System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting and storing historical traffic data and historical delivery data; means for obtaining a current location and a current speed; means for obtaining real-time traffic information, signal conditions, and weather information; means for analyzing the historical data and real-time information and calculating an optimal route; and means for presenting the calculated optimal 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] In the parcel delivery and delivery industries, labor shortages are becoming more serious in response to increasing consumer demand, increasing workloads and making efficient deliveries more difficult. Furthermore, traffic jams and waiting at traffic lights cause delivery delays, increasing stress and fatigue for delivery workers, and increasing the risk of traffic accidents. This invention aims to solve these issues and improve the efficiency of travel, thereby increasing the number of deliveries per person and reducing the workload. [Means for solving the problem]

[0005] The present invention is a system that includes the following means: means for collecting and storing past traffic data and delivery history data; means for acquiring current location and speed; means for acquiring real-time traffic information, traffic light conditions, and weather information; means for analyzing the past data and real-time information to calculate an optimal route; and means for presenting the calculated optimal route. This system allows delivery personnel to travel along the optimal route by utilizing congestion predictions based on past data and real-time traffic information, thereby shortening delivery times and achieving efficient travel. Furthermore, because the system takes into account the timing of traffic light changes, safe, non-stop travel is possible, reducing workload and the risk of accidents.

[0006] "Past traffic data" refers to data that records past traffic conditions in specific areas and time periods, and is used to understand traffic congestion trends and flows.

[0007] "Delivery history data" refers to data that includes records of past delivery status, routes, arrival times, etc., and is used to analyze delivery efficiency and trends.

[0008] "Current location" refers to the specific location where the user is currently located, and is obtained by a location information system such as GPS.

[0009] "Current speed" indicates the speed at which the user is currently traveling, and is information obtained by a GPS, a speedometer, or the like.

[0010] "Real-time traffic information" refers to the latest data on ongoing traffic conditions, including road congestion, accident information, traffic volume, etc.

[0011] "Signal status" is information about the current state (red, green, etc.) of a particular traffic light and the timing of changes.

[0012] "Weather information" refers to information about current and forecasted weather conditions (e.g., sunny, rainy, snowy, etc.) that affect traffic conditions and delivery routes.

[0013] The "optimal route" refers to the shortest and safest route from your current location to your destination, and is calculated taking into account traffic congestion and waiting at traffic lights.

[0014] The term "user" refers to a person such as a delivery person who uses the system of the present invention and travels along a route presented by the system.

[0015] A "generative AI model" is an algorithmic model that uses machine learning techniques to analyze past data and predict future traffic conditions and trends. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] The present invention relates to an AI-based predictive navigation system that collects and analyzes past traffic data, current location and speed, real-time traffic information, traffic signal status, and weather information to present the optimal route to the user.

[0038] System configuration

[0039] server

[0040] The server is responsible for the following functions:

[0041] Collection and storage of historical traffic and delivery data

[0042] Get real-time traffic, traffic light, and weather information

[0043] Analysis of collected data

[0044] Calculating the best route

[0045] Sending calculated route information to the device

[0046] Terminal

[0047] The terminal is responsible for the following functions:

[0048] Get the user's current location and speed

[0049] Receiving optimal route information sent from the server

[0050] Visual and audio navigation notifications for received route information

[0051] Monitor your location in real time and request route recalculation when necessary

[0052] User

[0053] Users can use this system to efficiently carry out delivery work.

[0054] Program processing

[0055] 1. Collection of historical data: The server periodically collects past traffic data and delivery history data and stores it in a database.

[0056] Example: Obtain traffic data for a specific area for the past year from an API and store it.

[0057] 2. Obtaining current location and speed: The device obtains the user's current location and speed using GPS and sends this to the server.

[0058] Example: When a user leaves at 8 o'clock, the device acquires its location information and movement speed and sends them to the server.

[0059] 3. Obtaining real-time information: The server obtains real-time traffic, traffic light, and weather information from external APIs and stores it in a database.

[0060] Example: A server uses an API to retrieve current traffic data and accident information for roads.

[0061] 4. Data analysis and route calculation: The server uses collected historical data and real-time information to calculate the optimal route using a generative AI model.

[0062] Example: The server uses a machine learning model to analyze predicted traffic congestion trends for a specific time period and calculate the optimal route taking into account traffic light change timings.

[0063] 5. Sending and notifying route information: The server sends the optimal route information to the terminal, and the terminal notifies the user of the route information. Navigation is performed visually and by voice.

[0064] Example: The calculated route is sent in JSON format to the device, which then displays the optimal route on a map and starts voice guidance.

[0065] Specific examples

[0066] Example 1: Morning rush hour delivery

[0067] 1. The server analyzes past traffic data and predicts morning rush hour congestion.

[0068] 2. The device sends the user's current location and speed to the server.

[0069] 3. The server calculates the optimal route based on real-time information.

[0070] 4. The device notifies the user of the calculated optimal route.

[0071] 5. The user follows the route and reaches the destination safely.

[0072] Example 2: Route change due to sudden weather change

[0073] 1. The server predicts traffic congestion based on past data and real-time weather information.

[0074] 2. The device keeps updating the user's location and sends it to the server.

[0075] 3. When it starts to rain, the server recalculates based on real-time weather information.

[0076] 4. The device notifies the user of the recalculated route.

[0077] 5. The user follows the new route and completes the delivery efficiently.

[0078] In this way, the system is expected to improve the efficiency of travel and reduce workload by aggregating and analyzing large amounts of data in real time.

[0079] The processing flow will be explained below.

[0080] Step 1:

[0081] The server collects past traffic data and delivery history data and stores it in a database. It periodically issues API requests, organizes the obtained data using ETL (Extract, Transform, Load) processing, and stores it in the database.

[0082] Step 2:

[0083] When a user launches the delivery app, the device uses GPS to obtain the user's current location and speed, and then sends the obtained location and speed information to the server.

[0084] Step 3:

[0085] The server receives real-time traffic information, traffic light status, and weather information from external APIs based on the user's current location and speed, and stores this data in a database.

[0086] Step 4:

[0087] The server inputs historical traffic data and current real-time information into a generative AI model to predict congestion trends for specific times of day and areas. This prediction is carried out using machine learning algorithms.

[0088] Step 5:

[0089] The server calculates the optimal route between your current location and your destination based on the predictions, taking into account traffic light change timing and real-time traffic information.

[0090] Step 6:

[0091] The server sends the calculated optimal route information in JSON format to the device, which receives it and notifies the user with visual and voice navigation.

[0092] Step 7:

[0093] The device tracks the user's movements in real time and, based on location and speed information, sends a request to the server to calculate a new route as needed.

[0094] Step 8:

[0095] If the server detects a change in real-time information, it recalculates a new optimal route and retransmits it to the terminal, which then notifies the user of the updated route information.

[0096] Step 9:

[0097] The user travels along the optimal route and reaches the destination. If an unexpected obstacle or traffic jam occurs along the way, steps 7 and 8 are repeated to receive a new optimal route.

[0098] Example 1

[0099] 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."

[0100] Current traffic navigation systems lack the information necessary to present optimal routes to users, or are unable to respond to real-time changes in conditions. Furthermore, conventional systems often present routes with low prediction accuracy because they do not fully utilize past traffic data or weather information. The present invention aims to solve these problems and provide more accurate future prediction navigation.

[0101] 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.

[0102] In this invention, the server includes means for collecting and storing past traffic data and delivery history data, means for acquiring current location and speed, means for acquiring real-time traffic information, traffic light status, and weather information, means for analyzing the past data and real-time information with a generative AI model and calculating an optimal route, means for presenting the calculated optimal route to the user using visual and voice navigation, and means for monitoring location information in real time and requesting route recalculation as necessary. This makes it possible to respond to changes in conditions in real time and present highly accurate routes using past data.

[0103] "Past traffic data" refers to data that indicates traffic flow and conditions during a specific period in the past.

[0104] "Delivery history data" refers to data that records past delivery routes, times, and situations.

[0105] "Current location" is specific location information of the user's current location.

[0106] "Current speed" is information about the speed at which the user is currently moving.

[0107] "Real-time traffic information" means up-to-date information showing current traffic conditions.

[0108] "Signal status" is information indicating the current status of a traffic signal.

[0109] "Weather information" is information that indicates the current weather conditions.

[0110] A "generative AI model" is an artificial intelligence model that uses past data and real-time information to predict future traffic conditions.

[0111] The "optimal route" is the most efficient travel route for the user, taking into account traffic conditions, traffic signals, weather, etc.

[0112] "Visual navigation" is a method of visually guiding a user along a route using maps and graphics.

[0113] "Voice navigation" is a method of providing route guidance to a user using voice.

[0114] "Location monitoring" is the process of continually determining a user's current location.

[0115] "Route recalculation" is the process of recalculating the optimal route based on changed real-time information.

[0116] The present invention relates to an AI-based future prediction navigation system, which is mainly composed of a server, a terminal, and a user, and provides the user with the optimal route by utilizing past traffic data, current location and speed, real-time traffic information, traffic signal status, and weather information.

[0117] Server Roles

[0118] The server is responsible for:

[0119] 1. Collecting and storing historical traffic and delivery data:

[0120] The server periodically collects past traffic data and delivery history data from external traffic information APIs and stores them in a database. Specifically, it uses Google Maps APIs and other sources to obtain and store traffic data for specific areas over the past year.

[0121] 2. Get real-time information:

[0122] The server obtains real-time traffic information, traffic light status, and weather information from external APIs (e.g., traffic information API, weather information API) and stores it in a database. This allows you to grasp current traffic volume, road accident information, traffic light timing, weather changes, etc. in real time.

[0123] 3. Data analysis and route calculation:

[0124] The server calculates the optimal route using a generative AI model (artificial intelligence model) based on collected historical data and real-time information. It uses a machine learning model to analyze predicted congestion trends during specific time periods and calculates the optimal route taking into account the timing of traffic light changes.

[0125] 4. Sending route information:

[0126] The server sends the calculated optimal route information to the device in JSON format, which is transferred to the device in real time.

[0127] Device Role

[0128] The terminal is responsible for:

[0129] 1. Get current location and speed:

[0130] The device uses GPS to acquire the user's current location and speed, which are then processed by software on the device and sent to a server.

[0131] 2. Route information reception and notification:

[0132] The device receives the optimal route information sent from the server and notifies the user of it through visual and voice navigation. Specifically, the optimal route is displayed on a map and voice guidance begins. For example, when the user heads towards their destination, the optimal route is displayed on the map app and voice guidance such as "Turn right" is given.

[0133] 3. Real-time location monitoring and route recalculation requests:

[0134] The device monitors the user's location information in real time and sends a route recalculation request to the server as needed. For example, if traffic congestion or accident information is detected, the device automatically requests a re-route from the server, and the optimal route is recalculated.

[0135] User Roles

[0136] Users use this system to travel efficiently, and can reach their destination safely and quickly by following the optimal route provided by the system. For example, if a user leaves at 8:00, the GPS acquires their location information and travel speed, which are then sent to the server. The optimal route is then calculated based on real-time information, and the device provides visual and voice navigation.

[0137] Prompt Sentence Examples

[0138] An example of a prompt for a generative AI model is:

[0139] "Calculate the optimal delivery route using historical traffic data and current real-time information, especially taking into account designated traffic light timings and weather information."

[0140] In this way, by aggregating and analyzing large amounts of data in real time, this system is expected to provide users with the optimal route, making travel more efficient and reducing workload.

[0141] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0142] Step 1: Collect historical data

[0143] The server collects past traffic data and delivery history data from an external traffic information API and stores it in a database.

[0144] Input: Traffic information API URL and required authentication information.

[0145] Data processing: Request historical traffic data using API and store the acquired data in a database in JSON format.

[0146] Output: Historical traffic and delivery history data stored in a database.

[0147] Specific operation: The server sends a request to the Google Maps API to obtain traffic data for a specific area for the past year and stores it in a database.

[0148] Step 2: Get your current location and speed

[0149] The device acquires the user's current location and speed using a GPS module and transmits this information to the server.

[0150] Input: Current location and speed information obtained by the device's GPS module.

[0151] Data processing: The acquired location and speed information is converted into JSON format and sent to the server.

[0152] Output: Current location and speed data sent to the server.

[0153] How it works: The device's GPS module measures the current location and speed, and dedicated software converts this into JSON format, which is then sent to the server.

[0154] Step 3: Obtaining real-time information

[0155] The server retrieves real-time traffic, traffic light, and weather information from external APIs and stores it in a database.

[0156] Input: Traffic and Weather API URLs and authentication information.

[0157] Data processing: Send API requests and store the acquired real-time information in a database.

[0158] Output: Real-time traffic information, traffic light status, and weather information stored in a database.

[0159] Specific operation: The server sends requests to the traffic information API and weather information API and adds the returned data to the database.

[0160] Step 4: Analyze data and calculate route

[0161] The server calculates the optimal route using a generative AI model based on collected historical data and real-time information.

[0162] Input: Historical traffic data stored in the database, delivery history data, real-time traffic information, traffic light status, and weather information.

[0163] Data calculation: This data is input into the generative AI model to calculate the optimal route.

[0164] Output: Calculated optimal route information.

[0165] Specific operation: Using a machine learning model, the system analyzes predicted congestion trends for specific time periods and calculates the optimal route taking into account the timing of traffic light changes.

[0166] Step 5: Send and notify route information

[0167] The server transmits the calculated optimum route information to the terminal, which notifies the user of the information through visual and voice navigation.

[0168] Input: Optimal route information calculated by the generative AI model.

[0169] Data processing: Convert optimal route information into JSON format and send it to the terminal.

[0170] Output: The optimal route information sent to the device.

[0171] Specific operation: The server sends optimal route information to the device, and the device displays the received information on the map app and starts voice guidance.

[0172] Step 6: Monitor real-time location and recalculate route

[0173] The device continuously monitors the user's location in real time and, if necessary, sends a route recalculation request to the server, which then recalculates using the new data.

[0174] Input: Current location and speed obtained from the device's GPS module.

[0175] Data processing: Receives a recalculation request from the terminal and recalculates the route based on new real-time information.

[0176] Output: The new recalculated optimal route information.

[0177] Specific operation: The device monitors the user's location information and, if necessary, sends a recalculation request to the server. The server then recalculates based on the new real-time data and sends the results to the device.

[0178] (Application example 1)

[0179] 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."

[0180] Conventional food delivery services lack an appropriate navigation system for drivers to deliver food efficiently. In particular, they need to comprehensively analyze past traffic data, real-time traffic information, weather information, and other factors to present drivers with the optimal route. Furthermore, real-time location updates and the associated route recalculation are difficult, which can increase the workload of drivers and extend delivery times. An efficient system is needed to solve these problems.

[0181] 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.

[0182] In this invention, the server includes means for collecting and storing past traffic data and delivery history data, means for acquiring current location and speed, means for acquiring real-time traffic information, traffic light status, and weather information, and means for notifying the driver of the calculation results visually and audibly. This makes it possible to automatically calculate the optimal route based on the collected past data and real-time information and present it to the driver through visual and audio navigation. It also performs real-time location updates and corresponding recalculations to support efficient delivery work.

[0183] "Past traffic data" refers to information about past traffic conditions, including information about traffic volume, congestion, average speed, and the like.

[0184] "Delivery history data" is a series of information related to delivery work performed during a specific period, and includes data such as delivery date and time, location information of the delivery destination, and delivery time.

[0185] "Location" refers to the physical location of a subject at a specific point in time, and is primarily obtained using GPS data.

[0186] "Current speed" refers to the speed of travel from a specific point to the next point, and is also obtained using technology such as GPS.

[0187] "Real-time traffic information" refers to the latest information on current traffic conditions, including current traffic volume, congestion information, accident information, etc.

[0188] "Traffic signal status" refers to the current and future status of traffic lights on roads, and includes information such as the timing at which the traffic lights change from red to green.

[0189] "Weather information" refers to information about weather conditions from the present to the near future, including precipitation such as rain and snow, wind speed, and temperature.

[0190] An "optimal route" refers to a route calculated to minimize time, cost, distance, etc. from a starting point to a destination.

[0191] "Visual navigation" refers to navigating through visual cues, such as maps, arrows, and color changes.

[0192] "Voice navigation" refers to a method of navigating through voice, where specific instructions or warnings are given to the user through voice prompts.

[0193] "Recalculation" refers to the process of recalculating the optimal route based on any changed conditions (e.g., traffic congestion or weather changes).

[0194] "Driver" refers to a driver who performs delivery work, and in this system, is the person who is provided with the optimal route for efficient delivery.

[0195] This invention relates to a food delivery optimal route navigation application that uses an AI-based future prediction navigation system. This system collects and analyzes past traffic data, delivery history data, current location and speed, real-time traffic information, traffic light status, and weather information to provide the driver with the optimal route.

[0196] System Configuration

[0197] server

[0198] The server is responsible for the following functions:

[0199] Collection and storage of historical traffic and delivery data

[0200] Get real-time traffic, traffic light, and weather information

[0201] Analyzing collected data and calculating optimal routes

[0202] Sending calculated route information to the device

[0203] Specifically, the server uses APIs to collect historical traffic data, real-time traffic information, and weather information. This information is stored in a database and analyzed using machine learning models. For example, traffic data from the past year is collected and congestion patterns in specific areas and time periods are analyzed. The optimal route for the driver is calculated, taking into account real-time weather information and traffic light conditions. The resulting optimal route information is then sent to the device in JSON format and displayed via visual and voice navigation.

[0204] Terminal

[0205] The terminal is responsible for the following functions:

[0206] Get the user's current location and speed

[0207] Receiving optimal route information sent from the server

[0208] Visual and audio navigation notifications for received route information

[0209] Monitor your location in real time and request route recalculation when necessary

[0210] For example, the device uses the smartphone's GPS function to obtain the driver's current location and speed, and sends this data to a server. It then obtains the optimal route information sent from the server and displays it on a map using Google Maps API or similar. It also uses a voice assistant to give voice instructions to the driver. Whenever the driver's location information changes, it requests a new route calculation from the server as needed.

[0211] User

[0212] Users use this system to efficiently perform delivery work. When a user starts a delivery, the device acquires his / her current location and speed information and sends it to the server. The server calculates the optimal route and sends the result to the device. The user then makes the delivery by following the optimal route through visual and voice navigation.

[0213] Program processing explanation

[0214] The server uses various external APIs to collect historical traffic data, real-time traffic information, traffic light status, and weather information. Machine learning models are used to analyze the data, utilizing libraries such as Python, TensorFlow, and PyTorch. The device uses Android or iOS development environments to acquire GPS data and send it to the server in real time. Google Maps API and voice assistants are used for navigation.

[0215] Specific examples

[0216] Example 1

[0217] The driver launches the smartphone app and inputs their starting and destination points. The server calculates the optimal route based on historical traffic data and real-time information. The results are sent to the smartphone and presented to the driver via visual and voice navigation. Real-time location information is updated during the delivery, and the route is recalculated based on traffic light conditions and weather information.

[0218] Example prompts to input to a generative AI model:

[0219] "Calculate the optimal route to get users from their current location to their delivery destination most efficiently based on historical traffic data, current location and speed, real-time traffic information, and weather information."

[0220] As described above, the system aims to improve the efficiency of delivery work by aggregating and analyzing large amounts of data in real time, calculating the optimal route, and providing it to drivers.

[0221] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0222] Step 1:

[0223] Collection and storage of historical data

[0224] The server collects past traffic data and delivery history data through the API and stores it in a database. This process involves obtaining data including traffic volume, congestion information, and accident information for a specific area over the past year. The raw data obtained from the API is used as input, and it is converted into a database format and stored. The output is formatted past traffic data and delivery history data.

[0225] Step 2:

[0226] Get current location and speed

[0227] The device uses its GPS function to obtain the current location and speed of the driver (user) and sends that data to the server. The input is real-time location data and speed information obtained from the device's GPS. This is sent to the server, and the location information and speed data received by the server are output.

[0228] Step 3:

[0229] Real-time information collection

[0230] The server retrieves real-time traffic, traffic light, and weather information from external APIs and stores it in a database. Specifically, it collects data including traffic volume data, accident information, traffic light change timing, weather changes, etc. It uses the real-time information retrieved from the API as input and stores it in a database in an organized format as output.

[0231] Step 4:

[0232] Analyzing data and calculating optimal routes

[0233] The server uses a generative AI model to calculate the optimal route based on historical traffic data, delivery history data, real-time traffic information, traffic light conditions, and weather information. In this process, it uses all collected and stored data as input and generates prompts for the generative AI model (e.g., "Based on historical traffic data, current location and current speed, real-time traffic information, and weather information, please calculate the optimal route for the user to most efficiently reach the delivery destination from their current location."). The output is the calculation result of the optimal route.

[0234] Step 5:

[0235] Notification of optimal routes

[0236] The server sends the calculated optimal route information to the device in JSON format. The device then notifies the user of the received route information as visual and voice navigation. Specifically, a map is displayed on the device and instructions are given by the voice assistant. The input is the optimal route information sent from the server, and the output is the visual and voice navigation information presented to the user.

[0237] Step 6:

[0238] Real-time location monitoring and route recalculation

[0239] The device keeps updating the driver's location information to the server in real time. If necessary, the server recalculates the optimal route based on the new location information and sends it to the device. The request is to send the new location information, and the output is the route information recalculated and updated by the server.

[0240] Through these steps, this system will significantly improve the efficiency of food delivery, reduce the workload of drivers, and enable them to reach their destinations more quickly.

[0241] 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.

[0242] This invention combines an AI-based predictive navigation system with an emotion engine. This system collects and analyzes past traffic data, current location and speed, real-time traffic information, traffic light status, and weather information to present the optimal route to the user. Furthermore, the emotion engine recognizes the user's emotional state and adjusts the navigation accordingly, reducing stress and fatigue for the user.

[0243] System configuration

[0244] server

[0245] The server is responsible for the following functions:

[0246] Collection and storage of historical traffic and delivery data

[0247] Get real-time traffic, traffic light, and weather information

[0248] Analysis of collected data

[0249] Calculating the best route

[0250] Sending calculated route information to the device

[0251] Processing user emotion recognition data

[0252] Terminal

[0253] The terminal is responsible for the following functions:

[0254] Get the user's current location and speed

[0255] Receiving optimal route information sent from the server

[0256] Visual and audio navigation notifications for received route information

[0257] Equipped with an emotion engine that recognizes emotions from the user's voice and facial expressions

[0258] Sending emotion recognition results to the server

[0259] Monitor your location in real time and request route recalculation when necessary

[0260] User

[0261] Users can use this system to efficiently perform delivery tasks and receive suggestions based on emotion recognition.

[0262] Program processing

[0263] 1. Collection of historical data: The server periodically collects past traffic data and delivery history data and stores it in a database.

[0264] Example: Obtain traffic data for a specific area for the past year from an API and store it.

[0265] 2. Obtaining current location and speed: The device obtains the user's current location and speed using GPS and sends this to the server.

[0266] Example: When a user leaves at 8 o'clock, the device acquires its location information and movement speed and sends them to the server.

[0267] 3. Obtaining real-time information: The server obtains real-time traffic, traffic light, and weather information from external APIs and stores it in a database.

[0268] Example: A server uses an API to retrieve current traffic data and accident information for roads.

[0269] 4. Data analysis and route calculation: The server uses historical traffic data and real-time information to calculate the optimal route using a generative AI model.

[0270] Example: The server uses a machine learning model to analyze predicted traffic congestion trends for a specific time period and calculate the optimal route taking into account traffic light change timings.

[0271] 5. Sending and notifying route information: The server sends the optimal route information in JSON format to the device, which receives it and notifies the user through visual and voice navigation.

[0272] Example: The calculated route is sent in JSON format to the device, which then displays the optimal route on a map and starts voice guidance.

[0273] 6. Emotion Recognition: The device's emotion engine recognizes the user's emotions from their voice and facial expressions and sends the data to the server, which processes the information and makes suggestions to reduce the user's stress and fatigue.

[0274] For example, if the user is tired, the device will suggest relaxing music and the server will guide them to resting spots.

[0275] Specific examples

[0276] Example 1: Morning rush hour delivery

[0277] 1. The server analyzes past traffic data and predicts morning rush hour congestion.

[0278] 2. The device sends the user's current location and speed to the server.

[0279] 3. The server calculates the optimal route based on real-time information.

[0280] 4. The device notifies the user of the calculated optimal route.

[0281] 5. The emotion engine recognizes the user's stress, and the server suggests relaxing music.

[0282] 6. The user follows the suggestions and reaches the destination comfortably.

[0283] Example 2: Route changes due to sudden weather changes and emotion recognition

[0284] 1. The server predicts traffic congestion based on past data and real-time weather information.

[0285] 2. The device keeps updating the user's location and sends it to the server.

[0286] 3. When it starts to rain, the server recalculates based on real-time weather information.

[0287] 4. The device notifies the user of the recalculated route.

[0288] 5. The emotion engine recognizes the user's stress, and the server suggests rest areas.

[0289] 6. The user follows the new route, completes the delivery efficiently, and takes the suggested breaks.

[0290] In this way, the system is expected to improve the efficiency of transportation and reduce workloads by aggregating and analyzing large amounts of data in real time. In addition, by combining it with an emotion engine, it can reduce stress and fatigue for users and provide a safe and comfortable delivery environment.

[0291] The processing flow will be explained below.

[0292] Step 1:

[0293] The server periodically collects past traffic data and delivery history data and stores it in a database. Specifically, it issues API requests, organizes the acquired data using ETL processing, and stores it.

[0294] Step 2:

[0295] When a user launches the delivery app, the device uses GPS to obtain the user's current location and speed, and then sends the obtained location and speed information to the server.

[0296] Step 3:

[0297] The server receives real-time traffic, traffic light, and weather information from external APIs based on the user's current location and speed, and stores this data in a database.

[0298] Step 4:

[0299] The server inputs historical traffic data and real-time information into a generative AI model to predict congestion trends for specific time periods and areas. Specifically, this prediction is performed using machine learning algorithms (e.g., LSTM and CNN).

[0300] Step 5:

[0301] The server calculates the optimal route between the current location and the destination based on the predictions, taking into account the timing of traffic lights changing and real-time traffic information. Specifically, it uses the Dijkstra algorithm and the A algorithm.

[0302] Step 6:

[0303] The server sends the calculated optimal route information in JSON format to the device, which receives it and notifies the user with visual and voice navigation.

[0304] Step 7:

[0305] The device's emotion engine recognizes emotions from the user's voice and facial expressions and sends the data to the server, using voice tone analysis and facial recognition technology.

[0306] Step 8:

[0307] The server processes the user's emotion recognition data and makes suggestions to reduce the user's stress and fatigue. Specifically, if the server determines that the user is feeling stressed, it will suggest relaxing music. If the server determines that the user is extremely tired, it will guide the user to the nearest rest area.

[0308] Step 9:

[0309] The device will provide visual and audio notifications to the user based on the suggestions sent from the server, play suggested relaxing music, and navigate to the nearest rest spot.

[0310] Step 10:

[0311] The user travels along the optimized route and reaches their destination. They also follow emotion-based suggestions to relax and take breaks as needed. If an unexpected obstacle or traffic jam occurs along the way, they repeat the process from step 7 and receive a new optimized route and suggestions.

[0312] In this way, the system aggregates and analyzes large amounts of data in real time and recognizes the user's emotional state, thereby improving travel efficiency and reducing workload.

[0313] Example 2

[0314] 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."

[0315] While conventional navigation systems can provide optimal routes based on traffic and weather information, they do not take into account the user's emotional state. This often results in users feeling stressed or tired while traveling, which can lead to significant mental and physical strain, especially during long trips or when faced with a tight schedule. Therefore, there is a need for a navigation system that can recognize the user's emotional state in real time and make suggestions and adjustments accordingly.

[0316] 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.

[0317] In this invention, the server includes means for collecting and storing past traffic data and delivery history data, means for acquiring the user's current location and current speed using GPS, means for acquiring real-time traffic information, traffic light status, and weather information, means for analyzing past data and real-time information using a generative AI model to calculate an optimal route, means for transmitting the calculated optimal route to the terminal and presenting it by visual and voice navigation, means for recognizing emotions from the user's voice and facial expressions and processing the emotion data, and means for making suggestions to reduce the user's stress and fatigue based on the user's emotion recognition data. This enables navigation that takes the user's emotional state into consideration, providing efficient and stress-free travel.

[0318] "Past traffic data" refers to information relating to traffic flow, congestion, accident occurrence, etc. during a specific period in the past.

[0319] "Delivery history data" is information relating to the history of past deliveries, including delivery destinations, delivery times, and routes.

[0320] "Current location" refers to the user's current location, and is location information obtained by GPS.

[0321] "Current speed" refers to the speed at which the user is currently moving, and is a value measured by GPS.

[0322] "Real-time traffic information" means instantly updated information about current road conditions and traffic flow.

[0323] "Signal status" is information about the current status of road traffic lights and the timing of signal changes.

[0324] "Weather information" is information about the current weather conditions, including whether it is rainy or sunny, the temperature, etc.

[0325] A "generative AI model" is a computer model that makes predictions and classifications based on machine learning algorithms.

[0326] An "optimal route" is the most efficient travel route that saves time and distance, calculated based on specified conditions.

[0327] "Visual navigation" is a navigation method that presents information visually, such as maps and directions.

[0328] "Voice navigation" is a navigation method that provides directions and routes to the user through voice.

[0329] "Emotion recognition" is a technology that analyzes and recognizes a user's emotional state from their voice and facial expressions.

[0330] "Emotion recognition data" is data regarding a user's emotional state obtained using emotion recognition technology.

[0331] "Suggestions for reducing stress and fatigue" are specific actions and advice that the system provides to relieve the stress and fatigue that the user is feeling.

[0332] This invention combines an AI-based predictive navigation system with an emotion engine. This system collects and analyzes past traffic data, current location and speed, real-time traffic information, traffic light status, and weather information to present the optimal route to the user. Furthermore, the emotion engine recognizes the user's emotional state and adjusts navigation accordingly, reducing stress and fatigue for the user.

[0333] Server processing

[0334] The server first collects past traffic data and delivery history data through an external API and stores it in a database. The software used includes a database management system (e.g., MySQL) and an API communication tool (e.g., the Requests library). The server periodically calls the API and retrieves data in JSON format.

[0335] The server then retrieves real-time traffic, traffic light, and weather information from external APIs and stores it in a database, using the Python requests library for this process.

[0336] The server calculates the optimal route using a generative AI model (e.g., TensorFlow) based on historical traffic data and real-time information. This generative AI model learns from historical data and predicts traffic patterns by time of day.

[0337] The calculated optimal route is sent to the device in JSON format. Communication is via the HTTP protocol and a RESTful API. The server formats the calculated route information as JSON and sends it via an API endpoint accessible by the device.

[0338] Furthermore, the server processes emotion recognition data sent from the device and makes suggestions to reduce the user's stress and fatigue using machine learning algorithms that analyze the emotion data and generate specific actions (e.g., suggesting relaxing music or providing directions to rest areas).

[0339] What the device is doing

[0340] The device acquires the user's current location and speed using the GPS module and sends it to the server. Specifically, the device collects data through location services (e.g., Google Maps API) and sends it to the server using an HTTP POST request.

[0341] The device receives the optimal route information sent from the server and notifies the user through visual and voice navigation. The navigation application used is Mapbox. It uses Mapbox's API to display the route on a map and start voice guidance.

[0342] The emotion engine installed in the device recognizes emotions from the user's voice and facial expressions. This process uses emotion recognition algorithms (e.g., OpenCV and Python speech analysis libraries), analyzes the user's emotional state, and sends the results to the server.

[0343] The device monitors the user's location in real time and sends route recalculation requests to the server as needed. Location information is periodically updated using the Google Maps API to provide a superior user experience.

[0344] Specific examples

[0345] Example 1: Morning rush hour delivery

[0346] 1. The server analyzes historical traffic data and uses a TensorFlow model to predict morning rush hour congestion.

[0347] 2. The device uses the Google Maps API to obtain the user's current location and speed and sends them to the server.

[0348] 3. The server retrieves the real-time information and calculates the optimal route based on historical data and real-time information.

[0349] 4. The device uses Mapbox to provide visual and audio notification of the calculated optimal route to the user.

[0350] 5. The emotion engine analyzes the user's facial expressions using OpenCV and recognizes their stress level. The server then suggests relaxing music.

[0351] 6. The user follows the suggestions and reaches the destination comfortably while listening to relaxing music.

[0352] Example prompt:

[0353] "Please explain the process your system uses to ensure users can deliver goods comfortably during the morning rush hour. Please include specific examples of optimal route calculation and emotion recognition."

[0354] Example 2: Route changes due to sudden weather changes and emotion recognition

[0355] 1. The server predicts traffic congestion based on past data and real-time weather information. It retrieves weather data using the Python requests library.

[0356] 2. The device keeps updating the user's location through the Google Maps API and sends it to the server.

[0357] 3. When it starts to rain, the server recalculates based on real-time weather information and recalculates the optimal route using the TensorFlow model.

[0358] 4. The device notifies the user of the new route and tells them to "proceed to the new route" through voice guidance.

[0359] 5. The emotion engine recognizes the user's stress level, and the server suggests rest areas. The user's stress level is determined using facial recognition and voice analysis.

[0360] 6. The user follows the new route, completes the delivery efficiently, and takes the suggested breaks.

[0361] Example prompt:

[0362] Please explain the process of changing the route due to sudden weather changes and how the emotion engine reduces user stress. Please also include specific examples of weather information acquisition and emotion recognition.

[0363] As described above, this system combines real-time data analysis and emotion recognition to provide users with efficient and stress-free travel.

[0364] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0365] Step 1:

[0366] Collection of historical data

[0367] The server periodically collects past traffic data and delivery history data through an external API and stores it in a database. Specifically, it calls the API using the Python requests library to retrieve data in JSON format. The retrieved data is then saved in a MySQL database using the INSERT statement.

[0368] Input: Past traffic and delivery history data obtained from the API (e.g., API endpoint " / past_traffic_data")

[0369] Output: Traffic and delivery history data stored in a MySQL database

[0370] Step 2:

[0371] Get current location and speed

[0372] The device acquires the user's current location and speed using the GPS module and sends it to the server. Specifically, the device acquires the location and speed data using the device's location information service (e.g., Google Maps API) and sends it to the server via an HTTP POST request.

[0373] Input: Current location and speed obtained from the GPS module (e.g., latitude 35.6895, longitude 139.6917, speed 50km / h)

[0374] Output: Location and speed data sent to the server

[0375] Step 3:

[0376] Obtaining real-time information

[0377] The server uses an external API to obtain real-time traffic, traffic light, and weather information and stores it in a database. Specifically, it uses the Python requests library to call the API, obtains data in JSON format, and stores it in a MySQL database.

[0378] Input: Real-time traffic information, traffic light status, and weather information obtained from the API (e.g., API endpoint " / current_traffic_data")

[0379] Output: Real-time traffic, traffic light and weather information stored in a MySQL database

[0380] Step 4:

[0381] Data analysis and route calculation

[0382] The server uses a generative AI model (e.g., TensorFlow) to calculate the optimal route based on historical traffic data and real-time information. This involves inputting historical and real-time data and using the AI ​​model to output the calculated optimal route.

[0383] Input: Historical traffic data, real-time traffic information, traffic signal status, weather information

[0384] Output: The optimal route calculated by the generative AI model

[0385] Step 5:

[0386] Sending and notifying route information

[0387] The server then sends the calculated optimal route in JSON format to the device, which then receives it and provides visual and voice navigation to the user. Specifically, the device uses Mapbox to display the route on a map and provide voice guidance.

[0388] Input: Optimal route calculated by the generative AI model (JSON format)

[0389] Output: Visual and audio guidance on the device (e.g., "Turn right next")

[0390] Step 6:

[0391] emotion recognition

[0392] The device's emotion engine recognizes emotions from the user's voice and facial expressions and sends the data to the server. Specifically, it uses emotion recognition algorithms (e.g., OpenCV and Python speech analysis libraries) to analyze the user's emotional state and sends the results to the server.

[0393] Input: User's voice and facial expression data

[0394] Output: Emotion recognition data sent to the server

[0395] Step 7:

[0396] Real-time location monitoring

[0397] The device monitors the user's location in real time and sends a route recalculation request to the server as needed. Specifically, it periodically updates the location using the Google Maps API and notifies the server whenever the location changes.

[0398] Input: Real-time location information from the GPS module

[0399] Output: Location information sent to the server and route recalculation requests if necessary

[0400] (Application example 2)

[0401] 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."

[0402] Conventional navigation systems for autonomous vehicles calculate optimal routes based on past traffic data and real-time information, but they do not take into account the emotional state of passengers, making it difficult to provide both safety and comfort. Furthermore, they do not take into account stress and fatigue caused by sudden changes in weather or traffic conditions, resulting in low user satisfaction.

[0403] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting and storing past traffic data and delivery history data, means for acquiring a current location and current speed, means for acquiring real-time traffic information, traffic light conditions, and weather information, means for analyzing the past data and real-time information and calculating an optimal route, means for presenting the calculated optimal route, means for recognizing a user's emotion from voice and image, and means for processing the recognized emotion data and making suggestions to reduce the user's stress and fatigue. This enables an autonomous vehicle to travel safely and comfortably while taking the user's emotional state into consideration.

[0404] "Historical traffic data" refers to data that includes past traffic conditions and delivery history for a specific area, and is used by the navigation system to calculate optimal routes.

[0405] "Delivery history data" refers to information such as past delivery times, routes, and speeds, and is useful for improving the efficiency of logistics and delivery operations.

[0406] "Current location" refers to information indicating the location of a user or vehicle, and is obtained using technologies such as GPS.

[0407] "Current speed" refers to the speed at which a user or vehicle is currently traveling, measured in real time.

[0408] "Real-time traffic information" refers to data that is obtained in real time about current traffic conditions, and includes information about accidents and congestion.

[0409] "Signal status" is information indicating the status of traffic signals on roads, and includes data such as the color and timing of signals.

[0410] "Weather information" is information indicating the current weather conditions and forecasts, and includes weather data such as rain, snow, and sunny weather.

[0411] The "optimal route" refers to the most efficient and safe travel route calculated by comprehensively taking into account traffic conditions, weather, and other factors.

[0412] "Presentation means" refers to the means for notifying the user of the calculated optimal route visually or audibly.

[0413] "Means for recognizing a user's emotions from voice and images" refers to technology for identifying a user's emotional state using voice analysis and image analysis.

[0414] "Means for processing emotional data" refers to technology that generates suggestions and measures to reduce the user's stress and fatigue based on the recognized emotional data.

[0415] A "generative AI model" refers to a machine learning model used to predict future situations based on past data and real-time information.

[0416] "Prompt" refers to the language used to give specific instructions to an AI model, including specific questions or requests.

[0417] This invention is a navigation system for autonomous vehicles that combines an AI-based future prediction navigation system with an emotion engine. This system collects and analyzes a wide variety of data and provides the optimal route taking into account the user's emotional state to ensure safe and comfortable driving of autonomous vehicles.

[0418] System configuration

[0419] server

[0420] The server is responsible for the following functions:

[0421] Collection and storage of past traffic data: The server periodically collects the user's delivery history data and past traffic data and stores them in a database.

[0422] Acquisition and storage of real-time information: The server acquires real-time traffic information, traffic light status, and weather information from external APIs and stores this data.

[0423] Data analysis and route calculation: The server analyzes the collected historical data and real-time information using a generative AI model to calculate the optimal route.

[0424] Route information distribution: The calculated optimal route information is sent to the terminal in JSON format.

[0425] Emotion data processing: Processes emotion recognition data sent from the device and makes suggestions to reduce the user's stress and fatigue.

[0426] Terminal

[0427] The terminal is responsible for the following functions:

[0428] Obtaining current location and speed: The user's current location and current moving speed are obtained using GPS and sent to the server.

[0429] Receiving and presenting route information: Receives optimal route information sent from the server and notifies the user through visual and voice navigation.

[0430] Equipped with an emotion engine: Recognizes the user's emotions from voice and facial expressions, and sends the emotion data to the server.

[0431] User

[0432] Using this system, users can travel comfortably and efficiently. By accepting stress reduction suggestions based on emotion recognition, they can reduce fatigue and stress during travel.

[0433] How it works

[0434] 1. Obtaining historical data and real-time information: The server periodically collects historical and real-time traffic information, including obtaining traffic information using APIs, checking traffic signal status, and collecting weather data.

[0435] 2. Obtaining current location and speed: The device uses GPS technology to obtain the user's current location and speed and transmits them to the server.

[0436] 3. Calculate optimal route: The server calculates the optimal route using a generative AI model based on historical data and real-time information.

[0437] 4. Route delivery and notification: The calculated optimal route is sent to the device in JSON format, and the device notifies the user visually and audibly.

[0438] 5. Emotion recognition and processing: The device's emotion engine analyzes the user's voice and facial expressions and sends the emotional data to the server, which processes the information and makes suggestions to reduce the user's stress and fatigue.

[0439] Specific examples

[0440] Example 1: Morning rush hour delivery

[0441] The server analyzes past traffic data and predicts congestion during the morning rush hour. The device sends the user's current location and speed to the server, which then calculates the optimal route based on real-time information. The user is then notified of the optimal route. The emotion engine recognizes the user's stress, and the server suggests relaxing music.

[0442] Example 2: Route changes due to sudden weather changes and emotion recognition

[0443] The server predicts traffic congestion based on past data and real-time weather information. The device continuously updates the user's current location and sends it to the server. When it starts to rain, the server recalculates the route based on real-time weather information. The device notifies the user of the recalculated route, the emotion engine recognizes the user's stress, and the server suggests rest stops.

[0444] Example prompt for a generative AI model:

[0445] "Calculate the most efficient route based on historical data and real-time information to provide shorter routes during the morning rush hour."

[0446] This system not only allows users to travel to their destinations efficiently and comfortably using self-driving vehicles, but also reduces stress and fatigue along the way.

[0447] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0448] Step 1: Collect and store historical data

[0449] The server collects past traffic data and delivery history data and stores it in a database. Specifically, it retrieves traffic information from the past few years from an external API and integrates it with the delivery history database. This provides data for analyzing past traffic conditions and delivery route trends. The input is the output of the past traffic data API, and the output is a database update that stores this data.

[0450] Step 2: Get your location and speed

[0451] The device uses GPS technology to obtain the user's current location and speed. It then sends the location and speed information to the server. The input is location and speed information from the GPS sensor, and the output is a data packet containing this information sent to the server.

[0452] Step 3: Obtaining real-time information

[0453] The server obtains real-time traffic information, traffic light status, and weather information through external APIs. This information is stored in a database. The inputs are the real-time traffic information API, traffic light status API, and weather information API, and the output is data containing this real-time information.

[0454] Step 4: Data analysis and route calculation

[0455] The server uses a generative AI model to analyze past traffic data, current location, current speed, and real-time information to calculate the optimal route. The inputs are past traffic data, current location, current speed, and real-time traffic information, and the output is optimal route information. Specifically, the server sends a prompt to the generative AI model to instruct it to calculate the optimal route. An example of a prompt is as follows:

[0456] "Calculate the most efficient route based on historical data and real-time information to provide shorter routes during the morning rush hour."

[0457] Step 5: Send and submit your route

[0458] The server sends the calculated optimal route information in JSON format to the device, which then notifies the user of this information as visual and audio navigation. The input is the JSON data of the optimal route information, and the output is navigation information that the user can confirm visually and audibly.

[0459] Step 6: Recognize emotions

[0460] The device's emotion engine uses a camera and microphone to recognize emotions from the user's voice and facial expressions. The recognized emotion data is sent to the server. The inputs are the user's voice data and camera footage, and the output is analyzed emotion data.

[0461] Step 7: Processing emotional data and making stress reduction suggestions

[0462] The server processes the emotional data sent from the device and generates suggestions to reduce the user's stress and fatigue. The input is emotional data, and the output is specific suggestions for reducing stress. Specifically, the server suggests relaxing music and resting spots based on the results of data processing.

[0463] Through the above steps, the system of the present invention enables an autonomous vehicle to provide an optimal route while taking into account the emotional state of the user, enabling comfortable and safe travel.

[0464] 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.

[0465] 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.

[0466] 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.

[0467] [Second embodiment]

[0468] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0469] 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.

[0470] 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).

[0471] 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.

[0472] 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.

[0473] 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).

[0474] 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.

[0475] 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.

[0476] 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.

[0477] 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.

[0478] 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.

[0479] 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."

[0480] The present invention relates to an AI-based predictive navigation system that collects and analyzes past traffic data, current location and speed, real-time traffic information, traffic signal status, and weather information to present the optimal route to the user.

[0481] System configuration

[0482] server

[0483] The server is responsible for the following functions:

[0484] Collection and storage of historical traffic and delivery data

[0485] Get real-time traffic, traffic light, and weather information

[0486] Analysis of collected data

[0487] Calculating the best route

[0488] Sending calculated route information to the device

[0489] Terminal

[0490] The terminal is responsible for the following functions:

[0491] Get the user's current location and speed

[0492] Receiving optimal route information sent from the server

[0493] Visual and audio navigation notifications for received route information

[0494] Monitor your location in real time and request route recalculation when necessary

[0495] User

[0496] Users can use this system to efficiently carry out delivery work.

[0497] Program processing

[0498] 1. Collection of historical data: The server periodically collects past traffic data and delivery history data and stores it in a database.

[0499] Example: Obtain traffic data for a specific area for the past year from an API and store it.

[0500] 2. Obtaining current location and speed: The device obtains the user's current location and speed using GPS and sends this to the server.

[0501] Example: When a user leaves at 8 o'clock, the device acquires its location information and movement speed and sends them to the server.

[0502] 3. Obtaining real-time information: The server obtains real-time traffic, traffic light, and weather information from external APIs and stores it in a database.

[0503] Example: A server uses an API to retrieve current traffic data and accident information for roads.

[0504] 4. Data analysis and route calculation: The server uses collected historical data and real-time information to calculate the optimal route using a generative AI model.

[0505] Example: The server uses a machine learning model to analyze predicted traffic congestion trends for a specific time period and calculate the optimal route taking into account traffic light change timings.

[0506] 5. Sending and notifying route information: The server sends the optimal route information to the terminal, and the terminal notifies the user of the route information. Navigation is performed visually and by voice.

[0507] Example: The calculated route is sent in JSON format to the device, which then displays the optimal route on a map and starts voice guidance.

[0508] Specific examples

[0509] Example 1: Morning rush hour delivery

[0510] 1. The server analyzes past traffic data and predicts morning rush hour congestion.

[0511] 2. The device sends the user's current location and speed to the server.

[0512] 3. The server calculates the optimal route based on real-time information.

[0513] 4. The device notifies the user of the calculated optimal route.

[0514] 5. The user follows the route and reaches the destination safely.

[0515] Example 2: Route change due to sudden weather change

[0516] 1. The server predicts traffic congestion based on past data and real-time weather information.

[0517] 2. The device keeps updating the user's location and sends it to the server.

[0518] 3. When it starts to rain, the server recalculates based on real-time weather information.

[0519] 4. The device notifies the user of the recalculated route.

[0520] 5. The user follows the new route and completes the delivery efficiently.

[0521] In this way, the system is expected to improve the efficiency of travel and reduce workload by aggregating and analyzing large amounts of data in real time.

[0522] The processing flow will be explained below.

[0523] Step 1:

[0524] The server collects past traffic data and delivery history data and stores it in a database. It periodically issues API requests, organizes the obtained data using ETL (Extract, Transform, Load) processing, and stores it in the database.

[0525] Step 2:

[0526] When a user launches the delivery app, the device uses GPS to obtain the user's current location and speed, and then sends the obtained location and speed information to the server.

[0527] Step 3:

[0528] The server receives real-time traffic information, traffic light status, and weather information from external APIs based on the user's current location and speed, and stores this data in a database.

[0529] Step 4:

[0530] The server inputs historical traffic data and current real-time information into a generative AI model to predict congestion trends for specific times of day and areas. This prediction is carried out using machine learning algorithms.

[0531] Step 5:

[0532] The server calculates the optimal route between your current location and your destination based on the predictions, taking into account traffic light change timing and real-time traffic information.

[0533] Step 6:

[0534] The server sends the calculated optimal route information in JSON format to the device, which receives it and notifies the user with visual and voice navigation.

[0535] Step 7:

[0536] The device tracks the user's movements in real time and, based on location and speed information, sends a request to the server to calculate a new route as needed.

[0537] Step 8:

[0538] If the server detects a change in real-time information, it recalculates a new optimal route and retransmits it to the terminal, which then notifies the user of the updated route information.

[0539] Step 9:

[0540] The user travels along the optimal route and reaches the destination. If an unexpected obstacle or traffic jam occurs along the way, steps 7 and 8 are repeated to receive a new optimal route.

[0541] Example 1

[0542] 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."

[0543] Current traffic navigation systems lack the information necessary to present optimal routes to users, or are unable to respond to real-time changes in conditions. Furthermore, conventional systems often present routes with low prediction accuracy because they do not fully utilize past traffic data or weather information. The present invention aims to solve these problems and provide more accurate future prediction navigation.

[0544] 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.

[0545] In this invention, the server includes means for collecting and storing past traffic data and delivery history data, means for acquiring current location and speed, means for acquiring real-time traffic information, traffic light status, and weather information, means for analyzing the past data and real-time information with a generative AI model and calculating an optimal route, means for presenting the calculated optimal route to the user using visual and voice navigation, and means for monitoring location information in real time and requesting route recalculation as necessary. This makes it possible to respond to changes in conditions in real time and present highly accurate routes using past data.

[0546] "Past traffic data" refers to data that indicates traffic flow and conditions during a specific period in the past.

[0547] "Delivery history data" refers to data that records past delivery routes, times, and situations.

[0548] "Current location" is specific location information of the user's current location.

[0549] "Current speed" is information about the speed at which the user is currently moving.

[0550] "Real-time traffic information" means up-to-date information showing current traffic conditions.

[0551] "Signal status" is information indicating the current status of a traffic signal.

[0552] "Weather information" is information that indicates the current weather conditions.

[0553] A "generative AI model" is an artificial intelligence model that uses past data and real-time information to predict future traffic conditions.

[0554] The "optimal route" is the most efficient travel route for the user, taking into account traffic conditions, traffic signals, weather, etc.

[0555] "Visual navigation" is a method of visually guiding a user along a route using maps and graphics.

[0556] "Voice navigation" is a method of providing route guidance to a user using voice.

[0557] "Location monitoring" is the process of continually determining a user's current location.

[0558] "Route recalculation" is the process of recalculating the optimal route based on changed real-time information.

[0559] The present invention relates to an AI-based future prediction navigation system, which is mainly composed of a server, a terminal, and a user, and provides the user with the optimal route by utilizing past traffic data, current location and speed, real-time traffic information, traffic signal status, and weather information.

[0560] Server Roles

[0561] The server is responsible for:

[0562] 1. Collecting and storing historical traffic and delivery data:

[0563] The server periodically collects past traffic data and delivery history data from external traffic information APIs and stores them in a database. Specifically, it uses Google Maps APIs and other sources to obtain and store traffic data for specific areas over the past year.

[0564] 2. Get real-time information:

[0565] The server obtains real-time traffic information, traffic light status, and weather information from external APIs (e.g., traffic information API, weather information API) and stores it in a database. This allows you to grasp current traffic volume, road accident information, traffic light timing, weather changes, etc. in real time.

[0566] 3. Data analysis and route calculation:

[0567] The server calculates the optimal route using a generative AI model (artificial intelligence model) based on collected historical data and real-time information. It uses a machine learning model to analyze predicted congestion trends during specific time periods and calculates the optimal route taking into account the timing of traffic light changes.

[0568] 4. Sending route information:

[0569] The server sends the calculated optimal route information to the device in JSON format, which is transferred to the device in real time.

[0570] Device Role

[0571] The terminal is responsible for:

[0572] 1. Get current location and speed:

[0573] The device uses GPS to acquire the user's current location and speed, which are then processed by software on the device and sent to a server.

[0574] 2. Route information reception and notification:

[0575] The device receives the optimal route information sent from the server and notifies the user of it through visual and voice navigation. Specifically, the optimal route is displayed on a map and voice guidance begins. For example, when the user heads towards their destination, the optimal route is displayed on the map app and voice guidance such as "Turn right" is given.

[0576] 3. Real-time location monitoring and route recalculation requests:

[0577] The device monitors the user's location information in real time and sends a route recalculation request to the server as needed. For example, if traffic congestion or accident information is detected, the device automatically requests a re-route from the server, and the optimal route is recalculated.

[0578] User Roles

[0579] Users use this system to travel efficiently, and can reach their destination safely and quickly by following the optimal route provided by the system. For example, if a user leaves at 8:00, the GPS acquires their location information and travel speed, which are then sent to the server. The optimal route is then calculated based on real-time information, and the device provides visual and voice navigation.

[0580] Prompt Sentence Examples

[0581] An example of a prompt for a generative AI model is:

[0582] "Calculate the optimal delivery route using historical traffic data and current real-time information, especially taking into account designated traffic light timings and weather information."

[0583] In this way, by aggregating and analyzing large amounts of data in real time, this system is expected to provide users with the optimal route, making travel more efficient and reducing workload.

[0584] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0585] Step 1: Collect historical data

[0586] The server collects past traffic data and delivery history data from an external traffic information API and stores it in a database.

[0587] Input: Traffic information API URL and required authentication information.

[0588] Data processing: Request historical traffic data using API and store the acquired data in a database in JSON format.

[0589] Output: Historical traffic and delivery history data stored in a database.

[0590] Specific operation: The server sends a request to the Google Maps API to obtain traffic data for a specific area for the past year and stores it in a database.

[0591] Step 2: Get your current location and speed

[0592] The device acquires the user's current location and speed using a GPS module and transmits this information to the server.

[0593] Input: Current location and speed information obtained by the device's GPS module.

[0594] Data processing: The acquired location and speed information is converted into JSON format and sent to the server.

[0595] Output: Current location and speed data sent to the server.

[0596] How it works: The device's GPS module measures the current location and speed, and dedicated software converts this into JSON format, which is then sent to the server.

[0597] Step 3: Obtaining real-time information

[0598] The server retrieves real-time traffic, traffic light, and weather information from external APIs and stores it in a database.

[0599] Input: Traffic and Weather API URLs and authentication information.

[0600] Data processing: Send API requests and store the acquired real-time information in a database.

[0601] Output: Real-time traffic information, traffic light status, and weather information stored in a database.

[0602] Specific operation: The server sends requests to the traffic information API and weather information API and adds the returned data to the database.

[0603] Step 4: Analyze data and calculate route

[0604] The server calculates the optimal route using a generative AI model based on collected historical data and real-time information.

[0605] Input: Historical traffic data stored in the database, delivery history data, real-time traffic information, traffic light status, and weather information.

[0606] Data calculation: This data is input into the generative AI model to calculate the optimal route.

[0607] Output: Calculated optimal route information.

[0608] Specific operation: Using a machine learning model, the system analyzes predicted congestion trends for specific time periods and calculates the optimal route taking into account the timing of traffic light changes.

[0609] Step 5: Send and notify route information

[0610] The server transmits the calculated optimum route information to the terminal, which notifies the user of the information through visual and voice navigation.

[0611] Input: Optimal route information calculated by the generative AI model.

[0612] Data processing: Convert optimal route information into JSON format and send it to the terminal.

[0613] Output: The optimal route information sent to the device.

[0614] Specific operation: The server sends optimal route information to the device, and the device displays the received information on the map app and starts voice guidance.

[0615] Step 6: Monitor real-time location and recalculate route

[0616] The device continuously monitors the user's location in real time and, if necessary, sends a route recalculation request to the server, which then recalculates using the new data.

[0617] Input: Current location and speed obtained from the device's GPS module.

[0618] Data processing: Receives a recalculation request from the terminal and recalculates the route based on new real-time information.

[0619] Output: The new recalculated optimal route information.

[0620] Specific operation: The device monitors the user's location information and, if necessary, sends a recalculation request to the server. The server then recalculates based on the new real-time data and sends the results to the device.

[0621] (Application example 1)

[0622] 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."

[0623] Conventional food delivery services lack an appropriate navigation system for drivers to deliver food efficiently. In particular, they need to comprehensively analyze past traffic data, real-time traffic information, weather information, and other factors to present drivers with the optimal route. Furthermore, real-time location updates and the associated route recalculation are difficult, which can increase the workload of drivers and extend delivery times. An efficient system is needed to solve these problems.

[0624] 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.

[0625] In this invention, the server includes means for collecting and storing past traffic data and delivery history data, means for acquiring current location and speed, means for acquiring real-time traffic information, traffic light status, and weather information, and means for notifying the driver of the calculation results visually and audibly. This makes it possible to automatically calculate the optimal route based on the collected past data and real-time information and present it to the driver through visual and audio navigation. It also performs real-time location updates and corresponding recalculations to support efficient delivery work.

[0626] "Past traffic data" refers to information about past traffic conditions, including information about traffic volume, congestion, average speed, and the like.

[0627] "Delivery history data" is a series of information related to delivery work performed during a specific period, and includes data such as delivery date and time, location information of the delivery destination, and delivery time.

[0628] "Location" refers to the physical location of a subject at a specific point in time, and is primarily obtained using GPS data.

[0629] "Current speed" refers to the speed of travel from a specific point to the next point, and is also obtained using technology such as GPS.

[0630] "Real-time traffic information" refers to the latest information on current traffic conditions, including current traffic volume, congestion information, accident information, etc.

[0631] "Traffic signal status" refers to the current and future status of traffic lights on roads, and includes information such as the timing at which the traffic lights change from red to green.

[0632] "Weather information" refers to information about weather conditions from the present to the near future, including precipitation such as rain and snow, wind speed, and temperature.

[0633] An "optimal route" refers to a route calculated to minimize time, cost, distance, etc. from a starting point to a destination.

[0634] "Visual navigation" refers to navigating through visual cues, such as maps, arrows, and color changes.

[0635] "Voice navigation" refers to a method of navigating through voice, where specific instructions or warnings are given to the user through voice prompts.

[0636] "Recalculation" refers to the process of recalculating the optimal route based on any changed conditions (e.g., traffic congestion or weather changes).

[0637] "Driver" refers to a driver who performs delivery work, and in this system, is the person who is provided with the optimal route for efficient delivery.

[0638] This invention relates to a food delivery optimal route navigation application that uses an AI-based future prediction navigation system. This system collects and analyzes past traffic data, delivery history data, current location and speed, real-time traffic information, traffic light status, and weather information to provide the driver with the optimal route.

[0639] System Configuration

[0640] server

[0641] The server is responsible for the following functions:

[0642] Collection and storage of historical traffic and delivery data

[0643] Get real-time traffic, traffic light, and weather information

[0644] Analyzing collected data and calculating optimal routes

[0645] Sending calculated route information to the device

[0646] Specifically, the server uses APIs to collect historical traffic data, real-time traffic information, and weather information. This information is stored in a database and analyzed using machine learning models. For example, traffic data from the past year is collected and congestion patterns in specific areas and time periods are analyzed. The optimal route for the driver is calculated, taking into account real-time weather information and traffic light conditions. The resulting optimal route information is sent to the device in JSON format and displayed via visual and voice navigation.

[0647] Terminal

[0648] The terminal is responsible for the following functions:

[0649] Get the user's current location and speed

[0650] Receiving optimal route information sent from the server

[0651] Visual and audio navigation notifications for received route information

[0652] Monitor your location in real time and request route recalculation when necessary

[0653] For example, the device uses the smartphone's GPS function to obtain the driver's current location and speed, and sends this data to a server. It then obtains the optimal route information sent from the server and displays it on a map using Google Maps API or similar. It also uses a voice assistant to give voice instructions to the driver. Whenever the driver's location information changes, it requests a new route calculation from the server as needed.

[0654] User

[0655] Users use this system to efficiently perform delivery work. When a user starts a delivery, the device acquires his / her current location and speed information and sends it to the server. The server calculates the optimal route and sends the result to the device. The user then makes the delivery by following the optimal route through visual and voice navigation.

[0656] Program processing explanation

[0657] The server uses various external APIs to collect historical traffic data, real-time traffic information, traffic light status, and weather information. Machine learning models are used to analyze the data, utilizing libraries such as Python, TensorFlow, and PyTorch. The device uses Android or iOS development environments to acquire GPS data and send it to the server in real time. Google Maps API and voice assistants are used for navigation.

[0658] Specific examples

[0659] Example 1

[0660] The driver launches the smartphone app and inputs their starting and destination points. The server calculates the optimal route based on historical traffic data and real-time information. The results are sent to the smartphone and presented to the driver via visual and voice navigation. Real-time location information is updated during the delivery, and the route is recalculated based on traffic light conditions and weather information.

[0661] Example prompts to input to a generative AI model:

[0662] "Calculate the optimal route to get users from their current location to their delivery destination most efficiently based on historical traffic data, current location and speed, real-time traffic information, and weather information."

[0663] As described above, the system aims to improve the efficiency of delivery work by aggregating and analyzing large amounts of data in real time, calculating the optimal route, and providing it to drivers.

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

[0665] Step 1:

[0666] Collection and storage of historical data

[0667] The server collects past traffic data and delivery history data through the API and stores it in a database. This process involves obtaining data including traffic volume, congestion information, and accident information for a specific area over the past year. The raw data obtained from the API is used as input, and it is converted into a database format and stored. The output is formatted past traffic data and delivery history data.

[0668] Step 2:

[0669] Get current location and speed

[0670] The device uses its GPS function to obtain the current location and speed of the driver (user) and sends that data to the server. The input is real-time location data and speed information obtained from the device's GPS. This is sent to the server, and the location information and speed data received by the server are output.

[0671] Step 3:

[0672] Real-time information collection

[0673] The server retrieves real-time traffic, traffic light, and weather information from external APIs and stores it in a database. Specifically, it collects data including traffic volume data, accident information, traffic light change timing, weather changes, etc. It uses the real-time information retrieved from the API as input and stores it in a database in an organized format as output.

[0674] Step 4:

[0675] Analyzing data and calculating optimal routes

[0676] The server uses a generative AI model to calculate the optimal route based on historical traffic data, delivery history data, real-time traffic information, traffic light conditions, and weather information. In this process, it uses all collected and stored data as input and generates prompts for the generative AI model (e.g., "Based on historical traffic data, current location and current speed, real-time traffic information, and weather information, please calculate the optimal route for the user to most efficiently reach the delivery destination from their current location."). The output is the calculation result of the optimal route.

[0677] Step 5:

[0678] Notification of optimal routes

[0679] The server sends the calculated optimal route information to the device in JSON format. The device then notifies the user of the received route information as visual and voice navigation. Specifically, a map is displayed on the device and instructions are given by the voice assistant. The input is the optimal route information sent from the server, and the output is the visual and voice navigation information presented to the user.

[0680] Step 6:

[0681] Real-time location monitoring and route recalculation

[0682] The device keeps updating the driver's location information to the server in real time. If necessary, the server recalculates the optimal route based on the new location information and sends it to the device. The request is to send the new location information, and the output is the route information recalculated and updated by the server.

[0683] Through these steps, this system will significantly improve the efficiency of food delivery, reduce the workload of drivers, and enable them to reach their destinations more quickly.

[0684] 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.

[0685] This invention combines an AI-based predictive navigation system with an emotion engine. This system collects and analyzes past traffic data, current location and speed, real-time traffic information, traffic light status, and weather information to present the optimal route to the user. Furthermore, the emotion engine recognizes the user's emotional state and adjusts the navigation accordingly, reducing stress and fatigue for the user.

[0686] System configuration

[0687] server

[0688] The server is responsible for the following functions:

[0689] Collection and storage of historical traffic and delivery data

[0690] Get real-time traffic, traffic light, and weather information

[0691] Analysis of collected data

[0692] Calculating the best route

[0693] Sending calculated route information to the device

[0694] Processing user emotion recognition data

[0695] Terminal

[0696] The terminal is responsible for the following functions:

[0697] Get the user's current location and speed

[0698] Receiving optimal route information sent from the server

[0699] Visual and audio navigation notifications for received route information

[0700] Equipped with an emotion engine that recognizes emotions from the user's voice and facial expressions

[0701] Sending emotion recognition results to the server

[0702] Monitor your location in real time and request route recalculation when necessary

[0703] User

[0704] Users can use this system to efficiently perform delivery tasks and receive suggestions based on emotion recognition.

[0705] Program processing

[0706] 1. Collection of historical data: The server periodically collects past traffic data and delivery history data and stores it in a database.

[0707] Example: Obtain traffic data for a specific area for the past year from an API and store it.

[0708] 2. Obtaining current location and speed: The device obtains the user's current location and speed using GPS and sends this to the server.

[0709] Example: When a user leaves at 8 o'clock, the device acquires its location information and movement speed and sends them to the server.

[0710] 3. Obtaining real-time information: The server obtains real-time traffic, traffic light, and weather information from external APIs and stores it in a database.

[0711] Example: A server uses an API to retrieve current traffic data and accident information for roads.

[0712] 4. Data analysis and route calculation: The server uses historical traffic data and real-time information to calculate the optimal route using a generative AI model.

[0713] Example: The server uses a machine learning model to analyze predicted traffic congestion trends for a specific time period and calculate the optimal route taking into account traffic light change timings.

[0714] 5. Sending and notifying route information: The server sends the optimal route information in JSON format to the device, which receives it and notifies the user through visual and voice navigation.

[0715] Example: The calculated route is sent in JSON format to the device, which then displays the optimal route on a map and starts voice guidance.

[0716] 6. Emotion Recognition: The device's emotion engine recognizes the user's emotions from their voice and facial expressions and sends the data to the server, which processes the information and makes suggestions to reduce the user's stress and fatigue.

[0717] For example, if the user is tired, the device will suggest relaxing music and the server will guide them to resting spots.

[0718] Specific examples

[0719] Example 1: Morning rush hour delivery

[0720] 1. The server analyzes past traffic data and predicts morning rush hour congestion.

[0721] 2. The device sends the user's current location and speed to the server.

[0722] 3. The server calculates the optimal route based on real-time information.

[0723] 4. The device notifies the user of the calculated optimal route.

[0724] 5. The emotion engine recognizes the user's stress, and the server suggests relaxing music.

[0725] 6. The user follows the suggestions and reaches the destination comfortably.

[0726] Example 2: Route changes due to sudden weather changes and emotion recognition

[0727] 1. The server predicts traffic congestion based on past data and real-time weather information.

[0728] 2. The device keeps updating the user's location and sends it to the server.

[0729] 3. When it starts to rain, the server recalculates based on real-time weather information.

[0730] 4. The device notifies the user of the recalculated route.

[0731] 5. The emotion engine recognizes the user's stress, and the server suggests rest areas.

[0732] 6. The user follows the new route, completes the delivery efficiently, and takes the suggested breaks.

[0733] In this way, the system is expected to improve the efficiency of transportation and reduce workloads by aggregating and analyzing large amounts of data in real time. In addition, by combining it with an emotion engine, it can reduce stress and fatigue for users and provide a safe and comfortable delivery environment.

[0734] The processing flow will be explained below.

[0735] Step 1:

[0736] The server periodically collects past traffic data and delivery history data and stores it in a database. Specifically, it issues API requests, organizes the acquired data using ETL processing, and stores it.

[0737] Step 2:

[0738] When a user launches the delivery app, the device uses GPS to obtain the user's current location and speed, and then sends the obtained location and speed information to the server.

[0739] Step 3:

[0740] The server receives real-time traffic, traffic light, and weather information from external APIs based on the user's current location and speed, and stores this data in a database.

[0741] Step 4:

[0742] The server inputs historical traffic data and real-time information into a generative AI model to predict congestion trends for specific time periods and areas. Specifically, this prediction is performed using machine learning algorithms (e.g., LSTM and CNN).

[0743] Step 5:

[0744] The server calculates the optimal route between the current location and the destination based on the predictions, taking into account the timing of traffic lights changing and real-time traffic information. Specifically, it uses the Dijkstra algorithm and the A algorithm.

[0745] Step 6:

[0746] The server sends the calculated optimal route information in JSON format to the device, which receives it and notifies the user with visual and voice navigation.

[0747] Step 7:

[0748] The device's emotion engine recognizes emotions from the user's voice and facial expressions and sends the data to the server, using voice tone analysis and facial recognition technology.

[0749] Step 8:

[0750] The server processes the user's emotion recognition data and makes suggestions to reduce the user's stress and fatigue. Specifically, if the server determines that the user is feeling stressed, it will suggest relaxing music. If the server determines that the user is extremely tired, it will guide the user to the nearest rest area.

[0751] Step 9:

[0752] The device will provide visual and audio notifications to the user based on the suggestions sent from the server, play suggested relaxing music, and navigate to the nearest rest spot.

[0753] Step 10:

[0754] The user travels along the optimized route and reaches their destination. They also follow emotion-based suggestions to relax and take breaks as needed. If an unexpected obstacle or traffic jam occurs along the way, they repeat the process from step 7 and receive a new optimized route and suggestions.

[0755] In this way, the system aggregates and analyzes large amounts of data in real time and recognizes the user's emotional state, thereby improving travel efficiency and reducing workload.

[0756] Example 2

[0757] 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."

[0758] While conventional navigation systems can provide optimal routes based on traffic and weather information, they do not take into account the user's emotional state. This often results in users feeling stressed or tired while traveling, which can lead to significant mental and physical strain, especially during long trips or when faced with a tight schedule. Therefore, there is a need for a navigation system that can recognize the user's emotional state in real time and make suggestions and adjustments accordingly.

[0759] 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.

[0760] In this invention, the server includes means for collecting and storing past traffic data and delivery history data, means for acquiring the user's current location and current speed using GPS, means for acquiring real-time traffic information, traffic light status, and weather information, means for analyzing past data and real-time information using a generative AI model to calculate an optimal route, means for transmitting the calculated optimal route to the terminal and presenting it by visual and voice navigation, means for recognizing emotions from the user's voice and facial expressions and processing the emotion data, and means for making suggestions to reduce the user's stress and fatigue based on the user's emotion recognition data. This enables navigation that takes the user's emotional state into consideration, providing efficient and stress-free travel.

[0761] "Past traffic data" refers to information relating to traffic flow, congestion, accident occurrence, etc. during a specific period in the past.

[0762] "Delivery history data" is information relating to the history of past deliveries, including delivery destinations, delivery times, and routes.

[0763] "Current location" refers to the user's current location, and is location information obtained by GPS.

[0764] "Current speed" refers to the speed at which the user is currently moving, and is a value measured by GPS.

[0765] "Real-time traffic information" means information that is updated immediately about current road conditions and traffic flow.

[0766] "Signal status" is information about the current status of road traffic lights and the timing of signal changes.

[0767] "Weather information" is information about the current weather conditions, including whether it is rainy or sunny, the temperature, etc.

[0768] A "generative AI model" is a computer model that makes predictions and classifications based on machine learning algorithms.

[0769] An "optimal route" is the most efficient travel route that saves time and distance, calculated based on specified conditions.

[0770] "Visual navigation" is a navigation method that presents information visually, such as maps and directions.

[0771] "Voice navigation" is a navigation method that provides directions and routes to the user through voice.

[0772] "Emotion recognition" is a technology that analyzes and recognizes a user's emotional state from their voice and facial expressions.

[0773] "Emotion recognition data" is data regarding a user's emotional state obtained using emotion recognition technology.

[0774] "Suggestions for reducing stress and fatigue" are specific actions and advice that the system provides to relieve the stress and fatigue that the user is feeling.

[0775] This invention combines an AI-based predictive navigation system with an emotion engine. This system collects and analyzes past traffic data, current location and speed, real-time traffic information, traffic light status, and weather information to present the optimal route to the user. Furthermore, the emotion engine recognizes the user's emotional state and adjusts navigation accordingly, reducing stress and fatigue for the user.

[0776] Server processing

[0777] The server first collects past traffic data and delivery history data through an external API and stores it in a database. The software used includes a database management system (e.g., MySQL) and an API communication tool (e.g., the Requests library). The server periodically calls the API and retrieves data in JSON format.

[0778] The server then retrieves real-time traffic, traffic light, and weather information from external APIs and stores it in a database, using the Python requests library for this process.

[0779] The server calculates the optimal route using a generative AI model (e.g., TensorFlow) based on historical traffic data and real-time information. This generative AI model learns from historical data and predicts traffic patterns by time of day.

[0780] The calculated optimal route is sent to the device in JSON format. Communication is via the HTTP protocol and a RESTful API. The server formats the calculated route information as JSON and sends it via an API endpoint accessible by the device.

[0781] Furthermore, the server processes emotion recognition data sent from the device and makes suggestions to reduce the user's stress and fatigue using machine learning algorithms that analyze the emotion data and generate specific actions (e.g., suggesting relaxing music or providing directions to rest areas).

[0782] What the device is doing

[0783] The device acquires the user's current location and speed using the GPS module and sends it to the server. Specifically, the device collects data through location services (e.g., Google Maps API) and sends it to the server using an HTTP POST request.

[0784] The device receives the optimal route information sent from the server and notifies the user through visual and voice navigation. The navigation application used is Mapbox. It uses Mapbox's API to display the route on a map and start voice guidance.

[0785] The emotion engine installed in the device recognizes emotions from the user's voice and facial expressions. This process uses emotion recognition algorithms (e.g., OpenCV and Python speech analysis libraries), analyzes the user's emotional state, and sends the results to the server.

[0786] The device monitors the user's location in real time and sends route recalculation requests to the server as needed. Location information is periodically updated using the Google Maps API to provide a superior user experience.

[0787] Specific examples

[0788] Example 1: Morning rush hour delivery

[0789] 1. The server analyzes historical traffic data and uses a TensorFlow model to predict morning rush hour congestion.

[0790] 2. The device uses the Google Maps API to obtain the user's current location and speed and sends them to the server.

[0791] 3. The server retrieves the real-time information and calculates the optimal route based on historical data and real-time information.

[0792] 4. The device uses Mapbox to provide visual and audio notification of the calculated optimal route to the user.

[0793] 5. The emotion engine analyzes the user's facial expressions using OpenCV and recognizes their stress level. The server then suggests relaxing music.

[0794] 6. The user follows the suggestions and reaches the destination comfortably while listening to relaxing music.

[0795] Example prompt:

[0796] "Please explain the process your system uses to ensure users can deliver goods comfortably during the morning rush hour. Please include specific examples of optimal route calculation and emotion recognition."

[0797] Example 2: Route changes due to sudden weather changes and emotion recognition

[0798] 1. The server predicts traffic congestion based on past data and real-time weather information. It retrieves weather data using the Python requests library.

[0799] 2. The device keeps updating the user's location through the Google Maps API and sends it to the server.

[0800] 3. When it starts to rain, the server recalculates based on real-time weather information and recalculates the optimal route using the TensorFlow model.

[0801] 4. The device notifies the user of the new route and tells them to "proceed to the new route" through voice guidance.

[0802] 5. The emotion engine recognizes the user's stress level, and the server suggests rest areas. The user's stress level is determined using facial recognition and voice analysis.

[0803] 6. The user follows the new route, completes the delivery efficiently, and takes the suggested breaks.

[0804] Example prompt:

[0805] Please explain the process of changing the route due to sudden weather changes and how the emotion engine reduces user stress. Please also include specific examples of weather information acquisition and emotion recognition.

[0806] As described above, this system combines real-time data analysis and emotion recognition to provide users with efficient and stress-free travel.

[0807] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0808] Step 1:

[0809] Collection of historical data

[0810] The server periodically collects past traffic data and delivery history data through an external API and stores it in a database. Specifically, it calls the API using the Python requests library to retrieve data in JSON format. The retrieved data is then saved in a MySQL database using the INSERT statement.

[0811] Input: Past traffic and delivery history data obtained from the API (e.g., API endpoint " / past_traffic_data")

[0812] Output: Traffic and delivery history data stored in a MySQL database

[0813] Step 2:

[0814] Get current location and speed

[0815] The device acquires the user's current location and speed using the GPS module and sends it to the server. Specifically, the device acquires the location and speed data using the device's location information service (e.g., Google Maps API) and sends it to the server via an HTTP POST request.

[0816] Input: Current location and speed obtained from the GPS module (e.g., latitude 35.6895, longitude 139.6917, speed 50km / h)

[0817] Output: Location and speed data sent to the server

[0818] Step 3:

[0819] Obtaining real-time information

[0820] The server uses an external API to obtain real-time traffic, traffic light, and weather information and stores it in a database. Specifically, it uses the Python requests library to call the API, obtains data in JSON format, and stores it in a MySQL database.

[0821] Input: Real-time traffic information, traffic light status, and weather information obtained from the API (e.g., API endpoint " / current_traffic_data")

[0822] Output: Real-time traffic, traffic light and weather information stored in a MySQL database

[0823] Step 4:

[0824] Data analysis and route calculation

[0825] The server uses a generative AI model (e.g., TensorFlow) to calculate the optimal route based on historical traffic data and real-time information. This involves inputting historical and real-time data and using the AI ​​model to output the calculated optimal route.

[0826] Input: Historical traffic data, real-time traffic information, traffic signal status, weather information

[0827] Output: The optimal route calculated by the generative AI model

[0828] Step 5:

[0829] Sending and notifying route information

[0830] The server then sends the calculated optimal route in JSON format to the device, which then receives it and provides visual and voice navigation to the user. Specifically, the device uses Mapbox to display the route on a map and provide voice guidance.

[0831] Input: Optimal route calculated by the generative AI model (JSON format)

[0832] Output: Visual and audio guidance on the device (e.g., "Turn right next")

[0833] Step 6:

[0834] emotion recognition

[0835] The device's emotion engine recognizes emotions from the user's voice and facial expressions and sends the data to the server. Specifically, it uses emotion recognition algorithms (e.g., OpenCV and Python speech analysis libraries) to analyze the user's emotional state and sends the results to the server.

[0836] Input: User's voice and facial expression data

[0837] Output: Emotion recognition data sent to the server

[0838] Step 7:

[0839] Real-time location monitoring

[0840] The device monitors the user's location in real time and sends a route recalculation request to the server as needed. Specifically, it periodically updates the location using the Google Maps API and notifies the server whenever the location changes.

[0841] Input: Real-time location information from the GPS module

[0842] Output: Location information sent to the server and route recalculation requests if necessary

[0843] (Application example 2)

[0844] 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."

[0845] Conventional navigation systems for autonomous vehicles calculate optimal routes based on past traffic data and real-time information, but they do not take into account the emotional state of passengers, making it difficult to provide both safety and comfort. Furthermore, they do not take into account stress and fatigue caused by sudden changes in weather or traffic conditions, resulting in low user satisfaction.

[0846] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting and storing past traffic data and delivery history data, means for acquiring a current location and current speed, means for acquiring real-time traffic information, traffic light conditions, and weather information, means for analyzing the past data and real-time information and calculating an optimal route, means for presenting the calculated optimal route, means for recognizing a user's emotion from voice and image, and means for processing the recognized emotion data and making suggestions to reduce the user's stress and fatigue. This enables an autonomous vehicle to travel safely and comfortably while taking the user's emotional state into consideration.

[0847] "Historical traffic data" refers to data that includes past traffic conditions and delivery history for a specific area, and is used by the navigation system to calculate optimal routes.

[0848] "Delivery history data" refers to information such as past delivery times, routes, and speeds, and is useful for improving the efficiency of logistics and delivery operations.

[0849] "Current location" refers to information indicating the location of a user or vehicle, and is obtained using technologies such as GPS.

[0850] "Current speed" refers to the speed at which a user or vehicle is currently traveling, measured in real time.

[0851] "Real-time traffic information" refers to data that is obtained in real time about current traffic conditions, and includes information about accidents and congestion.

[0852] "Signal status" is information indicating the status of traffic signals on roads, and includes data such as the color and timing of signals.

[0853] "Weather information" is information indicating the current weather conditions and forecasts, and includes weather data such as rain, snow, and sunny weather.

[0854] The "optimal route" refers to the most efficient and safe travel route calculated by comprehensively taking into account traffic conditions, weather, and other factors.

[0855] "Presentation means" refers to the means for notifying the user of the calculated optimal route visually or audibly.

[0856] "Means for recognizing a user's emotions from voice and images" refers to technology for identifying a user's emotional state using voice analysis and image analysis.

[0857] "Means for processing emotional data" refers to technology that generates suggestions and measures to reduce the user's stress and fatigue based on the recognized emotional data.

[0858] A "generative AI model" refers to a machine learning model used to predict future situations based on past data and real-time information.

[0859] "Prompt" refers to the language used to give specific instructions to an AI model, including specific questions or requests.

[0860] This invention is a navigation system for autonomous vehicles that combines an AI-based future prediction navigation system with an emotion engine. This system collects and analyzes a wide variety of data and provides the optimal route taking into account the user's emotional state to ensure safe and comfortable driving of autonomous vehicles.

[0861] System configuration

[0862] server

[0863] The server is responsible for the following functions:

[0864] Collection and storage of past traffic data: The server periodically collects the user's delivery history data and past traffic data and stores them in a database.

[0865] Acquisition and storage of real-time information: The server acquires real-time traffic information, traffic light status, and weather information from external APIs and stores this data.

[0866] Data analysis and route calculation: The server analyzes the collected historical data and real-time information using a generative AI model to calculate the optimal route.

[0867] Route information distribution: The calculated optimal route information is sent to the terminal in JSON format.

[0868] Emotion data processing: Processes emotion recognition data sent from the device and makes suggestions to reduce the user's stress and fatigue.

[0869] Terminal

[0870] The terminal is responsible for the following functions:

[0871] Obtaining current location and speed: The user's current location and current moving speed are obtained using GPS and sent to the server.

[0872] Receiving and presenting route information: Receives optimal route information sent from the server and notifies the user through visual and voice navigation.

[0873] Equipped with an emotion engine: Recognizes the user's emotions from voice and facial expressions, and sends the emotion data to the server.

[0874] User

[0875] Using this system, users can travel comfortably and efficiently. By accepting stress reduction suggestions based on emotion recognition, they can reduce fatigue and stress during travel.

[0876] How it works

[0877] 1. Obtaining historical data and real-time information: The server periodically collects historical and real-time traffic information, including obtaining traffic information using APIs, checking traffic signal status, and collecting weather data.

[0878] 2. Obtaining current location and speed: The device uses GPS technology to obtain the user's current location and speed and transmits them to the server.

[0879] 3. Calculate optimal route: The server calculates the optimal route using a generative AI model based on historical data and real-time information.

[0880] 4. Route delivery and notification: The calculated optimal route is sent to the device in JSON format, and the device notifies the user visually and audibly.

[0881] 5. Emotion recognition and processing: The device's emotion engine analyzes the user's voice and facial expressions and sends the emotional data to the server, which processes the information and makes suggestions to reduce the user's stress and fatigue.

[0882] Specific examples

[0883] Example 1: Morning rush hour delivery

[0884] The server analyzes past traffic data and predicts congestion during the morning rush hour. The device sends the user's current location and speed to the server, which then calculates the optimal route based on real-time information. The user is then notified of the optimal route. The emotion engine recognizes the user's stress, and the server suggests relaxing music.

[0885] Example 2: Route changes due to sudden weather changes and emotion recognition

[0886] The server predicts traffic congestion based on past data and real-time weather information. The device continuously updates the user's current location and sends it to the server. When it starts to rain, the server recalculates the route based on real-time weather information. The device notifies the user of the recalculated route, the emotion engine recognizes the user's stress, and the server suggests rest stops.

[0887] Example prompt for a generative AI model:

[0888] "Calculate the most efficient route based on historical data and real-time information to provide shorter routes during the morning rush hour."

[0889] This system not only allows users to travel to their destinations efficiently and comfortably using self-driving vehicles, but also reduces stress and fatigue along the way.

[0890] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0891] Step 1: Collect and store historical data

[0892] The server collects past traffic data and delivery history data and stores it in a database. Specifically, it retrieves traffic information from the past few years from an external API and integrates it with the delivery history database. This provides data for analyzing past traffic conditions and delivery route trends. The input is the output of the past traffic data API, and the output is a database update that stores this data.

[0893] Step 2: Get your location and speed

[0894] The device uses GPS technology to obtain the user's current location and speed. It then sends the location and speed information to the server. The input is location and speed information from the GPS sensor, and the output is a data packet containing this information sent to the server.

[0895] Step 3: Obtaining real-time information

[0896] The server obtains real-time traffic information, traffic light status, and weather information through external APIs. This information is stored in a database. The inputs are the real-time traffic information API, traffic light status API, and weather information API, and the output is data containing this real-time information.

[0897] Step 4: Data analysis and route calculation

[0898] The server uses a generative AI model to analyze past traffic data, current location, current speed, and real-time information to calculate the optimal route. The inputs are past traffic data, current location, current speed, and real-time traffic information, and the output is optimal route information. Specifically, the server sends a prompt to the generative AI model to instruct it to calculate the optimal route. An example of a prompt is as follows:

[0899] "Calculate the most efficient route based on historical data and real-time information to provide shorter routes during the morning rush hour."

[0900] Step 5: Send and submit your route

[0901] The server sends the calculated optimal route information in JSON format to the device, which then notifies the user of this information as visual and audio navigation. The input is the JSON data of the optimal route information, and the output is navigation information that the user can confirm visually and audibly.

[0902] Step 6: Recognize emotions

[0903] The device's emotion engine uses a camera and microphone to recognize emotions from the user's voice and facial expressions. The recognized emotion data is sent to the server. The inputs are the user's voice data and camera footage, and the output is analyzed emotion data.

[0904] Step 7: Processing emotional data and making stress reduction suggestions

[0905] The server processes the emotional data sent from the device and generates suggestions to reduce the user's stress and fatigue. The input is emotional data, and the output is specific suggestions for reducing stress. Specifically, the server suggests relaxing music and resting spots based on the results of data processing.

[0906] Through the above steps, the system of the present invention enables an autonomous vehicle to provide an optimal route while taking into account the emotional state of the user, enabling comfortable and safe travel.

[0907] 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.

[0908] 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.

[0909] 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.

[0910] [Third embodiment]

[0911] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0912] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0913] 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).

[0914] 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.

[0915] 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.

[0916] 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).

[0917] 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.

[0918] 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.

[0919] 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.

[0920] 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.

[0921] 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.

[0922] 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."

[0923] The present invention relates to an AI-based predictive navigation system that collects and analyzes past traffic data, current location and speed, real-time traffic information, traffic signal status, and weather information to present the optimal route to the user.

[0924] System configuration

[0925] server

[0926] The server is responsible for the following functions:

[0927] Collection and storage of historical traffic and delivery data

[0928] Get real-time traffic, traffic light, and weather information

[0929] Analysis of collected data

[0930] Calculating the best route

[0931] Sending calculated route information to the device

[0932] Terminal

[0933] The terminal is responsible for the following functions:

[0934] Get the user's current location and speed

[0935] Receiving optimal route information sent from the server

[0936] Visual and audio navigation notifications for received route information

[0937] Monitor your location in real time and request route recalculation when necessary

[0938] User

[0939] Users can use this system to efficiently carry out delivery work.

[0940] Program processing

[0941] 1. Collection of historical data: The server periodically collects past traffic data and delivery history data and stores it in a database.

[0942] Example: Obtain traffic data for a specific area for the past year from an API and store it.

[0943] 2. Obtaining current location and speed: The device obtains the user's current location and speed using GPS and sends this to the server.

[0944] Example: When a user leaves at 8 o'clock, the device acquires its location information and movement speed and sends them to the server.

[0945] 3. Obtaining real-time information: The server obtains real-time traffic, traffic light, and weather information from external APIs and stores it in a database.

[0946] Example: A server uses an API to retrieve current traffic data and accident information for roads.

[0947] 4. Data analysis and route calculation: The server uses collected historical data and real-time information to calculate the optimal route using a generative AI model.

[0948] Example: The server uses a machine learning model to analyze predicted traffic congestion trends for a specific time period and calculate the optimal route taking into account traffic light change timings.

[0949] 5. Sending and notifying route information: The server sends the optimal route information to the terminal, and the terminal notifies the user of the route information. Navigation is performed visually and by voice.

[0950] Example: The calculated route is sent in JSON format to the device, which then displays the optimal route on a map and starts voice guidance.

[0951] Specific examples

[0952] Example 1: Morning rush hour delivery

[0953] 1. The server analyzes past traffic data and predicts morning rush hour congestion.

[0954] 2. The device sends the user's current location and speed to the server.

[0955] 3. The server calculates the optimal route based on real-time information.

[0956] 4. The device notifies the user of the calculated optimal route.

[0957] 5. The user follows the route and reaches the destination safely.

[0958] Example 2: Route change due to sudden weather change

[0959] 1. The server predicts traffic congestion based on past data and real-time weather information.

[0960] 2. The device keeps updating the user's location and sends it to the server.

[0961] 3. When it starts to rain, the server recalculates based on real-time weather information.

[0962] 4. The device notifies the user of the recalculated route.

[0963] 5. The user follows the new route and completes the delivery efficiently.

[0964] In this way, the system is expected to improve the efficiency of travel and reduce workload by aggregating and analyzing large amounts of data in real time.

[0965] The processing flow will be explained below.

[0966] Step 1:

[0967] The server collects past traffic data and delivery history data and stores it in a database. It periodically issues API requests, organizes the obtained data using ETL (Extract, Transform, Load) processing, and stores it in the database.

[0968] Step 2:

[0969] When a user launches the delivery app, the device uses GPS to obtain the user's current location and speed, and then sends the obtained location and speed information to the server.

[0970] Step 3:

[0971] The server receives real-time traffic information, traffic light status, and weather information from external APIs based on the user's current location and speed, and stores this data in a database.

[0972] Step 4:

[0973] The server inputs historical traffic data and current real-time information into a generative AI model to predict congestion trends for specific times of day and areas. This prediction is carried out using machine learning algorithms.

[0974] Step 5:

[0975] The server calculates the optimal route between your current location and your destination based on the predictions, taking into account traffic light change timing and real-time traffic information.

[0976] Step 6:

[0977] The server sends the calculated optimal route information in JSON format to the device, which receives it and notifies the user with visual and voice navigation.

[0978] Step 7:

[0979] The device tracks the user's movements in real time and, based on location and speed information, sends a request to the server to calculate a new route as needed.

[0980] Step 8:

[0981] If the server detects a change in real-time information, it recalculates a new optimal route and retransmits it to the terminal, which then notifies the user of the updated route information.

[0982] Step 9:

[0983] The user travels along the optimal route and reaches the destination. If an unexpected obstacle or traffic jam occurs along the way, steps 7 and 8 are repeated to receive a new optimal route.

[0984] Example 1

[0985] 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."

[0986] Current traffic navigation systems lack the information necessary to present optimal routes to users, or are unable to respond to real-time changes in conditions. Furthermore, conventional systems often present routes with low prediction accuracy because they do not fully utilize past traffic data or weather information. The present invention aims to solve these problems and provide more accurate future prediction navigation.

[0987] 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.

[0988] In this invention, the server includes means for collecting and storing past traffic data and delivery history data, means for acquiring current location and speed, means for acquiring real-time traffic information, traffic light status, and weather information, means for analyzing the past data and real-time information with a generative AI model and calculating an optimal route, means for presenting the calculated optimal route to the user using visual and voice navigation, and means for monitoring location information in real time and requesting route recalculation as necessary. This makes it possible to respond to changes in conditions in real time and present highly accurate routes using past data.

[0989] "Past traffic data" refers to data that indicates traffic flow and conditions during a specific period in the past.

[0990] "Delivery history data" refers to data that records past delivery routes, times, and situations.

[0991] "Current location" is specific location information of the user's current location.

[0992] "Current speed" is information about the speed at which the user is currently moving.

[0993] "Real-time traffic information" means up-to-date information showing current traffic conditions.

[0994] "Signal status" is information indicating the current status of a traffic signal.

[0995] "Weather information" is information that indicates the current weather conditions.

[0996] A "generative AI model" is an artificial intelligence model that uses past data and real-time information to predict future traffic conditions.

[0997] The "optimal route" is the most efficient travel route for the user, taking into account traffic conditions, traffic signals, weather, etc.

[0998] "Visual navigation" is a method of visually guiding a user along a route using maps and graphics.

[0999] "Voice navigation" is a method of providing route guidance to a user using voice.

[1000] "Location monitoring" is the process of continually determining a user's current location.

[1001] "Route recalculation" is the process of recalculating the optimal route based on changed real-time information.

[1002] The present invention relates to an AI-based future prediction navigation system, which is mainly composed of a server, a terminal, and a user, and provides the user with the optimal route by utilizing past traffic data, current location and speed, real-time traffic information, traffic signal status, and weather information.

[1003] Server Roles

[1004] The server is responsible for:

[1005] 1. Collecting and storing historical traffic and delivery data:

[1006] The server periodically collects past traffic data and delivery history data from external traffic information APIs and stores them in a database. Specifically, it uses Google Maps APIs and other sources to obtain and store traffic data for specific areas over the past year.

[1007] 2. Get real-time information:

[1008] The server obtains real-time traffic information, traffic light status, and weather information from external APIs (e.g., traffic information API, weather information API) and stores it in a database. This allows you to grasp current traffic volume, road accident information, traffic light timing, weather changes, etc. in real time.

[1009] 3. Data analysis and route calculation:

[1010] The server calculates the optimal route using a generative AI model (artificial intelligence model) based on collected historical data and real-time information. It uses a machine learning model to analyze predicted congestion trends during specific time periods and calculates the optimal route taking into account the timing of traffic light changes.

[1011] 4. Sending route information:

[1012] The server sends the calculated optimal route information to the device in JSON format, which is transferred to the device in real time.

[1013] Device Role

[1014] The terminal is responsible for:

[1015] 1. Get current location and speed:

[1016] The device uses GPS to acquire the user's current location and speed, which are then processed by software on the device and sent to a server.

[1017] 2. Route information reception and notification:

[1018] The device receives the optimal route information sent from the server and notifies the user of it through visual and voice navigation. Specifically, the optimal route is displayed on a map and voice guidance begins. For example, when the user heads towards their destination, the optimal route is displayed on the map app and voice guidance such as "Turn right" is given.

[1019] 3. Real-time location monitoring and route recalculation requests:

[1020] The device monitors the user's location information in real time and sends a route recalculation request to the server as needed. For example, if traffic congestion or accident information is detected, the device automatically requests a re-route from the server, and the optimal route is recalculated.

[1021] User Roles

[1022] Users use this system to travel efficiently, and can reach their destination safely and quickly by following the optimal route provided by the system. For example, if a user leaves at 8:00, the GPS acquires their location information and travel speed, which are then sent to the server. The optimal route is then calculated based on real-time information, and the device provides visual and voice navigation.

[1023] Prompt Sentence Examples

[1024] An example of a prompt for a generative AI model is:

[1025] "Calculate the optimal delivery route using historical traffic data and current real-time information, especially taking into account designated traffic light timings and weather information."

[1026] In this way, by aggregating and analyzing large amounts of data in real time, this system is expected to provide users with the optimal route, making travel more efficient and reducing workload.

[1027] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1028] Step 1: Collect historical data

[1029] The server collects past traffic data and delivery history data from an external traffic information API and stores it in a database.

[1030] Input: Traffic information API URL and required authentication information.

[1031] Data processing: Request historical traffic data using API and store the acquired data in a database in JSON format.

[1032] Output: Historical traffic and delivery history data stored in a database.

[1033] Specific operation: The server sends a request to the Google Maps API to obtain traffic data for a specific area for the past year and stores it in a database.

[1034] Step 2: Get your current location and speed

[1035] The device acquires the user's current location and speed using a GPS module and transmits this information to the server.

[1036] Input: Current location and speed information obtained by the device's GPS module.

[1037] Data processing: The acquired location and speed information is converted into JSON format and sent to the server.

[1038] Output: Current location and speed data sent to the server.

[1039] How it works: The device's GPS module measures the current location and speed, and dedicated software converts this into JSON format, which is then sent to the server.

[1040] Step 3: Obtaining real-time information

[1041] The server retrieves real-time traffic, traffic light, and weather information from external APIs and stores it in a database.

[1042] Input: Traffic and Weather API URLs and authentication information.

[1043] Data processing: Send API requests and store the acquired real-time information in a database.

[1044] Output: Real-time traffic information, traffic light status, and weather information stored in a database.

[1045] Specific operation: The server sends requests to the traffic information API and weather information API and adds the returned data to the database.

[1046] Step 4: Analyze data and calculate route

[1047] The server calculates the optimal route using a generative AI model based on collected historical data and real-time information.

[1048] Input: Historical traffic data stored in the database, delivery history data, real-time traffic information, traffic light status, and weather information.

[1049] Data calculation: This data is input into the generative AI model to calculate the optimal route.

[1050] Output: Calculated optimal route information.

[1051] Specific operation: Using a machine learning model, the system analyzes predicted congestion trends for specific time periods and calculates the optimal route taking into account the timing of traffic light changes.

[1052] Step 5: Send and notify route information

[1053] The server transmits the calculated optimum route information to the terminal, which notifies the user of the information through visual and voice navigation.

[1054] Input: Optimal route information calculated by the generative AI model.

[1055] Data processing: Convert optimal route information into JSON format and send it to the terminal.

[1056] Output: The optimal route information sent to the device.

[1057] Specific operation: The server sends optimal route information to the device, and the device displays the received information on the map app and starts voice guidance.

[1058] Step 6: Monitor real-time location and recalculate route

[1059] The device continuously monitors the user's location in real time and, if necessary, sends a route recalculation request to the server, which then recalculates using the new data.

[1060] Input: Current location and speed obtained from the device's GPS module.

[1061] Data processing: Receives a recalculation request from the terminal and recalculates the route based on new real-time information.

[1062] Output: The new recalculated optimal route information.

[1063] Specific operation: The device monitors the user's location information and, if necessary, sends a recalculation request to the server. The server then recalculates based on the new real-time data and sends the results to the device.

[1064] (Application example 1)

[1065] 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."

[1066] Conventional food delivery services lack an appropriate navigation system for drivers to deliver food efficiently. In particular, they need to comprehensively analyze past traffic data, real-time traffic information, weather information, and other factors to present drivers with the optimal route. Furthermore, real-time location updates and the associated route recalculation are difficult, which can increase the workload of drivers and extend delivery times. An efficient system is needed to solve these problems.

[1067] 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.

[1068] In this invention, the server includes means for collecting and storing past traffic data and delivery history data, means for acquiring current location and speed, means for acquiring real-time traffic information, traffic light status, and weather information, and means for notifying the driver of the calculation results visually and audibly. This makes it possible to automatically calculate the optimal route based on the collected past data and real-time information and present it to the driver through visual and audio navigation. It also performs real-time location updates and corresponding recalculations to support efficient delivery work.

[1069] "Past traffic data" refers to information about past traffic conditions, including information about traffic volume, congestion, average speed, and the like.

[1070] "Delivery history data" is a series of information related to delivery work performed during a specific period, and includes data such as delivery date and time, location information of the delivery destination, and delivery time.

[1071] "Location" refers to the physical location of a subject at a specific point in time, and is primarily obtained using GPS data.

[1072] "Current speed" refers to the speed of travel from a specific point to the next point, and is also obtained using technology such as GPS.

[1073] "Real-time traffic information" refers to the latest information on current traffic conditions, including current traffic volume, congestion information, accident information, etc.

[1074] "Traffic signal status" refers to the current and future status of traffic lights on roads, and includes information such as the timing at which the traffic lights change from red to green.

[1075] "Weather information" refers to information about weather conditions from the present to the near future, including precipitation such as rain and snow, wind speed, and temperature.

[1076] An "optimal route" refers to a route calculated to minimize time, cost, distance, etc. from a starting point to a destination.

[1077] "Visual navigation" refers to navigating through visual cues, such as maps, arrows, and color changes.

[1078] "Voice navigation" refers to a method of navigating through voice, where specific instructions or warnings are given to the user through voice prompts.

[1079] "Recalculation" refers to the process of recalculating the optimal route based on any changed conditions (e.g., traffic congestion or weather changes).

[1080] "Driver" refers to a driver who performs delivery work, and in this system, is the person who is provided with the optimal route for efficient delivery.

[1081] This invention relates to a food delivery optimal route navigation application that uses an AI-based future prediction navigation system. This system collects and analyzes past traffic data, delivery history data, current location and speed, real-time traffic information, traffic light status, and weather information to provide the driver with the optimal route.

[1082] System Configuration

[1083] server

[1084] The server is responsible for the following functions:

[1085] Collection and storage of historical traffic and delivery data

[1086] Get real-time traffic, traffic light, and weather information

[1087] Analyzing collected data and calculating optimal routes

[1088] Sending calculated route information to the device

[1089] Specifically, the server uses APIs to collect historical traffic data, real-time traffic information, and weather information. This information is stored in a database and analyzed using machine learning models. For example, traffic data from the past year is collected and congestion patterns in specific areas and time periods are analyzed. The optimal route for the driver is calculated, taking into account real-time weather information and traffic light conditions. The resulting optimal route information is then sent to the device in JSON format and displayed via visual and voice navigation.

[1090] Terminal

[1091] The terminal is responsible for the following functions:

[1092] Get the user's current location and speed

[1093] Receiving optimal route information sent from the server

[1094] Visual and audio navigation notifications for received route information

[1095] Monitor your location in real time and request route recalculation when necessary

[1096] For example, the device uses the smartphone's GPS function to obtain the driver's current location and speed, and sends this data to a server. It then obtains the optimal route information sent from the server and displays it on a map using Google Maps API or similar. It also uses a voice assistant to give voice instructions to the driver. Whenever the driver's location information changes, it requests a new route calculation from the server as needed.

[1097] User

[1098] Users use this system to efficiently perform delivery work. When a user starts a delivery, the device acquires his / her current location and speed information and sends it to the server. The server calculates the optimal route and sends the result to the device. The user then makes the delivery by following the optimal route through visual and voice navigation.

[1099] Program processing explanation

[1100] The server uses various external APIs to collect historical traffic data, real-time traffic information, traffic light status, and weather information. Machine learning models are used to analyze the data, utilizing libraries such as Python, TensorFlow, and PyTorch. The device uses Android or iOS development environments to acquire GPS data and send it to the server in real time. Google Maps API and voice assistants are used for navigation.

[1101] Specific examples

[1102] Example 1

[1103] The driver launches the smartphone app and inputs their starting and destination points. The server calculates the optimal route based on historical traffic data and real-time information. The results are sent to the smartphone and presented to the driver via visual and voice navigation. Real-time location information is updated during the delivery, and the route is recalculated based on traffic light conditions and weather information.

[1104] Example prompts to input to a generative AI model:

[1105] "Calculate the optimal route to get users from their current location to their delivery destination most efficiently based on historical traffic data, current location and speed, real-time traffic information, and weather information."

[1106] As described above, the system aims to improve the efficiency of delivery work by aggregating and analyzing large amounts of data in real time, calculating the optimal route, and providing it to drivers.

[1107] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1108] Step 1:

[1109] Collection and storage of historical data

[1110] The server collects past traffic data and delivery history data through the API and stores it in a database. This process involves obtaining data including traffic volume, congestion information, and accident information for a specific area over the past year. The raw data obtained from the API is used as input, and it is converted into a database format and stored. The output is formatted past traffic data and delivery history data.

[1111] Step 2:

[1112] Get current location and speed

[1113] The device uses its GPS function to obtain the current location and speed of the driver (user) and sends that data to the server. The input is real-time location data and speed information obtained from the device's GPS. This is sent to the server, and the location information and speed data received by the server are output.

[1114] Step 3:

[1115] Real-time information collection

[1116] The server retrieves real-time traffic, traffic light, and weather information from external APIs and stores it in a database. Specifically, it collects data including traffic volume data, accident information, traffic light change timing, weather changes, etc. It uses the real-time information retrieved from the API as input and stores it in a database in an organized format as output.

[1117] Step 4:

[1118] Analyzing data and calculating optimal routes

[1119] The server uses a generative AI model to calculate the optimal route based on historical traffic data, delivery history data, real-time traffic information, traffic light conditions, and weather information. In this process, it uses all collected and stored data as input and generates prompts for the generative AI model (e.g., "Based on historical traffic data, current location and current speed, real-time traffic information, and weather information, please calculate the optimal route for the user to most efficiently reach the delivery destination from their current location."). The output is the calculation result of the optimal route.

[1120] Step 5:

[1121] Notification of optimal routes

[1122] The server sends the calculated optimal route information to the device in JSON format. The device then notifies the user of the received route information as visual and voice navigation. Specifically, a map is displayed on the device and instructions are given by the voice assistant. The input is the optimal route information sent from the server, and the output is the visual and voice navigation information presented to the user.

[1123] Step 6:

[1124] Real-time location monitoring and route recalculation

[1125] The device keeps updating the driver's location information to the server in real time. If necessary, the server recalculates the optimal route based on the new location information and sends it to the device. The request is to send the new location information, and the output is the route information recalculated and updated by the server.

[1126] Through these steps, this system will significantly improve the efficiency of food delivery, reduce the workload of drivers, and enable them to reach their destinations more quickly.

[1127] 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.

[1128] This invention combines an AI-based predictive navigation system with an emotion engine. This system collects and analyzes past traffic data, current location and speed, real-time traffic information, traffic light status, and weather information to present the optimal route to the user. Furthermore, the emotion engine recognizes the user's emotional state and adjusts the navigation accordingly, reducing stress and fatigue for the user.

[1129] System configuration

[1130] server

[1131] The server is responsible for the following functions:

[1132] Collection and storage of historical traffic and delivery data

[1133] Get real-time traffic, traffic light, and weather information

[1134] Analysis of collected data

[1135] Calculating the best route

[1136] Sending calculated route information to the device

[1137] Processing user emotion recognition data

[1138] Terminal

[1139] The terminal is responsible for the following functions:

[1140] Get the user's current location and speed

[1141] Receiving optimal route information sent from the server

[1142] Visual and audio navigation notifications for received route information

[1143] Equipped with an emotion engine that recognizes emotions from the user's voice and facial expressions

[1144] Sending emotion recognition results to the server

[1145] Monitor your location in real time and request route recalculation when necessary

[1146] User

[1147] Users can use this system to efficiently perform delivery tasks and receive suggestions based on emotion recognition.

[1148] Program processing

[1149] 1. Collection of historical data: The server periodically collects past traffic data and delivery history data and stores it in a database.

[1150] Example: Obtain traffic data for a specific area for the past year from an API and store it.

[1151] 2. Obtaining current location and speed: The device obtains the user's current location and speed using GPS and sends this to the server.

[1152] Example: When a user leaves at 8 o'clock, the device acquires its location information and movement speed and sends them to the server.

[1153] 3. Obtaining real-time information: The server obtains real-time traffic, traffic light, and weather information from external APIs and stores it in a database.

[1154] Example: A server uses an API to retrieve current traffic data and accident information for roads.

[1155] 4. Data analysis and route calculation: The server uses historical traffic data and real-time information to calculate the optimal route using a generative AI model.

[1156] Example: The server uses a machine learning model to analyze predicted traffic congestion trends for a specific time period and calculate the optimal route taking into account traffic light change timings.

[1157] 5. Sending and notifying route information: The server sends the optimal route information in JSON format to the device, which receives it and notifies the user through visual and voice navigation.

[1158] Example: The calculated route is sent in JSON format to the device, which then displays the optimal route on a map and starts voice guidance.

[1159] 6. Emotion Recognition: The device's emotion engine recognizes the user's emotions from their voice and facial expressions and sends the data to the server, which processes the information and makes suggestions to reduce the user's stress and fatigue.

[1160] For example, if the user is tired, the device will suggest relaxing music and the server will guide them to resting spots.

[1161] Specific examples

[1162] Example 1: Morning rush hour delivery

[1163] 1. The server analyzes past traffic data and predicts morning rush hour congestion.

[1164] 2. The device sends the user's current location and speed to the server.

[1165] 3. The server calculates the optimal route based on real-time information.

[1166] 4. The device notifies the user of the calculated optimal route.

[1167] 5. The emotion engine recognizes the user's stress, and the server suggests relaxing music.

[1168] 6. The user follows the suggestions and reaches the destination comfortably.

[1169] Example 2: Route changes due to sudden weather changes and emotion recognition

[1170] 1. The server predicts traffic congestion based on past data and real-time weather information.

[1171] 2. The device keeps updating the user's location and sends it to the server.

[1172] 3. When it starts to rain, the server recalculates based on real-time weather information.

[1173] 4. The device notifies the user of the recalculated route.

[1174] 5. The emotion engine recognizes the user's stress, and the server suggests rest areas.

[1175] 6. The user follows the new route, completes the delivery efficiently, and takes the suggested breaks.

[1176] In this way, the system is expected to improve the efficiency of transportation and reduce workloads by aggregating and analyzing large amounts of data in real time. In addition, by combining it with an emotion engine, it can reduce stress and fatigue for users and provide a safe and comfortable delivery environment.

[1177] The processing flow will be explained below.

[1178] Step 1:

[1179] The server periodically collects past traffic data and delivery history data and stores it in a database. Specifically, it issues API requests, organizes the acquired data using ETL processing, and stores it.

[1180] Step 2:

[1181] When a user launches the delivery app, the device uses GPS to obtain the user's current location and speed, and then sends the obtained location and speed information to the server.

[1182] Step 3:

[1183] The server receives real-time traffic, traffic light, and weather information from external APIs based on the user's current location and speed, and stores this data in a database.

[1184] Step 4:

[1185] The server inputs historical traffic data and real-time information into a generative AI model to predict congestion trends for specific time periods and areas. Specifically, this prediction is performed using machine learning algorithms (e.g., LSTM and CNN).

[1186] Step 5:

[1187] The server calculates the optimal route between the current location and the destination based on the predictions, taking into account the timing of traffic lights changing and real-time traffic information. Specifically, it uses the Dijkstra algorithm and the A algorithm.

[1188] Step 6:

[1189] The server sends the calculated optimal route information in JSON format to the device, which receives it and notifies the user with visual and voice navigation.

[1190] Step 7:

[1191] The device's emotion engine recognizes emotions from the user's voice and facial expressions and sends the data to the server, using voice tone analysis and facial recognition technology.

[1192] Step 8:

[1193] The server processes the user's emotion recognition data and makes suggestions to reduce the user's stress and fatigue. Specifically, if the server determines that the user is feeling stressed, it will suggest relaxing music. If the server determines that the user is extremely tired, it will guide the user to the nearest rest area.

[1194] Step 9:

[1195] The device will provide visual and audio notifications to the user based on the suggestions sent from the server, play suggested relaxing music, and navigate to the nearest rest spot.

[1196] Step 10:

[1197] The user travels along the optimized route and reaches their destination. They also follow emotion-based suggestions to relax and take breaks as needed. If an unexpected obstacle or traffic jam occurs along the way, they repeat the process from step 7 and receive a new optimized route and suggestions.

[1198] In this way, the system aggregates and analyzes large amounts of data in real time and recognizes the user's emotional state, thereby improving travel efficiency and reducing workload.

[1199] Example 2

[1200] 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."

[1201] While conventional navigation systems can provide optimal routes based on traffic and weather information, they do not take into account the user's emotional state. This often results in users feeling stressed or tired while traveling, which can lead to significant mental and physical strain, especially during long trips or when faced with a tight schedule. Therefore, there is a need for a navigation system that can recognize the user's emotional state in real time and make suggestions and adjustments accordingly.

[1202] 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.

[1203] In this invention, the server includes means for collecting and storing past traffic data and delivery history data, means for acquiring the user's current location and current speed using GPS, means for acquiring real-time traffic information, traffic light status, and weather information, means for analyzing past data and real-time information using a generative AI model to calculate an optimal route, means for transmitting the calculated optimal route to the terminal and presenting it by visual and voice navigation, means for recognizing emotions from the user's voice and facial expressions and processing the emotion data, and means for making suggestions to reduce the user's stress and fatigue based on the user's emotion recognition data. This enables navigation that takes the user's emotional state into consideration, providing efficient and stress-free travel.

[1204] "Past traffic data" refers to information relating to traffic flow, congestion, accident occurrence, etc. during a specific period in the past.

[1205] "Delivery history data" is information relating to the history of past deliveries, including delivery destinations, delivery times, and routes.

[1206] "Current location" refers to the user's current location, and is location information obtained by GPS.

[1207] "Current speed" refers to the speed at which the user is currently moving, and is a value measured by GPS.

[1208] "Real-time traffic information" means instantly updated information about current road conditions and traffic flow.

[1209] "Signal status" is information about the current status of road traffic lights and the timing of signal changes.

[1210] "Weather information" is information about the current weather conditions, including whether it is rainy or sunny, the temperature, etc.

[1211] A "generative AI model" is a computer model that makes predictions and classifications based on machine learning algorithms.

[1212] An "optimal route" is the most efficient travel route that saves time and distance, calculated based on specified conditions.

[1213] "Visual navigation" is a navigation method that presents information visually, such as maps and directions.

[1214] "Voice navigation" is a navigation method that provides directions and routes to the user through voice.

[1215] "Emotion recognition" is a technology that analyzes and recognizes a user's emotional state from their voice and facial expressions.

[1216] "Emotion recognition data" is data regarding a user's emotional state obtained using emotion recognition technology.

[1217] "Suggestions for reducing stress and fatigue" are specific actions and advice that the system provides to relieve the stress and fatigue that the user is feeling.

[1218] This invention combines an AI-based predictive navigation system with an emotion engine. This system collects and analyzes past traffic data, current location and speed, real-time traffic information, traffic light status, and weather information to present the optimal route to the user. Furthermore, the emotion engine recognizes the user's emotional state and adjusts navigation accordingly, reducing stress and fatigue for the user.

[1219] Server processing

[1220] The server first collects past traffic data and delivery history data through an external API and stores it in a database. The software used includes a database management system (e.g., MySQL) and an API communication tool (e.g., the Requests library). The server periodically calls the API and retrieves data in JSON format.

[1221] The server then retrieves real-time traffic, traffic light, and weather information from external APIs and stores it in a database, using the Python requests library for this process.

[1222] The server calculates the optimal route using a generative AI model (e.g., TensorFlow) based on historical traffic data and real-time information. This generative AI model learns from historical data and predicts traffic patterns by time of day.

[1223] The calculated optimal route is sent to the device in JSON format. Communication is via the HTTP protocol and a RESTful API. The server formats the calculated route information as JSON and sends it via an API endpoint accessible by the device.

[1224] Furthermore, the server processes emotion recognition data sent from the device and makes suggestions to reduce the user's stress and fatigue using machine learning algorithms that analyze the emotion data and generate specific actions (e.g., suggesting relaxing music or providing directions to rest areas).

[1225] What the device is doing

[1226] The device acquires the user's current location and speed using the GPS module and sends it to the server. Specifically, the device collects data through location services (e.g., Google Maps API) and sends it to the server using an HTTP POST request.

[1227] The device receives the optimal route information sent from the server and notifies the user through visual and voice navigation. The navigation application used is Mapbox. It uses Mapbox's API to display the route on a map and start voice guidance.

[1228] The emotion engine installed in the device recognizes emotions from the user's voice and facial expressions. This process uses emotion recognition algorithms (e.g., OpenCV and Python speech analysis libraries), analyzes the user's emotional state, and sends the results to the server.

[1229] The device monitors the user's location in real time and sends route recalculation requests to the server as needed. Location information is periodically updated using the Google Maps API to provide a superior user experience.

[1230] Specific examples

[1231] Example 1: Morning rush hour delivery

[1232] 1. The server analyzes historical traffic data and uses a TensorFlow model to predict morning rush hour congestion.

[1233] 2. The device uses the Google Maps API to obtain the user's current location and speed and sends them to the server.

[1234] 3. The server retrieves the real-time information and calculates the optimal route based on historical data and real-time information.

[1235] 4. The device uses Mapbox to provide visual and audio notification of the calculated optimal route to the user.

[1236] 5. The emotion engine analyzes the user's facial expressions using OpenCV and recognizes their stress level. The server then suggests relaxing music.

[1237] 6. The user follows the suggestions and reaches the destination comfortably while listening to relaxing music.

[1238] Example prompt:

[1239] "Please explain the process your system uses to ensure users can deliver goods comfortably during the morning rush hour. Please include specific examples of optimal route calculation and emotion recognition."

[1240] Example 2: Route changes due to sudden weather changes and emotion recognition

[1241] 1. The server predicts traffic congestion based on past data and real-time weather information. It retrieves weather data using the Python requests library.

[1242] 2. The device keeps updating the user's location through the Google Maps API and sends it to the server.

[1243] 3. When it starts to rain, the server recalculates based on real-time weather information and recalculates the optimal route using the TensorFlow model.

[1244] 4. The device notifies the user of the new route and tells them to "proceed to the new route" through voice guidance.

[1245] 5. The emotion engine recognizes the user's stress level, and the server suggests rest areas. The user's stress level is determined using facial recognition and voice analysis.

[1246] 6. The user follows the new route, completes the delivery efficiently, and takes the suggested breaks.

[1247] Example prompt:

[1248] Please explain the process of changing the route due to sudden weather changes and how the emotion engine reduces user stress. Please also include specific examples of weather information acquisition and emotion recognition.

[1249] As described above, this system combines real-time data analysis and emotion recognition to provide users with efficient and stress-free travel.

[1250] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1251] Step 1:

[1252] Collection of historical data

[1253] The server periodically collects past traffic data and delivery history data through an external API and stores it in a database. Specifically, it calls the API using the Python requests library to retrieve data in JSON format. The retrieved data is then saved in a MySQL database using the INSERT statement.

[1254] Input: Past traffic and delivery history data obtained from the API (e.g., API endpoint " / past_traffic_data")

[1255] Output: Traffic and delivery history data stored in a MySQL database

[1256] Step 2:

[1257] Get current location and speed

[1258] The device acquires the user's current location and speed using the GPS module and sends it to the server. Specifically, the device acquires the location and speed data using the device's location information service (e.g., Google Maps API) and sends it to the server via an HTTP POST request.

[1259] Input: Current location and speed obtained from the GPS module (e.g., latitude 35.6895, longitude 139.6917, speed 50km / h)

[1260] Output: Location and speed data sent to the server

[1261] Step 3:

[1262] Obtaining real-time information

[1263] The server uses an external API to obtain real-time traffic, traffic light, and weather information and stores it in a database. Specifically, it uses the Python requests library to call the API, obtains data in JSON format, and stores it in a MySQL database.

[1264] Input: Real-time traffic information, traffic light status, and weather information obtained from the API (e.g., API endpoint " / current_traffic_data")

[1265] Output: Real-time traffic, traffic light and weather information stored in a MySQL database

[1266] Step 4:

[1267] Data analysis and route calculation

[1268] The server uses a generative AI model (e.g., TensorFlow) to calculate the optimal route based on historical traffic data and real-time information. This involves inputting historical and real-time data and using the AI ​​model to output the calculated optimal route.

[1269] Input: Historical traffic data, real-time traffic information, traffic signal status, weather information

[1270] Output: The optimal route calculated by the generative AI model

[1271] Step 5:

[1272] Sending and notifying route information

[1273] The server then sends the calculated optimal route in JSON format to the device, which then receives it and provides visual and voice navigation to the user. Specifically, the device uses Mapbox to display the route on a map and provide voice guidance.

[1274] Input: Optimal route calculated by the generative AI model (JSON format)

[1275] Output: Visual and audio guidance on the device (e.g., "Turn right next")

[1276] Step 6:

[1277] emotion recognition

[1278] The device's emotion engine recognizes emotions from the user's voice and facial expressions and sends the data to the server. Specifically, it uses emotion recognition algorithms (e.g., OpenCV and Python speech analysis libraries) to analyze the user's emotional state and sends the results to the server.

[1279] Input: User's voice and facial expression data

[1280] Output: Emotion recognition data sent to the server

[1281] Step 7:

[1282] Real-time location monitoring

[1283] The device monitors the user's location in real time and sends a route recalculation request to the server as needed. Specifically, it periodically updates the location using the Google Maps API and notifies the server whenever the location changes.

[1284] Input: Real-time location information from the GPS module

[1285] Output: Location information sent to the server and route recalculation requests if necessary

[1286] (Application example 2)

[1287] 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."

[1288] Conventional navigation systems for autonomous vehicles calculate optimal routes based on past traffic data and real-time information, but they do not take into account the emotional state of passengers, making it difficult to provide both safety and comfort. Furthermore, they do not take into account stress and fatigue caused by sudden changes in weather or traffic conditions, resulting in low user satisfaction.

[1289] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting and storing past traffic data and delivery history data, means for acquiring a current location and current speed, means for acquiring real-time traffic information, traffic light conditions, and weather information, means for analyzing the past data and real-time information and calculating an optimal route, means for presenting the calculated optimal route, means for recognizing a user's emotion from voice and image, and means for processing the recognized emotion data and making suggestions to reduce the user's stress and fatigue. This enables an autonomous vehicle to travel safely and comfortably while taking the user's emotional state into consideration.

[1290] "Historical traffic data" refers to data that includes past traffic conditions and delivery history for a specific area, and is used by the navigation system to calculate optimal routes.

[1291] "Delivery history data" refers to information such as past delivery times, routes, and speeds, and is useful for improving the efficiency of logistics and delivery operations.

[1292] "Current location" refers to information indicating the location of a user or vehicle, and is obtained using technologies such as GPS.

[1293] "Current speed" refers to the speed at which a user or vehicle is currently traveling, measured in real time.

[1294] "Real-time traffic information" refers to data that is obtained in real time about current traffic conditions, and includes information about accidents and congestion.

[1295] "Signal status" is information indicating the status of traffic signals on roads, and includes data such as the color and timing of signals.

[1296] "Weather information" is information indicating the current weather conditions and forecasts, and includes weather data such as rain, snow, and sunny weather.

[1297] The "optimal route" refers to the most efficient and safe travel route calculated by comprehensively taking into account traffic conditions, weather, and other factors.

[1298] "Presentation means" refers to the means for notifying the user of the calculated optimal route visually or audibly.

[1299] "Means for recognizing a user's emotions from voice and images" refers to technology for identifying a user's emotional state using voice analysis and image analysis.

[1300] "Means for processing emotional data" refers to technology that generates suggestions and measures to reduce the user's stress and fatigue based on the recognized emotional data.

[1301] A "generative AI model" refers to a machine learning model used to predict future situations based on past data and real-time information.

[1302] "Prompt" refers to the language used to give specific instructions to an AI model, including specific questions or requests.

[1303] This invention is a navigation system for autonomous vehicles that combines an AI-based future prediction navigation system with an emotion engine. This system collects and analyzes a wide variety of data and provides the optimal route taking into account the user's emotional state to ensure safe and comfortable driving of autonomous vehicles.

[1304] System configuration

[1305] server

[1306] The server is responsible for the following functions:

[1307] Collection and storage of past traffic data: The server periodically collects the user's delivery history data and past traffic data and stores them in a database.

[1308] Acquisition and storage of real-time information: The server acquires real-time traffic information, traffic light status, and weather information from external APIs and stores this data.

[1309] Data analysis and route calculation: The server analyzes the collected historical data and real-time information using a generative AI model to calculate the optimal route.

[1310] Route information distribution: The calculated optimal route information is sent to the terminal in JSON format.

[1311] Emotion data processing: Processes emotion recognition data sent from the device and makes suggestions to reduce the user's stress and fatigue.

[1312] Terminal

[1313] The terminal is responsible for the following functions:

[1314] Obtaining current location and speed: The user's current location and current moving speed are obtained using GPS and sent to the server.

[1315] Receiving and presenting route information: Receives optimal route information sent from the server and notifies the user through visual and voice navigation.

[1316] Equipped with an emotion engine: Recognizes the user's emotions from voice and facial expressions, and sends the emotion data to the server.

[1317] User

[1318] Using this system, users can travel comfortably and efficiently. By accepting stress reduction suggestions based on emotion recognition, they can reduce fatigue and stress during travel.

[1319] How it works

[1320] 1. Obtaining historical data and real-time information: The server periodically collects historical and real-time traffic information, including obtaining traffic information using APIs, checking traffic signal status, and collecting weather data.

[1321] 2. Obtaining current location and speed: The device uses GPS technology to obtain the user's current location and speed and transmits them to the server.

[1322] 3. Calculate optimal route: The server calculates the optimal route using a generative AI model based on historical data and real-time information.

[1323] 4. Route delivery and notification: The calculated optimal route is sent to the device in JSON format, and the device notifies the user visually and audibly.

[1324] 5. Emotion recognition and processing: The device's emotion engine analyzes the user's voice and facial expressions and sends the emotional data to the server, which processes the information and makes suggestions to reduce the user's stress and fatigue.

[1325] Specific examples

[1326] Example 1: Morning rush hour delivery

[1327] The server analyzes past traffic data and predicts congestion during the morning rush hour. The device sends the user's current location and speed to the server, which then calculates the optimal route based on real-time information. The user is then notified of the optimal route. The emotion engine recognizes the user's stress, and the server suggests relaxing music.

[1328] Example 2: Route changes due to sudden weather changes and emotion recognition

[1329] The server predicts traffic congestion based on past data and real-time weather information. The device continuously updates the user's current location and sends it to the server. When it starts to rain, the server recalculates the route based on real-time weather information. The device notifies the user of the recalculated route, the emotion engine recognizes the user's stress, and the server suggests rest stops.

[1330] Example prompt for a generative AI model:

[1331] "Calculate the most efficient route based on historical data and real-time information to provide shorter routes during the morning rush hour."

[1332] This system not only allows users to travel to their destinations efficiently and comfortably using self-driving vehicles, but also reduces stress and fatigue along the way.

[1333] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1334] Step 1: Collect and store historical data

[1335] The server collects past traffic data and delivery history data and stores it in a database. Specifically, it retrieves traffic information from the past few years from an external API and integrates it with the delivery history database. This provides data for analyzing past traffic conditions and delivery route trends. The input is the output of the past traffic data API, and the output is a database update that stores this data.

[1336] Step 2: Get your location and speed

[1337] The device uses GPS technology to obtain the user's current location and speed. It then sends the location and speed information to the server. The input is location and speed information from the GPS sensor, and the output is a data packet containing this information sent to the server.

[1338] Step 3: Obtaining real-time information

[1339] The server obtains real-time traffic information, traffic light status, and weather information through external APIs. This information is stored in a database. The inputs are the real-time traffic information API, traffic light status API, and weather information API, and the output is data containing this real-time information.

[1340] Step 4: Data analysis and route calculation

[1341] The server uses a generative AI model to analyze past traffic data, current location, current speed, and real-time information to calculate the optimal route. The inputs are past traffic data, current location, current speed, and real-time traffic information, and the output is optimal route information. Specifically, the server sends a prompt to the generative AI model to instruct it to calculate the optimal route. An example of a prompt is as follows:

[1342] "Calculate the most efficient route based on historical data and real-time information to provide shorter routes during the morning rush hour."

[1343] Step 5: Send and submit your route

[1344] The server sends the calculated optimal route information in JSON format to the device, which then notifies the user of this information as visual and audio navigation. The input is the JSON data of the optimal route information, and the output is navigation information that the user can confirm visually and audibly.

[1345] Step 6: Recognize emotions

[1346] The device's emotion engine uses a camera and microphone to recognize emotions from the user's voice and facial expressions. The recognized emotion data is sent to the server. The inputs are the user's voice data and camera footage, and the output is analyzed emotion data.

[1347] Step 7: Processing emotional data and making stress reduction suggestions

[1348] The server processes the emotional data sent from the device and generates suggestions to reduce the user's stress and fatigue. The input is emotional data, and the output is specific suggestions for reducing stress. Specifically, the server suggests relaxing music and resting spots based on the results of data processing.

[1349] Through the above steps, the system of the present invention enables an autonomous vehicle to provide an optimal route while taking into account the emotional state of the user, enabling comfortable and safe travel.

[1350] 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.

[1351] 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.

[1352] 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.

[1353] [Fourth embodiment]

[1354] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1355] 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.

[1356] 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).

[1357] 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.

[1358] 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.

[1359] 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).

[1360] 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.

[1361] 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.

[1362] 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.

[1363] 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.

[1364] 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.

[1365] 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.

[1366] 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."

[1367] The present invention relates to an AI-based predictive navigation system that collects and analyzes past traffic data, current location and speed, real-time traffic information, traffic signal status, and weather information to present the optimal route to the user.

[1368] System configuration

[1369] server

[1370] The server is responsible for the following functions:

[1371] Collection and storage of historical traffic and delivery data

[1372] Get real-time traffic, traffic light, and weather information

[1373] Analysis of collected data

[1374] Calculating the best route

[1375] Sending calculated route information to the device

[1376] Terminal

[1377] The terminal is responsible for the following functions:

[1378] Get the user's current location and speed

[1379] Receiving optimal route information sent from the server

[1380] Visual and audio navigation notifications for received route information

[1381] Monitor your location in real time and request route recalculation when necessary

[1382] User

[1383] Users can use this system to efficiently carry out delivery work.

[1384] Program processing

[1385] 1. Collection of historical data: The server periodically collects past traffic data and delivery history data and stores it in a database.

[1386] Example: Obtain traffic data for a specific area for the past year from an API and store it.

[1387] 2. Obtaining current location and speed: The device obtains the user's current location and speed using GPS and sends this to the server.

[1388] Example: When a user leaves at 8 o'clock, the device acquires its location information and movement speed and sends them to the server.

[1389] 3. Obtaining real-time information: The server obtains real-time traffic, traffic light, and weather information from external APIs and stores it in a database.

[1390] Example: A server uses an API to retrieve current traffic data and accident information for roads.

[1391] 4. Data analysis and route calculation: The server uses collected historical data and real-time information to calculate the optimal route using a generative AI model.

[1392] Example: The server uses a machine learning model to analyze predicted traffic congestion trends for a specific time period and calculate the optimal route taking into account traffic light change timings.

[1393] 5. Sending and notifying route information: The server sends the optimal route information to the terminal, and the terminal notifies the user of the route information. Navigation is performed visually and by voice.

[1394] Example: The calculated route is sent in JSON format to the device, which then displays the optimal route on a map and starts voice guidance.

[1395] Specific examples

[1396] Example 1: Morning rush hour delivery

[1397] 1. The server analyzes past traffic data and predicts morning rush hour congestion.

[1398] 2. The device sends the user's current location and speed to the server.

[1399] 3. The server calculates the optimal route based on real-time information.

[1400] 4. The device notifies the user of the calculated optimal route.

[1401] 5. The user follows the route and reaches the destination safely.

[1402] Example 2: Route change due to sudden weather change

[1403] 1. The server predicts traffic congestion based on past data and real-time weather information.

[1404] 2. The device keeps updating the user's location and sends it to the server.

[1405] 3. When it starts to rain, the server recalculates based on real-time weather information.

[1406] 4. The device notifies the user of the recalculated route.

[1407] 5. The user follows the new route and completes the delivery efficiently.

[1408] In this way, the system is expected to improve the efficiency of travel and reduce workload by aggregating and analyzing large amounts of data in real time.

[1409] The processing flow will be explained below.

[1410] Step 1:

[1411] The server collects past traffic data and delivery history data and stores it in a database. It periodically issues API requests, organizes the obtained data using ETL (Extract, Transform, Load) processing, and stores it in the database.

[1412] Step 2:

[1413] When a user launches the delivery app, the device uses GPS to obtain the user's current location and speed, and then sends the obtained location and speed information to the server.

[1414] Step 3:

[1415] The server receives real-time traffic information, traffic light status, and weather information from external APIs based on the user's current location and speed, and stores this data in a database.

[1416] Step 4:

[1417] The server inputs historical traffic data and current real-time information into a generative AI model to predict congestion trends for specific times of day and areas. This prediction is carried out using machine learning algorithms.

[1418] Step 5:

[1419] The server calculates the optimal route between your current location and your destination based on the predictions, taking into account traffic light change timing and real-time traffic information.

[1420] Step 6:

[1421] The server sends the calculated optimal route information in JSON format to the device, which receives it and notifies the user with visual and voice navigation.

[1422] Step 7:

[1423] The device tracks the user's movements in real time and, based on location and speed information, sends a request to the server to calculate a new route as needed.

[1424] Step 8:

[1425] If the server detects a change in real-time information, it recalculates a new optimal route and retransmits it to the terminal, which then notifies the user of the updated route information.

[1426] Step 9:

[1427] The user travels along the optimal route and reaches the destination. If an unexpected obstacle or traffic jam occurs along the way, steps 7 and 8 are repeated to receive a new optimal route.

[1428] Example 1

[1429] 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."

[1430] Current traffic navigation systems lack the information necessary to present optimal routes to users, or are unable to respond to real-time changes in conditions. Furthermore, conventional systems often present routes with low prediction accuracy because they do not fully utilize past traffic data or weather information. The present invention aims to solve these problems and provide more accurate future prediction navigation.

[1431] 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.

[1432] In this invention, the server includes means for collecting and storing past traffic data and delivery history data, means for acquiring current location and speed, means for acquiring real-time traffic information, traffic light status, and weather information, means for analyzing the past data and real-time information with a generative AI model and calculating an optimal route, means for presenting the calculated optimal route to the user using visual and voice navigation, and means for monitoring location information in real time and requesting route recalculation as necessary. This makes it possible to respond to changes in conditions in real time and present highly accurate routes using past data.

[1433] "Past traffic data" refers to data that indicates traffic flow and conditions during a specific period in the past.

[1434] "Delivery history data" refers to data that records past delivery routes, times, and situations.

[1435] "Current location" is specific location information of the user's current location.

[1436] "Current speed" is information about the speed at which the user is currently moving.

[1437] "Real-time traffic information" means up-to-date information showing current traffic conditions.

[1438] "Signal status" is information indicating the current status of a traffic signal.

[1439] "Weather information" is information that indicates the current weather conditions.

[1440] A "generative AI model" is an artificial intelligence model that uses past data and real-time information to predict future traffic conditions.

[1441] The "optimal route" is the most efficient travel route for the user, taking into account traffic conditions, traffic signals, weather, etc.

[1442] "Visual navigation" is a method of visually guiding a user along a route using maps and graphics.

[1443] "Voice navigation" is a method of providing route guidance to a user using voice.

[1444] "Location monitoring" is the process of continually determining a user's current location.

[1445] "Route recalculation" is the process of recalculating the optimal route based on changed real-time information.

[1446] The present invention relates to an AI-based future prediction navigation system, which is mainly composed of a server, a terminal, and a user, and provides the user with the optimal route by utilizing past traffic data, current location and speed, real-time traffic information, traffic signal status, and weather information.

[1447] Server Roles

[1448] The server is responsible for:

[1449] 1. Collecting and storing historical traffic and delivery data:

[1450] The server periodically collects past traffic data and delivery history data from external traffic information APIs and stores them in a database. Specifically, it uses Google Maps APIs and other sources to obtain and store traffic data for specific areas over the past year.

[1451] 2. Get real-time information:

[1452] The server obtains real-time traffic information, traffic light status, and weather information from external APIs (e.g., traffic information API, weather information API) and stores it in a database. This allows you to grasp current traffic volume, road accident information, traffic light timing, weather changes, etc. in real time.

[1453] 3. Data analysis and route calculation:

[1454] The server calculates the optimal route using a generative AI model (artificial intelligence model) based on collected historical data and real-time information. It uses a machine learning model to analyze predicted congestion trends during specific time periods and calculates the optimal route taking into account the timing of traffic light changes.

[1455] 4. Sending route information:

[1456] The server sends the calculated optimal route information to the device in JSON format, which is transferred to the device in real time.

[1457] Device Role

[1458] The terminal is responsible for:

[1459] 1. Get current location and speed:

[1460] The device uses GPS to acquire the user's current location and speed, which are then processed by software on the device and sent to a server.

[1461] 2. Route information reception and notification:

[1462] The device receives the optimal route information sent from the server and notifies the user of it through visual and voice navigation. Specifically, the optimal route is displayed on a map and voice guidance begins. For example, when the user heads towards their destination, the optimal route is displayed on the map app and voice guidance such as "Turn right" is given.

[1463] 3. Real-time location monitoring and route recalculation requests:

[1464] The device monitors the user's location information in real time and sends a route recalculation request to the server as needed. For example, if traffic congestion or accident information is detected, the device automatically requests a re-route from the server, and the optimal route is recalculated.

[1465] User Roles

[1466] Users use this system to travel efficiently, and can reach their destination safely and quickly by following the optimal route provided by the system. For example, if a user leaves at 8:00, the GPS acquires their location information and travel speed, which are then sent to the server. The optimal route is then calculated based on real-time information, and the device provides visual and voice navigation.

[1467] Prompt Sentence Examples

[1468] An example of a prompt for a generative AI model is:

[1469] "Calculate the optimal delivery route using historical traffic data and current real-time information, especially taking into account designated traffic light timings and weather information."

[1470] In this way, by aggregating and analyzing large amounts of data in real time, this system is expected to provide users with the optimal route, making travel more efficient and reducing workload.

[1471] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1472] Step 1: Collect historical data

[1473] The server collects past traffic data and delivery history data from an external traffic information API and stores it in a database.

[1474] Input: Traffic information API URL and required authentication information.

[1475] Data processing: Request historical traffic data using API and store the acquired data in a database in JSON format.

[1476] Output: Historical traffic and delivery history data stored in a database.

[1477] Specific operation: The server sends a request to the Google Maps API to obtain traffic data for a specific area for the past year and stores it in a database.

[1478] Step 2: Get your current location and speed

[1479] The device acquires the user's current location and speed using a GPS module and transmits this information to the server.

[1480] Input: Current location and speed information obtained by the device's GPS module.

[1481] Data processing: The acquired location and speed information is converted into JSON format and sent to the server.

[1482] Output: Current location and speed data sent to the server.

[1483] How it works: The device's GPS module measures the current location and speed, and dedicated software converts this into JSON format, which is then sent to the server.

[1484] Step 3: Obtaining real-time information

[1485] The server retrieves real-time traffic, traffic light, and weather information from external APIs and stores it in a database.

[1486] Input: Traffic and Weather API URLs and authentication information.

[1487] Data processing: Send API requests and store the acquired real-time information in a database.

[1488] Output: Real-time traffic information, traffic light status, and weather information stored in a database.

[1489] Specific operation: The server sends requests to the traffic information API and weather information API and adds the returned data to the database.

[1490] Step 4: Analyze data and calculate route

[1491] The server calculates the optimal route using a generative AI model based on collected historical data and real-time information.

[1492] Input: Historical traffic data stored in the database, delivery history data, real-time traffic information, traffic light status, and weather information.

[1493] Data calculation: This data is input into the generative AI model to calculate the optimal route.

[1494] Output: Calculated optimal route information.

[1495] Specific operation: Using a machine learning model, the system analyzes predicted congestion trends for specific time periods and calculates the optimal route taking into account the timing of traffic light changes.

[1496] Step 5: Send and notify route information

[1497] The server transmits the calculated optimum route information to the terminal, which notifies the user of the information through visual and voice navigation.

[1498] Input: Optimal route information calculated by the generative AI model.

[1499] Data processing: Convert optimal route information into JSON format and send it to the terminal.

[1500] Output: The optimal route information sent to the device.

[1501] Specific operation: The server sends optimal route information to the device, and the device displays the received information on the map app and starts voice guidance.

[1502] Step 6: Monitor real-time location and recalculate route

[1503] The device continuously monitors the user's location in real time and, if necessary, sends a route recalculation request to the server, which then recalculates using the new data.

[1504] Input: Current location and speed obtained from the device's GPS module.

[1505] Data processing: Receives a recalculation request from the terminal and recalculates the route based on new real-time information.

[1506] Output: The new recalculated optimal route information.

[1507] Specific operation: The device monitors the user's location information and, if necessary, sends a recalculation request to the server. The server then recalculates based on the new real-time data and sends the results to the device.

[1508] (Application example 1)

[1509] 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."

[1510] Conventional food delivery services lack an appropriate navigation system for drivers to deliver food efficiently. In particular, they need to comprehensively analyze past traffic data, real-time traffic information, weather information, and other factors to present drivers with the optimal route. Furthermore, real-time location updates and the associated route recalculation are difficult, which can increase the workload of drivers and extend delivery times. An efficient system is needed to solve these problems.

[1511] 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.

[1512] In this invention, the server includes means for collecting and storing past traffic data and delivery history data, means for acquiring current location and speed, means for acquiring real-time traffic information, traffic light status, and weather information, and means for notifying the driver of the calculation results visually and audibly. This makes it possible to automatically calculate the optimal route based on the collected past data and real-time information and present it to the driver through visual and audio navigation. It also performs real-time location updates and corresponding recalculations to support efficient delivery work.

[1513] "Past traffic data" refers to information about past traffic conditions, including information about traffic volume, congestion, average speed, and the like.

[1514] "Delivery history data" is a series of information related to delivery work performed during a specific period, and includes data such as delivery date and time, location information of the delivery destination, and delivery time.

[1515] "Location" refers to the physical location of a subject at a specific point in time, and is primarily obtained using GPS data.

[1516] "Current speed" refers to the speed of travel from a specific point to the next point, and is also obtained using technology such as GPS.

[1517] "Real-time traffic information" refers to the latest information on current traffic conditions, including current traffic volume, congestion information, accident information, etc.

[1518] "Traffic signal status" refers to the current and future status of traffic lights on roads, and includes information such as the timing at which the traffic lights change from red to green.

[1519] "Weather information" refers to information about weather conditions from the present to the near future, including precipitation such as rain and snow, wind speed, and temperature.

[1520] An "optimal route" refers to a route calculated to minimize time, cost, distance, etc. from a starting point to a destination.

[1521] "Visual navigation" refers to navigating through visual cues, such as maps, arrows, and color changes.

[1522] "Voice navigation" refers to a method of navigating through voice, where specific instructions or warnings are given to the user through voice prompts.

[1523] "Recalculation" refers to the process of recalculating the optimal route based on any changed conditions (e.g., traffic congestion or weather changes).

[1524] "Driver" refers to a driver who performs delivery work, and in this system, is the person who is provided with the optimal route for efficient delivery.

[1525] This invention relates to a food delivery optimal route navigation application that uses an AI-based future prediction navigation system. This system collects and analyzes past traffic data, delivery history data, current location and speed, real-time traffic information, traffic light status, and weather information to provide the driver with the optimal route.

[1526] System Configuration

[1527] server

[1528] The server is responsible for the following functions:

[1529] Collection and storage of historical traffic and delivery data

[1530] Get real-time traffic, traffic light, and weather information

[1531] Analyzing collected data and calculating optimal routes

[1532] Sending calculated route information to the device

[1533] Specifically, the server uses APIs to collect historical traffic data, real-time traffic information, and weather information. This information is stored in a database and analyzed using machine learning models. For example, traffic data from the past year is collected and congestion patterns in specific areas and time periods are analyzed. The optimal route for the driver is calculated, taking into account real-time weather information and traffic light conditions. The resulting optimal route information is then sent to the device in JSON format and displayed via visual and voice navigation.

[1534] Terminal

[1535] The terminal is responsible for the following functions:

[1536] Get the user's current location and speed

[1537] Receiving optimal route information sent from the server

[1538] Visual and audio navigation notifications for received route information

[1539] Monitor your location in real time and request route recalculation when necessary

[1540] For example, the device uses the smartphone's GPS function to obtain the driver's current location and speed, and sends this data to a server. It then obtains the optimal route information sent from the server and displays it on a map using Google Maps API or similar. It also uses a voice assistant to give voice instructions to the driver. Whenever the driver's location information changes, it requests a new route calculation from the server as needed.

[1541] User

[1542] Users use this system to efficiently perform delivery work. When a user starts a delivery, the device acquires his / her current location and speed information and sends it to the server. The server calculates the optimal route and sends the result to the device. The user then makes the delivery by following the optimal route through visual and voice navigation.

[1543] Program processing explanation

[1544] The server uses various external APIs to collect historical traffic data, real-time traffic information, traffic light status, and weather information. Machine learning models are used to analyze the data, utilizing libraries such as Python, TensorFlow, and PyTorch. The device uses Android or iOS development environments to acquire GPS data and send it to the server in real time. Google Maps API and voice assistants are used for navigation.

[1545] Specific examples

[1546] Example 1

[1547] The driver launches the smartphone app and inputs their starting and destination points. The server calculates the optimal route based on historical traffic data and real-time information. The results are sent to the smartphone and presented to the driver via visual and voice navigation. Real-time location information is updated during the delivery, and the route is recalculated based on traffic light conditions and weather information.

[1548] Example prompts to input to a generative AI model:

[1549] "Calculate the optimal route to get users from their current location to their delivery destination most efficiently based on historical traffic data, current location and speed, real-time traffic information, and weather information."

[1550] As described above, the system aims to improve the efficiency of delivery work by aggregating and analyzing large amounts of data in real time, calculating the optimal route, and providing it to drivers.

[1551] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1552] Step 1:

[1553] Collection and storage of historical data

[1554] The server collects past traffic data and delivery history data through the API and stores it in a database. This process involves obtaining data including traffic volume, congestion information, and accident information for a specific area over the past year. The raw data obtained from the API is used as input, and it is converted into a database format and stored. The output is formatted past traffic data and delivery history data.

[1555] Step 2:

[1556] Get current location and speed

[1557] The device uses its GPS function to obtain the current location and speed of the driver (user) and sends that data to the server. The input is real-time location data and speed information obtained from the device's GPS. This is sent to the server, and the location information and speed data received by the server are output.

[1558] Step 3:

[1559] Real-time information collection

[1560] The server retrieves real-time traffic, traffic light, and weather information from external APIs and stores it in a database. Specifically, it collects data including traffic volume data, accident information, traffic light change timing, weather changes, etc. It uses the real-time information retrieved from the API as input and stores it in a database in an organized format as output.

[1561] Step 4:

[1562] Analyzing data and calculating optimal routes

[1563] The server uses a generative AI model to calculate the optimal route based on historical traffic data, delivery history data, real-time traffic information, traffic light conditions, and weather information. In this process, it uses all collected and stored data as input and generates prompts for the generative AI model (e.g., "Based on historical traffic data, current location and current speed, real-time traffic information, and weather information, please calculate the optimal route for the user to most efficiently reach the delivery destination from their current location."). The output is the calculation result of the optimal route.

[1564] Step 5:

[1565] Notification of optimal routes

[1566] The server sends the calculated optimal route information to the device in JSON format. The device then notifies the user of the received route information as visual and voice navigation. Specifically, a map is displayed on the device and instructions are given by the voice assistant. The input is the optimal route information sent from the server, and the output is the visual and voice navigation information presented to the user.

[1567] Step 6:

[1568] Real-time location monitoring and route recalculation

[1569] The device keeps updating the driver's location information to the server in real time. If necessary, the server recalculates the optimal route based on the new location information and sends it to the device. The request is to send the new location information, and the output is the route information recalculated and updated by the server.

[1570] Through these steps, this system will significantly improve the efficiency of food delivery, reduce the workload of drivers, and enable them to reach their destinations more quickly.

[1571] 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.

[1572] This invention combines an AI-based predictive navigation system with an emotion engine. This system collects and analyzes past traffic data, current location and speed, real-time traffic information, traffic light status, and weather information to present the optimal route to the user. Furthermore, the emotion engine recognizes the user's emotional state and adjusts the navigation accordingly, reducing stress and fatigue for the user.

[1573] System configuration

[1574] server

[1575] The server is responsible for the following functions:

[1576] Collection and storage of historical traffic and delivery data

[1577] Get real-time traffic, traffic light, and weather information

[1578] Analysis of collected data

[1579] Calculating the best route

[1580] Sending calculated route information to the device

[1581] Processing user emotion recognition data

[1582] Terminal

[1583] The terminal is responsible for the following functions:

[1584] Get the user's current location and speed

[1585] Receiving optimal route information sent from the server

[1586] Visual and audio navigation notifications for received route information

[1587] Equipped with an emotion engine that recognizes emotions from the user's voice and facial expressions

[1588] Sending emotion recognition results to the server

[1589] Monitor your location in real time and request route recalculation when necessary

[1590] User

[1591] Users can use this system to efficiently perform delivery tasks and receive suggestions based on emotion recognition.

[1592] Program processing

[1593] 1. Collection of historical data: The server periodically collects past traffic data and delivery history data and stores it in a database.

[1594] Example: Obtain traffic data for a specific area for the past year from an API and store it.

[1595] 2. Obtaining current location and speed: The device obtains the user's current location and speed using GPS and sends this to the server.

[1596] Example: When a user leaves at 8 o'clock, the device acquires its location information and movement speed and sends them to the server.

[1597] 3. Obtaining real-time information: The server obtains real-time traffic, traffic light, and weather information from external APIs and stores it in a database.

[1598] Example: A server uses an API to retrieve current traffic data and accident information for roads.

[1599] 4. Data analysis and route calculation: The server uses historical traffic data and real-time information to calculate the optimal route using a generative AI model.

[1600] Example: The server uses a machine learning model to analyze predicted traffic congestion trends for a specific time period and calculate the optimal route taking into account traffic light change timings.

[1601] 5. Sending and notifying route information: The server sends the optimal route information in JSON format to the device, which receives it and notifies the user through visual and voice navigation.

[1602] Example: The calculated route is sent in JSON format to the device, which then displays the optimal route on a map and starts voice guidance.

[1603] 6. Emotion Recognition: The device's emotion engine recognizes the user's emotions from their voice and facial expressions and sends the data to the server, which processes the information and makes suggestions to reduce the user's stress and fatigue.

[1604] For example, if the user is tired, the device will suggest relaxing music and the server will guide them to resting spots.

[1605] Specific examples

[1606] Example 1: Morning rush hour delivery

[1607] 1. The server analyzes past traffic data and predicts morning rush hour congestion.

[1608] 2. The device sends the user's current location and speed to the server.

[1609] 3. The server calculates the optimal route based on real-time information.

[1610] 4. The device notifies the user of the calculated optimal route.

[1611] 5. The emotion engine recognizes the user's stress, and the server suggests relaxing music.

[1612] 6. The user follows the suggestions and reaches the destination comfortably.

[1613] Example 2: Route changes due to sudden weather changes and emotion recognition

[1614] 1. The server predicts traffic congestion based on past data and real-time weather information.

[1615] 2. The device keeps updating the user's location and sends it to the server.

[1616] 3. When it starts to rain, the server recalculates based on real-time weather information.

[1617] 4. The device notifies the user of the recalculated route.

[1618] 5. The emotion engine recognizes the user's stress, and the server suggests rest areas.

[1619] 6. The user follows the new route, completes the delivery efficiently, and takes the suggested breaks.

[1620] In this way, the system is expected to improve the efficiency of transportation and reduce workloads by aggregating and analyzing large amounts of data in real time. In addition, by combining it with an emotion engine, it can reduce stress and fatigue for users and provide a safe and comfortable delivery environment.

[1621] The processing flow will be explained below.

[1622] Step 1:

[1623] The server periodically collects past traffic data and delivery history data and stores it in a database. Specifically, it issues API requests, organizes the acquired data using ETL processing, and stores it.

[1624] Step 2:

[1625] When a user launches the delivery app, the device uses GPS to obtain the user's current location and speed, and then sends the obtained location and speed information to the server.

[1626] Step 3:

[1627] The server receives real-time traffic, traffic light, and weather information from external APIs based on the user's current location and speed, and stores this data in a database.

[1628] Step 4:

[1629] The server inputs historical traffic data and real-time information into a generative AI model to predict congestion trends for specific time periods and areas. Specifically, this prediction is performed using machine learning algorithms (e.g., LSTM and CNN).

[1630] Step 5:

[1631] The server calculates the optimal route between the current location and the destination based on the predictions, taking into account the timing of traffic lights changing and real-time traffic information. Specifically, it uses the Dijkstra algorithm and the A algorithm.

[1632] Step 6:

[1633] The server sends the calculated optimal route information in JSON format to the device, which receives it and notifies the user with visual and voice navigation.

[1634] Step 7:

[1635] The device's emotion engine recognizes emotions from the user's voice and facial expressions and sends the data to the server, using voice tone analysis and facial recognition technology.

[1636] Step 8:

[1637] The server processes the user's emotion recognition data and makes suggestions to reduce the user's stress and fatigue. Specifically, if the server determines that the user is feeling stressed, it will suggest relaxing music. If the server determines that the user is extremely tired, it will guide the user to the nearest rest area.

[1638] Step 9:

[1639] The device will provide visual and audio notifications to the user based on the suggestions sent from the server, play suggested relaxing music, and navigate to the nearest rest spot.

[1640] Step 10:

[1641] The user travels along the optimized route and reaches their destination. They also follow emotion-based suggestions to relax and take breaks as needed. If an unexpected obstacle or traffic jam occurs along the way, they repeat the process from step 7 and receive a new optimized route and suggestions.

[1642] In this way, the system aggregates and analyzes large amounts of data in real time and recognizes the user's emotional state, thereby improving travel efficiency and reducing workload.

[1643] Example 2

[1644] 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."

[1645] While conventional navigation systems can provide optimal routes based on traffic and weather information, they do not take into account the user's emotional state. This often results in users feeling stressed or tired while traveling, which can lead to significant mental and physical strain, especially during long trips or when faced with a tight schedule. Therefore, there is a need for a navigation system that can recognize the user's emotional state in real time and make suggestions and adjustments accordingly.

[1646] 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.

[1647] In this invention, the server includes means for collecting and storing past traffic data and delivery history data, means for acquiring the user's current location and current speed using GPS, means for acquiring real-time traffic information, traffic light status, and weather information, means for analyzing past data and real-time information using a generative AI model to calculate an optimal route, means for transmitting the calculated optimal route to the terminal and presenting it by visual and voice navigation, means for recognizing emotions from the user's voice and facial expressions and processing the emotion data, and means for making suggestions to reduce the user's stress and fatigue based on the user's emotion recognition data. This enables navigation that takes the user's emotional state into consideration, providing efficient and stress-free travel.

[1648] "Past traffic data" refers to information relating to traffic flow, congestion, accident occurrence, etc. during a specific period in the past.

[1649] "Delivery history data" is information relating to the history of past deliveries, including delivery destinations, delivery times, and routes.

[1650] "Current location" refers to the user's current location, and is location information obtained by GPS.

[1651] "Current speed" refers to the speed at which the user is currently moving, and is a value measured by GPS.

[1652] "Real-time traffic information" means instantly updated information about current road conditions and traffic flow.

[1653] "Signal status" is information about the current status of road traffic lights and the timing of signal changes.

[1654] "Weather information" is information about the current weather conditions, including whether it is rainy or sunny, the temperature, etc.

[1655] A "generative AI model" is a computer model that makes predictions and classifications based on machine learning algorithms.

[1656] An "optimal route" is the most efficient travel route that saves time and distance, calculated based on specified conditions.

[1657] "Visual navigation" is a navigation method that presents information visually, such as maps and directions.

[1658] "Voice navigation" is a navigation method that provides directions and routes to the user through voice.

[1659] "Emotion recognition" is a technology that analyzes and recognizes a user's emotional state from their voice and facial expressions.

[1660] "Emotion recognition data" is data regarding a user's emotional state obtained using emotion recognition technology.

[1661] "Suggestions for reducing stress and fatigue" are specific actions and advice that the system provides to relieve the stress and fatigue that the user is feeling.

[1662] This invention combines an AI-based predictive navigation system with an emotion engine. This system collects and analyzes past traffic data, current location and speed, real-time traffic information, traffic light status, and weather information to present the optimal route to the user. Furthermore, the emotion engine recognizes the user's emotional state and adjusts navigation accordingly, reducing stress and fatigue for the user.

[1663] Server processing

[1664] The server first collects past traffic data and delivery history data through an external API and stores it in a database. The software used includes a database management system (e.g., MySQL) and an API communication tool (e.g., the Requests library). The server periodically calls the API and retrieves data in JSON format.

[1665] The server then retrieves real-time traffic, traffic light, and weather information from external APIs and stores it in a database, using the Python requests library for this process.

[1666] The server calculates the optimal route using a generative AI model (e.g., TensorFlow) based on historical traffic data and real-time information. This generative AI model learns from historical data and predicts traffic patterns by time of day.

[1667] The calculated optimal route is sent to the device in JSON format. Communication is via the HTTP protocol and a RESTful API. The server formats the calculated route information as JSON and sends it via an API endpoint accessible by the device.

[1668] Furthermore, the server processes emotion recognition data sent from the device and makes suggestions to reduce the user's stress and fatigue using machine learning algorithms that analyze the emotion data and generate specific actions (e.g., suggesting relaxing music or providing directions to rest areas).

[1669] What the device is doing

[1670] The device acquires the user's current location and speed using the GPS module and sends it to the server. Specifically, the device collects data through location services (e.g., Google Maps API) and sends it to the server using an HTTP POST request.

[1671] The device receives the optimal route information sent from the server and notifies the user through visual and voice navigation. The navigation application used is Mapbox. It uses Mapbox's API to display the route on a map and start voice guidance.

[1672] The emotion engine installed in the device recognizes emotions from the user's voice and facial expressions. This process uses emotion recognition algorithms (e.g., OpenCV and Python speech analysis libraries), analyzes the user's emotional state, and sends the results to the server.

[1673] The device monitors the user's location in real time and sends route recalculation requests to the server as needed. Location information is periodically updated using the Google Maps API to provide a superior user experience.

[1674] Specific examples

[1675] Example 1: Morning rush hour delivery

[1676] 1. The server analyzes historical traffic data and uses a TensorFlow model to predict morning rush hour congestion.

[1677] 2. The device uses the Google Maps API to obtain the user's current location and speed and sends them to the server.

[1678] 3. The server retrieves the real-time information and calculates the optimal route based on historical data and real-time information.

[1679] 4. The device uses Mapbox to provide visual and audio notification of the calculated optimal route to the user.

[1680] 5. The emotion engine analyzes the user's facial expressions using OpenCV and recognizes their stress level. The server then suggests relaxing music.

[1681] 6. The user follows the suggestions and reaches the destination comfortably while listening to relaxing music.

[1682] Example prompt:

[1683] "Please explain the process your system uses to ensure users can deliver goods comfortably during the morning rush hour. Please include specific examples of optimal route calculation and emotion recognition."

[1684] Example 2: Route changes due to sudden weather changes and emotion recognition

[1685] 1. The server predicts traffic congestion based on past data and real-time weather information. It retrieves weather data using the Python requests library.

[1686] 2. The device keeps updating the user's location through the Google Maps API and sends it to the server.

[1687] 3. When it starts to rain, the server recalculates based on real-time weather information and recalculates the optimal route using the TensorFlow model.

[1688] 4. The device notifies the user of the new route and tells them to "proceed along the new route" through voice guidance.

[1689] 5. The emotion engine recognizes the user's stress level, and the server suggests rest areas. The user's stress level is determined using facial recognition and voice analysis.

[1690] 6. The user follows the new route, completes the delivery efficiently, and takes the suggested breaks.

[1691] Example prompt:

[1692] Please explain the process of changing the route due to sudden weather changes and how the emotion engine reduces user stress. Please also include specific examples of weather information acquisition and emotion recognition.

[1693] As described above, this system combines real-time data analysis and emotion recognition to provide users with efficient and stress-free travel.

[1694] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1695] Step 1:

[1696] Collection of historical data

[1697] The server periodically collects past traffic data and delivery history data through an external API and stores it in a database. Specifically, it calls the API using the Python requests library to retrieve data in JSON format. The retrieved data is then saved in a MySQL database using the INSERT statement.

[1698] Input: Past traffic and delivery history data obtained from the API (e.g., API endpoint " / past_traffic_data")

[1699] Output: Traffic and delivery history data stored in a MySQL database

[1700] Step 2:

[1701] Get current location and speed

[1702] The device acquires the user's current location and speed using the GPS module and sends it to the server. Specifically, the device acquires the location and speed data using the device's location information service (e.g., Google Maps API) and sends it to the server via an HTTP POST request.

[1703] Input: Current location and speed obtained from the GPS module (e.g., latitude 35.6895, longitude 139.6917, speed 50km / h)

[1704] Output: Location and speed data sent to the server

[1705] Step 3:

[1706] Obtaining real-time information

[1707] The server uses an external API to obtain real-time traffic, traffic light, and weather information and stores it in a database. Specifically, it uses the Python requests library to call the API, obtains data in JSON format, and stores it in a MySQL database.

[1708] Input: Real-time traffic information, traffic light status, and weather information obtained from the API (e.g., API endpoint " / current_traffic_data")

[1709] Output: Real-time traffic, traffic light and weather information stored in a MySQL database

[1710] Step 4:

[1711] Data analysis and route calculation

[1712] The server uses a generative AI model (e.g., TensorFlow) to calculate the optimal route based on historical traffic data and real-time information. This involves inputting historical and real-time data and using the AI ​​model to output the calculated optimal route.

[1713] Input: Historical traffic data, real-time traffic information, traffic signal status, weather information

[1714] Output: The optimal route calculated by the generative AI model

[1715] Step 5:

[1716] Sending and notifying route information

[1717] The server then sends the calculated optimal route in JSON format to the device, which then receives it and provides visual and voice navigation to the user. Specifically, the device uses Mapbox to display the route on a map and provide voice guidance.

[1718] Input: Optimal route calculated by the generative AI model (JSON format)

[1719] Output: Visual and audio guidance on the device (e.g., "Turn right next")

[1720] Step 6:

[1721] emotion recognition

[1722] The device's emotion engine recognizes emotions from the user's voice and facial expressions and sends the data to the server. Specifically, it uses emotion recognition algorithms (e.g., OpenCV and Python speech analysis libraries) to analyze the user's emotional state and sends the results to the server.

[1723] Input: User's voice and facial expression data

[1724] Output: Emotion recognition data sent to the server

[1725] Step 7:

[1726] Real-time location monitoring

[1727] The device monitors the user's location in real time and sends a route recalculation request to the server as needed. Specifically, it periodically updates the location using the Google Maps API and notifies the server whenever the location changes.

[1728] Input: Real-time location information from the GPS module

[1729] Output: Location information sent to the server and route recalculation requests if necessary

[1730] (Application example 2)

[1731] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1732] Conventional navigation systems for autonomous vehicles calculate optimal routes based on past traffic data and real-time information, but they do not take into account the emotional state of passengers, making it difficult to provide both safety and comfort. Furthermore, they do not take into account stress and fatigue caused by sudden changes in weather or traffic conditions, resulting in low user satisfaction.

[1733] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting and storing past traffic data and delivery history data, means for acquiring a current location and current speed, means for acquiring real-time traffic information, traffic light conditions, and weather information, means for analyzing the past data and real-time information and calculating an optimal route, means for presenting the calculated optimal route, means for recognizing a user's emotion from voice and image, and means for processing the recognized emotion data and making suggestions to reduce the user's stress and fatigue. This enables an autonomous vehicle to travel safely and comfortably while taking the user's emotional state into consideration.

[1734] "Historical traffic data" refers to data that includes past traffic conditions and delivery history for a specific area, and is used by the navigation system to calculate optimal routes.

[1735] "Delivery history data" refers to information such as past delivery times, routes, and speeds, and is useful for improving the efficiency of logistics and delivery operations.

[1736] "Current location" refers to information indicating the location of a user or vehicle, and is obtained using technologies such as GPS.

[1737] "Current speed" refers to the speed at which a user or vehicle is currently traveling, measured in real time.

[1738] "Real-time traffic information" refers to data that is obtained in real time about current traffic conditions, and includes information about accidents and congestion.

[1739] "Signal status" is information indicating the status of traffic signals on roads, and includes data such as the color and timing of signals.

[1740] "Weather information" is information indicating the current weather conditions and forecasts, and includes weather data such as rain, snow, and sunny weather.

[1741] The "optimal route" refers to the most efficient and safe travel route calculated by comprehensively taking into account traffic conditions, weather, and other factors.

[1742] "Presentation means" refers to the means for notifying the user of the calculated optimal route visually or audibly.

[1743] "Means for recognizing a user's emotions from voice and images" refers to technology for identifying a user's emotional state using voice analysis and image analysis.

[1744] "Means for processing emotional data" refers to technology that generates suggestions and measures to reduce the user's stress and fatigue based on the recognized emotional data.

[1745] A "generative AI model" refers to a machine learning model used to predict future situations based on past data and real-time information.

[1746] "Prompt" refers to the language used to give specific instructions to an AI model, including specific questions or requests.

[1747] This invention is a navigation system for autonomous vehicles that combines an AI-based future prediction navigation system with an emotion engine. This system collects and analyzes a wide variety of data and provides the optimal route taking into account the user's emotional state to ensure safe and comfortable driving of autonomous vehicles.

[1748] System configuration

[1749] server

[1750] The server is responsible for the following functions:

[1751] Collection and storage of past traffic data: The server periodically collects the user's delivery history data and past traffic data and stores them in a database.

[1752] Acquisition and storage of real-time information: The server acquires real-time traffic information, traffic light status, and weather information from external APIs and stores this data.

[1753] Data analysis and route calculation: The server analyzes the collected historical data and real-time information using a generative AI model to calculate the optimal route.

[1754] Route information distribution: The calculated optimal route information is sent to the terminal in JSON format.

[1755] Emotion data processing: Processes emotion recognition data sent from the device and makes suggestions to reduce the user's stress and fatigue.

[1756] Terminal

[1757] The terminal is responsible for the following functions:

[1758] Obtaining current location and speed: The user's current location and current moving speed are obtained using GPS and sent to the server.

[1759] Receiving and presenting route information: Receives optimal route information sent from the server and notifies the user through visual and voice navigation.

[1760] Equipped with an emotion engine: Recognizes the user's emotions from voice and facial expressions, and sends the emotion data to the server.

[1761] User

[1762] Using this system, users can travel comfortably and efficiently. By accepting stress reduction suggestions based on emotion recognition, they can reduce fatigue and stress during travel.

[1763] How it works

[1764] 1. Obtaining historical data and real-time information: The server periodically collects historical and real-time traffic information, including obtaining traffic information using APIs, checking traffic signal status, and collecting weather data.

[1765] 2. Obtaining current location and speed: The device uses GPS technology to obtain the user's current location and speed and transmits them to the server.

[1766] 3. Calculate optimal route: The server calculates the optimal route using a generative AI model based on historical data and real-time information.

[1767] 4. Route delivery and notification: The calculated optimal route is sent to the device in JSON format, and the device notifies the user visually and audibly.

[1768] 5. Emotion recognition and processing: The device's emotion engine analyzes the user's voice and facial expressions and sends the emotional data to the server, which processes the information and makes suggestions to reduce the user's stress and fatigue.

[1769] Specific examples

[1770] Example 1: Morning rush hour delivery

[1771] The server analyzes past traffic data and predicts congestion during the morning rush hour. The device sends the user's current location and speed to the server, which then calculates the optimal route based on real-time information. The user is then notified of the optimal route. The emotion engine recognizes the user's stress, and the server suggests relaxing music.

[1772] Example 2: Route changes due to sudden weather changes and emotion recognition

[1773] The server predicts traffic congestion based on past data and real-time weather information. The device continuously updates the user's current location and sends it to the server. When it starts to rain, the server recalculates the route based on real-time weather information. The device notifies the user of the recalculated route, the emotion engine recognizes the user's stress, and the server suggests rest stops.

[1774] Example prompt for a generative AI model:

[1775] "Calculate the most efficient route based on historical data and real-time information to provide shorter routes during the morning rush hour."

[1776] This system not only allows users to travel to their destinations efficiently and comfortably using self-driving vehicles, but also reduces stress and fatigue along the way.

[1777] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1778] Step 1: Collect and store historical data

[1779] The server collects past traffic data and delivery history data and stores it in a database. Specifically, it retrieves traffic information from the past few years from an external API and integrates it with the delivery history database. This provides data for analyzing past traffic conditions and delivery route trends. The input is the output of the past traffic data API, and the output is a database update that stores this data.

[1780] Step 2: Get your current location and speed

[1781] The device uses GPS technology to obtain the user's current location and speed. It then sends the location and speed information to the server. The input is location and speed information from the GPS sensor, and the output is a data packet containing this information sent to the server.

[1782] Step 3: Obtaining real-time information

[1783] The server obtains real-time traffic information, traffic light status, and weather information through external APIs. This information is stored in a database. The inputs are the real-time traffic information API, traffic light status API, and weather information API, and the output is data containing this real-time information.

[1784] Step 4: Data analysis and route calculation

[1785] The server uses a generative AI model to analyze past traffic data, current location, current speed, and real-time information to calculate the optimal route. The inputs are past traffic data, current location, current speed, and real-time traffic information, and the output is optimal route information. Specifically, the server sends a prompt to the generative AI model to instruct it to calculate the optimal route. An example of a prompt is as follows:

[1786] "Calculate the most efficient route based on historical data and real-time information to provide shorter routes during the morning rush hour."

[1787] Step 5: Send and submit your route

[1788] The server sends the calculated optimal route information in JSON format to the device, which then notifies the user of this information as visual and audio navigation. The input is the JSON data of the optimal route information, and the output is navigation information that the user can confirm visually and audibly.

[1789] Step 6: Recognize emotions

[1790] The device's emotion engine uses a camera and microphone to recognize emotions from the user's voice and facial expressions. The recognized emotion data is sent to the server. The inputs are the user's voice data and camera footage, and the output is analyzed emotion data.

[1791] Step 7: Processing emotional data and making stress reduction suggestions

[1792] The server processes the emotional data sent from the device and generates suggestions to reduce the user's stress and fatigue. The input is emotional data, and the output is specific suggestions for reducing stress. Specifically, the server suggests relaxing music and resting spots based on the results of data processing.

[1793] Through the above steps, the system of the present invention enables an autonomous vehicle to provide an optimal route while taking into account the emotional state of the user, enabling comfortable and safe travel.

[1794] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1795] 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.

[1796] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1797] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1798] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1799] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1800] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1801] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1802] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1803] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1804] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1805] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1806] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1807] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1808] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1809] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1810] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1811] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1812] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1813] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1814] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1815] The following is further disclosed regarding the above embodiment.

[1816] (Claim 1)

[1817] a means for collecting and storing historical traffic and delivery history data;

[1818] A means for obtaining a current location and a current speed;

[1819] a means for obtaining real-time traffic, traffic light, and weather information;

[1820] means for analyzing the historical data and real-time information and calculating an optimal route;

[1821] means for presenting the calculated optimum route;

[1822] A system including:

[1823] (Claim 2)

[1824] 2. The system according to claim 1, wherein the means for calculating the optimum route calculates the route taking into account timing of traffic light changes.

[1825] (Claim 3)

[1826] 2. The system of claim 1, wherein the means for presenting the optimal route provides the user with visual and audio navigation.

[1827] "Example 1"

[1828] (Claim 1)

[1829] a means for collecting and storing historical traffic and delivery history data;

[1830] A means for obtaining a current location and a current speed;

[1831] a means for obtaining real-time traffic, traffic light, and weather information;

[1832] A means for analyzing the historical data and real-time information using a generating AI model to calculate an optimal route;

[1833] means for presenting the calculated optimum route to a user by visual and audio navigation;

[1834] A means to monitor location information in real time and request route recalculation if necessary;

[1835] A system including:

[1836] (Claim 2)

[1837] 2. The system according to claim 1, wherein the means for calculating the optimum route calculates the route taking into account timing of traffic light changes.

[1838] (Claim 3)

[1839] 2. The system according to claim 1, wherein the means for presenting an optimal route comprises means for transmitting the calculated optimal route to the terminal in a JSON format.

[1840] "Application Example 1"

[1841] (Claim 1)

[1842] a means for collecting and storing historical traffic and delivery history data;

[1843] A means for obtaining a current location and a current speed;

[1844] a means for obtaining real-time traffic, traffic light, and weather information;

[1845] means for analyzing the historical data and real-time information and calculating an optimal route;

[1846] means for presenting the calculated optimum route;

[1847] A means of notifying the driver of the calculation results visually and audibly;

[1848] A system including:

[1849] (Claim 2)

[1850] 2. The system according to claim 1, wherein the means for calculating the optimum route calculates the route taking into account timing of traffic lights and weather information.

[1851] (Claim 3)

[1852] 10. The system of claim 1, wherein the system notifies the driver of an optimal route through visual and audio navigation.

[1853] "Example 2: Combining Emotion Engines"

[1854] (Claim 1)

[1855] a means for collecting and storing historical traffic and delivery history data;

[1856] A means for obtaining a user's current location and current speed using a GPS;

[1857] a means for obtaining real-time traffic, traffic light, and weather information;

[1858] means for analyzing the historical data and real-time information and calculating an optimal route using a generative AI model;

[1859] means for transmitting the calculated optimum route to a terminal and presenting it by visual and audio navigation;

[1860] means for recognizing emotions from the user's voice and facial expressions and processing the emotion data;

[1861] a means for making suggestions to reduce stress and fatigue of the user based on the emotion recognition data of the user;

[1862] A system including:

[1863] (Claim 2)

[1864] 2. The system of claim 1, wherein the means for calculating the optimal route calculates the route taking into account traffic light change timings and real-time weather information.

[1865] (Claim 3)

[1866] 10. The system of claim 1, wherein suggestions for reducing stress and fatigue based on the emotion recognition are provided to the user via audio and visual notification.

[1867] "Application example 2 when combining emotion engines"

[1868] (Claim 1)

[1869] a means for collecting and storing historical traffic and delivery history data;

[1870] A means for obtaining a current location and a current speed;

[1871] a means for obtaining real-time traffic, traffic light, and weather information;

[1872] means for analyzing the historical data and real-time information and calculating an optimal route;

[1873] means for presenting the calculated optimum route;

[1874] means for recognizing a user's emotion from voice and image;

[1875] means for processing the recognized emotion data and making suggestions to reduce stress or fatigue of the user;

[1876] A system including:

[1877] (Claim 2)

[1878] 2. The system of claim 1, wherein the means for calculating an optimal route uses signal change timing and a generative AI model to calculate the route.

[1879] (Claim 3)

[1880] ...

Claims

1. a means for collecting and storing historical traffic and delivery history data; A means for obtaining a current location and a current speed; a means for obtaining real-time traffic, traffic light, and weather information; means for analyzing the historical data and real-time information and calculating an optimal route; means for presenting the calculated optimum route; A system including:

2. 2. The system according to claim 1, wherein the means for calculating the optimum route calculates the route taking into account timing of traffic light changes.

3. 2. The system of claim 1, wherein the means for presenting the optimal route provides the user with visual and audio navigation.

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