system

Abstract

We provide the system. [Solution] A means of acquiring multiple types of location data, A means for analyzing abnormal pedestrian flow using a congestion prediction model based on the aforementioned location data, A means for generating the optimal detour route based on predicted congestion, Means for notifying the mobile device's terminal of the aforementioned detour route, A means for an autonomous vehicle in motion to monitor changes in the situation in real time and update the aforementioned route, A means for receiving route data in real time in an autonomous vehicle and providing guidance using a display device and an audible device, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern urban life, sudden and unpredictable congestion situations have an adverse impact on the choice of means of transportation and schedules. In particular, temporary fluctuations in the flow of people due to events or construction are difficult to take countermeasures in advance, which causes problems of forcing stress and time waste on users. It is the main problem of the present invention to efficiently solve such problems caused by congestion and provide a means for users to move comfortably and smoothly.

Means for Solving the Problems

[0005] This invention provides means for acquiring multiple types of location data and analyzing abnormal pedestrian flow using a congestion prediction model based on this data. Furthermore, it provides means for generating an optimal detour route based on the predicted congestion situation. This makes it possible to notify the user's terminal of the detour route, monitor changes in the situation in real time while the user is traveling, and update the detour route as needed. By combining these means, users can avoid predicted congestion and achieve efficient travel.

[0006] "Location data" refers to information related to geographical locations and coordinates, and is used to determine the current and past locations of individuals and objects.

[0007] A "congestion prediction model" is a mathematical or statistical method used to predict anomalies in the flow of people and traffic at a specific location and time, based on past and present data.

[0008] "Abnormal human traffic" refers to a significant fluctuation in the number of people moving in a particular location or area compared to normal conditions, and the resulting congestion and delays.

[0009] A "detour route" is an alternative route selected to avoid anticipated congestion areas, and is intended to efficiently support users in reaching their destination.

[0010] A "terminal" refers to an electronic device that a user can carry with them and is used for receiving and communicating information.

[0011] "Real-time monitoring" means constantly acquiring the latest information and responding immediately to changes in status or situation. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the 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.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0026] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention relates to a system that utilizes diverse location data to predict congestion in real time and provides the optimal route based on that prediction. As an embodiment, a network system consisting of a server and user terminals is taken as an example.

[0034] First, the server acquires location data in real time from telecommunications carriers and transportation companies, and also receives anonymized location data from smartphone apps. This data is fed into a congestion prediction model installed on the server to detect abnormal pedestrian traffic and predict the likelihood of congestion at different locations and times.

[0035] The server analyzes this predictive data and generates an alternative route to the user's planned destination. The generated route is optimized to avoid congestion compared to the usual route, allowing for alternative modes of transport and reduced travel time. This information is immediately sent to the user's device and notified to the user.

[0036] Meanwhile, users can review and accept suggested routes through their devices. These devices may include a visual map display, text information, and voice guidance. As users travel, the server continuously monitors the situation in real time, sending updates to the device and optimizing the route if new congestion is predicted or existing conditions change.

[0037] For example, when a user tries to visit a tourist destination on a holiday, the server predicts peak hours based on past data and current congestion levels, and suggests alternative times and routes to the user. This allows the user to avoid crowds and travel with less stress.

[0038] This system allows users to avoid congestion during their daily commutes and trips for specific purposes, enabling them to enjoy efficient and comfortable travel.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The server acquires location data in real time from APIs of telecommunications carriers and transportation companies. This data includes current pedestrian traffic and the operating status of transportation services.

[0042] Step 2:

[0043] The server preprocesses the acquired data to remove errors and outliers, thereby ensuring data accuracy and reliability.

[0044] Step 3:

[0045] The server inputs the cleansed data into a congestion prediction model, which then analyzes unusual pedestrian traffic patterns by comparing them with historical data. The prediction model uses machine learning algorithms to assess the likelihood of congestion.

[0046] Step 4:

[0047] The server identifies congested areas and times based on the analysis results and generates the optimal detour route to avoid them. It simulates various routes and selects the most efficient one.

[0048] Step 5:

[0049] The server generates detour route information and sends it to the user's terminal for notification. This information includes a map display of the route and an estimated travel time.

[0050] Step 6:

[0051] The user reviews the suggested route on their device and decides whether to accept it. If selected, the device begins navigation and provides visual and audio guidance.

[0052] Step 7:

[0053] The device uses GPS functionality to track the user's location and monitors their progress in real time. If necessary, it can change direction or reset the destination.

[0054] Step 8:

[0055] The server continuously monitors the situation and recalculates routes in real time based on new pedestrian flow data and event information. If changes occur, updated information is sent to the terminal to notify the user.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] In today's transportation environment, the inability to respond quickly to real-time changes in congestion can hinder efficient travel. Conventional systems have resulted in users being unable to avoid congestion and being forced to endure stressful journeys. Therefore, there is a need to provide more accurate and rapid congestion forecasting and alternative routes.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes means for acquiring multiple types of location information data, means for analyzing abnormal pedestrian flow using a congestion prediction model based on the location information data and AI technology, and means for generating an optimal detour route based on the prediction results. This enables efficient movement that avoids congestion in real time.

[0061] "Location data" refers to information about geographical location, including the current location and movement path of users and objects.

[0062] "Generative AI technology" is a technology that uses artificial intelligence technology that mimics human thought to generate new predictive models and information from data.

[0063] A "congestion prediction model" is an algorithm or mathematical model used to predict abnormal pedestrian traffic and congestion based on past and present data.

[0064] "Communication equipment" refers to terminals or devices used for the purpose of sending and receiving information, and includes smartphones and computers.

[0065] A "detour route" is an alternative travel route to a destination that has been pre-set to avoid congestion.

[0066] This invention is a system that utilizes multiple location data to predict congestion in real time and then provides the user with the optimal detour route. Specifically, it consists of a server and a user terminal.

[0067] The server acquires various location data from communication networks and public transportation. This data collection utilizes communication carrier interfaces and publicly available APIs from transportation companies. The acquired data is input into a congestion prediction model that uses generative AI technology within the server. This model incorporates the latest machine learning algorithms and analyzes the data obtained in real time to predict abnormal pedestrian flow and congestion with high accuracy.

[0068] Subsequently, the server uses the Dijkstra algorithm and other methods to generate the optimal detour route to avoid congestion based on the predicted data. The generated route data is immediately transmitted to the user's communication device. The user's terminal displays a visual map, and may also provide text information or voice assistance. This allows the user to travel with less stress by avoiding congestion.

[0069] Furthermore, the server constantly monitors congestion levels while the user is traveling, re-optimizing alternative routes as needed and sending updated information to the terminal. This allows the user to receive the most suitable mode of transportation in real time.

[0070] As a concrete example, for a user visiting a tourist destination on a holiday, the server can use past and present congestion data to suggest the least congested time and route. This allows the user to reach their destination comfortably.

[0071] Examples of prompt statements for a generative AI model are as follows:

[0072] "I'm planning a visit to a tourist spot during the holidays. Could you tell me what time of day and route I should choose to avoid crowds?"

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The server collects location data through communication networks and public transportation APIs.

[0076] Specifically, the server periodically sends requests to the API endpoint to retrieve current pedestrian flow data and traffic condition data.

[0077] The input consists of data from communication networks and public transportation, while the output is location data stored on the server.

[0078] Step 2:

[0079] The server inputs the collected location data into a congestion prediction model.

[0080] This model uses generative AI technology and applies advanced machine learning algorithms to detect abnormal pedestrian flow patterns.

[0081] The input is location data, and the output is anomaly detection results and predicted congestion information.

[0082] In terms of specific operation, the server supplies data to machine learning models built on platforms such as Python, and generates results through batch processing.

[0083] Step 3:

[0084] The server generates the optimal detour route based on predicted congestion information.

[0085] Here, we use the Dijkstra algorithm to calculate travel paths that take real-time data into account.

[0086] The input is congestion information, and the output is the optimal detour route to avoid congestion.

[0087] In terms of specific operations, the server uses a data structure to perform route searching and saves the generated route.

[0088] Step 4:

[0089] The server sends the generated detour route to the user's terminal.

[0090] The device receives this information and displays it as a notification on the screen.

[0091] The input is the generated detour route, and the output is the terminal notification.

[0092] Specifically, the server uses a push notification service to send a message to the device.

[0093] Step 5:

[0094] The user reviews and selects the route information provided on the device.

[0095] The device displays a detailed map and provides directions.

[0096] The input is the routing information sent to the terminal, and the output is the user's selected action.

[0097] Users check their route through a dedicated application and begin their journey to their destination.

[0098] Step 6:

[0099] The server monitors congestion in real time while the user is on the move and updates alternative routes as needed.

[0100] The server will resend the route, which has been modified as needed, based on the new congestion forecast.

[0101] The input is the latest congestion information, and the output is updated route information.

[0102] Specifically, the server continuously retrieves data and provides the latest information by sending push notifications to the device again.

[0103] (Application Example 1)

[0104] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0105] This solution addresses the difficulty of providing the optimal route in real time for autonomous vehicles. In particular, it is required to optimize the route in a timely manner in response to congestion and changing conditions, thereby achieving safe and efficient operation.

[0106] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0107] In this invention, the server includes means for acquiring multiple types of location information data, means for analyzing abnormal pedestrian flow using a congestion prediction model based on the multiple types of location information data, and means for generating an optimal detour route based on the predicted congestion situation. This enables real-time route optimization for moving objects.

[0108] "Multiple types of location data" refers to location-specific information in various forms provided by communication networks, transportation methods, etc., that can be acquired in real time.

[0109] A "congestion prediction model" is a method that uses machine learning algorithms to detect anomalies in pedestrian flow, taking into account factors such as time of day, weather, and event information, and predicts future congestion.

[0110] A "detour route" is a route newly created to avoid congestion or reduce travel time, in contrast to the originally planned route.

[0111] An "autonomous vehicle" is a powered vehicle that recognizes the traffic environment and is automatically controlled without the need for driver intervention.

[0112] "Real-time updates" refers to the operation of receiving new information instantly even while a moving object is in motion, and dynamically correcting the route or instructions as needed.

[0113] "Display devices and sound devices" refer to hardware used to provide information to users visually and aurally, and include displays and speakers, respectively.

[0114] This invention is a system for optimizing the movement of autonomous vehicles and is configured as a network system including a server and terminals within the vehicle.

[0115] The server acquires location data provided in real time from communication networks and transportation methods. This data is input into a congestion prediction model built within the server. The congestion prediction model uses machine learning algorithms built with Python and TENSORFLOW® to detect anomalies in pedestrian flow, taking into account time of day, weather, and event information, and predicts future congestion. Based on the predicted congestion data, the optimal detour route is calculated. This route is reviewed each time new data is acquired and updated in real time.

[0116] The terminal uses a display and sound system to provide visual and auditory guidance to the driver of a vehicle, based on route data received from the server. Specifically, it displays the route and estimated time to the destination on the display and provides route guidance through the speaker. Furthermore, if new congestion occurs while driving, updated information is immediately transmitted from the server, and the terminal re-optimizes the route based on this information and continues to provide guidance.

[0117] For example, when a user is traveling to the city center by vehicle, the server can predict congestion and avoid the usual route, offering the user an alternative detour. This allows the user to avoid traffic jams and arrive at their destination smoothly.

[0118] Example of a prompt:

[0119] "Please explain how to generate alternative routes to avoid congestion in real time."

[0120] "Please suggest what algorithms should be used in machine learning models for congestion prediction."

[0121] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0122] Step 1:

[0123] The server acquires real-time location data from communication networks and transportation methods. This data input includes GPS information and traffic data. The data is temporarily stored in a database on the server and used for subsequent analysis.

[0124] Step 2:

[0125] The server inputs location data into a congestion prediction model. This model is built using machine learning algorithms with Python and TensorFlow. Data processing involves extracting abnormal pedestrian flow patterns and applying them to the prediction algorithm. The output provides information on predicted congestion times and areas.

[0126] Step 3:

[0127] The server generates the optimal detour route based on the congestion prediction results. At this stage, it combines existing map data with predicted congestion information and utilizes a real-time optimization algorithm. The generated route information is then ready to be sent to the terminal.

[0128] Step 4:

[0129] The terminal receives route data from the server and guides the user using a display device and audio equipment. It receives route data as input, displays a map and guidance information on the screen, and provides voice guidance through the speaker. The system conveys route information to the user in an easy-to-understand manner, both visually and aurally.

[0130] Step 5:

[0131] While the user is moving, the server continues to monitor traffic conditions and generates new route information as needed. In this process, location data is re-evaluated and fed into a predictive model to generate new output. The device receives this updated information and provides further directions to the user.

[0132] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0133] This invention is a system that combines congestion prediction technology and emotion recognition technology to provide users with a comfortable route while they are traveling. It consists of a server and a user terminal.

[0134] First, the server acquires real-time location information and operational status data from communication networks or transportation systems. This data is then used by a congestion prediction model running within the server to analyze pedestrian flow patterns and identify areas where congestion is expected.

[0135] Furthermore, the user's device has an emotion engine built in, which collects data from the user's voice input and biometric sensors. The emotion engine analyzes this data to estimate the user's current emotional state. This information is sent to the server, and the suggested alternative routes are adjusted based on the current emotional state.

[0136] The server generates the optimal detour route based on predicted congestion and the user's emotional state. This route is designed to avoid situations that would cause user discomfort, thus avoiding congestion while also considering the user's psychological comfort.

[0137] The generated route information is sent to the user's device, which then uses this information to perform real-time navigation. The user can review and select the route suggested by the device. During navigation, an emotion engine monitors the user's emotions in real time, and if a change is detected, it will suggest a different route as needed.

[0138] For example, if a user is feeling tired on their way home from work, the server can select a relaxed route and guide them to easily accessible rest stops. In this way, users can experience a physically and psychologically comfortable journey.

[0139] This system allows users to receive a travel experience that takes into account not only conventional traffic information but also their personal emotional state, resulting in a more personalized and optimized travel experience.

[0140] The following describes the processing flow.

[0141] Step 1:

[0142] The server obtains real-time location and operational status data from communication network and transportation APIs. This data is used to understand the current flow of people in the city.

[0143] Step 2:

[0144] The server stores the acquired data in a database and performs preprocessing. This identifies invalid and distorted data to improve data quality.

[0145] Step 3:

[0146] The server inputs pre-processed data into a congestion prediction model to analyze pedestrian flow patterns. The model uses machine learning algorithms to identify areas and time periods where congestion is predicted.

[0147] Step 4:

[0148] The device collects the user's voice input and biometric information (heart rate, facial expressions, etc.) using sensors and sends it to the emotion engine. The emotion engine analyzes the user's emotional state in real time.

[0149] Step 5:

[0150] The device sends analysis results to the server, providing information about the user's current emotional state. The server receives this data and customizes alternative routes based on the user's emotional state.

[0151] Step 6:

[0152] The server integrates congestion prediction information with user sentiment data to generate an optimal route that is psychologically and physically comfortable. This route not only avoids congestion as usual, but also takes into account the user's emotional state.

[0153] Step 7:

[0154] The server sends route information it has generated to the terminal. Based on this information, the terminal starts providing visual and audio navigation to the user.

[0155] Step 8:

[0156] The user accepts the device's navigation and begins moving. During the move, the emotion engine continuously monitors the user's emotions and detects any changes.

[0157] Step 9:

[0158] When the emotion engine detects a change in the user's emotions, the device sends new emotion data to the server. The server considers this information, recalculates the route as needed, and sends update information to the device.

[0159] (Example 2)

[0160] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0161] Conventional travel route suggestion systems only provide congestion avoidance based on traffic information, and do not adequately consider the psychological comfort of users during travel. Therefore, even if congestion is avoided, the user's emotional state may worsen, failing to improve the quality of the travel experience. Furthermore, the lack of functionality to flexibly respond to real-time changes in the environment and emotions makes it difficult to achieve the comfortable travel experience that users desire.

[0162] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0163] In this invention, the server includes means for acquiring multiple types of location information data, means for analyzing abnormal pedestrian traffic using a congestion prediction model, and means for estimating the user's emotional state. This makes it possible to generate and notify the user of an optimal detour route based on the real-time congestion situation and emotional state during their travel. As a result, the user can experience psychologically and physically comfortable travel.

[0164] "Location data" refers to data that shows the real-time geographical location of users and transportation systems.

[0165] A "congestion prediction model" is a machine learning algorithm that analyzes collected location data to predict abnormal patterns in pedestrian flow and identify areas where congestion is expected.

[0166] "User emotional state" refers to the user's psychological and emotional state, and is estimated based on data from voice input and biometric sensors.

[0167] A "detour route" is an alternative route to a destination designed to avoid congestion and take into consideration the user's emotional state.

[0168] "Monitoring changes in the situation in real time" refers to continuously observing changes in surrounding traffic conditions and the user's emotional state while the user is on the move.

[0169] This invention provides a system that offers a comfortable route in real time while a user is traveling. This system consists of a server and a user's terminal, and operates according to the following procedure.

[0170] First, the server acquires real-time location data from multiple locations. This data is obtained from communication networks and traffic information infrastructure and formatted in JSON or similar formats. The server then analyzes this location data using a congestion prediction model. This model is built using machine learning frameworks such as TensorFlow and identifies areas where congestion is expected.

[0171] Meanwhile, the user's device is equipped with an emotion engine. This emotion engine continuously collects the user's voice and biometric data using biometric sensors and microphones. Based on this, the device uses emotion analysis libraries such as the Affectiva SDK to estimate the user's current emotional state from the collected data.

[0172] The estimated user's emotional state is sent from the terminal to the server. The server integrates congestion predictions and user emotional state data and generates the optimal detour route using a generative AI model. This generative AI model selects a route that optimizes the user's psychological and physical comfort, taking into account the predicted congestion and emotional data.

[0173] The generated route information is sent from the server to the user's device and used for real-time navigation. The user can review and select the route presented on their device. Furthermore, the server will suggest a new route as needed, depending on changes in the user's situation and emotions during their journey.

[0174] As a concrete example, suppose a user is commuting home during peak hours. If the user is experiencing stress or fatigue, the server will suggest a relatively less crowded route and include stops along the way where the user can rest or relax.

[0175] An example of a prompt for a generative AI model would be, "Input user emotions and real-time traffic data, and suggest the optimal travel route for the current situation." This allows users to enjoy a personalized travel experience tailored to their individual needs.

[0176] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0177] Step 1:

[0178] The server acquires real-time location data from communication networks and traffic information infrastructure. This data includes GPS information and traffic service status data. The server receives this data, formats it, and prepares it for analysis. The input is location data, and the output is formatted data ready for analysis.

[0179] Step 2:

[0180] The server inputs formatted location data into a congestion prediction model. This model operates using machine learning frameworks such as TensorFlow and incorporates variables such as time, weather, and event information to identify areas where congestion is expected. The input is formatted data, and the output is congestion prediction information.

[0181] Step 3:

[0182] The device collects data from voice input and biometric sensors to estimate the user's emotional state. Specifically, it acquires voice data through the microphone and biometric data such as heart rate through sensors. This data is input to an emotion engine and analyzed. The input is voice and biometric data, and the output is the estimated result of the emotional state.

[0183] Step 4:

[0184] The device uses an emotion engine to analyze the user's emotional state. This analysis utilizes tools such as the Affectiva SDK to analyze voice tone and biometric information. The analysis quantifies the user's emotional state. The input for this step is voice and biometric information, and the output is a quantified emotional state.

[0185] Step 5:

[0186] The terminal sends its estimated emotional state to the server. The server integrates this emotional state data with previously acquired congestion prediction information to generate the optimal detour route. Using a generative AI model, it designs a route that takes into account the user's psychological comfort. The input for this step is the emotional state and congestion prediction information, and the output is the optimal route information.

[0187] Step 6:

[0188] The server sends optimal route information to the user's device. The device receives this information and presents it to the user in real time. The route is visualized on the device and becomes selectable by the user. The input is optimal route information, and the output is navigation information displayed on the user's device.

[0189] Step 7:

[0190] When a user experiences changes in their environment or emotions while traveling, the device monitors their emotional state again and sends update information to the server as needed. The server can then suggest a new route based on the new congestion information and emotional data. The input is the changed emotional state and new congestion information, and the output is the updated route suggestion.

[0191] (Application Example 2)

[0192] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0193] Travel in modern cities is often fraught with congestion and wasted time, and can also negatively impact users' emotional states. Furthermore, choosing a route that considers the comfort of individual users is difficult, and ensuring emotional comfort is particularly challenging. To address these issues, there is a need to provide a travel experience that takes into account not only congestion levels but also the user's personal emotional state.

[0194] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0195] In this invention, the server includes means for acquiring multiple types of location information data, means for analyzing abnormal pedestrian flow using a congestion prediction model based on the location information data, means for collecting and analyzing voice input and biometric information to estimate the user's emotional state, means for generating an optimal detour route based on the predicted congestion and emotional state, means for notifying the user's terminal of the detour route, and means for monitoring changes in the situation in real time while the user is moving and updating the detour route. This enables the user to obtain a physically and emotionally optimized route.

[0196] "Location data" refers to data that represents a geographical location, obtained from mobile devices and sensors.

[0197] A "congestion prediction model" is an algorithm that uses traffic data and pedestrian flow data to predict congestion levels in specific areas or routes in advance.

[0198] "Emotional state" refers to the psychological and emotional aspects of a user, estimated through voice and biometric data analysis.

[0199] An "emotion engine" is software or an algorithm used to analyze a user's emotions from voice and biometric information.

[0200] A "detour route" is a travel path that avoids congestion while also considering the comfort level of users based on their emotional state.

[0201] "Monitoring changes in the situation in real time" is a process of continuously acquiring data while on the move and instantly recognizing any fluctuations.

[0202] This invention is a system that improves the travel experience in urban environments and mainly consists of a server and a user terminal. The system provides users with optimized travel routes by combining congestion prediction and emotion recognition functions.

[0203] The server obtains real-time location information from location services and traffic data providers. This information is analyzed by a congestion prediction model, which makes predictions that take into account factors such as time of day, events, and weather. This prediction algorithm is implemented in programming languages ​​such as Python, and can utilize TensorFlow. Furthermore, it receives emotion data sent from the user's device, and the emotion engine analyzes data from voice input and biosensors. This determines the user's current psychological state.

[0204] The device notifies users in real time of alternative routes to avoid congestion through a navigation application. This application can be developed using a framework such as React Native. Furthermore, if the user's emotional state changes, the server-side emotion engine will immediately react and suggest a new route.

[0205] As a concrete example, consider a user enjoying shopping at a commercial facility in Tokyo. If the user shows signs of fatigue, the system can suggest avoiding crowded subways and taking a break at a cafe within walking distance. This allows the user to continue their journey while reducing stress.

[0206] An example of a prompt message would be, "Please describe the concept of a smartphone application that suggests relaxing alternative routes to avoid urban congestion when the user is feeling stressed." In this way, the invention provides users with a comfortable and efficient means of transportation in many situations.

[0207] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0208] Step 1:

[0209] The server retrieves location data in real time from location services and traffic data providers. Its inputs include the current time and the user's geographical location. The output is dynamic data for the target area. This information serves as foundational data necessary for subsequent processing.

[0210] Step 2:

[0211] The server runs a congestion prediction model using acquired location data. The inputs are real-time pedestrian flow data and a prediction algorithm. The data is analyzed using a machine learning algorithm implemented in Python, and the output is predicted congestion data. Here, the degree of congestion and its temporal changes are predicted.

[0212] Step 3:

[0213] The device acquires the user's voice input and biometric information. This is done using biosensors such as the smartphone's microphone or a smartwatch. The input consists of data obtained from biometric sensors and voice data, while the output is analytical data representing the user's emotional state.

[0214] Step 4:

[0215] The server receives emotional state data transmitted from the terminal and compares it with congestion prediction data. The inputs are emotional state data and congestion status data. Based on this data, it performs data calculations to determine alternative routes, and the output is optimized route information. The calculation uses an algorithm that takes user psychological comfort into consideration.

[0216] Step 5:

[0217] The device provides navigation to the user based on route information sent from the server. The input requires optimal route information, and the output is navigation instructions displayed to the user as visual and audio guidance. The device provides this guidance through an application built with React Native.

[0218] Step 6:

[0219] While the user is on the move, the device continuously updates emotional data and location information in real time and sends it to the server. The input is newly acquired voice data and biometric information, and the output is the updated emotional state and location data provided to the server. This process enables route re-suggestions in response to changes in the user's situation.

[0220] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0221] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0222] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0223] [Second Embodiment]

[0224] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0225] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0226] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0227] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0228] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0229] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0230] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0231] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0232] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0233] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0234] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0235] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0236] This invention relates to a system that utilizes diverse location data to predict congestion in real time and provides the optimal route based on that prediction. As an embodiment, a network system consisting of a server and user terminals is taken as an example.

[0237] First, the server acquires location data in real time from telecommunications carriers and transportation companies, and also receives anonymized location data from smartphone apps. This data is fed into a congestion prediction model installed on the server to detect abnormal pedestrian traffic and predict the likelihood of congestion at different locations and times.

[0238] The server analyzes this predictive data and generates an alternative route to the user's planned destination. The generated route is optimized to avoid congestion compared to the usual route, allowing for alternative modes of transport and reduced travel time. This information is immediately sent to the user's device and notified to the user.

[0239] Meanwhile, users can review and accept suggested routes through their devices. These devices may include a visual map display, text information, and voice guidance. As users travel, the server continuously monitors the situation in real time, sending updates to the device and optimizing the route if new congestion is predicted or existing conditions change.

[0240] For example, when a user tries to visit a tourist destination on a holiday, the server predicts peak hours based on past data and current congestion levels, and suggests alternative times and routes to the user. This allows the user to avoid crowds and travel with less stress.

[0241] This system allows users to avoid congestion during their daily commutes and trips for specific purposes, enabling them to enjoy efficient and comfortable travel.

[0242] The following describes the processing flow.

[0243] Step 1:

[0244] The server acquires location data in real time from APIs of telecommunications carriers and transportation companies. This data includes current pedestrian traffic and the operating status of transportation services.

[0245] Step 2:

[0246] The server preprocesses the acquired data to remove errors and outliers, thereby ensuring data accuracy and reliability.

[0247] Step 3:

[0248] The server inputs the cleansed data into a congestion prediction model, which then analyzes unusual pedestrian traffic patterns by comparing them with historical data. The prediction model uses machine learning algorithms to assess the likelihood of congestion.

[0249] Step 4:

[0250] The server identifies congested areas and times based on the analysis results and generates the optimal detour route to avoid them. It simulates various routes and selects the most efficient one.

[0251] Step 5:

[0252] The server generates detour route information and sends it to the user's terminal for notification. This information includes a map display of the route and an estimated travel time.

[0253] Step 6:

[0254] The user reviews the suggested route on their device and decides whether to accept it. If selected, the device begins navigation and provides visual and audio guidance.

[0255] Step 7:

[0256] The device uses GPS functionality to track the user's location and monitors their progress in real time. If necessary, it can change direction or reset the destination.

[0257] Step 8:

[0258] The server continuously monitors the situation and recalculates routes in real time based on new pedestrian flow data and event information. If changes occur, updated information is sent to the terminal to notify the user.

[0259] (Example 1)

[0260] Next, we will describe Example 1. 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."

[0261] In today's transportation environment, the inability to respond quickly to real-time changes in congestion can hinder efficient travel. Conventional systems have resulted in users being unable to avoid congestion and being forced to endure stressful journeys. Therefore, there is a need to provide more accurate and rapid congestion forecasting and alternative routes.

[0262] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0263] In this invention, the server includes means for acquiring multiple types of location information data, means for analyzing abnormal pedestrian flow using a congestion prediction model based on the location information data and AI technology, and means for generating an optimal detour route based on the prediction results. This enables efficient movement that avoids congestion in real time.

[0264] "Location data" refers to information about geographical location, including the current location and movement path of users and objects.

[0265] "Generative AI technology" is a technology that uses artificial intelligence technology that mimics human thought to generate new predictive models and information from data.

[0266] A "congestion prediction model" is an algorithm or mathematical model used to predict abnormal pedestrian traffic and congestion based on past and present data.

[0267] "Communication equipment" refers to terminals or devices used for the purpose of sending and receiving information, and includes smartphones and computers.

[0268] A "detour route" is an alternative travel route to a destination that has been pre-set to avoid congestion.

[0269] This invention is a system that utilizes multiple location data to predict congestion in real time and then provides the user with the optimal detour route. Specifically, it consists of a server and a user terminal.

[0270] The server acquires various location data from communication networks and public transportation. This data collection utilizes communication carrier interfaces and publicly available APIs from transportation companies. The acquired data is input into a congestion prediction model that uses generative AI technology within the server. This model incorporates the latest machine learning algorithms and analyzes the data obtained in real time to predict abnormal pedestrian flow and congestion with high accuracy.

[0271] Subsequently, the server uses the Dijkstra algorithm and other methods to generate the optimal detour route to avoid congestion based on the predicted data. The generated route data is immediately transmitted to the user's communication device. The user's terminal displays a visual map, and may also provide text information or voice assistance. This allows the user to travel with less stress by avoiding congestion.

[0272] Furthermore, the server constantly monitors congestion levels while the user is traveling, re-optimizing alternative routes as needed and sending updated information to the terminal. This allows the user to receive the most suitable mode of transportation in real time.

[0273] As a concrete example, for a user visiting a tourist destination on a holiday, the server can use past and present congestion data to suggest the least congested time and route. This allows the user to reach their destination comfortably.

[0274] Examples of prompt statements for a generative AI model are as follows:

[0275] "I'm planning a visit to a tourist spot during the holidays. Could you tell me what time of day and route I should choose to avoid crowds?"

[0276] The flow of the specific process in Example 1 will be described with reference to FIG. 11.

[0277] Step 1:

[0278] The server collects location information data through communication networks and APIs of public transportation agencies.

[0279] Specifically, the server periodically sends requests to the API endpoint to obtain current crowd flow data and traffic situation data.

[0280] The input is data from communication networks and public transportation agencies, and the output is location information data stored in the server.

[0281] Step 2:

[0282] The server inputs the collected location information data into the congestion prediction model.

[0283] This model uses generative AI technology and applies advanced machine learning algorithms to detect abnormal crowd flow patterns.

[0284] The input is location information data, and the output is the anomaly detection result and predicted congestion information.

[0285] As a specific operation, the server supplies data to a machine learning model built on a platform such as Python and generates results through batch processing.

[0286] Step 3:

[0287] The server generates an optimal detour route based on the predicted congestion information.

[0288] Here, the Dijkstra algorithm is used to calculate a travel route considering real-time data.

[0289] The input is congestion information, and the output is the optimal detour route to avoid congestion.

[0290] In terms of specific operations, the server uses a data structure to perform route searching and saves the generated route.

[0291] Step 4:

[0292] The server sends the generated detour route to the user's terminal.

[0293] The device receives this information and displays it as a notification on the screen.

[0294] The input is the generated detour route, and the output is the terminal notification.

[0295] Specifically, the server uses a push notification service to send a message to the device.

[0296] Step 5:

[0297] The user reviews and selects the route information provided on the device.

[0298] The device displays a detailed map and provides directions.

[0299] The input is the routing information sent to the terminal, and the output is the user's selected action.

[0300] Users check their route through a dedicated application and begin their journey to their destination.

[0301] Step 6:

[0302] The server monitors congestion in real time while the user is on the move and updates alternative routes as needed.

[0303] The server will resend the route, which has been modified as needed, based on the new congestion forecast.

[0304] The input is the latest traffic information, and the output is the updated route information.

[0305] As a specific operation, the server continuously acquires data at all times and provides the latest information by performing a Push notification to the terminal again.

[0306] (Application Example 1)

[0307] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0308] In an autonomous driving vehicle, it is difficult to provide an optimal driving route in real time. In particular, it is required to optimize the route in a timely manner according to traffic congestion and situation changes, and to achieve safe and efficient operation.

[0309] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0310] In this invention, the server includes means for acquiring a plurality of types of position information data, means for analyzing abnormal pedestrian flow using a congestion prediction model based on the plurality of types of position information data, and means for generating an optimal detour route based on the predicted congestion situation. Thereby, real-time route optimization in a moving body becomes possible.

[0311] The "plurality of types of position information data" is various forms of position-specific information provided from a communication network, a transportation means, etc., and can be acquired in real time.

[0312] The "congestion prediction model" is a method that uses a machine learning algorithm to detect abnormalities in pedestrian flow considering time zones, weather, event information, etc., and predicts future congestion.

[0313] A "detour route" is a route newly created to avoid congestion or reduce travel time, in contrast to the originally planned route.

[0314] An "autonomous vehicle" is a powered vehicle that recognizes the traffic environment and is automatically controlled without the need for driver intervention.

[0315] "Real-time updates" refers to the operation of receiving new information instantly even while a moving object is in motion, and dynamically correcting the route or instructions as needed.

[0316] "Display devices and sound devices" refer to hardware used to provide information to users visually and aurally, and include displays and speakers, respectively.

[0317] This invention is a system for optimizing the movement of autonomous vehicles and is configured as a network system including a server and terminals within the vehicle.

[0318] The server acquires location data provided in real time from communication networks and transportation methods. This data is input into a congestion prediction model built within the server. The congestion prediction model uses machine learning algorithms built with Python and TensorFlow to detect anomalies in pedestrian flow, taking into account time of day, weather, and event information, and predicts future congestion. Based on the predicted congestion data, the optimal detour route is calculated. This route is reviewed each time new data is acquired and updated in real time.

[0319] The terminal uses a display and sound system to provide visual and auditory guidance to the driver of a vehicle, based on route data received from the server. Specifically, it displays the route and estimated time to the destination on the display and provides route guidance through the speaker. Furthermore, if new congestion occurs while driving, updated information is immediately transmitted from the server, and the terminal re-optimizes the route based on this information and continues to provide guidance.

[0320] For example, when a user is traveling to the city center by vehicle, the server can predict congestion and avoid the usual route, offering the user an alternative detour. This allows the user to avoid traffic jams and arrive at their destination smoothly.

[0321] Example of a prompt:

[0322] "Please explain how to generate alternative routes to avoid congestion in real time."

[0323] "Please suggest what algorithms should be used in machine learning models for congestion prediction."

[0324] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0325] Step 1:

[0326] The server acquires real-time location data from communication networks and transportation methods. This data input includes GPS information and traffic data. The data is temporarily stored in a database on the server and used for subsequent analysis.

[0327] Step 2:

[0328] The server inputs location data into a congestion prediction model. This model is built using machine learning algorithms with Python and TensorFlow. Data processing involves extracting abnormal pedestrian flow patterns and applying them to the prediction algorithm. The output provides information on predicted congestion times and areas.

[0329] Step 3:

[0330] The server generates the optimal detour route based on the congestion prediction results. At this stage, it combines existing map data with predicted congestion information and utilizes a real-time optimization algorithm. The generated route information is then ready to be sent to the terminal.

[0331] Step 4:

[0332] The terminal receives route data from the server and guides the user using a display device and audio equipment. It receives route data as input, displays a map and guidance information on the screen, and provides voice guidance through the speaker. The system conveys route information to the user in an easy-to-understand manner, both visually and aurally.

[0333] Step 5:

[0334] While the user is moving, the server continues to monitor traffic conditions and generates new route information as needed. In this process, location data is re-evaluated and fed into a predictive model to generate new output. The device receives this updated information and provides further directions to the user.

[0335] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0336] This invention is a system that combines congestion prediction technology and emotion recognition technology to provide users with a comfortable route while they are traveling. It consists of a server and a user terminal.

[0337] First, the server acquires real-time location information and operational status data from communication networks or transportation systems. This data is then used by a congestion prediction model running within the server to analyze pedestrian flow patterns and identify areas where congestion is expected.

[0338] Furthermore, the user's device has an emotion engine built in, which collects data from the user's voice input and biometric sensors. The emotion engine analyzes this data to estimate the user's current emotional state. This information is sent to the server, and the suggested alternative routes are adjusted based on the current emotional state.

[0339] The server generates the optimal detour route based on predicted congestion and the user's emotional state. This route is designed to avoid situations that would cause user discomfort, thus avoiding congestion while also considering the user's psychological comfort.

[0340] The generated route information is sent to the user's device, which then uses this information to perform real-time navigation. The user can review and select the route suggested by the device. During navigation, an emotion engine monitors the user's emotions in real time, and if a change is detected, it will suggest a different route as needed.

[0341] For example, if a user is feeling tired on their way home from work, the server can select a relaxed route and guide them to easily accessible rest stops. In this way, users can experience a physically and psychologically comfortable journey.

[0342] This system allows users to receive a travel experience that takes into account not only conventional traffic information but also their personal emotional state, resulting in a more personalized and optimized travel experience.

[0343] The following describes the processing flow.

[0344] Step 1:

[0345] The server obtains real-time location and operational status data from communication network and transportation APIs. This data is used to understand the current flow of people in the city.

[0346] Step 2:

[0347] The server stores the acquired data in a database and performs preprocessing. This identifies invalid and distorted data to improve data quality.

[0348] Step 3:

[0349] The server inputs pre-processed data into a congestion prediction model to analyze pedestrian flow patterns. The model uses machine learning algorithms to identify areas and time periods where congestion is predicted.

[0350] Step 4:

[0351] The device collects the user's voice input and biometric information (heart rate, facial expressions, etc.) using sensors and sends it to the emotion engine. The emotion engine analyzes the user's emotional state in real time.

[0352] Step 5:

[0353] The device sends analysis results to the server, providing information about the user's current emotional state. The server receives this data and customizes alternative routes based on the user's emotional state.

[0354] Step 6:

[0355] The server integrates congestion prediction information with user sentiment data to generate an optimal route that is psychologically and physically comfortable. This route not only avoids congestion as usual, but also takes into account the user's emotional state.

[0356] Step 7:

[0357] The server sends route information it has generated to the terminal. Based on this information, the terminal starts providing visual and audio navigation to the user.

[0358] Step 8:

[0359] The user accepts the device's navigation and begins moving. During the move, the emotion engine continuously monitors the user's emotions and detects any changes.

[0360] Step 9:

[0361] When the emotion engine detects a change in the user's emotions, the device sends new emotion data to the server. The server considers this information, recalculates the route as needed, and sends update information to the device.

[0362] (Example 2)

[0363] Next, we will describe Example 2. 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".

[0364] Conventional travel route suggestion systems only provide congestion avoidance based on traffic information, and do not adequately consider the psychological comfort of users during travel. Therefore, even if congestion is avoided, the user's emotional state may worsen, failing to improve the quality of the travel experience. Furthermore, the lack of functionality to flexibly respond to real-time changes in the environment and emotions makes it difficult to achieve the comfortable travel experience that users desire.

[0365] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0366] In this invention, the server includes means for acquiring multiple types of location information data, means for analyzing abnormal pedestrian traffic using a congestion prediction model, and means for estimating the user's emotional state. This makes it possible to generate and notify the user of an optimal detour route based on the real-time congestion situation and emotional state during their travel. As a result, the user can experience psychologically and physically comfortable travel.

[0367] "Location data" refers to data that shows the real-time geographical location of users and transportation systems.

[0368] A "congestion prediction model" is a machine learning algorithm that analyzes collected location data to predict abnormal patterns in pedestrian flow and identify areas where congestion is expected.

[0369] "User emotional state" refers to the user's psychological and emotional state, and is estimated based on data from voice input and biometric sensors.

[0370] A "detour route" is an alternative route to a destination designed to avoid congestion and take into consideration the user's emotional state.

[0371] "Monitoring changes in the situation in real time" refers to continuously observing changes in surrounding traffic conditions and the user's emotional state while the user is on the move.

[0372] This invention provides a system that offers a comfortable route in real time while a user is traveling. This system consists of a server and a user's terminal, and operates according to the following procedure.

[0373] First, the server acquires real-time location data from multiple locations. This data is obtained from communication networks and traffic information infrastructure and formatted in JSON or similar formats. The server then analyzes this location data using a congestion prediction model. This model is built using machine learning frameworks such as TensorFlow and identifies areas where congestion is expected.

[0374] Meanwhile, the user's device is equipped with an emotion engine. This emotion engine continuously collects the user's voice and biometric data using biometric sensors and microphones. Based on this, the device uses emotion analysis libraries such as the Affectiva SDK to estimate the user's current emotional state from the collected data.

[0375] The estimated user's emotional state is sent from the terminal to the server. The server integrates congestion predictions and user emotional state data and generates the optimal detour route using a generative AI model. This generative AI model selects a route that optimizes the user's psychological and physical comfort, taking into account the predicted congestion and emotional data.

[0376] The generated route information is sent from the server to the user's device and used for real-time navigation. The user can review and select the route presented on their device. Furthermore, the server will suggest a new route as needed, depending on changes in the user's situation and emotions during their journey.

[0377] As a concrete example, suppose a user is commuting home during peak hours. If the user is experiencing stress or fatigue, the server will suggest a relatively less crowded route and include stops along the way where the user can rest or relax.

[0378] An example of a prompt for a generative AI model would be, "Input user emotions and real-time traffic data, and suggest the optimal travel route for the current situation." This allows users to enjoy a personalized travel experience tailored to their individual needs.

[0379] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0380] Step 1:

[0381] The server acquires real-time location data from communication networks and traffic information infrastructure. This data includes GPS information and traffic service status data. The server receives this data, formats it, and prepares it for analysis. The input is location data, and the output is formatted data ready for analysis.

[0382] Step 2:

[0383] The server inputs formatted location data into a congestion prediction model. This model operates using machine learning frameworks such as TensorFlow and incorporates variables such as time, weather, and event information to identify areas where congestion is expected. The input is formatted data, and the output is congestion prediction information.

[0384] Step 3:

[0385] The device collects data from voice input and biometric sensors to estimate the user's emotional state. Specifically, it acquires voice data through the microphone and biometric data such as heart rate through sensors. This data is input to an emotion engine and analyzed. The input is voice and biometric data, and the output is the estimated result of the emotional state.

[0386] Step 4:

[0387] The device uses an emotion engine to analyze the user's emotional state. This analysis utilizes tools such as the Affectiva SDK to analyze voice tone and biometric information. The analysis quantifies the user's emotional state. The input for this step is voice and biometric information, and the output is a quantified emotional state.

[0388] Step 5:

[0389] The terminal sends its estimated emotional state to the server. The server integrates this emotional state data with previously acquired congestion prediction information to generate the optimal detour route. Using a generative AI model, it designs a route that takes into account the user's psychological comfort. The input for this step is the emotional state and congestion prediction information, and the output is the optimal route information.

[0390] Step 6:

[0391] The server sends optimal route information to the user's device. The device receives this information and presents it to the user in real time. The route is visualized on the device and becomes selectable by the user. The input is optimal route information, and the output is navigation information displayed on the user's device.

[0392] Step 7:

[0393] When a user experiences changes in their environment or emotions while traveling, the device monitors their emotional state again and sends update information to the server as needed. The server can then suggest a new route based on the new congestion information and emotional data. The input is the changed emotional state and new congestion information, and the output is the updated route suggestion.

[0394] (Application Example 2)

[0395] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0396] Travel in modern cities is often fraught with congestion and wasted time, and can also negatively impact users' emotional states. Furthermore, choosing a route that considers the comfort of individual users is difficult, and ensuring emotional comfort is particularly challenging. To address these issues, there is a need to provide a travel experience that takes into account not only congestion levels but also the user's personal emotional state.

[0397] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0398] In this invention, the server includes means for acquiring multiple types of location information data, means for analyzing abnormal pedestrian flow using a congestion prediction model based on the location information data, means for collecting and analyzing voice input and biometric information to estimate the user's emotional state, means for generating an optimal detour route based on the predicted congestion and emotional state, means for notifying the user's terminal of the detour route, and means for monitoring changes in the situation in real time while the user is moving and updating the detour route. This enables the user to obtain a physically and emotionally optimized route.

[0399] "Location data" refers to data that represents a geographical location, obtained from mobile devices and sensors.

[0400] A "congestion prediction model" is an algorithm that uses traffic data and pedestrian flow data to predict congestion levels in specific areas or routes in advance.

[0401] "Emotional state" refers to the psychological and emotional aspects of a user, estimated through voice and biometric data analysis.

[0402] An "emotion engine" is software or an algorithm used to analyze a user's emotions from voice and biometric information.

[0403] A "detour route" is a travel path that avoids congestion while also considering the comfort level of users based on their emotional state.

[0404] "Monitoring changes in the situation in real time" is a process of continuously acquiring data while on the move and instantly recognizing any fluctuations.

[0405] This invention is a system that improves the travel experience in urban environments and mainly consists of a server and a user terminal. The system provides users with optimized travel routes by combining congestion prediction and emotion recognition functions.

[0406] The server obtains real-time location information from location services and traffic data providers. This information is analyzed by a congestion prediction model, which makes predictions that take into account factors such as time of day, events, and weather. This prediction algorithm is implemented in programming languages ​​such as Python, and can utilize TensorFlow. Furthermore, it receives emotion data sent from the user's device, and the emotion engine analyzes data from voice input and biosensors. This determines the user's current psychological state.

[0407] The device notifies users in real time of alternative routes to avoid congestion through a navigation application. This application can be developed using a framework such as React Native. Furthermore, if the user's emotional state changes, the server-side emotion engine will immediately react and suggest a new route.

[0408] As a concrete example, consider a user enjoying shopping at a commercial facility in Tokyo. If the user shows signs of fatigue, the system can suggest avoiding crowded subways and taking a break at a cafe within walking distance. This allows the user to continue their journey while reducing stress.

[0409] An example of a prompt message would be, "Please describe the concept of a smartphone application that suggests relaxing alternative routes to avoid urban congestion when the user is feeling stressed." In this way, the invention provides users with a comfortable and efficient means of transportation in many situations.

[0410] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0411] Step 1:

[0412] The server retrieves location data in real time from location services and traffic data providers. Its inputs include the current time and the user's geographical location. The output is dynamic data for the target area. This information serves as foundational data necessary for subsequent processing.

[0413] Step 2:

[0414] The server runs a congestion prediction model using acquired location data. The inputs are real-time pedestrian flow data and a prediction algorithm. The data is analyzed using a machine learning algorithm implemented in Python, and the output is predicted congestion data. Here, the degree of congestion and its temporal changes are predicted.

[0415] Step 3:

[0416] The device acquires the user's voice input and biometric information. This is done using biosensors such as the smartphone's microphone or a smartwatch. The input consists of data obtained from biometric sensors and voice data, while the output is analytical data representing the user's emotional state.

[0417] Step 4:

[0418] The server receives emotional state data transmitted from the terminal and compares it with congestion prediction data. The inputs are emotional state data and congestion status data. Based on this data, it performs data calculations to determine alternative routes, and the output is optimized route information. The calculation uses an algorithm that takes user psychological comfort into consideration.

[0419] Step 5:

[0420] The device provides navigation to the user based on route information sent from the server. The input requires optimal route information, and the output is navigation instructions displayed to the user as visual and audio guidance. The device provides this guidance through an application built with React Native.

[0421] Step 6:

[0422] While the user is on the move, the device continuously updates emotional data and location information in real time and sends it to the server. The input is newly acquired voice data and biometric information, and the output is the updated emotional state and location data provided to the server. This process enables route re-suggestions in response to changes in the user's situation.

[0423] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0424] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0425] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0426] [Third Embodiment]

[0427] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0428] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0429] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0430] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0431] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0432] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0433] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0434] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0435] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0436] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0437] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0438] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0439] This invention relates to a system that utilizes diverse location data to predict congestion in real time and provides the optimal route based on that prediction. As an embodiment, a network system consisting of a server and user terminals is taken as an example.

[0440] First, the server acquires location data in real time from telecommunications carriers and transportation companies, and also receives anonymized location data from smartphone apps. This data is fed into a congestion prediction model installed on the server to detect abnormal pedestrian traffic and predict the likelihood of congestion at different locations and times.

[0441] The server analyzes this predictive data and generates an alternative route to the user's planned destination. The generated route is optimized to avoid congestion compared to the usual route, allowing for alternative modes of transport and reduced travel time. This information is immediately sent to the user's device and notified to the user.

[0442] Meanwhile, users can review and accept suggested routes through their devices. These devices may include a visual map display, text information, and voice guidance. As users travel, the server continuously monitors the situation in real time, sending updates to the device and optimizing the route if new congestion is predicted or existing conditions change.

[0443] For example, when a user tries to visit a tourist destination on a holiday, the server predicts peak hours based on past data and current congestion levels, and suggests alternative times and routes to the user. This allows the user to avoid crowds and travel with less stress.

[0444] This system allows users to avoid congestion during their daily commutes and trips for specific purposes, enabling them to enjoy efficient and comfortable travel.

[0445] The following describes the processing flow.

[0446] Step 1:

[0447] The server acquires location data in real time from APIs of telecommunications carriers and transportation companies. This data includes current pedestrian traffic and the operating status of transportation services.

[0448] Step 2:

[0449] The server preprocesses the acquired data to remove errors and outliers, thereby ensuring data accuracy and reliability.

[0450] Step 3:

[0451] The server inputs the cleansed data into a congestion prediction model, which then analyzes unusual pedestrian traffic patterns by comparing them with historical data. The prediction model uses machine learning algorithms to assess the likelihood of congestion.

[0452] Step 4:

[0453] The server identifies congested areas and times based on the analysis results and generates the optimal detour route to avoid them. It simulates various routes and selects the most efficient one.

[0454] Step 5:

[0455] The server generates detour route information and sends it to the user's terminal for notification. This information includes a map display of the route and an estimated travel time.

[0456] Step 6:

[0457] The user reviews the suggested route on their device and decides whether to accept it. If selected, the device begins navigation and provides visual and audio guidance.

[0458] Step 7:

[0459] The device uses GPS functionality to track the user's location and monitors their progress in real time. If necessary, it can change direction or reset the destination.

[0460] Step 8:

[0461] The server continuously monitors the situation and recalculates routes in real time based on new pedestrian flow data and event information. If changes occur, updated information is sent to the terminal to notify the user.

[0462] (Example 1)

[0463] Next, we will describe Example 1. 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."

[0464] In today's transportation environment, the inability to respond quickly to real-time changes in congestion can hinder efficient travel. Conventional systems have resulted in users being unable to avoid congestion and being forced to endure stressful journeys. Therefore, there is a need to provide more accurate and rapid congestion forecasting and alternative routes.

[0465] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0466] In this invention, the server includes means for acquiring multiple types of location information data, means for analyzing abnormal pedestrian flow using a congestion prediction model based on the location information data and AI technology, and means for generating an optimal detour route based on the prediction results. This enables efficient movement that avoids congestion in real time.

[0467] "Location data" refers to information about geographical location, including the current location and movement path of users and objects.

[0468] "Generative AI technology" is a technology that uses artificial intelligence technology that mimics human thought to generate new predictive models and information from data.

[0469] A "congestion prediction model" is an algorithm or mathematical model used to predict abnormal pedestrian traffic and congestion based on past and present data.

[0470] "Communication equipment" refers to terminals or devices used for the purpose of sending and receiving information, and includes smartphones and computers.

[0471] A "detour route" is an alternative travel route to a destination that has been pre-set to avoid congestion.

[0472] This invention is a system that utilizes multiple location data to predict congestion in real time and then provides the user with the optimal detour route. Specifically, it consists of a server and a user terminal.

[0473] The server acquires various location data from communication networks and public transportation. This data collection utilizes communication carrier interfaces and publicly available APIs from transportation companies. The acquired data is input into a congestion prediction model that uses generative AI technology within the server. This model incorporates the latest machine learning algorithms and analyzes the data obtained in real time to predict abnormal pedestrian flow and congestion with high accuracy.

[0474] Subsequently, the server uses the Dijkstra algorithm and other methods to generate the optimal detour route to avoid congestion based on the predicted data. The generated route data is immediately transmitted to the user's communication device. The user's terminal displays a visual map, and may also provide text information or voice assistance. This allows the user to travel with less stress by avoiding congestion.

[0475] Furthermore, the server constantly monitors congestion levels while the user is traveling, re-optimizing alternative routes as needed and sending updated information to the terminal. This allows the user to receive the most suitable mode of transportation in real time.

[0476] As a concrete example, for a user visiting a tourist destination on a holiday, the server can use past and present congestion data to suggest the least congested time and route. This allows the user to reach their destination comfortably.

[0477] Examples of prompt statements for a generative AI model are as follows:

[0478] "I'm planning a visit to a tourist spot during the holidays. Could you tell me what time of day and route I should choose to avoid crowds?"

[0479] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0480] Step 1:

[0481] The server collects location data through communication networks and public transportation APIs.

[0482] Specifically, the server periodically sends requests to the API endpoint to retrieve current pedestrian flow data and traffic condition data.

[0483] The input consists of data from communication networks and public transportation, while the output is location data stored on the server.

[0484] Step 2:

[0485] The server inputs the collected location data into a congestion prediction model.

[0486] This model uses generative AI technology and applies advanced machine learning algorithms to detect abnormal pedestrian flow patterns.

[0487] The input is location data, and the output is anomaly detection results and predicted congestion information.

[0488] In terms of specific operation, the server supplies data to machine learning models built on platforms such as Python, and generates results through batch processing.

[0489] Step 3:

[0490] The server generates the optimal detour route based on predicted congestion information.

[0491] Here, we use the Dijkstra algorithm to calculate travel paths that take real-time data into account.

[0492] The input is congestion information, and the output is the optimal detour route to avoid congestion.

[0493] In terms of specific operations, the server uses a data structure to perform route searching and saves the generated route.

[0494] Step 4:

[0495] The server sends the generated detour route to the user's terminal.

[0496] The device receives this information and displays it as a notification on the screen.

[0497] The input is the generated detour route, and the output is the terminal notification.

[0498] Specifically, the server uses a push notification service to send a message to the device.

[0499] Step 5:

[0500] The user reviews and selects the route information provided on the device.

[0501] The device displays a detailed map and provides directions.

[0502] The input is the routing information sent to the terminal, and the output is the user's selected action.

[0503] Users check their route through a dedicated application and begin their journey to their destination.

[0504] Step 6:

[0505] The server monitors congestion in real time while the user is on the move and updates alternative routes as needed.

[0506] The server will resend the route, which has been modified as needed, based on the new congestion forecast.

[0507] The input is the latest congestion information, and the output is updated route information.

[0508] Specifically, the server continuously retrieves data and provides the latest information by sending push notifications to the device again.

[0509] (Application Example 1)

[0510] Next, we will explain Application Example 1. In the following explanation, 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."

[0511] This solution addresses the difficulty of providing the optimal route in real time for autonomous vehicles. In particular, it is required to optimize the route in a timely manner in response to congestion and changing conditions, thereby achieving safe and efficient operation.

[0512] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0513] In this invention, the server includes means for acquiring multiple types of location information data, means for analyzing abnormal pedestrian flow using a congestion prediction model based on the multiple types of location information data, and means for generating an optimal detour route based on the predicted congestion situation. This enables real-time route optimization for moving objects.

[0514] "Multiple types of location data" refers to location-specific information in various forms provided by communication networks, transportation methods, etc., that can be acquired in real time.

[0515] A "congestion prediction model" is a method that uses machine learning algorithms to detect anomalies in pedestrian flow, taking into account factors such as time of day, weather, and event information, and predicts future congestion.

[0516] A "detour route" is a route newly created to avoid congestion or reduce travel time, in contrast to the originally planned route.

[0517] An "autonomous vehicle" is a powered vehicle that recognizes the traffic environment and is automatically controlled without the need for driver intervention.

[0518] "Real-time updates" refers to the operation of receiving new information instantly even while a moving object is in motion, and dynamically correcting the route or instructions as needed.

[0519] "Display devices and sound devices" refer to hardware used to provide information to users visually and aurally, and include displays and speakers, respectively.

[0520] This invention is a system for optimizing the movement of autonomous vehicles and is configured as a network system including a server and terminals within the vehicle.

[0521] The server acquires location data provided in real time from communication networks and transportation methods. This data is input into a congestion prediction model built within the server. The congestion prediction model uses machine learning algorithms built with Python and TensorFlow to detect anomalies in pedestrian flow, taking into account time of day, weather, and event information, and predicts future congestion. Based on the predicted congestion data, the optimal detour route is calculated. This route is reviewed each time new data is acquired and updated in real time.

[0522] The terminal uses a display and sound system to provide visual and auditory guidance to the driver of a vehicle, based on route data received from the server. Specifically, it displays the route and estimated time to the destination on the display and provides route guidance through the speaker. Furthermore, if new congestion occurs while driving, updated information is immediately transmitted from the server, and the terminal re-optimizes the route based on this information and continues to provide guidance.

[0523] For example, when a user is traveling to the city center by vehicle, the server can predict congestion and avoid the usual route, offering the user an alternative detour. This allows the user to avoid traffic jams and arrive at their destination smoothly.

[0524] Example of a prompt:

[0525] "Please explain how to generate alternative routes to avoid congestion in real time."

[0526] "Please suggest what algorithms should be used in machine learning models for congestion prediction."

[0527] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0528] Step 1:

[0529] The server acquires real-time location data from communication networks and transportation methods. This data input includes GPS information and traffic data. The data is temporarily stored in a database on the server and used for subsequent analysis.

[0530] Step 2:

[0531] The server inputs location data into a congestion prediction model. This model is built using machine learning algorithms with Python and TensorFlow. Data processing involves extracting abnormal pedestrian flow patterns and applying them to the prediction algorithm. The output provides information on predicted congestion times and areas.

[0532] Step 3:

[0533] The server generates the optimal detour route based on the congestion prediction results. At this stage, it combines existing map data with predicted congestion information and utilizes a real-time optimization algorithm. The generated route information is then ready to be sent to the terminal.

[0534] Step 4:

[0535] The terminal receives route data from the server and guides the user using a display device and audio equipment. It receives route data as input, displays a map and guidance information on the screen, and provides voice guidance through the speaker. The system conveys route information to the user in an easy-to-understand manner, both visually and aurally.

[0536] Step 5:

[0537] While the user is moving, the server continues to monitor traffic conditions and generates new route information as needed. In this process, location data is re-evaluated and fed into a predictive model to generate new output. The device receives this updated information and provides further directions to the user.

[0538] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0539] This invention is a system that combines congestion prediction technology and emotion recognition technology to provide users with a comfortable route while they are traveling. It consists of a server and a user terminal.

[0540] First, the server acquires real-time location information and operational status data from communication networks or transportation systems. This data is then used by a congestion prediction model running within the server to analyze pedestrian flow patterns and identify areas where congestion is expected.

[0541] Furthermore, the user's device has an emotion engine built in, which collects data from the user's voice input and biometric sensors. The emotion engine analyzes this data to estimate the user's current emotional state. This information is sent to the server, and the suggested alternative routes are adjusted based on the current emotional state.

[0542] The server generates the optimal detour route based on predicted congestion and the user's emotional state. This route is designed to avoid situations that would cause user discomfort, thus avoiding congestion while also considering the user's psychological comfort.

[0543] The generated route information is sent to the user's device, which then uses this information to perform real-time navigation. The user can review and select the route suggested by the device. During navigation, an emotion engine monitors the user's emotions in real time, and if a change is detected, it will suggest a different route as needed.

[0544] For example, if a user is feeling tired on their way home from work, the server can select a relaxed route and guide them to easily accessible rest stops. In this way, users can experience a physically and psychologically comfortable journey.

[0545] This system allows users to receive a travel experience that takes into account not only conventional traffic information but also their personal emotional state, resulting in a more personalized and optimized travel experience.

[0546] The following describes the processing flow.

[0547] Step 1:

[0548] The server obtains real-time location and operational status data from communication network and transportation APIs. This data is used to understand the current flow of people in the city.

[0549] Step 2:

[0550] The server stores the acquired data in a database and performs preprocessing. This identifies invalid and distorted data to improve data quality.

[0551] Step 3:

[0552] The server inputs pre-processed data into a congestion prediction model to analyze pedestrian flow patterns. The model uses machine learning algorithms to identify areas and time periods where congestion is predicted.

[0553] Step 4:

[0554] The device collects the user's voice input and biometric information (heart rate, facial expressions, etc.) using sensors and sends it to the emotion engine. The emotion engine analyzes the user's emotional state in real time.

[0555] Step 5:

[0556] The device sends analysis results to the server, providing information about the user's current emotional state. The server receives this data and customizes alternative routes based on the user's emotional state.

[0557] Step 6:

[0558] The server integrates congestion prediction information with user sentiment data to generate an optimal route that is psychologically and physically comfortable. This route not only avoids congestion as usual, but also takes into account the user's emotional state.

[0559] Step 7:

[0560] The server sends route information it has generated to the terminal. Based on this information, the terminal starts providing visual and audio navigation to the user.

[0561] Step 8:

[0562] The user accepts the device's navigation and begins moving. During the move, the emotion engine continuously monitors the user's emotions and detects any changes.

[0563] Step 9:

[0564] When the emotion engine detects a change in the user's emotions, the device sends new emotion data to the server. The server considers this information, recalculates the route as needed, and sends update information to the device.

[0565] (Example 2)

[0566] Next, we will describe Example 2. 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."

[0567] Conventional travel route suggestion systems only provide congestion avoidance based on traffic information, and do not adequately consider the psychological comfort of users during travel. Therefore, even if congestion is avoided, the user's emotional state may worsen, failing to improve the quality of the travel experience. Furthermore, the lack of functionality to flexibly respond to real-time changes in the environment and emotions makes it difficult to achieve the comfortable travel experience that users desire.

[0568] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0569] In this invention, the server includes means for acquiring multiple types of location information data, means for analyzing abnormal pedestrian traffic using a congestion prediction model, and means for estimating the user's emotional state. This makes it possible to generate and notify the user of an optimal detour route based on the real-time congestion situation and emotional state during their travel. As a result, the user can experience psychologically and physically comfortable travel.

[0570] "Location data" refers to data that shows the real-time geographical location of users and transportation systems.

[0571] A "congestion prediction model" is a machine learning algorithm that analyzes collected location data to predict abnormal patterns in pedestrian flow and identify areas where congestion is expected.

[0572] "User emotional state" refers to the user's psychological and emotional state, and is estimated based on data from voice input and biometric sensors.

[0573] A "detour route" is an alternative route to a destination designed to avoid congestion and take into consideration the user's emotional state.

[0574] "Monitoring changes in the situation in real time" refers to continuously observing changes in surrounding traffic conditions and the user's emotional state while the user is on the move.

[0575] This invention provides a system that offers a comfortable route in real time while a user is traveling. This system consists of a server and a user's terminal, and operates according to the following procedure.

[0576] First, the server acquires real-time location data from multiple locations. This data is obtained from communication networks and traffic information infrastructure and formatted in JSON or similar formats. The server then analyzes this location data using a congestion prediction model. This model is built using machine learning frameworks such as TensorFlow and identifies areas where congestion is expected.

[0577] Meanwhile, the user's device is equipped with an emotion engine. This emotion engine continuously collects the user's voice and biometric data using biometric sensors and microphones. Based on this, the device uses emotion analysis libraries such as the Affectiva SDK to estimate the user's current emotional state from the collected data.

[0578] The estimated user's emotional state is sent from the terminal to the server. The server integrates congestion predictions and user emotional state data and generates the optimal detour route using a generative AI model. This generative AI model selects a route that optimizes the user's psychological and physical comfort, taking into account the predicted congestion and emotional data.

[0579] The generated route information is sent from the server to the user's device and used for real-time navigation. The user can review and select the route presented on their device. Furthermore, the server will suggest a new route as needed, depending on changes in the user's situation and emotions during their journey.

[0580] As a concrete example, suppose a user is commuting home during peak hours. If the user is experiencing stress or fatigue, the server will suggest a relatively less crowded route and include stops along the way where the user can rest or relax.

[0581] An example of a prompt for a generative AI model would be, "Input user emotions and real-time traffic data, and suggest the optimal travel route for the current situation." This allows users to enjoy a personalized travel experience tailored to their individual needs.

[0582] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0583] Step 1:

[0584] The server acquires real-time location data from communication networks and traffic information infrastructure. This data includes GPS information and traffic service status data. The server receives this data, formats it, and prepares it for analysis. The input is location data, and the output is formatted data ready for analysis.

[0585] Step 2:

[0586] The server inputs formatted location data into a congestion prediction model. This model operates using machine learning frameworks such as TensorFlow and incorporates variables such as time, weather, and event information to identify areas where congestion is expected. The input is formatted data, and the output is congestion prediction information.

[0587] Step 3:

[0588] The device collects data from voice input and biometric sensors to estimate the user's emotional state. Specifically, it acquires voice data through the microphone and biometric data such as heart rate through sensors. This data is input to an emotion engine and analyzed. The input is voice and biometric data, and the output is the estimated result of the emotional state.

[0589] Step 4:

[0590] The device uses an emotion engine to analyze the user's emotional state. This analysis utilizes tools such as the Affectiva SDK to analyze voice tone and biometric information. The analysis quantifies the user's emotional state. The input for this step is voice and biometric information, and the output is a quantified emotional state.

[0591] Step 5:

[0592] The terminal sends its estimated emotional state to the server. The server integrates this emotional state data with previously acquired congestion prediction information to generate the optimal detour route. Using a generative AI model, it designs a route that takes into account the user's psychological comfort. The input for this step is the emotional state and congestion prediction information, and the output is the optimal route information.

[0593] Step 6:

[0594] The server sends optimal route information to the user's device. The device receives this information and presents it to the user in real time. The route is visualized on the device and becomes selectable by the user. The input is optimal route information, and the output is navigation information displayed on the user's device.

[0595] Step 7:

[0596] When a user experiences changes in their environment or emotions while traveling, the device monitors their emotional state again and sends update information to the server as needed. The server can then suggest a new route based on the new congestion information and emotional data. The input is the changed emotional state and new congestion information, and the output is the updated route suggestion.

[0597] (Application Example 2)

[0598] Next, we will explain application example 2. In the following explanation, 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."

[0599] Travel in modern cities is often fraught with congestion and wasted time, and can also negatively impact users' emotional states. Furthermore, choosing a route that considers the comfort of individual users is difficult, and ensuring emotional comfort is particularly challenging. To address these issues, there is a need to provide a travel experience that takes into account not only congestion levels but also the user's personal emotional state.

[0600] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0601] In this invention, the server includes means for acquiring multiple types of location information data, means for analyzing abnormal pedestrian flow using a congestion prediction model based on the location information data, means for collecting and analyzing voice input and biometric information to estimate the user's emotional state, means for generating an optimal detour route based on the predicted congestion and emotional state, means for notifying the user's terminal of the detour route, and means for monitoring changes in the situation in real time while the user is moving and updating the detour route. This enables the user to obtain a physically and emotionally optimized route.

[0602] "Location data" refers to data that represents a geographical location, obtained from mobile devices and sensors.

[0603] A "congestion prediction model" is an algorithm that uses traffic data and pedestrian flow data to predict congestion levels in specific areas or routes in advance.

[0604] "Emotional state" refers to the psychological and emotional aspects of a user, estimated through voice and biometric data analysis.

[0605] An "emotion engine" is software or an algorithm used to analyze a user's emotions from voice and biometric information.

[0606] A "detour route" is a travel path that avoids congestion while also considering the comfort level of users based on their emotional state.

[0607] "Monitoring changes in the situation in real time" is a process of continuously acquiring data while on the move and instantly recognizing any fluctuations.

[0608] This invention is a system that improves the travel experience in urban environments and mainly consists of a server and a user terminal. The system provides users with optimized travel routes by combining congestion prediction and emotion recognition functions.

[0609] The server obtains real-time location information from location services and traffic data providers. This information is analyzed by a congestion prediction model, which makes predictions that take into account factors such as time of day, events, and weather. This prediction algorithm is implemented in programming languages ​​such as Python, and can utilize TensorFlow. Furthermore, it receives emotion data sent from the user's device, and the emotion engine analyzes data from voice input and biosensors. This determines the user's current psychological state.

[0610] The device notifies users in real time of alternative routes to avoid congestion through a navigation application. This application can be developed using a framework such as React Native. Furthermore, if the user's emotional state changes, the server-side emotion engine will immediately react and suggest a new route.

[0611] As a concrete example, consider a user enjoying shopping at a commercial facility in Tokyo. If the user shows signs of fatigue, the system can suggest avoiding crowded subways and taking a break at a cafe within walking distance. This allows the user to continue their journey while reducing stress.

[0612] An example of a prompt message would be, "Please describe the concept of a smartphone application that suggests relaxing alternative routes to avoid urban congestion when the user is feeling stressed." In this way, the invention provides users with a comfortable and efficient means of transportation in many situations.

[0613] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0614] Step 1:

[0615] The server retrieves location data in real time from location services and traffic data providers. Its inputs include the current time and the user's geographical location. The output is dynamic data for the target area. This information serves as foundational data necessary for subsequent processing.

[0616] Step 2:

[0617] The server runs a congestion prediction model using acquired location data. The inputs are real-time pedestrian flow data and a prediction algorithm. The data is analyzed using a machine learning algorithm implemented in Python, and the output is predicted congestion data. Here, the degree of congestion and its temporal changes are predicted.

[0618] Step 3:

[0619] The device acquires the user's voice input and biometric information. This is done using biosensors such as the smartphone's microphone or a smartwatch. The input consists of data obtained from biometric sensors and voice data, while the output is analytical data representing the user's emotional state.

[0620] Step 4:

[0621] The server receives emotional state data transmitted from the terminal and compares it with congestion prediction data. The inputs are emotional state data and congestion status data. Based on this data, it performs data calculations to determine alternative routes, and the output is optimized route information. The calculation uses an algorithm that takes user psychological comfort into consideration.

[0622] Step 5:

[0623] The device provides navigation to the user based on route information sent from the server. The input requires optimal route information, and the output is navigation instructions displayed to the user as visual and audio guidance. The device provides this guidance through an application built with React Native.

[0624] Step 6:

[0625] While the user is on the move, the device continuously updates emotional data and location information in real time and sends it to the server. The input is newly acquired voice data and biometric information, and the output is the updated emotional state and location data provided to the server. This process enables route re-suggestions in response to changes in the user's situation.

[0626] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0627] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0628] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0629] [Fourth Embodiment]

[0630] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0631] As shown in Figure 7, the 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.

[0632] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0633] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0634] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0635] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0636] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0637] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0638] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0639] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0640] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0641] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0642] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0643] This invention relates to a system that utilizes diverse location data to predict congestion in real time and provides the optimal route based on that prediction. As an embodiment, a network system consisting of a server and user terminals is taken as an example.

[0644] First, the server acquires location data in real time from telecommunications carriers and transportation companies, and also receives anonymized location data from smartphone apps. This data is fed into a congestion prediction model installed on the server to detect abnormal pedestrian traffic and predict the likelihood of congestion at different locations and times.

[0645] The server analyzes this predictive data and generates an alternative route to the user's planned destination. The generated route is optimized to avoid congestion compared to the usual route, allowing for alternative modes of transport and reduced travel time. This information is immediately sent to the user's device and notified to the user.

[0646] Meanwhile, users can review and accept suggested routes through their devices. These devices may include a visual map display, text information, and voice guidance. As users travel, the server continuously monitors the situation in real time, sending updates to the device and optimizing the route if new congestion is predicted or existing conditions change.

[0647] For example, when a user tries to visit a tourist destination on a holiday, the server predicts peak hours based on past data and current congestion levels, and suggests alternative times and routes to the user. This allows the user to avoid crowds and travel with less stress.

[0648] This system allows users to avoid congestion during their daily commutes and trips for specific purposes, enabling them to enjoy efficient and comfortable travel.

[0649] The following describes the processing flow.

[0650] Step 1:

[0651] The server acquires location data in real time from APIs of telecommunications carriers and transportation companies. This data includes current pedestrian traffic and the operating status of transportation services.

[0652] Step 2:

[0653] The server preprocesses the acquired data to remove errors and outliers, thereby ensuring data accuracy and reliability.

[0654] Step 3:

[0655] The server inputs the cleansed data into a congestion prediction model, which then analyzes unusual pedestrian traffic patterns by comparing them with historical data. The prediction model uses machine learning algorithms to assess the likelihood of congestion.

[0656] Step 4:

[0657] The server identifies congested areas and times based on the analysis results and generates the optimal detour route to avoid them. It simulates various routes and selects the most efficient one.

[0658] Step 5:

[0659] The server generates detour route information and sends it to the user's terminal for notification. This information includes a map display of the route and an estimated travel time.

[0660] Step 6:

[0661] The user reviews the suggested route on their device and decides whether to accept it. If selected, the device begins navigation and provides visual and audio guidance.

[0662] Step 7:

[0663] The device uses GPS functionality to track the user's location and monitors their progress in real time. If necessary, it can change direction or reset the destination.

[0664] Step 8:

[0665] The server continuously monitors the situation and recalculates routes in real time based on new pedestrian flow data and event information. If changes occur, updated information is sent to the terminal to notify the user.

[0666] (Example 1)

[0667] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0668] In today's transportation environment, the inability to respond quickly to real-time changes in congestion can hinder efficient travel. Conventional systems have resulted in users being unable to avoid congestion and being forced to endure stressful journeys. Therefore, there is a need to provide more accurate and rapid congestion forecasting and alternative routes.

[0669] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0670] In this invention, the server includes means for acquiring multiple types of location information data, means for analyzing abnormal pedestrian flow using a congestion prediction model based on the location information data and AI technology, and means for generating an optimal detour route based on the prediction results. This enables efficient movement that avoids congestion in real time.

[0671] "Location data" refers to information about geographical location, including the current location and movement path of users and objects.

[0672] "Generative AI technology" is a technology that uses artificial intelligence technology that mimics human thought to generate new predictive models and information from data.

[0673] A "congestion prediction model" is an algorithm or mathematical model used to predict abnormal pedestrian traffic and congestion based on past and present data.

[0674] "Communication equipment" refers to terminals or devices used for the purpose of sending and receiving information, and includes smartphones and computers.

[0675] A "detour route" is an alternative travel route to a destination that has been pre-set to avoid congestion.

[0676] This invention is a system that utilizes multiple location data to predict congestion in real time and then provides the user with the optimal detour route. Specifically, it consists of a server and a user terminal.

[0677] The server acquires various location data from communication networks and public transportation. This data collection utilizes communication carrier interfaces and publicly available APIs from transportation companies. The acquired data is input into a congestion prediction model that uses generative AI technology within the server. This model incorporates the latest machine learning algorithms and analyzes the data obtained in real time to predict abnormal pedestrian flow and congestion with high accuracy.

[0678] Subsequently, the server uses the Dijkstra algorithm and other methods to generate the optimal detour route to avoid congestion based on the predicted data. The generated route data is immediately transmitted to the user's communication device. The user's terminal displays a visual map, and may also provide text information or voice assistance. This allows the user to travel with less stress by avoiding congestion.

[0679] Furthermore, the server constantly monitors congestion levels while the user is traveling, re-optimizing alternative routes as needed and sending updated information to the terminal. This allows the user to receive the most suitable mode of transportation in real time.

[0680] As a concrete example, for a user visiting a tourist destination on a holiday, the server can use past and present congestion data to suggest the least congested time and route. This allows the user to reach their destination comfortably.

[0681] Examples of prompt statements for a generative AI model are as follows:

[0682] "I'm planning a visit to a tourist spot during the holidays. Could you tell me what time of day and route I should choose to avoid crowds?"

[0683] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0684] Step 1:

[0685] The server collects location data through communication networks and public transportation APIs.

[0686] Specifically, the server periodically sends requests to the API endpoint to retrieve current pedestrian flow data and traffic condition data.

[0687] The input consists of data from communication networks and public transportation, while the output is location data stored on the server.

[0688] Step 2:

[0689] The server inputs the collected location data into a congestion prediction model.

[0690] This model uses generative AI technology and applies advanced machine learning algorithms to detect abnormal pedestrian flow patterns.

[0691] The input is location data, and the output is anomaly detection results and predicted congestion information.

[0692] In terms of specific operation, the server supplies data to machine learning models built on platforms such as Python, and generates results through batch processing.

[0693] Step 3:

[0694] The server generates the optimal detour route based on predicted congestion information.

[0695] Here, we use the Dijkstra algorithm to calculate travel paths that take real-time data into account.

[0696] The input is congestion information, and the output is the optimal detour route to avoid congestion.

[0697] In terms of specific operations, the server uses a data structure to perform route searching and saves the generated route.

[0698] Step 4:

[0699] The server sends the generated detour route to the user's terminal.

[0700] The device receives this information and displays it as a notification on the screen.

[0701] The input is the generated detour route, and the output is the terminal notification.

[0702] Specifically, the server uses a push notification service to send a message to the device.

[0703] Step 5:

[0704] The user reviews and selects the route information provided on the device.

[0705] The device displays a detailed map and provides directions.

[0706] The input is the routing information sent to the terminal, and the output is the user's selected action.

[0707] Users check their route through a dedicated application and begin their journey to their destination.

[0708] Step 6:

[0709] The server monitors congestion in real time while the user is on the move and updates alternative routes as needed.

[0710] The server will resend the route, which has been modified as needed, based on the new congestion forecast.

[0711] The input is the latest congestion information, and the output is updated route information.

[0712] Specifically, the server continuously retrieves data and provides the latest information by sending push notifications to the device again.

[0713] (Application Example 1)

[0714] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0715] This solution addresses the difficulty of providing the optimal route in real time for autonomous vehicles. In particular, it is required to optimize the route in a timely manner in response to congestion and changing conditions, thereby achieving safe and efficient operation.

[0716] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0717] In this invention, the server includes means for acquiring multiple types of location information data, means for analyzing abnormal pedestrian flow using a congestion prediction model based on the multiple types of location information data, and means for generating an optimal detour route based on the predicted congestion situation. This enables real-time route optimization for moving objects.

[0718] "Multiple types of location data" refers to location-specific information in various forms provided by communication networks, transportation methods, etc., that can be acquired in real time.

[0719] A "congestion prediction model" is a method that uses machine learning algorithms to detect anomalies in pedestrian flow, taking into account factors such as time of day, weather, and event information, and predicts future congestion.

[0720] A "detour route" is a route newly created to avoid congestion or reduce travel time, in contrast to the originally planned route.

[0721] An "autonomous vehicle" is a powered vehicle that recognizes the traffic environment and is automatically controlled without the need for driver intervention.

[0722] "Real-time updates" refers to the operation of receiving new information instantly even while a moving object is in motion, and dynamically correcting the route or instructions as needed.

[0723] "Display devices and sound devices" refer to hardware used to provide information to users visually and aurally, and include displays and speakers, respectively.

[0724] This invention is a system for optimizing the movement of autonomous vehicles and is configured as a network system including a server and terminals within the vehicle.

[0725] The server acquires location data provided in real time from communication networks and transportation methods. This data is input into a congestion prediction model built within the server. The congestion prediction model uses machine learning algorithms built with Python and TensorFlow to detect anomalies in pedestrian flow, taking into account time of day, weather, and event information, and predicts future congestion. Based on the predicted congestion data, the optimal detour route is calculated. This route is reviewed each time new data is acquired and updated in real time.

[0726] The terminal uses a display and sound system to provide visual and auditory guidance to the driver of a vehicle, based on route data received from the server. Specifically, it displays the route and estimated time to the destination on the display and provides route guidance through the speaker. Furthermore, if new congestion occurs while driving, updated information is immediately transmitted from the server, and the terminal re-optimizes the route based on this information and continues to provide guidance.

[0727] For example, when a user is traveling to the city center by vehicle, the server can predict congestion and avoid the usual route, offering the user an alternative detour. This allows the user to avoid traffic jams and arrive at their destination smoothly.

[0728] Example of a prompt:

[0729] "Please explain how to generate alternative routes to avoid congestion in real time."

[0730] "Please suggest what algorithms should be used in machine learning models for congestion prediction."

[0731] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0732] Step 1:

[0733] The server acquires real-time location data from communication networks and transportation methods. This data input includes GPS information and traffic data. The data is temporarily stored in a database on the server and used for subsequent analysis.

[0734] Step 2:

[0735] The server inputs location data into a congestion prediction model. This model is built using machine learning algorithms with Python and TensorFlow. Data processing involves extracting abnormal pedestrian flow patterns and applying them to the prediction algorithm. The output provides information on predicted congestion times and areas.

[0736] Step 3:

[0737] The server generates the optimal detour route based on the congestion prediction results. At this stage, it combines existing map data with predicted congestion information and utilizes a real-time optimization algorithm. The generated route information is then ready to be sent to the terminal.

[0738] Step 4:

[0739] The terminal receives route data from the server and guides the user using a display device and audio equipment. It receives route data as input, displays a map and guidance information on the screen, and provides voice guidance through the speaker. The system conveys route information to the user in an easy-to-understand manner, both visually and aurally.

[0740] Step 5:

[0741] While the user is moving, the server continues to monitor traffic conditions and generates new route information as needed. In this process, location data is re-evaluated and fed into a predictive model to generate new output. The device receives this updated information and provides further directions to the user.

[0742] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0743] This invention is a system that combines congestion prediction technology and emotion recognition technology to provide users with a comfortable route while they are traveling. It consists of a server and a user terminal.

[0744] First, the server acquires real-time location information and operational status data from communication networks or transportation systems. This data is then used by a congestion prediction model running within the server to analyze pedestrian flow patterns and identify areas where congestion is expected.

[0745] Furthermore, the user's device has an emotion engine built in, which collects data from the user's voice input and biometric sensors. The emotion engine analyzes this data to estimate the user's current emotional state. This information is sent to the server, and the suggested alternative routes are adjusted based on the current emotional state.

[0746] The server generates the optimal detour route based on predicted congestion and the user's emotional state. This route is designed to avoid situations that would cause user discomfort, thus avoiding congestion while also considering the user's psychological comfort.

[0747] The generated route information is sent to the user's device, which then uses this information to perform real-time navigation. The user can review and select the route suggested by the device. During navigation, an emotion engine monitors the user's emotions in real time, and if a change is detected, it will suggest a different route as needed.

[0748] For example, if a user is feeling tired on their way home from work, the server can select a relaxed route and guide them to easily accessible rest stops. In this way, users can experience a physically and psychologically comfortable journey.

[0749] This system allows users to receive a travel experience that takes into account not only conventional traffic information but also their personal emotional state, resulting in a more personalized and optimized travel experience.

[0750] The following describes the processing flow.

[0751] Step 1:

[0752] The server obtains real-time location and operational status data from communication network and transportation APIs. This data is used to understand the current flow of people in the city.

[0753] Step 2:

[0754] The server stores the acquired data in a database and performs preprocessing. This identifies invalid and distorted data to improve data quality.

[0755] Step 3:

[0756] The server inputs pre-processed data into a congestion prediction model to analyze pedestrian flow patterns. The model uses machine learning algorithms to identify areas and time periods where congestion is predicted.

[0757] Step 4:

[0758] The device collects the user's voice input and biometric information (heart rate, facial expressions, etc.) using sensors and sends it to the emotion engine. The emotion engine analyzes the user's emotional state in real time.

[0759] Step 5:

[0760] The device sends analysis results to the server, providing information about the user's current emotional state. The server receives this data and customizes alternative routes based on the user's emotional state.

[0761] Step 6:

[0762] The server integrates congestion prediction information with user sentiment data to generate an optimal route that is psychologically and physically comfortable. This route not only avoids congestion as usual, but also takes into account the user's emotional state.

[0763] Step 7:

[0764] The server sends route information it has generated to the terminal. Based on this information, the terminal starts providing visual and audio navigation to the user.

[0765] Step 8:

[0766] The user accepts the device's navigation and begins moving. During the move, the emotion engine continuously monitors the user's emotions and detects any changes.

[0767] Step 9:

[0768] When the emotion engine detects a change in the user's emotions, the device sends new emotion data to the server. The server considers this information, recalculates the route as needed, and sends update information to the device.

[0769] (Example 2)

[0770] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0771] Conventional travel route suggestion systems only provide congestion avoidance based on traffic information, and do not adequately consider the psychological comfort of users during travel. Therefore, even if congestion is avoided, the user's emotional state may worsen, failing to improve the quality of the travel experience. Furthermore, the lack of functionality to flexibly respond to real-time changes in the environment and emotions makes it difficult to achieve the comfortable travel experience that users desire.

[0772] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0773] In this invention, the server includes means for acquiring multiple types of location information data, means for analyzing abnormal pedestrian traffic using a congestion prediction model, and means for estimating the user's emotional state. This makes it possible to generate and notify the user of an optimal detour route based on the real-time congestion situation and emotional state during their travel. As a result, the user can experience psychologically and physically comfortable travel.

[0774] "Location data" refers to data that shows the real-time geographical location of users and transportation systems.

[0775] A "congestion prediction model" is a machine learning algorithm that analyzes collected location data to predict abnormal patterns in pedestrian flow and identify areas where congestion is expected.

[0776] "User emotional state" refers to the user's psychological and emotional state, and is estimated based on data from voice input and biometric sensors.

[0777] A "detour route" is an alternative route to a destination designed to avoid congestion and take into consideration the user's emotional state.

[0778] "Monitoring changes in the situation in real time" refers to continuously observing changes in surrounding traffic conditions and the user's emotional state while the user is on the move.

[0779] This invention provides a system that offers a comfortable route in real time while a user is traveling. This system consists of a server and a user's terminal, and operates according to the following procedure.

[0780] First, the server acquires real-time location data from multiple locations. This data is obtained from communication networks and traffic information infrastructure and formatted in JSON or similar formats. The server then analyzes this location data using a congestion prediction model. This model is built using machine learning frameworks such as TensorFlow and identifies areas where congestion is expected.

[0781] Meanwhile, the user's device is equipped with an emotion engine. This emotion engine continuously collects the user's voice and biometric data using biometric sensors and microphones. Based on this, the device uses emotion analysis libraries such as the Affectiva SDK to estimate the user's current emotional state from the collected data.

[0782] The estimated user's emotional state is sent from the terminal to the server. The server integrates congestion predictions and user emotional state data and generates the optimal detour route using a generative AI model. This generative AI model selects a route that optimizes the user's psychological and physical comfort, taking into account the predicted congestion and emotional data.

[0783] The generated route information is sent from the server to the user's device and used for real-time navigation. The user can review and select the route presented on their device. Furthermore, the server will suggest a new route as needed, depending on changes in the user's situation and emotions during their journey.

[0784] As a concrete example, suppose a user is commuting home during peak hours. If the user is experiencing stress or fatigue, the server will suggest a relatively less crowded route and include stops along the way where the user can rest or relax.

[0785] An example of a prompt for a generative AI model would be, "Input user emotions and real-time traffic data, and suggest the optimal travel route for the current situation." This allows users to enjoy a personalized travel experience tailored to their individual needs.

[0786] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0787] Step 1:

[0788] The server acquires real-time location data from communication networks and traffic information infrastructure. This data includes GPS information and traffic service status data. The server receives this data, formats it, and prepares it for analysis. The input is location data, and the output is formatted data ready for analysis.

[0789] Step 2:

[0790] The server inputs formatted location data into a congestion prediction model. This model operates using machine learning frameworks such as TensorFlow and incorporates variables such as time, weather, and event information to identify areas where congestion is expected. The input is formatted data, and the output is congestion prediction information.

[0791] Step 3:

[0792] The device collects data from voice input and biometric sensors to estimate the user's emotional state. Specifically, it acquires voice data through the microphone and biometric data such as heart rate through sensors. This data is input to an emotion engine and analyzed. The input is voice and biometric data, and the output is the estimated result of the emotional state.

[0793] Step 4:

[0794] The device uses an emotion engine to analyze the user's emotional state. This analysis utilizes tools such as the Affectiva SDK to analyze voice tone and biometric information. The analysis quantifies the user's emotional state. The input for this step is voice and biometric information, and the output is a quantified emotional state.

[0795] Step 5:

[0796] The terminal sends its estimated emotional state to the server. The server integrates this emotional state data with previously acquired congestion prediction information to generate the optimal detour route. Using a generative AI model, it designs a route that takes into account the user's psychological comfort. The input for this step is the emotional state and congestion prediction information, and the output is the optimal route information.

[0797] Step 6:

[0798] The server sends optimal route information to the user's device. The device receives this information and presents it to the user in real time. The route is visualized on the device and becomes selectable by the user. The input is optimal route information, and the output is navigation information displayed on the user's device.

[0799] Step 7:

[0800] When a user experiences changes in their environment or emotions while traveling, the device monitors their emotional state again and sends update information to the server as needed. The server can then suggest a new route based on the new congestion information and emotional data. The input is the changed emotional state and new congestion information, and the output is the updated route suggestion.

[0801] (Application Example 2)

[0802] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0803] Travel in modern cities is often fraught with congestion and wasted time, and can also negatively impact users' emotional states. Furthermore, choosing a route that considers the comfort of individual users is difficult, and ensuring emotional comfort is particularly challenging. To address these issues, there is a need to provide a travel experience that takes into account not only congestion levels but also the user's personal emotional state.

[0804] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0805] In this invention, the server includes means for acquiring multiple types of location information data, means for analyzing abnormal pedestrian flow using a congestion prediction model based on the location information data, means for collecting and analyzing voice input and biometric information to estimate the user's emotional state, means for generating an optimal detour route based on the predicted congestion and emotional state, means for notifying the user's terminal of the detour route, and means for monitoring changes in the situation in real time while the user is moving and updating the detour route. This enables the user to obtain a physically and emotionally optimized route.

[0806] "Location data" refers to data that represents a geographical location, obtained from mobile devices and sensors.

[0807] A "congestion prediction model" is an algorithm that uses traffic data and pedestrian flow data to predict congestion levels in specific areas or routes in advance.

[0808] "Emotional state" refers to the psychological and emotional aspects of a user, estimated through voice and biometric data analysis.

[0809] An "emotion engine" is software or an algorithm used to analyze a user's emotions from voice and biometric information.

[0810] A "detour route" is a travel path that avoids congestion while also considering the comfort level of users based on their emotional state.

[0811] "Monitoring changes in the situation in real time" is a process of continuously acquiring data while on the move and instantly recognizing any fluctuations.

[0812] This invention is a system that improves the travel experience in urban environments and mainly consists of a server and a user terminal. The system provides users with optimized travel routes by combining congestion prediction and emotion recognition functions.

[0813] The server obtains real-time location information from location services and traffic data providers. This information is analyzed by a congestion prediction model, which makes predictions that take into account factors such as time of day, events, and weather. This prediction algorithm is implemented in programming languages ​​such as Python, and can utilize TensorFlow. Furthermore, it receives emotion data sent from the user's device, and the emotion engine analyzes data from voice input and biosensors. This determines the user's current psychological state.

[0814] The device notifies users in real time of alternative routes to avoid congestion through a navigation application. This application can be developed using a framework such as React Native. Furthermore, if the user's emotional state changes, the server-side emotion engine will immediately react and suggest a new route.

[0815] As a concrete example, consider a user enjoying shopping at a commercial facility in Tokyo. If the user shows signs of fatigue, the system can suggest avoiding crowded subways and taking a break at a cafe within walking distance. This allows the user to continue their journey while reducing stress.

[0816] An example of a prompt message would be, "Please describe the concept of a smartphone application that suggests relaxing alternative routes to avoid urban congestion when the user is feeling stressed." In this way, the invention provides users with a comfortable and efficient means of transportation in many situations.

[0817] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0818] Step 1:

[0819] The server retrieves location data in real time from location services and traffic data providers. Its inputs include the current time and the user's geographical location. The output is dynamic data for the target area. This information serves as foundational data necessary for subsequent processing.

[0820] Step 2:

[0821] The server runs a congestion prediction model using acquired location data. The inputs are real-time pedestrian flow data and a prediction algorithm. The data is analyzed using a machine learning algorithm implemented in Python, and the output is predicted congestion data. Here, the degree of congestion and its temporal changes are predicted.

[0822] Step 3:

[0823] The device acquires the user's voice input and biometric information. This is done using biosensors such as the smartphone's microphone or a smartwatch. The input consists of data obtained from biometric sensors and voice data, while the output is analytical data representing the user's emotional state.

[0824] Step 4:

[0825] The server receives emotional state data transmitted from the terminal and compares it with congestion prediction data. The inputs are emotional state data and congestion status data. Based on this data, it performs data calculations to determine alternative routes, and the output is optimized route information. The calculation uses an algorithm that takes user psychological comfort into consideration.

[0826] Step 5:

[0827] The device provides navigation to the user based on route information sent from the server. The input requires optimal route information, and the output is navigation instructions displayed to the user as visual and audio guidance. The device provides this guidance through an application built with React Native.

[0828] Step 6:

[0829] While the user is on the move, the device continuously updates emotional data and location information in real time and sends it to the server. The input is newly acquired voice data and biometric information, and the output is the updated emotional state and location data provided to the server. This process enables route re-suggestions in response to changes in the user's situation.

[0830] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0831] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0832] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0833] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0834] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0835] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0836] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0837] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0838] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0839] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0840] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0841] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0842] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0843] 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.

[0844] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0845] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0846] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0847] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0848] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0849] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0850] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0851] The following is further disclosed regarding the embodiments described above.

[0852] (Claim 1)

[0853] A means of acquiring multiple types of location data,

[0854] A means for analyzing abnormal pedestrian flow using a congestion prediction model based on the aforementioned location data,

[0855] A means of generating the optimal detour route based on predicted congestion,

[0856] Means for notifying the user's terminal of the aforementioned detour route,

[0857] A means for monitoring changes in the situation in real time while the user is moving and updating the detour route,

[0858] A system that includes this.

[0859] (Claim 2)

[0860] The system according to claim 1, wherein the location information data includes at least data provided from a communication network, and transportation operation information.

[0861] (Claim 3)

[0862] The system according to claim 1, wherein the congestion prediction model uses a machine learning algorithm that takes into account time of day and weather and event information.

[0863] "Example 1"

[0864] (Claim 1)

[0865] A means of acquiring multiple types of location data,

[0866] A means for analyzing abnormal pedestrian flow using a congestion prediction model based on the aforementioned location data and AI generation technology,

[0867] A means for generating the optimal detour route based on the prediction results,

[0868] Means for notifying the communication device of the aforementioned detour route,

[0869] Means for monitoring changes in the situation in real time while moving and updating the detour route,

[0870] A system that includes this.

[0871] (Claim 2)

[0872] The system according to claim 1, wherein the location information data includes at least data provided from a communication network and information on public transportation.

[0873] (Claim 3)

[0874] The system according to claim 1, wherein the congestion prediction model uses machine learning techniques that take into account time of day, weather, and event information.

[0875] "Application Example 1"

[0876] (Claim 1)

[0877] A means of acquiring multiple types of location data,

[0878] A means for analyzing abnormal pedestrian flow using a congestion prediction model based on the aforementioned location data,

[0879] A means for generating the optimal detour route based on predicted congestion,

[0880] Means for notifying the mobile device's terminal of the aforementioned detour route,

[0881] A means for an autonomous vehicle in motion to monitor changes in the situation in real time and update the aforementioned route,

[0882] A means for receiving route data in real time in an autonomous vehicle and providing guidance using a display device and an audible device,

[0883] A system that includes this.

[0884] (Claim 2)

[0885] The system according to claim 1, wherein the location information data includes at least data provided from a communication network, and operational information of a means of transport.

[0886] (Claim 3)

[0887] The system according to claim 1, wherein the congestion prediction model uses a machine learning algorithm that takes into account time of day, weather, and event information, and analyzes real-time data of moving objects to optimize the route.

[0888] "Example 2 of combining an emotion engine"

[0889] (Claim 1)

[0890] A means of acquiring multiple types of location data,

[0891] A means for analyzing abnormal pedestrian flow using a congestion prediction model based on the aforementioned location data,

[0892] A means of estimating the user's emotional state,

[0893] A means for generating an optimal detour route based on the aforementioned emotional state,

[0894] Means for notifying the user's terminal of the aforementioned detour route,

[0895] A means for monitoring changes in the situation and emotions in real time while the user is moving, and updating the detour route accordingly.

[0896] A system that includes this.

[0897] (Claim 2)

[0898] The system according to claim 1, wherein the location information data includes at least data provided from a communication network and transportation operation information, and the emotional state is based on data collected from voice input and biometric sensors.

[0899] (Claim 3)

[0900] The system according to claim 1, wherein the congestion prediction model uses a machine learning algorithm that takes into account time of day, weather, and event information, and the emotional state is based on emotional analysis using an analysis library.

[0901] "Application example 2 when combining with an emotional engine"

[0902] (Claim 1)

[0903] A means of acquiring multiple types of location data,

[0904] A means for analyzing abnormal pedestrian flow using a congestion prediction model based on the aforementioned location data,

[0905] A means for collecting and analyzing voice input and biometric information in order to estimate the user's emotional state,

[0906] A means for generating the optimal detour route based on predicted congestion and emotional states,

[0907] Means for notifying the user's terminal of the aforementioned detour route,

[0908] A means for monitoring changes in the situation in real time while the user is moving and updating the detour route,

[0909] A system that includes this.

[0910] (Claim 2)

[0911] The system according to claim 1, wherein the location information data includes at least data provided from a communication network and transportation operation information, and further analyzes voice and biometric information from the user using emotion recognition technology.

[0912] (Claim 3)

[0913] The system according to claim 1, wherein the congestion prediction model uses a machine learning algorithm that takes into account time of day, weather, and event information, and further proposes a new route in response to changes in the user's emotional state using an emotion engine. [Explanation of symbols]

[0914] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of acquiring multiple types of location data, A means for analyzing abnormal pedestrian flow using a congestion prediction model based on the aforementioned location data, A means for generating the optimal detour route based on predicted congestion, Means for notifying the mobile device's terminal of the aforementioned detour route, A means for an autonomous vehicle in motion to monitor changes in the situation in real time and update the aforementioned route, A means for receiving route data in real time in an autonomous vehicle and providing guidance using a display device and an audible device, A system that includes this.

2. The system according to claim 1, wherein the location information data includes at least data provided from a communication network, and operational information of a means of transport.

3. The system according to claim 1, wherein the congestion prediction model uses a machine learning algorithm that takes into account time of day, weather, and event information, and analyzes real-time data of moving objects to optimize the route.

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