System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting traffic accidents information, public security information, electric light and security camera information, and location information of public facilities; means for using a generative AI model to generate an optimal route based on the collected information and input conditions; means for displaying the generated route on a map and presenting it to a user; and means for tracking a current location of the user and updating the route in real time based on the latest accidents information and public security information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Current map applications only provide uniformly rational routes and are unable to generate routes that take into account safety and individual health conditions. As a result, they are unable to present routes that provide a safe and moderate amount of exercise, especially for users who need to exercise, such as the elderly, and there is a lack of support for them to go out with peace of mind. In the current situation where sarcopenia and maintaining a healthy age are major issues among middle-aged and elderly people, there is a need for services that provide optimal routes for these users. [Means for solving the problem]

[0005] The present invention solves the above problem by providing a system that includes: means for the user to input the starting point, destination, and conditions related to exercise volume and safety; means for collecting traffic accident information, public safety information, electric light and security camera information, and location information of public facilities; means for using a generative AI model to generate an optimal route based on the collected information and the conditions input by the user; means for displaying the generated route on a map and presenting it to the user; and means for tracking the user's current location and updating the route in real time based on the latest accident and public safety information.

[0006] This makes it possible for the system to generate individual routes that provide safe and moderate exercise for users, including the elderly, and provide an environment in which they can go out with peace of mind.Furthermore, it can track the user's current location and change the route in real time based on the latest information, and in the event of an emergency, it will appropriately suggest places to go to, so users can enjoy going out with greater peace of mind.

[0007] "Starting Point" means the location where a User sets the start of a Route.

[0008] "Destination" refers to the location that a user sets as the end point of a route.

[0009] "Movement amount" refers to quantitative indicators related to exercise, such as the number of steps, distance, and gradient required by the user.

[0010] "Safety" refers to factors that ensure users' safe travels, such as the risk of traffic accidents, the state of public safety, the installation of electric lights and security cameras, and the presence of public facilities.

[0011] "Traffic accident information" refers to information such as the frequency, location, and time of accidents in a specific area.

[0012] "Public safety information" refers to information such as crime rates in a particular area, police patrols, and types of crime.

[0013] "Light and security camera information" refers to information such as the location, number of streetlights and security cameras installed in a specific area, and their operating status.

[0014] "Location information of public facilities" refers to the location information of facilities that can be used in emergencies, such as police stations, evacuation shelters, hospitals, and parks.

[0015] "Means of collection" refers to methods and systems for collecting information on traffic accidents, public safety, lighting and security camera information, and location information of public facilities from various databases, news, social media, public information, etc.

[0016] "Generative AI model" refers to an artificial intelligence algorithm or system that automatically generates optimal routes based on collected information and user-entered conditions.

[0017] "Means for updating routes in real time" refers to methods or systems for instantly re-optimizing routes based on the user's current location and the latest accident and security information.

[0018] "Device" refers to a mobile information device such as a smartphone or tablet used by a user.

[0019] "Server" refers to a computer system that performs central computation and data management, including data collection, processing, and route generation. [Brief explanation of the drawings]

[0020] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0021] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0023] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0024] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0025] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0026] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0027] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0028] [First embodiment]

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

[0030] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0031] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0032] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0033] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0034] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0035] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0037] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0038] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0039] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0040] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0041] This invention is a map application system that utilizes a generative AI model to provide optimal routes that take into account the user's health and safety. This system supports safe and healthy outings by inputting user conditions, collecting necessary information, generating and presenting routes, and updating them in real time.

[0042] Overall system configuration

[0043] The system mainly consists of two components: a server and a terminal. The server is responsible for the central functions of data collection and route generation, while the terminal is responsible for the user interface and real-time tracking functions.

[0044] (1) User input

[0045] The user opens a map app, inputs their starting point and destination, and then specifies conditions for exercise volume (e.g., recommended number of steps and duration) and safety (e.g., wanting to travel through safe areas). The device then sends this information to the server.

[0046] (2) Data collection

[0047] The server collects the latest information on traffic accidents, public safety, the location of lights and security cameras, and the location of public facilities at that time, including data collected in real time from public data released by police stations and local governments, news, social media, etc.

[0048] (3) Route generation using generative AI models

[0049] The server inputs the collected information and user input into a generative AI model to generate the optimal route. This prioritizes routes that avoid areas with high traffic accident rates and areas with poor security. It also takes into account the number of steps and distance traveled to ensure the specified amount of exercise is achieved.

[0050] (4) Route presentation

[0051] The generated optimal route is sent from the server to the device, which displays it on a map and presents it visually to the user, allowing the user to see the entire route before setting off.

[0052] (5) Real-time feedback

[0053] Once the user actually leaves the house, the device will continuously track the user's current location using its GPS function. Real-time information on traffic accidents and public safety is collected and sent to the server. If necessary, the server will regenerate the route and send the updated route information to the device, which will immediately display it to the user.

[0054] Specific examples

[0055] For example, consider a case where a user sets a route from "home" to "supermarket" and specifies "5,000 steps" as the amount of exercise required per day. Since the user will be out at night, the user also sets the route with an emphasis on safety. Based on this information, the server collects the latest traffic accident and public safety information and generates a safe route with an appropriate amount of exercise.

[0056] The generated route passes through areas with many police stations and street lights, and avoids areas with a high incidence of traffic accidents. Once the user starts going out, the server monitors the latest information in real time while the current location is tracked by GPS, updating the route as necessary. For example, if a traffic accident occurs along the way, the server generates a new, safer route and sends it to the device. This allows the user to reach their destination safely and healthily.

[0057] The processing flow will be explained below.

[0058] Step 1:

[0059] A user opens a map app, enters a starting point and destination, and also sets criteria for exercise (e.g., recommended number of steps) and safety (e.g., passing through safe neighborhoods).

[0060] Step 2:

[0061] The device sends the user's input data, including the starting point, destination, exercise amount, and safety conditions, to the server.

[0062] Step 3:

[0063] The server collects traffic accident information, specifically using news, social media, and public databases from police stations to obtain the latest accident information for a specific area.

[0064] Step 4:

[0065] The server collects public safety information, such as public information from police stations and local governments, crime maps, and automated web scraping tools, to understand the public safety situation in a particular area.

[0066] Step 5:

[0067] The server collects information from lights and security cameras, obtains the necessary information from databases of local governments and public institutions, and maps the location information.

[0068] Step 6:

[0069] The server collects location information for public facilities, such as police stations, evacuation centers, hospitals, and parks, which users can use in emergencies, and updates the database.

[0070] Step 7:

[0071] The server consolidates all collected data and inputs it into a generative AI model, including accident information, public safety information, light and security camera locations, and public facility locations.

[0072] Step 8:

[0073] The generative AI model generates the optimal route based on the user's criteria (starting point, destination, amount of exercise, safety). The AI ​​model prioritizes routes that meet the specified amount of exercise while avoiding safe areas and areas with high traffic accident rates.

[0074] Step 9:

[0075] The server sends the generated route to the device.

[0076] Step 10:

[0077] The device displays the received route information on a map and presents it visually to the user, allowing the user to check the entire route before setting off.

[0078] Step 11:

[0079] The user starts traveling and follows a route.

[0080] Step 12:

[0081] The device uses GPS to track the user's current location in real time.

[0082] Step 13:

[0083] The server periodically collects the latest traffic accident and public safety information.

[0084] Step 14:

[0085] The server determines whether the route needs to be changed based on the user's current location and the latest accident and security information, and if so, generates a new, optimal route.

[0086] Step 15:

[0087] The server sends new route information to the device and notifies it in real time.

[0088] Step 16:

[0089] The device will redisplay the new route on the map and provide instructions to the user.

[0090] This allows users to navigate their route to their destination safely and healthily.

[0091] Example 1

[0092] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0093] Conventional map application systems have difficulty providing optimal routes that fully consider the user's safety and health. Furthermore, there is a lack of systems that can reflect the latest accident and public safety information in real time and flexibly adjust routes while the user is traveling. Therefore, there is a need for a system that provides routes that allow users to reach their destination safely and healthily.

[0094] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0095] In this invention, the server includes: means for inputting a starting point, a destination, and conditions related to exercise volume and safety; means for collecting information on traffic accidents, public safety, electric lights and security cameras, and the location of public facilities; means for using a generative AI model to generate an optimal route based on the collected information and the input conditions; means for displaying the generated route on a map and presenting it to the user; means for tracking the user's current location and updating the route in real time based on the latest accident and public safety information; means for collecting data in real time and regenerating the route as necessary; and means for checking for format errors. This enables the provision of a safe and healthy optimal route based on the conditions entered by the user, and updating the route in real time.

[0096] The "starting point" is the location where the user starts their journey, and is the location information that serves as the starting point when the map application system generates a route.

[0097] The "destination" is the location the user wishes to reach, and is the location information that serves as the end point when the map application system generates a route.

[0098] "Amount of exercise" indicates the degree of exercise that the user wants to achieve while traveling, and is a condition specified by indicators such as the number of steps and exercise time.

[0099] "Safety" refers to the criteria that indicate the importance placed on public safety and the low number of accidents when selecting a travel route, and is a standard for avoiding areas with a high incidence of traffic accidents and areas with poor public safety.

[0100] "Traffic accident information" is data showing the occurrence of traffic accidents in a specific area, and is information collected to ensure the safety of users' travel.

[0101] "Public safety information" is data showing the crime rate and public safety situation in a specific area, and is information collected to evaluate the safety of a user's travel route.

[0102] "Lighting information" is data that indicates the location and lighting conditions of streetlights installed in public areas such as roads and parks, and is information used to increase user safety when traveling at night.

[0103] "Security camera information" is data indicating the location and operation status of surveillance cameras installed in public areas, and is information used to select routes that users can travel safely.

[0104] "Location information of public facilities" is data indicating the locations of public facilities such as public toilets, police stations, hospitals, and evacuation shelters, and is information used to provide users with easy access routes to the facilities they need.

[0105] A "generative AI model" is an algorithm that uses artificial intelligence technology to generate optimal routes based on collected data and user input conditions.

[0106] A "prompt" is an instruction entered into a generative AI model, and is text that contains specific requests and conditions for route generation.

[0107] This invention is a map application system that utilizes a generative AI model to provide optimal routes that take into account the user's health and safety. This system supports safe and healthy outings by inputting user conditions, collecting necessary information, generating and presenting routes, and updating them in real time.

[0108] Overall system configuration

[0109] The system mainly consists of two components: a server and a terminal. The server is responsible for the central functions of data collection and route generation, while the terminal is responsible for the user interface and real-time tracking functions.

[0110] Hardware and Software

[0111] Server: Used for data collection and route generation. Specifically, a server machine with high-performance data processing capabilities is required.

[0112] Device: Used for user interface and real-time tracking. Can be a smartphone or tablet.

[0113] Generative AI models: Implemented using machine learning frameworks such as TensorFlow and PyTorch.

[0114] Program processing

[0115] User Input

[0116] The user opens a map app on their device, inputs their starting point and destination, and specifies conditions for exercise volume (e.g., recommended number of steps and duration) and safety (e.g., preferring to travel through safe areas). The device validates this information and then sends it to the server.

[0117] Data collection

[0118] The server collects the latest information on traffic accidents, public safety, the location of lights and security cameras, and the location of public facilities at that time. This data is collected in real time from public data released by police stations and local governments, news, social media, etc.

[0119] Route generation using generative AI models

[0120] The server inputs the collected information and user input into a generative AI model to generate the optimal route. The generative AI model prioritizes routes that avoid areas with high traffic accident rates and areas with poor security. It also takes into account the number of steps and distance exercised to ensure the specified amount of exercise is achieved.

[0121] Route suggestions

[0122] The generated optimal route is sent from the server to the device, which displays it on a map and presents it visually to the user, allowing the user to see the entire route before setting off.

[0123] Real-time feedback

[0124] Once the user begins to leave the house, the device will continuously track the user's current location using its GPS function. The server will monitor the latest traffic accident and public safety information in real time and update the route as necessary. For example, if a traffic accident occurs along the way, a new route will be generated and sent to the device, allowing the user to instantly find a safe route.

[0125] Specific examples

[0126] For example, consider a case where a user sets a route from "home" to "supermarket" and specifies "5,000 steps" as the amount of exercise required per day. Since the user will be out at night, the user also sets the route with an emphasis on safety. Based on this information, the server collects the latest traffic accident and public safety information and generates a safe route with an appropriate amount of exercise.

[0127] The generated route passes through areas with many police stations and street lights, and avoids areas with a high incidence of traffic accidents. Once the user starts going out, the server monitors the latest information in real time while the current location is tracked by GPS, and updates the route as necessary. For example, if a traffic accident occurs along the way, the server generates a new, safer route and sends it to the device. This allows the user to reach their destination safely and healthily.

[0128] Prompt Sentence Examples

[0129] "Set a route from home to the supermarket. Since I'll be out at night, safety is a priority. My goal is to walk 5,000 steps a day. Generate the optimal route taking into account the latest traffic accident and security information."

[0130] By inputting such a prompt, the generative AI model generates an appropriate route.

[0131] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0132] Step 1: User Input

[0133] User: Launches a map app on the device and inputs the starting point, destination, amount of exercise, and safety conditions. Specific input values ​​are: starting point "home," destination "supermarket," amount of exercise "5,000 steps," and safety "safe area."

[0134] Terminal: Validates the entered information, checks for formatting errors, and if there are no problems, sends the information to the server.

[0135] Input: starting point, destination, momentum, safety conditions.

[0136] Output: The input information sent to the server.

[0137] Step 2: Collect data

[0138] Server: Using APIs and scraping technology, information on traffic accidents, public safety, location information for lights and security cameras, and location information for public facilities is obtained in real time.

[0139] Input: Conditions for API requests and data scraping.

[0140] Output: A dataset of the latest traffic accident information, public safety information, information on electric lights and security cameras, and location information of public facilities.

[0141] As a specific example of how it works, the server collects traffic accident information from the police station's API, scrapes public safety information from social media, and obtains the location information of streetlights and security cameras from a GIS database.

[0142] Step 3: Route generation using generative AI models

[0143] Server: Combines the collected data with user input conditions, inputs appropriate prompts into the generative AI model, and generates the optimal route.

[0144] Input: Starting point, destination, amount of exercise, safety conditions, and any other information you have collected.

[0145] Output: Optimal route information.

[0146] As an example of specific operation, the server inputs prompt statements such as "Starting point: home, destination: supermarket, exercise amount: 5,000 steps, safety: safe area" into the generative AI model and performs calculations to generate a route.

[0147] Step 4: Route presentation

[0148] Server: Sends the optimal route information obtained from the generative AI model to the device.

[0149] Device: The received route information is visually displayed in a map application and presented to the user.

[0150] Input: Optimal route information.

[0151] Output: The route that is displayed to the user.

[0152] For example, the device will use a map app to generate a route and display it in an easy-to-understand format to the user, allowing the user to see the overall route before setting off.

[0153] Step 5: Real-time feedback

[0154] Device: Uses GPS to track the user's location in real time.

[0155] Server: Continuously monitors the latest traffic accident and public safety information, regenerates routes as needed, and sends them to the device.

[0156] Input: Current location, latest traffic accident information, and public safety information.

[0157] Output: The updated route information.

[0158] For example, when a user starts going out, the device tracks their current location using GPS. The server reevaluates the route based on real-time data, and if, for example, a traffic accident occurs along the way, it generates a new, safer route and sends it to the device.

[0159] (Application example 1)

[0160] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0161] In autonomous vehicles, it is difficult to provide an optimal route while simultaneously considering the safety and health of the user. Conventional route guidance systems are unable to update traffic accident and public safety information in real time and provide appropriate driving instructions to the user. Furthermore, since it is not possible to generate a route that balances both exercise and safety, there is a need to provide an environment in which users can travel with peace of mind. The present invention aims to solve these problems.

[0162] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0163] In this invention, the server includes: means for inputting conditions related to starting point, destination, and exercise volume and safety; means for collecting traffic accident information, public safety information, lighting and monitoring device information, and public facility location information; means for using a generative AI model to generate an optimal route based on the collected information and the input conditions; means for displaying the generated route on a map and presenting it to the user; means for tracking the user's current location and updating the route in real time based on the latest accident and public safety information; and means for displaying the optimal route on the display of the autonomous vehicle and providing driving instructions. This enables the user to travel with peace of mind and enables route generation that balances exercise volume and safety.

[0164] The "starting point" is the location where the user begins their journey.

[0165] A "destination" is a location that a user wishes to reach.

[0166] "Amount of exercise" refers to the amount of physical activity a user gets while moving, specifically the number of steps taken and the duration of exercise.

[0167] "Safety" refers to conditions for minimizing the dangers users encounter while traveling, and includes information on public safety and traffic accidents.

[0168] "Traffic accident information" is data relating to accidents that occur on roads.

[0169] "Public safety information" is data regarding the safety of an area.

[0170] "Lighting" refers to information about light source equipment such as street lights.

[0171] "Monitoring device information" is data relating to monitoring devices such as security cameras.

[0172] "Location information of public facilities" is data on the locations of public places and facilities such as police stations, fire stations, and parks.

[0173] "Means of collection" refers to the techniques and methods used to obtain the necessary data.

[0174] "Means using generative AI models" refers to artificial intelligence technology that creates optimal routes based on collected data and user conditions.

[0175] "User presentation means" refers to the method or technology used to visually display the generated route to the user.

[0176] "Tracking" is a technology that tracks a user's current location in real time.

[0177] A "route update means" is a method or technique for changing a route based on the latest information.

[0178] An "autonomous vehicle display" is a display device installed inside an autonomous vehicle.

[0179] A "means for providing driving instructions" is a method or technology for providing instructions to an automated driving vehicle.

[0180] The present invention is a map application system that uses generative AI models to provide optimal routes that take into account the safety and well-being of users, and is designed to be particularly effective in autonomous vehicles.

[0181] Overall system configuration

[0182] This system mainly consists of two components: a server and a terminal (display, etc.) inside the autonomous vehicle. The server is responsible for the central functions of data collection and route generation, while the terminal is responsible for the user interface and real-time tracking functions.

[0183] 1. User Input

[0184] The user (a passenger in an autonomous vehicle) uses a device to input their starting point and destination, and specify conditions for exercise amount (e.g., recommended number of steps and duration of exercise) and safety (e.g., passing through safe areas). The device then sends this information to a server.

[0185] 2. Data collection

[0186] The server collects the latest traffic accident information, public safety information, lighting and surveillance device information, and location information of public facilities at that time, including data collected in real time from public data of police stations and local governments, news, social media, etc.

[0187] 3. Route generation using generative AI models

[0188] The server inputs the collected information and user input into a generative AI model to generate the optimal route. This prioritizes routes that avoid areas with high traffic accident rates and areas with poor security. It also takes into account the number of steps and distance traveled to ensure the specified amount of exercise is achieved.

[0189] 4. Route presentation

[0190] The generated optimal route is sent from the server to the terminal, which then displays it on the vehicle's display, providing a visual route to the user and specific driving instructions to the autonomous vehicle.

[0191] 5. Real-time feedback

[0192] Once the autonomous vehicle begins moving, the device will use its GPS to continuously track the vehicle's current location. Real-time information on traffic accidents and public safety is collected and sent to the server. If necessary, the server will regenerate the route and send updated route information to the device. The device will immediately display this information to inform the user and provide new driving instructions to the autonomous vehicle.

[0193] Specific examples

[0194] For example, if a user sets a route from home to the supermarket and specifies 4,000 steps as the amount of exercise required per day, they can also set a safety priority for nighttime outings. Based on this information, the server collects the latest traffic accident and public safety information and generates a safe route with an appropriate amount of exercise. The generated route is displayed on the autonomous vehicle's display, and driving instructions are provided to the vehicle. Once the user begins their journey, the server monitors the latest information in real time while the current location is tracked by GPS, updating the route as necessary. For example, if a traffic accident occurs along the way, the server generates a new, safer route and sends it to the device. This allows the user to reach their destination safely and healthily.

[0195] Hardware and software used

[0196] The hardware includes a GPS module, vehicle display, and autonomous driving control system, while the software includes a generative AI model, API call functions for data collection, GPS location tracking software, and display management classes.

[0197] Prompt Sentence Examples

[0198] "Generate a route from your home to the supermarket. Requires 4000 steps, prioritizes safety, and takes into account the latest traffic accident and public safety information."

[0199] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0200] Step 1:

[0201] The user uses the device to input the starting point, destination, exercise amount (e.g., number of steps and exercise time), and safety conditions (e.g., passing through a safe area).

[0202] Input: User-specified starting point, destination, momentum, and safety criteria.

[0203] Output: The input condition data.

[0204] Specific operation: Through the terminal's user interface, the user inputs detailed conditions, including the destination and starting point.

[0205] Step 2:

[0206] The device sends the user's input data to the server.

[0207] Input: User-specified condition data.

[0208] Output: User criteria data sent to the server.

[0209] Specific operation: The device makes an API call to send data to a server over the Internet.

[0210] Step 3:

[0211] The server collects the latest traffic accident information, public safety information, lighting and surveillance device information, and location information of public facilities at that time.

[0212] Input: User criteria data.

[0213] Output: Collected public data, real-time data.

[0214] Specific operation: The server refers to external APIs and public databases to collect traffic accident information, public safety information, etc.

[0215] Step 4:

[0216] The information collected by the server and the user's input conditions are input into a generative AI model to generate the optimal route.

[0217] Input: Collected data, user condition data.

[0218] Output: Generated optimal route data.

[0219] How it works: The generative AI model uses machine learning algorithms to calculate a route with the optimal amount of momentum while avoiding areas prone to traffic accidents and areas with poor security.

[0220] Step 5:

[0221] The server sends the generated optimal route to the device.

[0222] Input: Generated optimal route data.

[0223] Output: Route data sent to the device.

[0224] Specific operation: The server sends the calculated route data to the terminal via the Internet.

[0225] Step 6:

[0226] The device will display the optimal route and present it to the user.

[0227] Input: Route data sent by the server.

[0228] Output: The route shown on the display.

[0229] Specific operation: The device visually displays the route on a map through the user interface and provides driving instructions to the autonomous vehicle control system.

[0230] Step 7:

[0231] When the user starts going out, the device uses GPS to track their current location in real time.

[0232] Input: GPS data.

[0233] Output: Current position data.

[0234] What it does: The device's GPS function continuously tracks the user's current location.

[0235] Step 8:

[0236] The server regenerates the route based on the latest collected traffic accident and security information, and sends the updated route information to the terminal.

[0237] Inputs: Real-time location data, latest traffic accident and public safety information.

[0238] Output: Updated route data.

[0239] What it does: The server re-runs the generative AI model based on the new information to generate a new route that is safe and healthy.

[0240] Step 9:

[0241] The device displays instantly updated route information and provides new driving instructions to the autonomous driving system.

[0242] Input: Updated route data.

[0243] Output: New route and driving instructions shown on the display.

[0244] Specific operation: The device instantly displays new route information on the map and provides new driving instructions to the autonomous vehicle.

[0245] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0246] This invention is a map application system that utilizes a generative AI model and an emotion engine to provide optimal routes that take into account the user's health, safety, and psychological state. The system supports safe and stress-free outings by allowing users to input their conditions, collecting necessary information, generating and presenting routes, updating them in real time, and recognizing their emotions.

[0247] Overall system configuration

[0248] The system mainly consists of three components: a server, a terminal, and an emotion engine. The server is responsible for the central functions of data collection and route generation, while the terminal is responsible for the user interface and real-time tracking. The emotion engine recognizes the user's emotional state in real time and sends feedback to the server.

[0249] (1) User input

[0250] The user opens the map app and inputs their starting point and destination. They also set conditions for exercise (e.g., recommended number of steps) and safety (e.g., passing through safe areas). The emotion engine then monitors the user's emotional state. Based on the results, the device sends the data to the server.

[0251] (2) Data collection

[0252] The server collects the latest information on traffic accidents, public safety, the location of lights and security cameras, and the location of public facilities at that time, including data collected in real time from public data released by police stations and local governments, news, social media, etc.

[0253] (3) Route generation using generative AI models

[0254] The server inputs the collected information, user input conditions, and emotional state information from the emotion engine into a generative AI model to generate the optimal route. This prioritizes routes that avoid areas with high traffic accident rates and areas with poor security, meet the specified exercise volume, and take the user's psychological state into consideration.

[0255] (4) Route presentation

[0256] The generated optimal route is sent from the server to the device, which displays it on a map and presents it visually to the user, allowing the user to check the entire route before setting off.

[0257] (5) Real-time feedback

[0258] Once the user actually leaves the house, the device will continuously track the user's current location using its GPS function. Real-time information on traffic accidents and public safety is collected and sent to the server. If necessary, the server will regenerate the route and send the updated route information to the device, which will immediately display it to the user.

[0259] (6) Feedback from the Emotion Engine

[0260] The emotion engine analyzes the user's emotional state (e.g., stress, relief, fear) in real time and sends feedback to the server. Based on this feedback, the server prioritizes nearby emergency shelters in case of an emergency. It also suggests routes through relaxing landscapes and quiet areas if the user is feeling stressed.

[0261] Specific examples

[0262] For example, consider the case where a user sets a route from "home" to "supermarket" and specifies "5,000 steps" as the amount of exercise required per day. Furthermore, since the user will be going out at night, the user sets the route with an emphasis on safety, and the emotion engine monitors the user's stress level. Based on this information, the server collects the latest traffic accident and security information and generates a safe route with an appropriate amount of exercise. If the emotion engine detects a high stress level, the server will prioritize routes with relaxing scenery.

[0263] The generated route takes into consideration a stress-free environment, passing through areas with police stations and plenty of street lights, and avoiding areas prone to traffic accidents. Once the user begins to go out, the server and emotion engine monitor the latest information in real time while the user's current location is tracked by GPS, updating the route as needed. For example, if a traffic accident occurs along the way or the user's stress level rises, the server regenerates a new, safer, and more relaxing route and sends it to the device. This allows the user to reach their destination safely and psychologically in a comfortable state.

[0264] The processing flow will be explained below.

[0265] Step 1:

[0266] The user opens the map app, inputs their starting point and destination, and sets criteria for exercise (e.g., recommended number of steps) and safety (e.g., passing through safe areas). The emotion engine then begins monitoring the user's emotional state in real time.

[0267] Step 2:

[0268] The device sends user input data to the server, including the starting point, destination, momentum, safety conditions, and initial emotional state information from the emotion engine.

[0269] Step 3:

[0270] The server collects traffic accident information, specifically using news, social media, and public databases from police stations to obtain the latest accident information for a specific area.

[0271] Step 4:

[0272] The server collects public safety information, using publicly available information from police stations and local governments, crime information maps, web scraping tools, and other tools to understand the public safety situation in a specific area.

[0273] Step 5:

[0274] The server collects information from lights and security cameras, obtains the necessary information from databases of local governments and public institutions, and maps the location information.

[0275] Step 6:

[0276] The server collects location information for public facilities, such as police stations, evacuation centers, hospitals, and parks, which users can use in emergencies, and updates the database.

[0277] Step 7:

[0278] The server integrates all collected data and inputs it into a generative AI model, including accident information, public safety information, light and security camera locations, public facility locations, and emotional state information provided by an emotion engine.

[0279] Step 8:

[0280] The generative AI model generates an optimal route based on the user's input and emotional state, meeting the specified exercise volume, avoiding areas with poor security and high traffic accident rates, and taking into account the psychological state (e.g., stress) detected by the emotion engine.

[0281] Step 9:

[0282] The server sends the generated route to the device.

[0283] Step 10:

[0284] The device displays the received route information on a map and presents it visually to the user, allowing the user to check the entire route before setting off.

[0285] Step 11:

[0286] The user starts traveling and follows a route.

[0287] Step 12:

[0288] The device uses GPS to track the user's current location in real time.

[0289] Step 13:

[0290] The emotion engine analyzes the user's emotional state (e.g., stress, relief, fear) in real time and sends that information to the server.

[0291] Step 14:

[0292] The server periodically collects the latest traffic accident and public safety information.

[0293] Step 15:

[0294] The server determines whether the route needs to be changed based on the user's current location, emotional state from the emotion engine, and the latest accident and security information. If necessary, it regenerates a new optimal route.

[0295] Step 16:

[0296] The server sends new route information to the device and notifies it in real time.

[0297] Step 17:

[0298] The device will redisplay the new route on the map and provide instructions to the user.

[0299] Step 18:

[0300] Users follow new routes to their destinations in a safe and psychologically comfortable environment.

[0301] Example 2

[0302] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0303] In modern society, safety and comfort are important when people go out. However, it is difficult to simultaneously avoid areas with high traffic accident rates and poor security, ensure an appropriate amount of exercise, and travel comfortably while reducing psychological stress. Furthermore, there is a need for a system that can re-suggest appropriate routes when conditions change in real time after a person has actually started going out.

[0304] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting conditions related to a starting point, a destination, and the amount of exercise and safety, a means for collecting information on traffic accidents, public safety, lighting and security devices, and the location information of public facilities, and a means for using a generative AI model to generate an optimal route based on the collected information and the input conditions. This makes it possible to provide an optimal route for a user to travel safely and comfortably to their destination, and to respond to situations that change in real time.

[0305] "Starting point" refers to the current location or the location designated by the user as the starting point of a journey.

[0306] "Destination" refers to the location the user ultimately wants to reach.

[0307] "Amount of exercise" refers to an indicator of the amount of physical activity, such as the number of steps taken and calories burned when a user moves around while out and about.

[0308] "Safety" refers to a safe environment that takes into consideration public safety and the risk of accidents when users travel.

[0309] "Conditions" refers to the exercise volume and safety requirements entered by the user.

[0310] "Traffic accident information" refers to data regarding the circumstances and locations of traffic accidents.

[0311] "Public safety information" refers to data on crime rates and safety in a particular area.

[0312] "Lighting Information" refers to data relating to the location of streetlights and other forms of lighting equipment.

[0313] "Security device information" refers to data regarding the location of security equipment such as security cameras and emergency notification devices.

[0314] "Location information of public facilities" refers to data regarding the locations of public facilities such as hospitals, police stations, schools, and parks.

[0315] A "generative AI model" refers to an algorithm that uses machine learning and artificial intelligence to generate optimal routes.

[0316] "Display device" refers to a device such as a smartphone or tablet that allows users to visually check the route.

[0317] "Tracking" refers to using technology such as GPS to determine a user's current location in real time.

[0318] "Mental state" refers to the user's mental state, such as emotions and stress level.

[0319] "Analysis" refers to processing data to extract meaningful information.

[0320] "Evacuation destination" refers to a location where users can evacuate to ensure their safety in the event of an emergency.

[0321] MODE FOR CARRYING OUT THE INVENTION

[0322] This invention is a map application system that utilizes a generative AI model and an emotion engine to provide optimal routes that take into account the user's health, safety, and psychological state. Below, we will explain each component of the system and its specific operation.

[0323] System configuration

[0324] This system mainly consists of three hardware components: a server, a terminal, and an emotion engine.

[0325] 1. Server

[0326] The server is responsible for data collection and route generation. Specifically, it collects information on traffic accidents, public safety, lighting, security devices, and the location of public facilities. This includes real-time data collection from public data from police stations and local governments, news, social media, and more. The collected data is stored in the server's database, and the optimal route is generated through a generative AI model.

[0327] 2. Terminal

[0328] The device is responsible for the user interface and real-time tracking. Specifically, the user opens the app, inputs their starting point and destination, and is shown a screen where they can set exercise and safety criteria. The device also uses GPS to track the user's current location and connects to a server to receive real-time route updates, which are displayed on a map.

[0329] 3. Emotion Engine

[0330] The emotion engine recognizes the user's emotional state in real time and sends that feedback to the server, allowing the route to be readjusted if the user is feeling stressed, for example.

[0331] Example of operation

[0332] For example, consider the case where a user sets a route from "home" to "supermarket." The user specifies "5,000 steps" as the amount of exercise required per day, and since they will be going out at night, they set the settings to prioritize safety. At this time, the emotion engine monitors the user's stress level. The server generates a safe route with an appropriate amount of exercise based on the latest traffic accident and security information collected.

[0333] The generated route takes into consideration a stress-free environment, passing through streets with many police stations and lighting, avoiding areas prone to traffic accidents, and so on. When the user begins to go out, the device tracks their current location using GPS, while the server and emotion engine monitor the latest information in real time and update the route as needed. For example, if a traffic accident occurs along the way or the user's stress level rises, the server regenerates a new, safer and more relaxing route and sends it to the device. This allows the user to reach their destination safely and psychologically in a comfortable state.

[0334] Prompt Sentence Examples

[0335] "Generate a safe and psychologically comfortable route from home to the supermarket that meets the 5,000-step physical activity requirement. If the emotion engine evaluates the user's stress level as high, provide a route that prioritizes relaxing scenery."

[0336] By combining a generative AI model with an emotion engine that analyzes the situation in real time, the system provides optimal routes tailored to each user's needs, which is particularly useful for users who prioritize safety and psychological comfort.

[0337] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0338] Step 1:

[0339] The user opens a map app. The device displays a user interface and provides a screen for entering the starting point and destination. Input: The user's starting point, destination, exercise amount, and safety conditions. Output: The set starting point, destination, exercise amount, and safety conditions.

[0340] Step 2:

[0341] The user inputs the starting point, destination, momentum, and safety conditions. The terminal sends this data to the server. Input: The user inputs the starting point, destination, momentum, and safety conditions. Output: Sends the input data to the server.

[0342] Step 3:

[0343] The server collects information on traffic accidents, public safety, lighting, security devices, and the location of public facilities. Sources of information include public data from police stations and local governments, news, and social media. Input: Collected information on traffic accidents, public safety, lighting, security devices, and public facilities. Output: Latest safety information database.

[0344] Step 4:

[0345] The information collected by the server and the entered conditions are input into a generative AI model to generate the optimal route. In particular, routes that avoid areas with high traffic accident rates and areas with poor security are prioritized. Input: User conditions and collected safety information. Output: Optimal route generated by the generative AI model.

[0346] Step 5:

[0347] The server sends optimal route information to the device. The device receives this information and draws the route on a map to present it visually. Input: Optimal route information sent from the server. Output: Route drawn on the map.

[0348] Step 6:

[0349] The user starts going out, and the device uses GPS to track the user's current location. Input: User movement information. Output: Real-time location data.

[0350] Step 7:

[0351] The server collects the latest traffic accident and public safety information in real time and checks whether the current route is appropriate. If necessary, the server regenerates the route and sends it to the device. Input: Real-time safety information and user location data. Output: Updated route information.

[0352] Step 8:

[0353] The emotion engine analyzes the user's psychological state in real time and sends the results to the server. Input: User's psychological data. Output: Analysis data of psychological state.

[0354] Step 9:

[0355] The server adjusts the route based on the emotion engine's feedback, providing a less stressful route if necessary. This new route information is sent to the device. Input: Emotion engine's feedback and current route information. Output: Adjusted route information.

[0356] (Application example 2)

[0357] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0358] Although conventional navigation systems can take into account traffic accident and public safety information, they are unable to reflect the user's psychological and health conditions in real time. This can lead to users passing through stressful environments or areas that cause anxiety. Even with a route update function, the route is not regenerated based on the user's emotional state, so the user's sense of security and comfort is not guaranteed.

[0359] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0360] In this invention, the server includes: means for inputting conditions related to the starting point, destination, and exercise volume and safety; means for collecting traffic accident information, safety information, lighting and monitoring device information, and location information of public facilities; means for using a generative AI model to generate an optimal route based on the collected information and the input conditions; means for displaying the generated route on a map and presenting it to the user; means for tracking the user's current location and updating the route in real time based on the latest accident and safety information; and means for using an emotion engine to analyze the user's emotional state and regenerate the route based on that. This allows the user's psychological and health state to be reflected in real time, enabling safe and comfortable travel.

[0361] "Start point" refers to the location where the user begins their journey.

[0362] "Destination" is the location where the user ends their journey.

[0363] "Movement" refers to the number of steps or degree of movement you wish to achieve during your trip.

[0364] "Safety" refers to the sense of security users feel while traveling and the local security situation.

[0365] "Traffic accident information" refers to data collected in real time about accidents and accidents on roads.

[0366] "Safety information" is data on the safety of a neighborhood, such as crime rates and the status of lighting facilities.

[0367] "Lighting" refers to streetlights and lamps that illuminate roads and other areas at night.

[0368] "Surveillance equipment" refers to cameras and security equipment used to monitor public order and safety.

[0369] "Public facilities" are facilities intended for public use, such as parks, police stations, and public toilets.

[0370] "Means of collecting information" refers to the methods and techniques used to collect the necessary data in real time.

[0371] A "generative AI model" is an artificial intelligence that uses collected data to automatically calculate and generate optimal routes.

[0372] An "emotion engine" is a technology that analyzes the user's psychological state in real time and provides that information to the system.

[0373] "Tracking" means tracking a user's current location in real time.

[0374] "Regenerating a route" means recalculating and generating an already generated route based on new conditions.

[0375] "Means for displaying on a map" refers to the methods and techniques used to visually present the generated route to the user.

[0376] An "evacuation site" is a location where users can go to ensure safety in the event of an emergency.

[0377] "Real-time" refers to the time when data and information are processed and updated immediately.

[0378] This invention is a navigation system installed in an autonomous vehicle that utilizes generative AI models and an emotion engine to provide optimal routes that take into account the user's emotional state and health in real time.

[0379] System configuration

[0380] The system consists of three main components:

[0381] 1. Server: Responsible for data collection and route generation.

[0382] 2. Terminal: Provides the user interface and real-time tracking functions.

[0383] 3. Emotion engine: Recognizes the user's emotional state in real time and sends feedback to the server.

[0384] Hardware and software used

[0385] Hardware:

[0386] Autonomous vehicle computer systems

[0387] GPS Modules

[0388] Camera and microphone (for emotion engine)

[0389] software:

[0390] Emotion recognition engine (e.g., Microsoft Azure Cognitive Services, Affectiva)

[0391] API communication library (e.g. requests)

[0392] System Operation Overview

[0393] 1. User Input

[0394] Users input their starting point and destination and set exercise and safety criteria, including recommended steps and safe areas. The emotion engine continuously monitors the user's emotional state and sends the results from the device to a server.

[0395] 2. Data collection

[0396] The server collects the latest traffic accident information, safety information, lighting and surveillance device information, and location information of public facilities, including public data from police stations and local governments, news, social media, and other sources.

[0397] 3. Route generation using generative AI models

[0398] The server inputs the collected information, user input conditions, and emotional state information from the emotion engine into a generative AI model to generate the optimal route. Routes that take into consideration the user's psychological state are prioritized, avoiding areas with high traffic accident rates and low safety.

[0399] 4. Route presentation

[0400] The generated optimal route is sent from the server to the device and displayed on a map, allowing the user to check the entire route before setting off.

[0401] 5. Real-time feedback

[0402] When the user begins to go out, the device continuously tracks the user's current location using its GPS function. The latest accident and safety information is collected in real time and sent to the server. If necessary, the server regenerates the route and sends updated route information to the device, which immediately displays it and notifies the user.

[0403] 6. Feedback from Emotion Engine

[0404] The emotion engine analyzes the user's emotional state (e.g., stress, relief, fear) in real time and sends feedback to the server. Based on this feedback, the server will prioritize nearby evacuation sites in an emergency, or suggest routes through relaxing landscapes or quiet areas if the user is feeling stressed.

[0405] Specific examples

[0406] For example, a user sets a route from "home" to "supermarket" and specifies "5,000 steps" as the amount of exercise required per day. Furthermore, since the user will be going out at night, the user sets the priority on safety, and the emotion engine monitors the user's stress level. Based on this information, the server collects the latest traffic accident and safety information and generates a safe route with an appropriate amount of exercise. If the emotion engine detects a high stress level, the server prioritizes routes with relaxing scenery.

[0407] Prompt Sentence Examples

[0408] Prompt statement:

[0409] Enter your current location and destination, and our emotion engine will monitor your emotional state and generate the best route in real time.

[0410] Starting point: Tokyo Station

[0411] Destination: Shibuya Station

[0412] Safety: High

[0413] Recommended steps: 5,000

[0414] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0415] Step 1:

[0416] User Input

[0417] The user inputs the starting point, destination, exercise amount (e.g., recommended number of steps), and safety conditions. The device receives this information, and the emotion engine monitors the user's emotional state.

[0418] Input: Starting point, destination, exercise amount, safety settings

[0419] Output: Condition data

[0420] Specific operation: The user enters the starting point, destination, exercise amount, and safety settings into the device, and the device sends this to the server.

[0421] Step 2:

[0422] Data collection

[0423] The server collects the latest traffic accident information, safety information (e.g., crime rates and nighttime lighting conditions), and facility information (e.g., the locations of public restrooms and police stations) in real time. This includes the ability to collect data from public information from police stations and local governments, news, social media, etc.

[0424] Input: Condition data

[0425] Output: Collected data

[0426] Specific operation: The server uses the specified API to collect traffic accident information, safety information, and facility information in real time.

[0427] Step 3:

[0428] Route generation using generative AI models

[0429] The server uses a generative AI model to generate an optimal route based on the collected data and the user's conditions, prioritizing routes that avoid areas with high traffic accident rates and areas with poor security, and that take the user's psychological state into consideration.

[0430] Input: Collected data, condition data

[0431] Output: Optimal route

[0432] Specific operation: The server inputs condition data and collected data into the generated AI model, and calculates and generates the optimal route.

[0433] Step 4:

[0434] Route suggestions

[0435] The generated optimal route is sent from the server to the terminal, which displays it on a map.

[0436] Input: Optimal Route

[0437] Output: Map display data

[0438] Specific operation: The server sends the generated optimal route to the device, which then visually displays it on the map app.

[0439] Step 5:

[0440] Real-time feedback

[0441] When the user starts going out, the device continuously tracks the user's current location using GPS. Traffic accident and safety information is collected in real time and sent to the server. If necessary, the server regenerates the route and sends new route information to the device.

[0442] Input: current user location, latest collected data

[0443] Output: Updated optimal route

[0444] How it works: The device tracks the user's current location using GPS and sends any new information collected to the server, which then regenerates the route based on that information and sends the updated route to the device.

[0445] Step 6:

[0446] Emotional Engine Feedback

[0447] The emotion engine analyzes the user's emotional state in real time and feeds that information back to the server, which then uses this information to suggest safe evacuation locations and regenerate relaxing routes.

[0448] Input: User's emotional state

[0449] Output: Emotional reflection route, evacuation site

[0450] Specific operation: The emotion engine interprets the user's emotional state and sends it to the server. The server then regenerates a new route or suggests evacuation locations depending on the emotional state.

[0451] Through these steps, the system can ensure user safety and psychological comfort and optimize the navigation of autonomous vehicles.

[0452] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0453] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0454] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0455] [Second embodiment]

[0456] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0457] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0458] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0459] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0460] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0461] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0462] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0463] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0464] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0465] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0466] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0467] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0468] This invention is a map application system that utilizes a generative AI model to provide optimal routes that take into account the user's health and safety. This system supports safe and healthy outings by inputting user conditions, collecting necessary information, generating and presenting routes, and updating them in real time.

[0469] Overall system configuration

[0470] The system mainly consists of two components: a server and a terminal. The server is responsible for the central functions of data collection and route generation, while the terminal is responsible for the user interface and real-time tracking functions.

[0471] (1) User input

[0472] The user opens a map app, inputs their starting point and destination, and then specifies conditions for exercise volume (e.g., recommended number of steps and duration) and safety (e.g., wanting to travel through safe areas). The device then sends this information to the server.

[0473] (2) Data collection

[0474] The server collects the latest information on traffic accidents, public safety, the location of lights and security cameras, and the location of public facilities at that time, including data collected in real time from public data released by police stations and local governments, news, social media, etc.

[0475] (3) Route generation using generative AI models

[0476] The server inputs the collected information and user input into a generative AI model to generate the optimal route. This prioritizes routes that avoid areas with high traffic accident rates and areas with poor security. It also takes into account the number of steps and distance traveled to ensure the specified amount of exercise is achieved.

[0477] (4) Route presentation

[0478] The generated optimal route is sent from the server to the device, which displays it on a map and presents it visually to the user, allowing the user to see the entire route before setting off.

[0479] (5) Real-time feedback

[0480] Once the user actually leaves the house, the device will continuously track the user's current location using its GPS function. Real-time information on traffic accidents and public safety is collected and sent to the server. If necessary, the server will regenerate the route and send the updated route information to the device, which will immediately display it to the user.

[0481] Specific examples

[0482] For example, consider a case where a user sets a route from "home" to "supermarket" and specifies "5,000 steps" as the amount of exercise required per day. Since the user will be out at night, the user also sets the route with an emphasis on safety. Based on this information, the server collects the latest traffic accident and public safety information and generates a safe route with an appropriate amount of exercise.

[0483] The generated route passes through areas with many police stations and street lights, and avoids areas with a high incidence of traffic accidents. Once the user starts going out, the server monitors the latest information in real time while the current location is tracked by GPS, updating the route as necessary. For example, if a traffic accident occurs along the way, the server generates a new, safer route and sends it to the device. This allows the user to reach their destination safely and healthily.

[0484] The processing flow will be explained below.

[0485] Step 1:

[0486] A user opens a map app, enters a starting point and destination, and also sets criteria for exercise (e.g., recommended number of steps) and safety (e.g., passing through safe neighborhoods).

[0487] Step 2:

[0488] The device sends the user's input data, including the starting point, destination, exercise amount, and safety conditions, to the server.

[0489] Step 3:

[0490] The server collects traffic accident information, specifically using news, social media, and public databases from police stations to obtain the latest accident information for a specific area.

[0491] Step 4:

[0492] The server collects public safety information, such as public information from police stations and local governments, crime maps, and automated web scraping tools, to understand the public safety situation in a particular area.

[0493] Step 5:

[0494] The server collects information from lights and security cameras, obtains the necessary information from databases of local governments and public institutions, and maps the location information.

[0495] Step 6:

[0496] The server collects location information for public facilities, such as police stations, evacuation centers, hospitals, and parks, which users can use in emergencies, and updates the database.

[0497] Step 7:

[0498] The server consolidates all collected data and inputs it into a generative AI model, including accident information, public safety information, light and security camera locations, and public facility locations.

[0499] Step 8:

[0500] The generative AI model generates the optimal route based on the user's criteria (starting point, destination, amount of exercise, safety). The AI ​​model prioritizes routes that meet the specified amount of exercise while avoiding safe areas and areas with high traffic accident rates.

[0501] Step 9:

[0502] The server sends the generated route to the device.

[0503] Step 10:

[0504] The device displays the received route information on a map and presents it visually to the user, allowing the user to check the entire route before setting off.

[0505] Step 11:

[0506] The user starts traveling and follows a route.

[0507] Step 12:

[0508] The device uses GPS to track the user's current location in real time.

[0509] Step 13:

[0510] The server periodically collects the latest traffic accident and public safety information.

[0511] Step 14:

[0512] The server determines whether the route needs to be changed based on the user's current location and the latest accident and security information, and if so, generates a new, optimal route.

[0513] Step 15:

[0514] The server sends new route information to the device and notifies it in real time.

[0515] Step 16:

[0516] The device will redisplay the new route on the map and provide instructions to the user.

[0517] This allows users to navigate their route to their destination safely and healthily.

[0518] Example 1

[0519] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0520] Conventional map application systems have difficulty providing optimal routes that fully consider the user's safety and health. Furthermore, there is a lack of systems that can reflect the latest accident and public safety information in real time and flexibly adjust routes while the user is traveling. Therefore, there is a need for a system that provides routes that allow users to reach their destination safely and healthily.

[0521] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0522] In this invention, the server includes: means for inputting a starting point, a destination, and conditions related to exercise volume and safety; means for collecting information on traffic accidents, public safety, electric lights and security cameras, and the location of public facilities; means for using a generative AI model to generate an optimal route based on the collected information and the input conditions; means for displaying the generated route on a map and presenting it to the user; means for tracking the user's current location and updating the route in real time based on the latest accident and public safety information; means for collecting data in real time and regenerating the route as necessary; and means for checking for format errors. This enables the provision of a safe and healthy optimal route based on the conditions entered by the user, and updating the route in real time.

[0523] The "starting point" is the location where the user starts their journey, and is the location information that serves as the starting point when the map application system generates a route.

[0524] The "destination" is the location the user wishes to reach, and is the location information that serves as the end point when the map application system generates a route.

[0525] "Amount of exercise" indicates the degree of exercise that the user wants to achieve while traveling, and is a condition specified by indicators such as the number of steps and exercise time.

[0526] "Safety" refers to the criteria that indicate the importance placed on public safety and the low number of accidents when selecting a travel route, and is a standard for avoiding areas with a high incidence of traffic accidents and areas with poor public safety.

[0527] "Traffic accident information" is data showing the occurrence of traffic accidents in a specific area, and is information collected to ensure the safety of users' travel.

[0528] "Public safety information" is data showing the crime rate and public safety situation in a specific area, and is information collected to evaluate the safety of a user's travel route.

[0529] "Lighting information" is data that indicates the location and lighting conditions of streetlights installed in public areas such as roads and parks, and is information used to increase user safety when traveling at night.

[0530] "Security camera information" is data indicating the location and operation status of surveillance cameras installed in public areas, and is information used to select routes that users can travel safely.

[0531] "Location information of public facilities" is data indicating the locations of public facilities such as public toilets, police stations, hospitals, and evacuation shelters, and is information used to provide users with easy access routes to the facilities they need.

[0532] A "generative AI model" is an algorithm that uses artificial intelligence technology to generate optimal routes based on collected data and user input conditions.

[0533] A "prompt" is an instruction entered into a generative AI model, and is text that contains specific requests and conditions for route generation.

[0534] This invention is a map application system that utilizes a generative AI model to provide optimal routes that take into account the user's health and safety. This system supports safe and healthy outings by inputting user conditions, collecting necessary information, generating and presenting routes, and updating them in real time.

[0535] Overall system configuration

[0536] The system mainly consists of two components: a server and a terminal. The server is responsible for the central functions of data collection and route generation, while the terminal is responsible for the user interface and real-time tracking functions.

[0537] Hardware and Software

[0538] Server: Used for data collection and route generation. Specifically, a server machine with high-performance data processing capabilities is required.

[0539] Device: Used for user interface and real-time tracking. Can be a smartphone or tablet.

[0540] Generative AI models: Implemented using machine learning frameworks such as TensorFlow and PyTorch.

[0541] Program processing

[0542] User Input

[0543] The user opens a map app on their device, inputs their starting point and destination, and specifies conditions for exercise volume (e.g., recommended number of steps and duration) and safety (e.g., preferring to travel through safe areas). The device validates this information and then sends it to the server.

[0544] Data collection

[0545] The server collects the latest information on traffic accidents, public safety, the location of lights and security cameras, and the location of public facilities at that time. This data is collected in real time from public data released by police stations and local governments, news, social media, etc.

[0546] Route generation using generative AI models

[0547] The server inputs the collected information and user input into a generative AI model to generate the optimal route. The generative AI model prioritizes routes that avoid areas with high traffic accident rates and areas with poor security. It also takes into account the number of steps and distance exercised to ensure the specified amount of exercise is achieved.

[0548] Route suggestions

[0549] The generated optimal route is sent from the server to the device, which displays it on a map and presents it visually to the user, allowing the user to see the entire route before setting off.

[0550] Real-time feedback

[0551] Once the user begins to leave the house, the device will continuously track the user's current location using its GPS function. The server will monitor the latest traffic accident and public safety information in real time and update the route as necessary. For example, if a traffic accident occurs along the way, a new route will be generated and sent to the device, allowing the user to instantly find a safe route.

[0552] Specific examples

[0553] For example, consider a case where a user sets a route from "home" to "supermarket" and specifies "5,000 steps" as the amount of exercise required per day. Since the user will be out at night, the user also sets the route with an emphasis on safety. Based on this information, the server collects the latest traffic accident and public safety information and generates a safe route with an appropriate amount of exercise.

[0554] The generated route passes through areas with many police stations and street lights, and avoids areas with a high incidence of traffic accidents. Once the user starts going out, the server monitors the latest information in real time while the current location is tracked by GPS, and updates the route as necessary. For example, if a traffic accident occurs along the way, the server generates a new, safer route and sends it to the device. This allows the user to reach their destination safely and healthily.

[0555] Prompt Sentence Examples

[0556] "Set a route from home to the supermarket. Since I'll be out at night, safety is a priority. My goal is to walk 5,000 steps a day. Generate the optimal route taking into account the latest traffic accident and security information."

[0557] By inputting such a prompt, the generative AI model generates an appropriate route.

[0558] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0559] Step 1: User Input

[0560] User: Launches a map app on the device and inputs the starting point, destination, amount of exercise, and safety conditions. Specific input values ​​are: starting point "home," destination "supermarket," amount of exercise "5,000 steps," and safety "safe area."

[0561] Terminal: Validates the entered information, checks for formatting errors, and if there are no problems, sends the information to the server.

[0562] Input: starting point, destination, momentum, safety conditions.

[0563] Output: The input information sent to the server.

[0564] Step 2: Collect data

[0565] Server: Using APIs and scraping technology, information on traffic accidents, public safety, location information for lights and security cameras, and location information for public facilities is obtained in real time.

[0566] Input: Conditions for API requests and data scraping.

[0567] Output: A dataset of the latest traffic accident information, public safety information, information on electric lights and security cameras, and location information of public facilities.

[0568] As a specific example of how it works, the server collects traffic accident information from the police station's API, scrapes public safety information from social media, and obtains the location information of streetlights and security cameras from a GIS database.

[0569] Step 3: Route generation using generative AI models

[0570] Server: Combines the collected data with user input conditions, inputs appropriate prompts into the generative AI model, and generates the optimal route.

[0571] Input: Starting point, destination, amount of exercise, safety conditions, and any other information you have collected.

[0572] Output: Optimal route information.

[0573] As an example of specific operation, the server inputs prompt statements such as "Starting point: home, destination: supermarket, exercise amount: 5,000 steps, safety: safe area" into the generative AI model and performs calculations to generate a route.

[0574] Step 4: Route presentation

[0575] Server: Sends the optimal route information obtained from the generative AI model to the device.

[0576] Device: The received route information is visually displayed in a map application and presented to the user.

[0577] Input: Optimal route information.

[0578] Output: The route that is displayed to the user.

[0579] For example, the device will use a map app to generate a route and display it in an easy-to-understand format to the user, allowing the user to see the overall route before setting off.

[0580] Step 5: Real-time feedback

[0581] Device: Uses GPS to track the user's location in real time.

[0582] Server: Continuously monitors the latest traffic accident and public safety information, regenerates routes as needed, and sends them to the device.

[0583] Input: Current location, latest traffic accident information, and public safety information.

[0584] Output: The updated route information.

[0585] For example, when a user starts going out, the device tracks their current location using GPS. The server reevaluates the route based on real-time data, and if, for example, a traffic accident occurs along the way, it generates a new, safer route and sends it to the device.

[0586] (Application example 1)

[0587] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0588] In autonomous vehicles, it is difficult to provide an optimal route while simultaneously considering the safety and health of the user. Conventional route guidance systems are unable to update traffic accident and public safety information in real time and provide appropriate driving instructions to the user. Furthermore, since it is not possible to generate a route that balances both exercise and safety, there is a need to provide an environment in which users can travel with peace of mind. The present invention aims to solve these problems.

[0589] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0590] In this invention, the server includes: means for inputting conditions related to starting point, destination, and exercise volume and safety; means for collecting traffic accident information, public safety information, lighting and monitoring device information, and public facility location information; means for using a generative AI model to generate an optimal route based on the collected information and the input conditions; means for displaying the generated route on a map and presenting it to the user; means for tracking the user's current location and updating the route in real time based on the latest accident and public safety information; and means for displaying the optimal route on the display of the autonomous vehicle and providing driving instructions. This enables the user to travel with peace of mind and enables route generation that balances exercise volume and safety.

[0591] The "starting point" is the location where the user begins their journey.

[0592] A "destination" is a location that a user wishes to reach.

[0593] "Amount of exercise" refers to the amount of physical activity a user gets while moving, specifically the number of steps taken and the duration of exercise.

[0594] "Safety" refers to conditions for minimizing the dangers users encounter while traveling, and includes information on public safety and traffic accidents.

[0595] "Traffic accident information" is data relating to accidents that occur on roads.

[0596] "Public safety information" is data regarding the safety of an area.

[0597] "Lighting" refers to information about light source equipment such as street lights.

[0598] "Monitoring device information" is data relating to monitoring devices such as security cameras.

[0599] "Location information of public facilities" is data on the locations of public places and facilities such as police stations, fire stations, and parks.

[0600] "Means of collection" refers to the techniques and methods used to obtain the necessary data.

[0601] "Means using generative AI models" refers to artificial intelligence technology that creates optimal routes based on collected data and user conditions.

[0602] "User presentation means" refers to the method or technology used to visually display the generated route to the user.

[0603] "Tracking" is a technology that tracks a user's current location in real time.

[0604] A "route update means" is a method or technique for changing a route based on the latest information.

[0605] An "autonomous vehicle display" is a display device installed inside an autonomous vehicle.

[0606] A "means for providing driving instructions" is a method or technology for providing instructions to an automated driving vehicle.

[0607] The present invention is a map application system that uses generative AI models to provide optimal routes that take into account the safety and well-being of users, and is designed to be particularly effective in autonomous vehicles.

[0608] Overall system configuration

[0609] This system mainly consists of two components: a server and a terminal (display, etc.) inside the autonomous vehicle. The server is responsible for the central functions of data collection and route generation, while the terminal is responsible for the user interface and real-time tracking functions.

[0610] 1. User Input

[0611] The user (a passenger in an autonomous vehicle) uses a device to input their starting point and destination, and specify conditions for exercise amount (e.g., recommended number of steps and duration of exercise) and safety (e.g., passing through safe areas). The device then sends this information to a server.

[0612] 2. Data collection

[0613] The server collects the latest traffic accident information, public safety information, lighting and surveillance device information, and location information of public facilities at that time, including data collected in real time from public data of police stations and local governments, news, social media, etc.

[0614] 3. Route generation using generative AI models

[0615] The server inputs the collected information and user input into a generative AI model to generate the optimal route. This prioritizes routes that avoid areas with high traffic accident rates and areas with poor security. It also takes into account the number of steps and distance traveled to ensure the specified amount of exercise is achieved.

[0616] 4. Route presentation

[0617] The generated optimal route is sent from the server to the terminal, which then displays it on the vehicle's display, providing a visual route to the user and specific driving instructions to the autonomous vehicle.

[0618] 5. Real-time feedback

[0619] Once the autonomous vehicle begins moving, the device will use its GPS to continuously track the vehicle's current location. Real-time information on traffic accidents and public safety is collected and sent to the server. If necessary, the server will regenerate the route and send updated route information to the device. The device will immediately display this information to inform the user and provide new driving instructions to the autonomous vehicle.

[0620] Specific examples

[0621] For example, if a user sets a route from home to the supermarket and specifies 4,000 steps as the amount of exercise required per day, they can also set a safety priority for nighttime outings. Based on this information, the server collects the latest traffic accident and public safety information and generates a safe route with an appropriate amount of exercise. The generated route is displayed on the autonomous vehicle's display, and driving instructions are provided to the vehicle. Once the user begins their journey, the server monitors the latest information in real time while the current location is tracked by GPS, updating the route as necessary. For example, if a traffic accident occurs along the way, the server generates a new, safer route and sends it to the device. This allows the user to reach their destination safely and healthily.

[0622] Hardware and software used

[0623] The hardware includes a GPS module, vehicle display, and autonomous driving control system, while the software includes a generative AI model, API call functions for data collection, GPS location tracking software, and display management classes.

[0624] Prompt Sentence Examples

[0625] "Generate a route from your home to the supermarket. Requires 4000 steps, prioritizes safety, and takes into account the latest traffic accident and public safety information."

[0626] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0627] Step 1:

[0628] The user uses the device to input the starting point, destination, exercise amount (e.g., number of steps and exercise time), and safety conditions (e.g., passing through a safe area).

[0629] Input: User-specified starting point, destination, momentum, and safety criteria.

[0630] Output: The input condition data.

[0631] Specific operation: Through the terminal's user interface, the user inputs detailed conditions, including the destination and starting point.

[0632] Step 2:

[0633] The device sends the user's input data to the server.

[0634] Input: User-specified condition data.

[0635] Output: User criteria data sent to the server.

[0636] Specific operation: The device makes an API call to send data to a server over the Internet.

[0637] Step 3:

[0638] The server collects the latest traffic accident information, public safety information, lighting and surveillance device information, and location information of public facilities at that time.

[0639] Input: User criteria data.

[0640] Output: Collected public data, real-time data.

[0641] Specific operation: The server refers to external APIs and public databases to collect traffic accident information, public safety information, etc.

[0642] Step 4:

[0643] The information collected by the server and the user's input conditions are input into a generative AI model to generate the optimal route.

[0644] Input: Collected data, user condition data.

[0645] Output: Generated optimal route data.

[0646] How it works: The generative AI model uses machine learning algorithms to calculate a route with the optimal amount of momentum while avoiding areas prone to traffic accidents and areas with poor security.

[0647] Step 5:

[0648] The server sends the generated optimal route to the device.

[0649] Input: Generated optimal route data.

[0650] Output: Route data sent to the device.

[0651] Specific operation: The server sends the calculated route data to the terminal via the Internet.

[0652] Step 6:

[0653] The device will display the optimal route and present it to the user.

[0654] Input: Route data sent by the server.

[0655] Output: The route shown on the display.

[0656] Specific operation: The device visually displays the route on a map through the user interface and provides driving instructions to the autonomous vehicle control system.

[0657] Step 7:

[0658] When the user starts going out, the device uses GPS to track their current location in real time.

[0659] Input: GPS data.

[0660] Output: Current position data.

[0661] What it does: The device's GPS function continuously tracks the user's current location.

[0662] Step 8:

[0663] The server regenerates the route based on the latest collected traffic accident and security information, and sends the updated route information to the terminal.

[0664] Inputs: Real-time location data, latest traffic accident and public safety information.

[0665] Output: Updated route data.

[0666] What it does: The server re-runs the generative AI model based on the new information to generate a new route that is safe and healthy.

[0667] Step 9:

[0668] The device displays instantly updated route information and provides new driving instructions to the autonomous driving system.

[0669] Input: Updated route data.

[0670] Output: New route and driving instructions shown on the display.

[0671] Specific operation: The device instantly displays new route information on the map and provides new driving instructions to the autonomous vehicle.

[0672] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0673] This invention is a map application system that utilizes a generative AI model and an emotion engine to provide optimal routes that take into account the user's health, safety, and psychological state. The system supports safe and stress-free outings by allowing users to input their conditions, collecting necessary information, generating and presenting routes, updating them in real time, and recognizing their emotions.

[0674] Overall system configuration

[0675] The system mainly consists of three components: a server, a terminal, and an emotion engine. The server is responsible for the central functions of data collection and route generation, while the terminal is responsible for the user interface and real-time tracking. The emotion engine recognizes the user's emotional state in real time and sends feedback to the server.

[0676] (1) User input

[0677] The user opens the map app and inputs their starting point and destination. They also set conditions for exercise (e.g., recommended number of steps) and safety (e.g., passing through safe areas). The emotion engine then monitors the user's emotional state. Based on the results, the device sends the data to the server.

[0678] (2) Data collection

[0679] The server collects the latest information on traffic accidents, public safety, the location of lights and security cameras, and the location of public facilities at that time, including data collected in real time from public data released by police stations and local governments, news, social media, etc.

[0680] (3) Route generation using generative AI models

[0681] The server inputs the collected information, user input conditions, and emotional state information from the emotion engine into a generative AI model to generate the optimal route. This prioritizes routes that avoid areas with high traffic accident rates and areas with poor security, meet the specified exercise volume, and take the user's psychological state into consideration.

[0682] (4) Route presentation

[0683] The generated optimal route is sent from the server to the device, which displays it on a map and presents it visually to the user, allowing the user to check the entire route before setting off.

[0684] (5) Real-time feedback

[0685] Once the user actually leaves the house, the device will continuously track the user's current location using its GPS function. Real-time information on traffic accidents and public safety is collected and sent to the server. If necessary, the server will regenerate the route and send the updated route information to the device, which will immediately display it to the user.

[0686] (6) Feedback from the Emotion Engine

[0687] The emotion engine analyzes the user's emotional state (e.g., stress, relief, fear) in real time and sends feedback to the server. Based on this feedback, the server prioritizes nearby emergency shelters in case of an emergency. It also suggests routes through relaxing landscapes and quiet areas if the user is feeling stressed.

[0688] Specific examples

[0689] For example, consider the case where a user sets a route from "home" to "supermarket" and specifies "5,000 steps" as the amount of exercise required per day. Furthermore, since the user will be going out at night, the user sets the route with an emphasis on safety, and the emotion engine monitors the user's stress level. Based on this information, the server collects the latest traffic accident and security information and generates a safe route with an appropriate amount of exercise. If the emotion engine detects a high stress level, the server will prioritize routes with relaxing scenery.

[0690] The generated route takes into consideration a stress-free environment, passing through areas with police stations and plenty of street lights, and avoiding areas prone to traffic accidents. Once the user begins to go out, the server and emotion engine monitor the latest information in real time while the user's current location is tracked by GPS, updating the route as needed. For example, if a traffic accident occurs along the way or the user's stress level rises, the server regenerates a new, safer, and more relaxing route and sends it to the device. This allows the user to reach their destination safely and psychologically in a comfortable state.

[0691] The processing flow will be explained below.

[0692] Step 1:

[0693] The user opens the map app, inputs their starting point and destination, and sets criteria for exercise (e.g., recommended number of steps) and safety (e.g., passing through safe areas). The emotion engine then begins monitoring the user's emotional state in real time.

[0694] Step 2:

[0695] The device sends user input data to the server, including the starting point, destination, momentum, safety conditions, and initial emotional state information from the emotion engine.

[0696] Step 3:

[0697] The server collects traffic accident information, specifically using news, social media, and public databases from police stations to obtain the latest accident information for a specific area.

[0698] Step 4:

[0699] The server collects public safety information, using publicly available information from police stations and local governments, crime information maps, web scraping tools, and other tools to understand the public safety situation in a specific area.

[0700] Step 5:

[0701] The server collects information from lights and security cameras, obtains the necessary information from databases of local governments and public institutions, and maps the location information.

[0702] Step 6:

[0703] The server collects location information for public facilities, such as police stations, evacuation centers, hospitals, and parks, which users can use in emergencies, and updates the database.

[0704] Step 7:

[0705] The server integrates all collected data and inputs it into a generative AI model, including accident information, public safety information, light and security camera locations, public facility locations, and emotional state information provided by an emotion engine.

[0706] Step 8:

[0707] The generative AI model generates an optimal route based on the user's input and emotional state, meeting the specified exercise volume, avoiding areas with poor security and high traffic accident rates, and taking into account the psychological state (e.g., stress) detected by the emotion engine.

[0708] Step 9:

[0709] The server sends the generated route to the device.

[0710] Step 10:

[0711] The device displays the received route information on a map and presents it visually to the user, allowing the user to check the entire route before setting off.

[0712] Step 11:

[0713] The user starts traveling and follows a route.

[0714] Step 12:

[0715] The device uses GPS to track the user's current location in real time.

[0716] Step 13:

[0717] The emotion engine analyzes the user's emotional state (e.g., stress, relief, fear) in real time and sends that information to the server.

[0718] Step 14:

[0719] The server periodically collects the latest traffic accident and public safety information.

[0720] Step 15:

[0721] The server determines whether the route needs to be changed based on the user's current location, emotional state from the emotion engine, and the latest accident and security information. If necessary, it regenerates a new optimal route.

[0722] Step 16:

[0723] The server sends new route information to the device and notifies it in real time.

[0724] Step 17:

[0725] The device will redisplay the new route on the map and provide instructions to the user.

[0726] Step 18:

[0727] Users follow new routes to their destinations in a safe and psychologically comfortable environment.

[0728] Example 2

[0729] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0730] In modern society, safety and comfort are important when people go out. However, it is difficult to simultaneously avoid areas with high traffic accident rates and poor security, ensure an appropriate amount of exercise, and travel comfortably while reducing psychological stress. Furthermore, there is a need for a system that can re-suggest appropriate routes when conditions change in real time after a person has actually started going out.

[0731] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting conditions related to a starting point, a destination, and the amount of exercise and safety, a means for collecting information on traffic accidents, public safety, lighting and security devices, and the location information of public facilities, and a means for using a generative AI model to generate an optimal route based on the collected information and the input conditions. This makes it possible to provide an optimal route for a user to travel safely and comfortably to their destination, and to respond to situations that change in real time.

[0732] "Starting point" refers to the current location or the location designated by the user as the starting point of a journey.

[0733] "Destination" refers to the location the user ultimately wants to reach.

[0734] "Amount of exercise" refers to an indicator of the amount of physical activity, such as the number of steps taken and calories burned when a user moves around while out and about.

[0735] "Safety" refers to a safe environment that takes into consideration public safety and the risk of accidents when users travel.

[0736] "Conditions" refers to the exercise volume and safety requirements entered by the user.

[0737] "Traffic accident information" refers to data regarding the circumstances and locations of traffic accidents.

[0738] "Public safety information" refers to data on crime rates and safety in a particular area.

[0739] "Lighting Information" refers to data relating to the location of streetlights and other forms of lighting equipment.

[0740] "Security device information" refers to data regarding the location of security equipment such as security cameras and emergency notification devices.

[0741] "Location information of public facilities" refers to data regarding the locations of public facilities such as hospitals, police stations, schools, and parks.

[0742] A "generative AI model" refers to an algorithm that uses machine learning and artificial intelligence to generate optimal routes.

[0743] "Display device" refers to a device such as a smartphone or tablet that allows users to visually check the route.

[0744] "Tracking" refers to using technology such as GPS to determine a user's current location in real time.

[0745] "Mental state" refers to the user's mental state, such as emotions and stress level.

[0746] "Analysis" refers to processing data to extract meaningful information.

[0747] "Evacuation destination" refers to a location where users can evacuate to ensure their safety in the event of an emergency.

[0748] MODE FOR CARRYING OUT THE INVENTION

[0749] This invention is a map application system that utilizes a generative AI model and an emotion engine to provide optimal routes that take into account the user's health, safety, and psychological state. Below, we will explain each component of the system and its specific operation.

[0750] System configuration

[0751] This system mainly consists of three hardware components: a server, a terminal, and an emotion engine.

[0752] 1. Server

[0753] The server is responsible for data collection and route generation. Specifically, it collects information on traffic accidents, public safety, lighting, security devices, and the location of public facilities. This includes real-time data collection from public data from police stations and local governments, news, social media, and more. The collected data is stored in the server's database, and the optimal route is generated through a generative AI model.

[0754] 2. Terminal

[0755] The device is responsible for the user interface and real-time tracking. Specifically, the user opens the app, inputs their starting point and destination, and is shown a screen where they can set exercise and safety criteria. The device also uses GPS to track the user's current location and connects to a server to receive real-time route updates, which are displayed on a map.

[0756] 3. Emotion Engine

[0757] The emotion engine recognizes the user's emotional state in real time and sends that feedback to the server, allowing the route to be readjusted if the user is feeling stressed, for example.

[0758] Example of operation

[0759] For example, consider the case where a user sets a route from "home" to "supermarket." The user specifies "5,000 steps" as the amount of exercise required per day, and since they will be going out at night, they set the settings to prioritize safety. At this time, the emotion engine monitors the user's stress level. The server generates a safe route with an appropriate amount of exercise based on the latest traffic accident and security information collected.

[0760] The generated route takes into consideration a stress-free environment, passing through streets with many police stations and lighting, avoiding areas prone to traffic accidents, and so on. When the user begins to go out, the device tracks their current location using GPS, while the server and emotion engine monitor the latest information in real time and update the route as needed. For example, if a traffic accident occurs along the way or the user's stress level rises, the server regenerates a new, safer and more relaxing route and sends it to the device. This allows the user to reach their destination safely and psychologically in a comfortable state.

[0761] Prompt Sentence Examples

[0762] "Generate a safe and psychologically comfortable route from home to the supermarket that meets the 5,000-step physical activity requirement. If the emotion engine evaluates the user's stress level as high, provide a route that prioritizes relaxing scenery."

[0763] By combining a generative AI model with an emotion engine that analyzes the situation in real time, the system provides optimal routes tailored to each user's needs, which is particularly useful for users who prioritize safety and psychological comfort.

[0764] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0765] Step 1:

[0766] The user opens a map app. The device displays a user interface and provides a screen for entering the starting point and destination. Input: The user's starting point, destination, exercise amount, and safety conditions. Output: The set starting point, destination, exercise amount, and safety conditions.

[0767] Step 2:

[0768] The user inputs the starting point, destination, momentum, and safety conditions. The terminal sends this data to the server. Input: The user inputs the starting point, destination, momentum, and safety conditions. Output: Sends the input data to the server.

[0769] Step 3:

[0770] The server collects information on traffic accidents, public safety, lighting, security devices, and the location of public facilities. Sources of information include public data from police stations and local governments, news, and social media. Input: Collected information on traffic accidents, public safety, lighting, security devices, and public facilities. Output: Latest safety information database.

[0771] Step 4:

[0772] The information collected by the server and the entered conditions are input into a generative AI model to generate the optimal route. In particular, routes that avoid areas with high traffic accident rates and areas with poor security are prioritized. Input: User conditions and collected safety information. Output: Optimal route generated by the generative AI model.

[0773] Step 5:

[0774] The server sends optimal route information to the device. The device receives this information and draws the route on a map to present it visually. Input: Optimal route information sent from the server. Output: Route drawn on the map.

[0775] Step 6:

[0776] The user starts going out, and the device uses GPS to track the user's current location. Input: User movement information. Output: Real-time location data.

[0777] Step 7:

[0778] The server collects the latest traffic accident and public safety information in real time and checks whether the current route is appropriate. If necessary, the server regenerates the route and sends it to the device. Input: Real-time safety information and user location data. Output: Updated route information.

[0779] Step 8:

[0780] The emotion engine analyzes the user's psychological state in real time and sends the results to the server. Input: User's psychological data. Output: Analysis data of psychological state.

[0781] Step 9:

[0782] The server adjusts the route based on the emotion engine's feedback, providing a less stressful route if necessary. This new route information is sent to the device. Input: Emotion engine's feedback and current route information. Output: Adjusted route information.

[0783] (Application example 2)

[0784] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0785] Although conventional navigation systems can take into account traffic accident and public safety information, they are unable to reflect the user's psychological and health conditions in real time. This can lead to users passing through stressful environments or areas that cause anxiety. Even with a route update function, the route is not regenerated based on the user's emotional state, so the user's sense of security and comfort is not guaranteed.

[0786] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0787] In this invention, the server includes: means for inputting conditions related to the starting point, destination, and exercise volume and safety; means for collecting traffic accident information, safety information, lighting and monitoring device information, and location information of public facilities; means for using a generative AI model to generate an optimal route based on the collected information and the input conditions; means for displaying the generated route on a map and presenting it to the user; means for tracking the user's current location and updating the route in real time based on the latest accident and safety information; and means for using an emotion engine to analyze the user's emotional state and regenerate the route based on that. This allows the user's psychological and health state to be reflected in real time, enabling safe and comfortable travel.

[0788] "Start point" refers to the location where the user begins their journey.

[0789] "Destination" is the location where the user ends their journey.

[0790] "Movement" refers to the number of steps or degree of movement you wish to achieve during your trip.

[0791] "Safety" refers to the sense of security users feel while traveling and the local security situation.

[0792] "Traffic accident information" refers to data collected in real time about accidents and accidents on roads.

[0793] "Safety information" is data on the safety of a neighborhood, such as crime rates and the status of lighting facilities.

[0794] "Lighting" refers to streetlights and lamps that illuminate roads and other areas at night.

[0795] "Surveillance equipment" refers to cameras and security equipment used to monitor public order and safety.

[0796] "Public facilities" are facilities intended for public use, such as parks, police stations, and public toilets.

[0797] "Means of collecting information" refers to the methods and techniques used to collect the necessary data in real time.

[0798] A "generative AI model" is an artificial intelligence that uses collected data to automatically calculate and generate optimal routes.

[0799] An "emotion engine" is a technology that analyzes the user's psychological state in real time and provides that information to the system.

[0800] "Tracking" means tracking a user's current location in real time.

[0801] "Regenerating a route" means recalculating and generating an already generated route based on new conditions.

[0802] "Means for displaying on a map" refers to the methods and techniques used to visually present the generated route to the user.

[0803] An "evacuation site" is a location where users can go to ensure safety in the event of an emergency.

[0804] "Real-time" refers to the time when data and information are processed and updated immediately.

[0805] This invention is a navigation system installed in an autonomous vehicle that utilizes generative AI models and an emotion engine to provide optimal routes that take into account the user's emotional state and health in real time.

[0806] System configuration

[0807] The system consists of three main components:

[0808] 1. Server: Responsible for data collection and route generation.

[0809] 2. Terminal: Provides the user interface and real-time tracking functions.

[0810] 3. Emotion engine: Recognizes the user's emotional state in real time and sends feedback to the server.

[0811] Hardware and software used

[0812] Hardware:

[0813] Autonomous vehicle computer systems

[0814] GPS Modules

[0815] Camera and microphone (for emotion engine)

[0816] software:

[0817] Emotion recognition engine (e.g., Microsoft Azure Cognitive Services, Affectiva)

[0818] API communication library (e.g. requests)

[0819] System Operation Overview

[0820] 1. User Input

[0821] Users input their starting point and destination and set exercise and safety criteria, including recommended steps and safe areas. The emotion engine continuously monitors the user's emotional state and sends the results from the device to a server.

[0822] 2. Data collection

[0823] The server collects the latest traffic accident information, safety information, lighting and surveillance device information, and location information of public facilities, including public data from police stations and local governments, news, social media, and other sources.

[0824] 3. Route generation using generative AI models

[0825] The server inputs the collected information, user input conditions, and emotional state information from the emotion engine into a generative AI model to generate the optimal route. Routes that take into consideration the user's psychological state are prioritized, avoiding areas with high traffic accident rates and low safety.

[0826] 4. Route presentation

[0827] The generated optimal route is sent from the server to the device and displayed on a map, allowing the user to check the entire route before setting off.

[0828] 5. Real-time feedback

[0829] When the user begins to go out, the device continuously tracks the user's current location using its GPS function. The latest accident and safety information is collected in real time and sent to the server. If necessary, the server regenerates the route and sends updated route information to the device, which immediately displays it and notifies the user.

[0830] 6. Feedback from Emotion Engine

[0831] The emotion engine analyzes the user's emotional state (e.g., stress, relief, fear) in real time and sends feedback to the server. Based on this feedback, the server will prioritize nearby evacuation sites in an emergency, or suggest routes through relaxing landscapes or quiet areas if the user is feeling stressed.

[0832] Specific examples

[0833] For example, a user sets a route from "home" to "supermarket" and specifies "5,000 steps" as the amount of exercise required per day. Furthermore, since the user will be going out at night, the user sets the priority on safety, and the emotion engine monitors the user's stress level. Based on this information, the server collects the latest traffic accident and safety information and generates a safe route with an appropriate amount of exercise. If the emotion engine detects a high stress level, the server prioritizes routes with relaxing scenery.

[0834] Prompt Sentence Examples

[0835] Prompt statement:

[0836] Enter your current location and destination, and our emotion engine will monitor your emotional state and generate the best route in real time.

[0837] Starting point: Tokyo Station

[0838] Destination: Shibuya Station

[0839] Safety: High

[0840] Recommended steps: 5,000

[0841] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0842] Step 1:

[0843] User Input

[0844] The user inputs the starting point, destination, exercise amount (e.g., recommended number of steps), and safety conditions. The device receives this information, and the emotion engine monitors the user's emotional state.

[0845] Input: Starting point, destination, exercise amount, safety settings

[0846] Output: Condition data

[0847] Specific operation: The user enters the starting point, destination, exercise amount, and safety settings into the device, and the device sends this to the server.

[0848] Step 2:

[0849] Data collection

[0850] The server collects the latest traffic accident information, safety information (e.g., crime rates and nighttime lighting conditions), and facility information (e.g., the locations of public restrooms and police stations) in real time. This includes the ability to collect data from public information from police stations and local governments, news, social media, etc.

[0851] Input: Condition data

[0852] Output: Collected data

[0853] Specific operation: The server uses the specified API to collect traffic accident information, safety information, and facility information in real time.

[0854] Step 3:

[0855] Route generation using generative AI models

[0856] The server uses a generative AI model to generate an optimal route based on the collected data and the user's conditions, prioritizing routes that avoid areas with high traffic accident rates and areas with poor security, and that take the user's psychological state into consideration.

[0857] Input: Collected data, condition data

[0858] Output: Optimal route

[0859] Specific operation: The server inputs condition data and collected data into the generated AI model, and calculates and generates the optimal route.

[0860] Step 4:

[0861] Route suggestions

[0862] The generated optimal route is sent from the server to the terminal, which displays it on a map.

[0863] Input: Optimal Route

[0864] Output: Map display data

[0865] Specific operation: The server sends the generated optimal route to the device, which then visually displays it on the map app.

[0866] Step 5:

[0867] Real-time feedback

[0868] When the user starts going out, the device continuously tracks the user's current location using GPS. Traffic accident and safety information is collected in real time and sent to the server. If necessary, the server regenerates the route and sends new route information to the device.

[0869] Input: current user location, latest collected data

[0870] Output: Updated optimal route

[0871] How it works: The device tracks the user's current location using GPS and sends any new information collected to the server, which then regenerates the route based on that information and sends the updated route to the device.

[0872] Step 6:

[0873] Emotional Engine Feedback

[0874] The emotion engine analyzes the user's emotional state in real time and feeds that information back to the server, which then uses this information to suggest safe evacuation locations and regenerate relaxing routes.

[0875] Input: User's emotional state

[0876] Output: Emotional reflection route, evacuation site

[0877] Specific operation: The emotion engine interprets the user's emotional state and sends it to the server. The server then regenerates a new route or suggests evacuation locations depending on the emotional state.

[0878] Through these steps, the system can ensure user safety and psychological comfort and optimize the navigation of autonomous vehicles.

[0879] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0880] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0881] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0882] [Third embodiment]

[0883] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0884] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0885] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0886] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0887] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0888] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0889] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0890] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0891] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0892] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0893] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0894] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0895] This invention is a map application system that utilizes a generative AI model to provide optimal routes that take into account the user's health and safety. This system supports safe and healthy outings by inputting user conditions, collecting necessary information, generating and presenting routes, and updating them in real time.

[0896] Overall system configuration

[0897] The system mainly consists of two components: a server and a terminal. The server is responsible for the central functions of data collection and route generation, while the terminal is responsible for the user interface and real-time tracking functions.

[0898] (1) User input

[0899] The user opens a map app, inputs their starting point and destination, and then specifies conditions for exercise volume (e.g., recommended number of steps and duration) and safety (e.g., wanting to travel through safe areas). The device then sends this information to the server.

[0900] (2) Data collection

[0901] The server collects the latest information on traffic accidents, public safety, the location of lights and security cameras, and the location of public facilities at that time, including data collected in real time from public data released by police stations and local governments, news, social media, etc.

[0902] (3) Route generation using generative AI models

[0903] The server inputs the collected information and user input into a generative AI model to generate the optimal route. This prioritizes routes that avoid areas with high traffic accident rates and areas with poor security. It also takes into account the number of steps and distance traveled to ensure the specified amount of exercise is achieved.

[0904] (4) Route presentation

[0905] The generated optimal route is sent from the server to the device, which displays it on a map and presents it visually to the user, allowing the user to see the entire route before setting off.

[0906] (5) Real-time feedback

[0907] Once the user actually leaves the house, the device will continuously track the user's current location using its GPS function. Real-time information on traffic accidents and public safety is collected and sent to the server. If necessary, the server will regenerate the route and send the updated route information to the device, which will immediately display it to the user.

[0908] Specific examples

[0909] For example, consider a case where a user sets a route from "home" to "supermarket" and specifies "5,000 steps" as the amount of exercise required per day. Since the user will be out at night, the user also sets the route with an emphasis on safety. Based on this information, the server collects the latest traffic accident and public safety information and generates a safe route with an appropriate amount of exercise.

[0910] The generated route passes through areas with many police stations and street lights, and avoids areas with a high incidence of traffic accidents. Once the user starts going out, the server monitors the latest information in real time while the current location is tracked by GPS, updating the route as necessary. For example, if a traffic accident occurs along the way, the server generates a new, safer route and sends it to the device. This allows the user to reach their destination safely and healthily.

[0911] The processing flow will be explained below.

[0912] Step 1:

[0913] A user opens a map app, enters a starting point and destination, and also sets criteria for exercise (e.g., recommended number of steps) and safety (e.g., passing through safe neighborhoods).

[0914] Step 2:

[0915] The device sends the user's input data, including the starting point, destination, exercise amount, and safety conditions, to the server.

[0916] Step 3:

[0917] The server collects traffic accident information, specifically using news, social media, and public databases from police stations to obtain the latest accident information for a specific area.

[0918] Step 4:

[0919] The server collects public safety information, such as public information from police stations and local governments, crime maps, and automated web scraping tools, to understand the public safety situation in a particular area.

[0920] Step 5:

[0921] The server collects information from lights and security cameras, obtains the necessary information from databases of local governments and public institutions, and maps the location information.

[0922] Step 6:

[0923] The server collects location information for public facilities, such as police stations, evacuation centers, hospitals, and parks, which users can use in emergencies, and updates the database.

[0924] Step 7:

[0925] The server consolidates all collected data and inputs it into a generative AI model, including accident information, public safety information, light and security camera locations, and public facility locations.

[0926] Step 8:

[0927] The generative AI model generates the optimal route based on the user's criteria (starting point, destination, amount of exercise, safety). The AI ​​model prioritizes routes that meet the specified amount of exercise while avoiding safe areas and areas with high traffic accident rates.

[0928] Step 9:

[0929] The server sends the generated route to the device.

[0930] Step 10:

[0931] The device displays the received route information on a map and presents it visually to the user, allowing the user to check the entire route before setting off.

[0932] Step 11:

[0933] The user starts traveling and follows a route.

[0934] Step 12:

[0935] The device uses GPS to track the user's current location in real time.

[0936] Step 13:

[0937] The server periodically collects the latest traffic accident and public safety information.

[0938] Step 14:

[0939] The server determines whether the route needs to be changed based on the user's current location and the latest accident and security information, and if so, generates a new, optimal route.

[0940] Step 15:

[0941] The server sends new route information to the device and notifies it in real time.

[0942] Step 16:

[0943] The device will redisplay the new route on the map and provide instructions to the user.

[0944] This allows users to navigate their route to their destination safely and healthily.

[0945] Example 1

[0946] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0947] Conventional map application systems have difficulty providing optimal routes that fully consider the user's safety and health. Furthermore, there is a lack of systems that can reflect the latest accident and public safety information in real time and flexibly adjust routes while the user is traveling. Therefore, there is a need for a system that provides routes that allow users to reach their destination safely and healthily.

[0948] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0949] In this invention, the server includes: means for inputting a starting point, a destination, and conditions related to exercise volume and safety; means for collecting information on traffic accidents, public safety, electric lights and security cameras, and the location of public facilities; means for using a generative AI model to generate an optimal route based on the collected information and the input conditions; means for displaying the generated route on a map and presenting it to the user; means for tracking the user's current location and updating the route in real time based on the latest accident and public safety information; means for collecting data in real time and regenerating the route as necessary; and means for checking for format errors. This enables the provision of a safe and healthy optimal route based on the conditions entered by the user, and updating the route in real time.

[0950] The "starting point" is the location where the user starts their journey, and is the location information that serves as the starting point when the map application system generates a route.

[0951] The "destination" is the location the user wishes to reach, and is the location information that serves as the end point when the map application system generates a route.

[0952] "Amount of exercise" indicates the degree of exercise that the user wants to achieve while traveling, and is a condition specified by indicators such as the number of steps and exercise time.

[0953] "Safety" refers to the criteria that indicate the importance placed on public safety and the low number of accidents when selecting a travel route, and is a standard for avoiding areas with a high incidence of traffic accidents and areas with poor public safety.

[0954] "Traffic accident information" is data showing the occurrence of traffic accidents in a specific area, and is information collected to ensure the safety of users' travel.

[0955] "Public safety information" is data showing the crime rate and public safety situation in a specific area, and is information collected to evaluate the safety of a user's travel route.

[0956] "Lighting information" is data that indicates the location and lighting conditions of streetlights installed in public areas such as roads and parks, and is information used to increase user safety when traveling at night.

[0957] "Security camera information" is data indicating the location and operation status of surveillance cameras installed in public areas, and is information used to select routes that users can travel safely.

[0958] "Location information of public facilities" is data indicating the locations of public facilities such as public toilets, police stations, hospitals, and evacuation shelters, and is information used to provide users with easy access routes to the facilities they need.

[0959] A "generative AI model" is an algorithm that uses artificial intelligence technology to generate optimal routes based on collected data and user input conditions.

[0960] A "prompt" is an instruction entered into a generative AI model, and is text that contains specific requests and conditions for route generation.

[0961] This invention is a map application system that utilizes a generative AI model to provide optimal routes that take into account the user's health and safety. This system supports safe and healthy outings by inputting user conditions, collecting necessary information, generating and presenting routes, and updating them in real time.

[0962] Overall system configuration

[0963] The system mainly consists of two components: a server and a terminal. The server is responsible for the central functions of data collection and route generation, while the terminal is responsible for the user interface and real-time tracking functions.

[0964] Hardware and Software

[0965] Server: Used for data collection and route generation. Specifically, a server machine with high-performance data processing capabilities is required.

[0966] Device: Used for user interface and real-time tracking. Can be a smartphone or tablet.

[0967] Generative AI models: Implemented using machine learning frameworks such as TensorFlow and PyTorch.

[0968] Program processing

[0969] User Input

[0970] The user opens a map app on their device, inputs their starting point and destination, and specifies conditions for exercise volume (e.g., recommended number of steps and duration) and safety (e.g., preferring to travel through safe areas). The device validates this information and then sends it to the server.

[0971] Data collection

[0972] The server collects the latest information on traffic accidents, public safety, the location of lights and security cameras, and the location of public facilities at that time. This data is collected in real time from public data released by police stations and local governments, news, social media, etc.

[0973] Route generation using generative AI models

[0974] The server inputs the collected information and user input into a generative AI model to generate the optimal route. The generative AI model prioritizes routes that avoid areas with high traffic accident rates and areas with poor security. It also takes into account the number of steps and distance exercised to ensure the specified amount of exercise is achieved.

[0975] Route suggestions

[0976] The generated optimal route is sent from the server to the device, which displays it on a map and presents it visually to the user, allowing the user to see the entire route before setting off.

[0977] Real-time feedback

[0978] Once the user begins to leave the house, the device will continuously track the user's current location using its GPS function. The server will monitor the latest traffic accident and public safety information in real time and update the route as necessary. For example, if a traffic accident occurs along the way, a new route will be generated and sent to the device, allowing the user to instantly find a safe route.

[0979] Specific examples

[0980] For example, consider a case where a user sets a route from "home" to "supermarket" and specifies "5,000 steps" as the amount of exercise required per day. Since the user will be out at night, the user also sets the route with an emphasis on safety. Based on this information, the server collects the latest traffic accident and public safety information and generates a safe route with an appropriate amount of exercise.

[0981] The generated route passes through areas with many police stations and street lights, and avoids areas with a high incidence of traffic accidents. Once the user starts going out, the server monitors the latest information in real time while the current location is tracked by GPS, and updates the route as necessary. For example, if a traffic accident occurs along the way, the server generates a new, safer route and sends it to the device. This allows the user to reach their destination safely and healthily.

[0982] Prompt Sentence Examples

[0983] "Set a route from home to the supermarket. Since I'll be out at night, safety is a priority. My goal is to walk 5,000 steps a day. Generate the optimal route taking into account the latest traffic accident and security information."

[0984] By inputting such a prompt, the generative AI model generates an appropriate route.

[0985] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0986] Step 1: User Input

[0987] User: Launches a map app on the device and inputs the starting point, destination, amount of exercise, and safety conditions. Specific input values ​​are: starting point "home," destination "supermarket," amount of exercise "5,000 steps," and safety "safe area."

[0988] Terminal: Validates the entered information, checks for formatting errors, and if there are no problems, sends the information to the server.

[0989] Input: starting point, destination, momentum, safety conditions.

[0990] Output: The input information sent to the server.

[0991] Step 2: Collect data

[0992] Server: Using APIs and scraping technology, information on traffic accidents, public safety, location information for lights and security cameras, and location information for public facilities is obtained in real time.

[0993] Input: Conditions for API requests and data scraping.

[0994] Output: A dataset of the latest traffic accident information, public safety information, information on electric lights and security cameras, and location information of public facilities.

[0995] As a specific example of how it works, the server collects traffic accident information from the police station's API, scrapes public safety information from social media, and obtains the location information of streetlights and security cameras from a GIS database.

[0996] Step 3: Route generation using generative AI models

[0997] Server: Combines the collected data with user input conditions, inputs appropriate prompts into the generative AI model, and generates the optimal route.

[0998] Input: Starting point, destination, amount of exercise, safety conditions, and any other information you have collected.

[0999] Output: Optimal route information.

[1000] As an example of specific operation, the server inputs prompt statements such as "Starting point: home, destination: supermarket, exercise amount: 5,000 steps, safety: safe area" into the generative AI model and performs calculations to generate a route.

[1001] Step 4: Route presentation

[1002] Server: Sends the optimal route information obtained from the generative AI model to the device.

[1003] Device: The received route information is visually displayed in a map application and presented to the user.

[1004] Input: Optimal route information.

[1005] Output: The route that is displayed to the user.

[1006] For example, the device will use a map app to generate a route and display it in an easy-to-understand format to the user, allowing the user to see the overall route before setting off.

[1007] Step 5: Real-time feedback

[1008] Device: Uses GPS to track the user's location in real time.

[1009] Server: Continuously monitors the latest traffic accident and public safety information, regenerates routes as needed, and sends them to the device.

[1010] Input: Current location, latest traffic accident information, and public safety information.

[1011] Output: The updated route information.

[1012] For example, when a user starts going out, the device tracks their current location using GPS. The server reevaluates the route based on real-time data, and if, for example, a traffic accident occurs along the way, it generates a new, safer route and sends it to the device.

[1013] (Application example 1)

[1014] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1015] In autonomous vehicles, it is difficult to provide an optimal route while simultaneously considering the safety and health of the user. Conventional route guidance systems are unable to update traffic accident and public safety information in real time and provide appropriate driving instructions to the user. Furthermore, since it is not possible to generate a route that balances both exercise and safety, there is a need to provide an environment in which users can travel with peace of mind. The present invention aims to solve these problems.

[1016] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1017] In this invention, the server includes: means for inputting conditions related to starting point, destination, and exercise volume and safety; means for collecting traffic accident information, public safety information, lighting and monitoring device information, and public facility location information; means for using a generative AI model to generate an optimal route based on the collected information and the input conditions; means for displaying the generated route on a map and presenting it to the user; means for tracking the user's current location and updating the route in real time based on the latest accident and public safety information; and means for displaying the optimal route on the display of the autonomous vehicle and providing driving instructions. This enables the user to travel with peace of mind and enables route generation that balances exercise volume and safety.

[1018] The "starting point" is the location where the user begins their journey.

[1019] A "destination" is a location that a user wishes to reach.

[1020] "Amount of exercise" refers to the amount of physical activity a user gets while moving, specifically the number of steps taken and the duration of exercise.

[1021] "Safety" refers to conditions for minimizing the dangers users encounter while traveling, and includes information on public safety and traffic accidents.

[1022] "Traffic accident information" is data relating to accidents that occur on roads.

[1023] "Public safety information" is data regarding the safety of an area.

[1024] "Lighting" refers to information about light source equipment such as street lights.

[1025] "Monitoring device information" is data relating to monitoring devices such as security cameras.

[1026] "Location information of public facilities" is data on the locations of public places and facilities such as police stations, fire stations, and parks.

[1027] "Means of collection" refers to the techniques and methods used to obtain the necessary data.

[1028] "Means using generative AI models" refers to artificial intelligence technology that creates optimal routes based on collected data and user conditions.

[1029] "User presentation means" refers to the method or technology used to visually display the generated route to the user.

[1030] "Tracking" is a technology that tracks a user's current location in real time.

[1031] A "route update means" is a method or technique for changing a route based on the latest information.

[1032] An "autonomous vehicle display" is a display device installed inside an autonomous vehicle.

[1033] A "means for providing driving instructions" is a method or technology for providing instructions to an automated driving vehicle.

[1034] The present invention is a map application system that uses generative AI models to provide optimal routes that take into account the safety and well-being of users, and is designed to be particularly effective in autonomous vehicles.

[1035] Overall system configuration

[1036] This system mainly consists of two components: a server and a terminal (display, etc.) inside the autonomous vehicle. The server is responsible for the central functions of data collection and route generation, while the terminal is responsible for the user interface and real-time tracking functions.

[1037] 1. User Input

[1038] The user (a passenger in an autonomous vehicle) uses a device to input their starting point and destination, and specify conditions for exercise amount (e.g., recommended number of steps and duration of exercise) and safety (e.g., passing through safe areas). The device then sends this information to a server.

[1039] 2. Data collection

[1040] The server collects the latest traffic accident information, public safety information, lighting and surveillance device information, and location information of public facilities at that time, including data collected in real time from public data of police stations and local governments, news, social media, etc.

[1041] 3. Route generation using generative AI models

[1042] The server inputs the collected information and user input into a generative AI model to generate the optimal route. This prioritizes routes that avoid areas with high traffic accident rates and areas with poor security. It also takes into account the number of steps and distance traveled to ensure the specified amount of exercise is achieved.

[1043] 4. Route presentation

[1044] The generated optimal route is sent from the server to the terminal, which then displays it on the vehicle's display, providing a visual route to the user and specific driving instructions to the autonomous vehicle.

[1045] 5. Real-time feedback

[1046] Once the autonomous vehicle begins moving, the device will use its GPS to continuously track the vehicle's current location. Real-time information on traffic accidents and public safety is collected and sent to the server. If necessary, the server will regenerate the route and send updated route information to the device. The device will immediately display this information to inform the user and provide new driving instructions to the autonomous vehicle.

[1047] Specific examples

[1048] For example, if a user sets a route from home to the supermarket and specifies 4,000 steps as the amount of exercise required per day, they can also set a safety priority for nighttime outings. Based on this information, the server collects the latest traffic accident and public safety information and generates a safe route with an appropriate amount of exercise. The generated route is displayed on the autonomous vehicle's display, and driving instructions are provided to the vehicle. Once the user begins their journey, the server monitors the latest information in real time while the current location is tracked by GPS, updating the route as necessary. For example, if a traffic accident occurs along the way, the server generates a new, safer route and sends it to the device. This allows the user to reach their destination safely and healthily.

[1049] Hardware and software used

[1050] The hardware includes a GPS module, vehicle display, and autonomous driving control system, while the software includes a generative AI model, API call functions for data collection, GPS location tracking software, and display management classes.

[1051] Prompt Sentence Examples

[1052] "Generate a route from your home to the supermarket. Requires 4000 steps, prioritizes safety, and takes into account the latest traffic accident and public safety information."

[1053] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1054] Step 1:

[1055] The user uses the device to input the starting point, destination, exercise amount (e.g., number of steps and exercise time), and safety conditions (e.g., passing through a safe area).

[1056] Input: User-specified starting point, destination, momentum, and safety criteria.

[1057] Output: The input condition data.

[1058] Specific operation: Through the terminal's user interface, the user inputs detailed conditions, including the destination and starting point.

[1059] Step 2:

[1060] The device sends the user's input data to the server.

[1061] Input: User-specified condition data.

[1062] Output: User criteria data sent to the server.

[1063] Specific operation: The device makes an API call to send data to a server over the Internet.

[1064] Step 3:

[1065] The server collects the latest traffic accident information, public safety information, lighting and surveillance device information, and location information of public facilities at that time.

[1066] Input: User criteria data.

[1067] Output: Collected public data, real-time data.

[1068] Specific operation: The server refers to external APIs and public databases to collect traffic accident information, public safety information, etc.

[1069] Step 4:

[1070] The information collected by the server and the user's input conditions are input into a generative AI model to generate the optimal route.

[1071] Input: Collected data, user condition data.

[1072] Output: Generated optimal route data.

[1073] How it works: The generative AI model uses machine learning algorithms to calculate a route with the optimal amount of momentum while avoiding areas prone to traffic accidents and areas with poor security.

[1074] Step 5:

[1075] The server sends the generated optimal route to the device.

[1076] Input: Generated optimal route data.

[1077] Output: Route data sent to the device.

[1078] Specific operation: The server sends the calculated route data to the terminal via the Internet.

[1079] Step 6:

[1080] The device will display the optimal route and present it to the user.

[1081] Input: Route data sent by the server.

[1082] Output: The route shown on the display.

[1083] Specific operation: The device visually displays the route on a map through the user interface and provides driving instructions to the autonomous vehicle control system.

[1084] Step 7:

[1085] When the user starts going out, the device uses GPS to track their current location in real time.

[1086] Input: GPS data.

[1087] Output: Current position data.

[1088] What it does: The device's GPS function continuously tracks the user's current location.

[1089] Step 8:

[1090] The server regenerates the route based on the latest collected traffic accident and security information, and sends the updated route information to the terminal.

[1091] Inputs: Real-time location data, latest traffic accident and public safety information.

[1092] Output: Updated route data.

[1093] What it does: The server re-runs the generative AI model based on the new information to generate a new route that is safe and healthy.

[1094] Step 9:

[1095] The device displays instantly updated route information and provides new driving instructions to the autonomous driving system.

[1096] Input: Updated route data.

[1097] Output: New route and driving instructions shown on the display.

[1098] Specific operation: The device instantly displays new route information on the map and provides new driving instructions to the autonomous vehicle.

[1099] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1100] This invention is a map application system that utilizes a generative AI model and an emotion engine to provide optimal routes that take into account the user's health, safety, and psychological state. The system supports safe and stress-free outings by allowing users to input their conditions, collecting necessary information, generating and presenting routes, updating them in real time, and recognizing their emotions.

[1101] Overall system configuration

[1102] The system mainly consists of three components: a server, a terminal, and an emotion engine. The server is responsible for the central functions of data collection and route generation, while the terminal is responsible for the user interface and real-time tracking. The emotion engine recognizes the user's emotional state in real time and sends feedback to the server.

[1103] (1) User input

[1104] The user opens the map app and inputs their starting point and destination. They also set conditions for exercise (e.g., recommended number of steps) and safety (e.g., passing through safe areas). The emotion engine then monitors the user's emotional state. Based on the results, the device sends the data to the server.

[1105] (2) Data collection

[1106] The server collects the latest information on traffic accidents, public safety, the location of lights and security cameras, and the location of public facilities at that time, including data collected in real time from public data released by police stations and local governments, news, social media, etc.

[1107] (3) Route generation using generative AI models

[1108] The server inputs the collected information, user input conditions, and emotional state information from the emotion engine into a generative AI model to generate the optimal route. This prioritizes routes that avoid areas with high traffic accident rates and areas with poor security, meet the specified exercise volume, and take the user's psychological state into consideration.

[1109] (4) Route presentation

[1110] The generated optimal route is sent from the server to the device, which displays it on a map and presents it visually to the user, allowing the user to check the entire route before setting off.

[1111] (5) Real-time feedback

[1112] Once the user actually leaves the house, the device will continuously track the user's current location using its GPS function. Real-time information on traffic accidents and public safety is collected and sent to the server. If necessary, the server will regenerate the route and send the updated route information to the device, which will immediately display it to the user.

[1113] (6) Feedback from the Emotion Engine

[1114] The emotion engine analyzes the user's emotional state (e.g., stress, relief, fear) in real time and sends feedback to the server. Based on this feedback, the server prioritizes nearby emergency shelters in case of an emergency. It also suggests routes through relaxing landscapes and quiet areas if the user is feeling stressed.

[1115] Specific examples

[1116] For example, consider the case where a user sets a route from "home" to "supermarket" and specifies "5,000 steps" as the amount of exercise required per day. Furthermore, since the user will be going out at night, the user sets the route with an emphasis on safety, and the emotion engine monitors the user's stress level. Based on this information, the server collects the latest traffic accident and security information and generates a safe route with an appropriate amount of exercise. If the emotion engine detects a high stress level, the server will prioritize routes with relaxing scenery.

[1117] The generated route takes into consideration a stress-free environment, passing through areas with police stations and plenty of street lights, and avoiding areas prone to traffic accidents. Once the user begins to go out, the server and emotion engine monitor the latest information in real time while the user's current location is tracked by GPS, updating the route as needed. For example, if a traffic accident occurs along the way or the user's stress level rises, the server regenerates a new, safer, and more relaxing route and sends it to the device. This allows the user to reach their destination safely and psychologically in a comfortable state.

[1118] The processing flow will be explained below.

[1119] Step 1:

[1120] The user opens the map app, inputs their starting point and destination, and sets criteria for exercise (e.g., recommended number of steps) and safety (e.g., passing through safe areas). The emotion engine then begins monitoring the user's emotional state in real time.

[1121] Step 2:

[1122] The device sends user input data to the server, including the starting point, destination, momentum, safety conditions, and initial emotional state information from the emotion engine.

[1123] Step 3:

[1124] The server collects traffic accident information, specifically using news, social media, and public databases from police stations to obtain the latest accident information for a specific area.

[1125] Step 4:

[1126] The server collects public safety information, using publicly available information from police stations and local governments, crime information maps, web scraping tools, and other tools to understand the public safety situation in a specific area.

[1127] Step 5:

[1128] The server collects information from lights and security cameras, obtains the necessary information from databases of local governments and public institutions, and maps the location information.

[1129] Step 6:

[1130] The server collects location information for public facilities, such as police stations, evacuation centers, hospitals, and parks, which users can use in emergencies, and updates the database.

[1131] Step 7:

[1132] The server integrates all collected data and inputs it into a generative AI model, including accident information, public safety information, light and security camera locations, public facility locations, and emotional state information provided by an emotion engine.

[1133] Step 8:

[1134] The generative AI model generates an optimal route based on the user's input and emotional state, meeting the specified exercise volume, avoiding areas with poor security and high traffic accident rates, and taking into account the psychological state (e.g., stress) detected by the emotion engine.

[1135] Step 9:

[1136] The server sends the generated route to the device.

[1137] Step 10:

[1138] The device displays the received route information on a map and presents it visually to the user, allowing the user to check the entire route before setting off.

[1139] Step 11:

[1140] The user starts traveling and follows a route.

[1141] Step 12:

[1142] The device uses GPS to track the user's current location in real time.

[1143] Step 13:

[1144] The emotion engine analyzes the user's emotional state (e.g., stress, relief, fear) in real time and sends that information to the server.

[1145] Step 14:

[1146] The server periodically collects the latest traffic accident and public safety information.

[1147] Step 15:

[1148] The server determines whether the route needs to be changed based on the user's current location, emotional state from the emotion engine, and the latest accident and security information. If necessary, it regenerates a new optimal route.

[1149] Step 16:

[1150] The server sends new route information to the device and notifies it in real time.

[1151] Step 17:

[1152] The device will redisplay the new route on the map and provide instructions to the user.

[1153] Step 18:

[1154] Users follow new routes to their destinations in a safe and psychologically comfortable environment.

[1155] Example 2

[1156] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1157] In modern society, safety and comfort are important when people go out. However, it is difficult to simultaneously avoid areas with high traffic accident rates and poor security, ensure an appropriate amount of exercise, and travel comfortably while reducing psychological stress. Furthermore, there is a need for a system that can re-suggest appropriate routes when conditions change in real time after a person has actually started going out.

[1158] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting conditions related to a starting point, a destination, and the amount of exercise and safety, a means for collecting information on traffic accidents, public safety, lighting and security devices, and the location information of public facilities, and a means for using a generative AI model to generate an optimal route based on the collected information and the input conditions. This makes it possible to provide an optimal route for a user to travel safely and comfortably to their destination, and to respond to situations that change in real time.

[1159] "Starting point" refers to the current location or the location designated by the user as the starting point of a journey.

[1160] "Destination" refers to the location the user ultimately wants to reach.

[1161] "Amount of exercise" refers to an indicator of the amount of physical activity, such as the number of steps taken and calories burned when a user moves around while out and about.

[1162] "Safety" refers to a safe environment that takes into consideration public safety and the risk of accidents when users travel.

[1163] "Conditions" refers to the exercise volume and safety requirements entered by the user.

[1164] "Traffic accident information" refers to data regarding the circumstances and locations of traffic accidents.

[1165] "Public safety information" refers to data on crime rates and safety in a particular area.

[1166] "Lighting Information" refers to data relating to the location of streetlights and other forms of lighting equipment.

[1167] "Security device information" refers to data regarding the location of security equipment such as security cameras and emergency notification devices.

[1168] "Location information of public facilities" refers to data regarding the locations of public facilities such as hospitals, police stations, schools, and parks.

[1169] A "generative AI model" refers to an algorithm that uses machine learning and artificial intelligence to generate optimal routes.

[1170] "Display device" refers to a device such as a smartphone or tablet that allows users to visually check the route.

[1171] "Tracking" refers to using technology such as GPS to determine a user's current location in real time.

[1172] "Mental state" refers to the user's mental state, such as emotions and stress level.

[1173] "Analysis" refers to processing data to extract meaningful information.

[1174] "Evacuation destination" refers to a location where users can evacuate to ensure their safety in the event of an emergency.

[1175] MODE FOR CARRYING OUT THE INVENTION

[1176] This invention is a map application system that utilizes a generative AI model and an emotion engine to provide optimal routes that take into account the user's health, safety, and psychological state. Below, we will explain each component of the system and its specific operation.

[1177] System configuration

[1178] This system mainly consists of three hardware components: a server, a terminal, and an emotion engine.

[1179] 1. Server

[1180] The server is responsible for data collection and route generation. Specifically, it collects information on traffic accidents, public safety, lighting, security devices, and the location of public facilities. This includes real-time data collection from public data from police stations and local governments, news, social media, and more. The collected data is stored in the server's database, and the optimal route is generated through a generative AI model.

[1181] 2. Terminal

[1182] The device is responsible for the user interface and real-time tracking. Specifically, the user opens the app, inputs their starting point and destination, and is shown a screen where they can set exercise and safety criteria. The device also uses GPS to track the user's current location and connects to a server to receive real-time route updates, which are displayed on a map.

[1183] 3. Emotion Engine

[1184] The emotion engine recognizes the user's emotional state in real time and sends that feedback to the server, allowing the route to be readjusted if the user is feeling stressed, for example.

[1185] Example of operation

[1186] For example, consider the case where a user sets a route from "home" to "supermarket." The user specifies "5,000 steps" as the amount of exercise required per day, and since they will be going out at night, they set the settings to prioritize safety. At this time, the emotion engine monitors the user's stress level. The server generates a safe route with an appropriate amount of exercise based on the latest traffic accident and security information collected.

[1187] The generated route takes into consideration a stress-free environment, passing through streets with many police stations and lighting, avoiding areas prone to traffic accidents, and so on. When the user begins to go out, the device tracks their current location using GPS, while the server and emotion engine monitor the latest information in real time and update the route as needed. For example, if a traffic accident occurs along the way or the user's stress level rises, the server regenerates a new, safer and more relaxing route and sends it to the device. This allows the user to reach their destination safely and psychologically in a comfortable state.

[1188] Prompt Sentence Examples

[1189] "Generate a safe and psychologically comfortable route from home to the supermarket that meets the 5,000-step physical activity requirement. If the emotion engine evaluates the user's stress level as high, provide a route that prioritizes relaxing scenery."

[1190] By combining a generative AI model with an emotion engine that analyzes the situation in real time, the system provides optimal routes tailored to each user's needs, which is particularly useful for users who prioritize safety and psychological comfort.

[1191] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1192] Step 1:

[1193] The user opens a map app. The device displays a user interface and provides a screen for entering the starting point and destination. Input: The user's starting point, destination, exercise amount, and safety conditions. Output: The set starting point, destination, exercise amount, and safety conditions.

[1194] Step 2:

[1195] The user inputs the starting point, destination, momentum, and safety conditions. The terminal sends this data to the server. Input: The user inputs the starting point, destination, momentum, and safety conditions. Output: Sends the input data to the server.

[1196] Step 3:

[1197] The server collects information on traffic accidents, public safety, lighting, security devices, and the location of public facilities. Sources of information include public data from police stations and local governments, news, and social media. Input: Collected information on traffic accidents, public safety, lighting, security devices, and public facilities. Output: Latest safety information database.

[1198] Step 4:

[1199] The information collected by the server and the entered conditions are input into a generative AI model to generate the optimal route. In particular, routes that avoid areas with high traffic accident rates and areas with poor security are prioritized. Input: User conditions and collected safety information. Output: Optimal route generated by the generative AI model.

[1200] Step 5:

[1201] The server sends optimal route information to the device. The device receives this information and draws the route on a map to present it visually. Input: Optimal route information sent from the server. Output: Route drawn on the map.

[1202] Step 6:

[1203] The user starts going out, and the device uses GPS to track the user's current location. Input: User movement information. Output: Real-time location data.

[1204] Step 7:

[1205] The server collects the latest traffic accident and public safety information in real time and checks whether the current route is appropriate. If necessary, the server regenerates the route and sends it to the device. Input: Real-time safety information and user location data. Output: Updated route information.

[1206] Step 8:

[1207] The emotion engine analyzes the user's psychological state in real time and sends the results to the server. Input: User's psychological data. Output: Analysis data of psychological state.

[1208] Step 9:

[1209] The server adjusts the route based on the emotion engine's feedback, providing a less stressful route if necessary. This new route information is sent to the device. Input: Emotion engine's feedback and current route information. Output: Adjusted route information.

[1210] (Application example 2)

[1211] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1212] Although conventional navigation systems can take into account traffic accident and public safety information, they are unable to reflect the user's psychological and health conditions in real time. This can lead to users passing through stressful environments or areas that cause anxiety. Even with a route update function, the route is not regenerated based on the user's emotional state, so the user's sense of security and comfort is not guaranteed.

[1213] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1214] In this invention, the server includes: means for inputting conditions related to the starting point, destination, and exercise volume and safety; means for collecting traffic accident information, safety information, lighting and monitoring device information, and location information of public facilities; means for using a generative AI model to generate an optimal route based on the collected information and the input conditions; means for displaying the generated route on a map and presenting it to the user; means for tracking the user's current location and updating the route in real time based on the latest accident and safety information; and means for using an emotion engine to analyze the user's emotional state and regenerate the route based on that. This allows the user's psychological and health state to be reflected in real time, enabling safe and comfortable travel.

[1215] "Start point" refers to the location where the user begins their journey.

[1216] "Destination" is the location where the user ends their journey.

[1217] "Movement" refers to the number of steps or degree of movement you wish to achieve during your trip.

[1218] "Safety" refers to the sense of security users feel while traveling and the local security situation.

[1219] "Traffic accident information" refers to data collected in real time about accidents and accidents on roads.

[1220] "Safety information" is data on the safety of a neighborhood, such as crime rates and the status of lighting facilities.

[1221] "Lighting" refers to streetlights and lamps that illuminate roads and other areas at night.

[1222] "Surveillance equipment" refers to cameras and security equipment used to monitor public order and safety.

[1223] "Public facilities" are facilities intended for public use, such as parks, police stations, and public toilets.

[1224] "Means of collecting information" refers to the methods and techniques used to collect the necessary data in real time.

[1225] A "generative AI model" is an artificial intelligence that uses collected data to automatically calculate and generate optimal routes.

[1226] An "emotion engine" is a technology that analyzes the user's psychological state in real time and provides that information to the system.

[1227] "Tracking" means tracking a user's current location in real time.

[1228] "Regenerating a route" means recalculating and generating an already generated route based on new conditions.

[1229] "Means for displaying on a map" refers to the methods and techniques used to visually present the generated route to the user.

[1230] An "evacuation site" is a location where users can go to ensure safety in the event of an emergency.

[1231] "Real-time" refers to the time when data and information are processed and updated immediately.

[1232] This invention is a navigation system installed in an autonomous vehicle that utilizes generative AI models and an emotion engine to provide optimal routes that take into account the user's emotional state and health in real time.

[1233] System configuration

[1234] The system consists of three main components:

[1235] 1. Server: Responsible for data collection and route generation.

[1236] 2. Terminal: Provides the user interface and real-time tracking functions.

[1237] 3. Emotion engine: Recognizes the user's emotional state in real time and sends feedback to the server.

[1238] Hardware and software used

[1239] Hardware:

[1240] Autonomous vehicle computer systems

[1241] GPS Modules

[1242] Camera and microphone (for emotion engine)

[1243] software:

[1244] Emotion recognition engine (e.g., Microsoft Azure Cognitive Services, Affectiva)

[1245] API communication library (e.g. requests)

[1246] System Operation Overview

[1247] 1. User Input

[1248] Users input their starting point and destination and set exercise and safety criteria, including recommended steps and safe areas. The emotion engine continuously monitors the user's emotional state and sends the results from the device to a server.

[1249] 2. Data collection

[1250] The server collects the latest traffic accident information, safety information, lighting and surveillance device information, and location information of public facilities, including public data from police stations and local governments, news, social media, and other sources.

[1251] 3. Route generation using generative AI models

[1252] The server inputs the collected information, user input conditions, and emotional state information from the emotion engine into a generative AI model to generate the optimal route. Routes that take into consideration the user's psychological state are prioritized, avoiding areas with high traffic accident rates and low safety.

[1253] 4. Route presentation

[1254] The generated optimal route is sent from the server to the device and displayed on a map, allowing the user to check the entire route before setting off.

[1255] 5. Real-time feedback

[1256] When the user begins to go out, the device continuously tracks the user's current location using its GPS function. The latest accident and safety information is collected in real time and sent to the server. If necessary, the server regenerates the route and sends updated route information to the device, which immediately displays it and notifies the user.

[1257] 6. Feedback from Emotion Engine

[1258] The emotion engine analyzes the user's emotional state (e.g., stress, relief, fear) in real time and sends feedback to the server. Based on this feedback, the server will prioritize nearby evacuation sites in an emergency, or suggest routes through relaxing landscapes or quiet areas if the user is feeling stressed.

[1259] Specific examples

[1260] For example, a user sets a route from "home" to "supermarket" and specifies "5,000 steps" as the amount of exercise required per day. Furthermore, since the user will be going out at night, the user sets the priority on safety, and the emotion engine monitors the user's stress level. Based on this information, the server collects the latest traffic accident and safety information and generates a safe route with an appropriate amount of exercise. If the emotion engine detects a high stress level, the server prioritizes routes with relaxing scenery.

[1261] Prompt Sentence Examples

[1262] Prompt statement:

[1263] Enter your current location and destination, and our emotion engine will monitor your emotional state and generate the best route in real time.

[1264] Starting point: Tokyo Station

[1265] Destination: Shibuya Station

[1266] Safety: High

[1267] Recommended steps: 5,000

[1268] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1269] Step 1:

[1270] User Input

[1271] The user inputs the starting point, destination, exercise amount (e.g., recommended number of steps), and safety conditions. The device receives this information, and the emotion engine monitors the user's emotional state.

[1272] Input: Starting point, destination, exercise amount, safety settings

[1273] Output: Condition data

[1274] Specific operation: The user enters the starting point, destination, exercise amount, and safety settings into the device, and the device sends this to the server.

[1275] Step 2:

[1276] Data collection

[1277] The server collects the latest traffic accident information, safety information (e.g., crime rates and nighttime lighting conditions), and facility information (e.g., the locations of public restrooms and police stations) in real time. This includes the ability to collect data from public information from police stations and local governments, news, social media, etc.

[1278] Input: Condition data

[1279] Output: Collected data

[1280] Specific operation: The server uses the specified API to collect traffic accident information, safety information, and facility information in real time.

[1281] Step 3:

[1282] Route generation using generative AI models

[1283] The server uses a generative AI model to generate an optimal route based on the collected data and the user's conditions, prioritizing routes that avoid areas with high traffic accident rates and areas with poor security, and that take the user's psychological state into consideration.

[1284] Input: Collected data, condition data

[1285] Output: Optimal route

[1286] Specific operation: The server inputs condition data and collected data into the generated AI model, and calculates and generates the optimal route.

[1287] Step 4:

[1288] Route suggestions

[1289] The generated optimal route is sent from the server to the terminal, which displays it on a map.

[1290] Input: Optimal Route

[1291] Output: Map display data

[1292] Specific operation: The server sends the generated optimal route to the device, which then visually displays it on the map app.

[1293] Step 5:

[1294] Real-time feedback

[1295] When the user starts going out, the device continuously tracks the user's current location using GPS. Traffic accident and safety information is collected in real time and sent to the server. If necessary, the server regenerates the route and sends new route information to the device.

[1296] Input: current user location, latest collected data

[1297] Output: Updated optimal route

[1298] How it works: The device tracks the user's current location using GPS and sends any new information collected to the server, which then regenerates the route based on that information and sends the updated route to the device.

[1299] Step 6:

[1300] Emotional Engine Feedback

[1301] The emotion engine analyzes the user's emotional state in real time and feeds that information back to the server, which then uses this information to suggest safe evacuation locations and regenerate relaxing routes.

[1302] Input: User's emotional state

[1303] Output: Emotional reflection route, evacuation site

[1304] Specific operation: The emotion engine interprets the user's emotional state and sends it to the server. The server then regenerates a new route or suggests evacuation locations depending on the emotional state.

[1305] Through these steps, the system can ensure user safety and psychological comfort and optimize the navigation of autonomous vehicles.

[1306] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1307] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1308] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1309] [Fourth embodiment]

[1310] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1311] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1312] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1313] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1314] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1315] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1316] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1317] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1318] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1319] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1320] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1321] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1322] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1323] This invention is a map application system that utilizes a generative AI model to provide optimal routes that take into account the user's health and safety. This system supports safe and healthy outings by inputting user conditions, collecting necessary information, generating and presenting routes, and updating them in real time.

[1324] Overall system configuration

[1325] The system mainly consists of two components: a server and a terminal. The server is responsible for the central functions of data collection and route generation, while the terminal is responsible for the user interface and real-time tracking functions.

[1326] (1) User input

[1327] The user opens a map app, inputs their starting point and destination, and then specifies conditions for exercise volume (e.g., recommended number of steps and duration) and safety (e.g., wanting to travel through safe areas). The device then sends this information to the server.

[1328] (2) Data collection

[1329] The server collects the latest information on traffic accidents, public safety, the location of lights and security cameras, and the location of public facilities at that time, including data collected in real time from public data released by police stations and local governments, news, social media, etc.

[1330] (3) Route generation using generative AI models

[1331] The server inputs the collected information and user input into a generative AI model to generate the optimal route. This prioritizes routes that avoid areas with high traffic accident rates and areas with poor security. It also takes into account the number of steps and distance traveled to ensure the specified amount of exercise is achieved.

[1332] (4) Route presentation

[1333] The generated optimal route is sent from the server to the device, which displays it on a map and presents it visually to the user, allowing the user to see the entire route before setting off.

[1334] (5) Real-time feedback

[1335] Once the user actually leaves the house, the device will continuously track the user's current location using its GPS function. Real-time information on traffic accidents and public safety is collected and sent to the server. If necessary, the server will regenerate the route and send the updated route information to the device, which will immediately display it to the user.

[1336] Specific examples

[1337] For example, consider a case where a user sets a route from "home" to "supermarket" and specifies "5,000 steps" as the amount of exercise required per day. Since the user will be out at night, the user also sets the route with an emphasis on safety. Based on this information, the server collects the latest traffic accident and public safety information and generates a safe route with an appropriate amount of exercise.

[1338] The generated route passes through areas with many police stations and street lights, and avoids areas with a high incidence of traffic accidents. Once the user starts going out, the server monitors the latest information in real time while the current location is tracked by GPS, updating the route as necessary. For example, if a traffic accident occurs along the way, the server generates a new, safer route and sends it to the device. This allows the user to reach their destination safely and healthily.

[1339] The processing flow will be explained below.

[1340] Step 1:

[1341] A user opens a map app, enters a starting point and destination, and also sets criteria for exercise (e.g., recommended number of steps) and safety (e.g., passing through safe neighborhoods).

[1342] Step 2:

[1343] The device sends the user's input data, including the starting point, destination, exercise amount, and safety conditions, to the server.

[1344] Step 3:

[1345] The server collects traffic accident information, specifically using news, social media, and public databases from police stations to obtain the latest accident information for a specific area.

[1346] Step 4:

[1347] The server collects public safety information, such as public information from police stations and local governments, crime maps, and automated web scraping tools, to understand the public safety situation in a particular area.

[1348] Step 5:

[1349] The server collects information from lights and security cameras, obtains the necessary information from databases of local governments and public institutions, and maps the location information.

[1350] Step 6:

[1351] The server collects location information for public facilities, such as police stations, evacuation centers, hospitals, and parks, which users can use in emergencies, and updates the database.

[1352] Step 7:

[1353] The server consolidates all collected data and inputs it into a generative AI model, including accident information, public safety information, light and security camera locations, and public facility locations.

[1354] Step 8:

[1355] The generative AI model generates the optimal route based on the user's criteria (starting point, destination, amount of exercise, safety). The AI ​​model prioritizes routes that meet the specified amount of exercise while avoiding safe areas and areas with high traffic accident rates.

[1356] Step 9:

[1357] The server sends the generated route to the device.

[1358] Step 10:

[1359] The device displays the received route information on a map and presents it visually to the user, allowing the user to check the entire route before setting off.

[1360] Step 11:

[1361] The user starts traveling and follows a route.

[1362] Step 12:

[1363] The device uses GPS to track the user's current location in real time.

[1364] Step 13:

[1365] The server periodically collects the latest traffic accident and public safety information.

[1366] Step 14:

[1367] The server determines whether the route needs to be changed based on the user's current location and the latest accident and security information, and if so, generates a new, optimal route.

[1368] Step 15:

[1369] The server sends new route information to the device and notifies it in real time.

[1370] Step 16:

[1371] The device will redisplay the new route on the map and provide instructions to the user.

[1372] This allows users to navigate their route to their destination safely and healthily.

[1373] Example 1

[1374] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1375] Conventional map application systems have difficulty providing optimal routes that fully consider the user's safety and health. Furthermore, there is a lack of systems that can reflect the latest accident and public safety information in real time and flexibly adjust routes while the user is traveling. Therefore, there is a need for a system that provides routes that allow users to reach their destination safely and healthily.

[1376] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1377] In this invention, the server includes: means for inputting a starting point, a destination, and conditions related to exercise volume and safety; means for collecting information on traffic accidents, public safety, electric lights and security cameras, and the location of public facilities; means for using a generative AI model to generate an optimal route based on the collected information and the input conditions; means for displaying the generated route on a map and presenting it to the user; means for tracking the user's current location and updating the route in real time based on the latest accident and public safety information; means for collecting data in real time and regenerating the route as necessary; and means for checking for format errors. This enables the provision of a safe and healthy optimal route based on the conditions entered by the user, and updating the route in real time.

[1378] The "starting point" is the location where the user starts their journey, and is the location information that serves as the starting point when the map application system generates a route.

[1379] The "destination" is the location the user wishes to reach, and is the location information that serves as the end point when the map application system generates a route.

[1380] "Amount of exercise" indicates the degree of exercise that the user wants to achieve while traveling, and is a condition specified by indicators such as the number of steps and exercise time.

[1381] "Safety" refers to the criteria that indicate the importance placed on public safety and the low number of accidents when selecting a travel route, and is a standard for avoiding areas with a high incidence of traffic accidents and areas with poor public safety.

[1382] "Traffic accident information" is data showing the occurrence of traffic accidents in a specific area, and is information collected to ensure the safety of users' travel.

[1383] "Public safety information" is data showing the crime rate and public safety situation in a specific area, and is information collected to evaluate the safety of a user's travel route.

[1384] "Lighting information" is data that indicates the location and lighting conditions of streetlights installed in public areas such as roads and parks, and is information used to increase user safety when traveling at night.

[1385] "Security camera information" is data indicating the location and operation status of surveillance cameras installed in public areas, and is information used to select routes that users can travel safely.

[1386] "Location information of public facilities" is data indicating the locations of public facilities such as public toilets, police stations, hospitals, and evacuation shelters, and is information used to provide users with easy access routes to the facilities they need.

[1387] A "generative AI model" is an algorithm that uses artificial intelligence technology to generate optimal routes based on collected data and user input conditions.

[1388] A "prompt" is an instruction entered into a generative AI model, and is text that contains specific requests and conditions for route generation.

[1389] This invention is a map application system that utilizes a generative AI model to provide optimal routes that take into account the user's health and safety. This system supports safe and healthy outings by inputting user conditions, collecting necessary information, generating and presenting routes, and updating them in real time.

[1390] Overall system configuration

[1391] The system mainly consists of two components: a server and a terminal. The server is responsible for the central functions of data collection and route generation, while the terminal is responsible for the user interface and real-time tracking functions.

[1392] Hardware and Software

[1393] Server: Used for data collection and route generation. Specifically, a server machine with high-performance data processing capabilities is required.

[1394] Device: Used for user interface and real-time tracking. Can be a smartphone or tablet.

[1395] Generative AI models: Implemented using machine learning frameworks such as TensorFlow and PyTorch.

[1396] Program processing

[1397] User Input

[1398] The user opens a map app on their device, inputs their starting point and destination, and specifies conditions for exercise volume (e.g., recommended number of steps and duration) and safety (e.g., preferring to travel through safe areas). The device validates this information and then sends it to the server.

[1399] Data collection

[1400] The server collects the latest information on traffic accidents, public safety, the location of lights and security cameras, and the location of public facilities at that time. This data is collected in real time from public data released by police stations and local governments, news, social media, etc.

[1401] Route generation using generative AI models

[1402] The server inputs the collected information and user input into a generative AI model to generate the optimal route. The generative AI model prioritizes routes that avoid areas with high traffic accident rates and areas with poor security. It also takes into account the number of steps and distance exercised to ensure the specified amount of exercise is achieved.

[1403] Route suggestions

[1404] The generated optimal route is sent from the server to the device, which displays it on a map and presents it visually to the user, allowing the user to see the entire route before setting off.

[1405] Real-time feedback

[1406] Once the user begins to leave the house, the device will continuously track the user's current location using its GPS function. The server will monitor the latest traffic accident and public safety information in real time and update the route as necessary. For example, if a traffic accident occurs along the way, a new route will be generated and sent to the device, allowing the user to instantly find a safe route.

[1407] Specific examples

[1408] For example, consider a case where a user sets a route from "home" to "supermarket" and specifies "5,000 steps" as the amount of exercise required per day. Since the user will be out at night, the user also sets the route with an emphasis on safety. Based on this information, the server collects the latest traffic accident and public safety information and generates a safe route with an appropriate amount of exercise.

[1409] The generated route passes through areas with many police stations and street lights, and avoids areas with a high incidence of traffic accidents. Once the user starts going out, the server monitors the latest information in real time while the current location is tracked by GPS, and updates the route as necessary. For example, if a traffic accident occurs along the way, the server generates a new, safer route and sends it to the device. This allows the user to reach their destination safely and healthily.

[1410] Prompt Sentence Examples

[1411] "Set a route from home to the supermarket. Since I'll be out at night, safety is a priority. My goal is to walk 5,000 steps a day. Generate the optimal route taking into account the latest traffic accident and security information."

[1412] By inputting such a prompt, the generative AI model generates an appropriate route.

[1413] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1414] Step 1: User Input

[1415] User: Launches a map app on the device and inputs the starting point, destination, amount of exercise, and safety conditions. Specific input values ​​are: starting point "home," destination "supermarket," amount of exercise "5,000 steps," and safety "safe area."

[1416] Terminal: Validates the entered information, checks for formatting errors, and if there are no problems, sends the information to the server.

[1417] Input: starting point, destination, momentum, safety conditions.

[1418] Output: The input information sent to the server.

[1419] Step 2: Collect data

[1420] Server: Using APIs and scraping technology, information on traffic accidents, public safety, location information for lights and security cameras, and location information for public facilities is obtained in real time.

[1421] Input: Conditions for API requests and data scraping.

[1422] Output: A dataset of the latest traffic accident information, public safety information, information on electric lights and security cameras, and location information of public facilities.

[1423] As a specific example of how it works, the server collects traffic accident information from the police station's API, scrapes public safety information from social media, and obtains the location information of streetlights and security cameras from a GIS database.

[1424] Step 3: Route generation using generative AI models

[1425] Server: Combines the collected data with user input conditions, inputs appropriate prompts into the generative AI model, and generates the optimal route.

[1426] Input: Starting point, destination, amount of exercise, safety conditions, and any other information you have collected.

[1427] Output: Optimal route information.

[1428] As an example of specific operation, the server inputs prompt statements such as "Starting point: home, destination: supermarket, exercise amount: 5,000 steps, safety: safe area" into the generative AI model and performs calculations to generate a route.

[1429] Step 4: Route presentation

[1430] Server: Sends the optimal route information obtained from the generative AI model to the device.

[1431] Device: The received route information is visually displayed in a map application and presented to the user.

[1432] Input: Optimal route information.

[1433] Output: The route that is displayed to the user.

[1434] For example, the device will use a map app to generate a route and display it in an easy-to-understand format to the user, allowing the user to see the overall route before setting off.

[1435] Step 5: Real-time feedback

[1436] Device: Uses GPS to track the user's location in real time.

[1437] Server: Continuously monitors the latest traffic accident and public safety information, regenerates routes as needed, and sends them to the device.

[1438] Input: Current location, latest traffic accident information, and public safety information.

[1439] Output: The updated route information.

[1440] For example, when a user starts going out, the device tracks their current location using GPS. The server reevaluates the route based on real-time data, and if, for example, a traffic accident occurs along the way, it generates a new, safer route and sends it to the device.

[1441] (Application example 1)

[1442] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1443] In autonomous vehicles, it is difficult to provide an optimal route while simultaneously considering the safety and health of the user. Conventional route guidance systems are unable to update traffic accident and public safety information in real time and provide appropriate driving instructions to the user. Furthermore, since it is not possible to generate a route that balances both exercise and safety, there is a need to provide an environment in which users can travel with peace of mind. The present invention aims to solve these problems.

[1444] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1445] In this invention, the server includes: means for inputting conditions related to starting point, destination, and exercise volume and safety; means for collecting traffic accident information, public safety information, lighting and monitoring device information, and public facility location information; means for using a generative AI model to generate an optimal route based on the collected information and the input conditions; means for displaying the generated route on a map and presenting it to the user; means for tracking the user's current location and updating the route in real time based on the latest accident and public safety information; and means for displaying the optimal route on the display of the autonomous vehicle and providing driving instructions. This enables the user to travel with peace of mind and enables route generation that balances exercise volume and safety.

[1446] The "starting point" is the location where the user begins their journey.

[1447] A "destination" is a location that a user wishes to reach.

[1448] "Amount of exercise" refers to the amount of physical activity a user gets while moving, specifically the number of steps taken and the duration of exercise.

[1449] "Safety" refers to conditions for minimizing the dangers users encounter while traveling, and includes information on public safety and traffic accidents.

[1450] "Traffic accident information" is data relating to accidents that occur on roads.

[1451] "Public safety information" is data regarding the safety of an area.

[1452] "Lighting" refers to information about light source equipment such as street lights.

[1453] "Monitoring device information" is data relating to monitoring devices such as security cameras.

[1454] "Location information of public facilities" is data on the locations of public places and facilities such as police stations, fire stations, and parks.

[1455] "Means of collection" refers to the techniques and methods used to obtain the necessary data.

[1456] "Means using generative AI models" refers to artificial intelligence technology that creates optimal routes based on collected data and user conditions.

[1457] "User presentation means" refers to the method or technology used to visually display the generated route to the user.

[1458] "Tracking" is a technology that tracks a user's current location in real time.

[1459] A "route update means" is a method or technique for changing a route based on the latest information.

[1460] An "autonomous vehicle display" is a display device installed inside an autonomous vehicle.

[1461] A "means for providing driving instructions" is a method or technology for providing instructions to an automated driving vehicle.

[1462] The present invention is a map application system that uses generative AI models to provide optimal routes that take into account the safety and well-being of users, and is designed to be particularly effective in autonomous vehicles.

[1463] Overall system configuration

[1464] This system mainly consists of two components: a server and a terminal (display, etc.) inside the autonomous vehicle. The server is responsible for the central functions of data collection and route generation, while the terminal is responsible for the user interface and real-time tracking functions.

[1465] 1. User Input

[1466] The user (a passenger in an autonomous vehicle) uses a device to input their starting point and destination, and specify conditions for exercise amount (e.g., recommended number of steps and duration of exercise) and safety (e.g., passing through safe areas). The device then sends this information to a server.

[1467] 2. Data collection

[1468] The server collects the latest traffic accident information, public safety information, lighting and surveillance device information, and location information of public facilities at that time, including data collected in real time from public data of police stations and local governments, news, social media, etc.

[1469] 3. Route generation using generative AI models

[1470] The server inputs the collected information and user input into a generative AI model to generate the optimal route. This prioritizes routes that avoid areas with high traffic accident rates and areas with poor security. It also takes into account the number of steps and distance traveled to ensure the specified amount of exercise is achieved.

[1471] 4. Route presentation

[1472] The generated optimal route is sent from the server to the terminal, which then displays it on the vehicle's display, providing a visual route to the user and specific driving instructions to the autonomous vehicle.

[1473] 5. Real-time feedback

[1474] Once the autonomous vehicle begins moving, the device will use its GPS to continuously track the vehicle's current location. Real-time information on traffic accidents and public safety is collected and sent to the server. If necessary, the server will regenerate the route and send updated route information to the device. The device will immediately display this information to inform the user and provide new driving instructions to the autonomous vehicle.

[1475] Specific examples

[1476] For example, if a user sets a route from home to the supermarket and specifies 4,000 steps as the amount of exercise required per day, they can also set a safety priority for nighttime outings. Based on this information, the server collects the latest traffic accident and public safety information and generates a safe route with an appropriate amount of exercise. The generated route is displayed on the autonomous vehicle's display, and driving instructions are provided to the vehicle. Once the user begins their journey, the server monitors the latest information in real time while the current location is tracked by GPS, updating the route as necessary. For example, if a traffic accident occurs along the way, the server generates a new, safer route and sends it to the device. This allows the user to reach their destination safely and healthily.

[1477] Hardware and software used

[1478] The hardware includes a GPS module, vehicle display, and autonomous driving control system, while the software includes a generative AI model, API call functions for data collection, GPS location tracking software, and display management classes.

[1479] Prompt Sentence Examples

[1480] "Generate a route from your home to the supermarket. Requires 4000 steps, prioritizes safety, and takes into account the latest traffic accident and public safety information."

[1481] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1482] Step 1:

[1483] The user uses the device to input the starting point, destination, exercise amount (e.g., number of steps and exercise time), and safety conditions (e.g., passing through a safe area).

[1484] Input: User-specified starting point, destination, momentum, and safety criteria.

[1485] Output: The input condition data.

[1486] Specific operation: Through the terminal's user interface, the user inputs detailed conditions, including the destination and starting point.

[1487] Step 2:

[1488] The device sends the user's input data to the server.

[1489] Input: User-specified condition data.

[1490] Output: User criteria data sent to the server.

[1491] Specific operation: The device makes an API call to send data to a server over the Internet.

[1492] Step 3:

[1493] The server collects the latest traffic accident information, public safety information, lighting and surveillance device information, and location information of public facilities at that time.

[1494] Input: User criteria data.

[1495] Output: Collected public data, real-time data.

[1496] Specific operation: The server refers to external APIs and public databases to collect traffic accident information, public safety information, etc.

[1497] Step 4:

[1498] The information collected by the server and the user's input conditions are input into a generative AI model to generate the optimal route.

[1499] Input: Collected data, user condition data.

[1500] Output: Generated optimal route data.

[1501] How it works: The generative AI model uses machine learning algorithms to calculate a route with the optimal amount of momentum while avoiding areas prone to traffic accidents and areas with poor security.

[1502] Step 5:

[1503] The server sends the generated optimal route to the device.

[1504] Input: Generated optimal route data.

[1505] Output: Route data sent to the device.

[1506] Specific operation: The server sends the calculated route data to the terminal via the Internet.

[1507] Step 6:

[1508] The device will display the optimal route and present it to the user.

[1509] Input: Route data sent by the server.

[1510] Output: The route shown on the display.

[1511] Specific operation: The device visually displays the route on a map through the user interface and provides driving instructions to the autonomous vehicle control system.

[1512] Step 7:

[1513] When the user starts going out, the device uses GPS to track their current location in real time.

[1514] Input: GPS data.

[1515] Output: Current position data.

[1516] What it does: The device's GPS function continuously tracks the user's current location.

[1517] Step 8:

[1518] The server regenerates the route based on the latest collected traffic accident and security information, and sends the updated route information to the terminal.

[1519] Inputs: Real-time location data, latest traffic accident and public safety information.

[1520] Output: Updated route data.

[1521] What it does: The server re-runs the generative AI model based on the new information to generate a new route that is safe and healthy.

[1522] Step 9:

[1523] The device displays instantly updated route information and provides new driving instructions to the autonomous driving system.

[1524] Input: Updated route data.

[1525] Output: New route and driving instructions shown on the display.

[1526] Specific operation: The device instantly displays new route information on the map and provides new driving instructions to the autonomous vehicle.

[1527] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1528] This invention is a map application system that utilizes a generative AI model and an emotion engine to provide optimal routes that take into account the user's health, safety, and psychological state. The system supports safe and stress-free outings by allowing users to input their conditions, collecting necessary information, generating and presenting routes, updating them in real time, and recognizing their emotions.

[1529] Overall system configuration

[1530] The system mainly consists of three components: a server, a terminal, and an emotion engine. The server is responsible for the central functions of data collection and route generation, while the terminal is responsible for the user interface and real-time tracking. The emotion engine recognizes the user's emotional state in real time and sends feedback to the server.

[1531] (1) User input

[1532] The user opens the map app and inputs their starting point and destination. They also set conditions for exercise (e.g., recommended number of steps) and safety (e.g., passing through safe areas). The emotion engine then monitors the user's emotional state. Based on the results, the device sends the data to the server.

[1533] (2) Data collection

[1534] The server collects the latest information on traffic accidents, public safety, the location of lights and security cameras, and the location of public facilities at that time, including data collected in real time from public data released by police stations and local governments, news, social media, etc.

[1535] (3) Route generation using generative AI models

[1536] The server inputs the collected information, user input conditions, and emotional state information from the emotion engine into a generative AI model to generate the optimal route. This prioritizes routes that avoid areas with high traffic accident rates and areas with poor security, meet the specified exercise volume, and take the user's psychological state into consideration.

[1537] (4) Route presentation

[1538] The generated optimal route is sent from the server to the device, which displays it on a map and presents it visually to the user, allowing the user to check the entire route before setting off.

[1539] (5) Real-time feedback

[1540] Once the user actually leaves the house, the device will continuously track the user's current location using its GPS function. Real-time information on traffic accidents and public safety is collected and sent to the server. If necessary, the server will regenerate the route and send the updated route information to the device, which will immediately display it to the user.

[1541] (6) Feedback from the Emotion Engine

[1542] The emotion engine analyzes the user's emotional state (e.g., stress, relief, fear) in real time and sends feedback to the server. Based on this feedback, the server prioritizes nearby emergency shelters in case of an emergency. It also suggests routes through relaxing landscapes and quiet areas if the user is feeling stressed.

[1543] Specific examples

[1544] For example, consider the case where a user sets a route from "home" to "supermarket" and specifies "5,000 steps" as the amount of exercise required per day. Furthermore, since the user will be going out at night, the user sets the route with an emphasis on safety, and the emotion engine monitors the user's stress level. Based on this information, the server collects the latest traffic accident and security information and generates a safe route with an appropriate amount of exercise. If the emotion engine detects a high stress level, the server will prioritize routes with relaxing scenery.

[1545] The generated route takes into consideration a stress-free environment, passing through areas with police stations and plenty of street lights, and avoiding areas prone to traffic accidents. Once the user begins to go out, the server and emotion engine monitor the latest information in real time while the user's current location is tracked by GPS, updating the route as needed. For example, if a traffic accident occurs along the way or the user's stress level rises, the server regenerates a new, safer, and more relaxing route and sends it to the device. This allows the user to reach their destination safely and psychologically in a comfortable state.

[1546] The processing flow will be explained below.

[1547] Step 1:

[1548] The user opens the map app, inputs their starting point and destination, and sets criteria for exercise (e.g., recommended number of steps) and safety (e.g., passing through safe areas). The emotion engine then begins monitoring the user's emotional state in real time.

[1549] Step 2:

[1550] The device sends user input data to the server, including the starting point, destination, momentum, safety conditions, and initial emotional state information from the emotion engine.

[1551] Step 3:

[1552] The server collects traffic accident information, specifically using news, social media, and public databases from police stations to obtain the latest accident information for a specific area.

[1553] Step 4:

[1554] The server collects public safety information, using publicly available information from police stations and local governments, crime information maps, web scraping tools, and other tools to understand the public safety situation in a specific area.

[1555] Step 5:

[1556] The server collects information from lights and security cameras, obtains the necessary information from databases of local governments and public institutions, and maps the location information.

[1557] Step 6:

[1558] The server collects location information for public facilities, such as police stations, evacuation centers, hospitals, and parks, which users can use in emergencies, and updates the database.

[1559] Step 7:

[1560] The server integrates all collected data and inputs it into a generative AI model, including accident information, public safety information, light and security camera locations, public facility locations, and emotional state information provided by an emotion engine.

[1561] Step 8:

[1562] The generative AI model generates an optimal route based on the user's input and emotional state, meeting the specified exercise volume, avoiding areas with poor security and high traffic accident rates, and taking into account the psychological state (e.g., stress) detected by the emotion engine.

[1563] Step 9:

[1564] The server sends the generated route to the device.

[1565] Step 10:

[1566] The device displays the received route information on a map and presents it visually to the user, allowing the user to check the entire route before setting off.

[1567] Step 11:

[1568] The user starts traveling and follows a route.

[1569] Step 12:

[1570] The device uses GPS to track the user's current location in real time.

[1571] Step 13:

[1572] The emotion engine analyzes the user's emotional state (e.g., stress, relief, fear) in real time and sends that information to the server.

[1573] Step 14:

[1574] The server periodically collects the latest traffic accident and public safety information.

[1575] Step 15:

[1576] The server determines whether the route needs to be changed based on the user's current location, emotional state from the emotion engine, and the latest accident and security information. If necessary, it regenerates a new optimal route.

[1577] Step 16:

[1578] The server sends new route information to the device and notifies it in real time.

[1579] Step 17:

[1580] The device will redisplay the new route on the map and provide instructions to the user.

[1581] Step 18:

[1582] Users follow new routes to their destinations in a safe and psychologically comfortable environment.

[1583] Example 2

[1584] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1585] In modern society, safety and comfort are important when people go out. However, it is difficult to simultaneously avoid areas with high traffic accident rates and poor security, ensure an appropriate amount of exercise, and travel comfortably while reducing psychological stress. Furthermore, there is a need for a system that can re-suggest appropriate routes when conditions change in real time after a person has actually started going out.

[1586] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting conditions related to a starting point, a destination, and the amount of exercise and safety, a means for collecting information on traffic accidents, public safety, lighting and security devices, and the location information of public facilities, and a means for using a generative AI model to generate an optimal route based on the collected information and the input conditions. This makes it possible to provide an optimal route for a user to travel safely and comfortably to their destination, and to respond to situations that change in real time.

[1587] "Starting point" refers to the current location or the location designated by the user as the starting point of a journey.

[1588] "Destination" refers to the location the user ultimately wants to reach.

[1589] "Amount of exercise" refers to an indicator of the amount of physical activity, such as the number of steps taken and calories burned when a user moves around while out and about.

[1590] "Safety" refers to a safe environment that takes into consideration public safety and the risk of accidents when users travel.

[1591] "Conditions" refers to the exercise volume and safety requirements entered by the user.

[1592] "Traffic accident information" refers to data regarding the circumstances and locations of traffic accidents.

[1593] "Public safety information" refers to data on crime rates and safety in a particular area.

[1594] "Lighting Information" refers to data relating to the location of streetlights and other forms of lighting equipment.

[1595] "Security device information" refers to data regarding the location of security equipment such as security cameras and emergency notification devices.

[1596] "Location information of public facilities" refers to data regarding the locations of public facilities such as hospitals, police stations, schools, and parks.

[1597] A "generative AI model" refers to an algorithm that uses machine learning and artificial intelligence to generate optimal routes.

[1598] "Display device" refers to a device such as a smartphone or tablet that allows users to visually check the route.

[1599] "Tracking" refers to using technology such as GPS to determine a user's current location in real time.

[1600] "Mental state" refers to the user's mental state, such as emotions and stress level.

[1601] "Analysis" refers to processing data to extract meaningful information.

[1602] "Evacuation destination" refers to a location where users can evacuate to ensure their safety in the event of an emergency.

[1603] MODE FOR CARRYING OUT THE INVENTION

[1604] This invention is a map application system that utilizes a generative AI model and an emotion engine to provide optimal routes that take into account the user's health, safety, and psychological state. Below, we will explain each component of the system and its specific operation.

[1605] System configuration

[1606] This system mainly consists of three hardware components: a server, a terminal, and an emotion engine.

[1607] 1. Server

[1608] The server is responsible for data collection and route generation. Specifically, it collects information on traffic accidents, public safety, lighting, security devices, and the location of public facilities. This includes real-time data collection from public data from police stations and local governments, news, social media, and more. The collected data is stored in the server's database, and the optimal route is generated through a generative AI model.

[1609] 2. Terminal

[1610] The device is responsible for the user interface and real-time tracking. Specifically, the user opens the app, inputs their starting point and destination, and is shown a screen where they can set exercise and safety criteria. The device also uses GPS to track the user's current location and connects to a server to receive real-time route updates, which are displayed on a map.

[1611] 3. Emotion Engine

[1612] The emotion engine recognizes the user's emotional state in real time and sends that feedback to the server, allowing the route to be readjusted if the user is feeling stressed, for example.

[1613] Example of operation

[1614] For example, consider the case where a user sets a route from "home" to "supermarket." The user specifies "5,000 steps" as the amount of exercise required per day, and since they will be going out at night, they set the settings to prioritize safety. At this time, the emotion engine monitors the user's stress level. The server generates a safe route with an appropriate amount of exercise based on the latest traffic accident and security information collected.

[1615] The generated route takes into consideration a stress-free environment, passing through streets with many police stations and lighting, avoiding areas prone to traffic accidents, and so on. When the user begins to go out, the device tracks their current location using GPS, while the server and emotion engine monitor the latest information in real time and update the route as needed. For example, if a traffic accident occurs along the way or the user's stress level rises, the server regenerates a new, safer and more relaxing route and sends it to the device. This allows the user to reach their destination safely and psychologically in a comfortable state.

[1616] Prompt Sentence Examples

[1617] "Generate a safe and psychologically comfortable route from home to the supermarket that meets the 5,000-step physical activity requirement. If the emotion engine evaluates the user's stress level as high, provide a route that prioritizes relaxing scenery."

[1618] By combining a generative AI model with an emotion engine that analyzes the situation in real time, the system provides optimal routes tailored to each user's needs, which is particularly useful for users who prioritize safety and psychological comfort.

[1619] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1620] Step 1:

[1621] The user opens a map app. The device displays a user interface and provides a screen for entering the starting point and destination. Input: The user's starting point, destination, exercise amount, and safety conditions. Output: The set starting point, destination, exercise amount, and safety conditions.

[1622] Step 2:

[1623] The user inputs the starting point, destination, momentum, and safety conditions. The terminal sends this data to the server. Input: The user inputs the starting point, destination, momentum, and safety conditions. Output: Sends the input data to the server.

[1624] Step 3:

[1625] The server collects information on traffic accidents, public safety, lighting, security devices, and the location of public facilities. Sources of information include public data from police stations and local governments, news, and social media. Input: Collected information on traffic accidents, public safety, lighting, security devices, and public facilities. Output: Latest safety information database.

[1626] Step 4:

[1627] The information collected by the server and the entered conditions are input into a generative AI model to generate the optimal route. In particular, routes that avoid areas with high traffic accident rates and areas with poor security are prioritized. Input: User conditions and collected safety information. Output: Optimal route generated by the generative AI model.

[1628] Step 5:

[1629] The server sends optimal route information to the device. The device receives this information and draws the route on a map to present it visually. Input: Optimal route information sent from the server. Output: Route drawn on the map.

[1630] Step 6:

[1631] The user starts going out, and the device uses GPS to track the user's current location. Input: User movement information. Output: Real-time location data.

[1632] Step 7:

[1633] The server collects the latest traffic accident and public safety information in real time and checks whether the current route is appropriate. If necessary, the server regenerates the route and sends it to the device. Input: Real-time safety information and user location data. Output: Updated route information.

[1634] Step 8:

[1635] The emotion engine analyzes the user's psychological state in real time and sends the results to the server. Input: User's psychological data. Output: Analysis data of psychological state.

[1636] Step 9:

[1637] The server adjusts the route based on the emotion engine's feedback, providing a less stressful route if necessary. This new route information is sent to the device. Input: Emotion engine's feedback and current route information. Output: Adjusted route information.

[1638] (Application example 2)

[1639] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1640] Although conventional navigation systems can take into account traffic accident and public safety information, they are unable to reflect the user's psychological and health conditions in real time. This can lead to users passing through stressful environments or areas that cause anxiety. Even with a route update function, the route is not regenerated based on the user's emotional state, so the user's sense of security and comfort is not guaranteed.

[1641] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1642] In this invention, the server includes: means for inputting conditions related to the starting point, destination, and exercise volume and safety; means for collecting traffic accident information, safety information, lighting and monitoring device information, and location information of public facilities; means for using a generative AI model to generate an optimal route based on the collected information and the input conditions; means for displaying the generated route on a map and presenting it to the user; means for tracking the user's current location and updating the route in real time based on the latest accident and safety information; and means for using an emotion engine to analyze the user's emotional state and regenerate the route based on that. This allows the user's psychological and health state to be reflected in real time, enabling safe and comfortable travel.

[1643] "Start point" refers to the location where the user begins their journey.

[1644] "Destination" is the location where the user ends their journey.

[1645] "Movement" refers to the number of steps or degree of movement you wish to achieve during your trip.

[1646] "Safety" refers to the sense of security users feel while traveling and the local security situation.

[1647] "Traffic accident information" refers to data collected in real time about accidents and accidents on roads.

[1648] "Safety information" is data on the safety of a neighborhood, such as crime rates and the status of lighting facilities.

[1649] "Lighting" refers to streetlights and lamps that illuminate roads and other areas at night.

[1650] "Surveillance equipment" refers to cameras and security equipment used to monitor public order and safety.

[1651] "Public facilities" are facilities intended for public use, such as parks, police stations, and public toilets.

[1652] "Means of collecting information" refers to the methods and techniques used to collect the necessary data in real time.

[1653] A "generative AI model" is an artificial intelligence that uses collected data to automatically calculate and generate optimal routes.

[1654] An "emotion engine" is a technology that analyzes the user's psychological state in real time and provides that information to the system.

[1655] "Tracking" means tracking a user's current location in real time.

[1656] "Regenerating a route" means recalculating and generating an already generated route based on new conditions.

[1657] "Means for displaying on a map" refers to the methods and techniques used to visually present the generated route to the user.

[1658] An "evacuation site" is a location where users can go to ensure safety in the event of an emergency.

[1659] "Real-time" refers to the time when data and information are processed and updated immediately.

[1660] This invention is a navigation system installed in an autonomous vehicle that utilizes generative AI models and an emotion engine to provide optimal routes that take into account the user's emotional state and health in real time.

[1661] System configuration

[1662] The system consists of three main components:

[1663] 1. Server: Responsible for data collection and route generation.

[1664] 2. Terminal: Provides the user interface and real-time tracking functions.

[1665] 3. Emotion engine: Recognizes the user's emotional state in real time and sends feedback to the server.

[1666] Hardware and software used

[1667] Hardware:

[1668] Autonomous vehicle computer systems

[1669] GPS Modules

[1670] Camera and microphone (for emotion engine)

[1671] software:

[1672] Emotion recognition engine (e.g., Microsoft Azure Cognitive Services, Affectiva)

[1673] API communication library (e.g. requests)

[1674] System Operation Overview

[1675] 1. User Input

[1676] Users input their starting point and destination and set exercise and safety criteria, including recommended steps and safe areas. The emotion engine continuously monitors the user's emotional state and sends the results from the device to a server.

[1677] 2. Data collection

[1678] The server collects the latest traffic accident information, safety information, lighting and surveillance device information, and location information of public facilities, including public data from police stations and local governments, news, social media, and other sources.

[1679] 3. Route generation using generative AI models

[1680] The server inputs the collected information, user input conditions, and emotional state information from the emotion engine into a generative AI model to generate the optimal route. Routes that take into consideration the user's psychological state are prioritized, avoiding areas with high traffic accident rates and low safety.

[1681] 4. Route presentation

[1682] The generated optimal route is sent from the server to the device and displayed on a map, allowing the user to check the entire route before setting off.

[1683] 5. Real-time feedback

[1684] When the user begins to go out, the device continuously tracks the user's current location using its GPS function. The latest accident and safety information is collected in real time and sent to the server. If necessary, the server regenerates the route and sends updated route information to the device, which immediately displays it and notifies the user.

[1685] 6. Feedback from Emotion Engine

[1686] The emotion engine analyzes the user's emotional state (e.g., stress, relief, fear) in real time and sends feedback to the server. Based on this feedback, the server will prioritize nearby evacuation sites in an emergency, or suggest routes through relaxing landscapes or quiet areas if the user is feeling stressed.

[1687] Specific examples

[1688] For example, a user sets a route from "home" to "supermarket" and specifies "5,000 steps" as the amount of exercise required per day. Furthermore, since the user will be going out at night, the user sets the priority on safety, and the emotion engine monitors the user's stress level. Based on this information, the server collects the latest traffic accident and safety information and generates a safe route with an appropriate amount of exercise. If the emotion engine detects a high stress level, the server prioritizes routes with relaxing scenery.

[1689] Prompt Sentence Examples

[1690] Prompt statement:

[1691] Enter your current location and destination, and our emotion engine will monitor your emotional state and generate the best route in real time.

[1692] Starting point: Tokyo Station

[1693] Destination: Shibuya Station

[1694] Safety: High

[1695] Recommended steps: 5,000

[1696] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1697] Step 1:

[1698] User Input

[1699] The user inputs the starting point, destination, exercise amount (e.g., recommended number of steps), and safety conditions. The device receives this information, and the emotion engine monitors the user's emotional state.

[1700] Input: Starting point, destination, exercise amount, safety settings

[1701] Output: Condition data

[1702] Specific operation: The user enters the starting point, destination, exercise amount, and safety settings into the device, and the device sends this to the server.

[1703] Step 2:

[1704] Data collection

[1705] The server collects the latest traffic accident information, safety information (e.g., crime rates and nighttime lighting conditions), and facility information (e.g., the locations of public restrooms and police stations) in real time. This includes the ability to collect data from public information from police stations and local governments, news, social media, etc.

[1706] Input: Condition data

[1707] Output: Collected data

[1708] Specific operation: The server uses the specified API to collect traffic accident information, safety information, and facility information in real time.

[1709] Step 3:

[1710] Route generation using generative AI models

[1711] The server uses a generative AI model to generate an optimal route based on the collected data and the user's conditions, prioritizing routes that avoid areas with high traffic accident rates and areas with poor security, and that take the user's psychological state into consideration.

[1712] Input: Collected data, condition data

[1713] Output: Optimal route

[1714] Specific operation: The server inputs condition data and collected data into the generated AI model, and calculates and generates the optimal route.

[1715] Step 4:

[1716] Route suggestions

[1717] The generated optimal route is sent from the server to the terminal, which displays it on a map.

[1718] Input: Optimal Route

[1719] Output: Map display data

[1720] Specific operation: The server sends the generated optimal route to the device, which then visually displays it on the map app.

[1721] Step 5:

[1722] Real-time feedback

[1723] When the user starts going out, the device continuously tracks the user's current location using GPS. Traffic accident and safety information is collected in real time and sent to the server. If necessary, the server regenerates the route and sends new route information to the device.

[1724] Input: current user location, latest collected data

[1725] Output: Updated optimal route

[1726] How it works: The device tracks the user's current location using GPS and sends any new information collected to the server, which then regenerates the route based on that information and sends the updated route to the device.

[1727] Step 6:

[1728] Emotional Engine Feedback

[1729] The emotion engine analyzes the user's emotional state in real time and feeds that information back to the server, which then uses this information to suggest safe evacuation locations and regenerate relaxing routes.

[1730] Input: User's emotional state

[1731] Output: Emotional reflection route, evacuation site

[1732] Specific operation: The emotion engine interprets the user's emotional state and sends it to the server. The server then regenerates a new route or suggests evacuation locations depending on the emotional state.

[1733] Through these steps, the system can ensure user safety and psychological comfort and optimize the navigation of autonomous vehicles.

[1734] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1735] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1736] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1737] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1738] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1739] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1740] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1741] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1742] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1743] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1744] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1745] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1746] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1747] 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.

[1748] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1749] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1750] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1751] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1752] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1753] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1754] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1755] The following is further disclosed regarding the above embodiment.

[1756] (Claim 1)

[1757] means for inputting starting and destination points and conditions relating to exercise and safety;

[1758] A means for collecting traffic accident information, public safety information, electric light and security camera information, and location information of public facilities;

[1759] A means using a generative AI model to generate an optimal route based on the collected information and input conditions;

[1760] A means for displaying the generated route on a map and presenting it to the user;

[1761] A means of tracking the user's current location and updating routes in real time based on the latest accident and security information;

[1762] A system including:

[1763] (Claim 2)

[1764] The system of claim 1 further comprising means for integrating collected traffic accident information, public safety information, electric light and security camera information, and location information of public facilities and supplying the information to the generative AI model.

[1765] (Claim 3)

[1766] 10. The system of claim 1, further comprising means for suggesting safe havens based on a current location of the user based on real-time location tracking.

[1767] "Example 1"

[1768] (Claim 1)

[1769] means for inputting starting and destination points and conditions relating to exercise and safety;

[1770] A means for collecting traffic accident information, public safety information, electric light and security camera information, and location information of public facilities;

[1771] A means using a generative AI model to generate an optimal route based on the collected information and input conditions;

[1772] A means for displaying the generated route on a map and presenting it to the user;

[1773] A means of tracking the user's current location and updating routes in real time based on the latest accident and security information;

[1774] A means of collecting data in real time and regenerating routes as needed;

[1775] a means for checking the format for errors;

[1776] A system including:

[1777] (Claim 2)

[1778] The system of claim 1 further comprising means for integrating collected traffic accident information, public safety information, electric light and security camera information, and location information of public facilities and supplying the information to the generative AI model.

[1779] (Claim 3)

[1780] 10. The system of claim 1, further comprising means for suggesting safe havens based on a current location of the user based on real-time location tracking.

[1781] "Application Example 1"

[1782] (Claim 1)

[1783] means for inputting starting and destination points and conditions relating to exercise and safety;

[1784] A means for collecting traffic accident information, public safety information, lighting and monitoring device information, and location information of public facilities;

[1785] A means using a generative AI model to generate an optimal route based on the collected information and input conditions;

[1786] A means for displaying the generated route on a map and presenting it to the user;

[1787] A means of tracking the user's current location and updating routes in real time based on the latest accident and security information;

[1788] a means for displaying an optimized route and providing driving instructions on a display of the autonomous vehicle;

[1789] A system including:

[1790] (Claim 2)

[1791] The system of claim 1, further comprising means for integrating the collected traffic accident information, public safety information, lighting and surveillance device information, and location information of public facilities and supplying the information to the generative AI model.

[1792] (Claim 3)

[1793] 10. The system of claim 1, further comprising means for suggesting safe havens from a current location based on real-time location tracking of the user.

[1794] "Example 2: Combining Emotion Engines"

[1795] (Claim 1)

[1796] means for inputting starting and destination points and conditions relating to exercise and safety;

[1797] A means for collecting traffic accident information, public safety information, lighting and security device information, and location information of public facilities;

[1798] A means using a generative AI model to generate an optimal route based on the collected information and input conditions;

[1799] means for displaying the generated route on a display device and presenting it to a user;

[1800] A means for tracking the user's current location and updating the route in real time based on the latest accident and security information;

[1801] A means to analyze the user's psychological state in real time and adjust the route based on that information,

[1802] A system including:

[1803] (Claim 2)

[1804] The system of claim 1, further comprising means for integrating the collected traffic accident information, public safety information, lighting and security device information, and location information of public facilities and supplying the information to the generative AI model.

[1805] (Claim 3)

[1806] 10. The system of claim 1, further comprising: means for suggesting safe havens from a current location based on real-time location tracking and mood analysis of the user.

[1807] "Application example 2 when combining emotion engines"

[1808] (Claim 1)

[1809] means for inputting starting and destination points and conditions relating to exercise and safety;

[1810] means for collecting traffic accident information, safety information, lighting and monitoring device information, and location information of public facilities;

[1811] A means using a generative AI model to generate an optimal route based on the collected information and input conditions;

[1812] A means for displaying the generated route on a map and presenting it to the user;

[1813] A means to track the user's current location and update routes in real time based on the latest accident and safety information;

[1814] a means using an emotion engine to analyze the user's emotional state and regenerate the route based on that;

[1815] A system including:

[1816] (Claim 2)

[1817] The system of claim 1, further comprising means for integrating the collected traffic accident information, safety information, lighting and monitoring device information, and location information of public facilities and supplying the information to the generative AI model.

[1818] (Claim 3)

[1819] 10. The system of claim 1, further comprising means for suggesting safe havens from a current location based on real-time location tracking of the user. [Explanation of symbols]

[1820] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for inputting starting and destination points and conditions relating to exercise and safety; A means for collecting traffic accident information, public safety information, electric light and security camera information, and location information of public facilities; A means using a generative AI model to generate an optimal route based on the collected information and input conditions; A means for displaying the generated route on a map and presenting it to the user; A means of tracking the user's current location and updating routes in real time based on the latest accident and security information; A system including:

2. The system of claim 1 further comprising means for integrating collected traffic accident information, public safety information, electric light and security camera information, and location information of public facilities and supplying the information to the generative AI model.

3. 10. The system of claim 1, further comprising means for suggesting safe havens from a current location based on real-time location tracking of the user.

Images