system

Abstract

To provide a system capable of providing highly accurate navigation even in an indoor environment and facilitating the movement of a user.SOLUTION: The system includes means for receiving a location information request from a user, means for searching for map information of a building on a network based on the received request, means for generating a virtual map using past travel data of the user and beacon information when the map information does not exist, means for integrating beacon, WiFi, GPS, and camera information to obtain a current location, means for calculating an optimal route based on the obtained current location information and map information of a destination, means for displaying the calculated route information on a display device, and means for continuously updating the current location information and recalculating a route if necessary.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] When moving around indoors, such as inside buildings or train stations, the accuracy of conventional map applications is low, making it difficult to determine one's current location and navigate to one's destination. There is a need to alleviate the anxiety and difficulties of moving around, particularly for wheelchair users, the elderly, and travelers. Another issue is that proper navigation cannot be provided in places where map information is unavailable or where updates are delayed. [Means for solving the problem]

[0005] To solve the above problems, a system is provided that includes the following means: a means for receiving a location information request from a user, and a means for searching for building map information on a network based on the received request; a means for generating a virtual map using the user's past driving data and beacon information if map information does not exist; a means for integrating beacon, WiFi, GPS, and camera information to obtain the current location, and a means for calculating an optimal route based on the obtained current location information and destination map information; a means for displaying the calculated route information on a display device, continuously updating the current location information, and recalculating the route as necessary. Using these specific means, highly accurate navigation can be provided even in indoor environments, facilitating the user's movement.

[0006] "User" refers to any individual or entity who needs navigation within a building or station.

[0007] "Location Request" means information or data that a User requests through an Application to navigate from their current location to a specific destination.

[0008] "Network" refers to a communication network such as the Internet or an intranet for data communication.

[0009] "Building map information" refers to data showing the relative positions of structures, facilities, passageways, etc. within buildings and stations.

[0010] A "virtual map" refers to map data that is created based on a user's driving data and beacon information when existing map information does not exist.

[0011] "Beacon information" refers to location information obtained based on signals transmitted from transmitters placed inside a building.

[0012] "WiFi" refers to the technology and network that uses wireless LAN for data communication.

[0013] "GPS" stands for Global Positioning System, a system that uses satellites to measure one's position on Earth.

[0014] "Camera information" refers to information for identifying the current location using image data acquired through a camera device.

[0015] "Current location information" refers to data relating to the user's current location.

[0016] An "optimal route" refers to the most efficient route from your current location to your destination.

[0017] "Calculation means" refers to a method or device that uses an algorithm or program to analyze data and derive the desired calculation result.

[0018] The term "display device" refers to a device for visually presenting information to a user, and in this invention particularly refers to augmented reality glasses and the like.

[0019] "Continuously updating" refers to obtaining new data in real time over time to keep the information up to date. [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 system that provides navigation in indoor environments such as buildings, train stations, etc. This system links users, terminals, and servers, enabling highly accurate location identification and navigation wherever users are.

[0042] System configuration

[0043] The system includes the following main components:

[0044] 1. Users

[0045] The user of the system and the entity requesting location information.

[0046] 2. Terminal

[0047] These are devices used by users, such as smartphones, tablets, and AR glasses. These devices acquire location information and display navigation information.

[0048] 3. Server

[0049] It is a central device that processes requests over the network and provides map information and route calculations.

[0050] Program processing flow

[0051] User operations

[0052] When a user wants to navigate from their current location to a destination within a building, they first launch a navigation app, enter their starting point and destination, and make a navigation request.

[0053] Terminal handling

[0054] The device receives requests from users and sends them to the server, and determines its current location by combining surrounding beacon signals, WiFi networks, GPS data, and camera information.

[0055] Server Processing

[0056] The server receives the request and location information from the device and searches the network for map information for the specified building or station. If map information does not exist, it generates a virtual map based on the user's past driving data and beacon information. It also calculates the optimal route based on the received current location and destination information.

[0057] Providing navigation information

[0058] The server then sends the calculated optimal route information to the device, which then receives the route information and displays the navigation information on a display device such as AR glasses or a smartphone screen. This allows the user to receive guidance to their destination in a visually easy-to-understand format.

[0059] Specific examples

[0060] As a concrete example, consider a situation where a wheelchair user is searching for the location of an elevator in a large shopping mall. In this case, the user sets the elevator as their destination in a navigation app and sends a request. The device determines the user's current location using beacons, Wi-Fi, and camera information and sends this information to the server. The server searches for the shopping mall's map information and generates a virtual map as needed. It then calculates the optimal route from the current location to the elevator and sends the route information to the device. The device displays this information on the AR glasses, and the user is guided to the elevator based on visual navigation. If the user's current location changes during travel, the device continuously updates its location information, and the server recalculates the route as needed and provides the latest navigation information.

[0061] This system will enable users to easily navigate difficult indoor spaces, which will be of particular help to wheelchair users and the elderly, and will also be a powerful tool for travelers to quickly reach their destinations in unfamiliar places.

[0062] The processing flow will be explained below.

[0063] Step 1:

[0064] The user launches the navigation app, inputs the starting point and destination, and sends a request, which inputs the necessary navigation information into the device.

[0065] Step 2:

[0066] The terminal receives the user's request and sends it to the server, where the request data is sent to the server using the HTTP protocol or similar.

[0067] Step 3:

[0068] The server analyzes the received request and searches for map information of the building through the network. If map information does not exist, it prepares to generate a virtual map.

[0069] Step 4:

[0070] The server generates a virtual map using past user driving data and beacon information as needed, and uses data analysis algorithms to create a virtual map structure.

[0071] Step 5:

[0072] To determine the device's current location, it acquires beacon, WiFi, GPS, and camera information, and combines this information to determine the device's current location.

[0073] Step 6:

[0074] The device sends its determined location to the server. The location data is packaged in an appropriate format (e.g., JSON) and sent to the server.

[0075] Step 7:

[0076] The server calculates the optimal route based on the current location information and map information received. Route calculations use a route search algorithm such as the A algorithm.

[0077] Step 8:

[0078] The server sends the calculation results (optimal route information) to the device. The route information is sent in an appropriate format so that all navigation-related data can be viewed on the device.

[0079] Step 9:

[0080] The optimal route information received by the device is displayed on a display device (e.g., AR glasses). Information is overlaid using AR technology to enable visual confirmation of navigation instructions.

[0081] Step 10:

[0082] The user moves to the destination according to the displayed navigation information. The user moves through the building based on the visual guide.

[0083] Step 11:

[0084] The device continuously monitors the user's current location while moving, periodically reacquiring sensor information and updating the current location.

[0085] Step 12:

[0086] The device sends updated location information to the server, and the server coordinates with the device to adjust the route based on changes in location information.

[0087] Step 13:

[0088] The server recalculates the route as needed and sends the latest navigation information to the device, ensuring that the user is always guided along the optimal route.

[0089] Step 14:

[0090] The device will then redisplay the latest route information and provide it to the user, allowing real-time navigation.

[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] This invention relates to a system for navigating indoor environments that acquires a user's current location information with high accuracy and provides the optimal route to reach a destination. Current technology has difficulty in identifying indoor locations and lacks a method for generating a virtual map when map information does not exist, making highly accurate navigation difficult. Furthermore, there is a lack of systems that can update location information in real time while the user is moving and flexibly recalculate routes. There is a need to solve these issues and provide users with constantly up-to-date navigation information.

[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 a means for receiving a location information request from a user, a means for searching for building map information on the network, and a means for generating a virtual map using the user's past driving data and beacon information if map information is not available. This enables highly accurate acquisition of the user's current location information and real-time location information updates. The terminal also includes a means for integrating beacon signals, WiFi networks, GPS data, and camera information to determine the current location, and a means for calculating an optimal route based on the acquired current location information and destination map information. The terminal also includes a means for displaying the calculated route information on a display device, continuously updating the current location information as the user moves, and recalculating the route as necessary. This allows the user to receive highly accurate navigation even in indoor environments, making navigation easier, especially for users with mobility difficulties or in unfamiliar locations.

[0096] A "user" is an entity that wishes to navigate using the system.

[0097] A "location information request" is a request from a user for navigation by specifying a starting point and a destination.

[0098] A "network" is a communication pathway between system components for communicating data.

[0099] "Building map information" is detailed information about the structure and routes inside a specified building.

[0100] A "virtual map" is a map generated based on the user's past driving data and beacon information when actual map information does not exist.

[0101] A "beacon signal" is a radio signal transmitted from a specific location, and by receiving this signal, the location can be identified.

[0102] A "WiFi network" is a network that uses wireless communication technology to send and receive data.

[0103] "GPS Data" means location information data obtained using the Global Positioning System.

[0104] "Camera information" refers to image and video data captured using a camera.

[0105] "Current location information" is information that identifies the user's location at the time the terminal is located.

[0106] A "destination" is the final destination to which a user seeks navigation.

[0107] An "optimal route" is the most efficient or shortest route from a current location to a destination.

[0108] A "display device" is a device for visually conveying navigation information to a user.

[0109] A "central aggregation device" is a device that processes and manages data from multiple terminals via a network, such as a server.

[0110] An "augmented reality device" is a device that displays digital information superimposed on the real world.

[0111] A "specific route planning algorithm" is a particular algorithmic technique used to calculate a route.

[0112] This invention is a system for providing highly accurate navigation in indoor environments. Specifically, it aims to link users, terminals, and servers to perform accurate location identification and navigation in real time, regardless of location.

[0113] System Configuration

[0114] The system consists of three main components:

[0115] 1. Users

[0116] They are the active users of the system and the ones who carry the terminals.

[0117] 2. Terminal

[0118] This refers to devices carried by users, such as smartphones, tablets, and AR glasses. The terminal acquires location information and acts as an interface for communicating with the server.

[0119] 3. Server

[0120] It is a central device that processes location information, searches map information, and calculates routes via the network.

[0121] Program processing flow

[0122] User operations

[0123] When a user requests navigation, they first launch a navigation app on their device and enter their starting point and destination, which then issues a navigation request.

[0124] Terminal handling

[0125] The device receives a request from the user and sends it to the server. The device also acquires surrounding beacon signals, WiFi networks, GPS data, and camera information to determine the current location. This acquired location information is also sent to the server.

[0126] Server Processing

[0127] The server receives the request and location information sent from the device. The server searches the network for map information for the specified building or station, and if it does not exist, it generates a virtual map using the user's past driving data and beacon information. It then calculates the optimal route based on the current location information and destination information. The algorithm used is expected to be the Dijkstra algorithm or the A algorithm.

[0128] Providing navigation information

[0129] The server sends the calculated optimal route information to the device. The device then displays the received route information on the AR glasses or smartphone screen. This allows the user to receive navigation in a visually easy-to-understand format. Furthermore, if the current location information changes as the user moves, the device continues to update the location information, and the server recalculates the route each time.

[0130] Specific examples

[0131] As a concrete example, consider a situation where a wheelchair user is searching for an elevator in a large shopping mall. The user launches a navigation app and sets the elevator as their destination. This request is sent from the device to the server. The device collects beacon signals, WiFi networks, GPS data, and camera information in real time to determine the user's current location. The server searches for the shopping mall's map information and generates a virtual map as needed. Next, it calculates the optimal route from the user's current location to the elevator and sends the route information to the device. The device displays this information on the AR glasses, allowing the user to head to the elevator with visual navigation in hand.

[0132] Prompt Sentence Examples

[0133] Here is an example prompt:

[0134] User: Launches the navigation app, enters the origin "Main Gate" and destination "Elevator", and submits the request.

[0135] Device: Sends the received request to the server and determines the current location based on beacon signals, WiFi networks, GPS data, and camera information.

[0136] Server: Calculates the optimal route based on the received current location information and destination information and sends it to the device.

[0137] Device: The received route information is displayed on the AR glasses, providing the user with visual navigation.

[0138] This system allows users to navigate indoors with high accuracy and in real time, and is particularly useful for users with mobility issues or in unfamiliar locations.

[0139] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0140] Step 1:

[0141] The user launches the navigation app and enters their starting point and destination. The entered data (starting point and destination) is sent to the device, which generates a navigation request. Input: starting point, destination. Output: navigation request.

[0142] Step 2:

[0143] The device receives a navigation request from the user and sends it to the server. The data sent includes the starting point, destination, and initial location information. Based on the received data, the device collects surrounding beacon signals, WiFi networks, GPS data, and camera information to determine the current location in real time. Input: Navigation request, various sensor data. Output: Current location information.

[0144] Step 3:

[0145] The server receives the navigation request and current location information sent from the device. The server first searches the network for map information for the specified building. If map information is not available, it generates a virtual map using the user's past driving data and beacon information. Input: Navigation request, current location information. Output: Map information or virtual map.

[0146] Step 4:

[0147] The server calculates the optimal route based on the received current location information and destination information. This process uses the Dijkstra algorithm or the A algorithm. The calculation results in each point on the route and corresponding navigation instructions. Input: Current location information, map information or virtual map. Output: Optimal route information.

[0148] Step 5:

[0149] The server sends the calculated optimal route information to the terminal. The data sent includes coordinates and instructions for each point on the route. Input: Optimal route information. Output: Route information.

[0150] Step 6:

[0151] The device analyzes the route information received from the server and presents it visually to the user, including arrows and visual guides displayed on the AR glasses or smartphone screen. Input: Route information. Output: Navigation display.

[0152] Step 7:

[0153] The user navigates based on the navigation information displayed on the device. If the user's current location changes as they move, the device continuously updates its location information and sends new location information to the server as needed. The server receives the new location information, recalculates the route as needed, and sends it to the device. This ensures that the user always receives the latest navigation information. Input: User's current location. Output: Updated route information and navigation display.

[0154] Each step works together to create a complete navigation process that helps users reach their destination safely. This system performs particularly well in indoor environments.

[0155] (Application example 1)

[0156] 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."

[0157] Current robot movement within factories is inefficient, making robot navigation difficult, especially in large factory facilities or complex layouts. This leads to problems such as reduced operational efficiency and reduced productivity. Furthermore, there is a risk that the robot will not reach its destination and will collide with obstacles during movement. The purpose of this invention is to solve these problems, optimize robot movement within factories, and provide efficient and safe navigation.

[0158] 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.

[0159] In this invention, the server includes a means for receiving a location information request from a user, a means for searching for building map information on the network, and a means for generating a virtual map using the user's past travel data and beacon information, thereby optimizing the robot's movement route within the factory and providing highly accurate navigation.

[0160] "User" refers to the entity that uses the system to request location information, and is primarily the operator or manager who manages the operation of robots within a factory.

[0161] A "terminal" is a device used by a user, and includes control devices such as smartphones, tablets, and dedicated terminals.

[0162] A "server" is a central device that processes requests over a network, searches for map information, and calculates routes.

[0163] A "location information request" refers to a request made by a user to confirm their current location or to request route guidance to a destination.

[0164] "Map information" is information that shows the layout of a building or factory, and is data necessary for route guidance.

[0165] A "virtual map" is virtual map information generated based on the user's past driving data and beacon information.

[0166] A "beacon" is a device that provides location information using short-range wireless communication, and is primarily used to identify indoor locations.

[0167] "WiFi" is a technology that uses wireless LAN technology for data communication, and is used for location determination and data transmission.

[0168] "GPS" refers to a satellite positioning system that measures positions on Earth with high precision.

[0169] "Camera information" is information for identifying the current location using video and image data captured by a camera device.

[0170] An "optimal route" refers to the most efficient route from the current point to the destination, calculated to minimize travel time and distance.

[0171] "Route calculation" refers to the process of deriving the optimal route between a specified current location and a destination.

[0172] A "display device" is a device that presents calculated route information and navigation information to a user. Examples include AR glasses and smartphone screens.

[0173] A "factory" is a facility where products are manufactured and assembled, and is the primary location where robots move.

[0174] A "robot" is a mechanical device that moves objects and processes products within a factory.

[0175] "Navigation" refers to the process of providing guidance along a route to a destination specified by a user.

[0176] The present invention is a system that optimizes the movement path of a robot within a factory and provides highly accurate navigation. This system is realized by linking users, terminals, and a server.

[0177] User operations

[0178] The user is an operator or administrator who manages the robot's movements and requests location information from the system. Specifically, the user launches a dedicated navigation app, inputs the starting point and destination, and sends the request.

[0179] Terminal handling

[0180] The terminal is a control device installed on the robot, and can be a smartphone, tablet, dedicated terminal, etc. The terminal first receives a request from the user and sends it to the server. The terminal also determines the robot's current location by combining surrounding beacon signals, WiFi networks, GPS data, and camera information.

[0181] Server Processing

[0182] The server receives requests and location information from devices via the network. It then searches for map information for the specified building or factory. If map information does not exist, it generates a virtual map based on the user's past driving data and beacon information. It then calculates the optimal route based on the received current location and destination information.

[0183] Providing navigation information

[0184] The server sends the calculated optimal route information to the terminal. The terminal receives this route information and displays it on a display device, such as the robot's control screen or AR glasses. This allows the robot to receive guidance to its destination in a visually easy-to-understand manner. If the current location changes during movement, the terminal continuously updates its location information, and the server recalculates the route as necessary to provide the latest navigation information.

[0185] Specific examples

[0186] As a concrete example, consider a situation in which a parts delivery robot in a factory transports parts from a central warehouse to a production line. The user sets the production line as the destination in a navigation app and sends a request. The device determines the robot's current location using beacons, Wi-Fi, and camera information, and sends this information to a server. The server searches map information within the factory and generates a virtual map as needed. It then calculates the optimal route from the current location to the production line and sends the route information to the device. The device displays this information on the robot's display device, and the robot heads to its destination according to the route presented.

[0187] Prompt Sentence Examples

[0188] "Update the robot's current coordinates and calculate the shortest path to the destination. The current coordinates are {'x': 1, 'y': 2} and the destination is {'x': 10, 'y': 5}."

[0189] This system improves the efficiency of robot movement within the factory, improving the operating efficiency of each manufacturing process. It also enables robots to reach their destinations safely and quickly, improving overall productivity. In this way, the present invention effectively solves various navigation issues within factories.

[0190] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0191] Step 1:

[0192] A user launches a navigation app, enters a starting point and destination, and sends a location request, which provides the device with the coordinates of the current location and destination.

[0193] Step 2:

[0194] The device receives a location request from the user. As input, it receives coordinate information of the starting point and destination specified by the user. The device sends this information to the server. As output, the received request data is forwarded to the server.

[0195] Step 3:

[0196] The server receives the request and location information from the terminal. The coordinate information of the current location and destination is provided to the server as input. The server uses this information to search the network for map information within the factory. The corresponding map information is obtained as output.

[0197] Step 4:

[0198] The server searches for map information and generates a virtual map if it does not exist. The input is the user's past driving data and beacon information. The server processes this data to generate a virtual map. The output is the newly generated virtual map information.

[0199] Step 5:

[0200] The server calculates the optimal route based on current location information and destination information. The server is provided with the coordinates of the current location, the coordinates of the destination, and map information (actual map or virtual map) as input. The server uses this data to apply a route calculation algorithm such as the A algorithm to derive the optimal route. The optimal route information is obtained as output.

[0201] Step 6:

[0202] The server sends the calculated optimal route information to the terminal. The input is the optimal route information calculated by the server. The server sends this to the terminal and provides it to the robot's control device. The output is the optimal route information transferred to the terminal.

[0203] Step 7:

[0204] The terminal receives the route information and presents it on a display device. The input is the optimal route information sent from the server. The terminal displays this information on a display device such as AR glasses or the robot's control screen. The output is visually easy-to-understand navigation information presented to the robot.

[0205] Step 8:

[0206] The robot moves towards the destination according to the presented route. The input is navigation information displayed on a display device. Based on this information, the robot uses its autonomous driving function to proceed along the route. The output is the robot's movement.

[0207] Step 9:

[0208] The terminal continuously updates the robot's current location. As input, it uses surrounding environmental data such as beacon signals, WiFi networks, GPS data, and camera information. The terminal integrates this data to re-determine the current location and sends it to the server. As output, the updated location information is provided to the server.

[0209] Step 10:

[0210] The server recalculates the route as needed based on the updated current location information. The updated current location and destination information are provided as input to the server. The server uses this to reapply algorithm A to calculate the latest optimal route. The output is the new route information.

[0211] Step 11:

[0212] The server sends new route information to the terminal, which then re-presents it on the display device. The input is the latest route information sent from the server. The terminal re-displays this information on the display device, providing the robot with up-to-date navigation information. The output is the route information shown to the robot, updated to the latest version.

[0213] 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.

[0214] This invention achieves more appropriate navigation by combining a system that receives location information requests from users and provides navigation within buildings or train stations with an emotion engine that recognizes the user's emotions. This system operates in cooperation with the user, terminal, server, and emotion engine.

[0215] System configuration

[0216] The system includes the following main components:

[0217] 1. Users

[0218] The entity that uses the system, requests location information, and receives navigation.

[0219] 2. Terminal

[0220] These are devices used by users, such as smartphones, tablets, and AR glasses, that acquire location information and display navigation information.

[0221] 3. Server

[0222] This is a device that processes requests via a network, searches for map information, and calculates routes.

[0223] 4. Emotion Engine

[0224] This module analyzes the user's facial expressions and voice to recognize emotions, allowing the navigation guidance method to be adjusted based on the user's emotions.

[0225] Program processing flow

[0226] User operations

[0227] The user launches the navigation app, inputs the starting point and destination, and sends a request, which inputs the necessary navigation information into the device.

[0228] Terminal handling

[0229] The device receives the user's request and sends it to the server. At the same time, the device acquires surrounding beacon signals, WiFi networks, GPS data, and camera information to determine its current location. It also uses the camera and microphone to collect the user's facial expressions and voice data, which are then sent to the emotion engine.

[0230] Emotion engine processing

[0231] The emotion engine analyzes the collected user facial and voice data to recognize the user's current emotions. The emotion data is sent to the server and reflected in the navigation route and guidance method.

[0232] Server Processing

[0233] The server receives requests from the device, along with location and emotion data. It searches the network for building map information and generates a virtual map as needed. It then calculates the optimal route based on the current location and destination information, taking emotion data into account. For example, if the user is feeling stressed, the server will select not only the shortest route but also a route that is mentally gentle.

[0234] Providing navigation information

[0235] The server then sends the calculated optimal route information to the device, which then receives the route information and displays the navigation information on a display device such as AR glasses or a smartphone screen. This allows the user to receive guidance to their destination in a visually easy-to-understand format.

[0236] Specific examples

[0237] As a concrete example, consider a situation where an elderly user wants to go to a specific store in a busy shopping mall. The user sets the store as a destination in the navigation app and sends a request. The device determines the user's current location based on beacons, Wi-Fi, and camera information, and sends this information to the server. At the same time, the device transmits the user's facial expressions and voice to the emotion engine.

[0238] The emotion engine analyzes the user's data and determines that the user is in a state of tension. The server searches for the shopping mall's map information, generates a virtual map as needed, and calculates the optimal route taking into account the user's level of tension. For example, it makes adjustments such as choosing wider aisles to avoid crowds or walking near elevators.

[0239] The optimal route information is sent to the device and displayed on the AR glasses. The user can follow the visual guide to the store with confidence. In this way, the system updates the user's current location and emotional state in real time, recalculating and providing the optimal route as needed.

[0240] This system allows users to easily navigate difficult indoor spaces, which is of particular help to the elderly and those who are prone to stress. It also enables travelers to reach their destinations quickly and safely, even in unfamiliar places.

[0241] The processing flow will be explained below.

[0242] Step 1:

[0243] The user launches the navigation app, inputs the starting point and destination, and sends a request, which inputs the necessary navigation information into the device.

[0244] Step 2:

[0245] The device receives the user's request and sends it to the server, while simultaneously acquiring surrounding beacon signals, WiFi networks, GPS data, and camera information to determine the device's current location.

[0246] Step 3:

[0247] The device collects the user's facial and voice data and sends it to an emotion engine, which is either built into the device or runs on the cloud.

[0248] Step 4:

[0249] The emotion engine analyzes the received facial expressions and voice data to recognize the user's emotional state, and sends the results of the judgment to the server.

[0250] Step 5:

[0251] The server receives the request from the device, along with the device's current location and emotional state, and searches the network for map information for the specified building or station.

[0252] Step 6:

[0253] If the server does not have map information, it generates a virtual map based on past user driving data and beacon information. It uses a data analysis algorithm to create a virtual map structure.

[0254] Step 7:

[0255] The server calculates the optimal route based on the current location information, destination information, and emotional state. For example, if the user is feeling stressed, the server will select a route that avoids crowds or passes near elevators.

[0256] Step 8:

[0257] The server sends the calculation results (optimal route information) to the device. The route information is sent in an appropriate format so that all navigation-related data can be viewed on the device.

[0258] Step 9:

[0259] The optimal route information received by the device is displayed on a display device (e.g., AR glasses). Information is overlaid using AR technology to enable visual confirmation of navigation instructions.

[0260] Step 10:

[0261] The user moves to the destination according to the displayed navigation information. The user moves through the building based on the visual guide.

[0262] Step 11:

[0263] The device continuously monitors the user's current location while moving, periodically reacquiring sensor information and updating the current location.

[0264] Step 12:

[0265] The device sends updated location information to the server, and the server coordinates with the device to adjust the route based on changes in location information.

[0266] Step 13:

[0267] The server recalculates the route as needed and sends the latest navigation information to the device, ensuring that the user is always guided along the optimal route.

[0268] Step 14:

[0269] The device will then redisplay the latest route information and provide it to the user, allowing real-time navigation.

[0270] Example 2

[0271] 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."

[0272] Currently, indoor and building navigation systems generally provide route guidance based on the user's location information. However, these systems do not take the user's emotional state into account, making it difficult to provide an optimal route if the user is feeling nervous or stressed. Therefore, in order to improve the user's sense of security and comfort, a system that recognizes the user's emotions and provides navigation that responds to them is needed.

[0273] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving a location information request from a user, means for searching for map information of buildings on a network, means for generating a virtual map using the user's past driving data and beacon information, means for integrating beacon, WiFi, GPS, and camera information to obtain a current location, means for obtaining facial expression data and voice data of the user, means for analyzing the facial expression data and voice data to recognize the user's emotional state, means for calculating an optimal route based on the obtained current location information, destination map information, and emotional state, means for displaying the calculated route information on a display device, and means for continuously updating the current location information and emotional state and recalculating the route as necessary. This enables navigation that takes the user's emotional state into consideration.

[0274] The "means for receiving location information requests from users" refers to the technical means for transmitting the information of the starting point and destination entered by the user through the navigation app to the server.

[0275] "Means for searching for building map information from a network" means technical means for obtaining detailed map data within a specified building via the Internet or other digital networks.

[0276] "Means for generating a virtual map" refers to a technical means for constructing a new map by utilizing the user's past driving data and beacon information to complement non-existent map information.

[0277] "Means for integrating beacon, WiFi, GPS, and camera information" refers to technical means for combining and processing data using these different location acquisition technologies to determine a user's current location.

[0278] The "means for acquiring the user's facial expression data and voice data" refers to a technical means for collecting the user's facial expression and voice using the camera and microphone of the terminal.

[0279] "Means for recognizing the user's emotional state" refers to technical means for analyzing the collected facial expression data and voice data and identifying the emotion the user is currently feeling (e.g., tension, relaxation, stress, etc.).

[0280] The "means for calculating the optimum route" refers to a technical means for calculating the most suitable route for the user, taking into account current location information, destination information, and the emotional state of the user.

[0281] "Means for displaying calculated route information on a display device" refers to the technical means for displaying information on a display device such as a smartphone or AR glasses in order to visually present the calculated navigation route to the user.

[0282] "Means for continuously updating current location information and emotional state and recalculating the route as needed" refers to technical means for updating the location information and emotional data of a user while they are moving in real time, and for recalculating the optimal route based on that information if needed.

[0283] The present invention is a system that provides more appropriate navigation to users by combining a navigation system for use in buildings or train stations with an emotion engine that recognizes the user's emotions. This system operates by linking the user, terminal, server, and emotion engine. Specific embodiments for implementing this system are described below.

[0284] System configuration

[0285] The system includes the following main components:

[0286] 1. Users

[0287] The entity that uses the system, requests location information, and receives navigation.

[0288] 2. Terminal

[0289] A device used by a user, such as a smartphone, tablet, or augmented reality glasses, that acquires location information and displays navigation information.

[0290] 3. Server

[0291] This is a device that processes requests via the network, searches for building map information, and calculates routes.

[0292] 4. Emotion Engine

[0293] This module analyzes the user's facial expressions and voice to recognize emotions, allowing the navigation guidance method to be adjusted based on the user's emotions.

[0294] Details of each element

[0295] 1. Users

[0296] A user uses a navigation app to input the information required for navigation (starting point and destination) and send a request. The user's role is to provide the system with location information and receive navigation guidance.

[0297] 2. Terminal

[0298] The device can be, for example, a smartphone or augmented reality glasses. The device has the following features:

[0299] Uses GPS, WiFi, beacons, and camera information to determine your location.

[0300] A camera and a microphone are used to capture facial expression data and voice data of the user.

[0301] The acquired data is sent to the server and emotion engine.

[0302] The navigation information received from the server is displayed to the user.

[0303] 3. Server

[0304] The server is the core component of the system and performs the following tasks:

[0305] It receives a location information request from a user and searches for building map information on the network.

[0306] If map information does not exist, a virtual map is generated using the user's past driving data and beacon information.

[0307] The system receives emotion data transmitted from the terminal and calculates the optimal route taking this into consideration.

[0308] The calculated route information is sent to the terminal.

[0309] 4. Emotion Engine

[0310] The emotion engine analyzes the user's facial expression and voice data to recognize their emotional state. This allows it to understand the user's state of tension, stress, relaxation, etc., and provide navigation accordingly. The emotion engine analyzes the data in real time and sends the results to the server.

[0311] Specific examples

[0312] As a specific example, consider a case where an elderly user wants to go to a specified store in a shopping mall.

[0313] 1. The user launches the navigation app, sets the store as the destination, and submits a request.

[0314] 2. The device determines its current location based on beacon, Wi-Fi, and camera information and sends it to the server. At the same time, it sends the user's facial expressions and voice to the emotion engine.

[0315] 3. The emotion engine analyzes the user's data and determines that they are in a state of tension.

[0316] 4. The server searches the network for building map information and generates a virtual map as needed. It calculates the optimal route, taking into account the user's sense of tension. For example, it will choose wider corridors to avoid crowds and walk near elevators.

[0317] 5. The calculated optimal route information is sent to the device and displayed on the screen of the augmented reality glasses or smartphone, allowing the user to follow the visual guidance to their destination with confidence.

[0318] Example prompts for generative AI models

[0319] "Provide navigation for users to reach their destination store in a shopping mall. Calculate the optimal route taking into account the user's current location and emotional state."

[0320] "The user is elderly and needs navigation to get to their destination on a train platform. If the user feels stressed, suggest an easy-to-walk route that avoids crowds."

[0321] In this way, the present invention provides a navigation system that enables users, particularly elderly people and those who are prone to stress, to reach their destination with peace of mind.

[0322] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0323] Step 1: Request user location

[0324] Input: The user enters a start point and destination into the navigation app.

[0325] Specific operation: The user launches the app on their smartphone, enters "home" as the starting point and "a store in the shopping mall" as the destination, and taps the send request button.

[0326] Output: The user's input information (starting point, destination) is sent to the terminal.

[0327] Step 2: Collect data from the device

[0328] Input: User location request and various data to determine current location (GPS, WiFi, beacon, camera information).

[0329] What it does: In the background, your device uses GPS to determine your location, captures WiFi and beacon signals, and uses the camera to capture video of your surroundings.

[0330] Output: Current location data, surrounding environment data, and user request information are sent to the server.

[0331] Step 3: Collecting user emotion data

[0332] Input: User's facial expression data and voice data acquired by the device's camera and microphone.

[0333] Specific operation: The device's camera captures the user's face and the microphone records the user's voice.

[0334] Output: The acquired facial expression data and voice data are sent to the emotion engine.

[0335] Step 4: Emotion Recognition in the Emotion Engine

[0336] Input: Facial expression data and speech data sent to the emotion engine.

[0337] Specific operation: The emotion engine uses facial expression recognition algorithms and voice analysis algorithms to analyze the user's emotional state, such as whether they are tense or relaxed.

[0338] Output: The analyzed emotional data (user's emotional state) is sent to the server.

[0339] Step 5: Searching for map information on the server and generating a virtual map

[0340] Input: User location request, current location data, and user emotional state data.

[0341] Specific operation: The server searches for map information for the building from the Internet or an internal database. If map information does not exist, it generates a virtual map based on the user's past driving data and beacon information.

[0342] Output: The acquired or generated map information is saved in the server.

[0343] Step 6: Optimal route calculation on the server

[0344] Input: Acquired map information, current location data, destination information, and user emotion data.

[0345] Specific operation: The server uses algorithm A to calculate the optimal route from the current location to the destination. At this time, it takes into account the user's emotional state, for example, selecting a route that takes a wide corridor if the user is nervous.

[0346] Output: The calculated optimal route information is sent to the terminal.

[0347] Step 7: Displaying navigation information

[0348] Input: Optimal route information sent from the server.

[0349] Specific operation: The route information received by the device is displayed on the screen of the augmented reality glasses or smartphone. Visual guidelines and arrows indicate the user's direction of travel.

[0350] Output: The user receives real-time visual navigation information and moves towards the destination.

[0351] Step 8: Continuously updating your location and emotional state

[0352] Input: User's location data, facial expression and voice data while moving.

[0353] Specific operation: The device periodically updates its location and emotional state, and sends new data to the server and emotion engine as needed.

[0354] Output: Updated location and emotion data are sent to the server, and the route is recalculated if necessary.

[0355] The above is a detailed explanation of each step of the processing flow of this system.

[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] Conventional navigation systems only provide routes based on the user's location information, and have the problem of not being able to provide navigation that takes into account the user's emotional state. This has led to a demand for navigation that allows users to reach their destination comfortably without feeling stressed or anxious.

[0359] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a location information request, means for searching for building map information on the network, means for generating a virtual map if map information does not exist, means for acquiring the current location by integrating beacon, WiFi, GPS, and camera information, means for analyzing the user's facial expressions and voice to recognize emotions, means for calculating an optimal route using the recognized emotion data, means for displaying the calculated route information on a display device, and means for continuously updating the current location information and emotion data and recalculating the route as necessary. This enables navigation that takes the user's emotional state into consideration, resulting in more comfortable and safe travel.

[0360] A "location information request" is a request that a user sends by specifying information about a departure point and a destination.

[0361] "Means for searching for building map information from a network" refers to a system for obtaining maps and location information within a building using a network such as the Internet.

[0362] A "means for generating a virtual map" is a mechanism for creating a virtual map based on the user's past data and surrounding information when actual map information does not exist.

[0363] "Means for obtaining current location" refers to a mechanism that integrates beacons, WiFi, GPS, and camera information to accurately determine the user's current location.

[0364] "Means for recognizing emotions" refers to a mechanism for understanding a user's emotional state by analyzing the user's facial expressions and voice.

[0365] The "means for calculating the optimal route" is a mechanism for calculating the optimal route for the user based on the acquired current location information and emotion data.

[0366] "Means for displaying on a display device" refers to a mechanism for displaying calculated route information on a display device such as a user's smartphone or augmented reality glasses.

[0367] The "means for recalculating the route" is a mechanism for continuously updating current location information and emotion data, and recalculating the route depending on the situation.

[0368] The present invention is a navigation system that takes into account the emotional state of the user, and is particularly applicable to autonomous vehicles. The system includes the following main components:

[0369] 1. Users

[0370] The entity that uses the system and issues a navigation request by specifying the starting point and destination.

[0371] 2. Terminal

[0372] A display device such as a smartphone or head-mounted display (HMD) that acquires the user's location information and emotional data and displays navigation information.

[0373] 3. Server

[0374] This device processes requests via the network, searches for map information, generates virtual maps, and calculates optimal routes.

[0375] 4. Emotion Engine

[0376] This module analyzes the user's facial expressions and voice to recognize their emotional state and reflects this in the navigation route.

[0377] Specific processing of the system

[0378] The system starts when the user launches a navigation app on their smartphone or HMD and specifies their starting point and destination. The device receives a location request from the user and sends it to a server. At the same time, it uses beacons, Wi-Fi, GPS, and camera information to determine the user's current location and sends this information to the server.

[0379] In addition, the device uses a camera and microphone to collect the user's facial expressions and voice, which are then sent to the emotion engine. The emotion engine analyzes the user's facial and voice data to recognize their current emotional state. The emotion data is then sent to the server, which uses this information to calculate the optimal route.

[0380] Hardware and software used

[0381] Hardware

[0382] Smartphone

[0383] Head-mounted display (HMD)

[0384] Camera and microphone

[0385] software

[0386] Python Program

[0387] OpenCV (image processing library)

[0388] Keras (machine learning library)

[0389] Geopy (geographic information library)

[0390] External API (route optimization)

[0391] Data processing and calculation

[0392] The device processes images captured by the camera using OpenCV to detect faces. The detected facial images are analyzed using an emotion recognition model using Keras. Audio data is also collected by the microphone and analyzed by the emotion engine. This data is sent to the server and used to calculate the optimal route.

[0393] The server uses Geopy to obtain the coordinates of the starting and destination points, optimizes the route using an external API, and selects a route that best suits the user's mental state based on emotion data.

[0394] Specific examples

[0395] For example, if the user is feeling stressed, the server can suggest scenic routes or roads with less traffic, using prompts like the following:

[0396] "Recognize emotions from the current facial expression and voice, and if the user is feeling stressed, calculate a relaxing route and provide navigation information. Use the following data:

[0397] Starting point: 35.6895, 139.6917

[0398] Destination: 34.6937, 135.5023

[0399] Emotion: 'Stress'

[0400] In this way, the system can take into account the user's emotional state and provide a more comfortable and secure navigation experience.

[0401] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0402] Step 1:

[0403] A user launches a navigation app and inputs their starting point and destination, which generates a navigation request. The input is the coordinate information of the starting point and destination, and the output is a navigation request.

[0404] Step 2:

[0405] The device receives navigation requests from the user and sends them to the server. At the same time, it acquires beacon signals, WiFi networks, GPS data, and camera information to determine the current location. The input is data from various sensors, and the output is the current location information.

[0406] Step 3:

[0407] The device uses a camera and microphone to collect facial and voice data from the user. The collected data is sent to the emotion engine. The input is camera footage and voice data, and the output is facial images and voice clips.

[0408] Step 4:

[0409] The emotion engine analyzes the received facial and voice data to recognize the user's emotions. For emotion recognition, it uses a generative AI model. The input is a face image and an audio clip, and the output is emotion data (e.g., "stressed" or "relieved").

[0410] Step 5:

[0411] The emotion engine sends the recognized emotion data to the server. The input is the emotion data, and the output is the completion of transmission to the server.

[0412] Step 6:

[0413] The server receives current location information, destination information, and emotion data from the user. It then searches for map information and generates a virtual map as needed. The input is various information data, and the output is map information.

[0414] Step 7:

[0415] The server calculates the optimal route based on the acquired map information and emotion data. The calculation uses the A algorithm and emotion data. The input is map information and emotion data, and the output is optimal route information.

[0416] Step 8:

[0417] The server sends the calculated optimal route information to the terminal. The input is the optimal route information, and the output is the completion of transmission to the terminal.

[0418] Step 9:

[0419] The terminal displays the received optimal route information on a display device such as a smartphone or HMD. The input is optimal route information, and the output is a visual navigation guide.

[0420] Step 10:

[0421] The device continuously updates its current location and emotion data, and requests route recalculation from the server as needed, providing optimal navigation in real time. The input is the current location and emotion data, and the output is updated navigation information.

[0422] 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.

[0423] 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.

[0424] 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.

[0425] [Second embodiment]

[0426] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0427] 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.

[0428] 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).

[0429] 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.

[0430] 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.

[0431] 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).

[0432] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0433] 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.

[0434] 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.

[0435] 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.

[0436] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0437] 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."

[0438] This invention is a system that provides navigation in indoor environments such as buildings, train stations, etc. This system links users, terminals, and servers, enabling highly accurate location identification and navigation wherever users are.

[0439] System configuration

[0440] The system includes the following main components:

[0441] 1. Users

[0442] The user of the system and the entity requesting location information.

[0443] 2. Terminal

[0444] These are devices used by users, such as smartphones, tablets, and AR glasses. These devices acquire location information and display navigation information.

[0445] 3. Server

[0446] It is a central device that processes requests over the network and provides map information and route calculations.

[0447] Program processing flow

[0448] User operations

[0449] When a user wants to navigate from their current location to a destination within a building, they first launch a navigation app, enter their starting point and destination, and make a navigation request.

[0450] Terminal handling

[0451] The device receives requests from users and sends them to the server, and determines its current location by combining surrounding beacon signals, WiFi networks, GPS data, and camera information.

[0452] Server Processing

[0453] The server receives the request and location information from the device and searches the network for map information for the specified building or station. If map information does not exist, it generates a virtual map based on the user's past driving data and beacon information. It also calculates the optimal route based on the received current location and destination information.

[0454] Providing navigation information

[0455] The server then sends the calculated optimal route information to the device, which then receives the route information and displays the navigation information on a display device such as AR glasses or a smartphone screen. This allows the user to receive guidance to their destination in a visually easy-to-understand format.

[0456] Specific examples

[0457] As a concrete example, consider a situation where a wheelchair user is searching for the location of an elevator in a large shopping mall. In this case, the user sets the elevator as their destination in a navigation app and sends a request. The device determines the user's current location using beacons, Wi-Fi, and camera information and sends this information to the server. The server searches for the shopping mall's map information and generates a virtual map as needed. It then calculates the optimal route from the current location to the elevator and sends the route information to the device. The device displays this information on the AR glasses, and the user is guided to the elevator based on visual navigation. If the user's current location changes during travel, the device continuously updates its location information, and the server recalculates the route as needed and provides the latest navigation information.

[0458] This system will enable users to easily navigate difficult indoor spaces, which will be of particular help to wheelchair users and the elderly, and will also be a powerful tool for travelers to quickly reach their destinations in unfamiliar places.

[0459] The processing flow will be explained below.

[0460] Step 1:

[0461] The user launches the navigation app, inputs the starting point and destination, and sends a request, which inputs the necessary navigation information into the device.

[0462] Step 2:

[0463] The terminal receives the user's request and sends it to the server, where the request data is sent to the server using the HTTP protocol or similar.

[0464] Step 3:

[0465] The server analyzes the received request and searches for map information of the building through the network. If map information does not exist, it prepares to generate a virtual map.

[0466] Step 4:

[0467] The server generates a virtual map using past user driving data and beacon information as needed, and uses data analysis algorithms to create a virtual map structure.

[0468] Step 5:

[0469] To determine the device's current location, it acquires beacon, WiFi, GPS, and camera information, and combines this information to determine the device's current location.

[0470] Step 6:

[0471] The device sends its determined location to the server. The location data is packaged in an appropriate format (e.g., JSON) and sent to the server.

[0472] Step 7:

[0473] The server calculates the optimal route based on the current location information and map information received. Route calculations use a route search algorithm such as the A algorithm.

[0474] Step 8:

[0475] The server sends the calculation results (optimal route information) to the device. The route information is sent in an appropriate format so that all navigation-related data can be viewed on the device.

[0476] Step 9:

[0477] The optimal route information received by the device is displayed on a display device (e.g., AR glasses). Information is overlaid using AR technology to enable visual confirmation of navigation instructions.

[0478] Step 10:

[0479] The user moves to the destination according to the displayed navigation information. The user moves through the building based on the visual guide.

[0480] Step 11:

[0481] The device continuously monitors the user's current location while moving, periodically reacquiring sensor information and updating the current location.

[0482] Step 12:

[0483] The device sends updated location information to the server, and the server coordinates with the device to adjust the route based on changes in location information.

[0484] Step 13:

[0485] The server recalculates the route as needed and sends the latest navigation information to the device, ensuring that the user is always guided along the optimal route.

[0486] Step 14:

[0487] The device will then redisplay the latest route information and provide it to the user, allowing real-time navigation.

[0488] Example 1

[0489] 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."

[0490] This invention relates to a system for navigating indoor environments that acquires a user's current location information with high accuracy and provides the optimal route to reach a destination. Current technology has difficulty in identifying indoor locations and lacks a method for generating a virtual map when map information does not exist, making highly accurate navigation difficult. Furthermore, there is a lack of systems that can update location information in real time while the user is moving and flexibly recalculate routes. There is a need to solve these issues and provide users with constantly up-to-date navigation information.

[0491] 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.

[0492] In this invention, the server includes a means for receiving a location information request from a user, a means for searching for building map information on the network, and a means for generating a virtual map using the user's past driving data and beacon information if map information is not available. This enables highly accurate acquisition of the user's current location information and real-time location information updates. The terminal also includes a means for integrating beacon signals, WiFi networks, GPS data, and camera information to determine the current location, and a means for calculating an optimal route based on the acquired current location information and destination map information. The terminal also includes a means for displaying the calculated route information on a display device, continuously updating the current location information as the user moves, and recalculating the route as necessary. This allows the user to receive highly accurate navigation even in indoor environments, making navigation easier, especially for users with mobility difficulties or in unfamiliar locations.

[0493] A "user" is an entity that wishes to navigate using the system.

[0494] A "location information request" is a request from a user for navigation by specifying a starting point and a destination.

[0495] A "network" is a communication pathway between system components for communicating data.

[0496] "Building map information" is detailed information about the structure and routes inside a specified building.

[0497] A "virtual map" is a map generated based on the user's past driving data and beacon information when actual map information does not exist.

[0498] A "beacon signal" is a radio signal transmitted from a specific location, and by receiving this signal, the location can be identified.

[0499] A "WiFi network" is a network that uses wireless communication technology to send and receive data.

[0500] "GPS Data" means location information data obtained using the Global Positioning System.

[0501] "Camera information" refers to image and video data captured using a camera.

[0502] "Current location information" is information that identifies the user's location at the time the terminal is located.

[0503] A "destination" is the final destination to which a user seeks navigation.

[0504] An "optimal route" is the most efficient or shortest route from a current location to a destination.

[0505] A "display device" is a device for visually conveying navigation information to a user.

[0506] A "central aggregation device" is a device that processes and manages data from multiple terminals via a network, such as a server.

[0507] An "augmented reality device" is a device that displays digital information superimposed on the real world.

[0508] A "specific route planning algorithm" is a particular algorithmic technique used to calculate a route.

[0509] This invention is a system for providing highly accurate navigation in indoor environments. Specifically, it aims to link users, terminals, and servers to perform accurate location identification and navigation in real time, regardless of location.

[0510] System Configuration

[0511] The system consists of three main components:

[0512] 1. Users

[0513] They are the active users of the system and the ones who carry the terminals.

[0514] 2. Terminal

[0515] This refers to devices carried by users, such as smartphones, tablets, and AR glasses. The terminal acquires location information and acts as an interface for communicating with the server.

[0516] 3. Server

[0517] It is a central device that processes location information, searches map information, and calculates routes via the network.

[0518] Program processing flow

[0519] User operations

[0520] When a user requests navigation, they first launch a navigation app on their device and enter their starting point and destination, which then issues a navigation request.

[0521] Terminal handling

[0522] The device receives a request from the user and sends it to the server. The device also acquires surrounding beacon signals, WiFi networks, GPS data, and camera information to determine the current location. This acquired location information is also sent to the server.

[0523] Server Processing

[0524] The server receives the request and location information sent from the device. The server searches the network for map information for the specified building or station, and if it does not exist, it generates a virtual map using the user's past driving data and beacon information. It then calculates the optimal route based on the current location information and destination information. The algorithm used is expected to be the Dijkstra algorithm or the A algorithm.

[0525] Providing navigation information

[0526] The server sends the calculated optimal route information to the device. The device then displays the received route information on the AR glasses or smartphone screen. This allows the user to receive navigation in a visually easy-to-understand format. Furthermore, if the current location information changes as the user moves, the device continues to update the location information, and the server recalculates the route each time.

[0527] Specific examples

[0528] As a concrete example, consider a situation where a wheelchair user is searching for an elevator in a large shopping mall. The user launches a navigation app and sets the elevator as their destination. This request is sent from the device to the server. The device collects beacon signals, WiFi networks, GPS data, and camera information in real time to determine the user's current location. The server searches for the shopping mall's map information and generates a virtual map as needed. Next, it calculates the optimal route from the user's current location to the elevator and sends the route information to the device. The device displays this information on the AR glasses, allowing the user to head to the elevator with visual navigation in hand.

[0529] Prompt Sentence Examples

[0530] Here is an example prompt:

[0531] User: Launches the navigation app, enters the origin "Main Gate" and destination "Elevator", and submits the request.

[0532] Device: Sends the received request to the server and determines the current location based on beacon signals, WiFi networks, GPS data, and camera information.

[0533] Server: Calculates the optimal route based on the received current location information and destination information and sends it to the device.

[0534] Device: The received route information is displayed on the AR glasses, providing the user with visual navigation.

[0535] This system allows users to navigate indoors with high accuracy and in real time, and is particularly useful for users with mobility issues or in unfamiliar locations.

[0536] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0537] Step 1:

[0538] The user launches the navigation app and enters their starting point and destination. The entered data (starting point and destination) is sent to the device, which generates a navigation request. Input: starting point, destination. Output: navigation request.

[0539] Step 2:

[0540] The device receives a navigation request from the user and sends it to the server. The data sent includes the starting point, destination, and initial location information. Based on the received data, the device collects surrounding beacon signals, WiFi networks, GPS data, and camera information to determine the current location in real time. Input: Navigation request, various sensor data. Output: Current location information.

[0541] Step 3:

[0542] The server receives the navigation request and current location information sent from the device. The server first searches the network for map information for the specified building. If map information is not available, it generates a virtual map using the user's past driving data and beacon information. Input: Navigation request, current location information. Output: Map information or virtual map.

[0543] Step 4:

[0544] The server calculates the optimal route based on the received current location information and destination information. This process uses the Dijkstra algorithm or the A algorithm. The calculation results in each point on the route and corresponding navigation instructions. Input: Current location information, map information or virtual map. Output: Optimal route information.

[0545] Step 5:

[0546] The server sends the calculated optimal route information to the terminal. The data sent includes coordinates and instructions for each point on the route. Input: Optimal route information. Output: Route information.

[0547] Step 6:

[0548] The device analyzes the route information received from the server and presents it visually to the user, including arrows and visual guides displayed on the AR glasses or smartphone screen. Input: Route information. Output: Navigation display.

[0549] Step 7:

[0550] The user navigates based on the navigation information displayed on the device. If the user's current location changes as they move, the device continuously updates its location information and sends new location information to the server as needed. The server receives the new location information, recalculates the route as needed, and sends it to the device. This ensures that the user always receives the latest navigation information. Input: User's current location. Output: Updated route information and navigation display.

[0551] Each step works together to create a complete navigation process that helps users reach their destination safely. This system performs particularly well in indoor environments.

[0552] (Application example 1)

[0553] 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."

[0554] Current robot movement within factories is inefficient, making robot navigation difficult, especially in large factory facilities or complex layouts. This leads to problems such as reduced operational efficiency and reduced productivity. Furthermore, there is a risk that the robot will not reach its destination and will collide with obstacles during movement. The purpose of this invention is to solve these problems, optimize robot movement within factories, and provide efficient and safe navigation.

[0555] 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.

[0556] In this invention, the server includes a means for receiving a location information request from a user, a means for searching for building map information on the network, and a means for generating a virtual map using the user's past travel data and beacon information, thereby optimizing the robot's movement route within the factory and providing highly accurate navigation.

[0557] "User" refers to the entity that uses the system to request location information, and is primarily the operator or manager who manages the operation of robots within a factory.

[0558] A "terminal" is a device used by a user, and includes control devices such as smartphones, tablets, and dedicated terminals.

[0559] A "server" is a central device that processes requests over a network, searches for map information, and calculates routes.

[0560] A "location information request" refers to a request made by a user to confirm their current location or to request route guidance to a destination.

[0561] "Map information" is information that shows the layout of a building or factory, and is data necessary for route guidance.

[0562] A "virtual map" is virtual map information generated based on the user's past driving data and beacon information.

[0563] A "beacon" is a device that provides location information using short-range wireless communication, and is primarily used to identify indoor locations.

[0564] "WiFi" is a technology that uses wireless LAN technology for data communication, and is used for location determination and data transmission.

[0565] "GPS" refers to a satellite positioning system that measures positions on Earth with high precision.

[0566] "Camera information" is information for identifying the current location using video and image data captured by a camera device.

[0567] An "optimal route" refers to the most efficient route from the current point to the destination, calculated to minimize travel time and distance.

[0568] "Route calculation" refers to the process of deriving the optimal route between a specified current location and a destination.

[0569] A "display device" is a device that presents calculated route information and navigation information to a user. Examples include AR glasses and smartphone screens.

[0570] A "factory" is a facility where products are manufactured and assembled, and is the primary location where robots move.

[0571] A "robot" is a mechanical device that moves objects and processes products within a factory.

[0572] "Navigation" refers to the process of providing guidance along a route to a destination specified by a user.

[0573] The present invention is a system that optimizes the movement path of a robot within a factory and provides highly accurate navigation. This system is realized by linking users, terminals, and a server.

[0574] User operations

[0575] The user is an operator or administrator who manages the robot's movements and requests location information from the system. Specifically, the user launches a dedicated navigation app, inputs the starting point and destination, and sends the request.

[0576] Terminal handling

[0577] The terminal is a control device installed on the robot, and can be a smartphone, tablet, dedicated terminal, etc. The terminal first receives a request from the user and sends it to the server. The terminal also determines the robot's current location by combining surrounding beacon signals, WiFi networks, GPS data, and camera information.

[0578] Server Processing

[0579] The server receives requests and location information from devices via the network. It then searches for map information for the specified building or factory. If map information does not exist, it generates a virtual map based on the user's past driving data and beacon information. It then calculates the optimal route based on the received current location and destination information.

[0580] Providing navigation information

[0581] The server sends the calculated optimal route information to the terminal. The terminal receives this route information and displays it on a display device, such as the robot's control screen or AR glasses. This allows the robot to receive guidance to its destination in a visually easy-to-understand manner. If the current location changes during movement, the terminal continuously updates its location information, and the server recalculates the route as necessary to provide the latest navigation information.

[0582] Specific examples

[0583] As a concrete example, consider a situation in which a parts delivery robot in a factory transports parts from a central warehouse to a production line. The user sets the production line as the destination in a navigation app and sends a request. The device determines the robot's current location using beacons, Wi-Fi, and camera information, and sends this information to a server. The server searches map information within the factory and generates a virtual map as needed. It then calculates the optimal route from the current location to the production line and sends the route information to the device. The device displays this information on the robot's display device, and the robot heads to its destination according to the route presented.

[0584] Prompt Sentence Examples

[0585] "Update the robot's current coordinates and calculate the shortest path to the destination. The current coordinates are {'x': 1, 'y': 2} and the destination is {'x': 10, 'y': 5}."

[0586] This system improves the efficiency of robot movement within the factory, improving the operating efficiency of each manufacturing process. It also enables robots to reach their destinations safely and quickly, improving overall productivity. In this way, the present invention effectively solves various navigation issues within factories.

[0587] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0588] Step 1:

[0589] A user launches a navigation app, enters a starting point and destination, and sends a location request, which provides the device with the coordinates of the current location and destination.

[0590] Step 2:

[0591] The device receives a location request from the user. As input, it receives coordinate information of the starting point and destination specified by the user. The device sends this information to the server. As output, the received request data is forwarded to the server.

[0592] Step 3:

[0593] The server receives the request and location information from the terminal. The coordinate information of the current location and destination is provided to the server as input. The server uses this information to search the network for map information within the factory. The corresponding map information is obtained as output.

[0594] Step 4:

[0595] The server searches for map information and generates a virtual map if it does not exist. The input is the user's past driving data and beacon information. The server processes this data to generate a virtual map. The output is the newly generated virtual map information.

[0596] Step 5:

[0597] The server calculates the optimal route based on current location information and destination information. The server is provided with the coordinates of the current location, the coordinates of the destination, and map information (actual map or virtual map) as input. The server uses this data to apply a route calculation algorithm such as the A algorithm to derive the optimal route. The optimal route information is obtained as output.

[0598] Step 6:

[0599] The server sends the calculated optimal route information to the terminal. The input is the optimal route information calculated by the server. The server sends this to the terminal and provides it to the robot's control device. The output is the optimal route information transferred to the terminal.

[0600] Step 7:

[0601] The terminal receives the route information and presents it on a display device. The input is the optimal route information sent from the server. The terminal displays this information on a display device such as AR glasses or the robot's control screen. The output is visually easy-to-understand navigation information presented to the robot.

[0602] Step 8:

[0603] The robot moves towards the destination according to the presented route. The input is navigation information displayed on a display device. Based on this information, the robot uses its autonomous driving function to proceed along the route. The output is the robot's movement.

[0604] Step 9:

[0605] The terminal continuously updates the robot's current location. As input, it uses surrounding environmental data such as beacon signals, WiFi networks, GPS data, and camera information. The terminal integrates this data to re-determine the current location and sends it to the server. As output, the updated location information is provided to the server.

[0606] Step 10:

[0607] The server recalculates the route as needed based on the updated current location information. The updated current location and destination information are provided as input to the server. The server uses this to reapply algorithm A to calculate the latest optimal route. The output is the new route information.

[0608] Step 11:

[0609] The server sends new route information to the terminal, which then re-presents it on the display device. The input is the latest route information sent from the server. The terminal re-displays this information on the display device, providing the robot with up-to-date navigation information. The output is the route information shown to the robot, updated to the latest version.

[0610] 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.

[0611] This invention achieves more appropriate navigation by combining a system that receives location information requests from users and provides navigation within buildings or train stations with an emotion engine that recognizes the user's emotions. This system operates in cooperation with the user, terminal, server, and emotion engine.

[0612] System configuration

[0613] The system includes the following main components:

[0614] 1. Users

[0615] The entity that uses the system, requests location information, and receives navigation.

[0616] 2. Terminal

[0617] These are devices used by users, such as smartphones, tablets, and AR glasses, that acquire location information and display navigation information.

[0618] 3. Server

[0619] This is a device that processes requests via a network, searches for map information, and calculates routes.

[0620] 4. Emotion Engine

[0621] This module analyzes the user's facial expressions and voice to recognize emotions, allowing the navigation guidance method to be adjusted based on the user's emotions.

[0622] Program processing flow

[0623] User operations

[0624] The user launches the navigation app, inputs the starting point and destination, and sends a request, which inputs the necessary navigation information into the device.

[0625] Terminal handling

[0626] The device receives the user's request and sends it to the server. At the same time, the device acquires surrounding beacon signals, WiFi networks, GPS data, and camera information to determine its current location. It also uses the camera and microphone to collect the user's facial expressions and voice data, which are then sent to the emotion engine.

[0627] Emotion engine processing

[0628] The emotion engine analyzes the collected user facial and voice data to recognize the user's current emotions. The emotion data is sent to the server and reflected in the navigation route and guidance method.

[0629] Server Processing

[0630] The server receives requests from the device, along with location and emotion data. It searches the network for building map information and generates a virtual map as needed. It then calculates the optimal route based on the current location and destination information, taking emotion data into account. For example, if the user is feeling stressed, the server will select not only the shortest route but also a route that is mentally gentle.

[0631] Providing navigation information

[0632] The server then sends the calculated optimal route information to the device, which then receives the route information and displays the navigation information on a display device such as AR glasses or a smartphone screen. This allows the user to receive guidance to their destination in a visually easy-to-understand format.

[0633] Specific examples

[0634] As a concrete example, consider a situation where an elderly user wants to go to a specific store in a busy shopping mall. The user sets the store as a destination in the navigation app and sends a request. The device determines the user's current location based on beacons, Wi-Fi, and camera information, and sends this information to the server. At the same time, the device transmits the user's facial expressions and voice to the emotion engine.

[0635] The emotion engine analyzes the user's data and determines that the user is in a state of tension. The server searches for the shopping mall's map information, generates a virtual map as needed, and calculates the optimal route taking into account the user's level of tension. For example, it makes adjustments such as choosing wider aisles to avoid crowds or walking near elevators.

[0636] The optimal route information is sent to the device and displayed on the AR glasses. The user can follow the visual guide to the store with confidence. In this way, the system updates the user's current location and emotional state in real time, recalculating and providing the optimal route as needed.

[0637] This system allows users to easily navigate difficult indoor spaces, which is of particular help to the elderly and those who are prone to stress. It also enables travelers to reach their destinations quickly and safely, even in unfamiliar places.

[0638] The processing flow will be explained below.

[0639] Step 1:

[0640] The user launches the navigation app, inputs the starting point and destination, and sends a request, which inputs the necessary navigation information into the device.

[0641] Step 2:

[0642] The device receives the user's request and sends it to the server, while simultaneously acquiring surrounding beacon signals, WiFi networks, GPS data, and camera information to determine the device's current location.

[0643] Step 3:

[0644] The device collects the user's facial and voice data and sends it to an emotion engine, which is either built into the device or runs on the cloud.

[0645] Step 4:

[0646] The emotion engine analyzes the received facial expressions and voice data to recognize the user's emotional state, and sends the results of the judgment to the server.

[0647] Step 5:

[0648] The server receives the request from the device, along with the device's current location and emotional state, and searches the network for map information for the specified building or station.

[0649] Step 6:

[0650] If the server does not have map information, it generates a virtual map based on past user driving data and beacon information. It uses a data analysis algorithm to create a virtual map structure.

[0651] Step 7:

[0652] The server calculates the optimal route based on the current location information, destination information, and emotional state. For example, if the user is feeling stressed, the server will select a route that avoids crowds or passes near elevators.

[0653] Step 8:

[0654] The server sends the calculation results (optimal route information) to the device. The route information is sent in an appropriate format so that all navigation-related data can be viewed on the device.

[0655] Step 9:

[0656] The optimal route information received by the device is displayed on a display device (e.g., AR glasses). Information is overlaid using AR technology to enable visual confirmation of navigation instructions.

[0657] Step 10:

[0658] The user moves to the destination according to the displayed navigation information. The user moves through the building based on the visual guide.

[0659] Step 11:

[0660] The device continuously monitors the user's current location while moving, periodically reacquiring sensor information and updating the current location.

[0661] Step 12:

[0662] The device sends updated location information to the server, and the server coordinates with the device to adjust the route based on changes in location information.

[0663] Step 13:

[0664] The server recalculates the route as needed and sends the latest navigation information to the device, ensuring that the user is always guided along the optimal route.

[0665] Step 14:

[0666] The device will then redisplay the latest route information and provide it to the user, allowing real-time navigation.

[0667] Example 2

[0668] 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."

[0669] Currently, indoor and building navigation systems generally provide route guidance based on the user's location information. However, these systems do not take the user's emotional state into account, making it difficult to provide an optimal route if the user is feeling nervous or stressed. Therefore, in order to improve the user's sense of security and comfort, a system that recognizes the user's emotions and provides navigation that responds to them is needed.

[0670] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving a location information request from a user, means for searching for map information of buildings on a network, means for generating a virtual map using the user's past driving data and beacon information, means for integrating beacon, WiFi, GPS, and camera information to obtain a current location, means for obtaining facial expression data and voice data of the user, means for analyzing the facial expression data and voice data to recognize the user's emotional state, means for calculating an optimal route based on the obtained current location information, destination map information, and emotional state, means for displaying the calculated route information on a display device, and means for continuously updating the current location information and emotional state and recalculating the route as necessary. This enables navigation that takes the user's emotional state into consideration.

[0671] The "means for receiving location information requests from users" refers to the technical means for transmitting the information of the starting point and destination entered by the user through the navigation app to the server.

[0672] "Means for searching for building map information from a network" means technical means for obtaining detailed map data within a specified building via the Internet or other digital networks.

[0673] "Means for generating a virtual map" refers to a technical means for constructing a new map by utilizing the user's past driving data and beacon information to complement non-existent map information.

[0674] "Means for integrating beacon, WiFi, GPS, and camera information" refers to technical means for combining and processing data using these different location acquisition technologies to determine a user's current location.

[0675] The "means for acquiring the user's facial expression data and voice data" refers to a technical means for collecting the user's facial expression and voice using the camera and microphone of the terminal.

[0676] "Means for recognizing the user's emotional state" refers to technical means for analyzing the collected facial expression data and voice data and identifying the emotion the user is currently feeling (e.g., tension, relaxation, stress, etc.).

[0677] The "means for calculating the optimum route" refers to a technical means for calculating the most suitable route for the user, taking into account current location information, destination information, and the emotional state of the user.

[0678] "Means for displaying calculated route information on a display device" refers to the technical means for displaying information on a display device such as a smartphone or AR glasses in order to visually present the calculated navigation route to the user.

[0679] "Means for continuously updating current location information and emotional state and recalculating the route as needed" refers to technical means for updating the location information and emotional data of a user while they are moving in real time, and for recalculating the optimal route based on that information if needed.

[0680] The present invention is a system that provides more appropriate navigation to users by combining a navigation system for use in buildings or train stations with an emotion engine that recognizes the user's emotions. This system operates by linking the user, terminal, server, and emotion engine. Specific embodiments for implementing this system are described below.

[0681] System configuration

[0682] The system includes the following main components:

[0683] 1. Users

[0684] The entity that uses the system, requests location information, and receives navigation.

[0685] 2. Terminal

[0686] A device used by a user, such as a smartphone, tablet, or augmented reality glasses, that acquires location information and displays navigation information.

[0687] 3. Server

[0688] This is a device that processes requests via the network, searches for building map information, and calculates routes.

[0689] 4. Emotion Engine

[0690] This module analyzes the user's facial expressions and voice to recognize emotions, allowing the navigation guidance method to be adjusted based on the user's emotions.

[0691] Details of each element

[0692] 1. Users

[0693] A user uses a navigation app to input the information required for navigation (starting point and destination) and send a request. The user's role is to provide the system with location information and receive navigation guidance.

[0694] 2. Terminal

[0695] The device can be, for example, a smartphone or augmented reality glasses. The device has the following features:

[0696] Uses GPS, WiFi, beacons, and camera information to determine your location.

[0697] A camera and a microphone are used to capture facial expression data and voice data of the user.

[0698] The acquired data is sent to the server and emotion engine.

[0699] The navigation information received from the server is displayed to the user.

[0700] 3. Server

[0701] The server is the core component of the system and performs the following tasks:

[0702] It receives a location information request from a user and searches for building map information on the network.

[0703] If map information does not exist, a virtual map is generated using the user's past driving data and beacon information.

[0704] The system receives emotion data transmitted from the terminal and calculates the optimal route taking this into consideration.

[0705] The calculated route information is sent to the terminal.

[0706] 4. Emotion Engine

[0707] The emotion engine analyzes the user's facial expression and voice data to recognize their emotional state. This allows it to understand the user's state of tension, stress, relaxation, etc., and provide navigation accordingly. The emotion engine analyzes the data in real time and sends the results to the server.

[0708] Specific examples

[0709] As a specific example, consider a case where an elderly user wants to go to a specified store in a shopping mall.

[0710] 1. The user launches the navigation app, sets the store as the destination, and submits a request.

[0711] 2. The device determines its current location based on beacon, Wi-Fi, and camera information and sends it to the server. At the same time, it sends the user's facial expressions and voice to the emotion engine.

[0712] 3. The emotion engine analyzes the user's data and determines that they are in a state of tension.

[0713] 4. The server searches the network for building map information and generates a virtual map as needed. It calculates the optimal route, taking into account the user's sense of tension. For example, it will choose wider corridors to avoid crowds and walk near elevators.

[0714] 5. The calculated optimal route information is sent to the device and displayed on the screen of the augmented reality glasses or smartphone, allowing the user to follow the visual guidance to their destination with confidence.

[0715] Example prompts for generative AI models

[0716] "Provide navigation for users to reach their destination store in a shopping mall. Calculate the optimal route taking into account the user's current location and emotional state."

[0717] "The user is elderly and needs navigation to get to their destination on a train platform. If the user feels stressed, suggest an easy-to-walk route that avoids crowds."

[0718] In this way, the present invention provides a navigation system that enables users, particularly elderly people and those who are prone to stress, to reach their destination with peace of mind.

[0719] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0720] Step 1: Request user location

[0721] Input: The user enters a start point and destination into the navigation app.

[0722] Specific operation: The user launches the app on their smartphone, enters "home" as the starting point and "a store in the shopping mall" as the destination, and taps the send request button.

[0723] Output: The user's input information (starting point, destination) is sent to the terminal.

[0724] Step 2: Collect data from the device

[0725] Input: User location request and various data to determine current location (GPS, WiFi, beacon, camera information).

[0726] What it does: In the background, your device uses GPS to determine your location, captures WiFi and beacon signals, and uses the camera to capture video of your surroundings.

[0727] Output: Current location data, surrounding environment data, and user request information are sent to the server.

[0728] Step 3: Collecting user emotion data

[0729] Input: User's facial expression data and voice data acquired by the device's camera and microphone.

[0730] Specific operation: The device's camera captures the user's face and the microphone records the user's voice.

[0731] Output: The acquired facial expression data and voice data are sent to the emotion engine.

[0732] Step 4: Emotion Recognition in the Emotion Engine

[0733] Input: Facial expression data and speech data sent to the emotion engine.

[0734] Specific operation: The emotion engine uses facial expression recognition algorithms and voice analysis algorithms to analyze the user's emotional state, such as whether they are tense or relaxed.

[0735] Output: The analyzed emotional data (user's emotional state) is sent to the server.

[0736] Step 5: Searching for map information on the server and generating a virtual map

[0737] Input: User location request, current location data, and user emotional state data.

[0738] Specific operation: The server searches for map information for the building from the Internet or an internal database. If map information does not exist, it generates a virtual map based on the user's past driving data and beacon information.

[0739] Output: The acquired or generated map information is saved in the server.

[0740] Step 6: Optimal route calculation on the server

[0741] Input: Acquired map information, current location data, destination information, and user emotion data.

[0742] Specific operation: The server uses algorithm A to calculate the optimal route from the current location to the destination. At this time, it takes into account the user's emotional state, for example, selecting a route that takes a wide corridor if the user is nervous.

[0743] Output: The calculated optimal route information is sent to the terminal.

[0744] Step 7: Displaying navigation information

[0745] Input: Optimal route information sent from the server.

[0746] Specific operation: The route information received by the device is displayed on the screen of the augmented reality glasses or smartphone. Visual guidelines and arrows indicate the user's direction of travel.

[0747] Output: The user receives real-time visual navigation information and moves towards the destination.

[0748] Step 8: Continuously updating your location and emotional state

[0749] Input: User's location data, facial expression and voice data while moving.

[0750] Specific operation: The device periodically updates its location and emotional state, and sends new data to the server and emotion engine as needed.

[0751] Output: Updated location and emotion data are sent to the server, and the route is recalculated if necessary.

[0752] The above is a detailed explanation of each step of the processing flow of this system.

[0753] (Application example 2)

[0754] 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."

[0755] Conventional navigation systems only provide routes based on the user's location information, and have the problem of not being able to provide navigation that takes into account the user's emotional state. This has led to a demand for navigation that allows users to reach their destination comfortably without feeling stressed or anxious.

[0756] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a location information request, means for searching for building map information on the network, means for generating a virtual map if map information does not exist, means for acquiring the current location by integrating beacon, WiFi, GPS, and camera information, means for analyzing the user's facial expressions and voice to recognize emotions, means for calculating an optimal route using the recognized emotion data, means for displaying the calculated route information on a display device, and means for continuously updating the current location information and emotion data and recalculating the route as necessary. This enables navigation that takes the user's emotional state into consideration, resulting in more comfortable and safe travel.

[0757] A "location information request" is a request that a user sends by specifying information about a departure point and a destination.

[0758] "Means for searching for building map information from a network" refers to a system for obtaining maps and location information within a building using a network such as the Internet.

[0759] A "means for generating a virtual map" is a mechanism for creating a virtual map based on the user's past data and surrounding information when actual map information does not exist.

[0760] "Means for obtaining current location" refers to a mechanism that integrates beacons, WiFi, GPS, and camera information to accurately determine the user's current location.

[0761] "Means for recognizing emotions" refers to a mechanism for understanding a user's emotional state by analyzing the user's facial expressions and voice.

[0762] The "means for calculating the optimal route" is a mechanism for calculating the optimal route for the user based on the acquired current location information and emotion data.

[0763] "Means for displaying on a display device" refers to a mechanism for displaying calculated route information on a display device such as a user's smartphone or augmented reality glasses.

[0764] The "means for recalculating the route" is a mechanism for continuously updating current location information and emotion data, and recalculating the route depending on the situation.

[0765] The present invention is a navigation system that takes into account the emotional state of the user, and is particularly applicable to autonomous vehicles. The system includes the following main components:

[0766] 1. Users

[0767] The entity that uses the system and issues a navigation request by specifying the starting point and destination.

[0768] 2. Terminal

[0769] A display device such as a smartphone or head-mounted display (HMD) that acquires the user's location information and emotional data and displays navigation information.

[0770] 3. Server

[0771] This device processes requests via the network, searches for map information, generates virtual maps, and calculates optimal routes.

[0772] 4. Emotion Engine

[0773] This module analyzes the user's facial expressions and voice to recognize their emotional state and reflects this in the navigation route.

[0774] Specific processing of the system

[0775] The system starts when the user launches a navigation app on their smartphone or HMD and specifies their starting point and destination. The device receives a location request from the user and sends it to a server. At the same time, it uses beacons, Wi-Fi, GPS, and camera information to determine the user's current location and sends this information to the server.

[0776] In addition, the device uses a camera and microphone to collect the user's facial expressions and voice, which are then sent to the emotion engine. The emotion engine analyzes the user's facial and voice data to recognize their current emotional state. The emotion data is then sent to the server, which uses this information to calculate the optimal route.

[0777] Hardware and software used

[0778] Hardware

[0779] Smartphone

[0780] Head-mounted display (HMD)

[0781] Camera and microphone

[0782] software

[0783] Python Program

[0784] OpenCV (image processing library)

[0785] Keras (machine learning library)

[0786] Geopy (geographic information library)

[0787] External API (route optimization)

[0788] Data processing and calculation

[0789] The device processes images captured by the camera using OpenCV to detect faces. The detected facial images are analyzed using an emotion recognition model using Keras. Audio data is also collected by the microphone and analyzed by the emotion engine. This data is sent to the server and used to calculate the optimal route.

[0790] The server uses Geopy to obtain the coordinates of the starting and destination points, optimizes the route using an external API, and selects a route that best suits the user's mental state based on emotion data.

[0791] Specific examples

[0792] For example, if the user is feeling stressed, the server can suggest scenic routes or roads with less traffic, using prompts like the following:

[0793] "Recognize emotions from the current facial expression and voice, and if the user is feeling stressed, calculate a relaxing route and provide navigation information. Use the following data:

[0794] Starting point: 35.6895, 139.6917

[0795] Destination: 34.6937, 135.5023

[0796] Emotion: 'Stress'

[0797] In this way, the system can take into account the user's emotional state and provide a more comfortable and secure navigation experience.

[0798] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0799] Step 1:

[0800] A user launches a navigation app and inputs their starting point and destination, which generates a navigation request. The input is the coordinate information of the starting point and destination, and the output is a navigation request.

[0801] Step 2:

[0802] The device receives navigation requests from the user and sends them to the server. At the same time, it acquires beacon signals, WiFi networks, GPS data, and camera information to determine the current location. The input is data from various sensors, and the output is the current location information.

[0803] Step 3:

[0804] The device uses a camera and microphone to collect facial and voice data from the user. The collected data is sent to the emotion engine. The input is camera footage and voice data, and the output is facial images and voice clips.

[0805] Step 4:

[0806] The emotion engine analyzes the received facial and voice data to recognize the user's emotions. For emotion recognition, it uses a generative AI model. The input is a face image and an audio clip, and the output is emotion data (e.g., "stressed" or "relieved").

[0807] Step 5:

[0808] The emotion engine sends the recognized emotion data to the server. The input is the emotion data, and the output is the completion of transmission to the server.

[0809] Step 6:

[0810] The server receives current location information, destination information, and emotion data from the user. It then searches for map information and generates a virtual map as needed. The input is various information data, and the output is map information.

[0811] Step 7:

[0812] The server calculates the optimal route based on the acquired map information and emotion data. The calculation uses the A algorithm and emotion data. The input is map information and emotion data, and the output is optimal route information.

[0813] Step 8:

[0814] The server sends the calculated optimal route information to the terminal. The input is the optimal route information, and the output is the completion of transmission to the terminal.

[0815] Step 9:

[0816] The terminal displays the received optimal route information on a display device such as a smartphone or HMD. The input is optimal route information, and the output is a visual navigation guide.

[0817] Step 10:

[0818] The device continuously updates its current location and emotion data, and requests route recalculation from the server as needed, providing optimal navigation in real time. The input is the current location and emotion data, and the output is updated navigation information.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] [Third embodiment]

[0823] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0824] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0825] 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).

[0826] 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.

[0827] 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.

[0828] 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).

[0829] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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."

[0835] This invention is a system that provides navigation in indoor environments such as buildings, train stations, etc. This system links users, terminals, and servers, enabling highly accurate location identification and navigation wherever users are.

[0836] System configuration

[0837] The system includes the following main components:

[0838] 1. Users

[0839] The user of the system and the entity requesting location information.

[0840] 2. Terminal

[0841] These are devices used by users, such as smartphones, tablets, and AR glasses. These devices acquire location information and display navigation information.

[0842] 3. Server

[0843] It is a central device that processes requests over the network and provides map information and route calculations.

[0844] Program processing flow

[0845] User operations

[0846] When a user wants to navigate from their current location to a destination within a building, they first launch a navigation app, enter their starting point and destination, and make a navigation request.

[0847] Terminal handling

[0848] The device receives requests from users and sends them to the server, and determines its current location by combining surrounding beacon signals, WiFi networks, GPS data, and camera information.

[0849] Server Processing

[0850] The server receives the request and location information from the device and searches the network for map information for the specified building or station. If map information does not exist, it generates a virtual map based on the user's past driving data and beacon information. It also calculates the optimal route based on the received current location and destination information.

[0851] Providing navigation information

[0852] The server then sends the calculated optimal route information to the device, which then receives the route information and displays the navigation information on a display device such as AR glasses or a smartphone screen. This allows the user to receive guidance to their destination in a visually easy-to-understand format.

[0853] Specific examples

[0854] As a concrete example, consider a situation where a wheelchair user is searching for the location of an elevator in a large shopping mall. In this case, the user sets the elevator as their destination in a navigation app and sends a request. The device determines the user's current location using beacons, Wi-Fi, and camera information and sends this information to the server. The server searches for the shopping mall's map information and generates a virtual map as needed. It then calculates the optimal route from the current location to the elevator and sends the route information to the device. The device displays this information on the AR glasses, and the user is guided to the elevator based on visual navigation. If the user's current location changes during travel, the device continuously updates its location information, and the server recalculates the route as needed and provides the latest navigation information.

[0855] This system will enable users to easily navigate difficult indoor spaces, which will be of particular help to wheelchair users and the elderly, and will also be a powerful tool for travelers to quickly reach their destinations in unfamiliar places.

[0856] The processing flow will be explained below.

[0857] Step 1:

[0858] The user launches the navigation app, inputs the starting point and destination, and sends a request, which inputs the necessary navigation information into the device.

[0859] Step 2:

[0860] The terminal receives the user's request and sends it to the server, where the request data is sent to the server using the HTTP protocol or similar.

[0861] Step 3:

[0862] The server analyzes the received request and searches for map information of the building through the network. If map information does not exist, it prepares to generate a virtual map.

[0863] Step 4:

[0864] The server generates a virtual map using past user driving data and beacon information as needed, and uses data analysis algorithms to create a virtual map structure.

[0865] Step 5:

[0866] To determine the device's current location, it acquires beacon, WiFi, GPS, and camera information, and combines this information to determine the device's current location.

[0867] Step 6:

[0868] The device sends its determined location to the server. The location data is packaged in an appropriate format (e.g., JSON) and sent to the server.

[0869] Step 7:

[0870] The server calculates the optimal route based on the current location information and map information received. Route calculations use a route search algorithm such as the A algorithm.

[0871] Step 8:

[0872] The server sends the calculation results (optimal route information) to the device. The route information is sent in an appropriate format so that all navigation-related data can be viewed on the device.

[0873] Step 9:

[0874] The optimal route information received by the device is displayed on a display device (e.g., AR glasses). Information is overlaid using AR technology to enable visual confirmation of navigation instructions.

[0875] Step 10:

[0876] The user moves to the destination according to the displayed navigation information. The user moves through the building based on the visual guide.

[0877] Step 11:

[0878] The device continuously monitors the user's current location while moving, periodically reacquiring sensor information and updating the current location.

[0879] Step 12:

[0880] The device sends updated location information to the server, and the server coordinates with the device to adjust the route based on changes in location information.

[0881] Step 13:

[0882] The server recalculates the route as needed and sends the latest navigation information to the device, ensuring that the user is always guided along the optimal route.

[0883] Step 14:

[0884] The device will then redisplay the latest route information and provide it to the user, allowing real-time navigation.

[0885] Example 1

[0886] 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."

[0887] This invention relates to a system for navigating indoor environments that acquires a user's current location information with high accuracy and provides the optimal route to reach a destination. Current technology has difficulty in identifying indoor locations and lacks a method for generating a virtual map when map information does not exist, making highly accurate navigation difficult. Furthermore, there is a lack of systems that can update location information in real time while the user is moving and flexibly recalculate routes. There is a need to solve these issues and provide users with constantly up-to-date navigation information.

[0888] 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.

[0889] In this invention, the server includes a means for receiving a location information request from a user, a means for searching for building map information on the network, and a means for generating a virtual map using the user's past driving data and beacon information if map information is not available. This enables highly accurate acquisition of the user's current location information and real-time location information updates. The terminal also includes a means for integrating beacon signals, WiFi networks, GPS data, and camera information to determine the current location, and a means for calculating an optimal route based on the acquired current location information and destination map information. The terminal also includes a means for displaying the calculated route information on a display device, continuously updating the current location information as the user moves, and recalculating the route as necessary. This allows the user to receive highly accurate navigation even in indoor environments, making navigation easier, especially for users with mobility difficulties or in unfamiliar locations.

[0890] A "user" is an entity that wishes to navigate using the system.

[0891] A "location information request" is a request from a user for navigation by specifying a starting point and a destination.

[0892] A "network" is a communication pathway between system components for communicating data.

[0893] "Building map information" is detailed information about the structure and routes inside a specified building.

[0894] A "virtual map" is a map generated based on the user's past driving data and beacon information when actual map information does not exist.

[0895] A "beacon signal" is a radio signal transmitted from a specific location, and by receiving this signal, the location can be identified.

[0896] A "WiFi network" is a network that uses wireless communication technology to send and receive data.

[0897] "GPS Data" means location information data obtained using the Global Positioning System.

[0898] "Camera information" refers to image and video data captured using a camera.

[0899] "Current location information" is information that identifies the user's location at the time the terminal is located.

[0900] A "destination" is the final destination to which a user seeks navigation.

[0901] An "optimal route" is the most efficient or shortest route from a current location to a destination.

[0902] A "display device" is a device for visually conveying navigation information to a user.

[0903] A "central aggregation device" is a device that processes and manages data from multiple terminals via a network, such as a server.

[0904] An "augmented reality device" is a device that displays digital information superimposed on the real world.

[0905] A "specific route planning algorithm" is a particular algorithmic technique used to calculate a route.

[0906] This invention is a system for providing highly accurate navigation in indoor environments. Specifically, it aims to link users, terminals, and servers to perform accurate location identification and navigation in real time, regardless of location.

[0907] System Configuration

[0908] The system consists of three main components:

[0909] 1. Users

[0910] They are the active users of the system and the ones who carry the terminals.

[0911] 2. Terminal

[0912] This refers to devices carried by users, such as smartphones, tablets, and AR glasses. The terminal acquires location information and acts as an interface for communicating with the server.

[0913] 3. Server

[0914] It is a central device that processes location information, searches map information, and calculates routes via the network.

[0915] Program processing flow

[0916] User operations

[0917] When a user requests navigation, they first launch a navigation app on their device and enter their starting point and destination, which then issues a navigation request.

[0918] Terminal handling

[0919] The device receives a request from the user and sends it to the server. The device also acquires surrounding beacon signals, WiFi networks, GPS data, and camera information to determine the current location. This acquired location information is also sent to the server.

[0920] Server Processing

[0921] The server receives the request and location information sent from the device. The server searches the network for map information for the specified building or station, and if it does not exist, it generates a virtual map using the user's past driving data and beacon information. It then calculates the optimal route based on the current location information and destination information. The algorithm used is expected to be the Dijkstra algorithm or the A algorithm.

[0922] Providing navigation information

[0923] The server sends the calculated optimal route information to the device. The device then displays the received route information on the AR glasses or smartphone screen. This allows the user to receive navigation in a visually easy-to-understand format. Furthermore, if the current location information changes as the user moves, the device continues to update the location information, and the server recalculates the route each time.

[0924] Specific examples

[0925] As a concrete example, consider a situation where a wheelchair user is searching for an elevator in a large shopping mall. The user launches a navigation app and sets the elevator as their destination. This request is sent from the device to the server. The device collects beacon signals, WiFi networks, GPS data, and camera information in real time to determine the user's current location. The server searches for the shopping mall's map information and generates a virtual map as needed. Next, it calculates the optimal route from the user's current location to the elevator and sends the route information to the device. The device displays this information on the AR glasses, allowing the user to head to the elevator with visual navigation in hand.

[0926] Prompt Sentence Examples

[0927] Here is an example prompt:

[0928] User: Launches the navigation app, enters the origin "Main Gate" and destination "Elevator", and submits the request.

[0929] Device: Sends the received request to the server and determines the current location based on beacon signals, WiFi networks, GPS data, and camera information.

[0930] Server: Calculates the optimal route based on the received current location information and destination information and sends it to the device.

[0931] Device: The received route information is displayed on the AR glasses, providing the user with visual navigation.

[0932] This system allows users to navigate indoors with high accuracy and in real time, and is particularly useful for users with mobility issues or in unfamiliar locations.

[0933] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0934] Step 1:

[0935] The user launches the navigation app and enters their starting point and destination. The entered data (starting point and destination) is sent to the device, which generates a navigation request. Input: starting point, destination. Output: navigation request.

[0936] Step 2:

[0937] The device receives a navigation request from the user and sends it to the server. The data sent includes the starting point, destination, and initial location information. Based on the received data, the device collects surrounding beacon signals, WiFi networks, GPS data, and camera information to determine the current location in real time. Input: Navigation request, various sensor data. Output: Current location information.

[0938] Step 3:

[0939] The server receives the navigation request and current location information sent from the device. The server first searches the network for map information for the specified building. If map information is not available, it generates a virtual map using the user's past driving data and beacon information. Input: Navigation request, current location information. Output: Map information or virtual map.

[0940] Step 4:

[0941] The server calculates the optimal route based on the received current location information and destination information. This process uses the Dijkstra algorithm or the A algorithm. The calculation results in each point on the route and corresponding navigation instructions. Input: Current location information, map information or virtual map. Output: Optimal route information.

[0942] Step 5:

[0943] The server sends the calculated optimal route information to the terminal. The data sent includes coordinates and instructions for each point on the route. Input: Optimal route information. Output: Route information.

[0944] Step 6:

[0945] The device analyzes the route information received from the server and presents it visually to the user, including arrows and visual guides displayed on the AR glasses or smartphone screen. Input: Route information. Output: Navigation display.

[0946] Step 7:

[0947] The user navigates based on the navigation information displayed on the device. If the user's current location changes as they move, the device continuously updates its location information and sends new location information to the server as needed. The server receives the new location information, recalculates the route as needed, and sends it to the device. This ensures that the user always receives the latest navigation information. Input: User's current location. Output: Updated route information and navigation display.

[0948] Each step works together to create a complete navigation process that helps users reach their destination safely. This system performs particularly well in indoor environments.

[0949] (Application example 1)

[0950] 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."

[0951] Current robot movement within factories is inefficient, making robot navigation difficult, especially in large factory facilities or complex layouts. This leads to problems such as reduced operational efficiency and reduced productivity. Furthermore, there is a risk that the robot will not reach its destination and will collide with obstacles during movement. The purpose of this invention is to solve these problems, optimize robot movement within factories, and provide efficient and safe navigation.

[0952] 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.

[0953] In this invention, the server includes a means for receiving a location information request from a user, a means for searching for building map information on the network, and a means for generating a virtual map using the user's past travel data and beacon information, thereby optimizing the robot's movement route within the factory and providing highly accurate navigation.

[0954] "User" refers to the entity that uses the system to request location information, and is primarily the operator or manager who manages the operation of robots within a factory.

[0955] A "terminal" is a device used by a user, and includes control devices such as smartphones, tablets, and dedicated terminals.

[0956] A "server" is a central device that processes requests over a network, searches for map information, and calculates routes.

[0957] A "location information request" refers to a request made by a user to confirm their current location or to request route guidance to a destination.

[0958] "Map information" is information that shows the layout of a building or factory, and is data necessary for route guidance.

[0959] A "virtual map" is virtual map information generated based on the user's past driving data and beacon information.

[0960] A "beacon" is a device that provides location information using short-range wireless communication, and is primarily used to identify indoor locations.

[0961] "WiFi" is a technology that uses wireless LAN technology for data communication, and is used for location determination and data transmission.

[0962] "GPS" refers to a satellite positioning system that measures positions on Earth with high precision.

[0963] "Camera information" is information for identifying the current location using video and image data captured by a camera device.

[0964] An "optimal route" refers to the most efficient route from the current point to the destination, calculated to minimize travel time and distance.

[0965] "Route calculation" refers to the process of deriving the optimal route between a specified current location and a destination.

[0966] A "display device" is a device that presents calculated route information and navigation information to a user. Examples include AR glasses and smartphone screens.

[0967] A "factory" is a facility where products are manufactured and assembled, and is the primary location where robots move.

[0968] A "robot" is a mechanical device that moves objects and processes products within a factory.

[0969] "Navigation" refers to the process of providing guidance along a route to a destination specified by a user.

[0970] The present invention is a system that optimizes the movement path of a robot within a factory and provides highly accurate navigation. This system is realized by linking users, terminals, and a server.

[0971] User operations

[0972] The user is an operator or administrator who manages the robot's movements and requests location information from the system. Specifically, the user launches a dedicated navigation app, inputs the starting point and destination, and sends the request.

[0973] Terminal handling

[0974] The terminal is a control device installed on the robot, and can be a smartphone, tablet, dedicated terminal, etc. The terminal first receives a request from the user and sends it to the server. The terminal also determines the robot's current location by combining surrounding beacon signals, WiFi networks, GPS data, and camera information.

[0975] Server Processing

[0976] The server receives requests and location information from devices via the network. It then searches for map information for the specified building or factory. If map information does not exist, it generates a virtual map based on the user's past driving data and beacon information. It then calculates the optimal route based on the received current location and destination information.

[0977] Providing navigation information

[0978] The server sends the calculated optimal route information to the terminal. The terminal receives this route information and displays it on a display device, such as the robot's control screen or AR glasses. This allows the robot to receive guidance to its destination in a visually easy-to-understand manner. If the current location changes during movement, the terminal continuously updates its location information, and the server recalculates the route as necessary to provide the latest navigation information.

[0979] Specific examples

[0980] As a concrete example, consider a situation in which a parts delivery robot in a factory transports parts from a central warehouse to a production line. The user sets the production line as the destination in a navigation app and sends a request. The device determines the robot's current location using beacons, Wi-Fi, and camera information, and sends this information to a server. The server searches map information within the factory and generates a virtual map as needed. It then calculates the optimal route from the current location to the production line and sends the route information to the device. The device displays this information on the robot's display device, and the robot heads to its destination according to the route presented.

[0981] Prompt Sentence Examples

[0982] "Update the robot's current coordinates and calculate the shortest path to the destination. The current coordinates are {'x': 1, 'y': 2} and the destination is {'x': 10, 'y': 5}."

[0983] This system improves the efficiency of robot movement within the factory, improving the operating efficiency of each manufacturing process. It also enables robots to reach their destinations safely and quickly, improving overall productivity. In this way, the present invention effectively solves various navigation issues within factories.

[0984] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0985] Step 1:

[0986] A user launches a navigation app, enters a starting point and destination, and sends a location request, which provides the device with the coordinates of the current location and destination.

[0987] Step 2:

[0988] The device receives a location request from the user. As input, it receives coordinate information of the starting point and destination specified by the user. The device sends this information to the server. As output, the received request data is forwarded to the server.

[0989] Step 3:

[0990] The server receives the request and location information from the terminal. The coordinate information of the current location and destination is provided to the server as input. The server uses this information to search the network for map information within the factory. The corresponding map information is obtained as output.

[0991] Step 4:

[0992] The server searches for map information and generates a virtual map if it does not exist. The input is the user's past driving data and beacon information. The server processes this data to generate a virtual map. The output is the newly generated virtual map information.

[0993] Step 5:

[0994] The server calculates the optimal route based on current location information and destination information. The server is provided with the coordinates of the current location, the coordinates of the destination, and map information (actual map or virtual map) as input. The server uses this data to apply a route calculation algorithm such as the A algorithm to derive the optimal route. The optimal route information is obtained as output.

[0995] Step 6:

[0996] The server sends the calculated optimal route information to the terminal. The input is the optimal route information calculated by the server. The server sends this to the terminal and provides it to the robot's control device. The output is the optimal route information transferred to the terminal.

[0997] Step 7:

[0998] The terminal receives the route information and presents it on a display device. The input is the optimal route information sent from the server. The terminal displays this information on a display device such as AR glasses or the robot's control screen. The output is visually easy-to-understand navigation information presented to the robot.

[0999] Step 8:

[1000] The robot moves towards the destination according to the presented route. The input is navigation information displayed on a display device. Based on this information, the robot uses its autonomous driving function to proceed along the route. The output is the robot's movement.

[1001] Step 9:

[1002] The terminal continuously updates the robot's current location. As input, it uses surrounding environmental data such as beacon signals, WiFi networks, GPS data, and camera information. The terminal integrates this data to re-determine the current location and sends it to the server. As output, the updated location information is provided to the server.

[1003] Step 10:

[1004] The server recalculates the route as needed based on the updated current location information. The updated current location and destination information are provided as input to the server. The server uses this to reapply algorithm A to calculate the latest optimal route. The output is the new route information.

[1005] Step 11:

[1006] The server sends new route information to the terminal, which then re-presents it on the display device. The input is the latest route information sent from the server. The terminal re-displays this information on the display device, providing the robot with up-to-date navigation information. The output is the route information shown to the robot, updated to the latest version.

[1007] 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.

[1008] This invention achieves more appropriate navigation by combining a system that receives location information requests from users and provides navigation within buildings or train stations with an emotion engine that recognizes the user's emotions. This system operates in cooperation with the user, terminal, server, and emotion engine.

[1009] System configuration

[1010] The system includes the following main components:

[1011] 1. Users

[1012] The entity that uses the system, requests location information, and receives navigation.

[1013] 2. Terminal

[1014] These are devices used by users, such as smartphones, tablets, and AR glasses, that acquire location information and display navigation information.

[1015] 3. Server

[1016] This is a device that processes requests via a network, searches for map information, and calculates routes.

[1017] 4. Emotion Engine

[1018] This module analyzes the user's facial expressions and voice to recognize emotions, allowing the navigation guidance method to be adjusted based on the user's emotions.

[1019] Program processing flow

[1020] User operations

[1021] The user launches the navigation app, inputs the starting point and destination, and sends a request, which inputs the necessary navigation information into the device.

[1022] Terminal handling

[1023] The device receives the user's request and sends it to the server. At the same time, the device acquires surrounding beacon signals, WiFi networks, GPS data, and camera information to determine its current location. It also uses the camera and microphone to collect the user's facial expressions and voice data, which are then sent to the emotion engine.

[1024] Emotion engine processing

[1025] The emotion engine analyzes the collected user facial and voice data to recognize the user's current emotions. The emotion data is sent to the server and reflected in the navigation route and guidance method.

[1026] Server Processing

[1027] The server receives requests from the device, along with location and emotion data. It searches the network for building map information and generates a virtual map as needed. It then calculates the optimal route based on the current location and destination information, taking emotion data into account. For example, if the user is feeling stressed, the server will select not only the shortest route but also a route that is mentally gentle.

[1028] Providing navigation information

[1029] The server then sends the calculated optimal route information to the device, which then receives the route information and displays the navigation information on a display device such as AR glasses or a smartphone screen. This allows the user to receive guidance to their destination in a visually easy-to-understand format.

[1030] Specific examples

[1031] As a concrete example, consider a situation where an elderly user wants to go to a specific store in a busy shopping mall. The user sets the store as a destination in the navigation app and sends a request. The device determines the user's current location based on beacons, Wi-Fi, and camera information, and sends this information to the server. At the same time, the device transmits the user's facial expressions and voice to the emotion engine.

[1032] The emotion engine analyzes the user's data and determines that the user is in a state of tension. The server searches for the shopping mall's map information, generates a virtual map as needed, and calculates the optimal route taking into account the user's level of tension. For example, it makes adjustments such as choosing wider aisles to avoid crowds or walking near elevators.

[1033] The optimal route information is sent to the device and displayed on the AR glasses. The user can follow the visual guide to the store with confidence. In this way, the system updates the user's current location and emotional state in real time, recalculating and providing the optimal route as needed.

[1034] This system allows users to easily navigate difficult indoor spaces, which is of particular help to the elderly and those who are prone to stress. It also enables travelers to reach their destinations quickly and safely, even in unfamiliar places.

[1035] The processing flow will be explained below.

[1036] Step 1:

[1037] The user launches the navigation app, inputs the starting point and destination, and sends a request, which inputs the necessary navigation information into the device.

[1038] Step 2:

[1039] The device receives the user's request and sends it to the server, while simultaneously acquiring surrounding beacon signals, WiFi networks, GPS data, and camera information to determine the device's current location.

[1040] Step 3:

[1041] The device collects the user's facial and voice data and sends it to an emotion engine, which is either built into the device or runs on the cloud.

[1042] Step 4:

[1043] The emotion engine analyzes the received facial expressions and voice data to recognize the user's emotional state, and sends the results of the judgment to the server.

[1044] Step 5:

[1045] The server receives the request from the device, along with the device's current location and emotional state, and searches the network for map information for the specified building or station.

[1046] Step 6:

[1047] If the server does not have map information, it generates a virtual map based on past user driving data and beacon information. It uses a data analysis algorithm to create a virtual map structure.

[1048] Step 7:

[1049] The server calculates the optimal route based on the current location information, destination information, and emotional state. For example, if the user is feeling stressed, the server will select a route that avoids crowds or passes near elevators.

[1050] Step 8:

[1051] The server sends the calculation results (optimal route information) to the device. The route information is sent in an appropriate format so that all navigation-related data can be viewed on the device.

[1052] Step 9:

[1053] The optimal route information received by the device is displayed on a display device (e.g., AR glasses). Information is overlaid using AR technology to enable visual confirmation of navigation instructions.

[1054] Step 10:

[1055] The user moves to the destination according to the displayed navigation information. The user moves through the building based on the visual guide.

[1056] Step 11:

[1057] The device continuously monitors the user's current location while moving, periodically reacquiring sensor information and updating the current location.

[1058] Step 12:

[1059] The device sends updated location information to the server, and the server coordinates with the device to adjust the route based on changes in location information.

[1060] Step 13:

[1061] The server recalculates the route as needed and sends the latest navigation information to the device, ensuring that the user is always guided along the optimal route.

[1062] Step 14:

[1063] The device will then redisplay the latest route information and provide it to the user, allowing real-time navigation.

[1064] Example 2

[1065] 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."

[1066] Currently, indoor and building navigation systems generally provide route guidance based on the user's location information. However, these systems do not take the user's emotional state into account, making it difficult to provide an optimal route if the user is feeling nervous or stressed. Therefore, in order to improve the user's sense of security and comfort, a system that recognizes the user's emotions and provides navigation that responds to them is needed.

[1067] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving a location information request from a user, means for searching for map information of buildings on a network, means for generating a virtual map using the user's past driving data and beacon information, means for integrating beacon, WiFi, GPS, and camera information to obtain a current location, means for obtaining facial expression data and voice data of the user, means for analyzing the facial expression data and voice data to recognize the user's emotional state, means for calculating an optimal route based on the obtained current location information, destination map information, and emotional state, means for displaying the calculated route information on a display device, and means for continuously updating the current location information and emotional state and recalculating the route as necessary. This enables navigation that takes the user's emotional state into consideration.

[1068] The "means for receiving location information requests from users" refers to the technical means for transmitting the information of the starting point and destination entered by the user through the navigation app to the server.

[1069] "Means for searching for building map information from a network" means technical means for obtaining detailed map data within a specified building via the Internet or other digital networks.

[1070] "Means for generating a virtual map" refers to a technical means for constructing a new map by utilizing the user's past driving data and beacon information to complement non-existent map information.

[1071] "Means for integrating beacon, WiFi, GPS, and camera information" refers to technical means for combining and processing data using these different location acquisition technologies to determine a user's current location.

[1072] The "means for acquiring the user's facial expression data and voice data" refers to a technical means for collecting the user's facial expression and voice using the camera and microphone of the terminal.

[1073] "Means for recognizing the user's emotional state" refers to technical means for analyzing the collected facial expression data and voice data and identifying the emotion the user is currently feeling (e.g., tension, relaxation, stress, etc.).

[1074] The "means for calculating the optimum route" refers to a technical means for calculating the most suitable route for the user, taking into account current location information, destination information, and the emotional state of the user.

[1075] "Means for displaying calculated route information on a display device" refers to the technical means for displaying information on a display device such as a smartphone or AR glasses in order to visually present the calculated navigation route to the user.

[1076] "Means for continuously updating current location information and emotional state and recalculating the route as needed" refers to technical means for updating the location information and emotional data of a user while they are moving in real time, and for recalculating the optimal route based on that information if needed.

[1077] The present invention is a system that provides more appropriate navigation to users by combining a navigation system for use in buildings or train stations with an emotion engine that recognizes the user's emotions. This system operates by linking the user, terminal, server, and emotion engine. Specific embodiments for implementing this system are described below.

[1078] System configuration

[1079] The system includes the following main components:

[1080] 1. Users

[1081] The entity that uses the system, requests location information, and receives navigation.

[1082] 2. Terminal

[1083] A device used by a user, such as a smartphone, tablet, or augmented reality glasses, that acquires location information and displays navigation information.

[1084] 3. Server

[1085] This is a device that processes requests via the network, searches for building map information, and calculates routes.

[1086] 4. Emotion Engine

[1087] This module analyzes the user's facial expressions and voice to recognize emotions, allowing the navigation guidance method to be adjusted based on the user's emotions.

[1088] Details of each element

[1089] 1. Users

[1090] A user uses a navigation app to input the information required for navigation (starting point and destination) and send a request. The user's role is to provide the system with location information and receive navigation guidance.

[1091] 2. Terminal

[1092] The device can be, for example, a smartphone or augmented reality glasses. The device has the following features:

[1093] Uses GPS, WiFi, beacons, and camera information to determine your location.

[1094] A camera and a microphone are used to capture facial expression data and voice data of the user.

[1095] The acquired data is sent to the server and emotion engine.

[1096] The navigation information received from the server is displayed to the user.

[1097] 3. Server

[1098] The server is the core component of the system and performs the following tasks:

[1099] It receives a location information request from a user and searches for building map information on the network.

[1100] If map information does not exist, a virtual map is generated using the user's past driving data and beacon information.

[1101] The system receives emotion data transmitted from the terminal and calculates the optimal route taking this into consideration.

[1102] The calculated route information is sent to the terminal.

[1103] 4. Emotion Engine

[1104] The emotion engine analyzes the user's facial expression and voice data to recognize their emotional state. This allows it to understand the user's state of tension, stress, relaxation, etc., and provide navigation accordingly. The emotion engine analyzes the data in real time and sends the results to the server.

[1105] Specific examples

[1106] As a specific example, consider a case where an elderly user wants to go to a specified store in a shopping mall.

[1107] 1. The user launches the navigation app, sets the store as the destination, and submits a request.

[1108] 2. The device determines its current location based on beacon, Wi-Fi, and camera information and sends it to the server. At the same time, it sends the user's facial expressions and voice to the emotion engine.

[1109] 3. The emotion engine analyzes the user's data and determines that they are in a state of tension.

[1110] 4. The server searches the network for building map information and generates a virtual map as needed. It calculates the optimal route, taking into account the user's sense of tension. For example, it will choose wider corridors to avoid crowds and walk near elevators.

[1111] 5. The calculated optimal route information is sent to the device and displayed on the screen of the augmented reality glasses or smartphone, allowing the user to follow the visual guidance to their destination with confidence.

[1112] Example prompts for generative AI models

[1113] "Provide navigation for users to reach their destination store in a shopping mall. Calculate the optimal route taking into account the user's current location and emotional state."

[1114] "The user is elderly and needs navigation to get to their destination on a train platform. If the user feels stressed, suggest an easy-to-walk route that avoids crowds."

[1115] In this way, the present invention provides a navigation system that enables users, particularly elderly people and those who are prone to stress, to reach their destination with peace of mind.

[1116] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1117] Step 1: Request user location

[1118] Input: The user enters a start point and destination into the navigation app.

[1119] Specific operation: The user launches the app on their smartphone, enters "home" as the starting point and "a store in the shopping mall" as the destination, and taps the send request button.

[1120] Output: The user's input information (starting point, destination) is sent to the terminal.

[1121] Step 2: Collect data from the device

[1122] Input: User location request and various data to determine current location (GPS, WiFi, beacon, camera information).

[1123] What it does: In the background, your device uses GPS to determine your location, captures WiFi and beacon signals, and uses the camera to capture video of your surroundings.

[1124] Output: Current location data, surrounding environment data, and user request information are sent to the server.

[1125] Step 3: Collecting user emotion data

[1126] Input: User's facial expression data and voice data acquired by the device's camera and microphone.

[1127] Specific operation: The device's camera captures the user's face and the microphone records the user's voice.

[1128] Output: The acquired facial expression data and voice data are sent to the emotion engine.

[1129] Step 4: Emotion Recognition in the Emotion Engine

[1130] Input: Facial expression data and speech data sent to the emotion engine.

[1131] Specific operation: The emotion engine uses facial expression recognition algorithms and voice analysis algorithms to analyze the user's emotional state, such as whether they are tense or relaxed.

[1132] Output: The analyzed emotional data (user's emotional state) is sent to the server.

[1133] Step 5: Searching for map information on the server and generating a virtual map

[1134] Input: User location request, current location data, and user emotional state data.

[1135] Specific operation: The server searches for map information for the building from the Internet or an internal database. If map information does not exist, it generates a virtual map based on the user's past driving data and beacon information.

[1136] Output: The acquired or generated map information is saved in the server.

[1137] Step 6: Optimal route calculation on the server

[1138] Input: Acquired map information, current location data, destination information, and user emotion data.

[1139] Specific operation: The server uses algorithm A to calculate the optimal route from the current location to the destination. At this time, it takes into account the user's emotional state, for example, selecting a route that takes a wide corridor if the user is nervous.

[1140] Output: The calculated optimal route information is sent to the terminal.

[1141] Step 7: Displaying navigation information

[1142] Input: Optimal route information sent from the server.

[1143] Specific operation: The route information received by the device is displayed on the screen of the augmented reality glasses or smartphone. Visual guidelines and arrows indicate the user's direction of travel.

[1144] Output: The user receives real-time visual navigation information and moves towards the destination.

[1145] Step 8: Continuously updating your location and emotional state

[1146] Input: User's location data, facial expression and voice data while moving.

[1147] Specific operation: The device periodically updates its location and emotional state, and sends new data to the server and emotion engine as needed.

[1148] Output: Updated location and emotion data are sent to the server, and the route is recalculated if necessary.

[1149] The above is a detailed explanation of each step of the processing flow of this system.

[1150] (Application example 2)

[1151] 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."

[1152] Conventional navigation systems only provide routes based on the user's location information, and have the problem of not being able to provide navigation that takes into account the user's emotional state. This has led to a demand for navigation that allows users to reach their destination comfortably without feeling stressed or anxious.

[1153] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a location information request, means for searching for building map information on the network, means for generating a virtual map if map information does not exist, means for acquiring the current location by integrating beacon, WiFi, GPS, and camera information, means for analyzing the user's facial expressions and voice to recognize emotions, means for calculating an optimal route using the recognized emotion data, means for displaying the calculated route information on a display device, and means for continuously updating the current location information and emotion data and recalculating the route as necessary. This enables navigation that takes the user's emotional state into consideration, resulting in more comfortable and safe travel.

[1154] A "location information request" is a request that a user sends by specifying information about a departure point and a destination.

[1155] "Means for searching for building map information from a network" refers to a system for obtaining maps and location information within a building using a network such as the Internet.

[1156] A "means for generating a virtual map" is a mechanism for creating a virtual map based on the user's past data and surrounding information when actual map information does not exist.

[1157] "Means for obtaining current location" refers to a mechanism that integrates beacons, WiFi, GPS, and camera information to accurately determine the user's current location.

[1158] "Means for recognizing emotions" refers to a mechanism for understanding a user's emotional state by analyzing the user's facial expressions and voice.

[1159] The "means for calculating the optimal route" is a mechanism for calculating the optimal route for the user based on the acquired current location information and emotion data.

[1160] "Means for displaying on a display device" refers to a mechanism for displaying calculated route information on a display device such as a user's smartphone or augmented reality glasses.

[1161] The "means for recalculating the route" is a mechanism for continuously updating current location information and emotion data, and recalculating the route depending on the situation.

[1162] The present invention is a navigation system that takes into account the emotional state of the user, and is particularly applicable to autonomous vehicles. The system includes the following main components:

[1163] 1. Users

[1164] The entity that uses the system and issues a navigation request by specifying the starting point and destination.

[1165] 2. Terminal

[1166] A display device such as a smartphone or head-mounted display (HMD) that acquires the user's location information and emotional data and displays navigation information.

[1167] 3. Server

[1168] This device processes requests via the network, searches for map information, generates virtual maps, and calculates optimal routes.

[1169] 4. Emotion Engine

[1170] This module analyzes the user's facial expressions and voice to recognize their emotional state and reflects this in the navigation route.

[1171] Specific processing of the system

[1172] The system starts when the user launches a navigation app on their smartphone or HMD and specifies their starting point and destination. The device receives a location request from the user and sends it to a server. At the same time, it uses beacons, Wi-Fi, GPS, and camera information to determine the user's current location and sends this information to the server.

[1173] In addition, the device uses a camera and microphone to collect the user's facial expressions and voice, which are then sent to the emotion engine. The emotion engine analyzes the user's facial and voice data to recognize their current emotional state. The emotion data is then sent to the server, which uses this information to calculate the optimal route.

[1174] Hardware and software used

[1175] Hardware

[1176] Smartphone

[1177] Head-mounted display (HMD)

[1178] Camera and microphone

[1179] software

[1180] Python Program

[1181] OpenCV (image processing library)

[1182] Keras (machine learning library)

[1183] Geopy (geographic information library)

[1184] External API (route optimization)

[1185] Data processing and calculation

[1186] The device processes images captured by the camera using OpenCV to detect faces. The detected facial images are analyzed using an emotion recognition model using Keras. Audio data is also collected by the microphone and analyzed by the emotion engine. This data is sent to the server and used to calculate the optimal route.

[1187] The server uses Geopy to obtain the coordinates of the starting and destination points, optimizes the route using an external API, and selects a route that best suits the user's mental state based on emotion data.

[1188] Specific examples

[1189] For example, if the user is feeling stressed, the server can suggest scenic routes or roads with less traffic, using prompts like the following:

[1190] "Recognize emotions from the current facial expression and voice, and if the user is feeling stressed, calculate a relaxing route and provide navigation information. Use the following data:

[1191] Starting point: 35.6895, 139.6917

[1192] Destination: 34.6937, 135.5023

[1193] Emotion: 'Stress'

[1194] In this way, the system can take into account the user's emotional state and provide a more comfortable and secure navigation experience.

[1195] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1196] Step 1:

[1197] A user launches a navigation app and inputs their starting point and destination, which generates a navigation request. The input is the coordinate information of the starting point and destination, and the output is a navigation request.

[1198] Step 2:

[1199] The device receives navigation requests from the user and sends them to the server. At the same time, it acquires beacon signals, WiFi networks, GPS data, and camera information to determine the current location. The input is data from various sensors, and the output is the current location information.

[1200] Step 3:

[1201] The device uses a camera and microphone to collect facial and voice data from the user. The collected data is sent to the emotion engine. The input is camera footage and voice data, and the output is facial images and voice clips.

[1202] Step 4:

[1203] The emotion engine analyzes the received facial and voice data to recognize the user's emotions. For emotion recognition, it uses a generative AI model. The input is a face image and an audio clip, and the output is emotion data (e.g., "stressed" or "relieved").

[1204] Step 5:

[1205] The emotion engine sends the recognized emotion data to the server. The input is the emotion data, and the output is the completion of transmission to the server.

[1206] Step 6:

[1207] The server receives current location information, destination information, and emotion data from the user. It then searches for map information and generates a virtual map as needed. The input is various information data, and the output is map information.

[1208] Step 7:

[1209] The server calculates the optimal route based on the acquired map information and emotion data. The calculation uses the A algorithm and emotion data. The input is map information and emotion data, and the output is optimal route information.

[1210] Step 8:

[1211] The server sends the calculated optimal route information to the terminal. The input is the optimal route information, and the output is the completion of transmission to the terminal.

[1212] Step 9:

[1213] The terminal displays the received optimal route information on a display device such as a smartphone or HMD. The input is optimal route information, and the output is a visual navigation guide.

[1214] Step 10:

[1215] The device continuously updates its current location and emotion data, and requests route recalculation from the server as needed, providing optimal navigation in real time. The input is the current location and emotion data, and the output is updated navigation information.

[1216] 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.

[1217] 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.

[1218] 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.

[1219] [Fourth embodiment]

[1220] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1221] 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.

[1222] 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).

[1223] 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.

[1224] 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.

[1225] 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).

[1226] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1227] 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.

[1228] 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.

[1229] 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.

[1230] 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.

[1231] 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.

[1232] 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."

[1233] This invention is a system that provides navigation in indoor environments such as buildings, train stations, etc. This system links users, terminals, and servers, enabling highly accurate location identification and navigation wherever users are.

[1234] System configuration

[1235] The system includes the following main components:

[1236] 1. Users

[1237] The user of the system and the entity requesting location information.

[1238] 2. Terminal

[1239] These are devices used by users, such as smartphones, tablets, and AR glasses. These devices acquire location information and display navigation information.

[1240] 3. Server

[1241] It is a central device that processes requests over the network and provides map information and route calculations.

[1242] Program processing flow

[1243] User operations

[1244] When a user wants to navigate from their current location to a destination within a building, they first launch a navigation app, enter their starting point and destination, and make a navigation request.

[1245] Terminal handling

[1246] The device receives requests from users and sends them to the server, and determines its current location by combining surrounding beacon signals, WiFi networks, GPS data, and camera information.

[1247] Server Processing

[1248] The server receives the request and location information from the device and searches the network for map information for the specified building or station. If map information does not exist, it generates a virtual map based on the user's past driving data and beacon information. It also calculates the optimal route based on the received current location and destination information.

[1249] Providing navigation information

[1250] The server then sends the calculated optimal route information to the device, which then receives the route information and displays the navigation information on a display device such as AR glasses or a smartphone screen. This allows the user to receive guidance to their destination in a visually easy-to-understand format.

[1251] Specific examples

[1252] As a concrete example, consider a situation where a wheelchair user is searching for the location of an elevator in a large shopping mall. In this case, the user sets the elevator as their destination in a navigation app and sends a request. The device determines the user's current location using beacons, Wi-Fi, and camera information and sends this information to the server. The server searches for the shopping mall's map information and generates a virtual map as needed. It then calculates the optimal route from the current location to the elevator and sends the route information to the device. The device displays this information on the AR glasses, and the user is guided to the elevator based on visual navigation. If the user's current location changes during travel, the device continuously updates its location information, and the server recalculates the route as needed and provides the latest navigation information.

[1253] This system will enable users to easily navigate difficult indoor spaces, which will be of particular help to wheelchair users and the elderly, and will also be a powerful tool for travelers to quickly reach their destinations in unfamiliar places.

[1254] The processing flow will be explained below.

[1255] Step 1:

[1256] The user launches the navigation app, inputs the starting point and destination, and sends a request, which inputs the necessary navigation information into the device.

[1257] Step 2:

[1258] The terminal receives the user's request and sends it to the server, where the request data is sent to the server using the HTTP protocol or similar.

[1259] Step 3:

[1260] The server analyzes the received request and searches for map information of the building through the network. If map information does not exist, it prepares to generate a virtual map.

[1261] Step 4:

[1262] The server generates a virtual map using past user driving data and beacon information as needed, and uses data analysis algorithms to create a virtual map structure.

[1263] Step 5:

[1264] To determine the device's current location, it acquires beacon, WiFi, GPS, and camera information, and combines this information to determine the device's current location.

[1265] Step 6:

[1266] The device sends its determined location to the server. The location data is packaged in an appropriate format (e.g., JSON) and sent to the server.

[1267] Step 7:

[1268] The server calculates the optimal route based on the current location information and map information received. Route calculations use a route search algorithm such as the A algorithm.

[1269] Step 8:

[1270] The server sends the calculation results (optimal route information) to the device. The route information is sent in an appropriate format so that all navigation-related data can be viewed on the device.

[1271] Step 9:

[1272] The optimal route information received by the device is displayed on a display device (e.g., AR glasses). Information is overlaid using AR technology to enable visual confirmation of navigation instructions.

[1273] Step 10:

[1274] The user moves to the destination according to the displayed navigation information. The user moves through the building based on the visual guide.

[1275] Step 11:

[1276] The device continuously monitors the user's current location while moving, periodically reacquiring sensor information and updating the current location.

[1277] Step 12:

[1278] The device sends updated location information to the server, and the server coordinates with the device to adjust the route based on changes in location information.

[1279] Step 13:

[1280] The server recalculates the route as needed and sends the latest navigation information to the device, ensuring that the user is always guided along the optimal route.

[1281] Step 14:

[1282] The device will then redisplay the latest route information and provide it to the user, allowing real-time navigation.

[1283] Example 1

[1284] 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."

[1285] This invention relates to a system for navigating indoor environments that acquires a user's current location information with high accuracy and provides the optimal route to reach a destination. Current technology has difficulty in identifying indoor locations and lacks a method for generating a virtual map when map information does not exist, making highly accurate navigation difficult. Furthermore, there is a lack of systems that can update location information in real time while the user is moving and flexibly recalculate routes. There is a need to solve these issues and provide users with constantly up-to-date navigation information.

[1286] 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.

[1287] In this invention, the server includes a means for receiving a location information request from a user, a means for searching for building map information on the network, and a means for generating a virtual map using the user's past driving data and beacon information if map information is not available. This enables highly accurate acquisition of the user's current location information and real-time location information updates. The terminal also includes a means for integrating beacon signals, WiFi networks, GPS data, and camera information to determine the current location, and a means for calculating an optimal route based on the acquired current location information and destination map information. The terminal also includes a means for displaying the calculated route information on a display device, continuously updating the current location information as the user moves, and recalculating the route as necessary. This allows the user to receive highly accurate navigation even in indoor environments, making navigation easier, especially for users with mobility difficulties or in unfamiliar locations.

[1288] A "user" is an entity that wishes to navigate using the system.

[1289] A "location information request" is a request from a user for navigation by specifying a starting point and a destination.

[1290] A "network" is a communication pathway between system components for communicating data.

[1291] "Building map information" is detailed information about the structure and routes inside a specified building.

[1292] A "virtual map" is a map generated based on the user's past driving data and beacon information when actual map information does not exist.

[1293] A "beacon signal" is a radio signal transmitted from a specific location, and by receiving this signal, the location can be identified.

[1294] A "WiFi network" is a network that uses wireless communication technology to send and receive data.

[1295] "GPS Data" means location information data obtained using the Global Positioning System.

[1296] "Camera information" refers to image and video data captured using a camera.

[1297] "Current location information" is information that identifies the user's location at the time the terminal is located.

[1298] A "destination" is the final destination to which a user seeks navigation.

[1299] An "optimal route" is the most efficient or shortest route from a current location to a destination.

[1300] A "display device" is a device for visually conveying navigation information to a user.

[1301] A "central aggregation device" is a device that processes and manages data from multiple terminals via a network, such as a server.

[1302] An "augmented reality device" is a device that displays digital information superimposed on the real world.

[1303] A "specific route planning algorithm" is a particular algorithmic technique used to calculate a route.

[1304] This invention is a system for providing highly accurate navigation in indoor environments. Specifically, it aims to link users, terminals, and servers to perform accurate location identification and navigation in real time, regardless of location.

[1305] System Configuration

[1306] The system consists of three main components:

[1307] 1. Users

[1308] They are the active users of the system and the ones who carry the terminals.

[1309] 2. Terminal

[1310] This refers to devices carried by users, such as smartphones, tablets, and AR glasses. The terminal acquires location information and acts as an interface for communicating with the server.

[1311] 3. Server

[1312] It is a central device that processes location information, searches map information, and calculates routes via the network.

[1313] Program processing flow

[1314] User operations

[1315] When a user requests navigation, they first launch a navigation app on their device and enter their starting point and destination, which then issues a navigation request.

[1316] Terminal handling

[1317] The device receives a request from the user and sends it to the server. The device also acquires surrounding beacon signals, WiFi networks, GPS data, and camera information to determine the current location. This acquired location information is also sent to the server.

[1318] Server Processing

[1319] The server receives the request and location information sent from the device. The server searches the network for map information for the specified building or station, and if it does not exist, it generates a virtual map using the user's past driving data and beacon information. It then calculates the optimal route based on the current location information and destination information. The algorithm used is expected to be the Dijkstra algorithm or the A algorithm.

[1320] Providing navigation information

[1321] The server sends the calculated optimal route information to the device. The device then displays the received route information on the AR glasses or smartphone screen. This allows the user to receive navigation in a visually easy-to-understand format. Furthermore, if the current location information changes as the user moves, the device continues to update the location information, and the server recalculates the route each time.

[1322] Specific examples

[1323] As a concrete example, consider a situation where a wheelchair user is searching for an elevator in a large shopping mall. The user launches a navigation app and sets the elevator as their destination. This request is sent from the device to the server. The device collects beacon signals, WiFi networks, GPS data, and camera information in real time to determine the user's current location. The server searches for the shopping mall's map information and generates a virtual map as needed. Next, it calculates the optimal route from the user's current location to the elevator and sends the route information to the device. The device displays this information on the AR glasses, allowing the user to head to the elevator with visual navigation in hand.

[1324] Prompt Sentence Examples

[1325] Here is an example prompt:

[1326] User: Launches the navigation app, enters the origin "Main Gate" and destination "Elevator", and submits the request.

[1327] Device: Sends the received request to the server and determines the current location based on beacon signals, WiFi networks, GPS data, and camera information.

[1328] Server: Calculates the optimal route based on the received current location information and destination information and sends it to the device.

[1329] Device: The received route information is displayed on the AR glasses, providing the user with visual navigation.

[1330] This system allows users to navigate indoors with high accuracy and in real time, and is particularly useful for users with mobility issues or in unfamiliar locations.

[1331] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1332] Step 1:

[1333] The user launches the navigation app and enters their starting point and destination. The entered data (starting point and destination) is sent to the device, which generates a navigation request. Input: starting point, destination. Output: navigation request.

[1334] Step 2:

[1335] The device receives a navigation request from the user and sends it to the server. The data sent includes the starting point, destination, and initial location information. Based on the received data, the device collects surrounding beacon signals, WiFi networks, GPS data, and camera information to determine the current location in real time. Input: Navigation request, various sensor data. Output: Current location information.

[1336] Step 3:

[1337] The server receives the navigation request and current location information sent from the device. The server first searches the network for map information for the specified building. If map information is not available, it generates a virtual map using the user's past driving data and beacon information. Input: Navigation request, current location information. Output: Map information or virtual map.

[1338] Step 4:

[1339] The server calculates the optimal route based on the received current location information and destination information. This process uses the Dijkstra algorithm or the A algorithm. The calculation results in each point on the route and corresponding navigation instructions. Input: Current location information, map information or virtual map. Output: Optimal route information.

[1340] Step 5:

[1341] The server sends the calculated optimal route information to the terminal. The data sent includes coordinates and instructions for each point on the route. Input: Optimal route information. Output: Route information.

[1342] Step 6:

[1343] The device analyzes the route information received from the server and presents it visually to the user, including arrows and visual guides displayed on the AR glasses or smartphone screen. Input: Route information. Output: Navigation display.

[1344] Step 7:

[1345] The user navigates based on the navigation information displayed on the device. If the user's current location changes as they move, the device continuously updates its location information and sends new location information to the server as needed. The server receives the new location information, recalculates the route as needed, and sends it to the device. This ensures that the user always receives the latest navigation information. Input: User's current location. Output: Updated route information and navigation display.

[1346] Each step works together to create a complete navigation process that helps users reach their destination safely. This system performs particularly well in indoor environments.

[1347] (Application example 1)

[1348] 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."

[1349] Current robot movement within factories is inefficient, making robot navigation difficult, especially in large factory facilities or complex layouts. This leads to problems such as reduced operational efficiency and reduced productivity. Furthermore, there is a risk that the robot will not reach its destination and will collide with obstacles during movement. The purpose of this invention is to solve these problems, optimize robot movement within factories, and provide efficient and safe navigation.

[1350] 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.

[1351] In this invention, the server includes a means for receiving a location information request from a user, a means for searching for building map information on the network, and a means for generating a virtual map using the user's past travel data and beacon information, thereby optimizing the robot's movement route within the factory and providing highly accurate navigation.

[1352] "User" refers to the entity that uses the system to request location information, and is primarily the operator or manager who manages the operation of robots within a factory.

[1353] A "terminal" is a device used by a user, and includes control devices such as smartphones, tablets, and dedicated terminals.

[1354] A "server" is a central device that processes requests over a network, searches for map information, and calculates routes.

[1355] A "location information request" refers to a request made by a user to confirm their current location or to request route guidance to a destination.

[1356] "Map information" is information that shows the layout of a building or factory, and is data necessary for route guidance.

[1357] A "virtual map" is virtual map information generated based on the user's past driving data and beacon information.

[1358] A "beacon" is a device that provides location information using short-range wireless communication, and is primarily used to identify indoor locations.

[1359] "WiFi" is a technology that uses wireless LAN technology for data communication, and is used for location determination and data transmission.

[1360] "GPS" refers to a satellite positioning system that measures positions on Earth with high precision.

[1361] "Camera information" is information for identifying the current location using video and image data captured by a camera device.

[1362] An "optimal route" refers to the most efficient route from the current point to the destination, calculated to minimize travel time and distance.

[1363] "Route calculation" refers to the process of deriving the optimal route between a specified current location and a destination.

[1364] A "display device" is a device that presents calculated route information and navigation information to a user. Examples include AR glasses and smartphone screens.

[1365] A "factory" is a facility where products are manufactured and assembled, and is the primary location where robots move.

[1366] A "robot" is a mechanical device that moves objects and processes products within a factory.

[1367] "Navigation" refers to the process of providing guidance along a route to a destination specified by a user.

[1368] The present invention is a system that optimizes the movement path of a robot within a factory and provides highly accurate navigation. This system is realized by linking users, terminals, and a server.

[1369] User operations

[1370] The user is an operator or administrator who manages the robot's movements and requests location information from the system. Specifically, the user launches a dedicated navigation app, inputs the starting point and destination, and sends the request.

[1371] Terminal handling

[1372] The terminal is a control device installed on the robot, and can be a smartphone, tablet, dedicated terminal, etc. The terminal first receives a request from the user and sends it to the server. The terminal also determines the robot's current location by combining surrounding beacon signals, WiFi networks, GPS data, and camera information.

[1373] Server Processing

[1374] The server receives requests and location information from devices via the network. It then searches for map information for the specified building or factory. If map information does not exist, it generates a virtual map based on the user's past driving data and beacon information. It then calculates the optimal route based on the received current location and destination information.

[1375] Providing navigation information

[1376] The server sends the calculated optimal route information to the terminal. The terminal receives this route information and displays it on a display device, such as the robot's control screen or AR glasses. This allows the robot to receive guidance to its destination in a visually easy-to-understand manner. If the current location changes during movement, the terminal continuously updates its location information, and the server recalculates the route as necessary to provide the latest navigation information.

[1377] Specific examples

[1378] As a concrete example, consider a situation in which a parts delivery robot in a factory transports parts from a central warehouse to a production line. The user sets the production line as the destination in a navigation app and sends a request. The device determines the robot's current location using beacons, Wi-Fi, and camera information, and sends this information to a server. The server searches map information within the factory and generates a virtual map as needed. It then calculates the optimal route from the current location to the production line and sends the route information to the device. The device displays this information on the robot's display device, and the robot heads to its destination according to the route presented.

[1379] Prompt Sentence Examples

[1380] "Update the robot's current coordinates and calculate the shortest path to the destination. The current coordinates are {'x': 1, 'y': 2} and the destination is {'x': 10, 'y': 5}."

[1381] This system improves the efficiency of robot movement within the factory, improving the operating efficiency of each manufacturing process. It also enables robots to reach their destinations safely and quickly, improving overall productivity. In this way, the present invention effectively solves various navigation issues within factories.

[1382] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1383] Step 1:

[1384] A user launches a navigation app, enters a starting point and destination, and sends a location request, which provides the device with the coordinates of the current location and destination.

[1385] Step 2:

[1386] The device receives a location request from the user. As input, it receives coordinate information of the starting point and destination specified by the user. The device sends this information to the server. As output, the received request data is forwarded to the server.

[1387] Step 3:

[1388] The server receives the request and location information from the terminal. The coordinate information of the current location and destination is provided to the server as input. The server uses this information to search the network for map information within the factory. The corresponding map information is obtained as output.

[1389] Step 4:

[1390] The server searches for map information and generates a virtual map if it does not exist. The input is the user's past driving data and beacon information. The server processes this data to generate a virtual map. The output is the newly generated virtual map information.

[1391] Step 5:

[1392] The server calculates the optimal route based on current location information and destination information. The server is provided with the coordinates of the current location, the coordinates of the destination, and map information (actual map or virtual map) as input. The server uses this data to apply a route calculation algorithm such as the A algorithm to derive the optimal route. The optimal route information is obtained as output.

[1393] Step 6:

[1394] The server sends the calculated optimal route information to the terminal. The input is the optimal route information calculated by the server. The server sends this to the terminal and provides it to the robot's control device. The output is the optimal route information transferred to the terminal.

[1395] Step 7:

[1396] The terminal receives the route information and presents it on a display device. The input is the optimal route information sent from the server. The terminal displays this information on a display device such as AR glasses or the robot's control screen. The output is visually easy-to-understand navigation information presented to the robot.

[1397] Step 8:

[1398] The robot moves towards the destination according to the presented route. The input is navigation information displayed on a display device. Based on this information, the robot uses its autonomous driving function to proceed along the route. The output is the robot's movement.

[1399] Step 9:

[1400] The terminal continuously updates the robot's current location. As input, it uses surrounding environmental data such as beacon signals, WiFi networks, GPS data, and camera information. The terminal integrates this data to re-determine the current location and sends it to the server. As output, the updated location information is provided to the server.

[1401] Step 10:

[1402] The server recalculates the route as needed based on the updated current location information. The updated current location and destination information are provided as input to the server. The server uses this to reapply algorithm A to calculate the latest optimal route. The output is the new route information.

[1403] Step 11:

[1404] The server sends new route information to the terminal, which then re-presents it on the display device. The input is the latest route information sent from the server. The terminal re-displays this information on the display device, providing the robot with up-to-date navigation information. The output is the route information shown to the robot, updated to the latest version.

[1405] 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.

[1406] This invention achieves more appropriate navigation by combining a system that receives location information requests from users and provides navigation within buildings or train stations with an emotion engine that recognizes the user's emotions. This system operates in cooperation with the user, terminal, server, and emotion engine.

[1407] System configuration

[1408] The system includes the following main components:

[1409] 1. Users

[1410] The entity that uses the system, requests location information, and receives navigation.

[1411] 2. Terminal

[1412] These are devices used by users, such as smartphones, tablets, and AR glasses, that acquire location information and display navigation information.

[1413] 3. Server

[1414] This is a device that processes requests via a network, searches for map information, and calculates routes.

[1415] 4. Emotion Engine

[1416] This module analyzes the user's facial expressions and voice to recognize emotions, allowing the navigation guidance method to be adjusted based on the user's emotions.

[1417] Program processing flow

[1418] User operations

[1419] The user launches the navigation app, inputs the starting point and destination, and sends a request, which inputs the necessary navigation information into the device.

[1420] Terminal handling

[1421] The device receives the user's request and sends it to the server. At the same time, the device acquires surrounding beacon signals, WiFi networks, GPS data, and camera information to determine its current location. It also uses the camera and microphone to collect the user's facial expressions and voice data, which are then sent to the emotion engine.

[1422] Emotion engine processing

[1423] The emotion engine analyzes the collected user facial and voice data to recognize the user's current emotions. The emotion data is sent to the server and reflected in the navigation route and guidance method.

[1424] Server Processing

[1425] The server receives requests from the device, along with location and emotion data. It searches the network for building map information and generates a virtual map as needed. It then calculates the optimal route based on the current location and destination information, taking emotion data into account. For example, if the user is feeling stressed, the server will select not only the shortest route but also a route that is mentally gentle.

[1426] Providing navigation information

[1427] The server then sends the calculated optimal route information to the device, which then receives the route information and displays the navigation information on a display device such as AR glasses or a smartphone screen. This allows the user to receive guidance to their destination in a visually easy-to-understand format.

[1428] Specific examples

[1429] As a concrete example, consider a situation where an elderly user wants to go to a specific store in a busy shopping mall. The user sets the store as a destination in the navigation app and sends a request. The device determines the user's current location based on beacons, Wi-Fi, and camera information, and sends this information to the server. At the same time, the device transmits the user's facial expressions and voice to the emotion engine.

[1430] The emotion engine analyzes the user's data and determines that the user is in a state of tension. The server searches for the shopping mall's map information, generates a virtual map as needed, and calculates the optimal route taking into account the user's level of tension. For example, it makes adjustments such as choosing wider aisles to avoid crowds or walking near elevators.

[1431] The optimal route information is sent to the device and displayed on the AR glasses. The user can follow the visual guide to the store with confidence. In this way, the system updates the user's current location and emotional state in real time, recalculating and providing the optimal route as needed.

[1432] This system allows users to easily navigate difficult indoor spaces, which is of particular help to the elderly and those who are prone to stress. It also enables travelers to reach their destinations quickly and safely, even in unfamiliar places.

[1433] The processing flow will be explained below.

[1434] Step 1:

[1435] The user launches the navigation app, inputs the starting point and destination, and sends a request, which inputs the necessary navigation information into the device.

[1436] Step 2:

[1437] The device receives the user's request and sends it to the server, while simultaneously acquiring surrounding beacon signals, WiFi networks, GPS data, and camera information to determine the device's current location.

[1438] Step 3:

[1439] The device collects the user's facial and voice data and sends it to an emotion engine, which is either built into the device or runs on the cloud.

[1440] Step 4:

[1441] The emotion engine analyzes the received facial expressions and voice data to recognize the user's emotional state, and sends the results of the judgment to the server.

[1442] Step 5:

[1443] The server receives the request from the device, along with the device's current location and emotional state, and searches the network for map information for the specified building or station.

[1444] Step 6:

[1445] If the server does not have map information, it generates a virtual map based on past user driving data and beacon information. It uses a data analysis algorithm to create a virtual map structure.

[1446] Step 7:

[1447] The server calculates the optimal route based on the current location information, destination information, and emotional state. For example, if the user is feeling stressed, the server will select a route that avoids crowds or passes near elevators.

[1448] Step 8:

[1449] The server sends the calculation results (optimal route information) to the device. The route information is sent in an appropriate format so that all navigation-related data can be viewed on the device.

[1450] Step 9:

[1451] The optimal route information received by the device is displayed on a display device (e.g., AR glasses). Information is overlaid using AR technology to enable visual confirmation of navigation instructions.

[1452] Step 10:

[1453] The user moves to the destination according to the displayed navigation information. The user moves through the building based on the visual guide.

[1454] Step 11:

[1455] The device continuously monitors the user's current location while moving, periodically reacquiring sensor information and updating the current location.

[1456] Step 12:

[1457] The device sends updated location information to the server, and the server coordinates with the device to adjust the route based on changes in location information.

[1458] Step 13:

[1459] The server recalculates the route as needed and sends the latest navigation information to the device, ensuring that the user is always guided along the optimal route.

[1460] Step 14:

[1461] The device will then redisplay the latest route information and provide it to the user, allowing real-time navigation.

[1462] Example 2

[1463] 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."

[1464] Currently, indoor and building navigation systems generally provide route guidance based on the user's location information. However, these systems do not take the user's emotional state into account, making it difficult to provide an optimal route if the user is feeling nervous or stressed. Therefore, in order to improve the user's sense of security and comfort, a system that recognizes the user's emotions and provides navigation that responds to them is needed.

[1465] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving a location information request from a user, means for searching for map information of buildings on a network, means for generating a virtual map using the user's past driving data and beacon information, means for integrating beacon, WiFi, GPS, and camera information to obtain a current location, means for obtaining facial expression data and voice data of the user, means for analyzing the facial expression data and voice data to recognize the user's emotional state, means for calculating an optimal route based on the obtained current location information, destination map information, and emotional state, means for displaying the calculated route information on a display device, and means for continuously updating the current location information and emotional state and recalculating the route as necessary. This enables navigation that takes the user's emotional state into consideration.

[1466] The "means for receiving location information requests from users" refers to the technical means for transmitting the information of the starting point and destination entered by the user through the navigation app to the server.

[1467] "Means for searching for building map information from a network" means technical means for obtaining detailed map data within a specified building via the Internet or other digital networks.

[1468] "Means for generating a virtual map" refers to a technical means for constructing a new map by utilizing the user's past driving data and beacon information to complement non-existent map information.

[1469] "Means for integrating beacon, WiFi, GPS, and camera information" refers to technical means for combining and processing data using these different location acquisition technologies to determine a user's current location.

[1470] The "means for acquiring the user's facial expression data and voice data" refers to a technical means for collecting the user's facial expression and voice using the camera and microphone of the terminal.

[1471] "Means for recognizing the user's emotional state" refers to technical means for analyzing the collected facial expression data and voice data and identifying the emotion the user is currently feeling (e.g., tension, relaxation, stress, etc.).

[1472] The "means for calculating the optimum route" refers to a technical means for calculating the most suitable route for the user, taking into account current location information, destination information, and the emotional state of the user.

[1473] "Means for displaying calculated route information on a display device" refers to the technical means for displaying information on a display device such as a smartphone or AR glasses in order to visually present the calculated navigation route to the user.

[1474] "Means for continuously updating current location information and emotional state and recalculating the route as needed" refers to technical means for updating the location information and emotional data of a user while they are moving in real time, and for recalculating the optimal route based on that information if needed.

[1475] The present invention is a system that provides more appropriate navigation to users by combining a navigation system for use in buildings or train stations with an emotion engine that recognizes the user's emotions. This system operates by linking the user, terminal, server, and emotion engine. Specific embodiments for implementing this system are described below.

[1476] System configuration

[1477] The system includes the following main components:

[1478] 1. Users

[1479] The entity that uses the system, requests location information, and receives navigation.

[1480] 2. Terminal

[1481] A device used by a user, such as a smartphone, tablet, or augmented reality glasses, that acquires location information and displays navigation information.

[1482] 3. Server

[1483] This is a device that processes requests via the network, searches for building map information, and calculates routes.

[1484] 4. Emotion Engine

[1485] This module analyzes the user's facial expressions and voice to recognize emotions, allowing the navigation guidance method to be adjusted based on the user's emotions.

[1486] Details of each element

[1487] 1. Users

[1488] A user uses a navigation app to input the information required for navigation (starting point and destination) and send a request. The user's role is to provide the system with location information and receive navigation guidance.

[1489] 2. Terminal

[1490] The device can be, for example, a smartphone or augmented reality glasses. The device has the following features:

[1491] Uses GPS, WiFi, beacons, and camera information to determine your location.

[1492] A camera and a microphone are used to capture facial expression data and voice data of the user.

[1493] The acquired data is sent to the server and emotion engine.

[1494] The navigation information received from the server is displayed to the user.

[1495] 3. Server

[1496] The server is the core component of the system and performs the following tasks:

[1497] It receives a location information request from a user and searches for building map information on the network.

[1498] If map information does not exist, a virtual map is generated using the user's past driving data and beacon information.

[1499] The system receives emotion data transmitted from the terminal and calculates the optimal route taking this into consideration.

[1500] The calculated route information is sent to the terminal.

[1501] 4. Emotion Engine

[1502] The emotion engine analyzes the user's facial expression and voice data to recognize their emotional state. This allows it to understand the user's state of tension, stress, relaxation, etc., and provide navigation accordingly. The emotion engine analyzes the data in real time and sends the results to the server.

[1503] Specific examples

[1504] As a specific example, consider a case where an elderly user wants to go to a specified store in a shopping mall.

[1505] 1. The user launches the navigation app, sets the store as the destination, and submits a request.

[1506] 2. The device determines its current location based on beacon, Wi-Fi, and camera information and sends it to the server. At the same time, it sends the user's facial expressions and voice to the emotion engine.

[1507] 3. The emotion engine analyzes the user's data and determines that they are in a state of tension.

[1508] 4. The server searches the network for building map information and generates a virtual map as needed. It calculates the optimal route, taking into account the user's sense of tension. For example, it will choose wider corridors to avoid crowds and walk near elevators.

[1509] 5. The calculated optimal route information is sent to the device and displayed on the screen of the augmented reality glasses or smartphone, allowing the user to follow the visual guidance to their destination with confidence.

[1510] Example prompts for generative AI models

[1511] "Provide navigation for users to reach their destination store in a shopping mall. Calculate the optimal route taking into account the user's current location and emotional state."

[1512] "The user is elderly and needs navigation to get to their destination on a train platform. If the user feels stressed, suggest an easy-to-walk route that avoids crowds."

[1513] In this way, the present invention provides a navigation system that enables users, particularly elderly people and those who are prone to stress, to reach their destination with peace of mind.

[1514] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1515] Step 1: Request user location

[1516] Input: The user enters a start point and destination into the navigation app.

[1517] Specific operation: The user launches the app on their smartphone, enters "home" as the starting point and "a store in the shopping mall" as the destination, and taps the send request button.

[1518] Output: The user's input information (starting point, destination) is sent to the terminal.

[1519] Step 2: Collect data from the device

[1520] Input: User location request and various data to determine current location (GPS, WiFi, beacon, camera information).

[1521] What it does: In the background, your device uses GPS to determine your location, captures WiFi and beacon signals, and uses the camera to capture video of your surroundings.

[1522] Output: Current location data, surrounding environment data, and user request information are sent to the server.

[1523] Step 3: Collecting user emotion data

[1524] Input: User's facial expression data and voice data acquired by the device's camera and microphone.

[1525] Specific operation: The device's camera captures the user's face and the microphone records the user's voice.

[1526] Output: The acquired facial expression data and voice data are sent to the emotion engine.

[1527] Step 4: Emotion Recognition in the Emotion Engine

[1528] Input: Facial expression data and speech data sent to the emotion engine.

[1529] Specific operation: The emotion engine uses facial expression recognition algorithms and voice analysis algorithms to analyze the user's emotional state, such as whether they are tense or relaxed.

[1530] Output: The analyzed emotional data (user's emotional state) is sent to the server.

[1531] Step 5: Searching for map information on the server and generating a virtual map

[1532] Input: User location request, current location data, and user emotional state data.

[1533] Specific operation: The server searches for map information for the building from the Internet or an internal database. If map information does not exist, it generates a virtual map based on the user's past driving data and beacon information.

[1534] Output: The acquired or generated map information is saved in the server.

[1535] Step 6: Optimal route calculation on the server

[1536] Input: Acquired map information, current location data, destination information, and user emotion data.

[1537] Specific operation: The server uses algorithm A to calculate the optimal route from the current location to the destination. At this time, it takes into account the user's emotional state, for example, selecting a route that takes a wide corridor if the user is nervous.

[1538] Output: The calculated optimal route information is sent to the terminal.

[1539] Step 7: Displaying navigation information

[1540] Input: Optimal route information sent from the server.

[1541] Specific operation: The route information received by the device is displayed on the screen of the augmented reality glasses or smartphone. Visual guidelines and arrows indicate the user's direction of travel.

[1542] Output: The user receives real-time visual navigation information and moves towards the destination.

[1543] Step 8: Continuously updating your location and emotional state

[1544] Input: User's location data, facial expression and voice data while moving.

[1545] Specific operation: The device periodically updates its location and emotional state, and sends new data to the server and emotion engine as needed.

[1546] Output: Updated location and emotion data are sent to the server, and the route is recalculated if necessary.

[1547] The above is a detailed explanation of each step of the processing flow of this system.

[1548] (Application example 2)

[1549] 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."

[1550] Conventional navigation systems only provide routes based on the user's location information, and have the problem of not being able to provide navigation that takes into account the user's emotional state. This has led to a demand for navigation that allows users to reach their destination comfortably without feeling stressed or anxious.

[1551] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a location information request, means for searching for building map information on the network, means for generating a virtual map if map information does not exist, means for acquiring the current location by integrating beacon, WiFi, GPS, and camera information, means for analyzing the user's facial expressions and voice to recognize emotions, means for calculating an optimal route using the recognized emotion data, means for displaying the calculated route information on a display device, and means for continuously updating the current location information and emotion data and recalculating the route as necessary. This enables navigation that takes the user's emotional state into consideration, resulting in more comfortable and safe travel.

[1552] A "location information request" is a request that a user sends by specifying information about a departure point and a destination.

[1553] "Means for searching for building map information from a network" refers to a system for obtaining maps and location information within a building using a network such as the Internet.

[1554] A "means for generating a virtual map" is a mechanism for creating a virtual map based on the user's past data and surrounding information when actual map information does not exist.

[1555] "Means for obtaining current location" refers to a mechanism that integrates beacons, WiFi, GPS, and camera information to accurately determine the user's current location.

[1556] "Means for recognizing emotions" refers to a mechanism for understanding a user's emotional state by analyzing the user's facial expressions and voice.

[1557] The "means for calculating the optimal route" is a mechanism for calculating the optimal route for the user based on the acquired current location information and emotion data.

[1558] "Means for displaying on a display device" refers to a mechanism for displaying calculated route information on a display device such as a user's smartphone or augmented reality glasses.

[1559] The "means for recalculating the route" is a mechanism for continuously updating current location information and emotion data, and recalculating the route depending on the situation.

[1560] The present invention is a navigation system that takes into account the emotional state of the user, and is particularly applicable to autonomous vehicles. The system includes the following main components:

[1561] 1. Users

[1562] The entity that uses the system and issues a navigation request by specifying the starting point and destination.

[1563] 2. Terminal

[1564] A display device such as a smartphone or head-mounted display (HMD) that acquires the user's location information and emotional data and displays navigation information.

[1565] 3. Server

[1566] This device processes requests via the network, searches for map information, generates virtual maps, and calculates optimal routes.

[1567] 4. Emotion Engine

[1568] This module analyzes the user's facial expressions and voice to recognize their emotional state and reflects this in the navigation route.

[1569] Specific processing of the system

[1570] The system starts when the user launches a navigation app on their smartphone or HMD and specifies their starting point and destination. The device receives a location request from the user and sends it to a server. At the same time, it uses beacons, Wi-Fi, GPS, and camera information to determine the user's current location and sends this information to the server.

[1571] In addition, the device uses a camera and microphone to collect the user's facial expressions and voice, which are then sent to the emotion engine. The emotion engine analyzes the user's facial and voice data to recognize their current emotional state. The emotion data is then sent to the server, which uses this information to calculate the optimal route.

[1572] Hardware and software used

[1573] Hardware

[1574] Smartphone

[1575] Head-mounted display (HMD)

[1576] Camera and microphone

[1577] software

[1578] Python Program

[1579] OpenCV (image processing library)

[1580] Keras (machine learning library)

[1581] Geopy (geographic information library)

[1582] External API (route optimization)

[1583] Data processing and calculation

[1584] The device processes images captured by the camera using OpenCV to detect faces. The detected facial images are analyzed using an emotion recognition model using Keras. Audio data is also collected by the microphone and analyzed by the emotion engine. This data is sent to the server and used to calculate the optimal route.

[1585] The server uses Geopy to obtain the coordinates of the starting and destination points, optimizes the route using an external API, and selects a route that best suits the user's mental state based on emotion data.

[1586] Specific examples

[1587] For example, if the user is feeling stressed, the server can suggest scenic routes or roads with less traffic, using prompts like the following:

[1588] "Recognize emotions from the current facial expression and voice, and if the user is feeling stressed, calculate a relaxing route and provide navigation information. Use the following data:

[1589] Starting point: 35.6895, 139.6917

[1590] Destination: 34.6937, 135.5023

[1591] Emotion: 'Stress'

[1592] In this way, the system can take into account the user's emotional state and provide a more comfortable and secure navigation experience.

[1593] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1594] Step 1:

[1595] A user launches a navigation app and inputs their starting point and destination, which generates a navigation request. The input is the coordinate information of the starting point and destination, and the output is a navigation request.

[1596] Step 2:

[1597] The device receives navigation requests from the user and sends them to the server. At the same time, it acquires beacon signals, WiFi networks, GPS data, and camera information to determine the current location. The input is data from various sensors, and the output is the current location information.

[1598] Step 3:

[1599] The device uses a camera and microphone to collect facial and voice data from the user. The collected data is sent to the emotion engine. The input is camera footage and voice data, and the output is facial images and voice clips.

[1600] Step 4:

[1601] The emotion engine analyzes the received facial and voice data to recognize the user's emotions. For emotion recognition, it uses a generative AI model. The input is a face image and an audio clip, and the output is emotion data (e.g., "stressed" or "relieved").

[1602] Step 5:

[1603] The emotion engine sends the recognized emotion data to the server. The input is the emotion data, and the output is the completion of transmission to the server.

[1604] Step 6:

[1605] The server receives current location information, destination information, and emotion data from the user. It then searches for map information and generates a virtual map as needed. The input is various information data, and the output is map information.

[1606] Step 7:

[1607] The server calculates the optimal route based on the acquired map information and emotion data. The calculation uses the A algorithm and emotion data. The input is map information and emotion data, and the output is optimal route information.

[1608] Step 8:

[1609] The server sends the calculated optimal route information to the terminal. The input is the optimal route information, and the output is the completion of transmission to the terminal.

[1610] Step 9:

[1611] The terminal displays the received optimal route information on a display device such as a smartphone or HMD. The input is optimal route information, and the output is a visual navigation guide.

[1612] Step 10:

[1613] The device continuously updates its current location and emotion data, and requests route recalculation from the server as needed, providing optimal navigation in real time. The input is the current location and emotion data, and the output is updated navigation information.

[1614] 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.

[1615] 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.

[1616] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1617] 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.

[1618] 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.

[1619] 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.

[1620] 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).

[1621] 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.

[1622] 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."

[1623] 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.

[1624] 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).

[1625] 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.

[1626] 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.

[1627] 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.

[1628] 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.

[1629] 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.

[1630] 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.

[1631] 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.

[1632] 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.

[1633] 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.

[1634] 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.

[1635] The following is further disclosed regarding the above embodiment.

[1636] (Claim 1)

[1637] means for receiving a location information request from a user;

[1638] means for searching for map information of buildings on a network based on the received request;

[1639] means for generating a virtual map using the user's past driving data and beacon information when the map information does not exist;

[1640] a means for integrating beacon, WiFi, GPS, and camera information to obtain a current location;

[1641] means for calculating an optimum route based on the acquired current location information and map information of the destination;

[1642] means for displaying the calculated route information on a display device;

[1643] A means of continually updating your location and recalculating your route as needed;

[1644] A system including:

[1645] (Claim 2)

[1646] The system of claim 1 , wherein the display device is an augmented reality glass.

[1647] (Claim 3)

[1648] 2. The system of claim 1, wherein the means for calculating the optimum route uses an A algorithm.

[1649] "Example 1"

[1650] (Claim 1)

[1651] means for receiving a location information request from a user;

[1652] means for searching for map information of buildings on a network based on the received request;

[1653] means for generating a virtual map using the user's past driving data and beacon information when the map information does not exist;

[1654] A means for integrating and acquiring beacon signals, WiFi networks, GPS data, and camera information to identify a user's current location;

[1655] means for calculating an optimum route based on the acquired current location information and map information of the destination;

[1656] means for displaying the calculated route information on a display device;

[1657] a means for continually updating the user's location as they move and recalculating the route as needed;

[1658] A system including a central aggregation device that performs the above processing.

[1659] (Claim 2)

[1660] 10. The system of claim 1, wherein the display device is an augmented reality device.

[1661] (Claim 3)

[1662] 2. The system of claim 1, wherein the means for calculating the optimum route uses a specific route search algorithm.

[1663] "Application Example 1"

[1664] (Claim 1)

[1665] means for receiving a location information request from a user;

[1666] means for searching for map information of buildings on a network based on the received request;

[1667] means for generating a virtual map using the user's past driving data and beacon information when the map information does not exist;

[1668] a means for integrating beacon, WiFi, GPS, and camera information to obtain a current location;

[1669] means for calculating an optimum route based on the acquired current location information and map information of the destination;

[1670] means for displaying the calculated route information on a display device;

[1671] A means of continually updating your location and recalculating your route as needed;

[1672] A means for optimizing the robot's movement path within the factory and providing highly accurate navigation;

[1673] A system including:

[1674] (Claim 2)

[1675] The system of claim 1 , wherein the display device is an augmented reality glass.

[1676] (Claim 3)

[1677] 2. The system of claim 1, wherein the means for calculating the optimum route uses an A algorithm.

[1678] "Example 2: Combining Emotion Engines"

[1679] (Claim 1)

[1680] means for receiving a location information request from a user;

[1681] means for searching for map information of buildings on a network based on the received request;

[1682] means for generating a virtual map using the user's past driving data and beacon information when the map information does not exist;

[1683] a means for integrating beacon, WiFi, GPS, and camera information to obtain a current location;

[1684] means for acquiring facial expression data and voice data of a user;

[1685] means for analyzing the facial expression data and voice data to recognize the emotional state of the user;

[1686] means for calculating an optimal route based on the acquired current location information, map information of the destination, and emotional state;

[1687] means for displaying the calculated route information on a display device;

[1688] a means for continually updating the location and emotional state and recalculating the route as needed;

[1689] A system including:

[1690] (Claim 2)

[1691] The system of claim 1 , wherein the display device is an augmented reality glass.

[1692] (Claim 3)

[1693] 2. The system of claim 1, wherein the means for calculating the optimum route uses an A algorithm.

[1694] "Application example 2 when combining emotion engines"

[1695] (Claim 1)

[1696] means for receiving a location information request from a user;

[1697] means for searching for map information of buildings on a network based on the received request;

[1698] means for generating a virtual map using the user's past driving data and beacon information when the map information does not exist;

[1699] a means for integrating beacon, WiFi, GPS, and camera information to obtain a current location;

[1700] means for calculating an optimum route based on the acquired current location information and map information of the destination;

[1701] means for recognizing emotions by analyzing facial expressions and voice of a user;

[1702] means for calculating an optimal route using the recognized emotion data;

[1703] means for displaying the calculated route information on a display device;

[1704] a means of continually updating location and emotion data and recalculating the route as needed;

[1705] A system including:

[1706] (Claim 2)

[1707] The system of claim 1 , wherein the display device is an augmented reality glass or a head-mounted display.

[1708] (Claim 3)

[1709] 2. The system of claim 1, wherein the means for calculating the optimal route uses an A algorithm and emotion data. [Explanation of symbols]

[1710] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving a location information request from a user; means for searching for map information of buildings on a network based on the received request; means for generating a virtual map using the user's past driving data and beacon information when the map information does not exist; a means for integrating beacon, WiFi, GPS, and camera information to obtain a current location; means for calculating an optimum route based on the acquired current location information and map information of the destination; means for displaying the calculated route information on a display device; A means of continually updating your location and recalculating your route as needed; A system including:

2. The system of claim 1 , wherein the display device is an augmented reality glass.

3. 2. The system of claim 1, wherein said means for calculating the optimum route uses an A algorithm.

Images