system

Abstract

The goal is to provide a system that efficiently delivers the user's expected route while also offering clear and easy-to-understand explanations. [Solution] A system comprising: means for receiving user input and generating multiple candidate routes; means for selecting the optimal route from the generated routes based on user specifications; means for generating navigation explanations to explain the selected route in an easy-to-understand manner for the user; means for transmitting navigation information to the user terminal; and means for collecting driving data from the terminal and using that information to improve the route selection algorithm.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a conventional in-vehicle navigation system, it has been difficult for a user to easily grasp a specific route desired by the user or a driving sequence along a route previously investigated. In addition, there has been a lack of navigation explanations based on roads frequently traveled by a driver or familiar scenery, and the movement to a destination may not be smooth. For this reason, there is a demand for a system that can efficiently provide a route expected by a user and give an easy-to-understand explanation.

Means for Solving the Problems

[0005] This invention provides a means for receiving route information input from a user and generating multiple candidate routes. This includes a means for selecting the optimal route, taking into account user specifications and past usage history. Furthermore, it can generate user-friendly navigation instructions based on the selected route and transmit them to the terminal. In addition, by utilizing driving data collected from the terminal and continuously improving the route selection algorithm, a system capable of achieving highly accurate and personalized navigation is constructed.

[0006] "User input" refers to information that the user provides to the navigation system, such as the starting point, destination, and desired route.

[0007] "Candidate routes" refer to multiple possible travel routes generated based on map data and traffic information.

[0008] The "optimal route" refers to the route that is most efficient in terms of arrival time and distance, selected considering the user's specified conditions and past usage history.

[0009] "Navigation instructions" refer to visual or audio instructions that clearly communicate the selected route to the user.

[0010] A "user terminal" refers to a device connected to a navigation system that provides users with route information, estimated arrival times, and other relevant data.

[0011] "Driving data" refers to information such as the route the user actually traveled, stops, driving time, and speed during a drive.

[0012] A "route selection algorithm" refers to the calculation procedures and rules used to select the optimal route from among candidate routes. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. <{ [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more nonvolatile storage devices that store various programs and various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention relates to a navigation system that allows a user to reach their destination via a route of their choice. The system operates by allowing the user to specify a starting point and destination, and optionally input the characteristics of their desired route. The main components of the system and their operation are described below.

[0035] server

[0036] The server plays a central role in the navigation system. It receives requests from the user's terminal and generates multiple candidate routes to reach the destination. This process utilizes map databases and real-time traffic information. Furthermore, the server uses generation AI to select the optimal route, taking into account user specifications and past history.

[0037] The server creates an easy-to-understand navigation description for the selected route. This description includes information on landmarks such as buildings and intersections, designed to facilitate the user's driving. The generated navigation description is then sent to the user's terminal.

[0038] terminal

[0039] The terminal is a device owned by the user (such as a smartphone with navigation capabilities or a dedicated device) and is responsible for receiving navigation information transmitted from the server. The terminal provides route guidance to the user visually or audibly, helping the user reach their destination smoothly.

[0040] User

[0041] The user is a driver using a navigation system to reach their destination. The user inputs their starting point, destination, and desired route information (if necessary) into the system. During driving, the user can efficiently travel by following the navigation provided on the terminal. After the trip, the user's actual driving data is sent to the server and used to improve the accuracy of the route selection algorithm for future trips.

[0042] For example, when a user specifies a route from home to work, the server selects routes that prioritize speed or routes similar to those used in the past, and provides specific directions to the terminal, such as "go straight when you see a cafe on your right," allowing the user to drive while seeing familiar scenery.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user enters the destination and departure point into the device and specifies the desired route conditions.

[0046] Step 2:

[0047] The terminal sends the information entered by the user to the server.

[0048] Step 3:

[0049] Based on the destination and departure point received by the server, it references a map database and real-time traffic information to generate multiple candidate routes.

[0050] Step 4:

[0051] The server uses generated AI to select the optimal route, taking into account the user's preferences and past historical data.

[0052] Step 5:

[0053] Based on the route selected by the server, a user-friendly navigation description is generated. This description includes landmark buildings and distinctive points of interest.

[0054] Step 6:

[0055] The server sends the generated navigation information to the terminal.

[0056] Step 7:

[0057] The device displays received navigation information and provides real-time instructions to the user through voice guidance and visual map displays.

[0058] Step 8:

[0059] The user drives according to the instructions on the device and heads towards the destination.

[0060] Step 9:

[0061] After the ride is complete, the device sends the collected ride data to the server. This data includes information about the actual route taken and the time taken.

[0062] Step 10:

[0063] The server updates the route selection algorithm based on the collected driving data, improving the accuracy of guidance for subsequent trips.

[0064] (Example 1)

[0065] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0066] In navigation systems, the challenge is to provide the optimal route that reflects the characteristics of the user's desired route while also considering real-time traffic conditions and past usage history. Users need to receive intuitively understandable guidance based on map information, efficiently reach their destination, and further improve the system's accuracy by feeding back the driving data.

[0067] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0068] In this invention, the server includes means for receiving user information input and constructing multiple candidate routes, means for selecting the optimal route from the constructed routes based on the user's specifications, and means for using a generation AI model to consider past usage history. This enables flexible and appropriate navigation guidance for the user.

[0069] A "user" is an individual or group that uses a navigation system to request route guidance from a starting point to a destination.

[0070] "Information input" refers to data that users provide to the navigation system, including their starting point, destination, and preferences or instructions regarding the route.

[0071] A "candidate route" refers to one of several selectable travel routes generated by the server to help the user reach their destination.

[0072] The "optimal route" refers to the most efficient and suitable travel route selected by the server, taking into account the user's specified conditions, past history, and traffic conditions.

[0073] "Guidance information" refers to information including navigation instructions that are provided in a way that is easy for users to intuitively understand regarding the selected route.

[0074] A "generative AI model" is an artificial intelligence technology used to generate appropriate routes and guidance information based on a user's past history and specified conditions.

[0075] "Real-time traffic information" refers to real-time data such as traffic conditions, congestion information, and road closures at the current time.

[0076] "Driving data" refers to records of the routes traveled by users and data detailing the movements made during that process.

[0077] A "route selection algorithm" is a computational method and procedure used in a navigation system to select the optimal route from multiple candidate routes.

[0078] This invention relates to a navigation system that provides the optimal route for a user to reach their destination. This system selects a route from the starting point to the destination based on information entered by the user and provides clear and easy-to-understand guidance along that route.

[0079] Server operation

[0080] The server plays a central role in this system. First, it receives the starting point, destination, and optional route features entered by the user via a terminal. The server generates multiple candidate routes using a map information database and real-time traffic information. External software, such as traffic APIs, may be used in this process.

[0081] Next, the server uses a generated AI model to select the optimal route, taking into account the user's past history and specified conditions. Therefore, the AI ​​model includes roles in generating navigation instructions and route scoring. The prompt used is: "Please select the optimal route from the starting point to the destination and generate directions including landmarks."

[0082] Based on the selected route, the server generates navigation instructions that are easy for the user to understand intuitively. These instructions include landmarks such as buildings and intersections.

[0083] Terminal operation

[0084] The terminal is responsible for receiving guidance information transmitted from the server and providing it to the user. This guidance is conveyed to the user via voice and visual means using smartphones and other mobile communication devices. The terminal can update traffic information in real time as needed and recalculate routes according to new conditions.

[0085] User actions

[0086] Users are guided to their destination using the navigation system. While driving, they can travel smoothly and efficiently by following the voice and visual instructions received from the terminal. After completing their journey, the user's actual driving data is sent from the terminal to the server and used to improve future route selection algorithms.

[0087] With the above configuration, this invention makes it possible to flexibly and quickly provide navigation that follows the route desired by the user.

[0088] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0089] Step 1:

[0090] The server receives information about the user's departure point, destination, and route characteristics transmitted from the terminal. It analyzes the received information and uses a map database and real-time traffic information to aggregate initial data for various candidate routes. The input is user condition data obtained from the terminal, and the output is a preliminary dataset of candidate routes.

[0091] Step 2:

[0092] The server uses a generative AI model to evaluate an aggregated dataset of candidate routes. Here, optimal routes are scored based on the user's past history and specified route features. The AI ​​model assigns scores to the candidate routes and selects the most suitable one. The input is the candidate route dataset, and the output is the selected optimal route after scoring.

[0093] Step 3:

[0094] The server generates user-friendly navigation instructions based on the selected optimal route. Using a generation AI model, it creates guidance text that includes specific landmarks and intersection information. The prompt used is "Create detailed directions from my current location to my destination, including landmarks." The input is the selected optimal route, and the output is user-friendly navigation instructions.

[0095] Step 4:

[0096] The terminal receives navigation instructions sent from the server and provides the user with audio or visual guidance. The receiving process also caches the guidance data in preparation for real-time updates. The input is the navigation instructions from the server, and the output is specific visual and audio guidance for the user.

[0097] Step 5:

[0098] The user drives to their destination following the navigation instructions provided by the terminal. Based on real-time traffic information during driving, the terminal can recalculate the route as needed and present updated directions. The input is the traffic conditions that change during driving, and the output is the updated real-time directions.

[0099] Step 6:

[0100] The terminal collects the user's actual driving data and sends it to the server after the session ends. The server analyzes the received driving data and uses it to improve the route selection algorithm. The input is the user's driving data, and the output is accumulated historical data and feedback for algorithm improvement.

[0101] (Application Example 1)

[0102] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0103] Modern navigation systems have limited ability to optimize routes based on user requests. Furthermore, they often fail to adequately consider real-time traffic conditions, resulting in inefficient travel. These problems are even more pronounced in autonomous vehicles, where more advanced navigation systems are needed to provide a safe and comfortable driving experience.

[0104] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0105] In this invention, the server includes means for receiving user input and generating multiple candidate routes, means for selecting the optimal route from the generated routes based on user specifications, and means for generating optimized navigation from traffic condition data. This makes it possible to provide efficient and safe travel routes that meet the user's needs.

[0106] "User input" refers to information that a user provides to the system, such as a specific starting point, destination, and desired route characteristics.

[0107] A "candidate route" is a set of multiple possible travel routes between a specified starting point and destination.

[0108] The "optimal route" is the most efficient and satisfying travel route selected based on the user's input.

[0109] "Directions" refer to guidance information presented in a format that users can easily understand regarding the selected route.

[0110] "User devices" refer to electronic devices such as smartphones and in-car systems that receive and display navigation information.

[0111] "Movement data" refers to data that includes the user's actual driving information and is used to improve the system's algorithms.

[0112] "Traffic condition data" refers to real-time information about traffic, such as current road congestion and traffic signal information.

[0113] To implement this invention, the server is primarily responsible for data processing and route optimization. The server is built using Flask with Python, receives route requests from users, and collects candidate routes using map APIs. Specifically, it uses Google® Maps API and OpenStreetMap API as map APIs to obtain the latest traffic conditions and geographic information. Next, it uses a generative AI model (for example, OpenAI®'s GPT-3®) to select the optimal route based on user-specified conditions and past travel history. Based on the selected route, the server generates a route description, creates optimized navigation information that also takes traffic data into consideration, and sends it to the user's device.

[0114] The terminals are user devices such as smartphones and in-car computers. These devices receive navigation information from a server and provide guidance to the user visually and audibly. Users input their origin and destination on their smartphone app or in-car system and add desired route characteristics as needed. The terminals then collect movement data from the user while driving and send it back to the server. This data is used to improve the route selection algorithm.

[0115] As a concrete example, when person A is going from their home to a shopping mall, specifying "a road with beautiful autumn foliage" will cause the system to suggest the optimal route according to that request. By inputting a prompt into the generating AI, it is possible to generate guidance such as, "Based on the route parameters specified by the user, please provide the most attractive and efficient route. For example, 'a road where you can enjoy autumn foliage during the autumn foliage season.' Based on the results, please generate real-time navigation instructions." In this way, a flexible and effective navigation system that responds to user input conditions is realized.

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] The user inputs their starting point, destination, and desired route characteristics using a smartphone app or in-vehicle system. This input information is transmitted via the application and passed to the server as a request. This step generates input data that can be customized according to the user's needs.

[0119] Step 2:

[0120] The server receives a request from the user and queries the map API. Using the Google Maps API and OpenStreetMap API, it generates multiple candidate routes using current map information and real-time traffic data. The output data obtained here contains detailed information about each candidate route.

[0121] Step 3:

[0122] The server uses a generative AI model (e.g., OpenAI GPT-3) to select the optimal route based on the user's input preferences and past history. The generative AI utilizes prompts to select the best route that satisfies the input conditions. In this step, the optimal route is determined based on the evaluation and selection criteria for each route.

[0123] Step 4:

[0124] The server generates detailed navigation instructions based on the selected optimal route. These instructions reflect traffic conditions and present route guidance in a user-friendly format. The output data for this step contains detailed route information.

[0125] Step 5:

[0126] The server transmits the generated navigation information to the user's device. The user's device receives this information and communicates it to the user through display and voice guidance. Here, a guide that the user can easily follow while driving is provided.

[0127] Step 6:

[0128] The user's device records driving data during operation and sends this data to the server. The server collects this data and uses it to improve the accuracy of future route selection algorithms. This improves the quality of navigation in subsequent trips.

[0129] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0130] This invention relates to a navigation system that incorporates an emotion engine to make user route guidance more personalized. In addition to basic navigation functions that select the optimal route based on user input and past usage history, this system provides more detailed and individualized route guidance by recognizing the user's emotional state.

[0131] server

[0132] The server receives route information from the user and generates multiple candidate routes. The server also receives information from the emotion engine and takes into account the user's emotional state if it influences route selection. For example, if the user wants to relax, the server might prioritize routes with good scenery.

[0133] terminal

[0134] The terminal serves as both a provider of route guidance and a device that operates an emotion engine. The terminal analyzes the user's voice input and video data from the camera to determine the user's emotional state in real time. The recognized emotions are sent from the terminal to the server and reflected in the route guidance information.

[0135] Emotional Engine

[0136] The emotion engine analyzes the tone of voice and facial expressions of the user when they speak into the device. This allows it to determine if the user is in a specific state (e.g., stressed or relaxed). This information is used to adjust navigation instructions, enabling route guidance tailored to the user's emotions.

[0137] User

[0138] Users benefit from the emotional engine when using a navigation system and receiving directions to their destination. For example, if a user is tired, the system can provide guidance tailored to their situation, such as recommending a route with rest facilities.

[0139] For example, if a user is feeling fatigued from driving for a long time, the emotional engine detects this state, and the server provides the user with relaxing information about the route, selects a route that includes appropriate rest stops, and guides them along the way. In this way, the user can reach their destination with a sense of security.

[0140] The following describes the processing flow.

[0141] Step 1:

[0142] The user enters the destination and departure point into the device and specifies the desired route conditions.

[0143] Step 2:

[0144] The device acquires the user's voice and facial expressions, which are then analyzed by an emotion engine. This analysis determines the user's emotional state.

[0145] Step 3:

[0146] The device sends emotional state information and routing requests to the server.

[0147] Step 4:

[0148] The server uses the received destination, departure point, and user sentiment information to match map data with real-time traffic information and generate multiple candidate routes.

[0149] Step 5:

[0150] The server uses AI generation to select the optimal route, taking into account the user's emotional state, past history, and preferences. For example, if the user wants to relax, it will prioritize scenic routes.

[0151] Step 6:

[0152] The server generates user-friendly navigation descriptions for the selected route. These descriptions are designed with consideration for the user's emotional state.

[0153] Step 7:

[0154] The server sends the generated navigation information to the terminal.

[0155] Step 8:

[0156] The device displays navigation information it has received and provides real-time instructions to the user through voice and map display.

[0157] Step 9:

[0158] The user drives according to the navigation on their device, safely reaching their destination via an emotionally sensitive route.

[0159] Step 10:

[0160] After the ride is complete, the device sends the collected ride data and emotion data to the server. This data is used to improve route selection for future rides.

[0161] (Example 2)

[0162] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0163] Current navigation systems select routes based solely on geographical information and traffic data, making it difficult to provide flexible and personalized guidance that takes into account the user's mood and emotional state. In particular, when a user is feeling fatigued or stressed, route guidance that reflects that state is required, but current systems are not adequately able to handle this.

[0164] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0165] In this invention, the server includes means for receiving user input and generating multiple candidate routes, means for selecting the optimal route from the generated routes based on the user's specifications and emotional state, and means for adjusting the selected route in cooperation with an emotion analysis device for analyzing the emotional state. This makes it possible to provide more personalized and reassuring route guidance that is tailored to the user's emotional state.

[0166] "User input" refers to the act of a user providing information about their destination or waypoints to the navigation system.

[0167] "Candidate routes" refer to the multiple routes that the server generates and that the user can choose to reach their destination.

[0168] The "optimal route" refers to the most appropriate route for the user, selected considering the user's specifications and emotional state.

[0169] "Guidance and explanation" refers to the content of audio and visual instructions provided to make it easier for users to understand the selected route.

[0170] A "user terminal" refers to a device used by a user to operate and utilize the navigation system.

[0171] "Driving information" refers to specific data collected from user terminals that is used for route selection and improvement.

[0172] "Emotional state" refers to the user's mental state, such as stress or relaxation, as recognized by the emotion analysis device.

[0173] An "emotion analysis device" refers to a device that analyzes audio and video data to determine the user's emotional state.

[0174] This invention is a system that provides personalized route guidance for users and navigation that takes their emotional state into consideration. It mainly consists of a server, a terminal, and an emotion analysis device. The specific roles and operations of each element are described below.

[0175] The server is hardware that generates multiple candidate routes based on route information received from the user. It utilizes software to integrate a map information database and real-time traffic data. Furthermore, it selects a route appropriate to the user's mental state based on emotional information received from an emotion analysis device. The server makes adjustments, such as prioritizing scenic routes when the user is feeling relaxed.

[0176] The terminal is user-facing hardware equipped with voice input and a camera, and has the capability to diagnose the user's emotional state in real time. The terminal is fitted with software to run an emotion analysis algorithm, and uses the on-device camera and microphone to record changes in the user's facial expressions and voice. This data is used to analyze the user's emotional state, and the results are sent to a server.

[0177] The emotion analysis device functions as an emotion engine, analyzing voice tone and facial expressions to determine whether the user is stressed or relaxed. The information obtained from emotion analysis is used to personalize the server's navigation instructions.

[0178] For example, if a user feels fatigued while driving, the device's emotion analyzer detects signs of fatigue and sends them to the server. The server then suggests a relaxing route and selects places where the user can take a break. This process allows the user to reach their destination with a sense of security.

[0179] An example of a prompt to input into a generative AI model is, "The user is seeking relaxation, so please suggest a route with good scenery." Such prompts support route selection based on sentiment analysis results.

[0180] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0181] Step 1:

[0182] The user inputs route information, including the destination, into the navigation system. This information is transmitted to the server via the terminal. The user's input includes geographical locations and route preferences. The server receives this information and performs data processing to generate multiple candidate routes. The output is a set of multiple candidate routes that the user can choose from.

[0183] Step 2:

[0184] The device collects the user's real-time emotional state using an emotion analysis device. Specifically, it captures the user's facial expressions with a camera and collects their voice tone with a microphone. The audio and video data, as input information, are analyzed by an emotion analysis algorithm to generate an output for a specific emotional state (e.g., relaxed, stressed).

[0185] Step 3:

[0186] The server receives the collected emotional state data and uses it to select candidate routes. This process adjusts the route based on the user's specific emotional state, such as wanting to relax. For example, routes with good scenery are prioritized. The input is emotional state data and initial candidate routes, and the output is the adjusted, optimal route.

[0187] Step 4:

[0188] The terminal receives pre-configured routes from the server and provides them to the user as audio and visual guidance. Specifically, the terminal uses its audio speaker and screen display device to provide real-time navigation guidance. The input is route information from the server, and the output is simple and easy-to-understand guidance for the user.

[0189] Step 5:

[0190] The user follows the instructions and continues driving. If a new emotional change occurs along the way, the terminal detects it again with an emotion analysis device, and the server re-evaluates the route as needed. The input is the newly collected emotional information, and the system output is the updated route guidance. Specifically, route changes are suggested in real time according to the user's comfort level.

[0191] (Application Example 2)

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

[0193] Conventional navigation systems fail to adequately improve user satisfaction and comfort because they provide route guidance without considering the user's current emotional state. Furthermore, because they cannot select routes that reflect the user's emotions in real time, it is difficult to provide optimal guidance tailored to the user's situation.

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

[0195] In this invention, the server includes means for receiving user input and generating multiple candidate routes, means for selecting the optimal route from the generated routes based on user specifications, and means for acquiring user emotion data through a device equipped with an emotion analysis function for analyzing the user's emotional state. This makes it possible to provide route guidance optimized for the user's emotional state.

[0196] "User" refers to a person or entity that receives route guidance using a navigation system.

[0197] "Input" refers to the operations or instructions a user gives to the system, and includes data related to destination information and specific preferences.

[0198] A "candidate route" refers to a set of multiple possible travel routes considered in order to reach a destination.

[0199] "Route selection" refers to the process of determining the optimal travel route from among multiple candidate routes.

[0200] "Guidance information" refers to instructions and explanations provided to the user based on the selected route, including route details and related precautions.

[0201] "Terminal" refers to a hardware device used by a user to access a navigation system, and includes smartphones, smart glasses, and other similar devices.

[0202] "Movement data" refers to information related to a user's movement obtained from a device, including location information, movement speed, and time.

[0203] "Emotion analysis function" refers to technology used to determine a user's emotional state, and it has the ability to analyze voice tone and facial expressions.

[0204] "Emotional data" refers to data that represents the user's emotional state, obtained through the emotion analysis function.

[0205] "Dynamic traffic information" refers to information about traffic conditions that is updated in real time, including road congestion levels and information about road disruptions.

[0206] The system that implements this application consists of three main elements: the user, the terminal, and the server.

[0207] The server receives destination and preference input information from the user. Furthermore, it receives real-time sentiment and movement data transmitted from the device. Based on this information, the server generates multiple candidate routes and selects the optimal route. During the selection process, sentiment data is used to determine the route best suited to the user's current state. The server utilizes cloud platforms such as Google Cloud and Amazon Web Services to process large amounts of data and also consider dynamic traffic information.

[0208] The system uses smart devices, such as smart glasses or smartphones, to acquire user movement information and emotional states. These devices are equipped with cameras and voice recognition capabilities to analyze the user's facial expressions and voice tone in real time. This process utilizes image processing libraries such as OpenCV and voice analysis software like Google Cloud Speech-to-Text. The emotion analysis function processes the acquired data using tools like TENSORFLOW (registered trademark) to determine the user's emotional state.

[0209] Users utilize the navigation system to receive optimized route guidance. For example, if a user experiencing stress during a long-distance drive, the server uses emotion analysis data to determine this and prioritizes routes with relaxing scenery. As a result, users can reach their destination comfortably and safely.

[0210] As a concrete example, an example prompt message is: "When the user is feeling stressed, please select a route that includes roads with a relaxing effect." This prompt allows the system to select an appropriate route based on the situation.

[0211] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0212] Step 1:

[0213] The device captures the user's voice and facial expressions. As input, the device uses its camera and microphone to acquire real-time video and audio data of the user. As output, this data is sent to the emotion analysis function.

[0214] Step 2:

[0215] An emotion analysis function operates within the device to evaluate the user's emotional state. The input is the audio and video data acquired in step 1. The device analyzes facial expressions using an image processing library (e.g., OpenCV) and speech tone using speech analysis software (e.g., Google Cloud Speech-to-Text). The output is the analyzed emotion data, which is sent from the device to the server.

[0216] Step 3:

[0217] The server receives transmitted sentiment data and user input information (such as destination and preferences). The input consists of sentiment data from the terminal and destination information from the user. Based on this, the server collects real-time traffic information and generates multiple candidate routes. The output is a list of the generated candidate routes.

[0218] Step 4:

[0219] The server considers emotional data and selects the optimal path. The input is the candidate paths generated in step 3 and the emotional data. The server uses a generative AI model to calculate the path best suited to the user's current emotional state. The output is the selected optimal path.

[0220] Step 5:

[0221] The server sends the selected route to the terminal and generates navigation information. The input is the optimal route selected in step 4. The server creates the navigation information and sends it to the terminal in a format that is easy for the user to understand. The output is the navigation information presented to the user.

[0222] Step 6:

[0223] The user receives guidance information through their device and travels according to the actual route. The input is guidance information sent from the server. The user receives guidance in real time using smart glasses or a smartphone. The output is the user's travel experience, which is an improvement in their sense of security and satisfaction during the journey to their destination.

[0224] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0225] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0226] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0227] [Second Embodiment]

[0228] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0229] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0230] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0231] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0232] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0233] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0234] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0235] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0236] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0237] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0238] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0239] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0240] This invention relates to a navigation system that allows a user to reach their destination via a route of their choice. The system operates by allowing the user to specify a starting point and destination, and optionally input the characteristics of their desired route. The main components of the system and their operation are described below.

[0241] server

[0242] The server plays a central role in the navigation system. It receives requests from the user's terminal and generates multiple candidate routes to reach the destination. This process utilizes map databases and real-time traffic information. Furthermore, the server uses generation AI to select the optimal route, taking into account user specifications and past history.

[0243] The server creates an easy-to-understand navigation description for the selected route. This description includes information on landmarks such as buildings and intersections, designed to facilitate the user's driving. The generated navigation description is then sent to the user's terminal.

[0244] terminal

[0245] The terminal is a device owned by the user (such as a smartphone with navigation capabilities or a dedicated device) and is responsible for receiving navigation information transmitted from the server. The terminal provides route guidance to the user visually or audibly, helping the user reach their destination smoothly.

[0246] User

[0247] The user is a driver using a navigation system to reach their destination. The user inputs their starting point, destination, and desired route information (if necessary) into the system. During driving, the user can efficiently travel by following the navigation provided on the terminal. After the trip, the user's actual driving data is sent to the server and used to improve the accuracy of the route selection algorithm for future trips.

[0248] For example, when a user specifies a route from home to work, the server selects routes that prioritize speed or routes similar to those used in the past, and provides specific directions to the terminal, such as "go straight when you see a cafe on your right," allowing the user to drive while seeing familiar scenery.

[0249] The following describes the processing flow.

[0250] Step 1:

[0251] The user enters the destination and departure point into the device and specifies the desired route conditions.

[0252] Step 2:

[0253] The terminal sends the information entered by the user to the server.

[0254] Step 3:

[0255] Based on the destination and departure point received by the server, it references a map database and real-time traffic information to generate multiple candidate routes.

[0256] Step 4:

[0257] The server uses generated AI to select the optimal route, taking into account the user's preferences and past historical data.

[0258] Step 5:

[0259] Based on the route selected by the server, a user-friendly navigation description is generated. This description includes landmark buildings and distinctive points of interest.

[0260] Step 6:

[0261] The server sends the generated navigation information to the terminal.

[0262] Step 7:

[0263] The device displays received navigation information and provides real-time instructions to the user through voice guidance and visual map displays.

[0264] Step 8:

[0265] The user drives according to the instructions on the device and heads towards the destination.

[0266] Step 9:

[0267] After the ride is complete, the device sends the collected ride data to the server. This data includes information about the actual route taken and the time taken.

[0268] Step 10:

[0269] The server updates the route selection algorithm based on the collected driving data, improving the accuracy of guidance for subsequent trips.

[0270] (Example 1)

[0271] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0272] In navigation systems, the challenge is to provide the optimal route that reflects the characteristics of the user's desired route while also considering real-time traffic conditions and past usage history. Users need to receive intuitively understandable guidance based on map information, efficiently reach their destination, and further improve the system's accuracy by feeding back the driving data.

[0273] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0274] In this invention, the server includes means for receiving user information input and constructing multiple candidate routes, means for selecting the optimal route from the constructed routes based on the user's specifications, and means for using a generation AI model to consider past usage history. This enables flexible and appropriate navigation guidance for the user.

[0275] A "user" is an individual or group that uses a navigation system to request route guidance from a starting point to a destination.

[0276] "Information input" refers to data that users provide to the navigation system, including their starting point, destination, and preferences or instructions regarding the route.

[0277] A "candidate route" refers to one of several selectable travel routes generated by the server to help the user reach their destination.

[0278] The "optimal route" refers to the most efficient and suitable travel route selected by the server, taking into account the user's specified conditions, past history, and traffic conditions.

[0279] "Guidance information" refers to information including navigation instructions that are provided in a way that is easy for users to intuitively understand regarding the selected route.

[0280] A "generative AI model" is an artificial intelligence technology used to generate appropriate routes and guidance information based on a user's past history and specified conditions.

[0281] "Real-time traffic information" refers to real-time data such as traffic conditions, congestion information, and road closures at the current time.

[0282] "Driving data" refers to records of the routes traveled by users and data detailing the movements made during that process.

[0283] A "route selection algorithm" is a computational method and procedure used in a navigation system to select the optimal route from multiple candidate routes.

[0284] The present invention relates to a navigation system that provides an optimal route for a user to reach a destination. This system is a mechanism that selects a route from the departure point to the destination based on the information input by the user and guides it in an easy-to-understand manner.

[0285] Server Operations

[0286] The server plays a central role in this system. First, it receives the departure point, destination, and optional route characteristics input by the user via the terminal. The server uses map information databases and real-time traffic information to generate multiple candidate routes. In this process, external software such as traffic APIs may be used.

[0287] Next, the server selects an optimal route considering the user's past history and specified conditions by utilizing a generative AI model. For this purpose, the AI model includes roles such as generating navigation instructions and route scoring. As a prompt sentence, "Please select the optimal route from the departure point to the destination and generate guidance including landmarks." is used.

[0288] Based on the selected route, the server generates navigation guidance that is intuitive and easy for the user to understand. This explanation includes buildings and intersections that serve as landmarks.

[0289] Terminal Operations

[0290] The terminal receives the guidance information transmitted from the server and plays the role of providing it to the user. This is a smartphone or other mobile communication device that conveys the guidance to the user in audio and visual ways. The terminal can update traffic information in real time as needed and recalculate the route according to new situations.

[0291] User Operations

[0292] Users are guided to their destination using the navigation system. While driving, they can travel smoothly and efficiently by following the voice and visual instructions received from the terminal. After completing their journey, the user's actual driving data is sent from the terminal to the server and used to improve future route selection algorithms.

[0293] With the above configuration, this invention makes it possible to flexibly and quickly provide navigation that follows the route desired by the user.

[0294] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0295] Step 1:

[0296] The server receives information about the user's departure point, destination, and route characteristics transmitted from the terminal. It analyzes the received information and uses a map database and real-time traffic information to aggregate initial data for various candidate routes. The input is user condition data obtained from the terminal, and the output is a preliminary dataset of candidate routes.

[0297] Step 2:

[0298] The server uses a generative AI model to evaluate an aggregated dataset of candidate routes. Here, optimal routes are scored based on the user's past history and specified route features. The AI ​​model assigns scores to the candidate routes and selects the most suitable one. The input is the candidate route dataset, and the output is the selected optimal route after scoring.

[0299] Step 3:

[0300] Based on the selected optimal route, the server generates a user-friendly navigation explanation. Using a generation AI model, it creates a guidance text that includes specific landmarks and intersection information. The prompt text "Create a detailed guide from the current location to the destination, including landmarks." is used. The input is the selected optimal route, and the output is the navigation explanation text for the user.

[0301] Step 4:

[0302] The terminal receives the navigation explanation sent from the server and provides guidance to the user either audibly or visually. In the receiving process, caching of the guidance data is also performed to prepare for real-time updates. The input is the navigation explanation from the server, and the output is the specific visual and audible guidance to the user.

[0303] Step 5:

[0304] The user drives to the destination according to the navigation guidance provided by the terminal. Based on the real-time traffic information during driving, the terminal can recalculate the route as needed and present updated guidance. The input is the changing traffic situation during driving, and the output is the updated real-time guidance.

[0305] Step 6:

[0306] The terminal collects the user's actual driving data and sends it to the server after the session ends. The server analyzes the received driving data and uses it to improve the route selection algorithm. The input is the user's driving data, and the output is the accumulated historical data and feedback for algorithm improvement.

[0307] (Application Example 1)

[0308] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0309] Modern navigation systems have limited ability to optimize routes based on user requests. Furthermore, they often fail to adequately consider real-time traffic conditions, resulting in inefficient travel. These problems are even more pronounced in autonomous vehicles, where more advanced navigation systems are needed to provide a safe and comfortable driving experience.

[0310] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0311] In this invention, the server includes means for receiving user input and generating multiple candidate routes, means for selecting the optimal route from the generated routes based on user specifications, and means for generating optimized navigation from traffic condition data. This makes it possible to provide efficient and safe travel routes that meet the user's needs.

[0312] "User input" refers to information that a user provides to the system, such as a specific starting point, destination, and desired route characteristics.

[0313] A "candidate route" is a set of multiple possible travel routes between a specified starting point and destination.

[0314] The "optimal route" is the most efficient and satisfying travel route selected based on the user's input.

[0315] "Directions" refer to guidance information presented in a format that users can easily understand regarding the selected route.

[0316] "User devices" refer to electronic devices such as smartphones and in-car systems that receive and display navigation information.

[0317] "Movement data" refers to data that includes the user's actual driving information and is used to improve the system's algorithms.

[0318] "Traffic condition data" refers to real-time information about traffic, such as current road congestion and traffic signal information.

[0319] To implement this invention, the server is primarily responsible for data processing and route optimization. Built using Flask with Python, the server receives route requests from users and collects candidate routes using map APIs. Specifically, it uses Google Maps API and OpenStreetMap API to obtain the latest traffic conditions and geographic information. Next, it uses a generative AI model (for example, OpenAI's GPT-3) to select the optimal route based on user-specified conditions and past travel history. Based on the selected route, the server generates a route description, creates optimized navigation information that also takes traffic data into consideration, and sends it to the user's device.

[0320] The terminals are user devices such as smartphones and in-car computers. These devices receive navigation information from a server and provide guidance to the user visually and audibly. Users input their origin and destination on their smartphone app or in-car system and add desired route characteristics as needed. The terminals then collect movement data from the user while driving and send it back to the server. This data is used to improve the route selection algorithm.

[0321] As a concrete example, when person A is going from their home to a shopping mall, specifying "a road with beautiful autumn foliage" will cause the system to suggest the optimal route according to that request. By inputting a prompt into the generating AI, it is possible to generate guidance such as, "Based on the route parameters specified by the user, please provide the most attractive and efficient route. For example, 'a road where you can enjoy autumn foliage during the autumn foliage season.' Based on the results, please generate real-time navigation instructions." In this way, a flexible and effective navigation system that responds to user input conditions is realized.

[0322] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0323] Step 1:

[0324] The user inputs their starting point, destination, and desired route characteristics using a smartphone app or in-vehicle system. This input information is transmitted via the application and passed to the server as a request. This step generates input data that can be customized according to the user's needs.

[0325] Step 2:

[0326] The server receives a request from the user and queries the map API. Using the Google Maps API and OpenStreetMap API, it generates multiple candidate routes using current map information and real-time traffic data. The output data obtained here contains detailed information about each candidate route.

[0327] Step 3:

[0328] The server uses a generative AI model (e.g., OpenAI GPT-3) to select the optimal route based on the user's input preferences and past history. The generative AI utilizes prompts to select the best route that satisfies the input conditions. In this step, the optimal route is determined based on the evaluation and selection criteria for each route.

[0329] Step 4:

[0330] The server generates detailed navigation instructions based on the selected optimal route. These instructions reflect traffic conditions and present route guidance in a user-friendly format. The output data for this step contains detailed route information.

[0331] Step 5:

[0332] The server transmits the generated navigation information to the user's device. The user's device receives this information and communicates it to the user through display and voice guidance. Here, a guide that the user can easily follow while driving is provided.

[0333] Step 6:

[0334] The user's device records driving data during operation and sends this data to the server. The server collects this data and uses it to improve the accuracy of future route selection algorithms. This improves the quality of navigation in subsequent trips.

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

[0336] This invention relates to a navigation system that incorporates an emotion engine to make user route guidance more personalized. In addition to basic navigation functions that select the optimal route based on user input and past usage history, this system provides more detailed and individualized route guidance by recognizing the user's emotional state.

[0337] server

[0338] The server receives route information from the user and generates multiple candidate routes. The server also receives information from the emotion engine and takes into account the user's emotional state if it influences route selection. For example, if the user wants to relax, the server might prioritize routes with good scenery.

[0339] terminal

[0340] The terminal serves as both a provider of route guidance and a device that operates an emotion engine. The terminal analyzes the user's voice input and video data from the camera to determine the user's emotional state in real time. The recognized emotions are sent from the terminal to the server and reflected in the route guidance information.

[0341] Emotional Engine

[0342] The emotion engine analyzes the tone of voice and facial expressions of the user when they speak into the device. This allows it to determine if the user is in a specific state (e.g., stressed or relaxed). This information is used to adjust navigation instructions, enabling route guidance tailored to the user's emotions.

[0343] User

[0344] Users benefit from the emotional engine when using a navigation system and receiving directions to their destination. For example, if a user is tired, the system can provide guidance tailored to their situation, such as recommending a route with rest facilities.

[0345] For example, if a user is feeling fatigued from driving for a long time, the emotional engine detects this state, and the server provides the user with relaxing information about the route, selects a route that includes appropriate rest stops, and guides them along the way. In this way, the user can reach their destination with a sense of security.

[0346] The following describes the processing flow.

[0347] Step 1:

[0348] The user enters the destination and departure point into the device and specifies the desired route conditions.

[0349] Step 2:

[0350] The device acquires the user's voice and facial expressions, which are then analyzed by an emotion engine. This analysis determines the user's emotional state.

[0351] Step 3:

[0352] The device sends emotional state information and routing requests to the server.

[0353] Step 4:

[0354] The server uses the received destination, departure point, and user sentiment information to match map data with real-time traffic information and generate multiple candidate routes.

[0355] Step 5:

[0356] The server uses AI generation to select the optimal route, taking into account the user's emotional state, past history, and preferences. For example, if the user wants to relax, it will prioritize scenic routes.

[0357] Step 6:

[0358] The server generates user-friendly navigation descriptions for the selected route. These descriptions are designed with consideration for the user's emotional state.

[0359] Step 7:

[0360] The server sends the generated navigation information to the terminal.

[0361] Step 8:

[0362] The device displays navigation information it has received and provides real-time instructions to the user through voice and map display.

[0363] Step 9:

[0364] The user drives according to the navigation on their device, safely reaching their destination via an emotionally sensitive route.

[0365] Step 10:

[0366] After the ride is complete, the device sends the collected ride data and emotion data to the server. This data is used to improve route selection for future rides.

[0367] (Example 2)

[0368] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0369] Current navigation systems select routes based solely on geographical information and traffic data, making it difficult to provide flexible and personalized guidance that takes into account the user's mood and emotional state. In particular, when a user is feeling fatigued or stressed, route guidance that reflects that state is required, but current systems are not adequately able to handle this.

[0370] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0371] In this invention, the server includes means for receiving user input and generating multiple candidate routes, means for selecting the optimal route from the generated routes based on the user's specifications and emotional state, and means for adjusting the selected route in cooperation with an emotion analysis device for analyzing the emotional state. This makes it possible to provide more personalized and reassuring route guidance that is tailored to the user's emotional state.

[0372] "User input" refers to the act of a user providing information about their destination or waypoints to the navigation system.

[0373] "Candidate routes" refer to the multiple routes that the server generates and that the user can choose to reach their destination.

[0374] The "optimal route" refers to the most appropriate route for the user, selected considering the user's specifications and emotional state.

[0375] "Guidance and explanation" refers to the content of audio and visual instructions provided to make it easier for users to understand the selected route.

[0376] A "user terminal" refers to a device used by a user to operate and utilize the navigation system.

[0377] "Driving information" refers to specific data collected from user terminals that is used for route selection and improvement.

[0378] "Emotional state" refers to the user's mental state, such as stress or relaxation, as recognized by the emotion analysis device.

[0379] An "emotion analysis device" refers to a device that analyzes audio and video data to determine the user's emotional state.

[0380] This invention is a system that provides personalized route guidance for users and navigation that takes their emotional state into consideration. It mainly consists of a server, a terminal, and an emotion analysis device. The specific roles and operations of each element are described below.

[0381] The server is hardware that generates multiple candidate routes based on route information received from the user. It utilizes software to integrate a map information database and real-time traffic data. Furthermore, it selects a route appropriate to the user's mental state based on emotional information received from an emotion analysis device. The server makes adjustments, such as prioritizing scenic routes when the user is feeling relaxed.

[0382] The terminal is user-facing hardware equipped with voice input and a camera, and has the capability to diagnose the user's emotional state in real time. The terminal is fitted with software to run an emotion analysis algorithm, and uses the on-device camera and microphone to record changes in the user's facial expressions and voice. This data is used to analyze the user's emotional state, and the results are sent to a server.

[0383] The emotion analysis device functions as an emotion engine, analyzing voice tone and facial expressions to determine whether the user is stressed or relaxed. The information obtained from emotion analysis is used to personalize the server's navigation instructions.

[0384] For example, if a user feels fatigued while driving, the device's emotion analyzer detects signs of fatigue and sends them to the server. The server then suggests a relaxing route and selects places where the user can take a break. This process allows the user to reach their destination with a sense of security.

[0385] An example of a prompt to input into a generative AI model is, "The user is seeking relaxation, so please suggest a route with good scenery." Such prompts support route selection based on sentiment analysis results.

[0386] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0387] Step 1:

[0388] The user inputs route information, including the destination, into the navigation system. This information is transmitted to the server via the terminal. The user's input includes geographical locations and route preferences. The server receives this information and performs data processing to generate multiple candidate routes. The output is a set of multiple candidate routes that the user can choose from.

[0389] Step 2:

[0390] The device collects the user's real-time emotional state using an emotion analysis device. Specifically, it captures the user's facial expressions with a camera and collects their voice tone with a microphone. The audio and video data, as input information, are analyzed by an emotion analysis algorithm to generate an output for a specific emotional state (e.g., relaxed, stressed).

[0391] Step 3:

[0392] The server receives the collected emotional state data and uses it to select candidate routes. This process adjusts the route based on the user's specific emotional state, such as wanting to relax. For example, routes with good scenery are prioritized. The input is emotional state data and initial candidate routes, and the output is the adjusted, optimal route.

[0393] Step 4:

[0394] The terminal receives pre-configured routes from the server and provides them to the user as audio and visual guidance. Specifically, the terminal uses its audio speaker and screen display device to provide real-time navigation guidance. The input is route information from the server, and the output is simple and easy-to-understand guidance for the user.

[0395] Step 5:

[0396] The user follows the instructions and continues driving. If a new emotional change occurs along the way, the terminal detects it again with an emotion analysis device, and the server re-evaluates the route as needed. The input is the newly collected emotional information, and the system output is the updated route guidance. Specifically, route changes are suggested in real time according to the user's comfort level.

[0397] (Application Example 2)

[0398] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0399] Conventional navigation systems fail to adequately improve user satisfaction and comfort because they provide route guidance without considering the user's current emotional state. Furthermore, because they cannot select routes that reflect the user's emotions in real time, it is difficult to provide optimal guidance tailored to the user's situation.

[0400] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0401] In this invention, the server includes means for receiving user input and generating multiple candidate routes, means for selecting the optimal route from the generated routes based on user specifications, and means for acquiring user emotion data through a device equipped with an emotion analysis function for analyzing the user's emotional state. This makes it possible to provide route guidance optimized for the user's emotional state.

[0402] "User" refers to a person or entity that receives route guidance using a navigation system.

[0403] "Input" refers to the operations or instructions a user gives to the system, and includes data related to destination information and specific preferences.

[0404] A "candidate route" refers to a set of multiple possible travel routes considered in order to reach a destination.

[0405] "Route selection" refers to the process of determining the optimal travel route from among multiple candidate routes.

[0406] "Guidance information" refers to instructions and explanations provided to the user based on the selected route, including route details and related precautions.

[0407] "Terminal" refers to a hardware device used by a user to access a navigation system, and includes smartphones, smart glasses, and other similar devices.

[0408] "Movement data" refers to information related to a user's movement obtained from a device, including location information, movement speed, and time.

[0409] "Emotion analysis function" refers to technology used to determine a user's emotional state, and it has the ability to analyze voice tone and facial expressions.

[0410] "Emotional data" refers to data that represents the user's emotional state, obtained through the emotion analysis function.

[0411] "Dynamic traffic information" refers to information about traffic conditions that is updated in real time, including road congestion levels and information about road disruptions.

[0412] The system that implements this application consists of three main elements: the user, the terminal, and the server.

[0413] The server receives destination and preference input information from the user. Furthermore, it receives real-time sentiment and movement data transmitted from the device. Based on this information, the server generates multiple candidate routes and selects the optimal route. During the selection process, sentiment data is used to determine the route best suited to the user's current state. The server utilizes cloud platforms such as Google Cloud and Amazon Web Services to process large amounts of data and also consider dynamic traffic information.

[0414] The system uses smart devices, such as smart glasses or smartphones, to acquire user movement information and emotional states. These devices are equipped with cameras and voice recognition capabilities to analyze the user's facial expressions and voice tone in real time. This process utilizes image processing libraries such as OpenCV and voice analysis software like Google Cloud Speech-to-Text. The emotion analysis function processes the acquired data using TensorFlow or similar tools to determine the user's emotional state.

[0415] Users utilize the navigation system to receive optimized route guidance. For example, if a user experiencing stress during a long-distance drive, the server uses emotion analysis data to determine this and prioritizes routes with relaxing scenery. As a result, users can reach their destination comfortably and safely.

[0416] As a concrete example, an example prompt message is: "When the user is feeling stressed, please select a route that includes roads with a relaxing effect." This prompt allows the system to select an appropriate route based on the situation.

[0417] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0418] Step 1:

[0419] The device captures the user's voice and facial expressions. As input, the device uses its camera and microphone to acquire real-time video and audio data of the user. As output, this data is sent to the emotion analysis function.

[0420] Step 2:

[0421] An emotion analysis function operates within the device to evaluate the user's emotional state. The input is the audio and video data acquired in step 1. The device analyzes facial expressions using an image processing library (e.g., OpenCV) and speech tone using speech analysis software (e.g., Google Cloud Speech-to-Text). The output is the analyzed emotion data, which is sent from the device to the server.

[0422] Step 3:

[0423] The server receives transmitted sentiment data and user input information (such as destination and preferences). The input consists of sentiment data from the terminal and destination information from the user. Based on this, the server collects real-time traffic information and generates multiple candidate routes. The output is a list of the generated candidate routes.

[0424] Step 4:

[0425] The server considers emotional data and selects the optimal path. The input is the candidate paths generated in step 3 and the emotional data. The server uses a generative AI model to calculate the path best suited to the user's current emotional state. The output is the selected optimal path.

[0426] Step 5:

[0427] The server sends the selected route to the terminal and generates navigation information. The input is the optimal route selected in step 4. The server creates the navigation information and sends it to the terminal in a format that is easy for the user to understand. The output is the navigation information presented to the user.

[0428] Step 6:

[0429] The user receives guidance information through their device and travels according to the actual route. The input is guidance information sent from the server. The user receives guidance in real time using smart glasses or a smartphone. The output is the user's travel experience, which is an improvement in their sense of security and satisfaction during the journey to their destination.

[0430] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0431] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0432] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0433] [Third Embodiment]

[0434] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0435] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0436] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0437] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0438] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0439] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0440] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0441] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0442] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0443] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0444] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0445] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0446] This invention relates to a navigation system that allows a user to reach their destination via a route of their choice. The system operates by allowing the user to specify a starting point and destination, and optionally input the characteristics of their desired route. The main components of the system and their operation are described below.

[0447] server

[0448] The server plays a central role in the navigation system. It receives requests from the user's terminal and generates multiple candidate routes to reach the destination. This process utilizes map databases and real-time traffic information. Furthermore, the server uses generation AI to select the optimal route, taking into account user specifications and past history.

[0449] The server creates user-friendly navigation instructions for the selected route. These instructions include information on landmarks such as buildings and intersections, designed to facilitate the user's driving. The generated navigation instructions are then sent to the user's terminal.

[0450] terminal

[0451] The terminal is a device owned by the user (such as a smartphone with navigation capabilities or a dedicated device) and is responsible for receiving navigation information transmitted from the server. The terminal provides route guidance to the user visually or audibly, helping the user reach their destination smoothly.

[0452] User

[0453] The user is a driver using a navigation system to reach their destination. The user inputs their starting point, destination, and desired route information (if necessary) into the system. During driving, the user can efficiently travel by following the navigation provided on the terminal. After the trip, the user's actual driving data is sent to the server and used to improve the accuracy of the route selection algorithm for future trips.

[0454] For example, when a user specifies a route from home to work, the server selects routes that prioritize speed or routes similar to those used in the past, and provides specific directions to the terminal, such as "go straight when you see a cafe on your right," allowing the user to drive while seeing familiar scenery.

[0455] The following describes the processing flow.

[0456] Step 1:

[0457] The user enters the destination and departure point into the device and specifies the desired route conditions.

[0458] Step 2:

[0459] The terminal sends the information entered by the user to the server.

[0460] Step 3:

[0461] Based on the destination and departure point received by the server, it references a map database and real-time traffic information to generate multiple candidate routes.

[0462] Step 4:

[0463] The server uses generated AI to select the optimal route, taking into account the user's preferences and past historical data.

[0464] Step 5:

[0465] Based on the route selected by the server, a user-friendly navigation description is generated. This description includes landmark buildings and distinctive points of interest.

[0466] Step 6:

[0467] The server sends the generated navigation information to the terminal.

[0468] Step 7:

[0469] The device displays received navigation information and provides real-time instructions to the user through voice guidance and visual map displays.

[0470] Step 8:

[0471] The user drives according to the instructions on the device and heads towards the destination.

[0472] Step 9:

[0473] After the ride is complete, the device sends the collected ride data to the server. This data includes information about the actual route taken and the time taken.

[0474] Step 10:

[0475] The server updates the route selection algorithm based on the collected driving data, improving the accuracy of guidance for subsequent trips.

[0476] (Example 1)

[0477] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0478] In navigation systems, the challenge is to provide the optimal route that reflects the characteristics of the user's desired route while also considering real-time traffic conditions and past usage history. Users need to receive intuitively understandable guidance based on map information, efficiently reach their destination, and further improve the system's accuracy by feeding back the driving data.

[0479] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0480] In this invention, the server includes means for receiving user information input and constructing multiple candidate routes, means for selecting the optimal route from the constructed routes based on the user's specifications, and means for using a generation AI model to consider past usage history. This enables flexible and appropriate navigation guidance for the user.

[0481] A "user" is an individual or group that uses a navigation system to request route guidance from a starting point to a destination.

[0482] "Information input" refers to data that users provide to the navigation system, including their starting point, destination, and preferences or instructions regarding the route.

[0483] A "candidate route" refers to one of several selectable travel routes generated by the server to help the user reach their destination.

[0484] The "optimal route" refers to the most efficient and suitable travel route selected by the server, taking into account the user's specified conditions, past history, and traffic conditions.

[0485] "Guidance information" refers to information including navigation instructions that are provided in a way that is easy for users to intuitively understand regarding the selected route.

[0486] A "generative AI model" is an artificial intelligence technology used to generate appropriate routes and guidance information based on a user's past history and specified conditions.

[0487] "Real-time traffic information" refers to real-time data such as traffic conditions, congestion information, and road closures at the current time.

[0488] "Driving data" refers to records of the routes traveled by users and data detailing the movements made during that process.

[0489] A "route selection algorithm" is a computational method and procedure used in a navigation system to select the optimal route from multiple candidate routes.

[0490] This invention relates to a navigation system that provides the optimal route for a user to reach their destination. This system selects a route from the starting point to the destination based on information entered by the user and provides clear and easy-to-understand guidance along that route.

[0491] Server operation

[0492] The server plays a central role in this system. First, it receives the starting point, destination, and optional route features entered by the user via a terminal. The server generates multiple candidate routes using a map information database and real-time traffic information. External software, such as traffic APIs, may be used in this process.

[0493] Next, the server uses a generated AI model to select the optimal route, taking into account the user's past history and specified conditions. Therefore, the AI ​​model includes roles in generating navigation instructions and route scoring. The prompt used is: "Please select the optimal route from the starting point to the destination and generate directions including landmarks."

[0494] Based on the selected route, the server generates navigation instructions that are easy for the user to understand intuitively. These instructions include landmarks such as buildings and intersections.

[0495] Terminal operation

[0496] The terminal is responsible for receiving guidance information transmitted from the server and providing it to the user. This guidance is conveyed to the user via voice and visual means using smartphones and other mobile communication devices. The terminal can update traffic information in real time as needed and recalculate routes according to new conditions.

[0497] User actions

[0498] Users are guided to their destination using the navigation system. While driving, they can travel smoothly and efficiently by following the voice and visual instructions received from the terminal. After completing their journey, the user's actual driving data is sent from the terminal to the server and used to improve future route selection algorithms.

[0499] With the above configuration, this invention makes it possible to flexibly and quickly provide navigation that follows the route desired by the user.

[0500] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0501] Step 1:

[0502] The server receives information about the user's departure point, destination, and route characteristics transmitted from the terminal. It analyzes the received information and uses a map database and real-time traffic information to aggregate initial data for various candidate routes. The input is user condition data obtained from the terminal, and the output is a preliminary dataset of candidate routes.

[0503] Step 2:

[0504] The server uses a generative AI model to evaluate an aggregated dataset of candidate routes. Here, optimal routes are scored based on the user's past history and specified route features. The AI ​​model assigns scores to the candidate routes and selects the most suitable one. The input is the candidate route dataset, and the output is the selected optimal route after scoring.

[0505] Step 3:

[0506] The server generates user-friendly navigation instructions based on the selected optimal route. Using a generation AI model, it creates guidance text that includes specific landmarks and intersection information. The prompt used is "Create detailed directions from my current location to my destination, including landmarks." The input is the selected optimal route, and the output is user-friendly navigation instructions.

[0507] Step 4:

[0508] The terminal receives navigation instructions sent from the server and provides the user with audio or visual guidance. The receiving process also caches the guidance data in preparation for real-time updates. The input is the navigation instructions from the server, and the output is specific visual and audio guidance for the user.

[0509] Step 5:

[0510] The user drives to their destination following the navigation instructions provided by the terminal. Based on real-time traffic information during driving, the terminal can recalculate the route as needed and present updated directions. The input is the traffic conditions that change during driving, and the output is the updated real-time directions.

[0511] Step 6:

[0512] The terminal collects the user's actual driving data and sends it to the server after the session ends. The server analyzes the received driving data and uses it to improve the route selection algorithm. The input is the user's driving data, and the output is accumulated historical data and feedback for algorithm improvement.

[0513] (Application Example 1)

[0514] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0515] Modern navigation systems have limited ability to optimize routes based on user requests. Furthermore, they often fail to adequately consider real-time traffic conditions, resulting in inefficient travel. These problems are even more pronounced in autonomous vehicles, where more advanced navigation systems are needed to provide a safe and comfortable driving experience.

[0516] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0517] In this invention, the server includes means for receiving user input and generating multiple candidate routes, means for selecting the optimal route from the generated routes based on user specifications, and means for generating optimized navigation from traffic condition data. This makes it possible to provide efficient and safe travel routes that meet the user's needs.

[0518] "User input" refers to information that a user provides to the system, such as a specific starting point, destination, and desired route characteristics.

[0519] A "candidate route" is a set of multiple possible travel routes between a specified starting point and destination.

[0520] The "optimal route" is the most efficient and satisfying travel route selected based on the user's input.

[0521] "Directions" refer to guidance information presented in a format that users can easily understand regarding the selected route.

[0522] "User devices" refer to electronic devices such as smartphones and in-car systems that receive and display navigation information.

[0523] "Movement data" refers to data that includes the user's actual driving information and is used to improve the system's algorithms.

[0524] "Traffic condition data" refers to real-time information about traffic, such as current road congestion and traffic signal information.

[0525] To implement this invention, the server is primarily responsible for data processing and route optimization. Built using Flask with Python, the server receives route requests from users and collects candidate routes using map APIs. Specifically, it uses Google Maps API and OpenStreetMap API to obtain the latest traffic conditions and geographic information. Next, it uses a generative AI model (for example, OpenAI's GPT-3) to select the optimal route based on user-specified conditions and past travel history. Based on the selected route, the server generates a route description, creates optimized navigation information that also takes traffic data into consideration, and sends it to the user's device.

[0526] The terminals are user devices such as smartphones and in-car computers. These devices receive navigation information from a server and provide guidance to the user visually and audibly. Users input their origin and destination on their smartphone app or in-car system and add desired route characteristics as needed. The terminals then collect movement data from the user while driving and send it back to the server. This data is used to improve the route selection algorithm.

[0527] As a concrete example, when person A is going from their home to a shopping mall, specifying "a road with beautiful autumn foliage" will cause the system to suggest the optimal route according to that request. By inputting a prompt into the generating AI, it is possible to generate guidance such as, "Based on the route parameters specified by the user, please provide the most attractive and efficient route. For example, 'a road where you can enjoy autumn foliage during the autumn foliage season.' Based on the results, please generate real-time navigation instructions." In this way, a flexible and effective navigation system that responds to user input conditions is realized.

[0528] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0529] Step 1:

[0530] The user inputs their starting point, destination, and desired route characteristics using a smartphone app or in-vehicle system. This input information is transmitted via the application and passed to the server as a request. This step generates input data that can be customized according to the user's needs.

[0531] Step 2:

[0532] The server receives a request from the user and queries the map API. Using the Google Maps API and OpenStreetMap API, it generates multiple candidate routes using current map information and real-time traffic data. The output data obtained here contains detailed information about each candidate route.

[0533] Step 3:

[0534] The server uses a generative AI model (e.g., OpenAI GPT-3) to select the optimal route based on the user's input preferences and past history. The generative AI utilizes prompts to select the best route that satisfies the input conditions. In this step, the optimal route is determined based on the evaluation and selection criteria for each route.

[0535] Step 4:

[0536] The server generates detailed navigation instructions based on the selected optimal route. These instructions reflect traffic conditions and present route guidance in a user-friendly format. The output data for this step contains detailed route information.

[0537] Step 5:

[0538] The server transmits the generated navigation information to the user's device. The user's device receives this information and communicates it to the user through display and voice guidance. Here, a guide that the user can easily follow while driving is provided.

[0539] Step 6:

[0540] The user's device records driving data during operation and sends this data to the server. The server collects this data and uses it to improve the accuracy of future route selection algorithms. This improves the quality of navigation in subsequent trips.

[0541] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0542] This invention relates to a navigation system that incorporates an emotion engine to make user route guidance more personalized. In addition to basic navigation functions that select the optimal route based on user input and past usage history, this system provides more detailed and individualized route guidance by recognizing the user's emotional state.

[0543] server

[0544] The server receives route information from the user and generates multiple candidate routes. The server also receives information from the emotion engine and takes into account the user's emotional state if it influences route selection. For example, if the user wants to relax, the server might prioritize routes with good scenery.

[0545] terminal

[0546] The terminal serves as both a provider of route guidance and a device that operates an emotion engine. The terminal analyzes the user's voice input and video data from the camera to determine the user's emotional state in real time. The recognized emotions are sent from the terminal to the server and reflected in the route guidance information.

[0547] Emotional Engine

[0548] The emotion engine analyzes the tone of voice and facial expressions of the user when they speak into the device. This allows it to determine if the user is in a specific state (e.g., stressed or relaxed). This information is used to adjust navigation instructions, enabling route guidance tailored to the user's emotions.

[0549] User

[0550] Users benefit from the emotional engine when using a navigation system and receiving directions to their destination. For example, if a user is tired, the system can provide guidance tailored to their situation, such as recommending a route with rest facilities.

[0551] For example, if a user is feeling fatigued from driving for a long time, the emotional engine detects this state, and the server provides the user with relaxing information about the route, selects a route that includes appropriate rest stops, and guides them along the way. In this way, the user can reach their destination with a sense of security.

[0552] The following describes the processing flow.

[0553] Step 1:

[0554] The user enters the destination and departure point into the device and specifies the desired route conditions.

[0555] Step 2:

[0556] The device acquires the user's voice and facial expressions, which are then analyzed by an emotion engine. This analysis determines the user's emotional state.

[0557] Step 3:

[0558] The device sends emotional state information and routing requests to the server.

[0559] Step 4:

[0560] The server uses the received destination, departure point, and user sentiment information to match map data with real-time traffic information and generate multiple candidate routes.

[0561] Step 5:

[0562] The server uses AI generation to select the optimal route, taking into account the user's emotional state, past history, and preferences. For example, if the user wants to relax, it will prioritize scenic routes.

[0563] Step 6:

[0564] The server generates user-friendly navigation descriptions for the selected route. These descriptions are designed with consideration for the user's emotional state.

[0565] Step 7:

[0566] The server sends the generated navigation information to the terminal.

[0567] Step 8:

[0568] The device displays navigation information it has received and provides real-time instructions to the user through voice and map display.

[0569] Step 9:

[0570] The user drives according to the navigation on their device, safely reaching their destination via an emotionally sensitive route.

[0571] Step 10:

[0572] After the ride is complete, the device sends the collected ride data and emotion data to the server. This data is used to improve route selection for future rides.

[0573] (Example 2)

[0574] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0575] Current navigation systems select routes based solely on geographical information and traffic data, making it difficult to provide flexible and personalized guidance that takes into account the user's mood and emotional state. In particular, when a user is feeling fatigued or stressed, route guidance that reflects that state is required, but current systems are not adequately able to handle this.

[0576] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0577] In this invention, the server includes means for receiving user input and generating multiple candidate routes, means for selecting the optimal route from the generated routes based on the user's specifications and emotional state, and means for adjusting the selected route in cooperation with an emotion analysis device for analyzing the emotional state. This makes it possible to provide more personalized and reassuring route guidance that is tailored to the user's emotional state.

[0578] "User input" refers to the act of a user providing information about their destination or waypoints to the navigation system.

[0579] "Candidate routes" refer to the multiple routes that the server generates and that the user can choose to reach their destination.

[0580] The "optimal route" refers to the most appropriate route for the user, selected considering the user's specifications and emotional state.

[0581] "Guidance and explanation" refers to the content of audio and visual instructions provided to make it easier for users to understand the selected route.

[0582] A "user terminal" refers to a device used by a user to operate and utilize the navigation system.

[0583] "Driving information" refers to specific data collected from user terminals that is used for route selection and improvement.

[0584] "Emotional state" refers to the user's mental state, such as stress or relaxation, as recognized by the emotion analysis device.

[0585] An "emotion analysis device" refers to a device that analyzes audio and video data to determine the user's emotional state.

[0586] This invention is a system that provides personalized route guidance for users and navigation that takes their emotional state into consideration. It mainly consists of a server, a terminal, and an emotion analysis device. The specific roles and operations of each element are described below.

[0587] The server is hardware that generates multiple candidate routes based on route information received from the user. It utilizes software to integrate a map information database and real-time traffic data. Furthermore, it selects a route appropriate to the user's mental state based on emotional information received from an emotion analysis device. The server makes adjustments, such as prioritizing scenic routes when the user is feeling relaxed.

[0588] The terminal is user-facing hardware equipped with voice input and a camera, and has the capability to diagnose the user's emotional state in real time. The terminal is fitted with software to run an emotion analysis algorithm, and uses the on-device camera and microphone to record changes in the user's facial expressions and voice. This data is used to analyze the user's emotional state, and the results are sent to a server.

[0589] The emotion analysis device functions as an emotion engine, analyzing voice tone and facial expressions to determine whether the user is stressed or relaxed. The information obtained from emotion analysis is used to personalize the server's navigation instructions.

[0590] For example, if a user feels fatigued while driving, the device's emotion analyzer detects signs of fatigue and sends them to the server. The server then suggests a relaxing route and selects places where the user can take a break. This process allows the user to reach their destination with a sense of security.

[0591] An example of a prompt to input into a generative AI model is, "The user is seeking relaxation, so please suggest a route with good scenery." Such prompts support route selection based on sentiment analysis results.

[0592] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0593] Step 1:

[0594] The user inputs route information, including the destination, into the navigation system. This information is transmitted to the server via the terminal. The user's input includes geographical locations and route preferences. The server receives this information and performs data processing to generate multiple candidate routes. The output is a set of multiple candidate routes that the user can choose from.

[0595] Step 2:

[0596] The device collects the user's real-time emotional state using an emotion analysis device. Specifically, it captures the user's facial expressions with a camera and collects their voice tone with a microphone. The audio and video data, as input information, are analyzed by an emotion analysis algorithm to generate an output for a specific emotional state (e.g., relaxed, stressed).

[0597] Step 3:

[0598] The server receives the collected emotional state data and uses it to select candidate routes. This process adjusts the route based on the user's specific emotional state, such as wanting to relax. For example, routes with good scenery are prioritized. The input is emotional state data and initial candidate routes, and the output is the adjusted, optimal route.

[0599] Step 4:

[0600] The terminal receives pre-configured routes from the server and provides them to the user as audio and visual guidance. Specifically, the terminal uses its audio speaker and screen display device to provide real-time navigation guidance. The input is route information from the server, and the output is simple and easy-to-understand guidance for the user.

[0601] Step 5:

[0602] The user follows the instructions and continues driving. If a new emotional change occurs along the way, the terminal detects it again with an emotion analysis device, and the server re-evaluates the route as needed. The input is the newly collected emotional information, and the system output is the updated route guidance. Specifically, route changes are suggested in real time according to the user's comfort level.

[0603] (Application Example 2)

[0604] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0605] Conventional navigation systems fail to adequately improve user satisfaction and comfort because they provide route guidance without considering the user's current emotional state. Furthermore, because they cannot select routes that reflect the user's emotions in real time, it is difficult to provide optimal guidance tailored to the user's situation.

[0606] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0607] In this invention, the server includes means for receiving user input and generating multiple candidate routes, means for selecting the optimal route from the generated routes based on user specifications, and means for acquiring user emotion data through a device equipped with an emotion analysis function for analyzing the user's emotional state. This makes it possible to provide route guidance optimized for the user's emotional state.

[0608] "User" refers to a person or entity that receives route guidance using a navigation system.

[0609] "Input" refers to the operations or instructions a user gives to the system, and includes data related to destination information and specific preferences.

[0610] A "candidate route" refers to a set of multiple possible travel routes considered in order to reach a destination.

[0611] "Route selection" refers to the process of determining the optimal travel route from among multiple candidate routes.

[0612] "Guidance information" refers to instructions and explanations provided to the user based on the selected route, including route details and related precautions.

[0613] "Terminal" refers to a hardware device used by a user to access a navigation system, and includes smartphones, smart glasses, and other similar devices.

[0614] "Movement data" refers to information related to a user's movement obtained from a device, including location information, movement speed, and time.

[0615] "Emotion analysis function" refers to technology used to determine a user's emotional state, and it has the ability to analyze voice tone and facial expressions.

[0616] "Emotional data" refers to data that represents the user's emotional state, obtained through the emotion analysis function.

[0617] "Dynamic traffic information" refers to information about traffic conditions that is updated in real time, including road congestion levels and information about road disruptions.

[0618] The system that implements this application consists of three main elements: the user, the terminal, and the server.

[0619] The server receives destination and preference input information from the user. Furthermore, it receives real-time sentiment and movement data transmitted from the device. Based on this information, the server generates multiple candidate routes and selects the optimal route. During the selection process, sentiment data is used to determine the route best suited to the user's current state. The server utilizes cloud platforms such as Google Cloud and Amazon Web Services to process large amounts of data and also consider dynamic traffic information.

[0620] The system uses smart devices, such as smart glasses or smartphones, to acquire user movement information and emotional states. These devices are equipped with cameras and voice recognition capabilities to analyze the user's facial expressions and voice tone in real time. This process utilizes image processing libraries such as OpenCV and voice analysis software like Google Cloud Speech-to-Text. The emotion analysis function processes the acquired data using TensorFlow or similar tools to determine the user's emotional state.

[0621] Users utilize the navigation system to receive optimized route guidance. For example, if a user experiencing stress during a long-distance drive, the server uses emotion analysis data to determine this and prioritizes routes with relaxing scenery. As a result, users can reach their destination comfortably and safely.

[0622] As a concrete example, an example prompt message is: "When the user is feeling stressed, please select a route that includes roads with a relaxing effect." This prompt allows the system to select an appropriate route based on the situation.

[0623] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0624] Step 1:

[0625] The device captures the user's voice and facial expressions. As input, the device uses its camera and microphone to acquire real-time video and audio data of the user. As output, this data is sent to the emotion analysis function.

[0626] Step 2:

[0627] An emotion analysis function operates within the device to evaluate the user's emotional state. The input is the audio and video data acquired in step 1. The device analyzes facial expressions using an image processing library (e.g., OpenCV) and speech tone using speech analysis software (e.g., Google Cloud Speech-to-Text). The output is the analyzed emotion data, which is sent from the device to the server.

[0628] Step 3:

[0629] The server receives transmitted sentiment data and user input information (such as destination and preferences). The input consists of sentiment data from the terminal and destination information from the user. Based on this, the server collects real-time traffic information and generates multiple candidate routes. The output is a list of the generated candidate routes.

[0630] Step 4:

[0631] The server considers emotional data and selects the optimal path. The input is the candidate paths generated in step 3 and the emotional data. The server uses a generative AI model to calculate the path best suited to the user's current emotional state. The output is the selected optimal path.

[0632] Step 5:

[0633] The server sends the selected route to the terminal and generates navigation information. The input is the optimal route selected in step 4. The server creates the navigation information and sends it to the terminal in a format that is easy for the user to understand. The output is the navigation information presented to the user.

[0634] Step 6:

[0635] The user receives guidance information through their device and travels according to the actual route. The input is guidance information sent from the server. The user receives guidance in real time using smart glasses or a smartphone. The output is the user's travel experience, which is an improvement in their sense of security and satisfaction during the journey to their destination.

[0636] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0637] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0638] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0639] [Fourth Embodiment]

[0640] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0641] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0642] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0643] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0644] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0645] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0646] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0647] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0648] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0649] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0650] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0651] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0652] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0653] This invention relates to a navigation system that allows a user to reach their destination via a route of their choice. The system operates by allowing the user to specify a starting point and destination, and optionally input the characteristics of their desired route. The main components of the system and their operation are described below.

[0654] server

[0655] The server plays a central role in the navigation system. It receives requests from the user's terminal and generates multiple candidate routes to reach the destination. This process utilizes map databases and real-time traffic information. Furthermore, the server uses generation AI to select the optimal route, taking into account user specifications and past history.

[0656] The server creates user-friendly navigation instructions for the selected route. These instructions include information on landmarks such as buildings and intersections, designed to facilitate the user's driving. The generated navigation instructions are then sent to the user's terminal.

[0657] terminal

[0658] The terminal is a device owned by the user (such as a smartphone with navigation capabilities or a dedicated device) and is responsible for receiving navigation information transmitted from the server. The terminal provides route guidance to the user visually or audibly, helping the user reach their destination smoothly.

[0659] User

[0660] The user is a driver using a navigation system to reach their destination. The user inputs their starting point, destination, and desired route information (if necessary) into the system. During driving, the user can efficiently travel by following the navigation provided on the terminal. After the trip, the user's actual driving data is sent to the server and used to improve the accuracy of the route selection algorithm for future trips.

[0661] For example, when a user specifies a route from home to work, the server selects routes that prioritize speed or routes similar to those used in the past, and provides specific directions to the terminal, such as "go straight when you see a cafe on your right," allowing the user to drive while seeing familiar scenery.

[0662] The following describes the processing flow.

[0663] Step 1:

[0664] The user enters the destination and departure point into the device and specifies the desired route conditions.

[0665] Step 2:

[0666] The terminal sends the information entered by the user to the server.

[0667] Step 3:

[0668] Based on the destination and departure point received by the server, it references a map database and real-time traffic information to generate multiple candidate routes.

[0669] Step 4:

[0670] The server uses generated AI to select the optimal route, taking into account the user's preferences and past historical data.

[0671] Step 5:

[0672] Based on the route selected by the server, a user-friendly navigation description is generated. This description includes landmark buildings and distinctive points of interest.

[0673] Step 6:

[0674] The server sends the generated navigation information to the terminal.

[0675] Step 7:

[0676] The device displays received navigation information and provides real-time instructions to the user through voice guidance and visual map displays.

[0677] Step 8:

[0678] The user drives according to the instructions on the device and heads towards the destination.

[0679] Step 9:

[0680] After the ride is complete, the device sends the collected ride data to the server. This data includes information about the actual route taken and the time taken.

[0681] Step 10:

[0682] The server updates the route selection algorithm based on the collected driving data, improving the accuracy of guidance for subsequent trips.

[0683] (Example 1)

[0684] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0685] In navigation systems, the challenge is to provide the optimal route that reflects the characteristics of the user's desired route while also considering real-time traffic conditions and past usage history. Users need to receive intuitively understandable guidance based on map information, efficiently reach their destination, and further improve the system's accuracy by feeding back the driving data.

[0686] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0687] In this invention, the server includes means for receiving user information input and constructing multiple candidate routes, means for selecting the optimal route from the constructed routes based on the user's specifications, and means for using a generation AI model to consider past usage history. This enables flexible and appropriate navigation guidance for the user.

[0688] A "user" is an individual or group that uses a navigation system to request route guidance from a starting point to a destination.

[0689] "Information input" refers to data that users provide to the navigation system, including their starting point, destination, and preferences or instructions regarding the route.

[0690] A "candidate route" refers to one of several selectable travel routes generated by the server to help the user reach their destination.

[0691] The "optimal route" refers to the most efficient and suitable travel route selected by the server, taking into account the user's specified conditions, past history, and traffic conditions.

[0692] "Guidance information" refers to information including navigation instructions that are provided in a way that is easy for users to intuitively understand regarding the selected route.

[0693] A "generative AI model" is an artificial intelligence technology used to generate appropriate routes and guidance information based on a user's past history and specified conditions.

[0694] "Real-time traffic information" refers to real-time data such as traffic conditions, congestion information, and road closures at the current time.

[0695] "Driving data" refers to records of the routes traveled by users and data detailing the movements made during that process.

[0696] A "route selection algorithm" is a computational method and procedure used in a navigation system to select the optimal route from multiple candidate routes.

[0697] This invention relates to a navigation system that provides the optimal route for a user to reach their destination. This system selects a route from the starting point to the destination based on information entered by the user and provides clear and easy-to-understand guidance along that route.

[0698] Server operation

[0699] The server plays a central role in this system. First, it receives the starting point, destination, and optional route features entered by the user via a terminal. The server generates multiple candidate routes using a map information database and real-time traffic information. External software, such as traffic APIs, may be used in this process.

[0700] Next, the server uses a generated AI model to select the optimal route, taking into account the user's past history and specified conditions. Therefore, the AI ​​model includes roles in generating navigation instructions and route scoring. The prompt used is: "Please select the optimal route from the starting point to the destination and generate directions including landmarks."

[0701] Based on the selected route, the server generates navigation instructions that are easy for the user to understand intuitively. These instructions include landmarks such as buildings and intersections.

[0702] Terminal operation

[0703] The terminal is responsible for receiving guidance information transmitted from the server and providing it to the user. This guidance is conveyed to the user via voice and visual means using smartphones and other mobile communication devices. The terminal can update traffic information in real time as needed and recalculate routes according to new conditions.

[0704] User actions

[0705] Users are guided to their destination using the navigation system. While driving, they can travel smoothly and efficiently by following the voice and visual instructions received from the terminal. After completing their journey, the user's actual driving data is sent from the terminal to the server and used to improve future route selection algorithms.

[0706] With the above configuration, this invention makes it possible to flexibly and quickly provide navigation that follows the route desired by the user.

[0707] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0708] Step 1:

[0709] The server receives information about the user's departure point, destination, and route characteristics transmitted from the terminal. It analyzes the received information and uses a map database and real-time traffic information to aggregate initial data for various candidate routes. The input is user condition data obtained from the terminal, and the output is a preliminary dataset of candidate routes.

[0710] Step 2:

[0711] The server uses a generative AI model to evaluate an aggregated dataset of candidate routes. Here, optimal routes are scored based on the user's past history and specified route features. The AI ​​model assigns scores to the candidate routes and selects the most suitable one. The input is the candidate route dataset, and the output is the selected optimal route after scoring.

[0712] Step 3:

[0713] The server generates user-friendly navigation instructions based on the selected optimal route. Using a generation AI model, it creates guidance text that includes specific landmarks and intersection information. The prompt used is "Create detailed directions from my current location to my destination, including landmarks." The input is the selected optimal route, and the output is user-friendly navigation instructions.

[0714] Step 4:

[0715] The terminal receives navigation instructions sent from the server and provides the user with audio or visual guidance. The receiving process also caches the guidance data in preparation for real-time updates. The input is the navigation instructions from the server, and the output is specific visual and audio guidance for the user.

[0716] Step 5:

[0717] The user drives to their destination following the navigation instructions provided by the terminal. Based on real-time traffic information during driving, the terminal can recalculate the route as needed and present updated directions. The input is the traffic conditions that change during driving, and the output is the updated real-time directions.

[0718] Step 6:

[0719] The terminal collects the user's actual driving data and sends it to the server after the session ends. The server analyzes the received driving data and uses it to improve the route selection algorithm. The input is the user's driving data, and the output is accumulated historical data and feedback for algorithm improvement.

[0720] (Application Example 1)

[0721] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0722] Modern navigation systems have limited ability to optimize routes based on user requests. Furthermore, they often fail to adequately consider real-time traffic conditions, resulting in inefficient travel. These problems are even more pronounced in autonomous vehicles, where more advanced navigation systems are needed to provide a safe and comfortable driving experience.

[0723] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0724] In this invention, the server includes means for receiving user input and generating multiple candidate routes, means for selecting the optimal route from the generated routes based on user specifications, and means for generating optimized navigation from traffic condition data. This makes it possible to provide efficient and safe travel routes that meet the user's needs.

[0725] "User input" refers to information that a user provides to the system, such as a specific starting point, destination, and desired route characteristics.

[0726] A "candidate route" is a set of multiple possible travel routes between a specified starting point and destination.

[0727] The "optimal route" is the most efficient and satisfying travel route selected based on the user's input.

[0728] "Directions" refer to guidance information presented in a format that users can easily understand regarding the selected route.

[0729] "User devices" refer to electronic devices such as smartphones and in-car systems that receive and display navigation information.

[0730] "Movement data" refers to data that includes the user's actual driving information and is used to improve the system's algorithms.

[0731] "Traffic condition data" refers to real-time information about traffic, such as current road congestion and traffic signal information.

[0732] To implement this invention, the server is primarily responsible for data processing and route optimization. Built using Flask with Python, the server receives route requests from users and collects candidate routes using map APIs. Specifically, it uses Google Maps API and OpenStreetMap API to obtain the latest traffic conditions and geographic information. Next, it uses a generative AI model (for example, OpenAI's GPT-3) to select the optimal route based on user-specified conditions and past travel history. Based on the selected route, the server generates a route description, creates optimized navigation information that also takes traffic data into consideration, and sends it to the user's device.

[0733] The terminals are user devices such as smartphones and in-car computers. These devices receive navigation information from a server and provide guidance to the user visually and audibly. Users input their origin and destination on their smartphone app or in-car system and add desired route characteristics as needed. The terminals then collect movement data from the user while driving and send it back to the server. This data is used to improve the route selection algorithm.

[0734] As a concrete example, when person A is going from their home to a shopping mall, specifying "a road with beautiful autumn foliage" will cause the system to suggest the optimal route according to that request. By inputting a prompt into the generating AI, it is possible to generate guidance such as, "Based on the route parameters specified by the user, please provide the most attractive and efficient route. For example, 'a road where you can enjoy autumn foliage during the autumn foliage season.' Based on the results, please generate real-time navigation instructions." In this way, a flexible and effective navigation system that responds to user input conditions is realized.

[0735] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0736] Step 1:

[0737] The user inputs their starting point, destination, and desired route characteristics using a smartphone app or in-vehicle system. This input information is transmitted via the application and passed to the server as a request. This step generates input data that can be customized according to the user's needs.

[0738] Step 2:

[0739] The server receives a request from the user and queries the map API. Using the Google Maps API and OpenStreetMap API, it generates multiple candidate routes using current map information and real-time traffic data. The output data obtained here contains detailed information about each candidate route.

[0740] Step 3:

[0741] The server uses a generative AI model (e.g., OpenAI GPT-3) to select the optimal route based on the user's input preferences and past history. The generative AI utilizes prompts to select the best route that satisfies the input conditions. In this step, the optimal route is determined based on the evaluation and selection criteria for each route.

[0742] Step 4:

[0743] The server generates detailed navigation instructions based on the selected optimal route. These instructions reflect traffic conditions and present route guidance in a user-friendly format. The output data for this step contains detailed route information.

[0744] Step 5:

[0745] The server transmits the generated navigation information to the user's device. The user's device receives this information and communicates it to the user through display and voice guidance. Here, a guide that the user can easily follow while driving is provided.

[0746] Step 6:

[0747] The user's device records driving data during operation and sends this data to the server. The server collects this data and uses it to improve the accuracy of future route selection algorithms. This improves the quality of navigation in subsequent trips.

[0748] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0749] This invention relates to a navigation system that incorporates an emotion engine to make user route guidance more personalized. In addition to basic navigation functions that select the optimal route based on user input and past usage history, this system provides more detailed and individualized route guidance by recognizing the user's emotional state.

[0750] server

[0751] The server receives route information from the user and generates multiple candidate routes. The server also receives information from the emotion engine and takes into account the user's emotional state if it influences route selection. For example, if the user wants to relax, the server might prioritize routes with good scenery.

[0752] terminal

[0753] The terminal serves as both a provider of route guidance and a device that operates an emotion engine. The terminal analyzes the user's voice input and video data from the camera to determine the user's emotional state in real time. The recognized emotions are sent from the terminal to the server and reflected in the route guidance information.

[0754] Emotional Engine

[0755] The emotion engine analyzes the tone of voice and facial expressions of the user when they speak into the device. This allows it to determine if the user is in a specific state (e.g., stressed or relaxed). This information is used to adjust navigation instructions, enabling route guidance tailored to the user's emotions.

[0756] User

[0757] Users benefit from the emotional engine when using a navigation system and receiving directions to their destination. For example, if a user is tired, the system can provide guidance tailored to their situation, such as recommending a route with rest facilities.

[0758] For example, if a user is feeling fatigued from driving for a long time, the emotional engine detects this state, and the server provides the user with relaxing information about the route, selects a route that includes appropriate rest stops, and guides them along the way. In this way, the user can reach their destination with a sense of security.

[0759] The following describes the processing flow.

[0760] Step 1:

[0761] The user enters the destination and departure point into the device and specifies the desired route conditions.

[0762] Step 2:

[0763] The device acquires the user's voice and facial expressions, which are then analyzed by an emotion engine. This analysis determines the user's emotional state.

[0764] Step 3:

[0765] The device sends emotional state information and routing requests to the server.

[0766] Step 4:

[0767] The server uses the received destination, departure point, and user sentiment information to match map data with real-time traffic information and generate multiple candidate routes.

[0768] Step 5:

[0769] The server uses AI generation to select the optimal route, taking into account the user's emotional state, past history, and preferences. For example, if the user wants to relax, it will prioritize scenic routes.

[0770] Step 6:

[0771] The server generates user-friendly navigation descriptions for the selected route. These descriptions are designed with consideration for the user's emotional state.

[0772] Step 7:

[0773] The server sends the generated navigation information to the terminal.

[0774] Step 8:

[0775] The device displays navigation information it has received and provides real-time instructions to the user through voice and map display.

[0776] Step 9:

[0777] The user drives according to the navigation on their device, safely reaching their destination via an emotionally sensitive route.

[0778] Step 10:

[0779] After the ride is complete, the device sends the collected ride data and emotion data to the server. This data is used to improve route selection for future rides.

[0780] (Example 2)

[0781] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0782] Current navigation systems select routes based solely on geographical information and traffic data, making it difficult to provide flexible and personalized guidance that takes into account the user's mood and emotional state. In particular, when a user is feeling fatigued or stressed, route guidance that reflects that state is required, but current systems are not adequately able to handle this.

[0783] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0784] In this invention, the server includes means for receiving user input and generating multiple candidate routes, means for selecting the optimal route from the generated routes based on the user's specifications and emotional state, and means for adjusting the selected route in cooperation with an emotion analysis device for analyzing the emotional state. This makes it possible to provide more personalized and reassuring route guidance that is tailored to the user's emotional state.

[0785] "User input" refers to the act of a user providing information about their destination or waypoints to the navigation system.

[0786] "Candidate routes" refer to the multiple routes that the server generates and that the user can choose to reach their destination.

[0787] The "optimal route" refers to the most appropriate route for the user, selected considering the user's specifications and emotional state.

[0788] "Guidance and explanation" refers to the content of audio and visual instructions provided to make it easier for users to understand the selected route.

[0789] A "user terminal" refers to a device used by a user to operate and utilize the navigation system.

[0790] "Driving information" refers to specific data collected from user terminals that is used for route selection and improvement.

[0791] "Emotional state" refers to the user's mental state, such as stress or relaxation, as recognized by the emotion analysis device.

[0792] An "emotion analysis device" refers to a device that analyzes audio and video data to determine the user's emotional state.

[0793] This invention is a system that provides personalized route guidance for users and navigation that takes their emotional state into consideration. It mainly consists of a server, a terminal, and an emotion analysis device. The specific roles and operations of each element are described below.

[0794] The server is hardware that generates multiple candidate routes based on route information received from the user. It utilizes software to integrate a map information database and real-time traffic data. Furthermore, it selects a route appropriate to the user's mental state based on emotional information received from an emotion analysis device. The server makes adjustments, such as prioritizing scenic routes when the user is feeling relaxed.

[0795] The terminal is user-facing hardware equipped with voice input and a camera, and has the capability to diagnose the user's emotional state in real time. The terminal is fitted with software to run an emotion analysis algorithm, and uses the on-device camera and microphone to record changes in the user's facial expressions and voice. This data is used to analyze the user's emotional state, and the results are sent to a server.

[0796] The emotion analysis device functions as an emotion engine, analyzing voice tone and facial expressions to determine whether the user is stressed or relaxed. The information obtained from emotion analysis is used to personalize the server's navigation instructions.

[0797] For example, if a user feels fatigued while driving, the device's emotion analyzer detects signs of fatigue and sends them to the server. The server then suggests a relaxing route and selects places where the user can take a break. This process allows the user to reach their destination with a sense of security.

[0798] An example of a prompt to input into a generative AI model is, "The user is seeking relaxation, so please suggest a route with good scenery." Such prompts support route selection based on sentiment analysis results.

[0799] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0800] Step 1:

[0801] The user inputs route information, including the destination, into the navigation system. This information is transmitted to the server via the terminal. The user's input includes geographical locations and route preferences. The server receives this information and performs data processing to generate multiple candidate routes. The output is a set of multiple candidate routes that the user can choose from.

[0802] Step 2:

[0803] The device collects the user's real-time emotional state using an emotion analysis device. Specifically, it captures the user's facial expressions with a camera and collects their voice tone with a microphone. The audio and video data, as input information, are analyzed by an emotion analysis algorithm to generate an output for a specific emotional state (e.g., relaxed, stressed).

[0804] Step 3:

[0805] The server receives the collected emotional state data and uses it to select candidate routes. This process adjusts the route based on the user's specific emotional state, such as wanting to relax. For example, routes with good scenery are prioritized. The input is emotional state data and initial candidate routes, and the output is the adjusted, optimal route.

[0806] Step 4:

[0807] The terminal receives pre-configured routes from the server and provides them to the user as audio and visual guidance. Specifically, the terminal uses its audio speaker and screen display device to provide real-time navigation guidance. The input is route information from the server, and the output is simple and easy-to-understand guidance for the user.

[0808] Step 5:

[0809] The user follows the instructions and continues driving. If a new emotional change occurs along the way, the terminal detects it again with an emotion analysis device, and the server re-evaluates the route as needed. The input is the newly collected emotional information, and the system output is the updated route guidance. Specifically, route changes are suggested in real time according to the user's comfort level.

[0810] (Application Example 2)

[0811] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0812] Conventional navigation systems fail to adequately improve user satisfaction and comfort because they provide route guidance without considering the user's current emotional state. Furthermore, because they cannot select routes that reflect the user's emotions in real time, it is difficult to provide optimal guidance tailored to the user's situation.

[0813] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0814] In this invention, the server includes means for receiving user input and generating multiple candidate routes, means for selecting the optimal route from the generated routes based on user specifications, and means for acquiring user emotion data through a device equipped with an emotion analysis function for analyzing the user's emotional state. This makes it possible to provide route guidance optimized for the user's emotional state.

[0815] "User" refers to a person or entity that receives route guidance using a navigation system.

[0816] "Input" refers to the operations or instructions a user gives to the system, and includes data related to destination information and specific preferences.

[0817] A "candidate route" refers to a set of multiple possible travel routes considered in order to reach a destination.

[0818] "Route selection" refers to the process of determining the optimal travel route from among multiple candidate routes.

[0819] "Guidance information" refers to instructions and explanations provided to the user based on the selected route, including route details and related precautions.

[0820] "Terminal" refers to a hardware device used by a user to access a navigation system, and includes smartphones, smart glasses, and other similar devices.

[0821] "Movement data" refers to information related to a user's movement obtained from a device, including location information, movement speed, and time.

[0822] "Emotion analysis function" refers to technology used to determine a user's emotional state, and it has the ability to analyze voice tone and facial expressions.

[0823] "Emotional data" refers to data that represents the user's emotional state, obtained through the emotion analysis function.

[0824] "Dynamic traffic information" refers to information about traffic conditions that is updated in real time, including road congestion levels and information about road disruptions.

[0825] The system that implements this application consists of three main elements: the user, the terminal, and the server.

[0826] The server receives destination and preference input information from the user. Furthermore, it receives real-time sentiment and movement data transmitted from the device. Based on this information, the server generates multiple candidate routes and selects the optimal route. During the selection process, sentiment data is used to determine the route best suited to the user's current state. The server utilizes cloud platforms such as Google Cloud and Amazon Web Services to process large amounts of data and also consider dynamic traffic information.

[0827] The system uses smart devices, such as smart glasses or smartphones, to acquire user movement information and emotional states. These devices are equipped with cameras and voice recognition capabilities to analyze the user's facial expressions and voice tone in real time. This process utilizes image processing libraries such as OpenCV and voice analysis software like Google Cloud Speech-to-Text. The emotion analysis function processes the acquired data using TensorFlow or similar tools to determine the user's emotional state.

[0828] Users utilize the navigation system to receive optimized route guidance. For example, if a user experiencing stress during a long-distance drive, the server uses emotion analysis data to determine this and prioritizes routes with relaxing scenery. As a result, users can reach their destination comfortably and safely.

[0829] As a concrete example, an example prompt message is: "When the user is feeling stressed, please select a route that includes roads with a relaxing effect." This prompt allows the system to select an appropriate route based on the situation.

[0830] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0831] Step 1:

[0832] The device captures the user's voice and facial expressions. As input, the device uses its camera and microphone to acquire real-time video and audio data of the user. As output, this data is sent to the emotion analysis function.

[0833] Step 2:

[0834] An emotion analysis function operates within the device to evaluate the user's emotional state. The input is the audio and video data acquired in step 1. The device analyzes facial expressions using an image processing library (e.g., OpenCV) and speech tone using speech analysis software (e.g., Google Cloud Speech-to-Text). The output is the analyzed emotion data, which is sent from the device to the server.

[0835] Step 3:

[0836] The server receives transmitted sentiment data and user input information (such as destination and preferences). The input consists of sentiment data from the terminal and destination information from the user. Based on this, the server collects real-time traffic information and generates multiple candidate routes. The output is a list of the generated candidate routes.

[0837] Step 4:

[0838] The server considers emotional data and selects the optimal path. The input is the candidate paths generated in step 3 and the emotional data. The server uses a generative AI model to calculate the path best suited to the user's current emotional state. The output is the selected optimal path.

[0839] Step 5:

[0840] The server sends the selected route to the terminal and generates navigation information. The input is the optimal route selected in step 4. The server creates the navigation information and sends it to the terminal in a format that is easy for the user to understand. The output is the navigation information presented to the user.

[0841] Step 6:

[0842] The user receives guidance information through their device and travels according to the actual route. The input is guidance information sent from the server. The user receives guidance in real time using smart glasses or a smartphone. The output is the user's travel experience, which is an improvement in their sense of security and satisfaction during the journey to their destination.

[0843] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0844] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0845] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0846] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0847] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0848] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0849] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0850] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0851] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0852] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0853] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0854] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0855] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0856] 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.

[0857] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0858] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0859] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0860] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0861] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0862] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0863] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0864] The following is further disclosed regarding the embodiments described above.

[0865] (Claim 1)

[0866] A means for receiving user input and generating multiple candidate paths,

[0867] A means for selecting the optimal route from the generated routes based on user specifications,

[0868] A means for generating navigation instructions to explain the selected route in an easy-to-understand manner for the user,

[0869] A means for transmitting navigation information to the user terminal,

[0870] A means of collecting driving data from a terminal and using that information to improve the route selection algorithm,

[0871] A system that includes this.

[0872] (Claim 2)

[0873] The system according to claim 1, wherein the navigation description is generated taking into account route information previously used by the user.

[0874] (Claim 3)

[0875] The system according to claim 1, which generates multiple candidate routes using real-time traffic information.

[0876] "Example 1"

[0877] (Claim 1)

[0878] A means of receiving user information input and constructing multiple candidate routes,

[0879] A means for selecting the optimal route from the constructed routes based on user specifications,

[0880] A means for creating guidance information to explain the selected route in an easy-to-understand manner to users,

[0881] A means for sending guidance information to the user's device,

[0882] A means for aggregating movement data from devices and using that information to improve the route selection algorithm,

[0883] A method for considering the user's past history when selecting a route using a generative AI model,

[0884] A system that includes this.

[0885] (Claim 2)

[0886] The system according to claim 1, which generates the aforementioned guidance information taking into consideration route information and distinctive landmarks previously used by the user.

[0887] (Claim 3)

[0888] The system according to claim 1, which generates multiple candidate routes using real-time traffic information.

[0889] "Application Example 1"

[0890] (Claim 1)

[0891] A means for receiving user input and generating multiple candidate paths,

[0892] A means for selecting the optimal route from the generated routes based on user specifications,

[0893] A means for generating a route explanation that makes the selected route easy for the user to understand,

[0894] A means for transmitting navigation information to a user device,

[0895] A means of collecting movement data from devices and using that information to improve the route selection algorithm,

[0896] A means of generating optimized navigation from traffic condition data,

[0897] A system that includes this.

[0898] (Claim 2)

[0899] The system according to claim 1, wherein the aforementioned route description is generated taking into account route information previously used by the user.

[0900] (Claim 3)

[0901] The system according to claim 1, which generates multiple candidate routes using real-time traffic information and provides signal information as route guidance.

[0902] "Example 2 of combining an emotion engine"

[0903] (Claim 1)

[0904] A means for receiving user input and generating multiple candidate paths,

[0905] A means for selecting the optimal route from the generated routes based on user specifications,

[0906] A means for generating guidance instructions to explain the selected route in an easy-to-understand manner for the user,

[0907] A means of sending guidance information to the user terminal,

[0908] A means for collecting driving information from a terminal and using that information to improve the route selection method,

[0909] A means of recognizing the user's emotional state and adjusting route selection based on that information,

[0910] Means including an emotion analysis device for analyzing the aforementioned emotional state,

[0911] A system that includes this.

[0912] (Claim 2)

[0913] The system according to claim 1, wherein the aforementioned guidance and explanation are generated taking into account the route information and emotional information previously used by the user.

[0914] (Claim 3)

[0915] The system according to claim 1, which generates multiple candidate routes using real-time traffic information and the user's emotional state.

[0916] "Application example 2 when combining with an emotional engine"

[0917] (Claim 1)

[0918] A means for receiving user input and generating multiple candidate paths,

[0919] A means for selecting the optimal route from the generated routes based on user specifications,

[0920] A means for generating guidance information to explain the selected route in an easy-to-understand manner to the user,

[0921] A means of sending guidance information to the user terminal,

[0922] A means of collecting movement data from a terminal and using that information to improve a route selection algorithm,

[0923] A means for acquiring user emotional data through a device equipped with an emotion analysis function for analyzing the user's emotional state,

[0924] A means of adjusting the path based on acquired emotional data,

[0925] A system that includes this.

[0926] (Claim 2)

[0927] The system according to claim 1, which generates the aforementioned guidance information taking into account route information previously used by the user.

[0928] (Claim 3)

[0929] The system according to claim 1, which generates multiple candidate routes using dynamic traffic information. [Explanation of symbols]

[0930] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for receiving user input and generating multiple candidate paths, A means for selecting the optimal route from the generated routes based on user specifications, A means for generating navigation instructions to explain the selected route in an easy-to-understand manner for the user, A means for transmitting navigation information to the user terminal, A means of collecting driving data from a terminal and using that information to improve the route selection algorithm, A system that includes this.

2. The system according to claim 1, wherein the navigation description is generated taking into account route information previously used by the user.

3. The system according to claim 1, which generates multiple candidate routes using real-time traffic information.

Images