system

Abstract

A system is provided.SOLUTION: Means for recognizing speech of a user and converting the speech into text data, means for transmitting the text data to a server, means for acquiring attribute information and current GPS data of the user and querying a database, means for generating an optimal route based on landscape information, a tourist spot, and evaluation information, means for navigating the generated route to the user, means for acquiring feedback of the user and transmitting the feedback to the server, and means for analyzing the feedback, A system comprising: means for re-evaluating route information; means for updating navigation based on the evaluated route information; and means for updating a database and training a AI model using the obtained feedback information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional car navigation systems focus on providing the shortest and fastest route to a destination, which leaves users who want to enjoy the drive itself unsatisfactory. In particular, users who want to enjoy the scenery and tourist spots while driving face the challenge of having to manually research and set a large amount of information in order to find the optimal route that takes these elements into account. To solve this problem, there is a need for a system that automatically generates a route that allows users to drive comfortably, using information on scenery, tourist spots, and driver impressions. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including: means for recognizing a user's speech and converting it into text data; means for transmitting the text data to a server; means for acquiring the user's attribute information and current GPS data and consulting a database; means for generating an optimal route based on scenic information, tourist spots, and rating information; means for navigating the generated route to the user; means for acquiring user feedback and transmitting it to a server; means for analyzing the feedback and reevaluating the route information; means for updating the navigation system based on the reevaluated route information; and means for updating the database using the acquired feedback data and training an AI model. This system provides a "pleasant" driving route optimized for each user, maximizing the enjoyment of driving.

[0006] "User" means an individual or organization that uses the subject car navigation system.

[0007] "Utterance" refers to instructions or requests that the user inputs verbally.

[0008] "Speech recognition" is a technology that converts voice signals into text data.

[0009] "Text data" is data in character format generated by speech recognition.

[0010] A "server" is a computer system that runs databases and algorithms and provides the results to a terminal.

[0011] "GPS data" is data that indicates the user's current location information.

[0012] The "database" is a system that stores information such as user attribute information, landscape information, tourist spots, and driver feedback.

[0013] "Scenery information" is data about the beauty and attractiveness of the scenery of a particular place.

[0014] "Tourist attractions" are information about places that users would like to visit.

[0015] "Review Information" means review data for a specific route or location provided by a driver or a third party.

[0016] "Route generation" is the process of designing the optimal driving route based on the acquired data.

[0017] "Navigating" is the act of guiding the user along the generated route.

[0018] "Feedback" refers to opinions and evaluations provided by users to the system.

[0019] "Reevaluation" is the process of reconsidering existing route information based on feedback.

[0020] An "AI model" is an artificial intelligence algorithm that generates routes and performs feedback analysis based on data. [Brief explanation of the drawings]

[0021] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6]FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0022] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0023] First, the terms used in the following description will be explained.

[0024] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0025] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0026] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0027] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0028] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0032] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0033] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0034] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0035] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0036] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0039] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0040] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0041] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0042] The present invention provides a car navigation system that generates a "pleasant" route for the user to enjoy driving itself. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes in detail the preferred embodiments of the present invention.

[0043] First, the user starts the car navigation system and verbally inputs the route they wish to drive. For example, they can say, "I want to take a pleasant mountain road." The device picks up this speech through a microphone and converts it into text data using speech recognition technology. This text data is then sent directly to the server.

[0044] The server first retrieves the user's attribute information (past driving data and preferences) from a database based on the user's ID. It also retrieves the user's current GPS data to confirm the user's location. Next, it queries the database for information on the surrounding scenery, tourist spots, and feedback from past drivers, and extracts several route candidates.

[0045] Based on this information, the server runs an algorithm to generate the optimal route. This algorithm combines the acquired scenic information, tourist spots, and rating information to calculate a "pleasantness" score for each route. The score takes into account factors such as scenic beauty, safety, traffic volume, and travel time. After calculating the score for each route candidate, the server selects the route with the highest score and generates detailed directions for it.

[0046] The generated route information is sent to the device, which then begins navigating the user based on this information. Specifically, a detailed map and route directions are displayed on the device screen, and voice guidance is provided to instruct the user on the next action. For example, guidance such as "Turn right in 100 meters. This is the road with a beautiful view of the lake" is provided.

[0047] While the user is enjoying the drive, the device monitors the user's driving data in real time and provides an interface where the user can input feedback. For example, if the user inputs feedback such as "This road is great," it is sent to the server.

[0048] The server analyzes the feedback it receives and reevaluates the route information, possibly recalculating a new route and sending the updated route information back to the device. The device then updates its navigation based on this new route information, providing new guidance such as, "Up ahead, if you turn right, there's a scenic road."

[0049] The server then stores the feedback data in a database and uses it to train the AI ​​model for future route generation. This allows the system to continually evolve and propose more accurate and "comfortable" driving routes.

[0050] In this way, the car navigation system of the present invention provides a very useful service to users who enjoy driving by generating and navigating the optimal driving route in real time based on the user's spoken requests.

[0051] The processing flow will be explained below.

[0052] Step 1:

[0053] The user starts the car navigation system and verbally inputs the route he or she wants to drive. For example, he or she says, "I want to take a nice mountain road."

[0054] Step 2:

[0055] The terminal captures the user's voice through a microphone.

[0056] Step 3:

[0057] The device converts the acquired voice data into text data using voice recognition technology, and sends the converted text, such as "I want to walk along a pleasant mountain path," to the server.

[0058] Step 4:

[0059] The server retrieves the user's attribute information (past driving data and preferences) from the database based on the user's ID, and simultaneously retrieves GPS data showing the user's current location.

[0060] Step 5:

[0061] Based on the GPS data, the server searches a database for information on scenery around the current location, tourist spots, highly rated driving routes, and more.

[0062] Step 6:

[0063] The server runs an algorithm to generate the optimal route based on the acquired scenic, tourist spot, and rating information. The algorithm calculates a "pleasantness" score for each route, taking into account factors such as scenic beauty, safety, traffic volume, and travel time.

[0064] Step 7:

[0065] The server selects the route with the highest score as the optimal route and generates detailed directions for it, which are then sent to the device.

[0066] Step 8:

[0067] The device then begins navigating the user based on the route information it receives. Specifically, it displays a map and route on the device screen and provides voice guidance on the next action, such as "Turn right in 100 meters. This is the road with a beautiful view of the lake."

[0068] Step 9:

[0069] The device monitors the user's driving data in real time and provides an interface where the user can input feedback, for example, "This road is great."

[0070] Step 10:

[0071] The terminal transmits the obtained feedback to the server.

[0072] Step 11:

[0073] The server analyzes the received feedback, reevaluates the route information, possibly recalculating a new route, and sends the updated route information to the device.

[0074] Step 12:

[0075] The device receives the new route information and updates the navigation, providing new directions such as, "Up ahead, if you turn right, there's a scenic road."

[0076] Step 13:

[0077] The server stores the feedback data in a database and trains the AI ​​model to use it for future route generation. This allows the system to evolve and propose more accurate and "comfortable" driving routes.

[0078] Example 1

[0079] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0080] Conventional car navigation systems have had difficulty generating "pleasant" routes for users to enjoy driving. In particular, they were unable to generate optimal routes by comprehensively evaluating factors such as scenery, beauty, safety, and traffic volume. It was also difficult to reflect user preferences and past feedback, resulting in insufficient real-time route updates and improvements. This resulted in low user satisfaction.

[0081] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0082] In this invention, the server includes means for acquiring user attribute information and current location data and querying a database, means for generating an optimal route based on scenery information, tourist spots, and rating information, and means for guiding the generated route to the user, thereby enabling the generation of a "pleasant" route in real time based on the user's utterances.

[0083] "Voice recognition of user speech" refers to the recognition of a user's actions of inputting instructions or information into a system by voice as a digital signal.

[0084] "Means for converting into text data" refers to the process of expressing voice data acquired by a voice recognition system as text information.

[0085] The "means for transmitting to a server" refers to a method for transmitting the converted text data to a remote server via a network.

[0086] "User attribute information" refers to information stored in a database, such as a user's past behavior, preferences, and individual characteristics.

[0087] "Current location data" is information that indicates the physical location of the user, and is typically obtained as GPS data.

[0088] "Means for querying a database" refers to a method for searching and retrieving required information from stored data.

[0089] "Scenery information" refers to data about the natural scenery and landscapes around roads.

[0090] "Tourist spots" refer to places that are considered worth visiting while driving.

[0091] "Rating information" refers to data based on past user feedback and reviews.

[0092] "Means for generating an optimal route" refers to the process of calculating and selecting the most suitable route for a user from multiple candidate routes.

[0093] "Means for guiding the user along the generated route" refers to a method for providing the user with visual and audio navigation along the selected route.

[0094] "User Feedback" means User opinions and impressions regarding driving routes and use of the System.

[0095] "Means for analyzing feedback" refers to a method for analyzing and evaluating feedback data obtained from users.

[0096] "Means for reevaluating route information" refers to the process of reviewing and improving existing route information based on the feedback obtained.

[0097] The "means for updating guidance" refers to a method for updating the currently performed navigation with the latest information based on the reevaluated route information.

[0098] "Means for training an artificial intelligence model" refers to the method by which the acquired data is used to train a machine learning algorithm and improve the accuracy of the system.

[0099] The present invention provides a car navigation system that generates a "pleasant" route for enjoying the drive itself. This system allows the user to register their driving needs through voice input, generates an optimal route that meets those needs, and navigates in real time.

[0100] First, the user starts the car navigation system and verbally inputs the route they wish to drive. For example, they can say, "I want to take a nice mountain road." The device picks up this speech through the microphone and converts it into text data using speech recognition technology. Specifically, the Google Cloud Speech-to-Text API is used. The converted text data is then sent to the server.

[0101] The server first retrieves the user's attribute information (past driving data and preferences) from a database based on the user's ID. This database is typically a relational database management system (RDBMS) such as Amazon RDS or MySQL. It also retrieves the user's current GPS data to confirm their location.

[0102] The server then searches a database for information on the surrounding scenery, tourist attractions, and past driver feedback, and extracts several route candidates, including information on scenic beauty, safety, and tourist attraction popularity.

[0103] Based on this information, the server runs an algorithm to generate the optimal route. This algorithm is implemented in Python and uses machine learning libraries (e.g., TensorFlow). The algorithm combines the acquired landscape information, tourist spot information, and rating information to calculate a "pleasantness" score for each route. This score takes into account factors such as scenic beauty, safety, traffic volume, and travel time. A score is calculated for each route candidate, and the route with the highest score is selected. Detailed directions for that route are then generated.

[0104] The generated route information is sent to the device, which then begins navigating the user based on this information. Specifically, a detailed map and directions are displayed on the device screen, and voice instructions are given using the Google Text-to-Speech API to instruct the user on the next action. For example, directions such as "Turn right in 100 meters. This is the road with a beautiful view of the lake" are provided.

[0105] While the user is enjoying the drive, the device monitors the user's driving data in real time and provides an interface where the user can input feedback. For example, if the user inputs feedback such as "This road is great," it will be sent to the server.

[0106] The server analyzes the feedback it receives and reevaluates the route information, possibly recalculating a new route and sending the updated route information back to the device. The device then updates its navigation based on this new route information, providing new guidance such as, "Up ahead, if you turn right, there's a scenic road."

[0107] The server then stores the feedback data in a database and uses it to train the AI ​​model for future route generation, allowing the system to continually evolve and propose more accurate, "comfortable" driving routes.

[0108] Specific examples

[0109] As a specific example, the prompt sentence in which the user utters "I want to walk along a pleasant mountain path" is shown below.

[0110] Prompt Sentence Examples

[0111] User ID: 12345

[0112] Current location: Latitude 34.052235, Longitude -118.243683

[0113] Route request: I want to go through a pleasant mountain path.

[0114] Past preferences: I like mountain roads, and prefer roads with little traffic.

[0115] Based on this information, the system generates an optimal driving route and provides real-time navigation.

[0116] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0117] Step 1:

[0118] The user starts the car navigation system and inputs the route they wish to drive by speech. For example, they might say, "I want to take a nice mountain road." The input data is the user's voice. The output data after conversion is speech data.

[0119] Step 2:

[0120] The device receives this speech through a microphone and converts it into text data using speech recognition technology. Specifically, it uses the Google Cloud Speech-to-Text API. The input voice data is output as text data.

[0121] Step 3:

[0122] The terminal sends the converted text data (e.g., "I want to walk along a pleasant mountain path") to the server using the HTTPS protocol. The input data is the text data, and the output data is the result of sending the text data to the server.

[0123] Step 4:

[0124] The server retrieves the user's attribute information (past driving data and preferences) from the database based on the user's ID. It also retrieves GPS data and confirms the user's current location. The input data is the user ID and current GPS data, and the output data is attribute information and current location data.

[0125] Step 5:

[0126] The server queries a database of landscape information around the current location, tourist spots, and past driver feedback information to extract several route candidates. The input data is the current location and user attribute information, and the output data is multiple route candidates.

[0127] Step 6:

[0128] The server uses a machine learning algorithm implemented in Python (e.g., TensorFlow) to calculate a "feel-good" score for each route candidate. The score calculation takes into account factors such as scenic beauty, safety, traffic volume, and travel time. The input data are the route candidates, and the output data is the "feel-good" score for each route.

[0129] Step 7:

[0130] The server selects the route with the highest score based on the calculated scores and generates detailed directions for it. Specifically, it uses a map data API (e.g., Google Maps API). The input data are the "comfort" score and route candidates, and the output data are detailed directions.

[0131] Step 8:

[0132] The server sends the generated route information to the terminal. Communication is via HTTPS. The input data is the detailed route information, and the output data is the transmission result to the terminal.

[0133] Step 9:

[0134] The device displays the received route information on the screen and provides voice guidance for the next action using the Google Text-to-Speech API. For example, guidance such as "Turn right after 100 meters. This is the road with a beautiful lake view" is provided. The input data is detailed route information, and the output data is navigation guidance for the user.

[0135] Step 10:

[0136] The user inputs a piece of feedback during navigation, for example, "This road is great." The input data is the user's feedback, and the output data is the text data of the feedback.

[0137] Step 11:

[0138] The terminal obtains the user's feedback and sends it to the server. The input data is the text data of the feedback, and the output data is the result of the transmission to the server.

[0139] Step 12:

[0140] The server analyzes the received feedback and reevaluates the route information if necessary, possibly recalculating a new route. The input data is the feedback, and the output data is the reevaluated route information.

[0141] Step 13:

[0142] The server sends the updated route information to the terminal, again using the HTTPS protocol. The input data is the re-evaluated route information, and the output data is the result sent to the terminal.

[0143] Step 14:

[0144] The device updates the navigation based on the new route information and provides new guidance such as, "If you turn right up ahead, there is a road with a beautiful view." The input data is the reevaluated route information, and the output data is the updated navigation information.

[0145] (Application example 1)

[0146] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0147] Conventional car navigation systems focus on providing the shortest or fastest route to a destination. However, there are few systems that provide "comfortable" routes that allow users to enjoy the drive itself. Furthermore, systems for autonomous vehicles that navigate routes that reflect user preferences are still immature. In these circumstances, there is a need to maximize the comfort and enjoyment users feel while driving.

[0148] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0149] In this invention, the server includes means for speech recognition of a user's speech and converting it into text data, means for transmitting the text data, means for acquiring user attribute information and current location information and consulting a database, means for generating an optimal route based on scenic information, tourist spots, and rating information, means for presenting the generated route, means for acquiring and transmitting user feedback, means for analyzing the feedback and reevaluating the route information, means for updating route instructions based on the reevaluated route information, means for updating and learning from the database using the acquired feedback data, and means for being installed in an autonomous vehicle and for automatically traveling a "pleasant" route specified by the user. This enables the user to take a "pleasant" route during a drive, providing a comfortable and enjoyable driving experience.

[0150] The "means for recognizing user speech and converting it into text data" refers to a device or software that has the function of recognizing what the user has said and converting it from speech to text data.

[0151] The "means for transmitting the text data to a server" refers to a device or software that has the function of transmitting the converted text data to a server using a communication means such as the Internet.

[0152] "Means for obtaining user attribute information and current GPS data and querying a database" refers to devices or software that have the function of obtaining past data and current location information about a user and searching a database based on that data.

[0153] "Means for generating optimal routes based on scenery information, tourist spots, and evaluation information" refers to devices or software that have the function of calculating the optimal driving route for a user based on the surrounding scenery, tourist spots, and past evaluation data.

[0154] The "means for navigating the generated route to the user" refers to a device or software that has the function of providing visual and audio guidance to the user based on the calculated route.

[0155] The "means for acquiring user feedback and transmitting it to the server" refers to a device or software that has the function of collecting user evaluations and comments about the route and transmitting them to the server.

[0156] The "means for analyzing feedback and re-evaluating route information" refers to a device or software that has the function of analyzing collected feedback and recalculating the route evaluation based on that feedback.

[0157] The "means for updating navigation based on reevaluated route information" refers to a device or software that has the function of updating the current navigation route to reflect the reevaluated route information.

[0158] "Means for updating the database using acquired feedback data and training the AI ​​model" refers to devices or software that have the function of adding feedback data acquired from users to the database and training the AI ​​model based on that data.

[0159] "A means to be installed in an autonomous vehicle and to automatically drive along a 'comfortable' route specified by the user" is a system that has the function of allowing an autonomous vehicle to automatically drive along a comfortable route specified by the user.

[0160] The present invention is a system for automatically driving a self-driving vehicle along a "pleasant" route designated by a user. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described in detail below with reference to the accompanying drawings.

[0161] First, the user starts the car navigation system via a smartphone or on-board display and inputs the route they wish to drive by speech. For example, they can say, "I want to take a nice road along the sea." The device picks up this speech through the microphone and converts it into text data using the SpeechRecognition library. This text data is then sent to the server via the Internet.

[0162] The server first retrieves the user's attribute information (past driving data and preferences) from a database based on the user's ID. It also retrieves the user's current GPS data to confirm the user's location. Next, it queries the database for information on the surrounding scenery, tourist spots, and feedback from past drivers, and extracts several route candidates.

[0163] Based on this information, the server runs an algorithm to generate the optimal route. This algorithm combines the acquired scenic information, tourist spots, and rating information to calculate a "pleasantness" score for each route. The score takes into account factors such as scenic beauty, safety, traffic volume, and travel time. After calculating the score for each route candidate, the server selects the route with the highest score and generates detailed directions for it.

[0164] The generated route information is sent to the device, which then begins navigating the user based on this information. Specifically, a detailed map and route directions are displayed on the device screen, and voice guidance is provided to instruct the user on the next action. For example, guidance such as "Turn right in 100 meters. This is the road with a beautiful view of the lake" is provided.

[0165] While the user is enjoying the drive, the device monitors the user's driving data in real time and provides an interface where the user can input feedback. For example, if the user inputs feedback such as "This road is great," it is sent to the server.

[0166] The server analyzes the feedback it receives and reevaluates the route information, possibly recalculating a new route and sending the updated route information back to the device. The device then updates its navigation based on this new route information, providing new guidance such as, "Up ahead, if you turn right, there's a scenic road."

[0167] Furthermore, the server stores the feedback data in a database and uses it to train the generative AI model for future route generation. This allows the system to constantly evolve and propose more accurate and "comfortable" driving routes.

[0168] As a specific example, a prompt sentence such as "Please tell me the best route if I want to take a pleasant road along the seashore" can be used.

[0169] In this way, the car navigation system of the present invention provides a very useful service to users who enjoy driving by generating and navigating the optimal driving route in real time based on the user's spoken requests.

[0170] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0171] Step 1:

[0172] A user starts a car navigation system via a smartphone or an in-car display and utters, "I want to take a nice road along the sea." The input is the user's voice data, and the output is the voice data being input to the device.

[0173] Step 2:

[0174] The device receives the user's speech through a microphone and converts the speech data into text using the SpeechRecognition library. The input is the user's speech data, and the output is the data converted from speech to text.

[0175] Step 3:

[0176] The terminal transmits the converted text data to a server via the Internet. The input is the text data, and the output is the text data transmitted to the server.

[0177] Step 4:

[0178] The server retrieves the user's attribute information (past driving data and preferences) from a database based on the user's ID. It also retrieves current GPS data. The input is text data and GPS data, and the output is the user's attribute information and current location information.

[0179] Step 5:

[0180] The server uses the user's attribute information and current location to query a database for information on the surrounding scenery, tourist spots, and past driver feedback, and extracts several route candidates. The input is the user's attribute information and current location information, and the output is route candidate information.

[0181] Step 6:

[0182] The server runs an algorithm to generate the optimal route based on the acquired information. This algorithm calculates a "pleasantness" score for each route. The inputs are landscape information, tourist spots, and rating information, and the output is the optimal route information.

[0183] Step 7:

[0184] The server sends the optimal route information to the terminal. The input is the optimal route information, and the output is the route information sent to the terminal.

[0185] Step 8:

[0186] The device then begins navigating the user based on the route information it receives. Specifically, it displays a detailed map and directions on the screen and provides voice guidance to guide the user on their next steps. The input is route information, and the output is navigation instructions for the user.

[0187] Step 9:

[0188] While the user is enjoying the drive, the terminal monitors the user's driving data in real time and provides an interface to obtain feedback from the user. The input is the user's driving data and feedback, and the output is the collected driving data and feedback information.

[0189] Step 10:

[0190] The terminal sends the collected feedback information to the server. The input is the feedback information, and the output is the data sent to the server.

[0191] Step 11:

[0192] The server analyzes the received feedback and re-evaluates the route information. The input is the feedback information and the output is the re-evaluated route information.

[0193] Step 12:

[0194] The server sends the re-evaluated route information to the terminal, which then updates the navigation. The input is the re-evaluated route information, and the output is the updated navigation information.

[0195] Step 13:

[0196] The server stores the obtained feedback data in a database and trains the generative AI model. The input is the feedback data, and the output is an updated AI model and database.

[0197] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0198] The present invention combines a car navigation system that generates a "pleasant" route for the user to enjoy driving itself with an emotion engine that recognizes the user's emotions. The embodiments of the present invention will be described in detail below.

[0199] First, the user starts the car navigation system and verbally inputs the route they wish to drive. For example, they can say, "I want to take a pleasant mountain road." The device picks up this speech through a microphone and converts it into text data using speech recognition technology. This text data is then sent directly to the server.

[0200] The server first retrieves the user's attribute information (past driving data and preferences) from a database based on the user's ID. It then retrieves GPS data showing the user's current location. At the same time, it evaluates the user's current emotional state using an emotion engine that recognizes emotions from the user's speech and behavior.

[0201] The server uses GPS data to query a database of surrounding scenery, tourist spots, and past driver feedback to identify potential routes. Based on this information, the server runs an algorithm to generate the optimal route. The algorithm combines the acquired scenery, tourist spots, and rating information to calculate a "pleasantness" score for each route. The score takes into account factors such as scenic beauty, safety, traffic volume, and travel time.

[0202] In addition, the emotion engine also incorporates the user's emotional information into the score calculation. For example, if the user wants to relax, routes with less traffic and more natural scenery will be prioritized. The algorithm selects the route with the highest score as the optimal route and generates detailed directions for it.

[0203] The generated route information is sent to the device, which then begins navigating the user based on this information. Specifically, a detailed map and route directions are displayed on the device screen, and voice guidance is provided to instruct the user on the next action. Directions such as "Turn right in 100 meters. This is the road with a beautiful view of the lake" are provided.

[0204] While the user is enjoying the drive, the device monitors the user's driving data in real time and continuously evaluates the user's emotional state through an emotion engine. At the same time, it provides an interface where the user can input feedback. For example, if the user inputs feedback such as "This road is great," it will be sent to the server.

[0205] The server analyzes the received feedback and reevaluates the route information. In some cases, it recalculates a new route and sends the updated route information to the device, taking into account the emotion engine's evaluation results. The device then updates its navigation system based on this new route information and provides new guidance, such as "If you turn right up ahead, there's a road with a beautiful view."

[0206] The server then stores the acquired feedback data and emotional information in a database and trains the AI ​​model to use it for future route generation. This allows the system to continually evolve and provide a "feel-good" driving route that reflects the user's emotional state and preferences.

[0207] In this way, the car navigation system of the present invention provides a very useful service to users who enjoy driving by generating and navigating the optimal driving route in real time based on the user's spoken requests and emotional information.

[0208] The processing flow will be explained below.

[0209] Step 1:

[0210] The user starts the car navigation system and verbally inputs the route he or she wants to drive. For example, he or she says, "I want to take a nice mountain road."

[0211] Step 2:

[0212] The terminal captures the user's voice through a microphone.

[0213] Step 3:

[0214] The device converts the acquired voice data into text data using voice recognition technology, and sends the converted text data, "I want to walk along a pleasant mountain path," to the server.

[0215] Step 4:

[0216] The server retrieves the user's attribute information (past driving data and preferences) from the database based on the user's ID, and simultaneously retrieves GPS data showing the user's current location.

[0217] Step 5:

[0218] The server uses an emotion engine to recognize the user's current emotional state from their speech and behavior, for example, assessing whether they want to relax or seek stimulation.

[0219] Step 6:

[0220] Based on the GPS data, the server searches a database for information on scenery around the current location, tourist spots, highly rated driving routes, and more.

[0221] Step 7:

[0222] The server runs an algorithm to generate the optimal route based on the acquired scenic, tourist spot, and rating information. The algorithm calculates a "feel-good" score for each route, taking into account factors such as scenic beauty, safety, traffic volume, and travel time.

[0223] Step 8:

[0224] The server adjusts the score calculation based on the emotional information obtained from the emotion engine. For example, if the user wants to relax, routes with less traffic and more natural scenery will be prioritized.

[0225] Step 9:

[0226] The server selects the route with the highest score as the optimal route and generates detailed directions for it, which are then sent to the device.

[0227] Step 10:

[0228] The device then begins navigating the user based on the route information it receives. Specifically, it displays a map and route on the device screen and provides voice guidance on the next action, such as "Turn right in 100 meters. This is the road with a beautiful view of the lake."

[0229] Step 11:

[0230] The device monitors the user's driving data in real time, continuously evaluates the user's emotional state through an emotion engine, and provides an interface for users to input feedback.

[0231] Step 12:

[0232] The device transmits the acquired feedback and emotion data to the server.

[0233] Step 13:

[0234] The server analyzes the received feedback and sentiment data, reevaluates the route information, and recalculates a new route if necessary.

[0235] Step 14:

[0236] The server sends the updated route information to the device, which also reflects the evaluation results of the emotion engine.

[0237] Step 15:

[0238] The device receives the new route information and updates the navigation, providing new directions such as, "Up ahead, if you turn right, there's a scenic road."

[0239] Step 16:

[0240] The server stores the acquired feedback data and emotional information in a database and uses it to train the AI ​​model for future route generation. This allows the system to evolve and improve its ability to suggest more accurate and "comfortable" driving routes.

[0241] Example 2

[0242] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0243] Conventional car navigation systems lack sufficient consideration for the user's emotional state and individual preferences, resulting in incomplete navigation for a specific route. Furthermore, their lack of functionality for incorporating user feedback in real time makes it difficult for users to fully enjoy their drive. To address these issues, a system is needed that can generate and update optimal driving routes in real time, taking into account the user's emotional state and preferences.

[0244] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for converting a user's utterance into text data using a voice recognition means, means for transmitting the text data to the server, means for acquiring user attribute information and current location information and consulting a database, means for generating an optimal route based on scenic information, tourist attractions, and feedback information, means for navigating the generated route for the user, means for acquiring user feedback and transmitting it to the server, means for analyzing the feedback and reevaluating the route information, means for updating the navigation based on the reevaluated route information, means for updating the database using the acquired feedback data and emotion information and training the artificial intelligence model, and means for using an emotion recognition engine that recognizes emotions from the user's utterances and actions. This enables the generation and navigation of an optimal route in real time based on the user's emotional state and preferences.

[0245] The "voice recognition means" is a technology that acquires the user's speech through a microphone and converts it into text data using voice recognition technology.

[0246] The "server" is a computer system that has data processing, analysis, and storage functions, manages user attribute information, current location information, scenic information, tourist spots, and feedback information, and generates routes and performs navigation.

[0247] "User attribute information" refers to information including the user's past driving data and preferences, and is data stored in a database.

[0248] "Current location information" refers to the user's current geographical location data obtained using location identification technology such as a GPS module.

[0249] The "database" is an information management system that stores user attribute information, current location information, landscape information, tourist spots, and feedback information, and allows the server to query and use this information.

[0250] "Landscape information" refers to information about natural and artificial landscapes in a particular geographical location.

[0251] A "tourist destination" is a place or facility that is a tourist attraction in a particular area.

[0252] "Feedback information" is information relating to opinions and impressions provided by the user while driving.

[0253] "Route generation means" is a technology that calculates and suggests the optimal driving route for the user based on the acquired data.

[0254] "Navigation means" is a technology that instructs the user on the next action by voice or on-screen display based on the generated route.

[0255] The "means for obtaining feedback" is a technique for obtaining feedback information input by a user and transmitting it to a server.

[0256] The "means for analyzing feedback" is a technique for analyzing the acquired feedback information on the server and reevaluating the route information.

[0257] The "means for updating navigation" is a technique for updating navigation information provided to a user based on reevaluated route information.

[0258] An "artificial intelligence model" is an algorithm or model that learns using captured feedback data and emotional information to help generate future routes.

[0259] An "emotion recognition engine" is a technology or algorithm for recognizing and evaluating emotions from a user's speech and behavior.

[0260] This invention relates to a car navigation system that generates an optimal driving route based on the user's emotional state and preferences, and navigates the user in real time. This system improves the user's driving experience by combining a voice recognition means, an emotion recognition engine, and an artificial intelligence model.

[0261] First, the user starts the car navigation system and inputs their desired driving route by voice. For example, they might say, "I want to take a pleasant mountain road." The device picks up this speech through the microphone and converts it into text data using voice recognition technology (for example, a general voice recognition API). This text data is then sent directly to the server.

[0262] The server retrieves the user's attribute information (past driving data and preferences) from a database based on the user's ID. At the same time, it uses a GPS module to identify the user's current location. It then uses an emotion recognition engine (e.g., a general emotion analysis API) to evaluate the user's current emotional state. This allows it to recognize emotions from the user's speech and behavior, and uses this information to help generate a route.

[0263] The server then uses the acquired GPS data and information from a database to query the surrounding landscape (e.g., open-source map data), tourist attractions, and past driver feedback. It then uses a route generation algorithm (e.g., A algorithm or Dijkstra algorithm) to integrate this information and calculate a "pleasantness" score for each route, which includes scenic beauty, safety, traffic volume, and travel time.

[0264] Once the optimal route is generated, the information is sent to the device, which displays a detailed map and directions to the user, and provides voice instructions such as, "Turn right in 100 meters. This is the road with a beautiful lake view." This allows the user to be navigated in real time to their destination.

[0265] While the user is enjoying the drive, the device monitors driving data in real time and continuously evaluates the user's emotional state through an emotion recognition engine. At the same time, the user can input feedback through the interface. For example, if the user inputs feedback such as "This road is great," the information is sent to the server.

[0266] The server analyzes the received feedback and reevaluates the route information. If necessary, it recalculates a new route and sends the updated route information to the device, taking into account the evaluation results of the emotion recognition engine. The device then guides the user along the new route. For example, it provides new guidance such as, "If you turn right up ahead, there is a road with a beautiful view."

[0267] Furthermore, the server stores the acquired feedback data and emotional information in a database and trains a generative AI model (e.g., a typical deep learning model) on it, allowing the system to continually evolve and provide a "comfortable" driving route that reflects the user's emotional state and preferences.

[0268] Specific examples

[0269] When a user voice-inputs something like "I want to drive while looking at the beautiful coastline," the device converts it into text using voice recognition technology and sends it to the server. The server takes into account past driving data, preferences, and current emotional state to generate a scenic route along the coastline. The device then guides the user, saying, "Turn left in 3 kilometers and you'll find a beautiful drive with ocean views." If, during the drive, the user gives feedback such as "This road is wonderful," the server uses that information to generate a more optimized route.

[0270] Example prompts for generative AI models

[0271] "If a user wants to relax, what factors would you consider to suggest the best driving route?"

[0272] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0273] Step 1:

[0274] The user starts the car navigation system and verbally inputs the route they want to drive. For example, they might say, "I want to take a nice mountain road." This utterance becomes the input.

[0275] Step 2:

[0276] The device picks up the user's speech through a microphone and converts it into text data using speech recognition technology. Specifically, it uses a speech recognition API. The input is the speech and the output is text data. This text data is sent directly to the server.

[0277] Step 3:

[0278] The server retrieves the user's attribute information (past driving data and preferences) from a database based on the user's ID. It also identifies the user's current location using a GPS module. The input is the user ID and GPS data, and the output is the user's attribute information and current location information.

[0279] Step 4:

[0280] The server uses an emotion recognition engine to evaluate the user's current emotional state. This is a process of analyzing emotions from the user's voice and behavior. The input is the user's voice data and behavior data, and the output is the evaluation result of the user's emotional state.

[0281] Step 5:

[0282] The server uses the acquired GPS data and information from the database to query information on the surrounding scenery, tourist spots, and past driver feedback. This provides the data necessary for route generation. The input is the GPS data and information from the database, and the output is the query results.

[0283] Step 6:

[0284] The server uses a route generation algorithm to calculate a "feel good" score for each route based on the query results. The algorithm uses A or Dijkstra's algorithm. The input is the query results, and the output is the "feel good" score.

[0285] Step 7:

[0286] The server selects the route with the highest score as the optimal route and generates detailed directions for it. The input is the "feel good" score, and the output is detailed directions for the optimal route. This information is sent to the device.

[0287] Step 8:

[0288] The device provides the user with detailed maps and voice guidance based on the route information sent from the server. For example, it provides navigation such as "Turn right 100 meters ahead." The input is detailed directions for the optimal route, and the output is navigation information.

[0289] Step 9:

[0290] While the user is enjoying the drive, the device monitors driving data in real time and continuously evaluates the user's emotional state through an emotion recognition engine. The input is driving data and emotion data, and the output is a real-time emotion evaluation result.

[0291] Step 10:

[0292] The user inputs feedback through the interface, such as "This road is great." The device then sends this feedback to the server.

[0293] Step 11:

[0294] The server analyzes the received feedback and re-evaluates the route information. The input is the feedback information and the output is the re-evaluated route information.

[0295] Step 12:

[0296] The server recalculates a new route if necessary and sends the updated route information to the device, which then updates its navigation based on the new route information and provides new instructions to the user. The input is the re-evaluated route information, and the output is the updated navigation information.

[0297] Step 13:

[0298] The server stores the acquired feedback data and emotional information in a database and trains the generative AI model based on this data. This allows the system to evolve future route generation to better reflect the user's emotional state and preferences. The input is feedback data and emotional information, and the output is the trained generative AI model.

[0299] (Application example 2)

[0300] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0301] In modern society, the demand for food delivery is increasing, resulting in an increased workload for delivery drivers. However, conventional navigation systems do not provide route suggestions that take into account the emotional state of delivery drivers, resulting in situations where stress and fatigue easily accumulate. Therefore, there is a need for route guidance that recognizes the emotional state of delivery drivers in real time and reduces their stress. The present invention solves this problem.

[0302] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0303] In this invention, the server includes means for speech recognition of a user's speech and converting it into text data, means for transmitting the text data to the server, means for acquiring the user's attribute information and current GPS data and consulting a database, means for generating an optimal route based on scenic information, tourist spots, and rating information, means for navigating the generated route to the user, means for acquiring user feedback and transmitting it to the server, means for analyzing the feedback and reevaluating the route information, means for updating the navigation based on the reevaluated route information, means for updating the database using the acquired feedback data and training the AI ​​model, means for recognizing the emotional state of a delivery driver in real time and providing an optimal route based on the driver's emotions, and means for selecting roads with good scenery or low traffic to reduce stress. This enables optimal route guidance based on the emotional state of the delivery driver, reducing driver stress and improving work efficiency.

[0304] A "user" is an individual or organization that uses the system.

[0305] An "utterance" is a spoken instruction or request made by a user to the system.

[0306] "Speech recognition" is a technology that converts a user's speech into text data.

[0307] "Text data" is character string information converted using voice recognition technology.

[0308] A "server" is a computer system that performs data processing and analysis.

[0309] "Attribute information" is individual information including a user's preferences, past behavior history, and the like.

[0310] "GPS data" is Global Positioning System data that indicates the user's current location.

[0311] A "database" is a system for storing and managing information.

[0312] "Scenery information" is data about the scenery and natural environment around the route.

[0313] "Tourist spots" are places or famous places that tourists should visit.

[0314] "Rating information" is feedback and opinion data collected from past users.

[0315] A "route" is a path from a starting point to a destination.

[0316] "Navigation" is an action or function that provides directions for a user to reach a destination.

[0317] "Feedback" refers to the impressions and evaluations provided by users after use.

[0318] "Reevaluation" is the act of reviewing a previous evaluation based on collected feedback.

[0319] "Navigation update" refers to updating the guidance information to the latest version based on new route information.

[0320] An "AI model" is a computational model for learning and prediction in artificial intelligence.

[0321] "Emotional state" is the user's current mental and emotional state.

[0322] "Real-time" means that data is processed and reflected immediately.

[0323] "Stress" refers to the psychological pressure or fatigue felt by the user.

[0324] A "scenic road" is a route that is rich in natural scenery and visually enjoyable.

[0325] A "low volume road" is a route with few vehicles on it, making it safe and relaxing.

[0326] This invention is a system that generates and navigates optimal routes based on the emotional state of delivery drivers. This system operates through the cooperation of a terminal and a server.

[0327] First, the delivery driver, who is the user, speaks to the device. The speech might be something like, "I'm tired, so I'd like a relaxing route." The device then receives this speech and converts it into text data using speech recognition technology. This speech recognition technology uses a service such as the Google Speech-to-Text API.

[0328] The converted text data is then sent to a server. The server retrieves attribute information from a database based on the user's ID. This attribute information includes past driving data and individual preferences. The server also retrieves GPS data sent from the device to determine the user's current location.

[0329] The server then uses the speech data sent from the device to activate an emotion engine to evaluate the user's emotional state. This emotion engine uses, for example, the Microsoft Azure Emotion API. The acquired emotional state is evaluated as "tired."

[0330] Next, the server retrieves information about the scenery around the current location, tourist spots, and feedback from past drivers from a database to generate route candidates. Route candidates can be obtained using the Google Maps API. The generated routes are scored for "comfort" based on an evaluation algorithm. This score takes into account factors such as scenic beauty, low traffic volume, and safety.

[0331] The evaluation algorithm also reflects the user's emotional state, as determined by the emotion engine. For example, if the user is "tired," scenic routes and routes with less traffic will be prioritized. Ultimately, the route with the highest score is selected and sent to the device.

[0332] The device will then begin navigating the user based on the selected route information. Specifically, it will display a map and provide voice guidance. For example, "Turn right in 100 meters. This road will take you past a scenic lake."

[0333] While the user is driving, the device continues to monitor driving data in real time. At the same time, it continuously evaluates the user's emotional state through the emotion engine. As a result, if the user inputs feedback such as "This road is great," the feedback will be sent to the server.

[0334] The server analyzes the received feedback and reevaluates the route information, possibly calculating a new route and sending it back to the device. This reevaluation allows the system to continually evolve and more accurately reflect the user's emotional state and preferences.

[0335] The hardware used includes smartphones (iOS, Android) and their built-in GPS and microphones, while the software uses a speech recognition API (Google Speech-to-Text API), an emotion engine API (Microsoft Azure Emotion API), and a navigation API (Google Maps API).

[0336] Examples of prompts:

[0337] "If a user is looking to relax, they should search for routes with beautiful scenery."

[0338] Based on this embodiment, delivery drivers can receive optimal route guidance according to their emotional state, enabling them to work efficiently while reducing stress during work.

[0339] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0340] Step 1:

[0341] The device receives the user's speech. When the user says, "I'm tired, so I'd like a relaxing route," the device's microphone captures this speech. The input is the user's speech, and the output is audio data.

[0342] Step 2:

[0343] The device converts the acquired voice data into text data using voice recognition technology. This process uses the Google Speech-to-Text API. The input is voice data, and the output is text data such as "I'm tired, so I'd like a relaxing route."

[0344] Step 3:

[0345] The terminal transmits the converted text data to the server. The input is the text data, and the output is the result of transmitting the text data to the server.

[0346] Step 4:

[0347] The server retrieves attribute information from the database based on the user's ID. The attribute information includes past driving data and individual preferences. The input is the user's ID, and the output is attribute information.

[0348] Step 5:

[0349] The server receives the GPS data sent from the device and identifies the user's current location. The input is the GPS data and the output is the user's current location.

[0350] Step 6:

[0351] The server inputs the text data sent from the device into the emotion engine and evaluates the user's emotional state. This uses the Microsoft Azure Emotion API. The input is text data, and the output is an emotional state such as "tired."

[0352] Step 7:

[0353] The server retrieves information about the scenery around the current location, tourist spots, and past feedback from a database and generates route candidates using the Google Maps API. The input is the current location and destination, and the output is a list of route candidates.

[0354] Step 8:

[0355] The server runs a rating algorithm to calculate a "feel-good" score for each route candidate. The rating algorithm takes into account scenic beauty, traffic volume, safety, and the user's emotional state. The input is the list of route candidates and the user's emotional state, and the output is a score for each route.

[0356] Step 9:

[0357] The server selects the route with the highest score and sends it to the terminal. The input is the route score, and the output is the selected optimal route.

[0358] Step 10:

[0359] The device then begins navigation based on the selected route information. Specifically, it displays a map and provides voice guidance. For example, "Turn right after 100 meters. This road passes by a scenic lake." The input is the optimal route information, and the output is navigation instructions.

[0360] Step 11:

[0361] The device monitors driving data in real time while driving and continuously evaluates the user's emotional state through an emotion engine. The input is driving data and emotional state, and the output is the continuous emotion evaluation result.

[0362] Step 12:

[0363] When the user inputs feedback, the terminal sends the feedback to the server. The input is the user's feedback, and the output is the result of the transmission to the server.

[0364] Step 13:

[0365] The server analyzes the received feedback, reevaluates the route information, and possibly recalculates a new route and sends it to the device. The input is the feedback, and the output is the reevaluated route.

[0366] Step 14:

[0367] The server updates the database using the feedback data it receives and trains the AI ​​model. The input is the feedback data, and the output is the updated AI model.

[0368] Examples of prompts:

[0369] "If a user is looking to relax, they should search for routes with beautiful scenery."

[0370] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0371] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0372] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0373] [Second embodiment]

[0374] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0375] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0376] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0377] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0378] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0379] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0380] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0381] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0382] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0383] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0384] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0385] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0386] The present invention provides a car navigation system that generates a "pleasant" route for the user to enjoy driving itself. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes in detail the preferred embodiments of the present invention.

[0387] First, the user starts the car navigation system and verbally inputs the route they wish to drive. For example, they can say, "I want to take a pleasant mountain road." The device picks up this speech through a microphone and converts it into text data using speech recognition technology. This text data is then sent directly to the server.

[0388] The server first retrieves the user's attribute information (past driving data and preferences) from a database based on the user's ID. It also retrieves the user's current GPS data to confirm the user's location. Next, it queries the database for information on the surrounding scenery, tourist spots, and feedback from past drivers, and extracts several route candidates.

[0389] Based on this information, the server runs an algorithm to generate the optimal route. This algorithm combines the acquired scenic information, tourist spots, and rating information to calculate a "pleasantness" score for each route. The score takes into account factors such as scenic beauty, safety, traffic volume, and travel time. After calculating the score for each route candidate, the server selects the route with the highest score and generates detailed directions for it.

[0390] The generated route information is sent to the device, which then begins navigating the user based on this information. Specifically, a detailed map and route directions are displayed on the device screen, and voice guidance is provided to instruct the user on the next action. For example, guidance such as "Turn right in 100 meters. This is the road with a beautiful view of the lake" is provided.

[0391] While the user is enjoying the drive, the device monitors the user's driving data in real time and provides an interface where the user can input feedback. For example, if the user inputs feedback such as "This road is great," it is sent to the server.

[0392] The server analyzes the feedback it receives and reevaluates the route information, possibly recalculating a new route and sending the updated route information back to the device. The device then updates its navigation based on this new route information, providing new guidance such as, "Up ahead, if you turn right, there's a scenic road."

[0393] The server then stores the feedback data in a database and uses it to train the AI ​​model for future route generation. This allows the system to continually evolve and propose more accurate and "comfortable" driving routes.

[0394] In this way, the car navigation system of the present invention provides a very useful service to users who enjoy driving by generating and navigating the optimal driving route in real time based on the user's spoken requests.

[0395] The processing flow will be explained below.

[0396] Step 1:

[0397] The user starts the car navigation system and verbally inputs the route he or she wants to drive. For example, he or she says, "I want to take a nice mountain road."

[0398] Step 2:

[0399] The terminal captures the user's voice through a microphone.

[0400] Step 3:

[0401] The device converts the acquired voice data into text data using voice recognition technology, and sends the converted text, such as "I want to walk along a pleasant mountain path," to the server.

[0402] Step 4:

[0403] The server retrieves the user's attribute information (past driving data and preferences) from the database based on the user's ID, and simultaneously retrieves GPS data showing the user's current location.

[0404] Step 5:

[0405] Based on the GPS data, the server searches a database for information on scenery around the current location, tourist spots, highly rated driving routes, and more.

[0406] Step 6:

[0407] The server runs an algorithm to generate the optimal route based on the acquired scenic, tourist spot, and rating information. The algorithm calculates a "pleasantness" score for each route, taking into account factors such as scenic beauty, safety, traffic volume, and travel time.

[0408] Step 7:

[0409] The server selects the route with the highest score as the optimal route and generates detailed directions for it, which are then sent to the device.

[0410] Step 8:

[0411] The device then begins navigating the user based on the route information it receives. Specifically, it displays a map and route on the device screen and provides voice guidance on the next action, such as "Turn right in 100 meters. This is the road with a beautiful view of the lake."

[0412] Step 9:

[0413] The device monitors the user's driving data in real time and provides an interface where the user can input feedback, for example, "This road is great."

[0414] Step 10:

[0415] The terminal transmits the obtained feedback to the server.

[0416] Step 11:

[0417] The server analyzes the received feedback, reevaluates the route information, possibly recalculating a new route, and sends the updated route information to the device.

[0418] Step 12:

[0419] The device receives the new route information and updates the navigation, providing new directions such as, "Up ahead, if you turn right, there's a scenic road."

[0420] Step 13:

[0421] The server stores the feedback data in a database and trains the AI ​​model to use it for future route generation. This allows the system to evolve and propose more accurate and "comfortable" driving routes.

[0422] Example 1

[0423] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0424] Conventional car navigation systems have had difficulty generating "pleasant" routes for users to enjoy driving. In particular, they were unable to generate optimal routes by comprehensively evaluating factors such as scenery, beauty, safety, and traffic volume. It was also difficult to reflect user preferences and past feedback, resulting in insufficient real-time route updates and improvements. This resulted in low user satisfaction.

[0425] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0426] In this invention, the server includes means for acquiring user attribute information and current location data and querying a database, means for generating an optimal route based on scenery information, tourist spots, and rating information, and means for guiding the generated route to the user, thereby enabling the generation of a "pleasant" route in real time based on the user's utterances.

[0427] "Voice recognition of user speech" refers to the recognition of a user's actions of inputting instructions or information into a system by voice as a digital signal.

[0428] "Means for converting into text data" refers to the process of expressing voice data acquired by a voice recognition system as text information.

[0429] The "means for transmitting to a server" refers to a method for transmitting the converted text data to a remote server via a network.

[0430] "User attribute information" refers to information stored in a database, such as a user's past behavior, preferences, and individual characteristics.

[0431] "Current location data" is information that indicates the physical location of the user, and is typically obtained as GPS data.

[0432] "Means for querying a database" refers to a method for searching and retrieving required information from stored data.

[0433] "Scenery information" refers to data about the natural scenery and landscapes around roads.

[0434] "Tourist spots" refer to places that are considered worth visiting while driving.

[0435] "Rating information" refers to data based on past user feedback and reviews.

[0436] "Means for generating an optimal route" refers to the process of calculating and selecting the most suitable route for a user from multiple candidate routes.

[0437] "Means for guiding the user along the generated route" refers to a method for providing the user with visual and audio navigation along the selected route.

[0438] "User Feedback" means User opinions and impressions regarding driving routes and use of the System.

[0439] "Means for analyzing feedback" refers to a method for analyzing and evaluating feedback data obtained from users.

[0440] "Means for reevaluating route information" refers to the process of reviewing and improving existing route information based on the feedback obtained.

[0441] The "means for updating guidance" refers to a method for updating the currently performed navigation with the latest information based on the reevaluated route information.

[0442] "Means for training an artificial intelligence model" refers to the method by which the acquired data is used to train a machine learning algorithm and improve the accuracy of the system.

[0443] The present invention provides a car navigation system that generates a "pleasant" route for enjoying the drive itself. This system allows the user to register their driving needs through voice input, generates an optimal route that meets those needs, and navigates in real time.

[0444] First, the user starts the car navigation system and verbally inputs the route they wish to drive. For example, they can say, "I want to take a nice mountain road." The device picks up this speech through the microphone and converts it into text data using speech recognition technology. Specifically, the Google Cloud Speech-to-Text API is used. The converted text data is then sent to the server.

[0445] The server first retrieves the user's attribute information (past driving data and preferences) from a database based on the user's ID. This database is typically a relational database management system (RDBMS) such as Amazon RDS or MySQL. It also retrieves the user's current GPS data to confirm their location.

[0446] The server then searches a database for information on the surrounding scenery, tourist attractions, and past driver feedback, and extracts several route candidates, including information on scenic beauty, safety, and tourist attraction popularity.

[0447] Based on this information, the server runs an algorithm to generate the optimal route. This algorithm is implemented in Python and uses machine learning libraries (e.g., TensorFlow). The algorithm combines the acquired landscape information, tourist spot information, and rating information to calculate a "pleasantness" score for each route. This score takes into account factors such as scenic beauty, safety, traffic volume, and travel time. A score is calculated for each route candidate, and the route with the highest score is selected. Detailed directions for that route are then generated.

[0448] The generated route information is sent to the device, which then begins navigating the user based on this information. Specifically, a detailed map and directions are displayed on the device screen, and voice instructions are given using the Google Text-to-Speech API to instruct the user on the next action. For example, directions such as "Turn right in 100 meters. This is the road with a beautiful view of the lake" are provided.

[0449] While the user is enjoying the drive, the device monitors the user's driving data in real time and provides an interface where the user can input feedback. For example, if the user inputs feedback such as "This road is great," it will be sent to the server.

[0450] The server analyzes the feedback it receives and reevaluates the route information, possibly recalculating a new route and sending the updated route information back to the device. The device then updates its navigation based on this new route information, providing new guidance such as, "Up ahead, if you turn right, there's a scenic road."

[0451] The server then stores the feedback data in a database and uses it to train the AI ​​model for future route generation, allowing the system to continually evolve and propose more accurate, "comfortable" driving routes.

[0452] Specific examples

[0453] As a specific example, the prompt sentence in which the user utters "I want to walk along a pleasant mountain path" is shown below.

[0454] Prompt Sentence Examples

[0455] User ID: 12345

[0456] Current location: Latitude 34.052235, Longitude -118.243683

[0457] Route request: I want to go through a pleasant mountain path.

[0458] Past preferences: I like mountain roads, and prefer roads with little traffic.

[0459] Based on this information, the system generates an optimal driving route and provides real-time navigation.

[0460] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0461] Step 1:

[0462] The user starts the car navigation system and inputs the route they wish to drive by speech. For example, they might say, "I want to take a nice mountain road." The input data is the user's voice. The output data after conversion is speech data.

[0463] Step 2:

[0464] The device receives this speech through a microphone and converts it into text data using speech recognition technology. Specifically, it uses the Google Cloud Speech-to-Text API. The input voice data is output as text data.

[0465] Step 3:

[0466] The terminal sends the converted text data (e.g., "I want to walk along a pleasant mountain path") to the server using the HTTPS protocol. The input data is the text data, and the output data is the result of sending the text data to the server.

[0467] Step 4:

[0468] The server retrieves the user's attribute information (past driving data and preferences) from the database based on the user's ID. It also retrieves GPS data and confirms the user's current location. The input data is the user ID and current GPS data, and the output data is attribute information and current location data.

[0469] Step 5:

[0470] The server queries a database of landscape information around the current location, tourist spots, and past driver feedback information to extract several route candidates. The input data is the current location and user attribute information, and the output data is multiple route candidates.

[0471] Step 6:

[0472] The server uses a machine learning algorithm implemented in Python (e.g., TensorFlow) to calculate a "feel-good" score for each route candidate. The score calculation takes into account factors such as scenic beauty, safety, traffic volume, and travel time. The input data are the route candidates, and the output data is the "feel-good" score for each route.

[0473] Step 7:

[0474] The server selects the route with the highest score based on the calculated scores and generates detailed directions for it. Specifically, it uses a map data API (e.g., Google Maps API). The input data are the "comfort" score and route candidates, and the output data are detailed directions.

[0475] Step 8:

[0476] The server sends the generated route information to the terminal. Communication is via HTTPS. The input data is the detailed route information, and the output data is the transmission result to the terminal.

[0477] Step 9:

[0478] The device displays the received route information on the screen and provides voice guidance for the next action using the Google Text-to-Speech API. For example, guidance such as "Turn right after 100 meters. This is the road with a beautiful lake view" is provided. The input data is detailed route information, and the output data is navigation guidance for the user.

[0479] Step 10:

[0480] The user inputs a piece of feedback during navigation, for example, "This road is great." The input data is the user's feedback, and the output data is the text data of the feedback.

[0481] Step 11:

[0482] The terminal obtains the user's feedback and sends it to the server. The input data is the text data of the feedback, and the output data is the result of the transmission to the server.

[0483] Step 12:

[0484] The server analyzes the received feedback and reevaluates the route information if necessary, possibly recalculating a new route. The input data is the feedback, and the output data is the reevaluated route information.

[0485] Step 13:

[0486] The server sends the updated route information to the terminal, again using the HTTPS protocol. The input data is the re-evaluated route information, and the output data is the result sent to the terminal.

[0487] Step 14:

[0488] The device updates the navigation based on the new route information and provides new guidance such as, "If you turn right up ahead, there is a road with a beautiful view." The input data is the reevaluated route information, and the output data is the updated navigation information.

[0489] (Application example 1)

[0490] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0491] Conventional car navigation systems focus on providing the shortest or fastest route to a destination. However, there are few systems that provide "comfortable" routes that allow users to enjoy the drive itself. Furthermore, systems for autonomous vehicles that navigate routes that reflect user preferences are still immature. In these circumstances, there is a need to maximize the comfort and enjoyment users feel while driving.

[0492] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0493] In this invention, the server includes means for speech recognition of a user's speech and converting it into text data, means for transmitting the text data, means for acquiring user attribute information and current location information and consulting a database, means for generating an optimal route based on scenic information, tourist spots, and rating information, means for presenting the generated route, means for acquiring and transmitting user feedback, means for analyzing the feedback and reevaluating the route information, means for updating route instructions based on the reevaluated route information, means for updating and learning from the database using the acquired feedback data, and means for being installed in an autonomous vehicle and for automatically traveling a "pleasant" route specified by the user. This enables the user to take a "pleasant" route during a drive, providing a comfortable and enjoyable driving experience.

[0494] The "means for recognizing user speech and converting it into text data" refers to a device or software that has the function of recognizing what the user has said and converting it from speech to text data.

[0495] The "means for transmitting the text data to a server" refers to a device or software that has the function of transmitting the converted text data to a server using a communication means such as the Internet.

[0496] "Means for obtaining user attribute information and current GPS data and querying a database" refers to devices or software that have the function of obtaining past data and current location information about a user and searching a database based on that data.

[0497] "Means for generating optimal routes based on scenery information, tourist spots, and evaluation information" refers to devices or software that have the function of calculating the optimal driving route for a user based on the surrounding scenery, tourist spots, and past evaluation data.

[0498] The "means for navigating the generated route to the user" refers to a device or software that has the function of providing visual and audio guidance to the user based on the calculated route.

[0499] The "means for acquiring user feedback and transmitting it to the server" refers to a device or software that has the function of collecting user evaluations and comments about the route and transmitting them to the server.

[0500] The "means for analyzing feedback and re-evaluating route information" refers to a device or software that has the function of analyzing collected feedback and recalculating the route evaluation based on that feedback.

[0501] The "means for updating navigation based on reevaluated route information" refers to a device or software that has the function of updating the current navigation route to reflect the reevaluated route information.

[0502] "Means for updating the database using acquired feedback data and training the AI ​​model" refers to devices or software that have the function of adding feedback data acquired from users to the database and training the AI ​​model based on that data.

[0503] "A means to be installed in an autonomous vehicle and to automatically drive along a 'comfortable' route specified by the user" is a system that has the function of allowing an autonomous vehicle to automatically drive along a comfortable route specified by the user.

[0504] The present invention is a system for automatically driving a self-driving vehicle along a "pleasant" route designated by a user. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described in detail below with reference to the accompanying drawings.

[0505] First, the user starts the car navigation system via a smartphone or on-board display and inputs the route they wish to drive by speech. For example, they can say, "I want to take a nice road along the sea." The device picks up this speech through the microphone and converts it into text data using the SpeechRecognition library. This text data is then sent to the server via the Internet.

[0506] The server first retrieves the user's attribute information (past driving data and preferences) from a database based on the user's ID. It also retrieves the user's current GPS data to confirm the user's location. Next, it queries the database for information on the surrounding scenery, tourist spots, and feedback from past drivers, and extracts several route candidates.

[0507] Based on this information, the server runs an algorithm to generate the optimal route. This algorithm combines the acquired scenic information, tourist spots, and rating information to calculate a "pleasantness" score for each route. The score takes into account factors such as scenic beauty, safety, traffic volume, and travel time. After calculating the score for each route candidate, the server selects the route with the highest score and generates detailed directions for it.

[0508] The generated route information is sent to the device, which then begins navigating the user based on this information. Specifically, a detailed map and route directions are displayed on the device screen, and voice guidance is provided to instruct the user on the next action. For example, guidance such as "Turn right in 100 meters. This is the road with a beautiful view of the lake" is provided.

[0509] While the user is enjoying the drive, the device monitors the user's driving data in real time and provides an interface where the user can input feedback. For example, if the user inputs feedback such as "This road is great," it is sent to the server.

[0510] The server analyzes the feedback it receives and reevaluates the route information, possibly recalculating a new route and sending the updated route information back to the device. The device then updates its navigation based on this new route information, providing new guidance such as, "Up ahead, if you turn right, there's a scenic road."

[0511] Furthermore, the server stores the feedback data in a database and uses it to train the generative AI model for future route generation. This allows the system to constantly evolve and propose more accurate and "comfortable" driving routes.

[0512] As a specific example, a prompt sentence such as "Please tell me the best route if I want to take a pleasant road along the seashore" can be used.

[0513] In this way, the car navigation system of the present invention provides a very useful service to users who enjoy driving by generating and navigating the optimal driving route in real time based on the user's spoken requests.

[0514] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0515] Step 1:

[0516] A user starts a car navigation system via a smartphone or an in-car display and utters, "I want to take a nice road along the sea." The input is the user's voice data, and the output is the voice data being input to the device.

[0517] Step 2:

[0518] The device receives the user's speech through a microphone and converts the speech data into text using the SpeechRecognition library. The input is the user's speech data, and the output is the data converted from speech to text.

[0519] Step 3:

[0520] The terminal transmits the converted text data to a server via the Internet. The input is the text data, and the output is the text data transmitted to the server.

[0521] Step 4:

[0522] The server retrieves the user's attribute information (past driving data and preferences) from a database based on the user's ID. It also retrieves current GPS data. The input is text data and GPS data, and the output is the user's attribute information and current location information.

[0523] Step 5:

[0524] The server uses the user's attribute information and current location to query a database for information on the surrounding scenery, tourist spots, and past driver feedback, and extracts several route candidates. The input is the user's attribute information and current location information, and the output is route candidate information.

[0525] Step 6:

[0526] The server runs an algorithm to generate the optimal route based on the acquired information. This algorithm calculates a "pleasantness" score for each route. The inputs are landscape information, tourist spots, and rating information, and the output is the optimal route information.

[0527] Step 7:

[0528] The server sends the optimal route information to the terminal. The input is the optimal route information, and the output is the route information sent to the terminal.

[0529] Step 8:

[0530] The device then begins navigating the user based on the route information it receives. Specifically, it displays a detailed map and directions on the screen and provides voice guidance to guide the user on their next steps. The input is route information, and the output is navigation instructions for the user.

[0531] Step 9:

[0532] While the user is enjoying the drive, the terminal monitors the user's driving data in real time and provides an interface to obtain feedback from the user. The input is the user's driving data and feedback, and the output is the collected driving data and feedback information.

[0533] Step 10:

[0534] The terminal sends the collected feedback information to the server. The input is the feedback information, and the output is the data sent to the server.

[0535] Step 11:

[0536] The server analyzes the received feedback and re-evaluates the route information. The input is the feedback information and the output is the re-evaluated route information.

[0537] Step 12:

[0538] The server sends the re-evaluated route information to the terminal, which then updates the navigation. The input is the re-evaluated route information, and the output is the updated navigation information.

[0539] Step 13:

[0540] The server stores the obtained feedback data in a database and trains the generative AI model. The input is the feedback data, and the output is an updated AI model and database.

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

[0542] The present invention combines a car navigation system that generates a "pleasant" route for the user to enjoy driving itself with an emotion engine that recognizes the user's emotions. The embodiments of the present invention will be described in detail below.

[0543] First, the user starts the car navigation system and verbally inputs the route they wish to drive. For example, they can say, "I want to take a pleasant mountain road." The device picks up this speech through a microphone and converts it into text data using speech recognition technology. This text data is then sent directly to the server.

[0544] The server first retrieves the user's attribute information (past driving data and preferences) from a database based on the user's ID. It then retrieves GPS data showing the user's current location. At the same time, it evaluates the user's current emotional state using an emotion engine that recognizes emotions from the user's speech and behavior.

[0545] The server uses GPS data to query a database of surrounding scenery, tourist spots, and past driver feedback to identify potential routes. Based on this information, the server runs an algorithm to generate the optimal route. The algorithm combines the acquired scenery, tourist spots, and rating information to calculate a "pleasantness" score for each route. The score takes into account factors such as scenic beauty, safety, traffic volume, and travel time.

[0546] In addition, the emotion engine also incorporates the user's emotional information into the score calculation. For example, if the user wants to relax, routes with less traffic and more natural scenery will be prioritized. The algorithm selects the route with the highest score as the optimal route and generates detailed directions for it.

[0547] The generated route information is sent to the device, which then begins navigating the user based on this information. Specifically, a detailed map and route directions are displayed on the device screen, and voice guidance is provided to instruct the user on the next action. Directions such as "Turn right in 100 meters. This is the road with a beautiful view of the lake" are provided.

[0548] While the user is enjoying the drive, the device monitors the user's driving data in real time and continuously evaluates the user's emotional state through an emotion engine. At the same time, it provides an interface where the user can input feedback. For example, if the user inputs feedback such as "This road is great," it will be sent to the server.

[0549] The server analyzes the received feedback and reevaluates the route information. In some cases, it recalculates a new route and sends the updated route information to the device, taking into account the emotion engine's evaluation results. The device then updates its navigation system based on this new route information and provides new guidance, such as "If you turn right up ahead, there's a road with a beautiful view."

[0550] The server then stores the acquired feedback data and emotional information in a database and trains the AI ​​model to use it for future route generation. This allows the system to continually evolve and provide a "feel-good" driving route that reflects the user's emotional state and preferences.

[0551] In this way, the car navigation system of the present invention provides a very useful service to users who enjoy driving by generating and navigating the optimal driving route in real time based on the user's spoken requests and emotional information.

[0552] The processing flow will be explained below.

[0553] Step 1:

[0554] The user starts the car navigation system and verbally inputs the route he or she wants to drive. For example, he or she says, "I want to take a nice mountain road."

[0555] Step 2:

[0556] The terminal captures the user's voice through a microphone.

[0557] Step 3:

[0558] The device converts the acquired voice data into text data using voice recognition technology, and sends the converted text data, "I want to walk along a pleasant mountain path," to the server.

[0559] Step 4:

[0560] The server retrieves the user's attribute information (past driving data and preferences) from the database based on the user's ID, and simultaneously retrieves GPS data showing the user's current location.

[0561] Step 5:

[0562] The server uses an emotion engine to recognize the user's current emotional state from their speech and behavior, for example, assessing whether they want to relax or seek stimulation.

[0563] Step 6:

[0564] Based on the GPS data, the server searches a database for information on scenery around the current location, tourist spots, highly rated driving routes, and more.

[0565] Step 7:

[0566] The server runs an algorithm to generate the optimal route based on the acquired scenic, tourist spot, and rating information. The algorithm calculates a "feel-good" score for each route, taking into account factors such as scenic beauty, safety, traffic volume, and travel time.

[0567] Step 8:

[0568] The server adjusts the score calculation based on the emotional information obtained from the emotion engine. For example, if the user wants to relax, routes with less traffic and more natural scenery will be prioritized.

[0569] Step 9:

[0570] The server selects the route with the highest score as the optimal route and generates detailed directions for it, which are then sent to the device.

[0571] Step 10:

[0572] The device then begins navigating the user based on the route information it receives. Specifically, it displays a map and route on the device screen and provides voice guidance on the next action, such as "Turn right in 100 meters. This is the road with a beautiful view of the lake."

[0573] Step 11:

[0574] The device monitors the user's driving data in real time, continuously evaluates the user's emotional state through an emotion engine, and provides an interface for users to input feedback.

[0575] Step 12:

[0576] The device transmits the acquired feedback and emotion data to the server.

[0577] Step 13:

[0578] The server analyzes the received feedback and sentiment data, reevaluates the route information, and recalculates a new route if necessary.

[0579] Step 14:

[0580] The server sends the updated route information to the device, which also reflects the evaluation results of the emotion engine.

[0581] Step 15:

[0582] The device receives the new route information and updates the navigation, providing new directions such as, "Up ahead, if you turn right, there's a scenic road."

[0583] Step 16:

[0584] The server stores the acquired feedback data and emotional information in a database and uses it to train the AI ​​model for future route generation. This allows the system to evolve and improve its ability to suggest more accurate and "comfortable" driving routes.

[0585] Example 2

[0586] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0587] Conventional car navigation systems lack sufficient consideration for the user's emotional state and individual preferences, resulting in incomplete navigation for a specific route. Furthermore, their lack of functionality for incorporating user feedback in real time makes it difficult for users to fully enjoy their drive. To address these issues, a system is needed that can generate and update optimal driving routes in real time, taking into account the user's emotional state and preferences.

[0588] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for converting a user's utterance into text data using a voice recognition means, means for transmitting the text data to the server, means for acquiring user attribute information and current location information and consulting a database, means for generating an optimal route based on scenic information, tourist attractions, and feedback information, means for navigating the generated route for the user, means for acquiring user feedback and transmitting it to the server, means for analyzing the feedback and reevaluating the route information, means for updating the navigation based on the reevaluated route information, means for updating the database using the acquired feedback data and emotion information and training the artificial intelligence model, and means for using an emotion recognition engine that recognizes emotions from the user's utterances and actions. This enables the generation and navigation of an optimal route in real time based on the user's emotional state and preferences.

[0589] The "voice recognition means" is a technology that acquires the user's speech through a microphone and converts it into text data using voice recognition technology.

[0590] The "server" is a computer system that has data processing, analysis, and storage functions, manages user attribute information, current location information, scenic information, tourist spots, and feedback information, and generates routes and performs navigation.

[0591] "User attribute information" refers to information including the user's past driving data and preferences, and is data stored in a database.

[0592] "Current location information" refers to the user's current geographical location data obtained using location identification technology such as a GPS module.

[0593] The "database" is an information management system that stores user attribute information, current location information, landscape information, tourist spots, and feedback information, and allows the server to query and use this information.

[0594] "Landscape information" refers to information about natural and artificial landscapes in a particular geographical location.

[0595] A "tourist destination" is a place or facility that is a tourist attraction in a particular area.

[0596] "Feedback information" is information relating to opinions and impressions provided by the user while driving.

[0597] "Route generation means" is a technology that calculates and suggests the optimal driving route for the user based on the acquired data.

[0598] "Navigation means" is a technology that instructs the user on the next action by voice or on-screen display based on the generated route.

[0599] The "means for obtaining feedback" is a technique for obtaining feedback information input by a user and transmitting it to a server.

[0600] The "means for analyzing feedback" is a technique for analyzing the acquired feedback information on the server and reevaluating the route information.

[0601] The "means for updating navigation" is a technique for updating navigation information provided to a user based on reevaluated route information.

[0602] An "artificial intelligence model" is an algorithm or model that learns using captured feedback data and emotional information to help generate future routes.

[0603] An "emotion recognition engine" is a technology or algorithm for recognizing and evaluating emotions from a user's speech and behavior.

[0604] This invention relates to a car navigation system that generates an optimal driving route based on the user's emotional state and preferences, and navigates the user in real time. This system improves the user's driving experience by combining a voice recognition means, an emotion recognition engine, and an artificial intelligence model.

[0605] First, the user starts the car navigation system and inputs their desired driving route by voice. For example, they might say, "I want to take a pleasant mountain road." The device picks up this speech through the microphone and converts it into text data using voice recognition technology (for example, a general voice recognition API). This text data is then sent directly to the server.

[0606] The server retrieves the user's attribute information (past driving data and preferences) from a database based on the user's ID. At the same time, it uses a GPS module to identify the user's current location. It then uses an emotion recognition engine (e.g., a general emotion analysis API) to evaluate the user's current emotional state. This allows it to recognize emotions from the user's speech and behavior, and uses this information to help generate a route.

[0607] The server then uses the acquired GPS data and information from a database to query the surrounding landscape (e.g., open-source map data), tourist attractions, and past driver feedback. It then uses a route generation algorithm (e.g., A algorithm or Dijkstra algorithm) to integrate this information and calculate a "pleasantness" score for each route, which includes scenic beauty, safety, traffic volume, and travel time.

[0608] Once the optimal route is generated, the information is sent to the device, which displays a detailed map and directions to the user, and provides voice instructions such as, "Turn right in 100 meters. This is the road with a beautiful lake view." This allows the user to be navigated in real time to their destination.

[0609] While the user is enjoying the drive, the device monitors driving data in real time and continuously evaluates the user's emotional state through an emotion recognition engine. At the same time, the user can input feedback through the interface. For example, if the user inputs feedback such as "This road is great," the information is sent to the server.

[0610] The server analyzes the received feedback and reevaluates the route information. If necessary, it recalculates a new route and sends the updated route information to the device, taking into account the evaluation results of the emotion recognition engine. The device then guides the user along the new route. For example, it provides new guidance such as, "If you turn right up ahead, there is a road with a beautiful view."

[0611] Furthermore, the server stores the acquired feedback data and emotional information in a database and trains a generative AI model (e.g., a typical deep learning model) on it, allowing the system to continually evolve and provide a "comfortable" driving route that reflects the user's emotional state and preferences.

[0612] Specific examples

[0613] When a user voice-inputs something like "I want to drive while looking at the beautiful coastline," the device converts it into text using voice recognition technology and sends it to the server. The server takes into account past driving data, preferences, and current emotional state to generate a scenic route along the coastline. The device then guides the user, saying, "Turn left in 3 kilometers and you'll find a beautiful drive with ocean views." If, during the drive, the user gives feedback such as "This road is wonderful," the server uses that information to generate a more optimized route.

[0614] Example prompts for generative AI models

[0615] "If a user wants to relax, what factors would you consider to suggest the best driving route?"

[0616] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0617] Step 1:

[0618] The user starts the car navigation system and verbally inputs the route they want to drive. For example, they might say, "I want to take a nice mountain road." This utterance becomes the input.

[0619] Step 2:

[0620] The device picks up the user's speech through a microphone and converts it into text data using speech recognition technology. Specifically, it uses a speech recognition API. The input is the speech and the output is text data. This text data is sent directly to the server.

[0621] Step 3:

[0622] The server retrieves the user's attribute information (past driving data and preferences) from a database based on the user's ID. It also identifies the user's current location using a GPS module. The input is the user ID and GPS data, and the output is the user's attribute information and current location information.

[0623] Step 4:

[0624] The server uses an emotion recognition engine to evaluate the user's current emotional state. This is a process of analyzing emotions from the user's voice and behavior. The input is the user's voice data and behavior data, and the output is the evaluation result of the user's emotional state.

[0625] Step 5:

[0626] The server uses the acquired GPS data and information from the database to query information on the surrounding scenery, tourist spots, and past driver feedback. This provides the data necessary for route generation. The input is the GPS data and information from the database, and the output is the query results.

[0627] Step 6:

[0628] The server uses a route generation algorithm to calculate a "feel good" score for each route based on the query results. The algorithm uses A or Dijkstra's algorithm. The input is the query results, and the output is the "feel good" score.

[0629] Step 7:

[0630] The server selects the route with the highest score as the optimal route and generates detailed directions for it. The input is the "feel good" score, and the output is detailed directions for the optimal route. This information is sent to the device.

[0631] Step 8:

[0632] The device provides the user with detailed maps and voice guidance based on the route information sent from the server. For example, it provides navigation such as "Turn right 100 meters ahead." The input is detailed directions for the optimal route, and the output is navigation information.

[0633] Step 9:

[0634] While the user is enjoying the drive, the device monitors driving data in real time and continuously evaluates the user's emotional state through an emotion recognition engine. The input is driving data and emotion data, and the output is a real-time emotion evaluation result.

[0635] Step 10:

[0636] The user inputs feedback through the interface, such as "This road is great." The device then sends this feedback to the server.

[0637] Step 11:

[0638] The server analyzes the received feedback and re-evaluates the route information. The input is the feedback information and the output is the re-evaluated route information.

[0639] Step 12:

[0640] The server recalculates a new route if necessary and sends the updated route information to the device, which then updates its navigation based on the new route information and provides new instructions to the user. The input is the re-evaluated route information, and the output is the updated navigation information.

[0641] Step 13:

[0642] The server stores the acquired feedback data and emotional information in a database and trains the generative AI model based on this data. This allows the system to evolve future route generation to better reflect the user's emotional state and preferences. The input is feedback data and emotional information, and the output is the trained generative AI model.

[0643] (Application example 2)

[0644] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0645] In modern society, the demand for food delivery is increasing, resulting in an increased workload for delivery drivers. However, conventional navigation systems do not provide route suggestions that take into account the emotional state of delivery drivers, resulting in situations where stress and fatigue easily accumulate. Therefore, there is a need for route guidance that recognizes the emotional state of delivery drivers in real time and reduces their stress. The present invention solves this problem.

[0646] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0647] In this invention, the server includes means for speech recognition of a user's speech and converting it into text data, means for transmitting the text data to the server, means for acquiring the user's attribute information and current GPS data and consulting a database, means for generating an optimal route based on scenic information, tourist spots, and rating information, means for navigating the generated route to the user, means for acquiring user feedback and transmitting it to the server, means for analyzing the feedback and reevaluating the route information, means for updating the navigation based on the reevaluated route information, means for updating the database using the acquired feedback data and training the AI ​​model, means for recognizing the emotional state of a delivery driver in real time and providing an optimal route based on the driver's emotions, and means for selecting roads with good scenery or low traffic to reduce stress. This enables optimal route guidance based on the emotional state of the delivery driver, reducing driver stress and improving work efficiency.

[0648] A "user" is an individual or organization that uses the system.

[0649] An "utterance" is a spoken instruction or request made by a user to the system.

[0650] "Speech recognition" is a technology that converts a user's speech into text data.

[0651] "Text data" is character string information converted using voice recognition technology.

[0652] A "server" is a computer system that performs data processing and analysis.

[0653] "Attribute information" is individual information including a user's preferences, past behavior history, and the like.

[0654] "GPS data" is Global Positioning System data that indicates the user's current location.

[0655] A "database" is a system for storing and managing information.

[0656] "Scenery information" is data about the scenery and natural environment around the route.

[0657] "Tourist spots" are places or famous places that tourists should visit.

[0658] "Rating information" is feedback and opinion data collected from past users.

[0659] A "route" is a path from a starting point to a destination.

[0660] "Navigation" is an action or function that provides directions for a user to reach a destination.

[0661] "Feedback" refers to the impressions and evaluations provided by users after use.

[0662] "Reevaluation" is the act of reviewing a previous evaluation based on collected feedback.

[0663] "Navigation update" refers to updating the guidance information to the latest version based on new route information.

[0664] An "AI model" is a computational model for learning and prediction in artificial intelligence.

[0665] "Emotional state" is the user's current mental and emotional state.

[0666] "Real-time" means that data is processed and reflected immediately.

[0667] "Stress" refers to the psychological pressure or fatigue felt by the user.

[0668] A "scenic road" is a route that is rich in natural scenery and visually enjoyable.

[0669] A "low volume road" is a route with few vehicles on it, making it safe and relaxing.

[0670] This invention is a system that generates and navigates optimal routes based on the emotional state of delivery drivers. This system operates through the cooperation of a terminal and a server.

[0671] First, the delivery driver, who is the user, speaks to the device. The speech might be something like, "I'm tired, so I'd like a relaxing route." The device then receives this speech and converts it into text data using speech recognition technology. This speech recognition technology uses a service such as the Google Speech-to-Text API.

[0672] The converted text data is then sent to a server. The server retrieves attribute information from a database based on the user's ID. This attribute information includes past driving data and individual preferences. The server also retrieves GPS data sent from the device to determine the user's current location.

[0673] The server then uses the speech data sent from the device to activate an emotion engine to evaluate the user's emotional state. This emotion engine uses, for example, the Microsoft Azure Emotion API. The acquired emotional state is evaluated as "tired."

[0674] Next, the server retrieves information about the scenery around the current location, tourist spots, and feedback from past drivers from a database to generate route candidates. Route candidates can be obtained using the Google Maps API. The generated routes are scored for "comfort" based on an evaluation algorithm. This score takes into account factors such as scenic beauty, low traffic volume, and safety.

[0675] The evaluation algorithm also reflects the user's emotional state, as determined by the emotion engine. For example, if the user is "tired," scenic routes and routes with less traffic will be prioritized. Ultimately, the route with the highest score is selected and sent to the device.

[0676] The device will then begin navigating the user based on the selected route information. Specifically, it will display a map and provide voice guidance. For example, "Turn right in 100 meters. This road will take you past a scenic lake."

[0677] While the user is driving, the device continues to monitor driving data in real time. At the same time, it continuously evaluates the user's emotional state through the emotion engine. As a result, if the user inputs feedback such as "This road is great," the feedback will be sent to the server.

[0678] The server analyzes the received feedback and reevaluates the route information, possibly calculating a new route and sending it back to the device. This reevaluation allows the system to continually evolve and more accurately reflect the user's emotional state and preferences.

[0679] The hardware used includes smartphones (iOS, Android) and their built-in GPS and microphones, while the software uses a speech recognition API (Google Speech-to-Text API), an emotion engine API (Microsoft Azure Emotion API), and a navigation API (Google Maps API).

[0680] Examples of prompts:

[0681] "If a user is looking to relax, they should search for routes with beautiful scenery."

[0682] Based on this embodiment, delivery drivers can receive optimal route guidance according to their emotional state, enabling them to work efficiently while reducing stress during work.

[0683] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0684] Step 1:

[0685] The device receives the user's speech. When the user says, "I'm tired, so I'd like a relaxing route," the device's microphone captures this speech. The input is the user's speech, and the output is audio data.

[0686] Step 2:

[0687] The device converts the acquired voice data into text data using voice recognition technology. This process uses the Google Speech-to-Text API. The input is voice data, and the output is text data such as "I'm tired, so I'd like a relaxing route."

[0688] Step 3:

[0689] The terminal transmits the converted text data to the server. The input is the text data, and the output is the result of transmitting the text data to the server.

[0690] Step 4:

[0691] The server retrieves attribute information from the database based on the user's ID. The attribute information includes past driving data and individual preferences. The input is the user's ID, and the output is attribute information.

[0692] Step 5:

[0693] The server receives the GPS data sent from the device and identifies the user's current location. The input is the GPS data and the output is the user's current location.

[0694] Step 6:

[0695] The server inputs the text data sent from the device into the emotion engine and evaluates the user's emotional state. This uses the Microsoft Azure Emotion API. The input is text data, and the output is an emotional state such as "tired."

[0696] Step 7:

[0697] The server retrieves information about the scenery around the current location, tourist spots, and past feedback from a database and generates route candidates using the Google Maps API. The input is the current location and destination, and the output is a list of route candidates.

[0698] Step 8:

[0699] The server runs a rating algorithm to calculate a "feel-good" score for each route candidate. The rating algorithm takes into account scenic beauty, traffic volume, safety, and the user's emotional state. The input is the list of route candidates and the user's emotional state, and the output is a score for each route.

[0700] Step 9:

[0701] The server selects the route with the highest score and sends it to the terminal. The input is the route score, and the output is the selected optimal route.

[0702] Step 10:

[0703] The device then begins navigation based on the selected route information. Specifically, it displays a map and provides voice guidance. For example, "Turn right after 100 meters. This road passes by a scenic lake." The input is the optimal route information, and the output is navigation instructions.

[0704] Step 11:

[0705] The device monitors driving data in real time while driving and continuously evaluates the user's emotional state through an emotion engine. The input is driving data and emotional state, and the output is the continuous emotion evaluation result.

[0706] Step 12:

[0707] When the user inputs feedback, the terminal sends the feedback to the server. The input is the user's feedback, and the output is the result of the transmission to the server.

[0708] Step 13:

[0709] The server analyzes the received feedback, reevaluates the route information, and possibly recalculates a new route and sends it to the device. The input is the feedback, and the output is the reevaluated route.

[0710] Step 14:

[0711] The server updates the database using the feedback data it receives and trains the AI ​​model. The input is the feedback data, and the output is the updated AI model.

[0712] Examples of prompts:

[0713] "If a user is looking to relax, they should search for routes with beautiful scenery."

[0714] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0715] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0716] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0717] [Third embodiment]

[0718] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0719] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0720] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0721] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0722] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0723] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0724] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0725] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0726] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0727] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0728] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0729] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0730] The present invention provides a car navigation system that generates a "pleasant" route for the user to enjoy driving itself. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes in detail the preferred embodiments of the present invention.

[0731] First, the user starts the car navigation system and verbally inputs the route they wish to drive. For example, they can say, "I want to take a pleasant mountain road." The device picks up this speech through a microphone and converts it into text data using speech recognition technology. This text data is then sent directly to the server.

[0732] The server first retrieves the user's attribute information (past driving data and preferences) from a database based on the user's ID. It also retrieves the user's current GPS data to confirm the user's location. Next, it queries the database for information on the surrounding scenery, tourist spots, and feedback from past drivers, and extracts several route candidates.

[0733] Based on this information, the server runs an algorithm to generate the optimal route. This algorithm combines the acquired scenic information, tourist spots, and rating information to calculate a "pleasantness" score for each route. The score takes into account factors such as scenic beauty, safety, traffic volume, and travel time. After calculating the score for each route candidate, the server selects the route with the highest score and generates detailed directions for it.

[0734] The generated route information is sent to the device, which then begins navigating the user based on this information. Specifically, a detailed map and route directions are displayed on the device screen, and voice guidance is provided to instruct the user on the next action. For example, guidance such as "Turn right in 100 meters. This is the road with a beautiful view of the lake" is provided.

[0735] While the user is enjoying the drive, the device monitors the user's driving data in real time and provides an interface where the user can input feedback. For example, if the user inputs feedback such as "This road is great," it is sent to the server.

[0736] The server analyzes the feedback it receives and reevaluates the route information, possibly recalculating a new route and sending the updated route information back to the device. The device then updates its navigation based on this new route information, providing new guidance such as, "Up ahead, if you turn right, there's a scenic road."

[0737] The server then stores the feedback data in a database and uses it to train the AI ​​model for future route generation. This allows the system to continually evolve and propose more accurate and "comfortable" driving routes.

[0738] In this way, the car navigation system of the present invention provides a very useful service to users who enjoy driving by generating and navigating the optimal driving route in real time based on the user's spoken requests.

[0739] The processing flow will be explained below.

[0740] Step 1:

[0741] The user starts the car navigation system and verbally inputs the route he or she wants to drive. For example, he or she says, "I want to take a nice mountain road."

[0742] Step 2:

[0743] The terminal captures the user's voice through a microphone.

[0744] Step 3:

[0745] The device converts the acquired voice data into text data using voice recognition technology, and sends the converted text, such as "I want to walk along a pleasant mountain path," to the server.

[0746] Step 4:

[0747] The server retrieves the user's attribute information (past driving data and preferences) from the database based on the user's ID, and simultaneously retrieves GPS data showing the user's current location.

[0748] Step 5:

[0749] Based on the GPS data, the server searches a database for information on scenery around the current location, tourist spots, highly rated driving routes, and more.

[0750] Step 6:

[0751] The server runs an algorithm to generate the optimal route based on the acquired scenic, tourist spot, and rating information. The algorithm calculates a "pleasantness" score for each route, taking into account factors such as scenic beauty, safety, traffic volume, and travel time.

[0752] Step 7:

[0753] The server selects the route with the highest score as the optimal route and generates detailed directions for it, which are then sent to the device.

[0754] Step 8:

[0755] The device then begins navigating the user based on the route information it receives. Specifically, it displays a map and route on the device screen and provides voice guidance on the next action, such as "Turn right in 100 meters. This is the road with a beautiful view of the lake."

[0756] Step 9:

[0757] The device monitors the user's driving data in real time and provides an interface where the user can input feedback, for example, "This road is great."

[0758] Step 10:

[0759] The terminal transmits the obtained feedback to the server.

[0760] Step 11:

[0761] The server analyzes the received feedback, reevaluates the route information, possibly recalculating a new route, and sends the updated route information to the device.

[0762] Step 12:

[0763] The device receives the new route information and updates the navigation, providing new directions such as, "Up ahead, if you turn right, there's a scenic road."

[0764] Step 13:

[0765] The server stores the feedback data in a database and trains the AI ​​model to use it for future route generation. This allows the system to evolve and propose more accurate and "comfortable" driving routes.

[0766] Example 1

[0767] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0768] Conventional car navigation systems have had difficulty generating "pleasant" routes for users to enjoy driving. In particular, they were unable to generate optimal routes by comprehensively evaluating factors such as scenery, beauty, safety, and traffic volume. It was also difficult to reflect user preferences and past feedback, resulting in insufficient real-time route updates and improvements. This resulted in low user satisfaction.

[0769] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0770] In this invention, the server includes means for acquiring user attribute information and current location data and querying a database, means for generating an optimal route based on scenery information, tourist spots, and rating information, and means for guiding the generated route to the user, thereby enabling the generation of a "pleasant" route in real time based on the user's utterances.

[0771] "Voice recognition of user speech" refers to the recognition of a user's actions of inputting instructions or information into a system by voice as a digital signal.

[0772] "Means for converting into text data" refers to the process of expressing voice data acquired by a voice recognition system as text information.

[0773] The "means for transmitting to a server" refers to a method for transmitting the converted text data to a remote server via a network.

[0774] "User attribute information" refers to information stored in a database, such as a user's past behavior, preferences, and individual characteristics.

[0775] "Current location data" is information that indicates the physical location of the user, and is typically obtained as GPS data.

[0776] "Means for querying a database" refers to a method for searching and retrieving required information from stored data.

[0777] "Scenery information" refers to data about the natural scenery and landscapes around roads.

[0778] "Tourist spots" refer to places that are considered worth visiting while driving.

[0779] "Rating information" refers to data based on past user feedback and reviews.

[0780] "Means for generating an optimal route" refers to the process of calculating and selecting the most suitable route for a user from multiple candidate routes.

[0781] "Means for guiding the user along the generated route" refers to a method for providing the user with visual and audio navigation along the selected route.

[0782] "User Feedback" means User opinions and impressions regarding driving routes and use of the System.

[0783] "Means for analyzing feedback" refers to a method for analyzing and evaluating feedback data obtained from users.

[0784] "Means for reevaluating route information" refers to the process of reviewing and improving existing route information based on the feedback obtained.

[0785] The "means for updating guidance" refers to a method for updating the currently performed navigation with the latest information based on the reevaluated route information.

[0786] "Means for training an artificial intelligence model" refers to the method by which the acquired data is used to train a machine learning algorithm and improve the accuracy of the system.

[0787] The present invention provides a car navigation system that generates a "pleasant" route for enjoying the drive itself. This system allows the user to register their driving needs through voice input, generates an optimal route that meets those needs, and navigates in real time.

[0788] First, the user starts the car navigation system and verbally inputs the route they wish to drive. For example, they can say, "I want to take a nice mountain road." The device picks up this speech through the microphone and converts it into text data using speech recognition technology. Specifically, the Google Cloud Speech-to-Text API is used. The converted text data is then sent to the server.

[0789] The server first retrieves the user's attribute information (past driving data and preferences) from a database based on the user's ID. This database is typically a relational database management system (RDBMS) such as Amazon RDS or MySQL. It also retrieves the user's current GPS data to confirm their location.

[0790] The server then searches a database for information on the surrounding scenery, tourist attractions, and past driver feedback, and extracts several route candidates, including information on scenic beauty, safety, and tourist attraction popularity.

[0791] Based on this information, the server runs an algorithm to generate the optimal route. This algorithm is implemented in Python and uses machine learning libraries (e.g., TensorFlow). The algorithm combines the acquired landscape information, tourist spot information, and rating information to calculate a "pleasantness" score for each route. This score takes into account factors such as scenic beauty, safety, traffic volume, and travel time. A score is calculated for each route candidate, and the route with the highest score is selected. Detailed directions for that route are then generated.

[0792] The generated route information is sent to the device, which then begins navigating the user based on this information. Specifically, a detailed map and directions are displayed on the device screen, and voice instructions are given using the Google Text-to-Speech API to instruct the user on the next action. For example, directions such as "Turn right in 100 meters. This is the road with a beautiful view of the lake" are provided.

[0793] While the user is enjoying the drive, the device monitors the user's driving data in real time and provides an interface where the user can input feedback. For example, if the user inputs feedback such as "This road is great," it will be sent to the server.

[0794] The server analyzes the feedback it receives and reevaluates the route information, possibly recalculating a new route and sending the updated route information back to the device. The device then updates its navigation based on this new route information, providing new guidance such as, "Up ahead, if you turn right, there's a scenic road."

[0795] The server then stores the feedback data in a database and uses it to train the AI ​​model for future route generation, allowing the system to continually evolve and propose more accurate, "comfortable" driving routes.

[0796] Specific examples

[0797] As a specific example, the prompt sentence in which the user utters "I want to walk along a pleasant mountain path" is shown below.

[0798] Prompt Sentence Examples

[0799] User ID: 12345

[0800] Current location: Latitude 34.052235, Longitude -118.243683

[0801] Route request: I want to go through a pleasant mountain path.

[0802] Past preferences: I like mountain roads, and prefer roads with little traffic.

[0803] Based on this information, the system generates an optimal driving route and provides real-time navigation.

[0804] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0805] Step 1:

[0806] The user starts the car navigation system and inputs the route they wish to drive by speech. For example, they might say, "I want to take a nice mountain road." The input data is the user's voice. The output data after conversion is speech data.

[0807] Step 2:

[0808] The device receives this speech through a microphone and converts it into text data using speech recognition technology. Specifically, it uses the Google Cloud Speech-to-Text API. The input voice data is output as text data.

[0809] Step 3:

[0810] The terminal sends the converted text data (e.g., "I want to walk along a pleasant mountain path") to the server using the HTTPS protocol. The input data is the text data, and the output data is the result of sending the text data to the server.

[0811] Step 4:

[0812] The server retrieves the user's attribute information (past driving data and preferences) from the database based on the user's ID. It also retrieves GPS data and confirms the user's current location. The input data is the user ID and current GPS data, and the output data is attribute information and current location data.

[0813] Step 5:

[0814] The server queries a database of landscape information around the current location, tourist spots, and past driver feedback information to extract several route candidates. The input data is the current location and user attribute information, and the output data is multiple route candidates.

[0815] Step 6:

[0816] The server uses a machine learning algorithm implemented in Python (e.g., TensorFlow) to calculate a "feel-good" score for each route candidate. The score calculation takes into account factors such as scenic beauty, safety, traffic volume, and travel time. The input data are the route candidates, and the output data is the "feel-good" score for each route.

[0817] Step 7:

[0818] The server selects the route with the highest score based on the calculated scores and generates detailed directions for it. Specifically, it uses a map data API (e.g., Google Maps API). The input data are the "comfort" score and route candidates, and the output data are detailed directions.

[0819] Step 8:

[0820] The server sends the generated route information to the terminal. Communication is via HTTPS. The input data is the detailed route information, and the output data is the transmission result to the terminal.

[0821] Step 9:

[0822] The device displays the received route information on the screen and provides voice guidance for the next action using the Google Text-to-Speech API. For example, guidance such as "Turn right after 100 meters. This is the road with a beautiful lake view" is provided. The input data is detailed route information, and the output data is navigation guidance for the user.

[0823] Step 10:

[0824] The user inputs a piece of feedback during navigation, for example, "This road is great." The input data is the user's feedback, and the output data is the text data of the feedback.

[0825] Step 11:

[0826] The terminal obtains the user's feedback and sends it to the server. The input data is the text data of the feedback, and the output data is the result of the transmission to the server.

[0827] Step 12:

[0828] The server analyzes the received feedback and reevaluates the route information if necessary, possibly recalculating a new route. The input data is the feedback, and the output data is the reevaluated route information.

[0829] Step 13:

[0830] The server sends the updated route information to the terminal, again using the HTTPS protocol. The input data is the re-evaluated route information, and the output data is the result sent to the terminal.

[0831] Step 14:

[0832] The device updates the navigation based on the new route information and provides new guidance such as, "If you turn right up ahead, there is a road with a beautiful view." The input data is the reevaluated route information, and the output data is the updated navigation information.

[0833] (Application example 1)

[0834] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0835] Conventional car navigation systems focus on providing the shortest or fastest route to a destination. However, there are few systems that provide "comfortable" routes that allow users to enjoy the drive itself. Furthermore, systems for autonomous vehicles that navigate routes that reflect user preferences are still immature. In these circumstances, there is a need to maximize the comfort and enjoyment users feel while driving.

[0836] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0837] In this invention, the server includes means for speech recognition of a user's speech and converting it into text data, means for transmitting the text data, means for acquiring user attribute information and current location information and consulting a database, means for generating an optimal route based on scenic information, tourist spots, and rating information, means for presenting the generated route, means for acquiring and transmitting user feedback, means for analyzing the feedback and reevaluating the route information, means for updating route instructions based on the reevaluated route information, means for updating and learning from the database using the acquired feedback data, and means for being installed in an autonomous vehicle and for automatically traveling a "pleasant" route specified by the user. This enables the user to take a "pleasant" route during a drive, providing a comfortable and enjoyable driving experience.

[0838] The "means for recognizing user speech and converting it into text data" refers to a device or software that has the function of recognizing what the user has said and converting it from speech to text data.

[0839] The "means for transmitting the text data to a server" refers to a device or software that has the function of transmitting the converted text data to a server using a communication means such as the Internet.

[0840] "Means for obtaining user attribute information and current GPS data and querying a database" refers to devices or software that have the function of obtaining past data and current location information about a user and searching a database based on that data.

[0841] "Means for generating optimal routes based on scenery information, tourist spots, and evaluation information" refers to devices or software that have the function of calculating the optimal driving route for a user based on the surrounding scenery, tourist spots, and past evaluation data.

[0842] The "means for navigating the generated route to the user" refers to a device or software that has the function of providing visual and audio guidance to the user based on the calculated route.

[0843] The "means for acquiring user feedback and transmitting it to the server" refers to a device or software that has the function of collecting user evaluations and comments about the route and transmitting them to the server.

[0844] The "means for analyzing feedback and re-evaluating route information" refers to a device or software that has the function of analyzing collected feedback and recalculating the route evaluation based on that feedback.

[0845] The "means for updating navigation based on reevaluated route information" refers to a device or software that has the function of updating the current navigation route to reflect the reevaluated route information.

[0846] "Means for updating the database using acquired feedback data and training the AI ​​model" refers to devices or software that have the function of adding feedback data acquired from users to the database and training the AI ​​model based on that data.

[0847] "A means to be installed in an autonomous vehicle and to automatically drive along a 'comfortable' route specified by the user" is a system that has the function of allowing an autonomous vehicle to automatically drive along a comfortable route specified by the user.

[0848] The present invention is a system for automatically driving a self-driving vehicle along a "pleasant" route designated by a user. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described in detail below with reference to the accompanying drawings.

[0849] First, the user starts the car navigation system via a smartphone or on-board display and inputs the route they wish to drive by speech. For example, they can say, "I want to take a nice road along the sea." The device picks up this speech through the microphone and converts it into text data using the SpeechRecognition library. This text data is then sent to the server via the Internet.

[0850] The server first retrieves the user's attribute information (past driving data and preferences) from a database based on the user's ID. It also retrieves the user's current GPS data to confirm the user's location. Next, it queries the database for information on the surrounding scenery, tourist spots, and feedback from past drivers, and extracts several route candidates.

[0851] Based on this information, the server runs an algorithm to generate the optimal route. This algorithm combines the acquired scenic information, tourist spots, and rating information to calculate a "pleasantness" score for each route. The score takes into account factors such as scenic beauty, safety, traffic volume, and travel time. After calculating the score for each route candidate, the server selects the route with the highest score and generates detailed directions for it.

[0852] The generated route information is sent to the device, which then begins navigating the user based on this information. Specifically, a detailed map and route directions are displayed on the device screen, and voice guidance is provided to instruct the user on the next action. For example, guidance such as "Turn right in 100 meters. This is the road with a beautiful view of the lake" is provided.

[0853] While the user is enjoying the drive, the device monitors the user's driving data in real time and provides an interface where the user can input feedback. For example, if the user inputs feedback such as "This road is great," it is sent to the server.

[0854] The server analyzes the feedback it receives and reevaluates the route information, possibly recalculating a new route and sending the updated route information back to the device. The device then updates its navigation based on this new route information, providing new guidance such as, "Up ahead, if you turn right, there's a scenic road."

[0855] Furthermore, the server stores the feedback data in a database and uses it to train the generative AI model for future route generation. This allows the system to constantly evolve and propose more accurate and "comfortable" driving routes.

[0856] As a specific example, a prompt sentence such as "Please tell me the best route if I want to take a pleasant road along the seashore" can be used.

[0857] In this way, the car navigation system of the present invention provides a very useful service to users who enjoy driving by generating and navigating the optimal driving route in real time based on the user's spoken requests.

[0858] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0859] Step 1:

[0860] A user starts a car navigation system via a smartphone or an in-car display and utters, "I want to take a nice road along the sea." The input is the user's voice data, and the output is the voice data being input to the device.

[0861] Step 2:

[0862] The device receives the user's speech through a microphone and converts the speech data into text using the SpeechRecognition library. The input is the user's speech data, and the output is the data converted from speech to text.

[0863] Step 3:

[0864] The terminal transmits the converted text data to a server via the Internet. The input is the text data, and the output is the text data transmitted to the server.

[0865] Step 4:

[0866] The server retrieves the user's attribute information (past driving data and preferences) from a database based on the user's ID. It also retrieves current GPS data. The input is text data and GPS data, and the output is the user's attribute information and current location information.

[0867] Step 5:

[0868] The server uses the user's attribute information and current location to query a database for information on the surrounding scenery, tourist spots, and past driver feedback, and extracts several route candidates. The input is the user's attribute information and current location information, and the output is route candidate information.

[0869] Step 6:

[0870] The server runs an algorithm to generate the optimal route based on the acquired information. This algorithm calculates a "pleasantness" score for each route. The inputs are landscape information, tourist spots, and rating information, and the output is the optimal route information.

[0871] Step 7:

[0872] The server sends the optimal route information to the terminal. The input is the optimal route information, and the output is the route information sent to the terminal.

[0873] Step 8:

[0874] The device then begins navigating the user based on the route information it receives. Specifically, it displays a detailed map and directions on the screen and provides voice guidance to guide the user on their next steps. The input is route information, and the output is navigation instructions for the user.

[0875] Step 9:

[0876] While the user is enjoying the drive, the terminal monitors the user's driving data in real time and provides an interface to obtain feedback from the user. The input is the user's driving data and feedback, and the output is the collected driving data and feedback information.

[0877] Step 10:

[0878] The terminal sends the collected feedback information to the server. The input is the feedback information, and the output is the data sent to the server.

[0879] Step 11:

[0880] The server analyzes the received feedback and re-evaluates the route information. The input is the feedback information and the output is the re-evaluated route information.

[0881] Step 12:

[0882] The server sends the re-evaluated route information to the terminal, which then updates the navigation. The input is the re-evaluated route information, and the output is the updated navigation information.

[0883] Step 13:

[0884] The server stores the obtained feedback data in a database and trains the generative AI model. The input is the feedback data, and the output is an updated AI model and database.

[0885] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0886] The present invention combines a car navigation system that generates a "pleasant" route for the user to enjoy driving itself with an emotion engine that recognizes the user's emotions. The embodiments of the present invention will be described in detail below.

[0887] First, the user starts the car navigation system and verbally inputs the route they wish to drive. For example, they can say, "I want to take a pleasant mountain road." The device picks up this speech through a microphone and converts it into text data using speech recognition technology. This text data is then sent directly to the server.

[0888] The server first retrieves the user's attribute information (past driving data and preferences) from a database based on the user's ID. It then retrieves GPS data showing the user's current location. At the same time, it evaluates the user's current emotional state using an emotion engine that recognizes emotions from the user's speech and behavior.

[0889] The server uses GPS data to query a database of surrounding scenery, tourist spots, and past driver feedback to identify potential routes. Based on this information, the server runs an algorithm to generate the optimal route. The algorithm combines the acquired scenery, tourist spots, and rating information to calculate a "pleasantness" score for each route. The score takes into account factors such as scenic beauty, safety, traffic volume, and travel time.

[0890] In addition, the emotion engine also incorporates the user's emotional information into the score calculation. For example, if the user wants to relax, routes with less traffic and more natural scenery will be prioritized. The algorithm selects the route with the highest score as the optimal route and generates detailed directions for it.

[0891] The generated route information is sent to the device, which then begins navigating the user based on this information. Specifically, a detailed map and route directions are displayed on the device screen, and voice guidance is provided to instruct the user on the next action. Directions such as "Turn right in 100 meters. This is the road with a beautiful view of the lake" are provided.

[0892] While the user is enjoying the drive, the device monitors the user's driving data in real time and continuously evaluates the user's emotional state through an emotion engine. At the same time, it provides an interface where the user can input feedback. For example, if the user inputs feedback such as "This road is great," it will be sent to the server.

[0893] The server analyzes the received feedback and reevaluates the route information. In some cases, it recalculates a new route and sends the updated route information to the device, taking into account the emotion engine's evaluation results. The device then updates its navigation system based on this new route information and provides new guidance, such as "If you turn right up ahead, there's a road with a beautiful view."

[0894] The server then stores the acquired feedback data and emotional information in a database and trains the AI ​​model to use it for future route generation. This allows the system to continually evolve and provide a "feel-good" driving route that reflects the user's emotional state and preferences.

[0895] In this way, the car navigation system of the present invention provides a very useful service to users who enjoy driving by generating and navigating the optimal driving route in real time based on the user's spoken requests and emotional information.

[0896] The processing flow will be explained below.

[0897] Step 1:

[0898] The user starts the car navigation system and verbally inputs the route he or she wants to drive. For example, he or she says, "I want to take a nice mountain road."

[0899] Step 2:

[0900] The terminal captures the user's voice through a microphone.

[0901] Step 3:

[0902] The device converts the acquired voice data into text data using voice recognition technology, and sends the converted text data, "I want to walk along a pleasant mountain path," to the server.

[0903] Step 4:

[0904] The server retrieves the user's attribute information (past driving data and preferences) from the database based on the user's ID, and simultaneously retrieves GPS data showing the user's current location.

[0905] Step 5:

[0906] The server uses an emotion engine to recognize the user's current emotional state from their speech and behavior, for example, assessing whether they want to relax or seek stimulation.

[0907] Step 6:

[0908] Based on the GPS data, the server searches a database for information on scenery around the current location, tourist spots, highly rated driving routes, and more.

[0909] Step 7:

[0910] The server runs an algorithm to generate the optimal route based on the acquired scenic, tourist spot, and rating information. The algorithm calculates a "feel-good" score for each route, taking into account factors such as scenic beauty, safety, traffic volume, and travel time.

[0911] Step 8:

[0912] The server adjusts the score calculation based on the emotional information obtained from the emotion engine. For example, if the user wants to relax, routes with less traffic and more natural scenery will be prioritized.

[0913] Step 9:

[0914] The server selects the route with the highest score as the optimal route and generates detailed directions for it, which are then sent to the device.

[0915] Step 10:

[0916] The device then begins navigating the user based on the route information it receives. Specifically, it displays a map and route on the device screen and provides voice guidance on the next action, such as "Turn right in 100 meters. This is the road with a beautiful view of the lake."

[0917] Step 11:

[0918] The device monitors the user's driving data in real time, continuously evaluates the user's emotional state through an emotion engine, and provides an interface for users to input feedback.

[0919] Step 12:

[0920] The device transmits the acquired feedback and emotion data to the server.

[0921] Step 13:

[0922] The server analyzes the received feedback and sentiment data, reevaluates the route information, and recalculates a new route if necessary.

[0923] Step 14:

[0924] The server sends the updated route information to the device, which also reflects the evaluation results of the emotion engine.

[0925] Step 15:

[0926] The device receives the new route information and updates the navigation, providing new directions such as, "Up ahead, if you turn right, there's a scenic road."

[0927] Step 16:

[0928] The server stores the acquired feedback data and emotional information in a database and uses it to train the AI ​​model for future route generation. This allows the system to evolve and improve its ability to suggest more accurate and "comfortable" driving routes.

[0929] Example 2

[0930] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0931] Conventional car navigation systems lack sufficient consideration for the user's emotional state and individual preferences, resulting in incomplete navigation for a specific route. Furthermore, their lack of functionality for incorporating user feedback in real time makes it difficult for users to fully enjoy their drive. To address these issues, a system is needed that can generate and update optimal driving routes in real time, taking into account the user's emotional state and preferences.

[0932] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for converting a user's utterance into text data using a voice recognition means, means for transmitting the text data to the server, means for acquiring user attribute information and current location information and consulting a database, means for generating an optimal route based on scenic information, tourist attractions, and feedback information, means for navigating the generated route for the user, means for acquiring user feedback and transmitting it to the server, means for analyzing the feedback and reevaluating the route information, means for updating the navigation based on the reevaluated route information, means for updating the database using the acquired feedback data and emotion information and training the artificial intelligence model, and means for using an emotion recognition engine that recognizes emotions from the user's utterances and actions. This enables the generation and navigation of an optimal route in real time based on the user's emotional state and preferences.

[0933] The "voice recognition means" is a technology that acquires the user's speech through a microphone and converts it into text data using voice recognition technology.

[0934] The "server" is a computer system that has data processing, analysis, and storage functions, manages user attribute information, current location information, scenic information, tourist spots, and feedback information, and generates routes and performs navigation.

[0935] "User attribute information" refers to information including the user's past driving data and preferences, and is data stored in a database.

[0936] "Current location information" refers to the user's current geographical location data obtained using location identification technology such as a GPS module.

[0937] The "database" is an information management system that stores user attribute information, current location information, landscape information, tourist spots, and feedback information, and allows the server to query and use this information.

[0938] "Landscape information" refers to information about natural and artificial landscapes in a particular geographical location.

[0939] A "tourist destination" is a place or facility that is a tourist attraction in a particular area.

[0940] "Feedback information" is information relating to opinions and impressions provided by the user while driving.

[0941] "Route generation means" is a technology that calculates and suggests the optimal driving route for the user based on the acquired data.

[0942] "Navigation means" is a technology that instructs the user on the next action by voice or on-screen display based on the generated route.

[0943] The "means for obtaining feedback" is a technique for obtaining feedback information input by a user and transmitting it to a server.

[0944] The "means for analyzing feedback" is a technique for analyzing the acquired feedback information on the server and reevaluating the route information.

[0945] The "means for updating navigation" is a technique for updating navigation information provided to a user based on reevaluated route information.

[0946] An "artificial intelligence model" is an algorithm or model that learns using captured feedback data and emotional information to help generate future routes.

[0947] An "emotion recognition engine" is a technology or algorithm for recognizing and evaluating emotions from a user's speech and behavior.

[0948] This invention relates to a car navigation system that generates an optimal driving route based on the user's emotional state and preferences, and navigates the user in real time. This system improves the user's driving experience by combining a voice recognition means, an emotion recognition engine, and an artificial intelligence model.

[0949] First, the user starts the car navigation system and inputs their desired driving route by voice. For example, they might say, "I want to take a pleasant mountain road." The device picks up this speech through the microphone and converts it into text data using voice recognition technology (for example, a general voice recognition API). This text data is then sent directly to the server.

[0950] The server retrieves the user's attribute information (past driving data and preferences) from a database based on the user's ID. At the same time, it uses a GPS module to identify the user's current location. It then uses an emotion recognition engine (e.g., a general emotion analysis API) to evaluate the user's current emotional state. This allows it to recognize emotions from the user's speech and behavior, and uses this information to help generate a route.

[0951] The server then uses the acquired GPS data and information from a database to query the surrounding landscape (e.g., open-source map data), tourist attractions, and past driver feedback. It then uses a route generation algorithm (e.g., A algorithm or Dijkstra algorithm) to integrate this information and calculate a "pleasantness" score for each route, which includes scenic beauty, safety, traffic volume, and travel time.

[0952] Once the optimal route is generated, the information is sent to the device, which displays a detailed map and directions to the user, and provides voice instructions such as, "Turn right in 100 meters. This is the road with a beautiful lake view." This allows the user to be navigated in real time to their destination.

[0953] While the user is enjoying the drive, the device monitors driving data in real time and continuously evaluates the user's emotional state through an emotion recognition engine. At the same time, the user can input feedback through the interface. For example, if the user inputs feedback such as "This road is great," the information is sent to the server.

[0954] The server analyzes the received feedback and reevaluates the route information. If necessary, it recalculates a new route and sends the updated route information to the device, taking into account the evaluation results of the emotion recognition engine. The device then guides the user along the new route. For example, it provides new guidance such as, "If you turn right up ahead, there is a road with a beautiful view."

[0955] Furthermore, the server stores the acquired feedback data and emotional information in a database and trains a generative AI model (e.g., a typical deep learning model) on it, allowing the system to continually evolve and provide a "comfortable" driving route that reflects the user's emotional state and preferences.

[0956] Specific examples

[0957] When a user voice-inputs something like "I want to drive while looking at the beautiful coastline," the device converts it into text using voice recognition technology and sends it to the server. The server takes into account past driving data, preferences, and current emotional state to generate a scenic route along the coastline. The device then guides the user, saying, "Turn left in 3 kilometers and you'll find a beautiful drive with ocean views." If, during the drive, the user gives feedback such as "This road is wonderful," the server uses that information to generate a more optimized route.

[0958] Example prompts for generative AI models

[0959] "If a user wants to relax, what factors would you consider to suggest the best driving route?"

[0960] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0961] Step 1:

[0962] The user starts the car navigation system and verbally inputs the route they want to drive. For example, they might say, "I want to take a nice mountain road." This utterance becomes the input.

[0963] Step 2:

[0964] The device picks up the user's speech through a microphone and converts it into text data using speech recognition technology. Specifically, it uses a speech recognition API. The input is the speech and the output is text data. This text data is sent directly to the server.

[0965] Step 3:

[0966] The server retrieves the user's attribute information (past driving data and preferences) from a database based on the user's ID. It also identifies the user's current location using a GPS module. The input is the user ID and GPS data, and the output is the user's attribute information and current location information.

[0967] Step 4:

[0968] The server uses an emotion recognition engine to evaluate the user's current emotional state. This is a process of analyzing emotions from the user's voice and behavior. The input is the user's voice data and behavior data, and the output is the evaluation result of the user's emotional state.

[0969] Step 5:

[0970] The server uses the acquired GPS data and information from the database to query information on the surrounding scenery, tourist spots, and past driver feedback. This provides the data necessary for route generation. The input is the GPS data and information from the database, and the output is the query results.

[0971] Step 6:

[0972] The server uses a route generation algorithm to calculate a "feel good" score for each route based on the query results. The algorithm uses A or Dijkstra's algorithm. The input is the query results, and the output is the "feel good" score.

[0973] Step 7:

[0974] The server selects the route with the highest score as the optimal route and generates detailed directions for it. The input is the "feel good" score, and the output is detailed directions for the optimal route. This information is sent to the device.

[0975] Step 8:

[0976] The device provides the user with detailed maps and voice guidance based on the route information sent from the server. For example, it provides navigation such as "Turn right 100 meters ahead." The input is detailed directions for the optimal route, and the output is navigation information.

[0977] Step 9:

[0978] While the user is enjoying the drive, the device monitors driving data in real time and continuously evaluates the user's emotional state through an emotion recognition engine. The input is driving data and emotion data, and the output is a real-time emotion evaluation result.

[0979] Step 10:

[0980] The user inputs feedback through the interface, such as "This road is great." The device then sends this feedback to the server.

[0981] Step 11:

[0982] The server analyzes the received feedback and re-evaluates the route information. The input is the feedback information and the output is the re-evaluated route information.

[0983] Step 12:

[0984] The server recalculates a new route if necessary and sends the updated route information to the device, which then updates its navigation based on the new route information and provides new instructions to the user. The input is the re-evaluated route information, and the output is the updated navigation information.

[0985] Step 13:

[0986] The server stores the acquired feedback data and emotional information in a database and trains the generative AI model based on this data. This allows the system to evolve future route generation to better reflect the user's emotional state and preferences. The input is feedback data and emotional information, and the output is the trained generative AI model.

[0987] (Application example 2)

[0988] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0989] In modern society, the demand for food delivery is increasing, resulting in an increased workload for delivery drivers. However, conventional navigation systems do not provide route suggestions that take into account the emotional state of delivery drivers, resulting in situations where stress and fatigue easily accumulate. Therefore, there is a need for route guidance that recognizes the emotional state of delivery drivers in real time and reduces their stress. The present invention solves this problem.

[0990] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0991] In this invention, the server includes means for speech recognition of a user's speech and converting it into text data, means for transmitting the text data to the server, means for acquiring the user's attribute information and current GPS data and consulting a database, means for generating an optimal route based on scenic information, tourist spots, and rating information, means for navigating the generated route to the user, means for acquiring user feedback and transmitting it to the server, means for analyzing the feedback and reevaluating the route information, means for updating the navigation based on the reevaluated route information, means for updating the database using the acquired feedback data and training the AI ​​model, means for recognizing the emotional state of a delivery driver in real time and providing an optimal route based on the driver's emotions, and means for selecting roads with good scenery or low traffic to reduce stress. This enables optimal route guidance based on the emotional state of the delivery driver, reducing driver stress and improving work efficiency.

[0992] A "user" is an individual or organization that uses the system.

[0993] An "utterance" is a spoken instruction or request made by a user to the system.

[0994] "Speech recognition" is a technology that converts a user's speech into text data.

[0995] "Text data" is character string information converted using voice recognition technology.

[0996] A "server" is a computer system that performs data processing and analysis.

[0997] "Attribute information" is individual information including a user's preferences, past behavior history, and the like.

[0998] "GPS data" is Global Positioning System data that indicates the user's current location.

[0999] A "database" is a system for storing and managing information.

[1000] "Scenery information" is data about the scenery and natural environment around the route.

[1001] "Tourist spots" are places or famous places that tourists should visit.

[1002] "Rating information" is feedback and opinion data collected from past users.

[1003] A "route" is a path from a starting point to a destination.

[1004] "Navigation" is an action or function that provides directions for a user to reach a destination.

[1005] "Feedback" refers to the impressions and evaluations provided by users after use.

[1006] "Reevaluation" is the act of reviewing a previous evaluation based on collected feedback.

[1007] "Navigation update" refers to updating the guidance information to the latest version based on new route information.

[1008] An "AI model" is a computational model for learning and prediction in artificial intelligence.

[1009] "Emotional state" is the user's current mental and emotional state.

[1010] "Real-time" means that data is processed and reflected immediately.

[1011] "Stress" refers to the psychological pressure or fatigue felt by the user.

[1012] A "scenic road" is a route that is rich in natural scenery and visually enjoyable.

[1013] A "low volume road" is a route with few vehicles on it, making it safe and relaxing.

[1014] This invention is a system that generates and navigates optimal routes based on the emotional state of delivery drivers. This system operates through the cooperation of a terminal and a server.

[1015] First, the delivery driver, who is the user, speaks to the device. The speech might be something like, "I'm tired, so I'd like a relaxing route." The device then receives this speech and converts it into text data using speech recognition technology. This speech recognition technology uses a service such as the Google Speech-to-Text API.

[1016] The converted text data is then sent to a server. The server retrieves attribute information from a database based on the user's ID. This attribute information includes past driving data and individual preferences. The server also retrieves GPS data sent from the device to determine the user's current location.

[1017] The server then uses the speech data sent from the device to activate an emotion engine to evaluate the user's emotional state. This emotion engine uses, for example, the Microsoft Azure Emotion API. The acquired emotional state is evaluated as "tired."

[1018] Next, the server retrieves information about the scenery around the current location, tourist spots, and feedback from past drivers from a database to generate route candidates. Route candidates can be obtained using the Google Maps API. The generated routes are scored for "comfort" based on an evaluation algorithm. This score takes into account factors such as scenic beauty, low traffic volume, and safety.

[1019] The evaluation algorithm also reflects the user's emotional state, as determined by the emotion engine. For example, if the user is "tired," scenic routes and routes with less traffic will be prioritized. Ultimately, the route with the highest score is selected and sent to the device.

[1020] The device will then begin navigating the user based on the selected route information. Specifically, it will display a map and provide voice guidance. For example, "Turn right in 100 meters. This road will take you past a scenic lake."

[1021] While the user is driving, the device continues to monitor driving data in real time. At the same time, it continuously evaluates the user's emotional state through the emotion engine. As a result, if the user inputs feedback such as "This road is great," the feedback will be sent to the server.

[1022] The server analyzes the received feedback and reevaluates the route information, possibly calculating a new route and sending it back to the device. This reevaluation allows the system to continually evolve and more accurately reflect the user's emotional state and preferences.

[1023] The hardware used includes smartphones (iOS, Android) and their built-in GPS and microphones, while the software uses a speech recognition API (Google Speech-to-Text API), an emotion engine API (Microsoft Azure Emotion API), and a navigation API (Google Maps API).

[1024] Examples of prompts:

[1025] "If a user is looking to relax, they should search for routes with beautiful scenery."

[1026] Based on this embodiment, delivery drivers can receive optimal route guidance according to their emotional state, enabling them to work efficiently while reducing stress during work.

[1027] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1028] Step 1:

[1029] The device receives the user's speech. When the user says, "I'm tired, so I'd like a relaxing route," the device's microphone captures this speech. The input is the user's speech, and the output is audio data.

[1030] Step 2:

[1031] The device converts the acquired voice data into text data using voice recognition technology. This process uses the Google Speech-to-Text API. The input is voice data, and the output is text data such as "I'm tired, so I'd like a relaxing route."

[1032] Step 3:

[1033] The terminal transmits the converted text data to the server. The input is the text data, and the output is the result of transmitting the text data to the server.

[1034] Step 4:

[1035] The server retrieves attribute information from the database based on the user's ID. The attribute information includes past driving data and individual preferences. The input is the user's ID, and the output is attribute information.

[1036] Step 5:

[1037] The server receives the GPS data sent from the device and identifies the user's current location. The input is the GPS data and the output is the user's current location.

[1038] Step 6:

[1039] The server inputs the text data sent from the device into the emotion engine and evaluates the user's emotional state. This uses the Microsoft Azure Emotion API. The input is text data, and the output is an emotional state such as "tired."

[1040] Step 7:

[1041] The server retrieves information about the scenery around the current location, tourist spots, and past feedback from a database and generates route candidates using the Google Maps API. The input is the current location and destination, and the output is a list of route candidates.

[1042] Step 8:

[1043] The server runs a rating algorithm to calculate a "feel-good" score for each route candidate. The rating algorithm takes into account scenic beauty, traffic volume, safety, and the user's emotional state. The input is the list of route candidates and the user's emotional state, and the output is a score for each route.

[1044] Step 9:

[1045] The server selects the route with the highest score and sends it to the terminal. The input is the route score, and the output is the selected optimal route.

[1046] Step 10:

[1047] The device then begins navigation based on the selected route information. Specifically, it displays a map and provides voice guidance. For example, "Turn right after 100 meters. This road passes by a scenic lake." The input is the optimal route information, and the output is navigation instructions.

[1048] Step 11:

[1049] The device monitors driving data in real time while driving and continuously evaluates the user's emotional state through an emotion engine. The input is driving data and emotional state, and the output is the continuous emotion evaluation result.

[1050] Step 12:

[1051] When the user inputs feedback, the terminal sends the feedback to the server. The input is the user's feedback, and the output is the result of the transmission to the server.

[1052] Step 13:

[1053] The server analyzes the received feedback, reevaluates the route information, and possibly recalculates a new route and sends it to the device. The input is the feedback, and the output is the reevaluated route.

[1054] Step 14:

[1055] The server updates the database using the feedback data it receives and trains the AI ​​model. The input is the feedback data, and the output is the updated AI model.

[1056] Examples of prompts:

[1057] "If a user is looking to relax, they should search for routes with beautiful scenery."

[1058] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1059] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1060] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1061] [Fourth embodiment]

[1062] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1063] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1064] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1065] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1066] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1067] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1068] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1069] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1070] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1071] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1072] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1073] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1074] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1075] The present invention provides a car navigation system that generates a "pleasant" route for the user to enjoy driving itself. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes in detail the preferred embodiments of the present invention.

[1076] First, the user starts the car navigation system and verbally inputs the route they wish to drive. For example, they can say, "I want to take a pleasant mountain road." The device picks up this speech through a microphone and converts it into text data using speech recognition technology. This text data is then sent directly to the server.

[1077] The server first retrieves the user's attribute information (past driving data and preferences) from a database based on the user's ID. It also retrieves the user's current GPS data to confirm the user's location. Next, it queries the database for information on the surrounding scenery, tourist spots, and feedback from past drivers, and extracts several route candidates.

[1078] Based on this information, the server runs an algorithm to generate the optimal route. This algorithm combines the acquired scenic information, tourist spots, and rating information to calculate a "pleasantness" score for each route. The score takes into account factors such as scenic beauty, safety, traffic volume, and travel time. After calculating the score for each route candidate, the server selects the route with the highest score and generates detailed directions for it.

[1079] The generated route information is sent to the device, which then begins navigating the user based on this information. Specifically, a detailed map and route directions are displayed on the device screen, and voice guidance is provided to instruct the user on the next action. For example, guidance such as "Turn right in 100 meters. This is the road with a beautiful view of the lake" is provided.

[1080] While the user is enjoying the drive, the device monitors the user's driving data in real time and provides an interface where the user can input feedback. For example, if the user inputs feedback such as "This road is great," it is sent to the server.

[1081] The server analyzes the feedback it receives and reevaluates the route information, possibly recalculating a new route and sending the updated route information back to the device. The device then updates its navigation based on this new route information, providing new guidance such as, "Up ahead, if you turn right, there's a scenic road."

[1082] The server then stores the feedback data in a database and uses it to train the AI ​​model for future route generation. This allows the system to continually evolve and propose more accurate and "comfortable" driving routes.

[1083] In this way, the car navigation system of the present invention provides a very useful service to users who enjoy driving by generating and navigating the optimal driving route in real time based on the user's spoken requests.

[1084] The processing flow will be explained below.

[1085] Step 1:

[1086] The user starts the car navigation system and verbally inputs the route he or she wants to drive. For example, he or she says, "I want to take a nice mountain road."

[1087] Step 2:

[1088] The terminal captures the user's voice through a microphone.

[1089] Step 3:

[1090] The device converts the acquired voice data into text data using voice recognition technology, and sends the converted text, such as "I want to walk along a pleasant mountain path," to the server.

[1091] Step 4:

[1092] The server retrieves the user's attribute information (past driving data and preferences) from the database based on the user's ID, and simultaneously retrieves GPS data showing the user's current location.

[1093] Step 5:

[1094] Based on the GPS data, the server searches a database for information on scenery around the current location, tourist spots, highly rated driving routes, and more.

[1095] Step 6:

[1096] The server runs an algorithm to generate the optimal route based on the acquired scenic, tourist spot, and rating information. The algorithm calculates a "pleasantness" score for each route, taking into account factors such as scenic beauty, safety, traffic volume, and travel time.

[1097] Step 7:

[1098] The server selects the route with the highest score as the optimal route and generates detailed directions for it, which are then sent to the device.

[1099] Step 8:

[1100] The device then begins navigating the user based on the route information it receives. Specifically, it displays a map and route on the device screen and provides voice guidance on the next action, such as "Turn right in 100 meters. This is the road with a beautiful view of the lake."

[1101] Step 9:

[1102] The device monitors the user's driving data in real time and provides an interface where the user can input feedback, for example, "This road is great."

[1103] Step 10:

[1104] The terminal transmits the obtained feedback to the server.

[1105] Step 11:

[1106] The server analyzes the received feedback, reevaluates the route information, possibly recalculating a new route, and sends the updated route information to the device.

[1107] Step 12:

[1108] The device receives the new route information and updates the navigation, providing new directions such as, "Up ahead, if you turn right, there's a scenic road."

[1109] Step 13:

[1110] The server stores the feedback data in a database and trains the AI ​​model to use it for future route generation. This allows the system to evolve and propose more accurate and "comfortable" driving routes.

[1111] Example 1

[1112] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1113] Conventional car navigation systems have had difficulty generating "pleasant" routes for users to enjoy driving. In particular, they were unable to generate optimal routes by comprehensively evaluating factors such as scenery, beauty, safety, and traffic volume. It was also difficult to reflect user preferences and past feedback, resulting in insufficient real-time route updates and improvements. This resulted in low user satisfaction.

[1114] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1115] In this invention, the server includes means for acquiring user attribute information and current location data and querying a database, means for generating an optimal route based on scenery information, tourist spots, and rating information, and means for guiding the generated route to the user, thereby enabling the generation of a "pleasant" route in real time based on the user's utterances.

[1116] "Voice recognition of user speech" refers to the recognition of a user's actions of inputting instructions or information into a system by voice as a digital signal.

[1117] "Means for converting into text data" refers to the process of expressing voice data acquired by a voice recognition system as text information.

[1118] The "means for transmitting to a server" refers to a method for transmitting the converted text data to a remote server via a network.

[1119] "User attribute information" refers to information stored in a database, such as a user's past behavior, preferences, and individual characteristics.

[1120] "Current location data" is information that indicates the physical location of the user, and is typically obtained as GPS data.

[1121] "Means for querying a database" refers to a method for searching and retrieving required information from stored data.

[1122] "Scenery information" refers to data about the natural scenery and landscapes around roads.

[1123] "Tourist spots" refer to places that are considered worth visiting while driving.

[1124] "Rating information" refers to data based on past user feedback and reviews.

[1125] "Means for generating an optimal route" refers to the process of calculating and selecting the most suitable route for a user from multiple candidate routes.

[1126] "Means for guiding the user along the generated route" refers to a method for providing the user with visual and audio navigation along the selected route.

[1127] "User Feedback" means User opinions and impressions regarding driving routes and use of the System.

[1128] "Means for analyzing feedback" refers to a method for analyzing and evaluating feedback data obtained from users.

[1129] "Means for reevaluating route information" refers to the process of reviewing and improving existing route information based on the feedback obtained.

[1130] The "means for updating guidance" refers to a method for updating the currently performed navigation with the latest information based on the reevaluated route information.

[1131] "Means for training an artificial intelligence model" refers to the method by which the acquired data is used to train a machine learning algorithm and improve the accuracy of the system.

[1132] The present invention provides a car navigation system that generates a "pleasant" route for enjoying the drive itself. This system allows the user to register their driving needs through voice input, generates an optimal route that meets those needs, and navigates in real time.

[1133] First, the user starts the car navigation system and verbally inputs the route they wish to drive. For example, they can say, "I want to take a nice mountain road." The device picks up this speech through the microphone and converts it into text data using speech recognition technology. Specifically, the Google Cloud Speech-to-Text API is used. The converted text data is then sent to the server.

[1134] The server first retrieves the user's attribute information (past driving data and preferences) from a database based on the user's ID. This database is typically a relational database management system (RDBMS) such as Amazon RDS or MySQL. It also retrieves the user's current GPS data to confirm their location.

[1135] The server then searches a database for information on the surrounding scenery, tourist attractions, and past driver feedback, and extracts several route candidates, including information on scenic beauty, safety, and tourist attraction popularity.

[1136] Based on this information, the server runs an algorithm to generate the optimal route. This algorithm is implemented in Python and uses machine learning libraries (e.g., TensorFlow). The algorithm combines the acquired landscape information, tourist spot information, and rating information to calculate a "pleasantness" score for each route. This score takes into account factors such as scenic beauty, safety, traffic volume, and travel time. A score is calculated for each route candidate, and the route with the highest score is selected. Detailed directions for that route are then generated.

[1137] The generated route information is sent to the device, which then begins navigating the user based on this information. Specifically, a detailed map and directions are displayed on the device screen, and voice instructions are given using the Google Text-to-Speech API to instruct the user on the next action. For example, directions such as "Turn right in 100 meters. This is the road with a beautiful view of the lake" are provided.

[1138] While the user is enjoying the drive, the device monitors the user's driving data in real time and provides an interface where the user can input feedback. For example, if the user inputs feedback such as "This road is great," it will be sent to the server.

[1139] The server analyzes the feedback it receives and reevaluates the route information, possibly recalculating a new route and sending the updated route information back to the device. The device then updates its navigation based on this new route information, providing new guidance such as, "Up ahead, if you turn right, there's a scenic road."

[1140] The server then stores the feedback data in a database and uses it to train the AI ​​model for future route generation, allowing the system to continually evolve and propose more accurate, "comfortable" driving routes.

[1141] Specific examples

[1142] As a specific example, the prompt sentence in which the user utters "I want to walk along a pleasant mountain path" is shown below.

[1143] Prompt Sentence Examples

[1144] User ID: 12345

[1145] Current location: Latitude 34.052235, Longitude -118.243683

[1146] Route request: I want to go through a pleasant mountain path.

[1147] Past preferences: I like mountain roads, and prefer roads with little traffic.

[1148] Based on this information, the system generates an optimal driving route and provides real-time navigation.

[1149] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1150] Step 1:

[1151] The user starts the car navigation system and inputs the route they wish to drive by speech. For example, they might say, "I want to take a nice mountain road." The input data is the user's voice. The output data after conversion is speech data.

[1152] Step 2:

[1153] The device receives this speech through a microphone and converts it into text data using speech recognition technology. Specifically, it uses the Google Cloud Speech-to-Text API. The input voice data is output as text data.

[1154] Step 3:

[1155] The terminal sends the converted text data (e.g., "I want to walk along a pleasant mountain path") to the server using the HTTPS protocol. The input data is the text data, and the output data is the result of sending the text data to the server.

[1156] Step 4:

[1157] The server retrieves the user's attribute information (past driving data and preferences) from the database based on the user's ID. It also retrieves GPS data and confirms the user's current location. The input data is the user ID and current GPS data, and the output data is attribute information and current location data.

[1158] Step 5:

[1159] The server queries a database of landscape information around the current location, tourist spots, and past driver feedback information to extract several route candidates. The input data is the current location and user attribute information, and the output data is multiple route candidates.

[1160] Step 6:

[1161] The server uses a machine learning algorithm implemented in Python (e.g., TensorFlow) to calculate a "feel-good" score for each route candidate. The score calculation takes into account factors such as scenic beauty, safety, traffic volume, and travel time. The input data are the route candidates, and the output data is the "feel-good" score for each route.

[1162] Step 7:

[1163] The server selects the route with the highest score based on the calculated scores and generates detailed directions for it. Specifically, it uses a map data API (e.g., Google Maps API). The input data are the "comfort" score and route candidates, and the output data are detailed directions.

[1164] Step 8:

[1165] The server sends the generated route information to the terminal. Communication is via HTTPS. The input data is the detailed route information, and the output data is the transmission result to the terminal.

[1166] Step 9:

[1167] The device displays the received route information on the screen and provides voice guidance for the next action using the Google Text-to-Speech API. For example, guidance such as "Turn right after 100 meters. This is the road with a beautiful lake view" is provided. The input data is detailed route information, and the output data is navigation guidance for the user.

[1168] Step 10:

[1169] The user inputs a piece of feedback during navigation, for example, "This road is great." The input data is the user's feedback, and the output data is the text data of the feedback.

[1170] Step 11:

[1171] The terminal obtains the user's feedback and sends it to the server. The input data is the text data of the feedback, and the output data is the result of the transmission to the server.

[1172] Step 12:

[1173] The server analyzes the received feedback and reevaluates the route information if necessary, possibly recalculating a new route. The input data is the feedback, and the output data is the reevaluated route information.

[1174] Step 13:

[1175] The server sends the updated route information to the terminal, again using the HTTPS protocol. The input data is the re-evaluated route information, and the output data is the result sent to the terminal.

[1176] Step 14:

[1177] The device updates the navigation based on the new route information and provides new guidance such as, "If you turn right up ahead, there is a road with a beautiful view." The input data is the reevaluated route information, and the output data is the updated navigation information.

[1178] (Application example 1)

[1179] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1180] Conventional car navigation systems focus on providing the shortest or fastest route to a destination. However, there are few systems that provide "comfortable" routes that allow users to enjoy the drive itself. Furthermore, systems for autonomous vehicles that navigate routes that reflect user preferences are still immature. In these circumstances, there is a need to maximize the comfort and enjoyment users feel while driving.

[1181] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1182] In this invention, the server includes means for speech recognition of a user's speech and converting it into text data, means for transmitting the text data, means for acquiring user attribute information and current location information and consulting a database, means for generating an optimal route based on scenic information, tourist spots, and rating information, means for presenting the generated route, means for acquiring and transmitting user feedback, means for analyzing the feedback and reevaluating the route information, means for updating route instructions based on the reevaluated route information, means for updating and learning from the database using the acquired feedback data, and means for being installed in an autonomous vehicle and for automatically traveling a "pleasant" route specified by the user. This enables the user to take a "pleasant" route during a drive, providing a comfortable and enjoyable driving experience.

[1183] The "means for recognizing user speech and converting it into text data" refers to a device or software that has the function of recognizing what the user has said and converting it from speech to text data.

[1184] The "means for transmitting the text data to a server" refers to a device or software that has the function of transmitting the converted text data to a server using a communication means such as the Internet.

[1185] "Means for obtaining user attribute information and current GPS data and querying a database" refers to devices or software that have the function of obtaining past data and current location information about a user and searching a database based on that data.

[1186] "Means for generating optimal routes based on scenery information, tourist spots, and evaluation information" refers to devices or software that have the function of calculating the optimal driving route for a user based on the surrounding scenery, tourist spots, and past evaluation data.

[1187] The "means for navigating the generated route to the user" refers to a device or software that has the function of providing visual and audio guidance to the user based on the calculated route.

[1188] The "means for acquiring user feedback and transmitting it to the server" refers to a device or software that has the function of collecting user evaluations and comments about the route and transmitting them to the server.

[1189] The "means for analyzing feedback and re-evaluating route information" refers to a device or software that has the function of analyzing collected feedback and recalculating the route evaluation based on that feedback.

[1190] The "means for updating navigation based on reevaluated route information" refers to a device or software that has the function of updating the current navigation route to reflect the reevaluated route information.

[1191] "Means for updating the database using acquired feedback data and training the AI ​​model" refers to devices or software that have the function of adding feedback data acquired from users to the database and training the AI ​​model based on that data.

[1192] "A means to be installed in an autonomous vehicle and to automatically drive along a 'comfortable' route specified by the user" is a system that has the function of allowing an autonomous vehicle to automatically drive along a comfortable route specified by the user.

[1193] The present invention is a system for automatically driving a self-driving vehicle along a "pleasant" route designated by a user. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described in detail below with reference to the accompanying drawings.

[1194] First, the user starts the car navigation system via a smartphone or on-board display and inputs the route they wish to drive by speech. For example, they can say, "I want to take a nice road along the sea." The device picks up this speech through the microphone and converts it into text data using the SpeechRecognition library. This text data is then sent to the server via the Internet.

[1195] The server first retrieves the user's attribute information (past driving data and preferences) from a database based on the user's ID. It also retrieves the user's current GPS data to confirm the user's location. Next, it queries the database for information on the surrounding scenery, tourist spots, and feedback from past drivers, and extracts several route candidates.

[1196] Based on this information, the server runs an algorithm to generate the optimal route. This algorithm combines the acquired scenic information, tourist spots, and rating information to calculate a "pleasantness" score for each route. The score takes into account factors such as scenic beauty, safety, traffic volume, and travel time. After calculating the score for each route candidate, the server selects the route with the highest score and generates detailed directions for it.

[1197] The generated route information is sent to the device, which then begins navigating the user based on this information. Specifically, a detailed map and route directions are displayed on the device screen, and voice guidance is provided to instruct the user on the next action. For example, guidance such as "Turn right in 100 meters. This is the road with a beautiful view of the lake" is provided.

[1198] While the user is enjoying the drive, the device monitors the user's driving data in real time and provides an interface where the user can input feedback. For example, if the user inputs feedback such as "This road is great," it is sent to the server.

[1199] The server analyzes the feedback it receives and reevaluates the route information, possibly recalculating a new route and sending the updated route information back to the device. The device then updates its navigation based on this new route information, providing new guidance such as, "Up ahead, if you turn right, there's a scenic road."

[1200] Furthermore, the server stores the feedback data in a database and uses it to train the generative AI model for future route generation. This allows the system to constantly evolve and propose more accurate and "comfortable" driving routes.

[1201] As a specific example, a prompt sentence such as "Please tell me the best route if I want to take a pleasant road along the seashore" can be used.

[1202] In this way, the car navigation system of the present invention provides a very useful service to users who enjoy driving by generating and navigating the optimal driving route in real time based on the user's spoken requests.

[1203] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1204] Step 1:

[1205] A user starts a car navigation system via a smartphone or an in-car display and utters, "I want to take a nice road along the sea." The input is the user's voice data, and the output is the voice data being input to the device.

[1206] Step 2:

[1207] The device receives the user's speech through a microphone and converts the speech data into text using the SpeechRecognition library. The input is the user's speech data, and the output is the data converted from speech to text.

[1208] Step 3:

[1209] The terminal transmits the converted text data to a server via the Internet. The input is the text data, and the output is the text data transmitted to the server.

[1210] Step 4:

[1211] The server retrieves the user's attribute information (past driving data and preferences) from a database based on the user's ID. It also retrieves current GPS data. The input is text data and GPS data, and the output is the user's attribute information and current location information.

[1212] Step 5:

[1213] The server uses the user's attribute information and current location to query a database for information on the surrounding scenery, tourist spots, and past driver feedback, and extracts several route candidates. The input is the user's attribute information and current location information, and the output is route candidate information.

[1214] Step 6:

[1215] The server runs an algorithm to generate the optimal route based on the acquired information. This algorithm calculates a "pleasantness" score for each route. The inputs are landscape information, tourist spots, and rating information, and the output is the optimal route information.

[1216] Step 7:

[1217] The server sends the optimal route information to the terminal. The input is the optimal route information, and the output is the route information sent to the terminal.

[1218] Step 8:

[1219] The device then begins navigating the user based on the route information it receives. Specifically, it displays a detailed map and directions on the screen and provides voice guidance to guide the user on their next steps. The input is route information, and the output is navigation instructions for the user.

[1220] Step 9:

[1221] While the user is enjoying the drive, the terminal monitors the user's driving data in real time and provides an interface to obtain feedback from the user. The input is the user's driving data and feedback, and the output is the collected driving data and feedback information.

[1222] Step 10:

[1223] The terminal sends the collected feedback information to the server. The input is the feedback information, and the output is the data sent to the server.

[1224] Step 11:

[1225] The server analyzes the received feedback and re-evaluates the route information. The input is the feedback information and the output is the re-evaluated route information.

[1226] Step 12:

[1227] The server sends the re-evaluated route information to the terminal, which then updates the navigation. The input is the re-evaluated route information, and the output is the updated navigation information.

[1228] Step 13:

[1229] The server stores the obtained feedback data in a database and trains the generative AI model. The input is the feedback data, and the output is an updated AI model and database.

[1230] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1231] The present invention combines a car navigation system that generates a "pleasant" route for the user to enjoy driving itself with an emotion engine that recognizes the user's emotions. The embodiments of the present invention will be described in detail below.

[1232] First, the user starts the car navigation system and verbally inputs the route they wish to drive. For example, they can say, "I want to take a pleasant mountain road." The device picks up this speech through a microphone and converts it into text data using speech recognition technology. This text data is then sent directly to the server.

[1233] The server first retrieves the user's attribute information (past driving data and preferences) from a database based on the user's ID. It then retrieves GPS data showing the user's current location. At the same time, it evaluates the user's current emotional state using an emotion engine that recognizes emotions from the user's speech and behavior.

[1234] The server uses GPS data to query a database of surrounding scenery, tourist spots, and past driver feedback to identify potential routes. Based on this information, the server runs an algorithm to generate the optimal route. The algorithm combines the acquired scenery, tourist spots, and rating information to calculate a "pleasantness" score for each route. The score takes into account factors such as scenic beauty, safety, traffic volume, and travel time.

[1235] In addition, the emotion engine also incorporates the user's emotional information into the score calculation. For example, if the user wants to relax, routes with less traffic and more natural scenery will be prioritized. The algorithm selects the route with the highest score as the optimal route and generates detailed directions for it.

[1236] The generated route information is sent to the device, which then begins navigating the user based on this information. Specifically, a detailed map and route directions are displayed on the device screen, and voice guidance is provided to instruct the user on the next action. Directions such as "Turn right in 100 meters. This is the road with a beautiful view of the lake" are provided.

[1237] While the user is enjoying the drive, the device monitors the user's driving data in real time and continuously evaluates the user's emotional state through an emotion engine. At the same time, it provides an interface where the user can input feedback. For example, if the user inputs feedback such as "This road is great," it will be sent to the server.

[1238] The server analyzes the received feedback and reevaluates the route information. In some cases, it recalculates a new route and sends the updated route information to the device, taking into account the emotion engine's evaluation results. The device then updates its navigation system based on this new route information and provides new guidance, such as "If you turn right up ahead, there's a road with a beautiful view."

[1239] The server then stores the acquired feedback data and emotional information in a database and trains the AI ​​model to use it for future route generation. This allows the system to continually evolve and provide a "feel-good" driving route that reflects the user's emotional state and preferences.

[1240] In this way, the car navigation system of the present invention provides a very useful service to users who enjoy driving by generating and navigating the optimal driving route in real time based on the user's spoken requests and emotional information.

[1241] The processing flow will be explained below.

[1242] Step 1:

[1243] The user starts the car navigation system and verbally inputs the route he or she wants to drive. For example, he or she says, "I want to take a nice mountain road."

[1244] Step 2:

[1245] The terminal captures the user's voice through a microphone.

[1246] Step 3:

[1247] The device converts the acquired voice data into text data using voice recognition technology, and sends the converted text data, "I want to walk along a pleasant mountain path," to the server.

[1248] Step 4:

[1249] The server retrieves the user's attribute information (past driving data and preferences) from the database based on the user's ID, and simultaneously retrieves GPS data showing the user's current location.

[1250] Step 5:

[1251] The server uses an emotion engine to recognize the user's current emotional state from their speech and behavior, for example, assessing whether they want to relax or seek stimulation.

[1252] Step 6:

[1253] Based on the GPS data, the server searches a database for information on scenery around the current location, tourist spots, highly rated driving routes, and more.

[1254] Step 7:

[1255] The server runs an algorithm to generate the optimal route based on the acquired scenic, tourist spot, and rating information. The algorithm calculates a "feel-good" score for each route, taking into account factors such as scenic beauty, safety, traffic volume, and travel time.

[1256] Step 8:

[1257] The server adjusts the score calculation based on the emotional information obtained from the emotion engine. For example, if the user wants to relax, routes with less traffic and more natural scenery will be prioritized.

[1258] Step 9:

[1259] The server selects the route with the highest score as the optimal route and generates detailed directions for it, which are then sent to the device.

[1260] Step 10:

[1261] The device then begins navigating the user based on the route information it receives. Specifically, it displays a map and route on the device screen and provides voice guidance on the next action, such as "Turn right in 100 meters. This is the road with a beautiful view of the lake."

[1262] Step 11:

[1263] The device monitors the user's driving data in real time, continuously evaluates the user's emotional state through an emotion engine, and provides an interface for users to input feedback.

[1264] Step 12:

[1265] The device transmits the acquired feedback and emotion data to the server.

[1266] Step 13:

[1267] The server analyzes the received feedback and sentiment data, reevaluates the route information, and recalculates a new route if necessary.

[1268] Step 14:

[1269] The server sends the updated route information to the device, which also reflects the evaluation results of the emotion engine.

[1270] Step 15:

[1271] The device receives the new route information and updates the navigation, providing new directions such as, "Up ahead, if you turn right, there's a scenic road."

[1272] Step 16:

[1273] The server stores the acquired feedback data and emotional information in a database and uses it to train the AI ​​model for future route generation. This allows the system to evolve and improve its ability to suggest more accurate and "comfortable" driving routes.

[1274] Example 2

[1275] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1276] Conventional car navigation systems lack sufficient consideration for the user's emotional state and individual preferences, resulting in incomplete navigation for a specific route. Furthermore, their lack of functionality for incorporating user feedback in real time makes it difficult for users to fully enjoy their drive. To address these issues, a system is needed that can generate and update optimal driving routes in real time, taking into account the user's emotional state and preferences.

[1277] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for converting a user's utterance into text data using a voice recognition means, means for transmitting the text data to the server, means for acquiring user attribute information and current location information and consulting a database, means for generating an optimal route based on scenic information, tourist attractions, and feedback information, means for navigating the generated route for the user, means for acquiring user feedback and transmitting it to the server, means for analyzing the feedback and reevaluating the route information, means for updating the navigation based on the reevaluated route information, means for updating the database using the acquired feedback data and emotion information and training the artificial intelligence model, and means for using an emotion recognition engine that recognizes emotions from the user's utterances and actions. This enables the generation and navigation of an optimal route in real time based on the user's emotional state and preferences.

[1278] The "voice recognition means" is a technology that acquires the user's speech through a microphone and converts it into text data using voice recognition technology.

[1279] The "server" is a computer system that has data processing, analysis, and storage functions, manages user attribute information, current location information, scenic information, tourist spots, and feedback information, and generates routes and performs navigation.

[1280] "User attribute information" refers to information including the user's past driving data and preferences, and is data stored in a database.

[1281] "Current location information" refers to the user's current geographical location data obtained using location identification technology such as a GPS module.

[1282] The "database" is an information management system that stores user attribute information, current location information, landscape information, tourist spots, and feedback information, and allows the server to query and use this information.

[1283] "Landscape information" refers to information about natural and artificial landscapes in a particular geographical location.

[1284] A "tourist destination" is a place or facility that is a tourist attraction in a particular area.

[1285] "Feedback information" is information relating to opinions and impressions provided by the user while driving.

[1286] "Route generation means" is a technology that calculates and suggests the optimal driving route for the user based on the acquired data.

[1287] "Navigation means" is a technology that instructs the user on the next action by voice or on-screen display based on the generated route.

[1288] The "means for obtaining feedback" is a technique for obtaining feedback information input by a user and transmitting it to a server.

[1289] The "means for analyzing feedback" is a technique for analyzing the acquired feedback information on the server and reevaluating the route information.

[1290] The "means for updating navigation" is a technique for updating navigation information provided to a user based on reevaluated route information.

[1291] An "artificial intelligence model" is an algorithm or model that learns using captured feedback data and emotional information to help generate future routes.

[1292] An "emotion recognition engine" is a technology or algorithm for recognizing and evaluating emotions from a user's speech and behavior.

[1293] This invention relates to a car navigation system that generates an optimal driving route based on the user's emotional state and preferences, and navigates the user in real time. This system improves the user's driving experience by combining a voice recognition means, an emotion recognition engine, and an artificial intelligence model.

[1294] First, the user starts the car navigation system and inputs their desired driving route by voice. For example, they might say, "I want to take a pleasant mountain road." The device picks up this speech through the microphone and converts it into text data using voice recognition technology (for example, a general voice recognition API). This text data is then sent directly to the server.

[1295] The server retrieves the user's attribute information (past driving data and preferences) from a database based on the user's ID. At the same time, it uses a GPS module to identify the user's current location. It then uses an emotion recognition engine (e.g., a general emotion analysis API) to evaluate the user's current emotional state. This allows it to recognize emotions from the user's speech and behavior, and uses this information to help generate a route.

[1296] The server then uses the acquired GPS data and information from a database to query the surrounding landscape (e.g., open-source map data), tourist attractions, and past driver feedback. It then uses a route generation algorithm (e.g., A algorithm or Dijkstra algorithm) to integrate this information and calculate a "pleasantness" score for each route, which includes scenic beauty, safety, traffic volume, and travel time.

[1297] Once the optimal route is generated, the information is sent to the device, which displays a detailed map and directions to the user, and provides voice instructions such as, "Turn right in 100 meters. This is the road with a beautiful lake view." This allows the user to be navigated in real time to their destination.

[1298] While the user is enjoying the drive, the device monitors driving data in real time and continuously evaluates the user's emotional state through an emotion recognition engine. At the same time, the user can input feedback through the interface. For example, if the user inputs feedback such as "This road is great," the information is sent to the server.

[1299] The server analyzes the received feedback and reevaluates the route information. If necessary, it recalculates a new route and sends the updated route information to the device, taking into account the evaluation results of the emotion recognition engine. The device then guides the user along the new route. For example, it provides new guidance such as, "If you turn right up ahead, there is a road with a beautiful view."

[1300] Furthermore, the server stores the acquired feedback data and emotional information in a database and trains a generative AI model (e.g., a typical deep learning model) on it, allowing the system to continually evolve and provide a "comfortable" driving route that reflects the user's emotional state and preferences.

[1301] Specific examples

[1302] When a user voice-inputs something like "I want to drive while looking at the beautiful coastline," the device converts it into text using voice recognition technology and sends it to the server. The server takes into account past driving data, preferences, and current emotional state to generate a scenic route along the coastline. The device then guides the user, saying, "Turn left in 3 kilometers and you'll find a beautiful drive with ocean views." If, during the drive, the user gives feedback such as "This road is wonderful," the server uses that information to generate a more optimized route.

[1303] Example prompts for generative AI models

[1304] "If a user wants to relax, what factors would you consider to suggest the best driving route?"

[1305] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1306] Step 1:

[1307] The user starts the car navigation system and verbally inputs the route they want to drive. For example, they might say, "I want to take a nice mountain road." This utterance becomes the input.

[1308] Step 2:

[1309] The device picks up the user's speech through a microphone and converts it into text data using speech recognition technology. Specifically, it uses a speech recognition API. The input is the speech and the output is text data. This text data is sent directly to the server.

[1310] Step 3:

[1311] The server retrieves the user's attribute information (past driving data and preferences) from a database based on the user's ID. It also identifies the user's current location using a GPS module. The input is the user ID and GPS data, and the output is the user's attribute information and current location information.

[1312] Step 4:

[1313] The server uses an emotion recognition engine to evaluate the user's current emotional state. This is a process of analyzing emotions from the user's voice and behavior. The input is the user's voice data and behavior data, and the output is the evaluation result of the user's emotional state.

[1314] Step 5:

[1315] The server uses the acquired GPS data and information from the database to query information on the surrounding scenery, tourist spots, and past driver feedback. This provides the data necessary for route generation. The input is the GPS data and information from the database, and the output is the query results.

[1316] Step 6:

[1317] The server uses a route generation algorithm to calculate a "feel good" score for each route based on the query results. The algorithm uses A or Dijkstra's algorithm. The input is the query results, and the output is the "feel good" score.

[1318] Step 7:

[1319] The server selects the route with the highest score as the optimal route and generates detailed directions for it. The input is the "feel good" score, and the output is detailed directions for the optimal route. This information is sent to the device.

[1320] Step 8:

[1321] The device provides the user with detailed maps and voice guidance based on the route information sent from the server. For example, it provides navigation such as "Turn right 100 meters ahead." The input is detailed directions for the optimal route, and the output is navigation information.

[1322] Step 9:

[1323] While the user is enjoying the drive, the device monitors driving data in real time and continuously evaluates the user's emotional state through an emotion recognition engine. The input is driving data and emotion data, and the output is a real-time emotion evaluation result.

[1324] Step 10:

[1325] The user inputs feedback through the interface, such as "This road is great." The device then sends this feedback to the server.

[1326] Step 11:

[1327] The server analyzes the received feedback and re-evaluates the route information. The input is the feedback information and the output is the re-evaluated route information.

[1328] Step 12:

[1329] The server recalculates a new route if necessary and sends the updated route information to the device, which then updates its navigation based on the new route information and provides new instructions to the user. The input is the re-evaluated route information, and the output is the updated navigation information.

[1330] Step 13:

[1331] The server stores the acquired feedback data and emotional information in a database and trains the generative AI model based on this data. This allows the system to evolve future route generation to better reflect the user's emotional state and preferences. The input is feedback data and emotional information, and the output is the trained generative AI model.

[1332] (Application example 2)

[1333] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1334] In modern society, the demand for food delivery is increasing, resulting in an increased workload for delivery drivers. However, conventional navigation systems do not provide route suggestions that take into account the emotional state of delivery drivers, resulting in situations where stress and fatigue easily accumulate. Therefore, there is a need for route guidance that recognizes the emotional state of delivery drivers in real time and reduces their stress. The present invention solves this problem.

[1335] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1336] In this invention, the server includes means for speech recognition of a user's speech and converting it into text data, means for transmitting the text data to the server, means for acquiring the user's attribute information and current GPS data and consulting a database, means for generating an optimal route based on scenic information, tourist spots, and rating information, means for navigating the generated route to the user, means for acquiring user feedback and transmitting it to the server, means for analyzing the feedback and reevaluating the route information, means for updating the navigation based on the reevaluated route information, means for updating the database using the acquired feedback data and training the AI ​​model, means for recognizing the emotional state of a delivery driver in real time and providing an optimal route based on the driver's emotions, and means for selecting roads with good scenery or low traffic to reduce stress. This enables optimal route guidance based on the emotional state of the delivery driver, reducing driver stress and improving work efficiency.

[1337] A "user" is an individual or organization that uses the system.

[1338] An "utterance" is a spoken instruction or request made by a user to the system.

[1339] "Speech recognition" is a technology that converts a user's speech into text data.

[1340] "Text data" is character string information converted using voice recognition technology.

[1341] A "server" is a computer system that performs data processing and analysis.

[1342] "Attribute information" is individual information including a user's preferences, past behavior history, and the like.

[1343] "GPS data" is Global Positioning System data that indicates the user's current location.

[1344] A "database" is a system for storing and managing information.

[1345] "Scenery information" is data about the scenery and natural environment around the route.

[1346] "Tourist spots" are places or famous places that tourists should visit.

[1347] "Rating information" is feedback and opinion data collected from past users.

[1348] A "route" is a path from a starting point to a destination.

[1349] "Navigation" is an action or function that provides directions for a user to reach a destination.

[1350] "Feedback" refers to the impressions and evaluations provided by users after use.

[1351] "Reevaluation" is the act of reviewing a previous evaluation based on collected feedback.

[1352] "Navigation update" refers to updating the guidance information to the latest version based on new route information.

[1353] An "AI model" is a computational model for learning and prediction in artificial intelligence.

[1354] "Emotional state" is the user's current mental and emotional state.

[1355] "Real-time" means that data is processed and reflected immediately.

[1356] "Stress" refers to the psychological pressure or fatigue felt by the user.

[1357] A "scenic road" is a route that is rich in natural scenery and visually enjoyable.

[1358] A "low volume road" is a route with few vehicles on it, making it safe and relaxing.

[1359] This invention is a system that generates and navigates optimal routes based on the emotional state of delivery drivers. This system operates through the cooperation of a terminal and a server.

[1360] First, the delivery driver, who is the user, speaks to the device. The speech might be something like, "I'm tired, so I'd like a relaxing route." The device then receives this speech and converts it into text data using speech recognition technology. This speech recognition technology uses a service such as the Google Speech-to-Text API.

[1361] The converted text data is then sent to a server. The server retrieves attribute information from a database based on the user's ID. This attribute information includes past driving data and individual preferences. The server also retrieves GPS data sent from the device to determine the user's current location.

[1362] The server then uses the speech data sent from the device to activate an emotion engine to evaluate the user's emotional state. This emotion engine uses, for example, the Microsoft Azure Emotion API. The acquired emotional state is evaluated as "tired."

[1363] Next, the server retrieves information about the scenery around the current location, tourist spots, and feedback from past drivers from a database to generate route candidates. Route candidates can be obtained using the Google Maps API. The generated routes are scored for "comfort" based on an evaluation algorithm. This score takes into account factors such as scenic beauty, low traffic volume, and safety.

[1364] The evaluation algorithm also reflects the user's emotional state, as determined by the emotion engine. For example, if the user is "tired," scenic routes and routes with less traffic will be prioritized. Ultimately, the route with the highest score is selected and sent to the device.

[1365] The device will then begin navigating the user based on the selected route information. Specifically, it will display a map and provide voice guidance. For example, "Turn right in 100 meters. This road will take you past a scenic lake."

[1366] While the user is driving, the device continues to monitor driving data in real time. At the same time, it continuously evaluates the user's emotional state through the emotion engine. As a result, if the user inputs feedback such as "This road is great," the feedback will be sent to the server.

[1367] The server analyzes the received feedback and reevaluates the route information, possibly calculating a new route and sending it back to the device. This reevaluation allows the system to continually evolve and more accurately reflect the user's emotional state and preferences.

[1368] The hardware used includes smartphones (iOS, Android) and their built-in GPS and microphones, while the software uses a speech recognition API (Google Speech-to-Text API), an emotion engine API (Microsoft Azure Emotion API), and a navigation API (Google Maps API).

[1369] Examples of prompts:

[1370] "If a user is looking to relax, they should search for routes with beautiful scenery."

[1371] Based on this embodiment, delivery drivers can receive optimal route guidance according to their emotional state, enabling them to work efficiently while reducing stress during work.

[1372] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1373] Step 1:

[1374] The device receives the user's speech. When the user says, "I'm tired, so I'd like a relaxing route," the device's microphone captures this speech. The input is the user's speech, and the output is audio data.

[1375] Step 2:

[1376] The device converts the acquired voice data into text data using voice recognition technology. This process uses the Google Speech-to-Text API. The input is voice data, and the output is text data such as "I'm tired, so I'd like a relaxing route."

[1377] Step 3:

[1378] The terminal transmits the converted text data to the server. The input is the text data, and the output is the result of transmitting the text data to the server.

[1379] Step 4:

[1380] The server retrieves attribute information from the database based on the user's ID. The attribute information includes past driving data and individual preferences. The input is the user's ID, and the output is attribute information.

[1381] Step 5:

[1382] The server receives the GPS data sent from the device and identifies the user's current location. The input is the GPS data and the output is the user's current location.

[1383] Step 6:

[1384] The server inputs the text data sent from the device into the emotion engine and evaluates the user's emotional state. This uses the Microsoft Azure Emotion API. The input is text data, and the output is an emotional state such as "tired."

[1385] Step 7:

[1386] The server retrieves information about the scenery around the current location, tourist spots, and past feedback from a database and generates route candidates using the Google Maps API. The input is the current location and destination, and the output is a list of route candidates.

[1387] Step 8:

[1388] The server runs a rating algorithm to calculate a "feel-good" score for each route candidate. The rating algorithm takes into account scenic beauty, traffic volume, safety, and the user's emotional state. The input is the list of route candidates and the user's emotional state, and the output is a score for each route.

[1389] Step 9:

[1390] The server selects the route with the highest score and sends it to the terminal. The input is the route score, and the output is the selected optimal route.

[1391] Step 10:

[1392] The device then begins navigation based on the selected route information. Specifically, it displays a map and provides voice guidance. For example, "Turn right after 100 meters. This road passes by a scenic lake." The input is the optimal route information, and the output is navigation instructions.

[1393] Step 11:

[1394] The device monitors driving data in real time while driving and continuously evaluates the user's emotional state through an emotion engine. The input is driving data and emotional state, and the output is the continuous emotion evaluation result.

[1395] Step 12:

[1396] When the user inputs feedback, the terminal sends the feedback to the server. The input is the user's feedback, and the output is the result of the transmission to the server.

[1397] Step 13:

[1398] The server analyzes the received feedback, reevaluates the route information, and possibly recalculates a new route and sends it to the device. The input is the feedback, and the output is the reevaluated route.

[1399] Step 14:

[1400] The server updates the database using the feedback data it receives and trains the AI ​​model. The input is the feedback data, and the output is the updated AI model.

[1401] Examples of prompts:

[1402] "If a user is looking to relax, they should search for routes with beautiful scenery."

[1403] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1404] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1405] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1406] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1407] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1408] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1409] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1410] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1411] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1412] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1413] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1414] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1415] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1416] 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.

[1417] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1418] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1419] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1420] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1421] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1422] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1423] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1424] The following is further disclosed regarding the above embodiment.

[1425] (Claim 1)

[1426] A means for recognizing a user's speech and converting it into text data;

[1427] means for transmitting the text data to a server;

[1428] A means for obtaining user attribute information and current GPS data and querying a database;

[1429] A means for generating optimal routes based on landscape information, tourist spots, and rating information;

[1430] means for navigating the generated route to a user;

[1431] means for obtaining and transmitting user feedback to a server;

[1432] a means for analyzing the feedback and re-evaluating the route information;

[1433] a means for updating navigation based on the reevaluated route information;

[1434] a means for updating the database and training the AI ​​model using the obtained feedback data;

[1435] A system including:

[1436] (Claim 2)

[1437] 10. The system of claim 1, further comprising means for obtaining data based on user preferences from a database and generating an optimized route for each user.

[1438] (Claim 3)

[1439] 2. The system according to claim 1, further comprising means for calculating a "pleasant" score based on the acquired scenery information and rating information, and providing an optimal route.

[1440] (Claim 4)

[1441] 10. The system of claim 1, further comprising means for monitoring a user's driving situation in real time and dynamically adjusting navigation.

[1442] "Example 1"

[1443] (Claim 1)

[1444] A means for recognizing a user's speech and converting it into text data;

[1445] means for transmitting the text data to a server;

[1446] means for obtaining user attribute information and current location data and querying a database;

[1447] A means for generating an optimal route based on landscape information, tourist spots, and rating information;

[1448] means for guiding a user along the generated route;

[1449] means for obtaining and transmitting user feedback to a server;

[1450] a means for analyzing the feedback and re-evaluating the route information;

[1451] means for updating guidance based on the reevaluated route information;

[1452] a means for updating the database using the obtained feedback data and training the artificial intelligence model;

[1453] A system including:

[1454] (Claim 2)

[1455] 10. The system of claim 1, further comprising means for obtaining data based on user preferences from a database and generating an optimized route for each user.

[1456] (Claim 3)

[1457] 10. The system of claim 1, further comprising means for calculating a "pleasant" score based on the acquired scenery information and rating information, and providing an optimal route.

[1458] "Application Example 1"

[1459] (Claim 1)

[1460] A means for recognizing a user's speech and converting it into text data;

[1461] means for transmitting the text data to a server;

[1462] A means for obtaining user attribute information and current GPS data and querying a database;

[1463] A means for generating optimal routes based on landscape information, tourist spots, and rating information;

[1464] means for navigating the generated route to a user;

[1465] means for obtaining and transmitting user feedback to a server;

[1466] a means for analyzing the feedback and re-evaluating the route information;

[1467] a means for updating navigation based on the reevaluated route information;

[1468] a means for updating the database and training the AI ​​model using the obtained feedback data;

[1469] It is installed in an autonomous vehicle and is a means of automatically driving along a "comfortable" route specified by the user.

[1470] A system including:

[1471] (Claim 2)

[1472] 10. The system of claim 1, further comprising means for obtaining data based on user preferences from a database and generating an optimized route for each user.

[1473] (Claim 3)

[1474] 2. The system according to claim 1, further comprising means for calculating a "pleasant" score based on the acquired scenery information and rating information, and providing an optimal route.

[1475] "Example 2: Combining Emotion Engines"

[1476] (Claim 1)

[1477] means for converting a user's speech into text data using a speech recognition means;

[1478] means for transmitting the text data to a server;

[1479] A means for acquiring user attribute information and current location information and querying a database;

[1480] A means for generating an optimal route based on scenic information, tourist spots, and feedback information;

[1481] means for navigating the generated route to a user;

[1482] means for obtaining and transmitting user feedback to a server;

[1483] a means for analyzing the feedback and re-evaluating the route information;

[1484] a means for updating navigation based on the reevaluated route information;

[1485] means for updating the database and training the artificial intelligence model using the obtained feedback data and emotion information;

[1486] A means for using an emotion recognition engine that recognizes emotions from the user's utterances and actions;

[1487] A system including:

[1488] (Claim 2)

[1489] 10. The system of claim 1, further comprising means for obtaining data based on user preferences from a database and generating an optimized route for each user.

[1490] (Claim 3)

[1491] 2. The system of claim 1, further comprising means for calculating a "pleasant" score based on the acquired scenery information and rating information, and providing an optimal route taking into account the emotional state of the user.

[1492] "Application example 2 when combining emotion engines"

[1493] (Claim 1)

[1494] A means for recognizing a user's speech and converting it into text data;

[1495] means for transmitting the text data to a server;

[1496] A means for obtaining user attribute information and current GPS data and querying a database;

[1497] A means for generating optimal routes based on landscape information, tourist spots, and rating information;

[1498] means for navigating the generated route to a user;

[1499] means for obtaining and transmitting user feedback to a server;

[1500] a means for analyzing the feedback and re-evaluating the route information;

[1501] a means for updating navigation based on the reevaluated route information;

[1502] a means for updating the database and training the AI ​​model using the obtained feedback data;

[1503] A means of recognizing the emotional state of delivery drivers in real time and providing optimal routes based on the driver's emotions;

[1504] How to choose scenic or less trafficked roads to reduce stress,

[1505] A system including:

[1506] (Claim 2)

[1507] 10. The system of claim 1, further comprising means for obtaining data based on user preferences from a database and generating an optimized route for each user.

[1508] (Claim 3)

[1509] 2. The system according to claim 1, further comprising means for calculating a "pleasant" score based on the acquired scenery information and rating information, and providing an optimal route. [Explanation of symbols]

[1510] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for recognizing a user's speech and converting it into text data; means for transmitting the text data to a server; A means for obtaining user attribute information and current GPS data and querying a database; A means for generating optimal routes based on landscape information, tourist spots, and rating information; a means for navigating the generated route to a user; means for obtaining and transmitting user feedback to a server; a means for analyzing the feedback and re-evaluating the route information; a means for updating navigation based on the reevaluated route information; a means for updating the database and training the AI ​​model using the obtained feedback data; A system including:

2. 10. The system of claim 1, further comprising means for obtaining data based on user preferences from a database and generating an optimized route for each user.

3. 2. The system of claim 1, further comprising means for calculating a "pleasant" score based on the acquired scenery information and rating information, and providing an optimal route.

4. 10. The system of claim 1, further comprising means for monitoring a user's driving situation in real time and dynamically adjusting navigation.

Images