system

Abstract

We provide the system. [Solution] A voice conversion means for converting audio signals into text information, An analysis means for analyzing destination information from the aforementioned character information, A path calculation means that acquires dynamic information and calculates the optimal path, Information presentation means for presenting the optimal route, Means of providing voice guidance, Means for providing visual route display, A system that includes this.

Description

Technical Field

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

Background Art

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] Manually modifying a route due to changes in traffic conditions or operating a device to change the destination during driving is a factor that distracts the driver and poses a risk of compromising safety. Also, the selection of appropriate rest points affects driving comfort. However, conventional navigation systems have difficulty automatically and effectively managing these, and there is a need to support safer and more efficient driving.

Means for Solving the Problems

[0005] This invention includes a voice conversion means that converts voice data into text data, and an analysis means that can analyze the user's voice commands to set destination information. Furthermore, it includes a route calculation means that acquires real-time traffic data and calculates the optimal route. This provides a system equipped with a display means that dynamically corrects the route in response to changes in traffic conditions and presents it to the user. This system eliminates the need for the user to operate a device while driving, thereby improving safety. In addition, the analysis means can suggest rest stops, thereby improving driving comfort.

[0006] "Speech conversion means" refers to a device or software that has the function of acquiring speech data and converting it into text data.

[0007] "Analysis means" refers to a device or software that has the function of analyzing the user's intent from text data and extracting information about destinations and intermediate points.

[0008] "Real-time traffic data" refers to dynamic information that reflects current traffic conditions, including data on traffic congestion and accident occurrences.

[0009] A "route calculation means" is a device or software that has the function of calculating the optimal route to a destination based on acquired traffic data.

[0010] "Display means" refers to a device or software that has the function of presenting calculated route information or other notifications to the user visually or audibly.

[0011] A "rest stop" is a location proposed as a place where drivers can take a break while driving. [Brief explanation of the drawing]

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

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

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

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

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

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

[0018] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

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

[0020] [First Embodiment]

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

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

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

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

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

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

[0033] This invention is a navigation system that combines speech recognition technology and generative AI to help users move safely and efficiently. Embodiments of the invention are configured around speech conversion means, analysis means, route calculation means, and display means.

[0034] While driving, the user inputs their destination and waypoints by voice into the terminal. The terminal then uses a voice conversion device to convert the voice data into text data and sends that data to the server.

[0035] The server receives text data and uses an analysis tool to analyze the user's set destination and waypoints. Based on this analysis, the server uses real-time traffic data to calculate the optimal route to the destination.

[0036] The route calculation system dynamically generates the optimal route to avoid congestion and accidents based on traffic information acquired on the server. The calculated route information is sent to the terminal and notified to the user.

[0037] The terminal provides the user with route information received from the server through a display mechanism. This display includes voice guidance and visual route displays. Rest stop suggestions are also displayed on the terminal as information from the server, prompting the user to take breaks based on their selection.

[0038] For example, if a user instructs their device to "set Park Mall as the next destination," the voice conversion system converts the speech to text, and the server analyzes the intent. The server sets Park Mall as the destination and calculates the optimal route based on real-time traffic data. Route guidance is then displayed on the device, and notifications such as "Let's take a break at the next service area" are provided.

[0039] This invention allows users to maintain safety while driving, while keeping their gaze focused on the road. Furthermore, it enables efficient and comfortable driving by providing flexible route changes and rest stop suggestions according to traffic conditions.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user voice-inputs their destination into the device. The device then retrieves that voice data.

[0043] Step 2:

[0044] The terminal converts the audio data into text data using a speech conversion device. This text data is then sent to the server.

[0045] Step 3:

[0046] The server analyzes the received text data using an analysis tool and extracts information about the destination and intermediate points.

[0047] Step 4:

[0048] The server acquires real-time traffic data and uses that data to calculate the optimal route to the destination using a route calculation tool.

[0049] Step 5:

[0050] The calculated optimal route is sent from the server to the terminal.

[0051] Step 6:

[0052] The terminal displays the received optimal route information to the user via a display device. This includes voice guidance and visual route display.

[0053] Step 7:

[0054] While the user is driving, the server continuously monitors traffic conditions and recalculates the route as needed.

[0055] Step 8:

[0056] The server uses analysis tools to suggest rest locations and sends that information to the terminal. The terminal then notifies the user of the suggested rest locations.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] There is a need to provide a system that allows drivers to safely set their destination without taking their eyes off the road while traveling by car or other means, and to find an efficient route based on real-time traffic information. Furthermore, the system must be able to flexibly adapt to changes in traffic conditions.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] In this invention, the server includes a conversion means for converting voice information into text information, an analysis means for analyzing target location information from the text information, and a route calculation means for acquiring spatiotemporal information and calculating the optimal route. This makes it possible for the driver to set a destination without using their hands and to obtain a safe and optimal route based on traffic information.

[0062] "Voice information" refers to information acquired as voice data, including the user's voice instructions.

[0063] "Textual information" refers to text data converted from audio information, and is a data format used by systems for analysis.

[0064] "Conversion means" refers to a process or device that converts audio information into text information, and utilizes speech recognition technology.

[0065] "Analysis means" refers to a means for extracting information such as target locations from textual information, and for the system to understand and process it.

[0066] "Spatiotemporal information" refers to data that includes real-time traffic data and location information, and is used for route calculations.

[0067] A "path calculation means" is an algorithm or process that searches for and calculates the optimal path based on spatiotemporal information.

[0068] "Presentation means" refers to a device or method that visually or audibly notifies the user of the calculated optimal route.

[0069] "Device" refers to the entire system that integrates the above-mentioned means to support the user's safe and efficient travel to their destination.

[0070] This invention relates to a voice recognition-based navigation system that enables users to safely and efficiently set and reach their destinations while on the move. The system converts voice information into text information, calculates the optimal route using real-time spatiotemporal information, and provides that route to the user.

[0071] Performing voice input and conversion

[0072] Users can input their target location by voice while driving or using the terminal. This input is acquired through the terminal's built-in microphone. The terminal uses voice recognition software (e.g., a voice recognition cloud service) to convert the voice information into text. This conversion process is based on acoustic models and natural language processing techniques.

[0073] Data analysis and information provision

[0074] Text information is sent from the terminal to the server, which uses analysis tools to analyze the target location and necessary itinerary information. Based on the analyzed information, the optimal route is calculated, taking into account real-time traffic conditions and geographical data. The server can obtain the latest spatiotemporal information using external traffic information service APIs.

[0075] Route notification and display

[0076] Once the optimal route calculation is complete, the server sends that information to the terminal. The terminal provides the user with route information using visual map displays and voice guidance. This allows the user to efficiently navigate to their destination while keeping their eyes on the road, even while driving.

[0077] Specific examples and prompt statements

[0078] As a concrete example, suppose a user gives a voice command to their device saying, "Set the next destination to the shopping center." This voice command is converted into text, the server analyzes the intent, and sets the shopping center as the target location. Then, the optimal route based on actual traffic conditions is calculated, and the directions are displayed on the device.

[0079] As an example of a prompt, inputting "Design an algorithm that analyzes the user-specified destination and calculates the shortest route using the latest traffic information" into the generating AI model can optimize the analysis and route calculation process.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The user gives voice commands to the terminal to set a destination. For example, the user might say, "Set the next destination to the shopping center." The terminal receives this voice input and uses speech recognition software to convert the voice data into text data. The input is voice information, and the output is text information. Acoustic models and natural language processing techniques are used for this conversion.

[0083] Step 2:

[0084] The terminal sends the converted text information to the server. The server uses parsing tools to extract destination and additional information from this text information. The input for parsing is text information, and the output is structured data about the destination and travel information. In this process, the server uses a generative AI model to accurately interpret the user's instructions.

[0085] Step 3:

[0086] The server acquires spatiotemporal information and calculates the optimal route based on the analyzed information. It utilizes external traffic information service APIs to obtain real-time, fluctuating traffic data. The input is structured destination data and spatiotemporal information, and the output is optimal route information. Here, the route is flexibly adjusted according to traffic conditions.

[0087] Step 4:

[0088] The server sends the calculated optimal route information to the terminal. Based on this information, the terminal displays a visual map and starts voice guidance. The input is the optimal route information, and the output is visual and voice guidance to the user. Specifically, the terminal instructs the user to "move into the left lane 500 meters ahead."

[0089] Step 5:

[0090] The user follows notifications from the device and drives safely and efficiently towards their destination. If a break is needed, they can choose to stop at a rest stop suggested by the device. This ensures a comfortable journey to the destination while maintaining safety while driving.

[0091] (Application Example 1)

[0092] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0093] Autonomous vehicles are required to support safe and efficient travel while reducing the burden on passengers. Furthermore, they need to provide a comfortable travel environment by responding to real-time traffic conditions and suggesting rest stops.

[0094] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0095] In this invention, the server includes a voice conversion means for converting voice signals into text information, an analysis means for analyzing destination information from the text information, and a route calculation means for acquiring dynamic information and calculating the optimal route. This enables safe and efficient travel and comfortable driving assistance.

[0096] A "speech signal" is an electrical signal obtained by converting a speaker's voice into an electrical signal.

[0097] "Textual information" refers to text data generated based on audio signals.

[0098] "Speech conversion means" refers to a device or system that has the function of converting speech signals into text information.

[0099] "Destination information" refers to information about the user's intended location, including the destination and intermediate stops.

[0100] "Analysis means" refers to a device or system that has the function of analyzing destination information from textual information.

[0101] "Dynamic information" refers to real-time changes in traffic conditions and road information.

[0102] A "route calculation means" is a device or system that has the function of calculating the optimal route based on dynamic information.

[0103] "Information presentation means" refers to a device or system that has the function of providing a calculated route to the user visually or audibly.

[0104] "Means of providing voice guidance" refers to a system that provides users with routes and directions via voice.

[0105] "Means for providing visual route display" refers to a device or system that has the function of displaying a route on the user's screen or display in map format or similar.

[0106] This invention realizes a navigation system for autonomous vehicles that utilizes voice recognition technology and generative AI. The system sets destinations and waypoints based on the user's voice instructions, calculates the optimal route based on real-time traffic information, and provides it. A specific embodiment is shown below.

[0107] The server analyzes the user's voice input using a speech conversion device that converts "voice signals" into "text information." A terminal equipped with a microphone is used for this purpose. The speech recognition engine utilizes the Python speech_recognition library. The converted text information is sent to an analysis device that analyzes the user's intent. The analysis device extracts the "destination information" specified by the user. A generative AI model is involved in this analysis process to understand the user's intent with high accuracy.

[0108] Next, the server calculates the optimal route based on "dynamic information" that reflects real-time traffic conditions. The "route calculation means" uses API communication to obtain the latest traffic data. This information is then sent to the terminal as the calculated route.

[0109] The terminal provides route information to the user using information display means. This display includes visual route display on the screen and means of providing voice guidance. The user visually checks the map display and receives route instructions via voice guidance. For example, if the user says, "Set the next destination to the shopping center," the optimal route will be suggested based on that instruction. A voice notification such as "Let's take a break at the next service area" will also be provided.

[0110] By implementing this invention, users can safely maintain their gaze on the road while driving and achieve efficient travel. A specific prompt might be, "Generate route guidance text when the user inputs the next waypoint by voice. The destination is a shopping center." Based on this prompt, the generation AI model generates and provides contextually appropriate guidance.

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The user initiates voice input. The user speaks into a microphone installed in the vehicle, stating their destination and intermediate stops. This voice signal is captured by the terminal and transmitted to a voice conversion device.

[0114] Step 2:

[0115] The device converts audio signals into text information. Using a speech recognition engine, it converts the user's voice into text data. This process utilizes a speech recognition technology library to extract highly accurate text information, which is the output.

[0116] Step 3:

[0117] The server receives text information and uses analysis tools to analyze destination information. Using text information as input, it utilizes a generative AI model to identify the user's intended destination and intermediate points. This analysis result is output as destination information.

[0118] Step 4:

[0119] The server acquires dynamic information and calculates the optimal route. It obtains real-time traffic data via API communication and processes the dynamic information using a route calculation method. It generates a route that avoids congestion points and outputs the calculated optimal route.

[0120] Step 5:

[0121] The terminal provides the user with the optimal route through information presentation methods. It displays a visual route on the screen and provides route instructions via voice guidance. The user receives voice guidance from the terminal while confirming the route on the screen. Based on the voice guidance and displayed map, safe and efficient travel is achieved.

[0122] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0123] This invention relates to a navigation system that combines an emotion engine that analyzes user voice input and recognizes emotional states. This system includes a voice conversion means, an analysis means, a route calculation means, a display means, and an emotion engine. The embodiments of this invention aim to provide users with a safe and comfortable driving environment.

[0124] The user inputs their destination and intermediate stops by voice into the terminal. The terminal uses a voice conversion device to convert the voice data into text data and sends this text data to the server. The server uses an analysis device to analyze the user's intent from the text data and extracts destination information.

[0125] The emotion engine similarly analyzes the user's emotional state from their voice. This analysis allows it to recognize whether the user is stressed, relaxed, or otherwise emotionally unstable. The server incorporates this emotional data into route calculations, selecting routes and rest stops that are appropriate for the user's emotional state.

[0126] For example, if a user is feeling stressed due to a congested route, the emotion engine will detect this, and the server will prioritize suggesting less-loaded routes. Also, if it's determined that the user's stress levels are high due to prolonged driving, it will recommend relaxing rest stops. This information is communicated to the user through the device's display.

[0127] Furthermore, the server utilizes real-time traffic data to constantly calculate the optimal route and provides care based on the user's emotional state. The terminal supports safe and comfortable driving by providing the user with information received from the server through voice guidance and visual displays.

[0128] Thus, the present invention makes it possible to provide a more personalized navigation service by comprehensively utilizing the user's voice and emotional state.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] The user speaks into the device and says, "Set the next destination to the public library." The device then acquires the voice data through its microphone.

[0132] Step 2:

[0133] The terminal converts the audio data into text data using a speech conversion device and sends this text data to the server.

[0134] Step 3:

[0135] The server uses an analysis tool to extract destination information from the text data and recognizes "Citizen's Library" as the next destination.

[0136] Step 4:

[0137] The device simultaneously transmits voice data to the emotion engine, which analyzes the voice characteristics. The emotion engine determines the user's stress level and relaxation level.

[0138] Step 5:

[0139] The server acquires real-time traffic data and uses a route calculation tool to calculate the optimal route to the destination.

[0140] Step 6:

[0141] The server takes data from the emotion engine into consideration and, if the user is in a high-stress state, selects a route to reduce stress.

[0142] Step 7:

[0143] The server determines the optimal route based on sentiment analysis results and traffic conditions, and, if necessary, selects rest stops, then transmits this information to the terminal.

[0144] Step 8:

[0145] The terminal displays route information and rest stops received from the server to the user via a display device. Information is provided to the user while driving through visual displays and voice guidance.

[0146] (Example 2)

[0147] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0148] Conventional navigation systems have a problem in that they do not select routes or rest stops that take into account the user's emotional state, thus failing to reduce user stress and fatigue. Users are prone to mental and physical fatigue from long hours of driving and traffic jams, which can reduce driving safety. To solve this problem, there is a need to realize route guidance based on the user's emotional state.

[0149] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0150] In this invention, the server includes a voice conversion means for converting voice data into symbolic data, an analysis means for analyzing location information from the symbolic data, and an emotion recognition means for analyzing emotional state. This makes it possible to calculate routes and select rest stops while taking into account the user's emotional state.

[0151] "Speech conversion means" refers to a device or software for converting speech data into symbolic data.

[0152] "Analysis means" refers to a device or program that has the function of extracting location information from symbolic data and analyzing the user's intent.

[0153] "Emotion recognition means" refers to a device or program that has the function of analyzing the emotional state from the user's voice and providing the results to other system components.

[0154] "Selection means" refers to a device or program that has the function of selecting an appropriate resting place based on the user's emotional state.

[0155] "Dynamic information" refers to real-time traffic data and information about road conditions.

[0156] "Route calculation means" refers to a device or program for calculating the optimal route based on location information and dynamic information.

[0157] "Presentation means" refers to a device or program for providing users with optimal route information through visual or auditory means.

[0158] This invention relates to a comprehensive navigation system that provides a safe and comfortable driving environment based on user voice input. This system consists of voice conversion means, analysis means, emotion recognition means, selection means, route calculation means, and presentation means.

[0159] Users can input their destination and intermediate stops via voice input through their device. The device converts the voice data into text data using a voice conversion mechanism. For this purpose, it uses a common API for voice recognition software. The converted text data is sent to the server.

[0160] The server analyzes text data using analytical tools to extract user destination information. This process utilizes common natural language processing techniques as a generative AI model. Furthermore, the server analyzes the user's emotional state from their voice using emotion recognition tools. This allows it to assess whether the user is relaxed or stressed.

[0161] The server calculates the optimal route using route calculation means based on the user's emotional state and real-time traffic data. For example, if the user is tired while driving, the system suggests relaxing rest stops using selection means. The optimized route information is delivered to the terminal through presentation means and guided to the user visually or audibly.

[0162] For example, if a user is heading to a campsite with their family, they might voice-input "Tell me the route to the next campsite" into their device. Based on this information, the server calculates the route, and the device can then provide instructions such as, "There's a park on your right where you can relax and recover from driving fatigue."

[0163] An example of a prompt for a generative AI model might be: "I'm a little tired from driving for a long time. Please suggest a route that includes relaxing rest stops."

[0164] Thus, the present invention comprehensively utilizes the user's voice and emotional state to provide a more personalized navigation service.

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1:

[0167] The user inputs their destination and intermediate stops into the terminal by voice. This input is acquired by the terminal as digital voice data. Using a voice conversion method, the terminal converts the digital voice data into symbolic data (text data). Specifically, the terminal uses voice recognition software to analyze the waveform of the voice and converts its content into natural language text. The output is text data.

[0168] Step 2:

[0169] The terminal sends the converted text data to the server. The server receives the text data using an analysis tool and analyzes the location information within the text. Specifically, it utilizes a generative AI model to extract destination and waypoint information from the text. In this process, techniques such as noun extraction and place name recognition are used. The output is destination information data.

[0170] Step 3:

[0171] The server uses emotion recognition tools to analyze the user's emotional state from their digital voice data. This step involves analyzing factors such as voice tone, speaking speed, and linguistic characteristics to assess stress levels and relaxation. Specifically, emotion analysis is performed using machine learning algorithms. The output is data indicating the emotional state.

[0172] Step 4:

[0173] The server receives analyzed destination information and emotional state as input and uses a selection mechanism to choose an appropriate rest stop. This process takes into account real-time traffic data and the user's emotional state; for example, if the user is tired, it will choose a more comfortable rest stop. The output is information on recommended rest stops.

[0174] Step 5:

[0175] The server uses a route calculation system to calculate the optimal route based on real-time dynamic information. This optimization considers not only the shortest distance in normal travel time, but also the user's emotional state. The specific operation includes a calculation process utilizing a traffic information database. The output is optimized route data.

[0176] Step 6:

[0177] Upon receiving optimal route data and rest stop information from the server, the terminal notifies the user of the information using a presentation mechanism. Here, the user can receive instructions regarding the route and rest stops through visual map displays and voice guidance. The output provides route guidance and rest stop information that the user can view. Specifically, the terminal displays the guided route on its screen and provides navigation using speech synthesis technology.

[0178] (Application Example 2)

[0179] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0180] Conventional navigation systems merely presented the optimal route to the destination without considering the user's emotional state. Therefore, even when a user was feeling stressed or seeking a relaxing driving environment, it was difficult to suggest a suitable route, resulting in a failure to adequately ensure driving comfort and safety.

[0181] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0182] In this invention, the server includes a conversion means for converting voice data into text data, an analysis means for analyzing destination information and the user's emotional state from the text data, a calculation means for acquiring real-time traffic data and calculating the optimal route, and a presentation means for presenting the optimal route and providing information according to the emotional state. This makes it possible to adjust the driving environment based on the user's emotional state.

[0183] "Audio data" refers to data that is a digital representation of the audio signal emitted by a user.

[0184] "Text data" refers to string data obtained by converting audio data.

[0185] "Conversion means" refers to a device or software that has the role of converting audio data into text data.

[0186] "Analysis means" refers to a device or software that has the function of analyzing destination information and the user's emotional state from text data.

[0187] "Real-time traffic data" refers to data that shows the current traffic situation over time.

[0188] "Computation means" refers to a device or software that calculates the optimal route based on real-time traffic data and analysis results.

[0189] "Presentation means" refers to a device or software that provides the user with information that corresponds to the calculated optimal route and emotional state, either visually or audibly.

[0190] "Emotional state" refers to the emotions and moods a user is currently experiencing, including states such as stress and relaxation.

[0191] The system for realizing this invention includes a terminal for receiving voice data, a server for analyzing and processing the data, and a display device for providing feedback to the user. Voice data spoken by the user is first digitized by the voice input function of the terminal. The voice data is then sent to the server and converted into text data by a conversion means. Voice recognition software such as Google® Cloud Speech-to-Text can be used for voice conversion.

[0192] The server then uses an analysis tool to analyze destination information and the user's emotional state from the text data. A custom model using TENSORFLOW® is employed for the emotional state analysis, determining whether the user is stressed, relaxed, etc. The analysis results are then passed to a calculation tool, which calculates the optimal route based on real-time traffic data. This process utilizes the Google Maps API, enabling route selection that takes current traffic conditions into account.

[0193] Finally, the calculated route and suitable rest stops and relaxation spots are communicated to the user visually or audibly through the terminal's display mechanisms. This reduces stress and enables a more comfortable and safer driving experience.

[0194] For example, if a user voice-inputs, "I want to be guided to a quiet and calming place," the system will analyze this intention and emotional state, select the most comfortable route, and set the in-car sound environment to relaxing music. An example of a prompt to the generating AI model might be, "Please help design an application that analyzes the emotional state and intention from a user's voice input and suggests the optimal route. For example, if the user is feeling stressed, please give me some ideas for suggesting a quiet route or relaxation spot."

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] The user inputs their destination or specific requests by voice into the terminal. The voice data is captured by the terminal and digitized. This data is input through a microphone with voice input / output capabilities.

[0198] Step 2:

[0199] The terminal sends audio data to the server. The server first converts the audio data into text data. Speech recognition software such as Google Cloud Speech-to-Text is used for this conversion, and in this process, text data that corresponds to the audio is output.

[0200] Step 3:

[0201] The server uses analysis tools to analyze the text data in detail. The analysis employs an NLP engine to identify the user's destination information and emotional state from the text data. The analysis results output the destination and emotional status.

[0202] Step 4:

[0203] Based on the analysis results, the server begins acquiring real-time traffic data using computational methods. The Google Maps API is used for this real-time traffic data, and the optimal route is calculated based on this data. The output data is a customized route adjusted to the user's emotional state.

[0204] Step 5:

[0205] The server sends the calculated route to the terminal and provides the user with route information visually or audibly through the terminal's display mechanisms. Music and audio guides that take the user's emotional state into consideration are used to create a relaxing environment.

[0206] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0207] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0208] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0209] [Second Embodiment]

[0210] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0211] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0212] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0213] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0214] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0215] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0216] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0217] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0218] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0219] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0220] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0221] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0222] This invention is a navigation system that combines speech recognition technology and generative AI to help users move safely and efficiently. Embodiments of the invention are configured around speech conversion means, analysis means, route calculation means, and display means.

[0223] While driving, the user inputs their destination and waypoints by voice into the terminal. The terminal then uses a voice conversion device to convert the voice data into text data and sends that data to the server.

[0224] The server receives text data and uses an analysis tool to analyze the user's set destination and waypoints. Based on this analysis, the server uses real-time traffic data to calculate the optimal route to the destination.

[0225] The route calculation system dynamically generates the optimal route to avoid congestion and accidents based on traffic information acquired on the server. The calculated route information is sent to the terminal and notified to the user.

[0226] The terminal provides the user with route information received from the server through a display mechanism. This display includes voice guidance and visual route displays. Rest stop suggestions are also displayed on the terminal as information from the server, prompting the user to take breaks based on their selection.

[0227] For example, if a user instructs their device to "set Park Mall as the next destination," the voice conversion system converts the speech to text, and the server analyzes the intent. The server sets Park Mall as the destination and calculates the optimal route based on real-time traffic data. Route guidance is then displayed on the device, and notifications such as "Let's take a break at the next service area" are provided.

[0228] This invention allows users to maintain safety while driving, while keeping their gaze focused on the road. Furthermore, it enables efficient and comfortable driving by providing flexible route changes and rest stop suggestions according to traffic conditions.

[0229] The following describes the processing flow.

[0230] Step 1:

[0231] The user voice-inputs their destination into the device. The device then retrieves that voice data.

[0232] Step 2:

[0233] The terminal converts the audio data into text data using a speech conversion device. This text data is then sent to the server.

[0234] Step 3:

[0235] The server analyzes the received text data using an analysis tool and extracts information about the destination and intermediate points.

[0236] Step 4:

[0237] The server acquires real-time traffic data and uses that data to calculate the optimal route to the destination using a route calculation tool.

[0238] Step 5:

[0239] The calculated optimal route is sent from the server to the terminal.

[0240] Step 6:

[0241] The terminal displays the received optimal route information to the user via a display device. This includes voice guidance and visual route display.

[0242] Step 7:

[0243] While the user is driving, the server continuously monitors traffic conditions and recalculates the route as needed.

[0244] Step 8:

[0245] The server uses analysis tools to suggest rest locations and sends that information to the terminal. The terminal then notifies the user of the suggested rest locations.

[0246] (Example 1)

[0247] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0248] There is a need to provide a system that allows drivers to safely set their destination without taking their eyes off the road while traveling by car or other means, and to find an efficient route based on real-time traffic information. Furthermore, the system must be able to flexibly adapt to changes in traffic conditions.

[0249] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0250] In this invention, the server includes a conversion means for converting voice information into text information, an analysis means for analyzing target location information from the text information, and a route calculation means for acquiring spatiotemporal information and calculating the optimal route. This makes it possible for the driver to set a destination without using their hands and to obtain a safe and optimal route based on traffic information.

[0251] "Voice information" refers to information acquired as voice data, including the user's voice instructions.

[0252] "Textual information" refers to text data converted from audio information, and is a data format used by systems for analysis.

[0253] "Conversion means" refers to a process or device that converts audio information into text information, and utilizes speech recognition technology.

[0254] "Analysis means" refers to a means for extracting information such as target locations from textual information, and for the system to understand and process it.

[0255] "Spatiotemporal information" refers to data that includes real-time traffic data and location information, and is used for route calculations.

[0256] A "path calculation means" is an algorithm or process that searches for and calculates the optimal path based on spatiotemporal information.

[0257] "Presentation means" refers to a device or method that visually or audibly notifies the user of the calculated optimal route.

[0258] "Device" refers to the entire system that integrates the above-mentioned means to support the user's safe and efficient travel to their destination.

[0259] This invention relates to a voice recognition-based navigation system that enables users to safely and efficiently set and reach their destinations while on the move. The system converts voice information into text information, calculates the optimal route using real-time spatiotemporal information, and provides that route to the user.

[0260] Performing voice input and conversion

[0261] Users can input their target location by voice while driving or using the terminal. This input is acquired through the terminal's built-in microphone. The terminal uses voice recognition software (e.g., a voice recognition cloud service) to convert the voice information into text. This conversion process is based on acoustic models and natural language processing techniques.

[0262] Data analysis and information provision

[0263] Text information is sent from the terminal to the server, which uses analysis tools to analyze the target location and necessary itinerary information. Based on the analyzed information, the optimal route is calculated, taking into account real-time traffic conditions and geographical data. The server can obtain the latest spatiotemporal information using external traffic information service APIs.

[0264] Route notification and display

[0265] Once the optimal route calculation is complete, the server sends that information to the terminal. The terminal provides the user with route information using visual map displays and voice guidance. This allows the user to efficiently navigate to their destination while keeping their eyes on the road, even while driving.

[0266] Specific examples and prompt statements

[0267] As a concrete example, suppose a user gives a voice command to their device saying, "Set the next destination to the shopping center." This voice command is converted into text, the server analyzes the intent, and sets the shopping center as the target location. Then, the optimal route based on actual traffic conditions is calculated, and the directions are displayed on the device.

[0268] As an example of a prompt, inputting "Design an algorithm that analyzes the user-specified destination and calculates the shortest route using the latest traffic information" into the generating AI model can optimize the analysis and route calculation process.

[0269] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0270] Step 1:

[0271] The user gives voice commands to the terminal to set a destination. For example, the user might say, "Set the next destination to the shopping center." The terminal receives this voice input and uses speech recognition software to convert the voice data into text data. The input is voice information, and the output is text information. Acoustic models and natural language processing techniques are used for this conversion.

[0272] Step 2:

[0273] The terminal sends the converted text information to the server. The server uses parsing tools to extract destination and additional information from this text information. The input for parsing is text information, and the output is structured data about the destination and travel information. In this process, the server uses a generative AI model to accurately interpret the user's instructions.

[0274] Step 3:

[0275] The server acquires spatiotemporal information and calculates the optimal route based on the analyzed information. It utilizes external traffic information service APIs to obtain real-time, fluctuating traffic data. The input is structured destination data and spatiotemporal information, and the output is optimal route information. Here, the route is flexibly adjusted according to traffic conditions.

[0276] Step 4:

[0277] The server sends the calculated optimal route information to the terminal. Based on this information, the terminal displays a visual map and starts voice guidance. The input is the optimal route information, and the output is visual and voice guidance to the user. Specifically, the terminal instructs the user to "move into the left lane 500 meters ahead."

[0278] Step 5:

[0279] The user follows notifications from the device and drives safely and efficiently towards their destination. If a break is needed, they can choose to stop at a rest stop suggested by the device. This ensures a comfortable journey to the destination while maintaining safety while driving.

[0280] (Application Example 1)

[0281] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0282] Autonomous vehicles are required to support safe and efficient travel while reducing the burden on passengers. Furthermore, they need to provide a comfortable travel environment by responding to real-time traffic conditions and suggesting rest stops.

[0283] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0284] In this invention, the server includes voice conversion means for converting a voice signal into character information, analysis means for analyzing destination information from the character information, and route calculation means for acquiring dynamic information and calculating an optimal route. As a result, it becomes possible to achieve safe and efficient movement and provide comfortable driving support.

[0285] A "voice signal" is what is obtained by converting a speaker's voice into an electrical signal.

[0286] "Character information" is text data generated based on a voice signal.

[0287] "Voice conversion means" is a device or system having a function of converting a voice signal into character information.

[0288] "Destination information" is information regarding a destination point set by a user, including a destination and a transit point.

[0289] "Analysis means" is a device or system having a function of analyzing destination information from character information.

[0290] "Dynamic information" refers to traffic conditions and road information that change in real time.

[0291] "Route calculation means" is a device or system having a function of calculating an optimal route based on dynamic information.

[0292] "Information presentation means" is a device or system having a function of visually or aurally providing the calculated route to the user.

[0293] "Means for providing voice guidance" is a system that provides a route and guidance to the user by voice.

[0294] "Means for providing a visual route display" is a device or system having a function of displaying a route in a map format or the like on the user's screen or display.

[0295] This invention realizes a navigation system for autonomous vehicles that utilizes voice recognition technology and generative AI. The system sets destinations and waypoints based on the user's voice instructions, calculates the optimal route based on real-time traffic information, and provides it. A specific embodiment is shown below.

[0296] The server analyzes the user's voice input using a speech conversion device that converts "voice signals" into "text information." A terminal equipped with a microphone is used for this purpose. The speech recognition engine utilizes the Python speech_recognition library. The converted text information is sent to an analysis device that analyzes the user's intent. The analysis device extracts the "destination information" specified by the user. A generative AI model is involved in this analysis process to understand the user's intent with high accuracy.

[0297] Next, the server calculates the optimal route based on "dynamic information" that reflects real-time traffic conditions. The "route calculation means" uses API communication to obtain the latest traffic data. This information is then sent to the terminal as the calculated route.

[0298] The terminal provides route information to the user using information display means. This display includes visual route display on the screen and means of providing voice guidance. The user visually checks the map display and receives route instructions via voice guidance. For example, if the user says, "Set the next destination to the shopping center," the optimal route will be suggested based on that instruction. A voice notification such as "Let's take a break at the next service area" will also be provided.

[0299] By implementing this invention, users can safely maintain their gaze on the road while driving and achieve efficient travel. A specific prompt might be, "Generate route guidance text when the user inputs the next waypoint by voice. The destination is a shopping center." Based on this prompt, the generation AI model generates and provides contextually appropriate guidance.

[0300] The flow of the specific process in Application Example 1 will be described with reference to FIG. 12.

[0301] Step 1:

[0302] The user starts voice input. The user tells the destination and the route via point towards the microphone installed in the vehicle. This voice signal is captured by the terminal and transmitted to the voice conversion means.

[0303] Step 2:

[0304] The terminal converts the voice signal into character information. Using a voice recognition engine, the user's voice is converted into text data. This process utilizes a library of voice recognition technology, and the output is to extract highly accurate character information.

[0305] Step 3:

[0306] The server receives the character information and analyzes the destination information using analysis means. Taking the character information as input, the generated AI model is utilized to identify the destination and the route via point intended by the user. This analysis result is output as the destination information.

[0307] Step 4:

[0308] The server obtains dynamic information and calculates the optimal route. Real-time traffic data is obtained through API communication, and the dynamic information is processed by the route calculation means. A route that avoids congested points is generated, and the calculated optimal route is output.

[0309] Step 5:

[0310] The terminal provides the optimal route to the user by means of information presentation. A visual route display is shown on the display, and route guidance is given through voice guidance. The user receives the voice guidance from the terminal while checking the route on the screen. Based on the voice guidance and the displayed map, safe and efficient movement is realized.

[0311] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0312] This invention relates to a navigation system that combines an emotion engine that analyzes user voice input and recognizes emotional states. This system includes a voice conversion means, an analysis means, a route calculation means, a display means, and an emotion engine. The embodiments of this invention aim to provide users with a safe and comfortable driving environment.

[0313] The user inputs their destination and intermediate stops by voice into the terminal. The terminal uses a voice conversion device to convert the voice data into text data and sends this text data to the server. The server uses an analysis device to analyze the user's intent from the text data and extracts destination information.

[0314] The emotion engine similarly analyzes the user's emotional state from their voice. This analysis allows it to recognize whether the user is stressed, relaxed, or otherwise emotionally unstable. The server incorporates this emotional data into route calculations, selecting routes and rest stops that are appropriate for the user's emotional state.

[0315] For example, if a user is feeling stressed due to a congested route, the emotion engine will detect this, and the server will prioritize suggesting less-loaded routes. Also, if it's determined that the user's stress levels are high due to prolonged driving, it will recommend relaxing rest stops. This information is communicated to the user through the device's display.

[0316] Furthermore, the server utilizes real-time traffic data to constantly calculate the optimal route and provides care based on the user's emotional state. The terminal supports safe and comfortable driving by providing the user with information received from the server through voice guidance and visual displays.

[0317] Thus, the present invention makes it possible to provide a more personalized navigation service by comprehensively utilizing the user's voice and emotional state.

[0318] The following describes the processing flow.

[0319] Step 1:

[0320] The user speaks into the device and says, "Set the next destination to the public library." The device then acquires the voice data through its microphone.

[0321] Step 2:

[0322] The terminal converts the audio data into text data using a speech conversion device and sends this text data to the server.

[0323] Step 3:

[0324] The server uses an analysis tool to extract destination information from the text data and recognizes "Citizen's Library" as the next destination.

[0325] Step 4:

[0326] The device simultaneously transmits voice data to the emotion engine, which analyzes the voice characteristics. The emotion engine determines the user's stress level and relaxation level.

[0327] Step 5:

[0328] The server acquires real-time traffic data and uses a route calculation tool to calculate the optimal route to the destination.

[0329] Step 6:

[0330] The server takes data from the emotion engine into consideration and, if the user is in a high-stress state, selects a route to reduce stress.

[0331] Step 7:

[0332] The server determines the optimal route based on sentiment analysis results and traffic conditions, and, if necessary, selects rest stops, then transmits this information to the terminal.

[0333] Step 8:

[0334] The terminal displays route information and rest stops received from the server to the user via a display device. Information is provided to the user while driving through visual displays and voice guidance.

[0335] (Example 2)

[0336] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0337] Conventional navigation systems have a problem in that they do not select routes or rest stops that take into account the user's emotional state, thus failing to reduce user stress and fatigue. Users are prone to mental and physical fatigue from long hours of driving and traffic jams, which can reduce driving safety. To solve this problem, there is a need to realize route guidance based on the user's emotional state.

[0338] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0339] In this invention, the server includes a voice conversion means for converting voice data into symbolic data, an analysis means for analyzing location information from the symbolic data, and an emotion recognition means for analyzing emotional state. This makes it possible to calculate routes and select rest stops while taking into account the user's emotional state.

[0340] "Speech conversion means" refers to a device or software for converting speech data into symbolic data.

[0341] "Analysis means" refers to a device or program that has the function of extracting location information from symbolic data and analyzing the user's intent.

[0342] "Emotion recognition means" refers to a device or program that has the function of analyzing the emotional state from the user's voice and providing the results to other system components.

[0343] "Selection means" refers to a device or program that has the function of selecting an appropriate resting place based on the user's emotional state.

[0344] "Dynamic information" refers to real-time traffic data and information about road conditions.

[0345] "Route calculation means" refers to a device or program for calculating the optimal route based on location information and dynamic information.

[0346] "Presentation means" refers to a device or program for providing users with optimal route information through visual or auditory means.

[0347] This invention relates to a comprehensive navigation system that provides a safe and comfortable driving environment based on user voice input. This system consists of voice conversion means, analysis means, emotion recognition means, selection means, route calculation means, and presentation means.

[0348] Users can input their destination and intermediate stops via voice input through their device. The device converts the voice data into text data using a voice conversion mechanism. For this purpose, it uses a common API for voice recognition software. The converted text data is sent to the server.

[0349] The server analyzes text data using analytical tools to extract user destination information. This process utilizes common natural language processing techniques as a generative AI model. Furthermore, the server analyzes the user's emotional state from their voice using emotion recognition tools. This allows it to assess whether the user is relaxed or stressed.

[0350] The server calculates the optimal route using route calculation means based on the user's emotional state and real-time traffic data. For example, if the user is tired while driving, the system suggests relaxing rest stops using selection means. The optimized route information is delivered to the terminal through presentation means and guided to the user visually or audibly.

[0351] For example, if a user is heading to a campsite with their family, they might voice-input "Tell me the route to the next campsite" into their device. Based on this information, the server calculates the route, and the device can then provide instructions such as, "There's a park on your right where you can relax and recover from driving fatigue."

[0352] An example of a prompt for a generative AI model might be: "I'm a little tired from driving for a long time. Please suggest a route that includes relaxing rest stops."

[0353] Thus, the present invention comprehensively utilizes the user's voice and emotional state to provide a more personalized navigation service.

[0354] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0355] Step 1:

[0356] The user inputs their destination and intermediate stops into the terminal by voice. This input is acquired by the terminal as digital voice data. Using a voice conversion method, the terminal converts the digital voice data into symbolic data (text data). Specifically, the terminal uses voice recognition software to analyze the waveform of the voice and converts its content into natural language text. The output is text data.

[0357] Step 2:

[0358] The terminal sends the converted text data to the server. The server receives the text data using an analysis tool and analyzes the location information within the text. Specifically, it utilizes a generative AI model to extract destination and waypoint information from the text. In this process, techniques such as noun extraction and place name recognition are used. The output is destination information data.

[0359] Step 3:

[0360] The server uses emotion recognition tools to analyze the user's emotional state from their digital voice data. This step involves analyzing factors such as voice tone, speaking speed, and linguistic characteristics to assess stress levels and relaxation. Specifically, emotion analysis is performed using machine learning algorithms. The output is data indicating the emotional state.

[0361] Step 4:

[0362] The server receives analyzed destination information and emotional state as input and uses a selection mechanism to choose an appropriate rest stop. This process takes into account real-time traffic data and the user's emotional state; for example, if the user is tired, it will choose a more comfortable rest stop. The output is information on recommended rest stops.

[0363] Step 5:

[0364] The server uses a route calculation system to calculate the optimal route based on real-time dynamic information. This optimization considers not only the shortest distance in normal travel time, but also the user's emotional state. The specific operation includes a calculation process utilizing a traffic information database. The output is optimized route data.

[0365] Step 6:

[0366] Upon receiving optimal route data and rest stop information from the server, the terminal notifies the user of the information using a presentation mechanism. Here, the user can receive instructions regarding the route and rest stops through visual map displays and voice guidance. The output provides route guidance and rest stop information that the user can view. Specifically, the terminal displays the guided route on its screen and provides navigation using speech synthesis technology.

[0367] (Application Example 2)

[0368] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0369] Conventional navigation systems merely presented the optimal route to the destination without considering the user's emotional state. Therefore, even when a user was feeling stressed or seeking a relaxing driving environment, it was difficult to suggest a suitable route, resulting in a failure to adequately ensure driving comfort and safety.

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

[0371] In this invention, the server includes a conversion means for converting voice data into text data, an analysis means for analyzing destination information and the user's emotional state from the text data, a calculation means for acquiring real-time traffic data and calculating the optimal route, and a presentation means for presenting the optimal route and providing information according to the emotional state. This makes it possible to adjust the driving environment based on the user's emotional state.

[0372] "Audio data" refers to data that is a digital representation of the audio signal emitted by a user.

[0373] "Text data" refers to string data obtained by converting audio data.

[0374] "Conversion means" refers to a device or software that has the role of converting audio data into text data.

[0375] "Analysis means" refers to a device or software that has the function of analyzing destination information and the user's emotional state from text data.

[0376] "Real-time traffic data" refers to data that shows the current traffic situation over time.

[0377] "Computation means" refers to a device or software that calculates the optimal route based on real-time traffic data and analysis results.

[0378] "Presentation means" refers to a device or software that provides the user with information that corresponds to the calculated optimal route and emotional state, either visually or audibly.

[0379] "Emotional state" refers to the emotions and moods a user is currently experiencing, including states such as stress and relaxation.

[0380] The system for realizing this invention includes a terminal for receiving voice data, a server for analyzing and processing the data, and a display device for providing feedback to the user. Voice data spoken by the user is first digitized by the terminal's voice input function. The voice data is then sent to the server and converted into text data by a conversion means. Speech recognition software such as Google Cloud Speech-to-Text can be used for the voice conversion.

[0381] The server then uses an analysis tool to analyze destination information and the user's emotional state from the text data. A custom model using TensorFlow is employed for the emotional state analysis, determining whether the user is stressed, relaxed, etc. The analysis results are then passed to a computation tool, which calculates the optimal route based on real-time traffic data. This process utilizes the Google Maps API, enabling route selection that takes current traffic conditions into account.

[0382] Finally, the calculated route and suitable rest stops and relaxation spots are communicated to the user visually or audibly through the terminal's display mechanisms. This reduces stress and enables a more comfortable and safer driving experience.

[0383] For example, if a user voice-inputs, "I want to be guided to a quiet and calming place," the system will analyze this intention and emotional state, select the most comfortable route, and set the in-car sound environment to relaxing music. An example of a prompt to the generating AI model might be, "Please help design an application that analyzes the emotional state and intention from a user's voice input and suggests the optimal route. For example, if the user is feeling stressed, please give me some ideas for suggesting a quiet route or relaxation spot."

[0384] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0385] Step 1:

[0386] The user inputs their destination or specific requests by voice into the terminal. The voice data is captured by the terminal and digitized. This data is input through a microphone with voice input / output capabilities.

[0387] Step 2:

[0388] The terminal sends audio data to the server. The server first converts the audio data into text data. Speech recognition software such as Google Cloud Speech-to-Text is used for this conversion, and in this process, text data that corresponds to the audio is output.

[0389] Step 3:

[0390] The server uses analysis tools to analyze the text data in detail. The analysis employs an NLP engine to identify the user's destination information and emotional state from the text data. The analysis results output the destination and emotional status.

[0391] Step 4:

[0392] Based on the analysis results, the server begins acquiring real-time traffic data using computational methods. The Google Maps API is used for this real-time traffic data, and the optimal route is calculated based on this data. The output data is a customized route adjusted to the user's emotional state.

[0393] Step 5:

[0394] The server sends the calculated route to the terminal and provides the user with route information visually or audibly through the terminal's display mechanisms. Music and audio guides that take the user's emotional state into consideration are used to create a relaxing environment.

[0395] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0396] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0397] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0398] [Third Embodiment]

[0399] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0400] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0401] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0402] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0403] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0404] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0405] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0406] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0407] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0408] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0409] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0410] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0411] This invention is a navigation system that combines speech recognition technology and generative AI to help users move safely and efficiently. Embodiments of the invention are configured around speech conversion means, analysis means, route calculation means, and display means.

[0412] While driving, the user inputs their destination and waypoints by voice into the terminal. The terminal then uses a voice conversion device to convert the voice data into text data and sends that data to the server.

[0413] The server receives text data and uses an analysis tool to analyze the user's set destination and waypoints. Based on this analysis, the server uses real-time traffic data to calculate the optimal route to the destination.

[0414] The route calculation system dynamically generates the optimal route to avoid congestion and accidents based on traffic information acquired on the server. The calculated route information is sent to the terminal and notified to the user.

[0415] The terminal provides the user with route information received from the server through a display mechanism. This display includes voice guidance and visual route displays. Rest stop suggestions are also displayed on the terminal as information from the server, prompting the user to take breaks based on their selection.

[0416] For example, if a user instructs their device to "set Park Mall as the next destination," the voice conversion system converts the speech to text, and the server analyzes the intent. The server sets Park Mall as the destination and calculates the optimal route based on real-time traffic data. Route guidance is then displayed on the device, and notifications such as "Let's take a break at the next service area" are provided.

[0417] This invention allows users to maintain safety while driving, while keeping their gaze focused on the road. Furthermore, it enables efficient and comfortable driving by providing flexible route changes and rest stop suggestions according to traffic conditions.

[0418] The following describes the processing flow.

[0419] Step 1:

[0420] The user voice-inputs their destination into the device. The device then retrieves that voice data.

[0421] Step 2:

[0422] The terminal converts the audio data into text data using a speech conversion device. This text data is then sent to the server.

[0423] Step 3:

[0424] The server analyzes the received text data using an analysis tool and extracts information about the destination and intermediate points.

[0425] Step 4:

[0426] The server acquires real-time traffic data and uses that data to calculate the optimal route to the destination using a route calculation tool.

[0427] Step 5:

[0428] The calculated optimal route is sent from the server to the terminal.

[0429] Step 6:

[0430] The terminal displays the received optimal route information to the user via a display device. This includes voice guidance and visual route display.

[0431] Step 7:

[0432] While the user is driving, the server continuously monitors traffic conditions and recalculates the route as needed.

[0433] Step 8:

[0434] The server uses analysis tools to suggest rest locations and sends that information to the terminal. The terminal then notifies the user of the suggested rest locations.

[0435] (Example 1)

[0436] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0437] There is a need to provide a system that allows drivers to safely set their destination without taking their eyes off the road while traveling by car or other means, and to find an efficient route based on real-time traffic information. Furthermore, the system must be able to flexibly adapt to changes in traffic conditions.

[0438] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0439] In this invention, the server includes a conversion means for converting voice information into text information, an analysis means for analyzing target location information from the text information, and a route calculation means for acquiring spatiotemporal information and calculating the optimal route. This makes it possible for the driver to set a destination without using their hands and to obtain a safe and optimal route based on traffic information.

[0440] "Voice information" refers to information acquired as voice data, including the user's voice instructions.

[0441] "Textual information" refers to text data converted from audio information, and is a data format used by systems for analysis.

[0442] "Conversion means" refers to a process or device that converts audio information into text information, and utilizes speech recognition technology.

[0443] "Analysis means" refers to a means for extracting information such as target locations from textual information, and for the system to understand and process it.

[0444] "Spatiotemporal information" refers to data that includes real-time traffic data and location information, and is used for route calculations.

[0445] A "path calculation means" is an algorithm or process that searches for and calculates the optimal path based on spatiotemporal information.

[0446] "Presentation means" refers to a device or method that visually or audibly notifies the user of the calculated optimal route.

[0447] "Device" refers to the entire system that integrates the above-mentioned means to support the user's safe and efficient travel to their destination.

[0448] This invention relates to a voice recognition-based navigation system that enables users to safely and efficiently set and reach their destinations while on the move. The system converts voice information into text information, calculates the optimal route using real-time spatiotemporal information, and provides that route to the user.

[0449] Performing voice input and conversion

[0450] Users can input their target location by voice while driving or using the terminal. This input is acquired through the terminal's built-in microphone. The terminal uses voice recognition software (e.g., a voice recognition cloud service) to convert the voice information into text. This conversion process is based on acoustic models and natural language processing techniques.

[0451] Data analysis and information provision

[0452] Text information is sent from the terminal to the server, which uses analysis tools to analyze the target location and necessary itinerary information. Based on the analyzed information, the optimal route is calculated, taking into account real-time traffic conditions and geographical data. The server can obtain the latest spatiotemporal information using external traffic information service APIs.

[0453] Route notification and display

[0454] Once the optimal route calculation is complete, the server sends that information to the terminal. The terminal provides the user with route information using visual map displays and voice guidance. This allows the user to efficiently navigate to their destination while keeping their eyes on the road, even while driving.

[0455] Specific examples and prompt statements

[0456] As a concrete example, suppose a user gives a voice command to their device saying, "Set the next destination to the shopping center." This voice command is converted into text, the server analyzes the intent, and sets the shopping center as the target location. Then, the optimal route based on actual traffic conditions is calculated, and the directions are displayed on the device.

[0457] As an example of a prompt, inputting "Design an algorithm that analyzes the user-specified destination and calculates the shortest route using the latest traffic information" into the generating AI model can optimize the analysis and route calculation process.

[0458] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0459] Step 1:

[0460] The user gives voice commands to the terminal to set a destination. For example, the user might say, "Set the next destination to the shopping center." The terminal receives this voice input and uses speech recognition software to convert the voice data into text data. The input is voice information, and the output is text information. Acoustic models and natural language processing techniques are used for this conversion.

[0461] Step 2:

[0462] The terminal sends the converted text information to the server. The server uses parsing tools to extract destination and additional information from this text information. The input for parsing is text information, and the output is structured data about the destination and travel information. In this process, the server uses a generative AI model to accurately interpret the user's instructions.

[0463] Step 3:

[0464] The server acquires spatiotemporal information and calculates the optimal route based on the analyzed information. It utilizes external traffic information service APIs to obtain real-time, fluctuating traffic data. The input is structured destination data and spatiotemporal information, and the output is optimal route information. Here, the route is flexibly adjusted according to traffic conditions.

[0465] Step 4:

[0466] The server sends the calculated optimal route information to the terminal. Based on this information, the terminal displays a visual map and starts voice guidance. The input is the optimal route information, and the output is visual and voice guidance to the user. Specifically, the terminal instructs the user to "move into the left lane 500 meters ahead."

[0467] Step 5:

[0468] The user follows notifications from the device and drives safely and efficiently towards their destination. If a break is needed, they can choose to stop at a rest stop suggested by the device. This ensures a comfortable journey to the destination while maintaining safety while driving.

[0469] (Application Example 1)

[0470] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0471] Autonomous vehicles are required to support safe and efficient travel while reducing the burden on passengers. Furthermore, they need to provide a comfortable travel environment by responding to real-time traffic conditions and suggesting rest stops.

[0472] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0473] In this invention, the server includes a voice conversion means for converting voice signals into text information, an analysis means for analyzing destination information from the text information, and a route calculation means for acquiring dynamic information and calculating the optimal route. This enables safe and efficient travel and comfortable driving assistance.

[0474] A "speech signal" is an electrical signal obtained by converting a speaker's voice into an electrical signal.

[0475] "Textual information" refers to text data generated based on audio signals.

[0476] "Speech conversion means" refers to a device or system that has the function of converting speech signals into text information.

[0477] "Destination information" refers to information about the user's intended location, including the destination and intermediate stops.

[0478] "Analysis means" refers to a device or system that has the function of analyzing destination information from textual information.

[0479] "Dynamic information" refers to real-time changes in traffic conditions and road information.

[0480] A "route calculation means" is a device or system that has the function of calculating the optimal route based on dynamic information.

[0481] "Information presentation means" refers to a device or system that has the function of providing a calculated route to the user visually or audibly.

[0482] "Means of providing voice guidance" refers to a system that provides users with routes and directions via voice.

[0483] "Means for providing visual route display" refers to a device or system that has the function of displaying a route on the user's screen or display in map format or similar.

[0484] This invention realizes a navigation system for autonomous vehicles that utilizes voice recognition technology and generative AI. The system sets destinations and waypoints based on the user's voice instructions, calculates the optimal route based on real-time traffic information, and provides it. A specific embodiment is shown below.

[0485] The server analyzes the user's voice input using a speech conversion device that converts "voice signals" into "text information." A terminal equipped with a microphone is used for this purpose. The speech recognition engine utilizes the Python speech_recognition library. The converted text information is sent to an analysis device that analyzes the user's intent. The analysis device extracts the "destination information" specified by the user. A generative AI model is involved in this analysis process to understand the user's intent with high accuracy.

[0486] Next, the server calculates the optimal route based on "dynamic information" that reflects real-time traffic conditions. The "route calculation means" uses API communication to obtain the latest traffic data. This information is then sent to the terminal as the calculated route.

[0487] The terminal provides route information to the user using information display means. This display includes visual route display on the screen and means of providing voice guidance. The user visually checks the map display and receives route instructions via voice guidance. For example, if the user says, "Set the next destination to the shopping center," the optimal route will be suggested based on that instruction. A voice notification such as "Let's take a break at the next service area" will also be provided.

[0488] By implementing this invention, users can safely maintain their gaze on the road while driving and achieve efficient travel. A specific prompt might be, "Generate route guidance text when the user inputs the next waypoint by voice. The destination is a shopping center." Based on this prompt, the generation AI model generates and provides contextually appropriate guidance.

[0489] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0490] Step 1:

[0491] The user initiates voice input. The user speaks into a microphone installed in the vehicle, stating their destination and intermediate stops. This voice signal is captured by the terminal and transmitted to a voice conversion device.

[0492] Step 2:

[0493] The device converts audio signals into text information. Using a speech recognition engine, it converts the user's voice into text data. This process utilizes a speech recognition technology library to extract highly accurate text information, which is the output.

[0494] Step 3:

[0495] The server receives text information and uses analysis tools to analyze destination information. Using text information as input, it utilizes a generative AI model to identify the user's intended destination and intermediate points. This analysis result is output as destination information.

[0496] Step 4:

[0497] The server acquires dynamic information and calculates the optimal route. It obtains real-time traffic data via API communication and processes the dynamic information using a route calculation method. It generates a route that avoids congestion points and outputs the calculated optimal route.

[0498] Step 5:

[0499] The terminal provides the user with the optimal route through information presentation methods. It displays a visual route on the screen and provides route instructions via voice guidance. The user receives voice guidance from the terminal while confirming the route on the screen. Based on the voice guidance and displayed map, safe and efficient travel is achieved.

[0500] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0501] This invention relates to a navigation system that combines an emotion engine that analyzes user voice input and recognizes emotional states. This system includes a voice conversion means, an analysis means, a route calculation means, a display means, and an emotion engine. The embodiments of this invention aim to provide users with a safe and comfortable driving environment.

[0502] The user inputs their destination and intermediate stops by voice into the terminal. The terminal uses a voice conversion device to convert the voice data into text data and sends this text data to the server. The server uses an analysis device to analyze the user's intent from the text data and extracts destination information.

[0503] The emotion engine similarly analyzes the user's emotional state from their voice. This analysis allows it to recognize whether the user is stressed, relaxed, or otherwise emotionally unstable. The server incorporates this emotional data into route calculations, selecting routes and rest stops that are appropriate for the user's emotional state.

[0504] For example, if a user is feeling stressed due to a congested route, the emotion engine will detect this, and the server will prioritize suggesting less-loaded routes. Also, if it's determined that the user's stress levels are high due to prolonged driving, it will recommend relaxing rest stops. This information is communicated to the user through the device's display.

[0505] Furthermore, the server utilizes real-time traffic data to constantly calculate the optimal route and provides care based on the user's emotional state. The terminal supports safe and comfortable driving by providing the user with information received from the server through voice guidance and visual displays.

[0506] Thus, the present invention makes it possible to provide a more personalized navigation service by comprehensively utilizing the user's voice and emotional state.

[0507] The following describes the processing flow.

[0508] Step 1:

[0509] The user speaks into the device and says, "Set the next destination to the public library." The device then acquires the voice data through its microphone.

[0510] Step 2:

[0511] The terminal converts the audio data into text data using a speech conversion device and sends this text data to the server.

[0512] Step 3:

[0513] The server uses an analysis tool to extract destination information from the text data and recognizes "Citizen's Library" as the next destination.

[0514] Step 4:

[0515] The device simultaneously transmits voice data to the emotion engine, which analyzes the voice characteristics. The emotion engine determines the user's stress level and relaxation level.

[0516] Step 5:

[0517] The server acquires real-time traffic data and uses a route calculation tool to calculate the optimal route to the destination.

[0518] Step 6:

[0519] The server takes data from the emotion engine into consideration and, if the user is in a high-stress state, selects a route to reduce stress.

[0520] Step 7:

[0521] The server determines the optimal route based on sentiment analysis results and traffic conditions, and, if necessary, selects rest stops, then transmits this information to the terminal.

[0522] Step 8:

[0523] The terminal displays route information and rest stops received from the server to the user via a display device. Information is provided to the user while driving through visual displays and voice guidance.

[0524] (Example 2)

[0525] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0526] Conventional navigation systems have a problem in that they do not select routes or rest stops that take into account the user's emotional state, thus failing to reduce user stress and fatigue. Users are prone to mental and physical fatigue from long hours of driving and traffic jams, which can reduce driving safety. To solve this problem, there is a need to realize route guidance based on the user's emotional state.

[0527] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0528] In this invention, the server includes a voice conversion means for converting voice data into symbolic data, an analysis means for analyzing location information from the symbolic data, and an emotion recognition means for analyzing emotional state. This makes it possible to calculate routes and select rest stops while taking into account the user's emotional state.

[0529] "Speech conversion means" refers to a device or software for converting speech data into symbolic data.

[0530] "Analysis means" refers to a device or program that has the function of extracting location information from symbolic data and analyzing the user's intent.

[0531] "Emotion recognition means" refers to a device or program that has the function of analyzing the emotional state from the user's voice and providing the results to other system components.

[0532] "Selection means" refers to a device or program that has the function of selecting an appropriate resting place based on the user's emotional state.

[0533] "Dynamic information" refers to real-time traffic data and information about road conditions.

[0534] "Route calculation means" refers to a device or program for calculating the optimal route based on location information and dynamic information.

[0535] "Presentation means" refers to a device or program for providing users with optimal route information through visual or auditory means.

[0536] This invention relates to a comprehensive navigation system that provides a safe and comfortable driving environment based on user voice input. This system consists of voice conversion means, analysis means, emotion recognition means, selection means, route calculation means, and presentation means.

[0537] Users can input their destination and intermediate stops via voice input through their device. The device converts the voice data into text data using a voice conversion mechanism. For this purpose, it uses a common API for voice recognition software. The converted text data is sent to the server.

[0538] The server analyzes text data using analytical tools to extract user destination information. This process utilizes common natural language processing techniques as a generative AI model. Furthermore, the server analyzes the user's emotional state from their voice using emotion recognition tools. This allows it to assess whether the user is relaxed or stressed.

[0539] The server calculates the optimal route using route calculation means based on the user's emotional state and real-time traffic data. For example, if the user is tired while driving, the system suggests relaxing rest stops using selection means. The optimized route information is delivered to the terminal through presentation means and guided to the user visually or audibly.

[0540] For example, if a user is heading to a campsite with their family, they might voice-input "Tell me the route to the next campsite" into their device. Based on this information, the server calculates the route, and the device can then provide instructions such as, "There's a park on your right where you can relax and recover from driving fatigue."

[0541] An example of a prompt for a generative AI model might be: "I'm a little tired from driving for a long time. Please suggest a route that includes relaxing rest stops."

[0542] Thus, the present invention comprehensively utilizes the user's voice and emotional state to provide a more personalized navigation service.

[0543] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0544] Step 1:

[0545] The user inputs their destination and intermediate stops into the terminal by voice. This input is acquired by the terminal as digital voice data. Using a voice conversion method, the terminal converts the digital voice data into symbolic data (text data). Specifically, the terminal uses voice recognition software to analyze the waveform of the voice and converts its content into natural language text. The output is text data.

[0546] Step 2:

[0547] The terminal sends the converted text data to the server. The server receives the text data using an analysis tool and analyzes the location information within the text. Specifically, it utilizes a generative AI model to extract destination and waypoint information from the text. In this process, techniques such as noun extraction and place name recognition are used. The output is destination information data.

[0548] Step 3:

[0549] The server uses emotion recognition tools to analyze the user's emotional state from their digital voice data. This step involves analyzing factors such as voice tone, speaking speed, and linguistic characteristics to assess stress levels and relaxation. Specifically, emotion analysis is performed using machine learning algorithms. The output is data indicating the emotional state.

[0550] Step 4:

[0551] The server receives analyzed destination information and emotional state as input and uses a selection mechanism to choose an appropriate rest stop. This process takes into account real-time traffic data and the user's emotional state; for example, if the user is tired, it will choose a more comfortable rest stop. The output is information on recommended rest stops.

[0552] Step 5:

[0553] The server uses a route calculation system to calculate the optimal route based on real-time dynamic information. This optimization considers not only the shortest distance in normal travel time, but also the user's emotional state. The specific operation includes a calculation process utilizing a traffic information database. The output is optimized route data.

[0554] Step 6:

[0555] Upon receiving optimal route data and rest stop information from the server, the terminal notifies the user of the information using a presentation mechanism. Here, the user can receive instructions regarding the route and rest stops through visual map displays and voice guidance. The output provides route guidance and rest stop information that the user can view. Specifically, the terminal displays the guided route on its screen and provides navigation using speech synthesis technology.

[0556] (Application Example 2)

[0557] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0558] Conventional navigation systems merely presented the optimal route to the destination without considering the user's emotional state. Therefore, even when a user was feeling stressed or seeking a relaxing driving environment, it was difficult to suggest a suitable route, resulting in a failure to adequately ensure driving comfort and safety.

[0559] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0560] In this invention, the server includes a conversion means for converting voice data into text data, an analysis means for analyzing destination information and the user's emotional state from the text data, a calculation means for acquiring real-time traffic data and calculating the optimal route, and a presentation means for presenting the optimal route and providing information according to the emotional state. This makes it possible to adjust the driving environment based on the user's emotional state.

[0561] "Audio data" refers to data that is a digital representation of the audio signal emitted by a user.

[0562] "Text data" refers to string data obtained by converting audio data.

[0563] "Conversion means" refers to a device or software that has the role of converting audio data into text data.

[0564] "Analysis means" refers to a device or software that has the function of analyzing destination information and the user's emotional state from text data.

[0565] "Real-time traffic data" refers to data that shows the current traffic situation over time.

[0566] "Computation means" refers to a device or software that calculates the optimal route based on real-time traffic data and analysis results.

[0567] "Presentation means" refers to a device or software that provides the user with information that corresponds to the calculated optimal route and emotional state, either visually or audibly.

[0568] "Emotional state" refers to the emotions and moods a user is currently experiencing, including states such as stress and relaxation.

[0569] The system for realizing this invention includes a terminal for receiving voice data, a server for analyzing and processing the data, and a display device for providing feedback to the user. Voice data spoken by the user is first digitized by the terminal's voice input function. The voice data is then sent to the server and converted into text data by a conversion means. Speech recognition software such as Google Cloud Speech-to-Text can be used for the voice conversion.

[0570] The server then uses an analysis tool to analyze destination information and the user's emotional state from the text data. A custom model using TensorFlow is employed for the emotional state analysis, determining whether the user is stressed, relaxed, etc. The analysis results are then passed to a computation tool, which calculates the optimal route based on real-time traffic data. This process utilizes the Google Maps API, enabling route selection that takes current traffic conditions into account.

[0571] Finally, the calculated route and suitable rest stops and relaxation spots are communicated to the user visually or audibly through the terminal's display mechanisms. This reduces stress and enables a more comfortable and safer driving experience.

[0572] For example, if a user voice-inputs, "I want to be guided to a quiet and calming place," the system will analyze this intention and emotional state, select the most comfortable route, and set the in-car sound environment to relaxing music. An example of a prompt to the generating AI model might be, "Please help design an application that analyzes the emotional state and intention from a user's voice input and suggests the optimal route. For example, if the user is feeling stressed, please give me some ideas for suggesting a quiet route or relaxation spot."

[0573] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0574] Step 1:

[0575] The user inputs their destination or specific requests by voice into the terminal. The voice data is captured by the terminal and digitized. This data is input through a microphone with voice input / output capabilities.

[0576] Step 2:

[0577] The terminal sends audio data to the server. The server first converts the audio data into text data. Speech recognition software such as Google Cloud Speech-to-Text is used for this conversion, and in this process, text data that corresponds to the audio is output.

[0578] Step 3:

[0579] The server uses analysis tools to analyze the text data in detail. The analysis employs an NLP engine to identify the user's destination information and emotional state from the text data. The analysis results output the destination and emotional status.

[0580] Step 4:

[0581] Based on the analysis results, the server begins acquiring real-time traffic data using computational methods. The Google Maps API is used for this real-time traffic data, and the optimal route is calculated based on this data. The output data is a customized route adjusted to the user's emotional state.

[0582] Step 5:

[0583] The server sends the calculated route to the terminal and provides the user with route information visually or audibly through the terminal's display mechanisms. Music and audio guides that take the user's emotional state into consideration are used to create a relaxing environment.

[0584] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0585] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0586] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0587] [Fourth Embodiment]

[0588] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0589] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0590] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0591] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0592] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0593] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0594] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0595] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0596] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0597] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0598] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0599] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0600] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0601] This invention is a navigation system that combines speech recognition technology and generative AI to help users move safely and efficiently. Embodiments of the invention are configured around speech conversion means, analysis means, route calculation means, and display means.

[0602] While driving, the user inputs their destination and waypoints by voice into the terminal. The terminal then uses a voice conversion device to convert the voice data into text data and sends that data to the server.

[0603] The server receives text data and uses an analysis tool to analyze the user's set destination and waypoints. Based on this analysis, the server uses real-time traffic data to calculate the optimal route to the destination.

[0604] The route calculation system dynamically generates the optimal route to avoid congestion and accidents based on traffic information acquired on the server. The calculated route information is sent to the terminal and notified to the user.

[0605] The terminal provides the user with route information received from the server through a display mechanism. This display includes voice guidance and visual route displays. Rest stop suggestions are also displayed on the terminal as information from the server, prompting the user to take breaks based on their selection.

[0606] For example, if a user instructs their device to "set Park Mall as the next destination," the voice conversion system converts the speech to text, and the server analyzes the intent. The server sets Park Mall as the destination and calculates the optimal route based on real-time traffic data. Route guidance is then displayed on the device, and notifications such as "Let's take a break at the next service area" are provided.

[0607] This invention allows users to maintain safety while driving, while keeping their gaze focused on the road. Furthermore, it enables efficient and comfortable driving by providing flexible route changes and rest stop suggestions according to traffic conditions.

[0608] The following describes the processing flow.

[0609] Step 1:

[0610] The user voice-inputs their destination into the device. The device then retrieves that voice data.

[0611] Step 2:

[0612] The terminal converts the audio data into text data using a speech conversion device. This text data is then sent to the server.

[0613] Step 3:

[0614] The server analyzes the received text data using an analysis tool and extracts information about the destination and intermediate points.

[0615] Step 4:

[0616] The server acquires real-time traffic data and uses that data to calculate the optimal route to the destination using a route calculation tool.

[0617] Step 5:

[0618] The calculated optimal route is sent from the server to the terminal.

[0619] Step 6:

[0620] The terminal displays the received optimal route information to the user via a display device. This includes voice guidance and visual route display.

[0621] Step 7:

[0622] While the user is driving, the server continuously monitors traffic conditions and recalculates the route as needed.

[0623] Step 8:

[0624] The server uses analysis tools to suggest rest locations and sends that information to the terminal. The terminal then notifies the user of the suggested rest locations.

[0625] (Example 1)

[0626] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0627] There is a need to provide a system that allows drivers to safely set their destination without taking their eyes off the road while traveling by car or other means, and to find an efficient route based on real-time traffic information. Furthermore, the system must be able to flexibly adapt to changes in traffic conditions.

[0628] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0629] In this invention, the server includes a conversion means for converting voice information into text information, an analysis means for analyzing target location information from the text information, and a route calculation means for acquiring spatiotemporal information and calculating the optimal route. This makes it possible for the driver to set a destination without using their hands and to obtain a safe and optimal route based on traffic information.

[0630] "Voice information" refers to information acquired as voice data, including the user's voice instructions.

[0631] "Textual information" refers to text data converted from audio information, and is a data format used by systems for analysis.

[0632] "Conversion means" refers to a process or device that converts audio information into text information, and utilizes speech recognition technology.

[0633] "Analysis means" refers to a means for extracting information such as target locations from textual information, and for the system to understand and process it.

[0634] "Spatiotemporal information" refers to data that includes real-time traffic data and location information, and is used for route calculations.

[0635] A "path calculation means" is an algorithm or process that searches for and calculates the optimal path based on spatiotemporal information.

[0636] "Presentation means" refers to a device or method that visually or audibly notifies the user of the calculated optimal route.

[0637] "Device" refers to the entire system that integrates the above-mentioned means to support the user's safe and efficient travel to their destination.

[0638] This invention relates to a voice recognition-based navigation system that enables users to safely and efficiently set and reach their destinations while on the move. The system converts voice information into text information, calculates the optimal route using real-time spatiotemporal information, and provides that route to the user.

[0639] Performing voice input and conversion

[0640] Users can input their target location by voice while driving or using the terminal. This input is acquired through the terminal's built-in microphone. The terminal uses voice recognition software (e.g., a voice recognition cloud service) to convert the voice information into text. This conversion process is based on acoustic models and natural language processing techniques.

[0641] Data analysis and information provision

[0642] Text information is sent from the terminal to the server, which uses analysis tools to analyze the target location and necessary itinerary information. Based on the analyzed information, the optimal route is calculated, taking into account real-time traffic conditions and geographical data. The server can obtain the latest spatiotemporal information using external traffic information service APIs.

[0643] Route notification and display

[0644] Once the optimal route calculation is complete, the server sends that information to the terminal. The terminal provides the user with route information using visual map displays and voice guidance. This allows the user to efficiently navigate to their destination while keeping their eyes on the road, even while driving.

[0645] Specific examples and prompt statements

[0646] As a concrete example, suppose a user gives a voice command to their device saying, "Set the next destination to the shopping center." This voice command is converted into text, the server analyzes the intent, and sets the shopping center as the target location. Then, the optimal route based on actual traffic conditions is calculated, and the directions are displayed on the device.

[0647] As an example of a prompt, inputting "Design an algorithm that analyzes the user-specified destination and calculates the shortest route using the latest traffic information" into the generating AI model can optimize the analysis and route calculation process.

[0648] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0649] Step 1:

[0650] The user gives voice commands to the terminal to set a destination. For example, the user might say, "Set the next destination to the shopping center." The terminal receives this voice input and uses speech recognition software to convert the voice data into text data. The input is voice information, and the output is text information. Acoustic models and natural language processing techniques are used for this conversion.

[0651] Step 2:

[0652] The terminal sends the converted text information to the server. The server uses parsing tools to extract destination and additional information from this text information. The input for parsing is text information, and the output is structured data about the destination and travel information. In this process, the server uses a generative AI model to accurately interpret the user's instructions.

[0653] Step 3:

[0654] The server acquires spatiotemporal information and calculates the optimal route based on the analyzed information. It utilizes external traffic information service APIs to obtain real-time, fluctuating traffic data. The input is structured destination data and spatiotemporal information, and the output is optimal route information. Here, the route is flexibly adjusted according to traffic conditions.

[0655] Step 4:

[0656] The server sends the calculated optimal route information to the terminal. Based on this information, the terminal displays a visual map and starts voice guidance. The input is the optimal route information, and the output is visual and voice guidance to the user. Specifically, the terminal instructs the user to "move into the left lane 500 meters ahead."

[0657] Step 5:

[0658] The user follows notifications from the device and drives safely and efficiently towards their destination. If a break is needed, they can choose to stop at a rest stop suggested by the device. This ensures a comfortable journey to the destination while maintaining safety while driving.

[0659] (Application Example 1)

[0660] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0661] Autonomous vehicles are required to support safe and efficient travel while reducing the burden on passengers. Furthermore, they need to provide a comfortable travel environment by responding to real-time traffic conditions and suggesting rest stops.

[0662] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0663] In this invention, the server includes a voice conversion means for converting voice signals into text information, an analysis means for analyzing destination information from the text information, and a route calculation means for acquiring dynamic information and calculating the optimal route. This enables safe and efficient travel and comfortable driving assistance.

[0664] A "speech signal" is an electrical signal obtained by converting a speaker's voice into an electrical signal.

[0665] "Textual information" refers to text data generated based on audio signals.

[0666] "Speech conversion means" refers to a device or system that has the function of converting speech signals into text information.

[0667] "Destination information" refers to information about the user's intended location, including the destination and intermediate stops.

[0668] "Analysis means" refers to a device or system that has the function of analyzing destination information from textual information.

[0669] "Dynamic information" refers to real-time changes in traffic conditions and road information.

[0670] A "route calculation means" is a device or system that has the function of calculating the optimal route based on dynamic information.

[0671] "Information presentation means" refers to a device or system that has the function of providing a calculated route to the user visually or audibly.

[0672] "Means of providing voice guidance" refers to a system that provides users with routes and directions via voice.

[0673] "Means for providing visual route display" refers to a device or system that has the function of displaying a route on the user's screen or display in map format or similar.

[0674] This invention realizes a navigation system for autonomous vehicles that utilizes voice recognition technology and generative AI. The system sets destinations and waypoints based on the user's voice instructions, calculates the optimal route based on real-time traffic information, and provides it. A specific embodiment is shown below.

[0675] The server analyzes the user's voice input using a speech conversion device that converts "voice signals" into "text information." A terminal equipped with a microphone is used for this purpose. The speech recognition engine utilizes the Python speech_recognition library. The converted text information is sent to an analysis device that analyzes the user's intent. The analysis device extracts the "destination information" specified by the user. A generative AI model is involved in this analysis process to understand the user's intent with high accuracy.

[0676] Next, the server calculates the optimal route based on "dynamic information" that reflects real-time traffic conditions. The "route calculation means" uses API communication to obtain the latest traffic data. This information is then sent to the terminal as the calculated route.

[0677] The terminal provides route information to the user using information display means. This display includes visual route display on the screen and means of providing voice guidance. The user visually checks the map display and receives route instructions via voice guidance. For example, if the user says, "Set the next destination to the shopping center," the optimal route will be suggested based on that instruction. A voice notification such as "Let's take a break at the next service area" will also be provided.

[0678] By implementing this invention, users can safely maintain their gaze on the road while driving and achieve efficient travel. A specific prompt might be, "Generate route guidance text when the user inputs the next waypoint by voice. The destination is a shopping center." Based on this prompt, the generation AI model generates and provides contextually appropriate guidance.

[0679] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0680] Step 1:

[0681] The user initiates voice input. The user speaks into a microphone installed in the vehicle, stating their destination and intermediate stops. This voice signal is captured by the terminal and transmitted to a voice conversion device.

[0682] Step 2:

[0683] The device converts audio signals into text information. Using a speech recognition engine, it converts the user's voice into text data. This process utilizes a speech recognition technology library to extract highly accurate text information, which is the output.

[0684] Step 3:

[0685] The server receives text information and uses analysis tools to analyze destination information. Using text information as input, it utilizes a generative AI model to identify the user's intended destination and intermediate points. This analysis result is output as destination information.

[0686] Step 4:

[0687] The server acquires dynamic information and calculates the optimal route. It obtains real-time traffic data via API communication and processes the dynamic information using a route calculation method. It generates a route that avoids congestion points and outputs the calculated optimal route.

[0688] Step 5:

[0689] The terminal provides the user with the optimal route through information presentation methods. It displays a visual route on the screen and provides route instructions via voice guidance. The user receives voice guidance from the terminal while confirming the route on the screen. Based on the voice guidance and displayed map, safe and efficient travel is achieved.

[0690] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0691] This invention relates to a navigation system that combines an emotion engine that analyzes user voice input and recognizes emotional states. This system includes a voice conversion means, an analysis means, a route calculation means, a display means, and an emotion engine. The embodiments of this invention aim to provide users with a safe and comfortable driving environment.

[0692] The user inputs their destination and intermediate stops by voice into the terminal. The terminal uses a voice conversion device to convert the voice data into text data and sends this text data to the server. The server uses an analysis device to analyze the user's intent from the text data and extracts destination information.

[0693] The emotion engine similarly analyzes the user's emotional state from their voice. This analysis allows it to recognize whether the user is stressed, relaxed, or otherwise emotionally unstable. The server incorporates this emotional data into route calculations, selecting routes and rest stops that are appropriate for the user's emotional state.

[0694] For example, if a user is feeling stressed due to a congested route, the emotion engine will detect this, and the server will prioritize suggesting less-loaded routes. Also, if it's determined that the user's stress levels are high due to prolonged driving, it will recommend relaxing rest stops. This information is communicated to the user through the device's display.

[0695] Furthermore, the server utilizes real-time traffic data to constantly calculate the optimal route and provides care based on the user's emotional state. The terminal supports safe and comfortable driving by providing the user with information received from the server through voice guidance and visual displays.

[0696] Thus, the present invention makes it possible to provide a more personalized navigation service by comprehensively utilizing the user's voice and emotional state.

[0697] The following describes the processing flow.

[0698] Step 1:

[0699] The user speaks into the device and says, "Set the next destination to the public library." The device then acquires the voice data through its microphone.

[0700] Step 2:

[0701] The terminal converts the audio data into text data using a speech conversion device and sends this text data to the server.

[0702] Step 3:

[0703] The server uses an analysis tool to extract destination information from the text data and recognizes "Citizen's Library" as the next destination.

[0704] Step 4:

[0705] The device simultaneously transmits voice data to the emotion engine, which analyzes the voice characteristics. The emotion engine determines the user's stress level and relaxation level.

[0706] Step 5:

[0707] The server acquires real-time traffic data and uses a route calculation tool to calculate the optimal route to the destination.

[0708] Step 6:

[0709] The server takes data from the emotion engine into consideration and, if the user is in a high-stress state, selects a route to reduce stress.

[0710] Step 7:

[0711] The server determines the optimal route based on sentiment analysis results and traffic conditions, and, if necessary, selects rest stops, then transmits this information to the terminal.

[0712] Step 8:

[0713] The terminal displays route information and rest stops received from the server to the user via a display device. Information is provided to the user while driving through visual displays and voice guidance.

[0714] (Example 2)

[0715] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0716] Conventional navigation systems have a problem in that they do not select routes or rest stops that take into account the user's emotional state, thus failing to reduce user stress and fatigue. Users are prone to mental and physical fatigue from long hours of driving and traffic jams, which can reduce driving safety. To solve this problem, there is a need to realize route guidance based on the user's emotional state.

[0717] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0718] In this invention, the server includes a voice conversion means for converting voice data into symbolic data, an analysis means for analyzing location information from the symbolic data, and an emotion recognition means for analyzing emotional state. This makes it possible to calculate routes and select rest stops while taking into account the user's emotional state.

[0719] "Speech conversion means" refers to a device or software for converting speech data into symbolic data.

[0720] "Analysis means" refers to a device or program that has the function of extracting location information from symbolic data and analyzing the user's intent.

[0721] "Emotion recognition means" refers to a device or program that has the function of analyzing the emotional state from the user's voice and providing the results to other system components.

[0722] "Selection means" refers to a device or program that has the function of selecting an appropriate resting place based on the user's emotional state.

[0723] "Dynamic information" refers to real-time traffic data and information about road conditions.

[0724] "Route calculation means" refers to a device or program for calculating the optimal route based on location information and dynamic information.

[0725] "Presentation means" refers to a device or program for providing users with optimal route information through visual or auditory means.

[0726] This invention relates to a comprehensive navigation system that provides a safe and comfortable driving environment based on user voice input. This system consists of voice conversion means, analysis means, emotion recognition means, selection means, route calculation means, and presentation means.

[0727] Users can input their destination and intermediate stops via voice input through their device. The device converts the voice data into text data using a voice conversion mechanism. For this purpose, it uses a common API for voice recognition software. The converted text data is sent to the server.

[0728] The server analyzes text data using analytical tools to extract user destination information. This process utilizes common natural language processing techniques as a generative AI model. Furthermore, the server analyzes the user's emotional state from their voice using emotion recognition tools. This allows it to assess whether the user is relaxed or stressed.

[0729] The server calculates the optimal route using route calculation means based on the user's emotional state and real-time traffic data. For example, if the user is tired while driving, the system suggests relaxing rest stops using selection means. The optimized route information is delivered to the terminal through presentation means and guided to the user visually or audibly.

[0730] For example, if a user is heading to a campsite with their family, they might voice-input "Tell me the route to the next campsite" into their device. Based on this information, the server calculates the route, and the device can then provide instructions such as, "There's a park on your right where you can relax and recover from driving fatigue."

[0731] An example of a prompt for a generative AI model might be: "I'm a little tired from driving for a long time. Please suggest a route that includes relaxing rest stops."

[0732] Thus, the present invention comprehensively utilizes the user's voice and emotional state to provide a more personalized navigation service.

[0733] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0734] Step 1:

[0735] The user inputs their destination and intermediate stops into the terminal by voice. This input is acquired by the terminal as digital voice data. Using a voice conversion method, the terminal converts the digital voice data into symbolic data (text data). Specifically, the terminal uses voice recognition software to analyze the waveform of the voice and converts its content into natural language text. The output is text data.

[0736] Step 2:

[0737] The terminal sends the converted text data to the server. The server receives the text data using an analysis tool and analyzes the location information within the text. Specifically, it utilizes a generative AI model to extract destination and waypoint information from the text. In this process, techniques such as noun extraction and place name recognition are used. The output is destination information data.

[0738] Step 3:

[0739] The server uses emotion recognition tools to analyze the user's emotional state from their digital voice data. This step involves analyzing factors such as voice tone, speaking speed, and linguistic characteristics to assess stress levels and relaxation. Specifically, emotion analysis is performed using machine learning algorithms. The output is data indicating the emotional state.

[0740] Step 4:

[0741] The server receives analyzed destination information and emotional state as input and uses a selection mechanism to choose an appropriate rest stop. This process takes into account real-time traffic data and the user's emotional state; for example, if the user is tired, it will choose a more comfortable rest stop. The output is information on recommended rest stops.

[0742] Step 5:

[0743] The server uses a route calculation system to calculate the optimal route based on real-time dynamic information. This optimization considers not only the shortest distance in normal travel time, but also the user's emotional state. The specific operation includes a calculation process utilizing a traffic information database. The output is optimized route data.

[0744] Step 6:

[0745] Upon receiving optimal route data and rest stop information from the server, the terminal notifies the user of the information using a presentation mechanism. Here, the user can receive instructions regarding the route and rest stops through visual map displays and voice guidance. The output provides route guidance and rest stop information that the user can view. Specifically, the terminal displays the guided route on its screen and provides navigation using speech synthesis technology.

[0746] (Application Example 2)

[0747] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0748] Conventional navigation systems merely presented the optimal route to the destination without considering the user's emotional state. Therefore, even when a user was feeling stressed or seeking a relaxing driving environment, it was difficult to suggest a suitable route, resulting in a failure to adequately ensure driving comfort and safety.

[0749] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0750] In this invention, the server includes a conversion means for converting voice data into text data, an analysis means for analyzing destination information and the user's emotional state from the text data, a calculation means for acquiring real-time traffic data and calculating the optimal route, and a presentation means for presenting the optimal route and providing information according to the emotional state. This makes it possible to adjust the driving environment based on the user's emotional state.

[0751] "Audio data" refers to data that is a digital representation of the audio signal emitted by a user.

[0752] "Text data" refers to string data obtained by converting audio data.

[0753] "Conversion means" refers to a device or software that has the role of converting audio data into text data.

[0754] "Analysis means" refers to a device or software that has the function of analyzing destination information and the user's emotional state from text data.

[0755] "Real-time traffic data" refers to data that shows the current traffic situation over time.

[0756] "Computation means" refers to a device or software that calculates the optimal route based on real-time traffic data and analysis results.

[0757] "Presentation means" refers to a device or software that provides the user with information that corresponds to the calculated optimal route and emotional state, either visually or audibly.

[0758] "Emotional state" refers to the emotions and moods a user is currently experiencing, including states such as stress and relaxation.

[0759] The system for realizing this invention includes a terminal for receiving voice data, a server for analyzing and processing the data, and a display device for providing feedback to the user. Voice data spoken by the user is first digitized by the terminal's voice input function. The voice data is then sent to the server and converted into text data by a conversion means. Speech recognition software such as Google Cloud Speech-to-Text can be used for the voice conversion.

[0760] The server then uses an analysis tool to analyze destination information and the user's emotional state from the text data. A custom model using TensorFlow is employed for the emotional state analysis, determining whether the user is stressed, relaxed, etc. The analysis results are then passed to a computation tool, which calculates the optimal route based on real-time traffic data. This process utilizes the Google Maps API, enabling route selection that takes current traffic conditions into account.

[0761] Finally, the calculated route and suitable rest stops and relaxation spots are communicated to the user visually or audibly through the terminal's display mechanisms. This reduces stress and enables a more comfortable and safer driving experience.

[0762] For example, if a user voice-inputs, "I want to be guided to a quiet and calming place," the system will analyze this intention and emotional state, select the most comfortable route, and set the in-car sound environment to relaxing music. An example of a prompt to the generating AI model might be, "Please help design an application that analyzes the emotional state and intention from a user's voice input and suggests the optimal route. For example, if the user is feeling stressed, please give me some ideas for suggesting a quiet route or relaxation spot."

[0763] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0764] Step 1:

[0765] The user inputs their destination or specific requests by voice into the terminal. The voice data is captured by the terminal and digitized. This data is input through a microphone with voice input / output capabilities.

[0766] Step 2:

[0767] The terminal sends audio data to the server. The server first converts the audio data into text data. Speech recognition software such as Google Cloud Speech-to-Text is used for this conversion, and in this process, text data that corresponds to the audio is output.

[0768] Step 3:

[0769] The server uses analysis tools to analyze the text data in detail. The analysis employs an NLP engine to identify the user's destination information and emotional state from the text data. The analysis results output the destination and emotional status.

[0770] Step 4:

[0771] Based on the analysis results, the server begins acquiring real-time traffic data using computational methods. The Google Maps API is used for this real-time traffic data, and the optimal route is calculated based on this data. The output data is a customized route adjusted to the user's emotional state.

[0772] Step 5:

[0773] The server sends the calculated route to the terminal and provides the user with route information visually or audibly through the terminal's display mechanisms. Music and audio guides that take the user's emotional state into consideration are used to create a relaxing environment.

[0774] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0775] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0776] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0777] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0778] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0779] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0780] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0781] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0782] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0783] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0784] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0785] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0786] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0787] 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.

[0788] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0789] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0790] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0791] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0792] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0793] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0794] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0795] The following is further disclosed regarding the embodiments described above.

[0796] (Claim 1)

[0797] A speech conversion method for converting audio data into text data,

[0798] An analysis means for analyzing destination information from the aforementioned text data,

[0799] A route calculation method that acquires real-time traffic data and calculates the optimal route,

[0800] The display means for presenting the optimal route,

[0801] A system that includes this.

[0802] (Claim 2)

[0803] The system according to claim 1, characterized in that the analysis means has a function to suggest rest points.

[0804] (Claim 3)

[0805] The system according to claim 1, characterized in that the route calculation means includes a function to dynamically modify the route in consideration of traffic congestion.

[0806] "Example 1"

[0807] (Claim 1)

[0808] A conversion means for converting audio information into text information,

[0809] An analysis means for analyzing target location information from the aforementioned textual information,

[0810] A path calculation means that acquires spatiotemporal information and calculates the optimal path,

[0811] The presenting means for presenting the optimal route,

[0812] A device that includes this.

[0813] (Claim 2)

[0814] The apparatus according to claim 1, characterized in that the analysis means has a function to suggest a resting place.

[0815] (Claim 3)

[0816] The apparatus according to claim 1, characterized in that the route calculation means has a function to dynamically modify the route in consideration of traffic conditions.

[0817] "Application Example 1"

[0818] (Claim 1)

[0819] A voice conversion means for converting audio signals into text information,

[0820] An analysis means for analyzing destination information from the aforementioned character information,

[0821] A path calculation means that acquires dynamic information and calculates the optimal path,

[0822] Information presentation means for presenting the optimal route,

[0823] Means of providing voice guidance,

[0824] Means for providing visual route display,

[0825] A system that includes this.

[0826] (Claim 2)

[0827] The system according to claim 1, characterized in that the analysis means has a function to suggest a resting position.

[0828] (Claim 3)

[0829] The system according to claim 1, characterized in that the route calculation means has a function to dynamically modify the route taking congestion information into consideration.

[0830] "Example 2 of combining an emotion engine"

[0831] (Claim 1)

[0832] A speech conversion means for converting speech data into symbolic data,

[0833] An analysis means for analyzing location information from the aforementioned symbolic data,

[0834] A means of emotional recognition that analyzes emotional states,

[0835] A selection means for selecting a resting place based on the aforementioned emotional state,

[0836] A path calculation means that acquires dynamic information and calculates the optimal path,

[0837] The presenting means for presenting the optimal route,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, characterized in that the emotion recognition means has a function of analyzing the user's emotional state and providing that information to the path calculation means.

[0841] (Claim 3)

[0842] The system according to claim 1, characterized in that the selection means has a function to improve user comfort by suggesting resting places that are adapted to the user's emotional state.

[0843] "Application example 2 when combining with an emotional engine"

[0844] (Claim 1)

[0845] A conversion method for converting audio data into text data,

[0846] An analysis means for analyzing destination information and the user's emotional state from the aforementioned text data,

[0847] A computational means for acquiring real-time traffic data and calculating the optimal route,

[0848] A presentation means that presents the aforementioned optimal route and provides information according to the emotional state,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, characterized in that the analysis means has a function to suggest resting places and relaxation spots based on the user's emotional state.

[0852] (Claim 3)

[0853] The system according to claim 1, characterized in that the calculation means includes a function to dynamically modify the route taking into account traffic congestion and the user's emotional state. [Explanation of Symbols]

[0854] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A voice conversion means for converting audio signals into text information, An analysis means for analyzing destination information from the aforementioned character information, A path calculation means that acquires dynamic information and calculates the optimal path, Information presentation means for presenting the optimal route, Means of providing voice guidance, Means for providing visual route display, A system that includes this.

2. The system according to claim 1, characterized in that the analysis means includes a function for suggesting a resting position.

3. The system according to claim 1, characterized in that the route calculation means has a function to dynamically modify the route taking congestion information into consideration.

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