System

Abstract

A system is provided.SOLUTION: A system, comprising: means for receiving a voice input and converting the voice input into text data; means for analyzing the text data and understanding a user's intention; means for obtaining real-time road information and calculating an optimal route; and means for outputting the calculation result to the user by voice.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional car navigation systems use a user interface that mainly relies on touch screen operation, making operation cumbersome while driving and increasing the risk of serious accidents. Furthermore, their functionality for obtaining real-time road information and instantly proposing optimal routes is limited, making it difficult to reflect information on traffic congestion and accidents. For these reasons, there is a demand for car navigation systems that simultaneously provide convenience and safety while driving. [Means for solving the problem]

[0005] The present invention provides a system including a means for receiving voice input and converting the voice input into text data, a means for analyzing the text data and understanding the user's intent, a means for acquiring real-time road information and calculating the optimal route, and a means for outputting the calculation results to the user by voice. This allows the user to set up the car navigation system and change the route by voice alone while driving, without touching the screen. Furthermore, since road information is acquired in real time and the optimal route is calculated and provided by voice, the safety and efficiency of driving can be improved.

[0006] "Voice input" refers to the voice information uttered by the user through a microphone, and is the basic data for processing by the system.

[0007] "Text data" is character information obtained by analyzing voice input.

[0008] "Analysis" is a process for understanding the user's intent based on text data.

[0009] "Real-time road information" refers to the latest data showing current traffic and road conditions.

[0010] An "optimal route" is a route that will reach a user's destination in the shortest time or with the least traffic congestion.

[0011] "Audio output" is a means of conveying analysis results and calculation results to the user by voice.

[0012] "User intent" refers to the content of the wishes and commands that the user requests from the system through voice input. [Brief explanation of the drawings]

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

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

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

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

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

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

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

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0021] [First embodiment]

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

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

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

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

[0026] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0034] The present invention is a car navigation system that realizes a user's hands-free navigation request through voice input. This system functions mainly through cooperation between a terminal and a server.

[0035] Voice input and recognition

[0036] First, the user inputs a voice command into the device, such as "I want to turn left at the next intersection." The device is equipped with a microphone that receives this voice input. The received voice data is then sent from the device to the server.

[0037] Voice Recognition

[0038] The server analyzes the transmitted voice data using a voice recognition system and converts it into text data. For example, voice data such as "I want to turn left at the next intersection" is analyzed as text data such as "I want to turn left at the next intersection."

[0039] Intent analysis and request understanding

[0040] The server then uses a natural language processing engine to analyze the text data and understand the user's intent—in this case, the specific commands "next intersection" and "turn left"—to figure out the next route the user should take.

[0041] Real-time information acquisition and route optimization

[0042] The server also accesses the traffic information API and road information center to obtain real-time road information. This information includes current road conditions such as traffic congestion, accidents, and construction. Based on this, the server calculates the optimal route. This calculation reflects the user's instructions and suggests the optimal route to "turn left at the next intersection."

[0043] Audio output and user feedback

[0044] The calculated optimal route information is sent to the device as text data. The device then converts this text data into voice using a speech synthesis engine and provides guidance to the user. For example, voice guidance may be provided in the form of "Turn left 500 meters before the next intersection." If the user responds by saying "OK," the device again sends the voice input to the server and repeats the same process.

[0045] Specific examples

[0046] While actually driving, suppose the user says, "I want to go to a nearby gas station." The device receives this speech and sends it to the server. The server understands the intent to "find a nearby gas station" and obtains real-time information on nearby gas stations. It then suggests the optimal route and provides voice guidance to the user, such as, "Turn right at the intersection 500 meters ahead, then turn left at the next corner."

[0047] The system of this invention allows users to operate the car navigation system safely and efficiently using only their voice while driving, without using their hands. This system significantly improves user convenience and enables safer driving without distracting the driver.

[0048] The processing flow will be explained below.

[0049] Step 1:

[0050] User: Gives voice instructions to the car navigation system. For example, the user says, "I want to turn left at the next intersection."

[0051] Step 2:

[0052] Device: Receives audio input through a microphone, converts the audio data into a digital format, and prepares it for transmission to the server.

[0053] Step 3:

[0054] Terminal: Sends the received audio data to the server via network communication.

[0055] Step 4:

[0056] Server: Receives the voice data and passes it to a speech recognition engine. The speech recognition engine is used to convert the voice data into text data. For example, the text generated is "I would like to turn left at the next intersection."

[0057] Step 5:

[0058] Server: The converted text data is passed to a natural language processing engine. The natural language processing engine analyzes the text data and understands the user's intent. For example, it extracts commands such as "next intersection" and "turn left."

[0059] Step 6:

[0060] Server: After understanding the user's intent, the server accesses the traffic information API and road information center to obtain real-time road information, such as current traffic conditions and construction information.

[0061] Step 7:

[0062] Server: Calculates the optimal route based on the acquired real-time information and the user's intentions. For example, it generates specific navigation instructions such as "Turn left 500 meters before the next intersection."

[0063] Step 8:

[0064] Server: Sends calculated route information in text format to the device.

[0065] Step 9:

[0066] Terminal: Passes the received text data to a speech synthesis engine. The speech synthesis engine converts the text data into speech and provides guidance to the user. For example, it may provide a voice prompt saying, "Turn left 500 meters before the next intersection."

[0067] Step 10:

[0068] User: Listen to and follow the voice prompts. Provide further voice prompts as needed. For example, reply "Got it."

[0069] Step 11:

[0070] Terminal: Receives the user's response again and sends it to the server as voice data.

[0071] Step 12:

[0072] Server: Again performs speech recognition and natural language processing to determine if further instructions are needed. If so, return to step 1 and repeat the process.

[0073] Example 1

[0074] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0075] Conventional car navigation systems require users to operate the system manually, which increases the risk of distraction while driving. Therefore, there is a need for a hands-free method of navigating safely and efficiently while driving, improving user convenience.

[0076] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0077] In this invention, the server includes means for receiving voice input and converting the voice input into text data, means for analyzing the text data to understand the user's intention, and means for obtaining real-time traffic information and calculating the optimal route, thereby enabling the user to use navigation solely by voice while driving, without using their hands.

[0078] "Voice input" is the process of receiving a user's speech as machine-recognizable data.

[0079] "Text data" is data that expresses voice input as text information.

[0080] "User intent" refers to the actions or requests that a user intends to convey to a machine through voice input.

[0081] "Real-time traffic information" is data that indicates the current road conditions and traffic situation.

[0082] An "optimal route" is the route that most efficiently reaches the user's destination.

[0083] "Audio output" is the process of converting text data back into audio and conveying it to the user.

[0084] An "acoustic sensor" is a device that senses sound waves and converts them into a form that can be processed as audio data.

[0085] A "remote device" is a device that transmits data from a terminal to a server via the Internet or other communication means.

[0086] A "voice recognition system" is a general term for software and hardware used to convert voice data into text data.

[0087] The present invention is a car navigation system that realizes a user's hands-free navigation request through voice input. This system functions mainly through cooperation between a terminal and a server.

[0088] Voice input and recognition

[0089] First, the user inputs a voice command into the device, such as "I want to turn left at the next intersection." The device is equipped with a microphone that receives this voice input. The received voice data is sent from the device to the server. At this time, the device uses an Internet connection to encrypt the voice data according to a security protocol and send it to the server.

[0090] Voice Recognition

[0091] The server analyzes the transmitted voice data using a general-purpose voice recognition system (e.g., Google (registered trademark) Cloud Speech-to-Text) and converts it into text data. For example, the voice "I want to turn left at the next intersection" is returned as text data "I want to turn left at the next intersection."

[0092] Intent analysis and request understanding

[0093] The server passes the acquired text data to a general-purpose natural language processing engine (e.g., OpenAI (registered trademark) GPT-3 (registered trademark)) to understand the user's intention. From the text "I want to turn left at the next intersection," the server understands the action instruction "Turn left at the next intersection."

[0094] Real-time information acquisition and route optimization

[0095] The server also uses a general-purpose traffic information API (e.g., Google Maps API) to obtain real-time road information, including information on traffic congestion, accidents, and construction work. Based on this information, the server calculates the optimal route for the user's instructions. The calculation results are generated as text data.

[0096] Audio output and user feedback

[0097] The calculated optimal route information is sent to the device as text data. The device then converts this text data into voice data using a general-purpose speech synthesis engine (e.g., Amazon Polly) and provides guidance to the user. For example, the voice guidance may say, "Turn left 500 meters before the next intersection." If the user responds by saying "OK," the device again sends the voice input to the server and repeats the same process.

[0098] Specific examples

[0099] While actually driving, suppose the user says, "I want to go to a nearby gas station." The device receives this speech and sends it to the server. The server understands the intent to "find a nearby gas station" and obtains real-time information on nearby gas stations. It then suggests the optimal route and provides voice guidance to the user, such as, "Turn right at the intersection 500 meters ahead, then turn left at the next corner."

[0100] Prompt Sentence Examples

[0101] As an example of a specific prompt, you could input the following instructions to your generative AI model:

[0102] When a user says, "I want to turn left at the next intersection," please explain in detail the process of the program that analyzes their intention and calculates the next route to take. Use a general-purpose speech recognition system as the speech recognition system, a general-purpose natural language processing engine as the natural language processing engine, a general-purpose traffic information API as the traffic information API, and a general-purpose speech synthesis engine as the speech synthesis engine.

[0103] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0104] Step 1:

[0105] The user speaks a voice command, such as "I want to turn left at the next intersection," into the device's microphone, which captures this voice input, converts it into digital voice data, and sends it to the device's voice processing unit.

[0106] Input: User's voice command

[0107] Output: Digital audio data

[0108] Specific behavior:

[0109] A microphone in the terminal receives the user's voice and transmits it to the voice processing unit as digital voice data.

[0110] Step 2:

[0111] The device transmits the captured digital audio data over the internet to a server, where it encrypts the data and transmits it using a secure protocol.

[0112] Input: Digital audio data

[0113] Output: Audio data sent to the server

[0114] Specific behavior:

[0115] The device encrypts the voice data and sends it to a server via an internet connection.

[0116] Step 3:

[0117] The server sends the received voice data to a general-purpose voice recognition system (e.g., Google Cloud Speech-to-Text) and converts the voice into text data.

[0118] Input: Audio data sent to the server

[0119] Output: Text data

[0120] Specific behavior:

[0121] The server sends the voice data to a voice recognition system, which converts the digital voice data into text data.

[0122] Step 4:

[0123] The server passes the acquired text data to a general-purpose natural language processing engine (e.g., OpenAI GPT-3) to analyze the user's intent.

[0124] Input: Text data

[0125] Output: Parsed user intent

[0126] Specific behavior:

[0127] The server inputs the text data into a natural language processing engine and executes a process to extract the user's intent.

[0128] Step 5:

[0129] The server obtains real-time road information from a general-purpose traffic information API (e.g., Google Maps API) and uses this information to calculate the optimal route.

[0130] Input: Parsed user intent, real-time road information from traffic information API

[0131] Output: Text data of the optimal route

[0132] Specific behavior:

[0133] The server calculates the optimal route based on road information obtained from the traffic information API and the user's intentions, and generates it as text data.

[0134] Step 6:

[0135] The server sends the calculated optimal route as text data to the device, which converts this text data into voice data using a general-purpose speech synthesis engine (e.g., Amazon Polly).

[0136] Input: Text data of optimal route

[0137] Output: Audio data

[0138] Specific behavior:

[0139] The server transmits the text data to the terminal, and the terminal converts the text data into voice data using a voice synthesis engine.

[0140] Step 7:

[0141] The device uses a speech synthesis engine to generate voice data and plays it back to the user, for example, providing voice guidance such as "Turn left 500 meters before the next intersection."

[0142] Input: Audio data

[0143] Output: Audio output to the user

[0144] Specific behavior:

[0145] The device plays audio data through a speaker and guides the user to the optimal route.

[0146] (Application example 1)

[0147] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0148] Conventional car navigation systems require manual operation by the user, which can compromise safety while driving. Furthermore, route changes based on real-time traffic information are not made quickly, making it difficult to achieve efficient navigation. Furthermore, in autonomous vehicles, usability is poor because there is no interface that allows users to give voice instructions on destinations or route changes. There is a need for a system that can solve these issues and achieve safe and efficient autonomous driving.

[0149] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0150] In this invention, the server includes means for receiving voice input and converting the voice input into text data, means for analyzing the text data and understanding the user's intent, means for acquiring real-time traffic information and calculating an optimal route, means for outputting the calculated route information to the user by voice, means for linking the input destination and desired route to the autopilot system based on voice commands, and means for correcting the route in real time and re-navigating based on voice commands. This allows the user to specify destinations and route changes by voice without using their hands, and obtain the optimal route in real time, enabling safe and efficient automated driving.

[0151] "Voice input" refers to providing a user's spoken voice to the system as digital data.

[0152] "Text data" refers to digital information that has been converted from voice input into text.

[0153] "Real-time traffic information" refers to data showing current road and traffic conditions.

[0154] An "optimal route" refers to the most efficient and safe route calculated based on the user's specified conditions and intentions.

[0155] "Voice commands" refer to commands that a user gives to the system by voice, such as specifying a destination or instructing the system to change routes.

[0156] An "autopilot system" refers to a system that automatically drives a vehicle and allows it to reach a destination without manual operation by the user.

[0157] "Route correction" refers to the process of switching from a current route to another route.

[0158] "Navigation" refers to providing route guidance and direction to a destination.

[0159] "Server" refers to the central processing unit that analyzes voice input, obtains traffic information, and calculates routes.

[0160] A "voice recognition engine" refers to software or hardware for converting voice data into text data.

[0161] The system of the present invention is equipped with voice input, voice analysis, intent understanding, real-time information acquisition, route calculation, voice output, and linkage with an automated driving system, allowing the user to give hands-free instructions on destinations and route changes, realizing safe and efficient automated driving.

[0162] First, the user inputs a voice command into the system. This voice input is received by the device's built-in microphone and sent as digital data to the server. The server then uses a voice recognition engine to convert the voice data into text data.

[0163] The server then analyzes the converted text data and uses a natural language processing engine to understand the user's intent. For example, if a user says, "I want to go home," the system recognizes the intents "home" and "go home" and extracts the destination.

[0164] The server then uses a traffic information API to obtain real-time traffic information, collecting the latest information on congestion, accidents, road construction, etc. Based on this, it calculates the optimal route and sends that information to the device as text data.

[0165] The device uses a speech synthesis engine to convert the text data into speech, which is then fed back to the user, such as "Turn right at the next intersection." Each time the user inputs a new voice command, the system adjusts the route in real time and provides instructions to the autonomous driving system.

[0166] In this way, users can operate autonomous vehicles hands-free using only their voice.

[0167] For example, if a user says, "I want to go to a nearby gas station," the device receives the voice and sends it to the server. The server then understands the intent of "find a nearby gas station," obtains real-time information on nearby gas stations, calculates the optimal route, and provides voice guidance to the user, such as, "Turn right at the intersection 500 meters ahead, then turn left at the next corner."

[0168] Example prompts using generative AI models

[0169] "User said: 'I want to go to a nearby gas station.' Please output the generated navigation route."

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

[0171] Step 1:

[0172] The user issues a voice command.

[0173] For example, say, "I want to go to a nearby gas station." This voice input is captured as digital data by the device's microphone. The input is voice data, and the output is digitized voice data.

[0174] Step 2:

[0175] The terminal transmits the acquired voice data to the server.

[0176] A transmission process takes place and the server receives the audio data. The input is the digitized audio data, and the output is the same audio data received.

[0177] Step 3:

[0178] The server converts the received voice data into text data using a voice recognition engine.

[0179] Within the server, a speech recognition engine analyzes the digital voice data and generates corresponding text data. The input is voice data, and the output is text data.

[0180] Step 4:

[0181] The server analyzes the generated text data and performs natural language processing to understand the user's intent.

[0182] It uses a natural language processing engine to analyze instructions in text data and understand the user's desired destination and operation. The input is text data, and the output is data that indicates the user's intention.

[0183] Step 5:

[0184] The server accesses the traffic information API to obtain real-time traffic information.

[0185] It obtains the latest traffic data from the API and calculates the optimal route based on it. The input is the user's intention data, and the output is real-time traffic information and the calculated optimal route.

[0186] Step 6:

[0187] The server transmits the optimum route information to the terminal as text data.

[0188] A transmission process is performed and the terminal receives this information. The input is the text data of the calculated optimal route, and the output is the same text data sent to the terminal.

[0189] Step 7:

[0190] The terminal converts the received text data into voice using a voice synthesis engine and provides guidance to the user.

[0191] Text-to-speech conversion is performed, and a voice guidance message is generated: "Turn right at the intersection 500 meters ahead, then turn left at the next corner." The input is text data, and the output is voice guidance.

[0192] Step 8:

[0193] The user enters new voice commands and modifies the route as needed.

[0194] For example, issue a new command, such as "I want to stop at the cafe up ahead." The process repeats from step 1, correcting the route in real time. The input is a new voice command, and the output is a revised, optimized route and its instructions.

[0195] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0196] The present invention is a system for providing safe and efficient navigation while driving, which provides a navigation process including voice input, text conversion, user intent analysis, real-time road information acquisition, and sentiment analysis.

[0197] Voice input and recognition

[0198] First, the user speaks a voice command to the car navigation system, such as "I want to turn left at the next intersection." The device is equipped with a microphone that receives this voice input. The received voice data is then sent from the device to the server.

[0199] Speech recognition and text conversion

[0200] The server analyzes the transmitted voice data using a voice recognition engine and converts it into text data. The voice data "I want to turn left at the next intersection" is analyzed as text data "I want to turn left at the next intersection."

[0201] Intent analysis and request understanding

[0202] The server then uses a natural language processing engine to analyze the text data and understand the user's intent. By understanding specific commands such as "next intersection" and "turn left," the server knows the next route the user should take.

[0203] Emotion analysis

[0204] Furthermore, the server uses an emotion engine to recognize the user's emotions from the voice data. The emotion engine analyzes the voice tone, pitch, speed, etc. to identify the user's emotional state. For example, it determines whether the user is anxious or calm.

[0205] Real-time information acquisition and route optimization

[0206] The server accesses the traffic information API and road information center to obtain real-time road information, thereby obtaining current traffic conditions, construction information, etc. The server calculates the optimal route taking into account the obtained information, the user's intentions, and emotional state. Specific navigation instructions, such as "Turn left 500 meters before the next intersection," are generated.

[0207] Audio output and user feedback

[0208] The calculated optimal route information is sent to the device as text data. The device then converts this text data into speech using a speech synthesis engine and provides guidance to the user. For example, the device may provide a voice prompt saying, "Turn left 500 meters before the next intersection." By following this guidance, the user can achieve hands-free operation.

[0209] Specific examples

[0210] When a user says, "I want to go to a nearby gas station," the device receives the voice and sends it to the server. The server understands the user's intention and uses its emotion engine to recognize the user's sense of impatience. It then decides to guide the user to the nearest gas station via the fastest route. The voice guides the user, saying, "Turn right at the next traffic light, and the gas station is 500 meters ahead." This allows the user to reach their destination without feeling stressed.

[0211] This invention enables safe and efficient operation of car navigation systems using only voice commands without using the user's hands while driving, and provides guidance that takes the user's emotions into consideration, greatly improving the safety and comfort of driving.

[0212] The processing flow will be explained below.

[0213] Step 1:

[0214] User: Gives voice instructions to the car navigation system. For example, "I want to turn left at the next intersection."

[0215] Step 2:

[0216] Device: Collects user voice input through a microphone. Converts the voice data into a digital format and prepares it for transmission to the server.

[0217] Step 3:

[0218] Terminal: Sends the received voice data to the server via the network.

[0219] Step 4:

[0220] Server: The transmitted voice data is analyzed by a voice recognition engine and converted into text data. For example, the text data "I would like to turn left at the next intersection" is generated.

[0221] Step 5:

[0222] Server: The generated text data is passed to a natural language processing engine to understand the user's intent. For example, it identifies commands such as "turn left" at the "next intersection."

[0223] Step 6:

[0224] Server: Passes the voice data to the emotion engine and analyzes the user's emotional state. It analyzes the voice tone, pitch, speed, etc. to determine whether the user is anxious or calm.

[0225] Step 7:

[0226] Server: Based on the analyzed user's intentions and emotional state, the server accesses the traffic information API and road information center to obtain real-time road information, such as current traffic conditions, congestion, accidents, and construction information.

[0227] Step 8:

[0228] Server: Calculates the optimal route based on the acquired real-time information and the user's requests and emotional state. For example, specific navigation instructions such as "Turn left 500 meters before the next intersection" are generated.

[0229] Step 9:

[0230] Server: Sends calculated route information in text format to the device.

[0231] Step 10:

[0232] Terminal: The received text data is passed to a speech synthesis engine, which converts the text data into speech. Then, voice guidance is provided to the user. For example, it may say, "Turn left 500 meters before the next intersection."

[0233] Step 11:

[0234] User: Listens to and follows the voice prompts. For example, the user responds "Got it."

[0235] Step 12:

[0236] Terminal: The user's response is sent to the server again as voice data.

[0237] Step 13:

[0238] Server: Analyzes the user's response using a speech recognition and natural language processing engine, and optionally an emotion engine, to determine if further instructions are needed. Repeat the process as necessary.

[0239] Example 2

[0240] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0241] Conventional navigation systems can convert voice input into text, understand the user's intent, and calculate routes based on real-time road information. However, these systems often lack safety and comfort because they do not take the user's emotional state into account. The objective of this invention is to provide a navigation system that also takes the user's emotional state into account, thereby improving safety and efficiency while driving.

[0242] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving a voice input and converting the voice input into text data, means for analyzing the text data and understanding the user's intention, means for analyzing the user's emotional state from the voice data, means for acquiring real-time road information and calculating an optimal route taking into account the user's intention and emotional state, and means for outputting the calculation result to the user by voice. This enables navigation that takes into account the user's emotional state, significantly improving safety and efficiency during driving.

[0243] 1. "Voice input" is the process of capturing or recording a user's spoken voice.

[0244] 2. "Text data" refers to data that has been analyzed from voice input and converted into a string format.

[0245] 3. "User intent" refers to the commands or requests that the user makes to the system.

[0246] 4. "Emotional state" is information that expresses the user's mental and emotional state.

[0247] 5. "Real-time road information" means data on the latest traffic and road conditions.

[0248] 6. "Optimal route" refers to the most efficient and safe route calculated based on certain criteria.

[0249] 7. "Speech recognition engine" means software or hardware that analyzes voice data and converts it into text data.

[0250] 8. "Emotion Analysis Engine" means software or hardware for analyzing a user's emotional state from voice data.

[0251] 9. "Navigation system" means a system that provides users with the instructions and information they need to reach their destination.

[0252] The present invention is a system for providing safe and efficient navigation while driving, which provides a navigation process including voice input, text conversion, user intent analysis, real-time road information acquisition, and sentiment analysis.

[0253] Voice input and recognition

[0254] The user speaks a command to the car navigation system, such as "I want to turn left at the next intersection." The device is equipped with a microphone that receives this voice input. The received voice data is sent from the device to a server. The transmission is carried out over the Internet, and security protocols are used to ensure the safety of the data.

[0255] Speech recognition and text conversion

[0256] The server analyzes the transmitted voice data using a voice recognition engine (a typical example is a voice recognition API) and converts it into text data. The voice data uttered by the user, "I would like to turn left at the next intersection," is analyzed as text data saying, "I would like to turn left at the next intersection."

[0257] Intent analysis and request understanding

[0258] The server then analyzes the text data using a natural language processing engine (typically a generative AI model) to understand the user's intent, extracting specific instructions such as "next intersection" and "turn left" and figuring out the next route the user should take.

[0259] Emotion analysis

[0260] Furthermore, the server uses an emotion analysis engine to recognize the user's emotions from the voice data. The emotion analysis engine analyzes the voice tone, pitch, speed, etc. to identify the user's emotional state. For example, it determines whether the user is feeling impatient or calm.

[0261] Real-time information acquisition and route optimization

[0262] The server accesses the traffic information API and road information center to obtain real-time road information, thereby obtaining the latest traffic conditions, construction information, accident information, etc. The server calculates the optimal route taking into account the obtained information and the user's intentions and emotional state. For example, specific navigation instructions such as "Turn left 500 meters before the next intersection" are generated.

[0263] Audio output and user feedback

[0264] The calculated optimal route information is sent to the device as text data. The device then converts this text data into speech using a speech synthesis engine (a typical example is a speech synthesis API) and provides guidance to the user. For example, the device may provide a voice prompt saying, "Turn left 500 meters before the next intersection." By following this guidance, the user can achieve hands-free operation.

[0265] Specific examples

[0266] For example, if a user says, "I want to go to a nearby gas station," the device receives the voice and sends it to the server. The server understands the user's intention and uses an emotion analysis engine to recognize the user's sense of impatience. It then decides to guide the user to the nearest gas station via the fastest route. The voice guides the user, saying, "Turn right at the next traffic light, and the gas station is 500 meters ahead." This allows the user to reach their destination without feeling stressed.

[0267] Prompt Sentence Examples

[0268] An example of a prompt to be input to a generative AI model might be, "Generate appropriate navigation instructions if the user says they want to go to a nearby gas station."

[0269] Through the above specific process, the system can provide users with safe and efficient navigation.

[0270] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0271] Step 1:

[0272] The user speaks to the car navigation system, saying, "I want to turn left at the next intersection." The microphone on the device receives this voice input and records it as voice data. This voice data becomes the input for the next step.

[0273] Step 2:

[0274] The device sends the recorded audio data to the server. The transmission is over the Internet, and the data is secured by security protocols (e.g., SSL / TLS). The output of this step is the audio data sent to the server.

[0275] Step 3:

[0276] The server passes the received voice data to a voice recognition engine (e.g., a voice recognition API). The voice recognition engine analyzes the voice data and converts it into text data such as "I would like to turn left at the next intersection." This text data becomes the input for the next step.

[0277] Step 4:

[0278] The server passes the generated text data to a natural language processing engine (e.g., a generative AI model) for analysis. The natural language processing engine extracts the user's intent from the text, specifically the specific commands "next intersection" and "turn left." The results of this analysis become the input for the next step.

[0279] Step 5:

[0280] The server then passes the initial voice data to an emotion analysis engine, which analyzes the tone, pitch, and speed of the voice to identify the user's emotional state (e.g., anxious, calm, etc.). The results of this analysis are used as input for the next step.

[0281] Step 6:

[0282] Based on the analyzed user's intention and emotional state, the server accesses the traffic information API and road information center to obtain real-time road information. This obtained road information becomes the input for the next step.

[0283] Step 7:

[0284] The server calculates the optimal route based on the acquired road information, the user's intentions, and the user's emotional state. The route calculation uses an algorithm that combines the user's requests with real-time information. The result of this calculation is the input for the next step.

[0285] Step 8:

[0286] The server generates specific navigation instructions based on the optimal route information, such as "Turn left 500 meters before the next intersection." This navigation instruction becomes the input for the next step.

[0287] Step 9:

[0288] The generated navigation instructions are sent to the device as text data. The device passes this text data to a speech synthesis engine (e.g., a speech synthesis API) to convert it into speech. The output of this step is speech data.

[0289] Step 10:

[0290] The device then sends the generated voice data to the user through a speaker. For example, it may say, "Turn left 500 meters before the next intersection." By following this guidance, the user can achieve hands-free operation and reach their destination safely and efficiently.

[0291] (Application example 2)

[0292] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0293] Conventional navigation systems not only have the ability to understand user intentions and provide routes that take traffic information into account, but also have the problem of not being able to consider the emotional state of professional drivers such as delivery drivers. As a result, delivery efficiency may decrease and delivery workers' stress may increase. In the food delivery field in particular, maintaining the mental health of delivery workers is important in addition to delivery efficiency. Therefore, there is a need for a system that not only analyzes user intentions but also provides optimal routes that take emotions into account.

[0294] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving voice input and converting the voice input into text data, means for analyzing the text data and understanding the user's intention, means for analyzing the user's emotions from the voice data, means for acquiring real-time traffic information and calculating the optimal route taking into account the user's intention and emotional state, and means for outputting the calculation results to the user by voice. This enables the delivery person to select an efficient and less stressful route.

[0295] "Voice input" refers to the act of receiving a user's voice commands into a system and the system's interface.

[0296] "Text data" refers to character string data converted from voice input.

[0297] "User intent" refers to the user's goals and requests analyzed from voice input.

[0298] "Voice Data" means a digital representation of a user's voice input received by a system.

[0299] "Real-time traffic information" refers to up-to-date information about current traffic and road conditions.

[0300] An "optimal route" refers to the most efficient and safe route of travel calculated taking into account the user's intentions and emotional state.

[0301] "Means for outputting to the user by voice" refers to a method of notifying the user of calculation results and instructions using voice synthesis technology.

[0302] "Voice recording device" refers to a microphone or recording device for inputting user voice commands.

[0303] "Central processing unit" refers to a server or computer that analyzes voice commands and performs the necessary processing.

[0304] A "voice recognition engine" refers to software or a system for analyzing voice data and converting it into text data.

[0305] "Emotional state" refers to the user's psychological state and emotions analyzed from voice data.

[0306] A "delivery person" refers to a professional driver who delivers goods or food to a requested destination.

[0307] The embodiment of the present invention is configured as follows.

[0308] Generating a Program

[0309] This invention is a system that performs voice input, voice recognition, emotion analysis, acquisition of real-time traffic information, route optimization, and voice output to help delivery personnel select efficient and stress-free routes. The system is composed of elements such as a voice recording device, a central processing unit, a voice recognition engine, and an emotion analysis engine.

[0310] Processing Description

[0311] The server receives voice commands from the user using a voice recording device, such as a microphone or a smartphone. This voice data is sent to the server and converted into text data by a voice recognition engine.

[0312] The server then analyzes the converted text data using natural language processing technology to identify the user's intent, such as whether the user wants to head to a destination or take a specific route.

[0313] The server then uses an emotion analysis engine to identify the user's emotional state from the voice data, such as analyzing parameters like voice tone, pitch, and speed to determine whether the user is agitated or calm. This information is then used to optimize the route.

[0314] The server then retrieves real-time traffic information from relevant APIs and traffic information centers, including current traffic conditions, accident information, road construction information, etc.

[0315] Finally, the server calculates the optimal route based on the acquired traffic information, the user's intentions, and the user's emotional state. The calculated route information is provided to the user via a voice output device, such as a smartphone or a car speaker. At this time, text data is converted into voice using speech synthesis technology.

[0316] Specific examples

[0317] For example, if a delivery person says, "I want to get to my next delivery as quickly as possible," the voice recording device receives this speech and sends it to the server. The server first converts the speech into text data, then analyzes this data to determine the delivery person's intentions. It then uses an emotion analysis engine to recognize the delivery person's sense of impatience. It then calculates the fastest route and provides specific instructions via voice, such as, "Turn right at the next traffic light, and your delivery is 500 meters away."

[0318] Prompt Sentence Examples

[0319] If you give the voice command "I want to get to the next delivery destination as quickly as possible," and feel a sense of urgency, calculate the fastest route and provide feedback.

[0320] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0321] Step 1:

[0322] The user speaks a voice command into a voice recording device (smartphone or microphone). For example, a delivery person might say, "I want to get to the next delivery destination as soon as possible." This voice data is digitally recorded on the device.

[0323] Input: User's voice command

[0324] Output: Digitized audio data

[0325] Step 2:

[0326] The device sends the recorded audio data to a central processing unit (server), where it is converted into data packets and transferred to the server using a stable communication protocol.

[0327] Input: Digitized voice data

[0328] Output: Audio data sent to the server

[0329] Step 3:

[0330] The server converts the received voice data into text data using a voice recognition engine, which identifies phonemes from the voice waveform and converts them into corresponding text.

[0331] Input: Audio data

[0332] Output: Text data

[0333] Step 4:

[0334] The server uses a generative AI model to analyze the text data and identify the user's intent. For example, from the text "I want to get to the next delivery destination as quickly as possible," the server determines that the delivery person is looking for a fast route.

[0335] Input: Text data

[0336] Output: User intent

[0337] Step 5:

[0338] The server uses an emotion analysis engine to analyze the user's emotional state from the voice data, which analyzes the tone, pitch, speed, etc. of the voice to determine whether the user is agitated or calm.

[0339] Input: Audio data

[0340] Output: User's emotional state

[0341] Step 6:

[0342] The server uses a traffic information API to obtain real-time traffic information, including current traffic conditions, accident information, and road construction information.

[0343] Input: Traffic information API to request

[0344] Output: Real-time traffic information

[0345] Step 7:

[0346] The server integrates the user's intentions, emotional state, and real-time traffic information to calculate the optimal route using a route optimization algorithm. The calculation process prioritizes the delivery person's urgency and routes that avoid traffic congestion.

[0347] Input: User intent, user emotional state, real-time traffic information

[0348] Output: Optimal route

[0349] Step 8:

[0350] The server calculates the optimal route and sends it to the device as text data, which includes specific instructions such as "Turn right at the next traffic light and your delivery address is 500 meters away."

[0351] Input: Optimal Route

[0352] Output: Text data of route directions

[0353] Step 9:

[0354] The device uses a speech synthesis engine to convert the received text data into speech and provides guidance to the user via a voice output device (smartphone or car speaker).

[0355] Input: Text data for route directions

[0356] Output: Voice guidance

[0357] In this way, the entire process from the user uttering a voice command to receiving voice guidance along an optimized route is completed.

[0358] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0359] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0360] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0361] [Second embodiment]

[0362] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0363] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0364] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0365] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0366] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0367] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0368] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0369] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0370] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0371] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0372] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0373] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0374] The present invention is a car navigation system that realizes a user's hands-free navigation request through voice input. This system functions mainly through cooperation between a terminal and a server.

[0375] Voice input and recognition

[0376] First, the user inputs a voice command into the device, such as "I want to turn left at the next intersection." The device is equipped with a microphone that receives this voice input. The received voice data is then sent from the device to the server.

[0377] Voice Recognition

[0378] The server analyzes the transmitted voice data using a voice recognition system and converts it into text data. For example, voice data such as "I want to turn left at the next intersection" is analyzed as text data such as "I want to turn left at the next intersection."

[0379] Intent analysis and request understanding

[0380] The server then uses a natural language processing engine to analyze the text data and understand the user's intent—in this case, the specific commands "next intersection" and "turn left"—to figure out the next route the user should take.

[0381] Real-time information acquisition and route optimization

[0382] The server also accesses the traffic information API and road information center to obtain real-time road information. This information includes current road conditions such as traffic congestion, accidents, and construction. Based on this, the server calculates the optimal route. This calculation reflects the user's instructions and suggests the optimal route to "turn left at the next intersection."

[0383] Audio output and user feedback

[0384] The calculated optimal route information is sent to the device as text data. The device then converts this text data into voice using a speech synthesis engine and provides guidance to the user. For example, voice guidance may be provided in the form of "Turn left 500 meters before the next intersection." If the user responds by saying "OK," the device again sends the voice input to the server and repeats the same process.

[0385] Specific examples

[0386] While actually driving, suppose the user says, "I want to go to a nearby gas station." The device receives this speech and sends it to the server. The server understands the intent to "find a nearby gas station" and obtains real-time information on nearby gas stations. It then suggests the optimal route and provides voice guidance to the user, such as, "Turn right at the intersection 500 meters ahead, then turn left at the next corner."

[0387] The system of this invention allows users to operate the car navigation system safely and efficiently using only their voice while driving, without using their hands. This system significantly improves user convenience and enables safer driving without distracting the driver.

[0388] The processing flow will be explained below.

[0389] Step 1:

[0390] User: Gives voice instructions to the car navigation system. For example, the user says, "I want to turn left at the next intersection."

[0391] Step 2:

[0392] Device: Receives audio input through a microphone, converts the audio data into a digital format, and prepares it for transmission to the server.

[0393] Step 3:

[0394] Terminal: Sends the received audio data to the server via network communication.

[0395] Step 4:

[0396] Server: Receives the voice data and passes it to a speech recognition engine. The speech recognition engine is used to convert the voice data into text data. For example, the text generated is "I would like to turn left at the next intersection."

[0397] Step 5:

[0398] Server: The converted text data is passed to a natural language processing engine. The natural language processing engine analyzes the text data and understands the user's intent. For example, it extracts commands such as "next intersection" and "turn left."

[0399] Step 6:

[0400] Server: After understanding the user's intent, the server accesses the traffic information API and road information center to obtain real-time road information, such as current traffic conditions and construction information.

[0401] Step 7:

[0402] Server: Calculates the optimal route based on the acquired real-time information and the user's intentions. For example, it generates specific navigation instructions such as "Turn left 500 meters before the next intersection."

[0403] Step 8:

[0404] Server: Sends calculated route information in text format to the device.

[0405] Step 9:

[0406] Terminal: Passes the received text data to a speech synthesis engine. The speech synthesis engine converts the text data into speech and provides guidance to the user. For example, it may provide a voice prompt saying, "Turn left 500 meters before the next intersection."

[0407] Step 10:

[0408] User: Listen to and follow the voice prompts. Provide further voice prompts as needed. For example, reply "Got it."

[0409] Step 11:

[0410] Terminal: Receives the user's response again and sends it to the server as voice data.

[0411] Step 12:

[0412] Server: Again performs speech recognition and natural language processing to determine if further instructions are needed. If so, return to step 1 and repeat the process.

[0413] Example 1

[0414] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0415] Conventional car navigation systems require users to operate the system manually, which increases the risk of distraction while driving. Therefore, there is a need for a hands-free method of navigating safely and efficiently while driving, improving user convenience.

[0416] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0417] In this invention, the server includes means for receiving voice input and converting the voice input into text data, means for analyzing the text data to understand the user's intention, and means for obtaining real-time traffic information and calculating the optimal route, thereby enabling the user to use navigation solely by voice while driving, without using their hands.

[0418] "Voice input" is the process of receiving a user's speech as machine-recognizable data.

[0419] "Text data" is data that expresses voice input as text information.

[0420] "User intent" refers to the actions or requests that a user intends to convey to a machine through voice input.

[0421] "Real-time traffic information" is data that indicates the current road conditions and traffic situation.

[0422] An "optimal route" is the route that most efficiently reaches the user's destination.

[0423] "Audio output" is the process of converting text data back into audio and conveying it to the user.

[0424] An "acoustic sensor" is a device that senses sound waves and converts them into a form that can be processed as audio data.

[0425] A "remote device" is a device that transmits data from a terminal to a server via the Internet or other communication means.

[0426] A "voice recognition system" is a general term for software and hardware used to convert voice data into text data.

[0427] The present invention is a car navigation system that realizes a user's hands-free navigation request through voice input. This system functions mainly through cooperation between a terminal and a server.

[0428] Voice input and recognition

[0429] First, the user inputs a voice command into the device, such as "I want to turn left at the next intersection." The device is equipped with a microphone that receives this voice input. The received voice data is sent from the device to the server. At this time, the device uses an Internet connection to encrypt the voice data according to a security protocol and send it to the server.

[0430] Voice Recognition

[0431] The server analyzes the transmitted voice data using a general-purpose voice recognition system (for example, Google Cloud Speech-to-Text) and converts it into text data. For example, the voice message "I want to turn left at the next intersection" is returned as text data saying "I want to turn left at the next intersection."

[0432] Intent analysis and request understanding

[0433] The server passes the acquired text data to a general-purpose natural language processing engine (e.g., OpenAI GPT-3) to understand the user's intention. From the text "I want to turn left at the next intersection," the server understands the action instruction "Turn left at the next intersection."

[0434] Real-time information acquisition and route optimization

[0435] The server also uses a general-purpose traffic information API (e.g., Google Maps API) to obtain real-time road information, including information on traffic congestion, accidents, and construction work. Based on this information, the server calculates the optimal route for the user's instructions. The calculation results are generated as text data.

[0436] Audio output and user feedback

[0437] The calculated optimal route information is sent to the device as text data. The device then converts this text data into voice data using a general-purpose speech synthesis engine (e.g., Amazon Polly) and provides guidance to the user. For example, the voice guidance may say, "Turn left 500 meters before the next intersection." If the user responds by saying "OK," the device again sends the voice input to the server and repeats the same process.

[0438] Specific examples

[0439] While actually driving, suppose the user says, "I want to go to a nearby gas station." The device receives this speech and sends it to the server. The server understands the intent to "find a nearby gas station" and obtains real-time information on nearby gas stations. It then suggests the optimal route and provides voice guidance to the user, such as, "Turn right at the intersection 500 meters ahead, then turn left at the next corner."

[0440] Prompt Sentence Examples

[0441] As an example of a specific prompt, you could input the following instructions to your generative AI model:

[0442] When a user says, "I want to turn left at the next intersection," please explain in detail the process of the program that analyzes their intention and calculates the next route to take. Use a general-purpose speech recognition system as the speech recognition system, a general-purpose natural language processing engine as the natural language processing engine, a general-purpose traffic information API as the traffic information API, and a general-purpose speech synthesis engine as the speech synthesis engine.

[0443] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0444] Step 1:

[0445] The user speaks a voice command, such as "I want to turn left at the next intersection," into the device's microphone, which captures this voice input, converts it into digital voice data, and sends it to the device's voice processing unit.

[0446] Input: User's voice command

[0447] Output: Digital audio data

[0448] Specific behavior:

[0449] A microphone in the terminal receives the user's voice and transmits it to the voice processing unit as digital voice data.

[0450] Step 2:

[0451] The device transmits the captured digital audio data over the internet to a server, where it encrypts the data and transmits it using a secure protocol.

[0452] Input: Digital audio data

[0453] Output: Audio data sent to the server

[0454] Specific behavior:

[0455] The device encrypts the voice data and sends it to a server via an internet connection.

[0456] Step 3:

[0457] The server sends the received voice data to a general-purpose voice recognition system (e.g., Google Cloud Speech-to-Text) and converts the voice into text data.

[0458] Input: Audio data sent to the server

[0459] Output: Text data

[0460] Specific behavior:

[0461] The server sends the voice data to a voice recognition system, which converts the digital voice data into text data.

[0462] Step 4:

[0463] The server passes the acquired text data to a general-purpose natural language processing engine (e.g., OpenAI GPT-3) to analyze the user's intent.

[0464] Input: Text data

[0465] Output: Parsed user intent

[0466] Specific behavior:

[0467] The server inputs the text data into a natural language processing engine and executes a process to extract the user's intent.

[0468] Step 5:

[0469] The server obtains real-time road information from a general-purpose traffic information API (e.g., Google Maps API) and uses this information to calculate the optimal route.

[0470] Input: Parsed user intent, real-time road information from traffic information API

[0471] Output: Text data of the optimal route

[0472] Specific behavior:

[0473] The server calculates the optimal route based on road information obtained from the traffic information API and the user's intentions, and generates it as text data.

[0474] Step 6:

[0475] The server sends the calculated optimal route as text data to the device, which converts this text data into voice data using a general-purpose speech synthesis engine (e.g., Amazon Polly).

[0476] Input: Text data of optimal route

[0477] Output: Audio data

[0478] Specific behavior:

[0479] The server transmits the text data to the terminal, and the terminal converts the text data into voice data using a voice synthesis engine.

[0480] Step 7:

[0481] The device uses a speech synthesis engine to generate voice data and plays it back to the user, for example, providing voice guidance such as "Turn left 500 meters before the next intersection."

[0482] Input: Audio data

[0483] Output: Audio output to the user

[0484] Specific behavior:

[0485] The device plays audio data through a speaker and guides the user to the optimal route.

[0486] (Application example 1)

[0487] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0488] Conventional car navigation systems require manual operation by the user, which can compromise safety while driving. Furthermore, route changes based on real-time traffic information are not made quickly, making it difficult to achieve efficient navigation. Furthermore, in autonomous vehicles, usability is poor because there is no interface that allows users to give voice instructions on destinations or route changes. There is a need for a system that can solve these issues and achieve safe and efficient autonomous driving.

[0489] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0490] In this invention, the server includes means for receiving voice input and converting the voice input into text data, means for analyzing the text data and understanding the user's intent, means for acquiring real-time traffic information and calculating an optimal route, means for outputting the calculated route information to the user by voice, means for linking the input destination and desired route to the autopilot system based on voice commands, and means for correcting the route in real time and re-navigating based on voice commands. This allows the user to specify destinations and route changes by voice without using their hands, and obtain the optimal route in real time, enabling safe and efficient automated driving.

[0491] "Voice input" refers to providing a user's spoken voice to the system as digital data.

[0492] "Text data" refers to digital information that has been converted from voice input into text.

[0493] "Real-time traffic information" refers to data showing current road and traffic conditions.

[0494] An "optimal route" refers to the most efficient and safe route calculated based on the user's specified conditions and intentions.

[0495] "Voice commands" refer to commands that a user gives to the system by voice, such as specifying a destination or instructing the system to change routes.

[0496] An "autopilot system" refers to a system that automatically drives a vehicle and allows it to reach a destination without manual operation by the user.

[0497] "Route correction" refers to the process of switching from a current route to another route.

[0498] "Navigation" refers to providing route guidance and direction to a destination.

[0499] "Server" refers to the central processing unit that analyzes voice input, obtains traffic information, and calculates routes.

[0500] A "voice recognition engine" refers to software or hardware for converting voice data into text data.

[0501] The system of the present invention is equipped with voice input, voice analysis, intent understanding, real-time information acquisition, route calculation, voice output, and linkage with an automated driving system, allowing the user to give hands-free instructions on destinations and route changes, realizing safe and efficient automated driving.

[0502] First, the user inputs a voice command into the system. This voice input is received by the device's built-in microphone and sent as digital data to the server. The server then uses a voice recognition engine to convert the voice data into text data.

[0503] The server then analyzes the converted text data and uses a natural language processing engine to understand the user's intent. For example, if a user says, "I want to go home," the system recognizes the intents "home" and "go home" and extracts the destination.

[0504] The server then uses a traffic information API to obtain real-time traffic information, collecting the latest information on congestion, accidents, road construction, etc. Based on this, it calculates the optimal route and sends that information to the device as text data.

[0505] The device uses a speech synthesis engine to convert the text data into speech, which is then fed back to the user, such as "Turn right at the next intersection." Each time the user inputs a new voice command, the system adjusts the route in real time and provides instructions to the autonomous driving system.

[0506] In this way, users can operate autonomous vehicles hands-free using only their voice.

[0507] For example, if a user says, "I want to go to a nearby gas station," the device receives the voice and sends it to the server. The server then understands the intent of "find a nearby gas station," obtains real-time information on nearby gas stations, calculates the optimal route, and provides voice guidance to the user, such as, "Turn right at the intersection 500 meters ahead, then turn left at the next corner."

[0508] Example prompts using generative AI models

[0509] "User said: 'I want to go to a nearby gas station.' Please output the generated navigation route."

[0510] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0511] Step 1:

[0512] The user issues a voice command.

[0513] For example, say, "I want to go to a nearby gas station." This voice input is captured as digital data by the device's microphone. The input is voice data, and the output is digitized voice data.

[0514] Step 2:

[0515] The terminal transmits the acquired voice data to the server.

[0516] A transmission process takes place and the server receives the audio data. The input is the digitized audio data, and the output is the same audio data received.

[0517] Step 3:

[0518] The server converts the received voice data into text data using a voice recognition engine.

[0519] Within the server, a speech recognition engine analyzes the digital voice data and generates corresponding text data. The input is voice data, and the output is text data.

[0520] Step 4:

[0521] The server analyzes the generated text data and performs natural language processing to understand the user's intent.

[0522] It uses a natural language processing engine to analyze instructions in text data and understand the user's desired destination and operation. The input is text data, and the output is data that indicates the user's intention.

[0523] Step 5:

[0524] The server accesses the traffic information API to obtain real-time traffic information.

[0525] It obtains the latest traffic data from the API and calculates the optimal route based on it. The input is the user's intention data, and the output is real-time traffic information and the calculated optimal route.

[0526] Step 6:

[0527] The server transmits the optimum route information to the terminal as text data.

[0528] A transmission process is performed and the terminal receives this information. The input is the text data of the calculated optimal route, and the output is the same text data sent to the terminal.

[0529] Step 7:

[0530] The terminal converts the received text data into voice using a voice synthesis engine and provides guidance to the user.

[0531] Text-to-speech conversion is performed, and a voice guidance message is generated: "Turn right at the intersection 500 meters ahead, then turn left at the next corner." The input is text data, and the output is voice guidance.

[0532] Step 8:

[0533] The user enters new voice commands and modifies the route as needed.

[0534] For example, issue a new command, such as "I want to stop at the cafe up ahead." The process repeats from step 1, correcting the route in real time. The input is a new voice command, and the output is a revised, optimized route and its instructions.

[0535] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0536] The present invention is a system for providing safe and efficient navigation while driving, which provides a navigation process including voice input, text conversion, user intent analysis, real-time road information acquisition, and sentiment analysis.

[0537] Voice input and recognition

[0538] First, the user speaks a voice command to the car navigation system, such as "I want to turn left at the next intersection." The device is equipped with a microphone that receives this voice input. The received voice data is then sent from the device to the server.

[0539] Speech recognition and text conversion

[0540] The server analyzes the transmitted voice data using a voice recognition engine and converts it into text data. The voice data "I want to turn left at the next intersection" is analyzed as text data "I want to turn left at the next intersection."

[0541] Intent analysis and request understanding

[0542] The server then uses a natural language processing engine to analyze the text data and understand the user's intent. By understanding specific commands such as "next intersection" and "turn left," the server knows the next route the user should take.

[0543] Emotion analysis

[0544] Furthermore, the server uses an emotion engine to recognize the user's emotions from the voice data. The emotion engine analyzes the voice tone, pitch, speed, etc. to identify the user's emotional state. For example, it determines whether the user is anxious or calm.

[0545] Real-time information acquisition and route optimization

[0546] The server accesses the traffic information API and road information center to obtain real-time road information, thereby obtaining current traffic conditions, construction information, etc. The server calculates the optimal route taking into account the obtained information, the user's intentions, and emotional state. Specific navigation instructions, such as "Turn left 500 meters before the next intersection," are generated.

[0547] Audio output and user feedback

[0548] The calculated optimal route information is sent to the device as text data. The device then converts this text data into speech using a speech synthesis engine and provides guidance to the user. For example, the device may provide a voice prompt saying, "Turn left 500 meters before the next intersection." By following this guidance, the user can achieve hands-free operation.

[0549] Specific examples

[0550] When a user says, "I want to go to a nearby gas station," the device receives the voice and sends it to the server. The server understands the user's intention and uses its emotion engine to recognize the user's sense of impatience. It then decides to guide the user to the nearest gas station via the fastest route. The voice guides the user, saying, "Turn right at the next traffic light, and the gas station is 500 meters ahead." This allows the user to reach their destination without feeling stressed.

[0551] This invention enables safe and efficient operation of car navigation systems using only voice commands without using the user's hands while driving, and provides guidance that takes the user's emotions into consideration, greatly improving the safety and comfort of driving.

[0552] The processing flow will be explained below.

[0553] Step 1:

[0554] User: Gives voice instructions to the car navigation system. For example, "I want to turn left at the next intersection."

[0555] Step 2:

[0556] Device: Collects user voice input through a microphone. Converts the voice data into a digital format and prepares it for transmission to the server.

[0557] Step 3:

[0558] Terminal: Sends the received voice data to the server via the network.

[0559] Step 4:

[0560] Server: The transmitted voice data is analyzed by a voice recognition engine and converted into text data. For example, the text data "I would like to turn left at the next intersection" is generated.

[0561] Step 5:

[0562] Server: The generated text data is passed to a natural language processing engine to understand the user's intent. For example, it identifies commands such as "turn left" at the "next intersection."

[0563] Step 6:

[0564] Server: Passes the voice data to the emotion engine and analyzes the user's emotional state. It analyzes the voice tone, pitch, speed, etc. to determine whether the user is anxious or calm.

[0565] Step 7:

[0566] Server: Based on the analyzed user's intentions and emotional state, the server accesses the traffic information API and road information center to obtain real-time road information, such as current traffic conditions, congestion, accidents, and construction information.

[0567] Step 8:

[0568] Server: Calculates the optimal route based on the acquired real-time information and the user's requests and emotional state. For example, specific navigation instructions such as "Turn left 500 meters before the next intersection" are generated.

[0569] Step 9:

[0570] Server: Sends calculated route information in text format to the device.

[0571] Step 10:

[0572] Terminal: The received text data is passed to a speech synthesis engine, which converts the text data into speech. Then, voice guidance is provided to the user. For example, it may say, "Turn left 500 meters before the next intersection."

[0573] Step 11:

[0574] User: Listens to and follows the voice prompts. For example, the user responds "Got it."

[0575] Step 12:

[0576] Terminal: The user's response is sent to the server again as voice data.

[0577] Step 13:

[0578] Server: Analyzes the user's response using a speech recognition and natural language processing engine, and optionally an emotion engine, to determine if further instructions are needed. Repeat the process as necessary.

[0579] Example 2

[0580] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0581] Conventional navigation systems can convert voice input into text, understand the user's intent, and calculate routes based on real-time road information. However, these systems often lack safety and comfort because they do not take the user's emotional state into account. The objective of this invention is to provide a navigation system that also takes the user's emotional state into account, thereby improving safety and efficiency while driving.

[0582] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving a voice input and converting the voice input into text data, means for analyzing the text data and understanding the user's intention, means for analyzing the user's emotional state from the voice data, means for acquiring real-time road information and calculating an optimal route taking into account the user's intention and emotional state, and means for outputting the calculation result to the user by voice. This enables navigation that takes into account the user's emotional state, significantly improving safety and efficiency during driving.

[0583] 1. "Voice input" is the process of capturing or recording a user's spoken voice.

[0584] 2. "Text data" refers to data that has been analyzed from voice input and converted into a string format.

[0585] 3. "User intent" refers to the commands or requests that the user makes to the system.

[0586] 4. "Emotional state" is information that expresses the user's mental and emotional state.

[0587] 5. "Real-time road information" means data on the latest traffic and road conditions.

[0588] 6. "Optimal route" refers to the most efficient and safe route calculated based on certain criteria.

[0589] 7. "Speech recognition engine" means software or hardware that analyzes voice data and converts it into text data.

[0590] 8. "Emotion Analysis Engine" means software or hardware for analyzing a user's emotional state from voice data.

[0591] 9. "Navigation system" means a system that provides users with the instructions and information they need to reach their destination.

[0592] The present invention is a system for providing safe and efficient navigation while driving, which provides a navigation process including voice input, text conversion, user intent analysis, real-time road information acquisition, and sentiment analysis.

[0593] Voice input and recognition

[0594] The user speaks a command to the car navigation system, such as "I want to turn left at the next intersection." The device is equipped with a microphone that receives this voice input. The received voice data is sent from the device to a server. The transmission is carried out over the Internet, and security protocols are used to ensure the safety of the data.

[0595] Speech recognition and text conversion

[0596] The server analyzes the transmitted voice data using a voice recognition engine (a typical example is a voice recognition API) and converts it into text data. The voice data uttered by the user, "I would like to turn left at the next intersection," is analyzed as text data saying, "I would like to turn left at the next intersection."

[0597] Intent analysis and request understanding

[0598] The server then analyzes the text data using a natural language processing engine (typically a generative AI model) to understand the user's intent, extracting specific instructions such as "next intersection" and "turn left" and figuring out the next route the user should take.

[0599] Emotion analysis

[0600] Furthermore, the server uses an emotion analysis engine to recognize the user's emotions from the voice data. The emotion analysis engine analyzes the voice tone, pitch, speed, etc. to identify the user's emotional state. For example, it determines whether the user is feeling impatient or calm.

[0601] Real-time information acquisition and route optimization

[0602] The server accesses the traffic information API and road information center to obtain real-time road information, thereby obtaining the latest traffic conditions, construction information, accident information, etc. The server calculates the optimal route taking into account the obtained information and the user's intentions and emotional state. For example, specific navigation instructions such as "Turn left 500 meters before the next intersection" are generated.

[0603] Audio output and user feedback

[0604] The calculated optimal route information is sent to the device as text data. The device then converts this text data into speech using a speech synthesis engine (a typical example is a speech synthesis API) and provides guidance to the user. For example, the device may provide a voice prompt saying, "Turn left 500 meters before the next intersection." By following this guidance, the user can achieve hands-free operation.

[0605] Specific examples

[0606] For example, if a user says, "I want to go to a nearby gas station," the device receives the voice and sends it to the server. The server understands the user's intention and uses an emotion analysis engine to recognize the user's sense of impatience. It then decides to guide the user to the nearest gas station via the fastest route. The voice guides the user, saying, "Turn right at the next traffic light, and the gas station is 500 meters ahead." This allows the user to reach their destination without feeling stressed.

[0607] Prompt Sentence Examples

[0608] An example of a prompt to be input to a generative AI model might be, "Generate appropriate navigation instructions if the user says they want to go to a nearby gas station."

[0609] Through the above specific process, the system can provide users with safe and efficient navigation.

[0610] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0611] Step 1:

[0612] The user speaks to the car navigation system, saying, "I want to turn left at the next intersection." The microphone on the device receives this voice input and records it as voice data. This voice data becomes the input for the next step.

[0613] Step 2:

[0614] The device sends the recorded audio data to the server. The transmission is over the Internet, and the data is secured by security protocols (e.g., SSL / TLS). The output of this step is the audio data sent to the server.

[0615] Step 3:

[0616] The server passes the received voice data to a voice recognition engine (e.g., a voice recognition API). The voice recognition engine analyzes the voice data and converts it into text data such as "I would like to turn left at the next intersection." This text data becomes the input for the next step.

[0617] Step 4:

[0618] The server passes the generated text data to a natural language processing engine (e.g., a generative AI model) for analysis. The natural language processing engine extracts the user's intent from the text, specifically the specific commands "next intersection" and "turn left." The results of this analysis become the input for the next step.

[0619] Step 5:

[0620] The server then passes the initial voice data to an emotion analysis engine, which analyzes the tone, pitch, and speed of the voice to identify the user's emotional state (e.g., anxious, calm, etc.). The results of this analysis are used as input for the next step.

[0621] Step 6:

[0622] Based on the analyzed user's intention and emotional state, the server accesses the traffic information API and road information center to obtain real-time road information. This obtained road information becomes the input for the next step.

[0623] Step 7:

[0624] The server calculates the optimal route based on the acquired road information, the user's intentions, and the user's emotional state. The route calculation uses an algorithm that combines the user's requests with real-time information. The result of this calculation is the input for the next step.

[0625] Step 8:

[0626] The server generates specific navigation instructions based on the optimal route information, such as "Turn left 500 meters before the next intersection." This navigation instruction becomes the input for the next step.

[0627] Step 9:

[0628] The generated navigation instructions are sent to the device as text data. The device passes this text data to a speech synthesis engine (e.g., a speech synthesis API) to convert it into speech. The output of this step is speech data.

[0629] Step 10:

[0630] The device then sends the generated voice data to the user through a speaker. For example, it may say, "Turn left 500 meters before the next intersection." By following this guidance, the user can achieve hands-free operation and reach their destination safely and efficiently.

[0631] (Application example 2)

[0632] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0633] Conventional navigation systems not only have the ability to understand user intentions and provide routes that take traffic information into account, but also have the problem of not being able to consider the emotional state of professional drivers such as delivery drivers. As a result, delivery efficiency may decrease and delivery workers' stress may increase. In the food delivery field in particular, maintaining the mental health of delivery workers is important in addition to delivery efficiency. Therefore, there is a need for a system that not only analyzes user intentions but also provides optimal routes that take emotions into account.

[0634] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving voice input and converting the voice input into text data, means for analyzing the text data and understanding the user's intention, means for analyzing the user's emotions from the voice data, means for acquiring real-time traffic information and calculating the optimal route taking into account the user's intention and emotional state, and means for outputting the calculation results to the user by voice. This enables the delivery person to select an efficient and less stressful route.

[0635] "Voice input" refers to the act of receiving a user's voice commands into a system and the system's interface.

[0636] "Text data" refers to character string data converted from voice input.

[0637] "User intent" refers to the user's goals and requests analyzed from voice input.

[0638] "Voice Data" means a digital representation of a user's voice input received by a system.

[0639] "Real-time traffic information" refers to up-to-date information about current traffic and road conditions.

[0640] An "optimal route" refers to the most efficient and safe route of travel calculated taking into account the user's intentions and emotional state.

[0641] "Means for outputting to the user by voice" refers to a method of notifying the user of calculation results and instructions using voice synthesis technology.

[0642] "Voice recording device" refers to a microphone or recording device for inputting user voice commands.

[0643] "Central processing unit" refers to a server or computer that analyzes voice commands and performs the necessary processing.

[0644] A "voice recognition engine" refers to software or a system for analyzing voice data and converting it into text data.

[0645] "Emotional state" refers to the user's psychological state and emotions analyzed from voice data.

[0646] A "delivery person" refers to a professional driver who delivers goods or food to a requested destination.

[0647] The embodiment of the present invention is configured as follows.

[0648] Generating a Program

[0649] This invention is a system that performs voice input, voice recognition, emotion analysis, acquisition of real-time traffic information, route optimization, and voice output to help delivery personnel select efficient and stress-free routes. The system is composed of elements such as a voice recording device, a central processing unit, a voice recognition engine, and an emotion analysis engine.

[0650] Processing Description

[0651] The server receives voice commands from the user using a voice recording device, such as a microphone or a smartphone. This voice data is sent to the server and converted into text data by a voice recognition engine.

[0652] The server then analyzes the converted text data using natural language processing technology to identify the user's intent, such as whether the user wants to head to a destination or take a specific route.

[0653] The server then uses an emotion analysis engine to identify the user's emotional state from the voice data, such as analyzing parameters like voice tone, pitch, and speed to determine whether the user is agitated or calm. This information is then used to optimize the route.

[0654] The server then retrieves real-time traffic information from relevant APIs and traffic information centers, including current traffic conditions, accident information, road construction information, etc.

[0655] Finally, the server calculates the optimal route based on the acquired traffic information, the user's intentions, and the user's emotional state. The calculated route information is provided to the user via a voice output device, such as a smartphone or a car speaker. At this time, text data is converted into voice using speech synthesis technology.

[0656] Specific examples

[0657] For example, if a delivery person says, "I want to get to my next delivery as quickly as possible," the voice recording device receives this speech and sends it to the server. The server first converts the speech into text data, then analyzes this data to determine the delivery person's intentions. It then uses an emotion analysis engine to recognize the delivery person's sense of impatience. It then calculates the fastest route and provides specific instructions via voice, such as, "Turn right at the next traffic light, and your delivery is 500 meters away."

[0658] Prompt Sentence Examples

[0659] If you give the voice command "I want to get to the next delivery destination as quickly as possible," and feel a sense of urgency, calculate the fastest route and provide feedback.

[0660] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0661] Step 1:

[0662] The user speaks a voice command into a voice recording device (smartphone or microphone). For example, a delivery person might say, "I want to get to the next delivery destination as soon as possible." This voice data is digitally recorded on the device.

[0663] Input: User's voice command

[0664] Output: Digitized audio data

[0665] Step 2:

[0666] The device sends the recorded audio data to a central processing unit (server), where it is converted into data packets and transferred to the server using a stable communication protocol.

[0667] Input: Digitized voice data

[0668] Output: Audio data sent to the server

[0669] Step 3:

[0670] The server converts the received voice data into text data using a voice recognition engine, which identifies phonemes from the voice waveform and converts them into corresponding text.

[0671] Input: Audio data

[0672] Output: Text data

[0673] Step 4:

[0674] The server uses a generative AI model to analyze the text data and identify the user's intent. For example, from the text "I want to get to the next delivery destination as quickly as possible," the server determines that the delivery person is looking for a fast route.

[0675] Input: Text data

[0676] Output: User intent

[0677] Step 5:

[0678] The server uses an emotion analysis engine to analyze the user's emotional state from the voice data, which analyzes the tone, pitch, speed, etc. of the voice to determine whether the user is agitated or calm.

[0679] Input: Audio data

[0680] Output: User's emotional state

[0681] Step 6:

[0682] The server uses a traffic information API to obtain real-time traffic information, including current traffic conditions, accident information, and road construction information.

[0683] Input: Traffic information API to request

[0684] Output: Real-time traffic information

[0685] Step 7:

[0686] The server integrates the user's intentions, emotional state, and real-time traffic information to calculate the optimal route using a route optimization algorithm. The calculation process prioritizes the delivery person's urgency and routes that avoid traffic congestion.

[0687] Input: User intent, user emotional state, real-time traffic information

[0688] Output: Optimal route

[0689] Step 8:

[0690] The server calculates the optimal route and sends it to the device as text data, which includes specific instructions such as "Turn right at the next traffic light and your delivery address is 500 meters away."

[0691] Input: Optimal Route

[0692] Output: Text data of route directions

[0693] Step 9:

[0694] The device uses a speech synthesis engine to convert the received text data into speech and provides guidance to the user via a voice output device (smartphone or car speaker).

[0695] Input: Text data for route directions

[0696] Output: Voice guidance

[0697] In this way, the entire process from the user uttering a voice command to receiving voice guidance along an optimized route is completed.

[0698] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0699] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0700] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0701] [Third embodiment]

[0702] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0703] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0704] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0705] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0706] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0707] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0708] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0709] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0710] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0711] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0712] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0713] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0714] The present invention is a car navigation system that realizes a user's hands-free navigation request through voice input. This system functions mainly through cooperation between a terminal and a server.

[0715] Voice input and recognition

[0716] First, the user inputs a voice command into the device, such as "I want to turn left at the next intersection." The device is equipped with a microphone that receives this voice input. The received voice data is then sent from the device to the server.

[0717] Voice Recognition

[0718] The server analyzes the transmitted voice data using a voice recognition system and converts it into text data. For example, voice data such as "I want to turn left at the next intersection" is analyzed as text data such as "I want to turn left at the next intersection."

[0719] Intent analysis and request understanding

[0720] The server then uses a natural language processing engine to analyze the text data and understand the user's intent—in this case, the specific commands "next intersection" and "turn left"—to figure out the next route the user should take.

[0721] Real-time information acquisition and route optimization

[0722] The server also accesses the traffic information API and road information center to obtain real-time road information. This information includes current road conditions such as traffic congestion, accidents, and construction. Based on this, the server calculates the optimal route. This calculation reflects the user's instructions and suggests the optimal route to "turn left at the next intersection."

[0723] Audio output and user feedback

[0724] The calculated optimal route information is sent to the device as text data. The device then converts this text data into voice using a speech synthesis engine and provides guidance to the user. For example, voice guidance may be provided in the form of "Turn left 500 meters before the next intersection." If the user responds by saying "OK," the device again sends the voice input to the server and repeats the same process.

[0725] Specific examples

[0726] While actually driving, suppose the user says, "I want to go to a nearby gas station." The device receives this speech and sends it to the server. The server understands the intent to "find a nearby gas station" and obtains real-time information on nearby gas stations. It then suggests the optimal route and provides voice guidance to the user, such as, "Turn right at the intersection 500 meters ahead, then turn left at the next corner."

[0727] The system of this invention allows users to operate the car navigation system safely and efficiently using only their voice while driving, without using their hands. This system significantly improves user convenience and enables safer driving without distracting the driver.

[0728] The processing flow will be explained below.

[0729] Step 1:

[0730] User: Gives voice instructions to the car navigation system. For example, the user says, "I want to turn left at the next intersection."

[0731] Step 2:

[0732] Device: Receives audio input through a microphone, converts the audio data into a digital format, and prepares it for transmission to the server.

[0733] Step 3:

[0734] Terminal: Sends the received audio data to the server via network communication.

[0735] Step 4:

[0736] Server: Receives the voice data and passes it to a speech recognition engine. The speech recognition engine is used to convert the voice data into text data. For example, the text generated is "I would like to turn left at the next intersection."

[0737] Step 5:

[0738] Server: The converted text data is passed to a natural language processing engine. The natural language processing engine analyzes the text data and understands the user's intent. For example, it extracts commands such as "next intersection" and "turn left."

[0739] Step 6:

[0740] Server: After understanding the user's intent, the server accesses the traffic information API and road information center to obtain real-time road information, such as current traffic conditions and construction information.

[0741] Step 7:

[0742] Server: Calculates the optimal route based on the acquired real-time information and the user's intentions. For example, it generates specific navigation instructions such as "Turn left 500 meters before the next intersection."

[0743] Step 8:

[0744] Server: Sends calculated route information in text format to the device.

[0745] Step 9:

[0746] Terminal: Passes the received text data to a speech synthesis engine. The speech synthesis engine converts the text data into speech and provides guidance to the user. For example, it may provide a voice prompt saying, "Turn left 500 meters before the next intersection."

[0747] Step 10:

[0748] User: Listen to and follow the voice prompts. Provide further voice prompts as needed. For example, reply "Got it."

[0749] Step 11:

[0750] Terminal: Receives the user's response again and sends it to the server as voice data.

[0751] Step 12:

[0752] Server: Again performs speech recognition and natural language processing to determine if further instructions are needed. If so, return to step 1 and repeat the process.

[0753] Example 1

[0754] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0755] Conventional car navigation systems require users to operate the system manually, which increases the risk of distraction while driving. Therefore, there is a need for a hands-free method of navigating safely and efficiently while driving, improving user convenience.

[0756] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0757] In this invention, the server includes means for receiving voice input and converting the voice input into text data, means for analyzing the text data to understand the user's intention, and means for obtaining real-time traffic information and calculating the optimal route, thereby enabling the user to use navigation solely by voice while driving, without using their hands.

[0758] "Voice input" is the process of receiving a user's speech as machine-recognizable data.

[0759] "Text data" is data that expresses voice input as text information.

[0760] "User intent" refers to the actions or requests that a user intends to convey to a machine through voice input.

[0761] "Real-time traffic information" is data that indicates the current road conditions and traffic situation.

[0762] An "optimal route" is the route that most efficiently reaches the user's destination.

[0763] "Audio output" is the process of converting text data back into audio and conveying it to the user.

[0764] An "acoustic sensor" is a device that senses sound waves and converts them into a form that can be processed as audio data.

[0765] A "remote device" is a device that transmits data from a terminal to a server via the Internet or other communication means.

[0766] A "voice recognition system" is a general term for software and hardware used to convert voice data into text data.

[0767] The present invention is a car navigation system that realizes a user's hands-free navigation request through voice input. This system functions mainly through cooperation between a terminal and a server.

[0768] Voice input and recognition

[0769] First, the user inputs a voice command into the device, such as "I want to turn left at the next intersection." The device is equipped with a microphone that receives this voice input. The received voice data is sent from the device to the server. At this time, the device uses an Internet connection to encrypt the voice data according to a security protocol and send it to the server.

[0770] Voice Recognition

[0771] The server analyzes the transmitted voice data using a general-purpose voice recognition system (for example, Google Cloud Speech-to-Text) and converts it into text data. For example, the voice message "I want to turn left at the next intersection" is returned as text data saying "I want to turn left at the next intersection."

[0772] Intent analysis and request understanding

[0773] The server passes the acquired text data to a general-purpose natural language processing engine (e.g., OpenAI GPT-3) to understand the user's intention. From the text "I want to turn left at the next intersection," the server understands the action instruction "Turn left at the next intersection."

[0774] Real-time information acquisition and route optimization

[0775] The server also uses a general-purpose traffic information API (e.g., Google Maps API) to obtain real-time road information, including information on traffic congestion, accidents, and construction work. Based on this information, the server calculates the optimal route for the user's instructions. The calculation results are generated as text data.

[0776] Audio output and user feedback

[0777] The calculated optimal route information is sent to the device as text data. The device then converts this text data into voice data using a general-purpose speech synthesis engine (e.g., Amazon Polly) and provides guidance to the user. For example, the voice guidance may say, "Turn left 500 meters before the next intersection." If the user responds by saying "OK," the device again sends the voice input to the server and repeats the same process.

[0778] Specific examples

[0779] While actually driving, suppose the user says, "I want to go to a nearby gas station." The device receives this speech and sends it to the server. The server understands the intent to "find a nearby gas station" and obtains real-time information on nearby gas stations. It then suggests the optimal route and provides voice guidance to the user, such as, "Turn right at the intersection 500 meters ahead, then turn left at the next corner."

[0780] Prompt Sentence Examples

[0781] As an example of a specific prompt, you could input the following instructions to your generative AI model:

[0782] When a user says, "I want to turn left at the next intersection," please explain in detail the process of the program that analyzes their intention and calculates the next route to take. Use a general-purpose speech recognition system as the speech recognition system, a general-purpose natural language processing engine as the natural language processing engine, a general-purpose traffic information API as the traffic information API, and a general-purpose speech synthesis engine as the speech synthesis engine.

[0783] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0784] Step 1:

[0785] The user speaks a voice command, such as "I want to turn left at the next intersection," into the device's microphone, which captures this voice input, converts it into digital voice data, and sends it to the device's voice processing unit.

[0786] Input: User's voice command

[0787] Output: Digital audio data

[0788] Specific behavior:

[0789] A microphone in the terminal receives the user's voice and transmits it to the voice processing unit as digital voice data.

[0790] Step 2:

[0791] The device transmits the captured digital audio data over the internet to a server, where it encrypts the data and transmits it using a secure protocol.

[0792] Input: Digital audio data

[0793] Output: Audio data sent to the server

[0794] Specific behavior:

[0795] The device encrypts the voice data and sends it to a server via an internet connection.

[0796] Step 3:

[0797] The server sends the received voice data to a general-purpose voice recognition system (e.g., Google Cloud Speech-to-Text) and converts the voice into text data.

[0798] Input: Audio data sent to the server

[0799] Output: Text data

[0800] Specific behavior:

[0801] The server sends the voice data to a voice recognition system, which converts the digital voice data into text data.

[0802] Step 4:

[0803] The server passes the acquired text data to a general-purpose natural language processing engine (e.g., OpenAI GPT-3) to analyze the user's intent.

[0804] Input: Text data

[0805] Output: Parsed user intent

[0806] Specific behavior:

[0807] The server inputs the text data into a natural language processing engine and executes a process to extract the user's intent.

[0808] Step 5:

[0809] The server obtains real-time road information from a general-purpose traffic information API (e.g., Google Maps API) and uses this information to calculate the optimal route.

[0810] Input: Parsed user intent, real-time road information from traffic information API

[0811] Output: Text data of the optimal route

[0812] Specific behavior:

[0813] The server calculates the optimal route based on road information obtained from the traffic information API and the user's intentions, and generates it as text data.

[0814] Step 6:

[0815] The server sends the calculated optimal route as text data to the device, which converts this text data into voice data using a general-purpose speech synthesis engine (e.g., Amazon Polly).

[0816] Input: Text data of optimal route

[0817] Output: Audio data

[0818] Specific behavior:

[0819] The server transmits the text data to the terminal, and the terminal converts the text data into voice data using a voice synthesis engine.

[0820] Step 7:

[0821] The device uses a speech synthesis engine to generate voice data and plays it back to the user, for example, providing voice guidance such as "Turn left 500 meters before the next intersection."

[0822] Input: Audio data

[0823] Output: Audio output to the user

[0824] Specific behavior:

[0825] The device plays audio data through a speaker and guides the user to the optimal route.

[0826] (Application example 1)

[0827] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0828] Conventional car navigation systems require manual operation by the user, which can compromise safety while driving. Furthermore, route changes based on real-time traffic information are not made quickly, making it difficult to achieve efficient navigation. Furthermore, in autonomous vehicles, usability is poor because there is no interface that allows users to give voice instructions on destinations or route changes. There is a need for a system that can solve these issues and achieve safe and efficient autonomous driving.

[0829] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0830] In this invention, the server includes means for receiving voice input and converting the voice input into text data, means for analyzing the text data and understanding the user's intent, means for acquiring real-time traffic information and calculating an optimal route, means for outputting the calculated route information to the user by voice, means for linking the input destination and desired route to the autopilot system based on voice commands, and means for correcting the route in real time and re-navigating based on voice commands. This allows the user to specify destinations and route changes by voice without using their hands, and obtain the optimal route in real time, enabling safe and efficient automated driving.

[0831] "Voice input" refers to providing a user's spoken voice to the system as digital data.

[0832] "Text data" refers to digital information that has been converted from voice input into text.

[0833] "Real-time traffic information" refers to data showing current road and traffic conditions.

[0834] An "optimal route" refers to the most efficient and safe route calculated based on the user's specified conditions and intentions.

[0835] "Voice commands" refer to commands that a user gives to the system by voice, such as specifying a destination or instructing the system to change routes.

[0836] An "autopilot system" refers to a system that automatically drives a vehicle and allows it to reach a destination without manual operation by the user.

[0837] "Route correction" refers to the process of switching from a current route to another route.

[0838] "Navigation" refers to providing route guidance and direction to a destination.

[0839] "Server" refers to the central processing unit that analyzes voice input, obtains traffic information, and calculates routes.

[0840] A "voice recognition engine" refers to software or hardware for converting voice data into text data.

[0841] The system of the present invention is equipped with voice input, voice analysis, intent understanding, real-time information acquisition, route calculation, voice output, and linkage with an automated driving system, allowing the user to give hands-free instructions on destinations and route changes, realizing safe and efficient automated driving.

[0842] First, the user inputs a voice command into the system. This voice input is received by the device's built-in microphone and sent as digital data to the server. The server then uses a voice recognition engine to convert the voice data into text data.

[0843] The server then analyzes the converted text data and uses a natural language processing engine to understand the user's intent. For example, if a user says, "I want to go home," the system recognizes the intents "home" and "go home" and extracts the destination.

[0844] The server then uses a traffic information API to obtain real-time traffic information, collecting the latest information on congestion, accidents, road construction, etc. Based on this, it calculates the optimal route and sends that information to the device as text data.

[0845] The device uses a speech synthesis engine to convert the text data into speech, which is then fed back to the user, such as "Turn right at the next intersection." Each time the user inputs a new voice command, the system adjusts the route in real time and provides instructions to the autonomous driving system.

[0846] In this way, users can operate autonomous vehicles hands-free using only their voice.

[0847] For example, if a user says, "I want to go to a nearby gas station," the device receives the voice and sends it to the server. The server then understands the intent of "find a nearby gas station," obtains real-time information on nearby gas stations, calculates the optimal route, and provides voice guidance to the user, such as, "Turn right at the intersection 500 meters ahead, then turn left at the next corner."

[0848] Example prompts using generative AI models

[0849] "User said: 'I want to go to a nearby gas station.' Please output the generated navigation route."

[0850] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0851] Step 1:

[0852] The user issues a voice command.

[0853] For example, say, "I want to go to a nearby gas station." This voice input is captured as digital data by the device's microphone. The input is voice data, and the output is digitized voice data.

[0854] Step 2:

[0855] The terminal transmits the acquired voice data to the server.

[0856] A transmission process takes place and the server receives the audio data. The input is the digitized audio data, and the output is the same audio data received.

[0857] Step 3:

[0858] The server converts the received voice data into text data using a voice recognition engine.

[0859] Within the server, a speech recognition engine analyzes the digital voice data and generates corresponding text data. The input is voice data, and the output is text data.

[0860] Step 4:

[0861] The server analyzes the generated text data and performs natural language processing to understand the user's intent.

[0862] It uses a natural language processing engine to analyze instructions in text data and understand the user's desired destination and operation. The input is text data, and the output is data that indicates the user's intention.

[0863] Step 5:

[0864] The server accesses the traffic information API to obtain real-time traffic information.

[0865] It obtains the latest traffic data from the API and calculates the optimal route based on it. The input is the user's intention data, and the output is real-time traffic information and the calculated optimal route.

[0866] Step 6:

[0867] The server transmits the optimum route information to the terminal as text data.

[0868] A transmission process is performed and the terminal receives this information. The input is the text data of the calculated optimal route, and the output is the same text data sent to the terminal.

[0869] Step 7:

[0870] The terminal converts the received text data into voice using a voice synthesis engine and provides guidance to the user.

[0871] Text-to-speech conversion is performed, and a voice guidance message is generated: "Turn right at the intersection 500 meters ahead, then turn left at the next corner." The input is text data, and the output is voice guidance.

[0872] Step 8:

[0873] The user enters new voice commands and modifies the route as needed.

[0874] For example, issue a new command, such as "I want to stop at the cafe up ahead." The process repeats from step 1, correcting the route in real time. The input is a new voice command, and the output is a revised, optimized route and its instructions.

[0875] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0876] The present invention is a system for providing safe and efficient navigation while driving, which provides a navigation process including voice input, text conversion, user intent analysis, real-time road information acquisition, and sentiment analysis.

[0877] Voice input and recognition

[0878] First, the user speaks a voice command to the car navigation system, such as "I want to turn left at the next intersection." The device is equipped with a microphone that receives this voice input. The received voice data is then sent from the device to the server.

[0879] Speech recognition and text conversion

[0880] The server analyzes the transmitted voice data using a voice recognition engine and converts it into text data. The voice data "I want to turn left at the next intersection" is analyzed as text data "I want to turn left at the next intersection."

[0881] Intent analysis and request understanding

[0882] The server then uses a natural language processing engine to analyze the text data and understand the user's intent. By understanding specific commands such as "next intersection" and "turn left," the server knows the next route the user should take.

[0883] Emotion analysis

[0884] Furthermore, the server uses an emotion engine to recognize the user's emotions from the voice data. The emotion engine analyzes the voice tone, pitch, speed, etc. to identify the user's emotional state. For example, it determines whether the user is anxious or calm.

[0885] Real-time information acquisition and route optimization

[0886] The server accesses the traffic information API and road information center to obtain real-time road information, thereby obtaining current traffic conditions, construction information, etc. The server calculates the optimal route taking into account the obtained information, the user's intentions, and emotional state. Specific navigation instructions, such as "Turn left 500 meters before the next intersection," are generated.

[0887] Audio output and user feedback

[0888] The calculated optimal route information is sent to the device as text data. The device then converts this text data into speech using a speech synthesis engine and provides guidance to the user. For example, the device may provide a voice prompt saying, "Turn left 500 meters before the next intersection." By following this guidance, the user can achieve hands-free operation.

[0889] Specific examples

[0890] When a user says, "I want to go to a nearby gas station," the device receives the voice and sends it to the server. The server understands the user's intention and uses its emotion engine to recognize the user's sense of impatience. It then decides to guide the user to the nearest gas station via the fastest route. The voice guides the user, saying, "Turn right at the next traffic light, and the gas station is 500 meters ahead." This allows the user to reach their destination without feeling stressed.

[0891] This invention enables safe and efficient operation of car navigation systems using only voice commands without using the user's hands while driving, and provides guidance that takes the user's emotions into consideration, greatly improving the safety and comfort of driving.

[0892] The processing flow will be explained below.

[0893] Step 1:

[0894] User: Gives voice instructions to the car navigation system. For example, "I want to turn left at the next intersection."

[0895] Step 2:

[0896] Device: Collects user voice input through a microphone. Converts the voice data into a digital format and prepares it for transmission to the server.

[0897] Step 3:

[0898] Terminal: Sends the received voice data to the server via the network.

[0899] Step 4:

[0900] Server: The transmitted voice data is analyzed by a voice recognition engine and converted into text data. For example, the text data "I would like to turn left at the next intersection" is generated.

[0901] Step 5:

[0902] Server: The generated text data is passed to a natural language processing engine to understand the user's intent. For example, it identifies commands such as "turn left" at the "next intersection."

[0903] Step 6:

[0904] Server: Passes the voice data to the emotion engine and analyzes the user's emotional state. It analyzes the voice tone, pitch, speed, etc. to determine whether the user is anxious or calm.

[0905] Step 7:

[0906] Server: Based on the analyzed user's intentions and emotional state, the server accesses the traffic information API and road information center to obtain real-time road information, such as current traffic conditions, congestion, accidents, and construction information.

[0907] Step 8:

[0908] Server: Calculates the optimal route based on the acquired real-time information and the user's requests and emotional state. For example, specific navigation instructions such as "Turn left 500 meters before the next intersection" are generated.

[0909] Step 9:

[0910] Server: Sends calculated route information in text format to the device.

[0911] Step 10:

[0912] Terminal: The received text data is passed to a speech synthesis engine, which converts the text data into speech. Then, voice guidance is provided to the user. For example, it may say, "Turn left 500 meters before the next intersection."

[0913] Step 11:

[0914] User: Listens to and follows the voice prompts. For example, the user responds "Got it."

[0915] Step 12:

[0916] Terminal: The user's response is sent to the server again as voice data.

[0917] Step 13:

[0918] Server: Analyzes the user's response using a speech recognition and natural language processing engine, and optionally an emotion engine, to determine if further instructions are needed. Repeat the process as necessary.

[0919] Example 2

[0920] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0921] Conventional navigation systems can convert voice input into text, understand the user's intent, and calculate routes based on real-time road information. However, these systems often lack safety and comfort because they do not take the user's emotional state into account. The objective of this invention is to provide a navigation system that also takes the user's emotional state into account, thereby improving safety and efficiency while driving.

[0922] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving a voice input and converting the voice input into text data, means for analyzing the text data and understanding the user's intention, means for analyzing the user's emotional state from the voice data, means for acquiring real-time road information and calculating an optimal route taking into account the user's intention and emotional state, and means for outputting the calculation result to the user by voice. This enables navigation that takes into account the user's emotional state, significantly improving safety and efficiency during driving.

[0923] 1. "Voice input" is the process of capturing or recording a user's spoken voice.

[0924] 2. "Text data" refers to data that has been analyzed from voice input and converted into a string format.

[0925] 3. "User intent" refers to the commands or requests that the user makes to the system.

[0926] 4. "Emotional state" is information that expresses the user's mental and emotional state.

[0927] 5. "Real-time road information" means data on the latest traffic and road conditions.

[0928] 6. "Optimal route" refers to the most efficient and safe route calculated based on certain criteria.

[0929] 7. "Speech recognition engine" means software or hardware that analyzes voice data and converts it into text data.

[0930] 8. "Emotion Analysis Engine" means software or hardware for analyzing a user's emotional state from voice data.

[0931] 9. "Navigation system" means a system that provides users with the instructions and information they need to reach their destination.

[0932] The present invention is a system for providing safe and efficient navigation while driving, which provides a navigation process including voice input, text conversion, user intent analysis, real-time road information acquisition, and sentiment analysis.

[0933] Voice input and recognition

[0934] The user speaks a command to the car navigation system, such as "I want to turn left at the next intersection." The device is equipped with a microphone that receives this voice input. The received voice data is sent from the device to a server. The transmission is carried out over the Internet, and security protocols are used to ensure the safety of the data.

[0935] Speech recognition and text conversion

[0936] The server analyzes the transmitted voice data using a voice recognition engine (a typical example is a voice recognition API) and converts it into text data. The voice data uttered by the user, "I would like to turn left at the next intersection," is analyzed as text data saying, "I would like to turn left at the next intersection."

[0937] Intent analysis and request understanding

[0938] The server then analyzes the text data using a natural language processing engine (typically a generative AI model) to understand the user's intent, extracting specific instructions such as "next intersection" and "turn left" and figuring out the next route the user should take.

[0939] Emotion analysis

[0940] Furthermore, the server uses an emotion analysis engine to recognize the user's emotions from the voice data. The emotion analysis engine analyzes the voice tone, pitch, speed, etc. to identify the user's emotional state. For example, it determines whether the user is feeling impatient or calm.

[0941] Real-time information acquisition and route optimization

[0942] The server accesses the traffic information API and road information center to obtain real-time road information, thereby obtaining the latest traffic conditions, construction information, accident information, etc. The server calculates the optimal route taking into account the obtained information and the user's intentions and emotional state. For example, specific navigation instructions such as "Turn left 500 meters before the next intersection" are generated.

[0943] Audio output and user feedback

[0944] The calculated optimal route information is sent to the device as text data. The device then converts this text data into speech using a speech synthesis engine (a typical example is a speech synthesis API) and provides guidance to the user. For example, the device may provide a voice prompt saying, "Turn left 500 meters before the next intersection." By following this guidance, the user can achieve hands-free operation.

[0945] Specific examples

[0946] For example, if a user says, "I want to go to a nearby gas station," the device receives the voice and sends it to the server. The server understands the user's intention and uses an emotion analysis engine to recognize the user's sense of impatience. It then decides to guide the user to the nearest gas station via the fastest route. The voice guides the user, saying, "Turn right at the next traffic light, and the gas station is 500 meters ahead." This allows the user to reach their destination without feeling stressed.

[0947] Prompt Sentence Examples

[0948] An example of a prompt to be input to a generative AI model might be, "Generate appropriate navigation instructions if the user says they want to go to a nearby gas station."

[0949] Through the above specific process, the system can provide users with safe and efficient navigation.

[0950] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0951] Step 1:

[0952] The user speaks to the car navigation system, saying, "I want to turn left at the next intersection." The microphone on the device receives this voice input and records it as voice data. This voice data becomes the input for the next step.

[0953] Step 2:

[0954] The device sends the recorded audio data to the server. The transmission is over the Internet, and the data is secured by security protocols (e.g., SSL / TLS). The output of this step is the audio data sent to the server.

[0955] Step 3:

[0956] The server passes the received voice data to a voice recognition engine (e.g., a voice recognition API). The voice recognition engine analyzes the voice data and converts it into text data such as "I would like to turn left at the next intersection." This text data becomes the input for the next step.

[0957] Step 4:

[0958] The server passes the generated text data to a natural language processing engine (e.g., a generative AI model) for analysis. The natural language processing engine extracts the user's intent from the text, specifically the specific commands "next intersection" and "turn left." The results of this analysis become the input for the next step.

[0959] Step 5:

[0960] The server then passes the initial voice data to an emotion analysis engine, which analyzes the tone, pitch, and speed of the voice to identify the user's emotional state (e.g., anxious, calm, etc.). The results of this analysis are used as input for the next step.

[0961] Step 6:

[0962] Based on the analyzed user's intention and emotional state, the server accesses the traffic information API and road information center to obtain real-time road information. This obtained road information becomes the input for the next step.

[0963] Step 7:

[0964] The server calculates the optimal route based on the acquired road information, the user's intentions, and the user's emotional state. The route calculation uses an algorithm that combines the user's requests with real-time information. The result of this calculation is the input for the next step.

[0965] Step 8:

[0966] The server generates specific navigation instructions based on the optimal route information, such as "Turn left 500 meters before the next intersection." This navigation instruction becomes the input for the next step.

[0967] Step 9:

[0968] The generated navigation instructions are sent to the device as text data. The device passes this text data to a speech synthesis engine (e.g., a speech synthesis API) to convert it into speech. The output of this step is speech data.

[0969] Step 10:

[0970] The device then sends the generated voice data to the user through a speaker. For example, it may say, "Turn left 500 meters before the next intersection." By following this guidance, the user can achieve hands-free operation and reach their destination safely and efficiently.

[0971] (Application example 2)

[0972] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0973] Conventional navigation systems not only have the ability to understand user intentions and provide routes that take traffic information into account, but also have the problem of not being able to consider the emotional state of professional drivers such as delivery drivers. As a result, delivery efficiency may decrease and delivery workers' stress may increase. In the food delivery field in particular, maintaining the mental health of delivery workers is important in addition to delivery efficiency. Therefore, there is a need for a system that not only analyzes user intentions but also provides optimal routes that take emotions into account.

[0974] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving voice input and converting the voice input into text data, means for analyzing the text data and understanding the user's intention, means for analyzing the user's emotions from the voice data, means for acquiring real-time traffic information and calculating the optimal route taking into account the user's intention and emotional state, and means for outputting the calculation results to the user by voice. This enables the delivery person to select an efficient and less stressful route.

[0975] "Voice input" refers to the act of receiving a user's voice commands into a system and the system's interface.

[0976] "Text data" refers to character string data converted from voice input.

[0977] "User intent" refers to the user's goals and requests analyzed from voice input.

[0978] "Voice Data" means a digital representation of a user's voice input received by a system.

[0979] "Real-time traffic information" refers to up-to-date information about current traffic and road conditions.

[0980] An "optimal route" refers to the most efficient and safe route of travel calculated taking into account the user's intentions and emotional state.

[0981] "Means for outputting to the user by voice" refers to a method of notifying the user of calculation results and instructions using voice synthesis technology.

[0982] "Voice recording device" refers to a microphone or recording device for inputting user voice commands.

[0983] "Central processing unit" refers to a server or computer that analyzes voice commands and performs the necessary processing.

[0984] A "voice recognition engine" refers to software or a system for analyzing voice data and converting it into text data.

[0985] "Emotional state" refers to the user's psychological state and emotions analyzed from voice data.

[0986] A "delivery person" refers to a professional driver who delivers goods or food to a requested destination.

[0987] The embodiment of the present invention is configured as follows.

[0988] Generating a Program

[0989] This invention is a system that performs voice input, voice recognition, emotion analysis, acquisition of real-time traffic information, route optimization, and voice output to help delivery personnel select efficient and stress-free routes. The system is composed of elements such as a voice recording device, a central processing unit, a voice recognition engine, and an emotion analysis engine.

[0990] Processing Description

[0991] The server receives voice commands from the user using a voice recording device, such as a microphone or a smartphone. This voice data is sent to the server and converted into text data by a voice recognition engine.

[0992] The server then analyzes the converted text data using natural language processing technology to identify the user's intent, such as whether the user wants to head to a destination or take a specific route.

[0993] The server then uses an emotion analysis engine to identify the user's emotional state from the voice data, such as analyzing parameters like voice tone, pitch, and speed to determine whether the user is agitated or calm. This information is then used to optimize the route.

[0994] The server then retrieves real-time traffic information from relevant APIs and traffic information centers, including current traffic conditions, accident information, road construction information, etc.

[0995] Finally, the server calculates the optimal route based on the acquired traffic information, the user's intentions, and the user's emotional state. The calculated route information is provided to the user via a voice output device, such as a smartphone or a car speaker. At this time, text data is converted into voice using speech synthesis technology.

[0996] Specific examples

[0997] For example, if a delivery person says, "I want to get to my next delivery as quickly as possible," the voice recording device receives this speech and sends it to the server. The server first converts the speech into text data, then analyzes this data to determine the delivery person's intentions. It then uses an emotion analysis engine to recognize the delivery person's sense of impatience. It then calculates the fastest route and provides specific instructions via voice, such as, "Turn right at the next traffic light, and your delivery is 500 meters away."

[0998] Prompt Sentence Examples

[0999] If you give the voice command "I want to get to the next delivery destination as quickly as possible," and feel a sense of urgency, calculate the fastest route and provide feedback.

[1000] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1001] Step 1:

[1002] The user speaks a voice command into a voice recording device (smartphone or microphone). For example, a delivery person might say, "I want to get to the next delivery destination as soon as possible." This voice data is digitally recorded on the device.

[1003] Input: User's voice command

[1004] Output: Digitized audio data

[1005] Step 2:

[1006] The device sends the recorded audio data to a central processing unit (server), where it is converted into data packets and transferred to the server using a stable communication protocol.

[1007] Input: Digitized voice data

[1008] Output: Audio data sent to the server

[1009] Step 3:

[1010] The server converts the received voice data into text data using a voice recognition engine, which identifies phonemes from the voice waveform and converts them into corresponding text.

[1011] Input: Audio data

[1012] Output: Text data

[1013] Step 4:

[1014] The server uses a generative AI model to analyze the text data and identify the user's intent. For example, from the text "I want to get to the next delivery destination as quickly as possible," the server determines that the delivery person is looking for a fast route.

[1015] Input: Text data

[1016] Output: User intent

[1017] Step 5:

[1018] The server uses an emotion analysis engine to analyze the user's emotional state from the voice data, which analyzes the tone, pitch, speed, etc. of the voice to determine whether the user is agitated or calm.

[1019] Input: Audio data

[1020] Output: User's emotional state

[1021] Step 6:

[1022] The server uses a traffic information API to obtain real-time traffic information, including current traffic conditions, accident information, and road construction information.

[1023] Input: Traffic information API to request

[1024] Output: Real-time traffic information

[1025] Step 7:

[1026] The server integrates the user's intentions, emotional state, and real-time traffic information to calculate the optimal route using a route optimization algorithm. The calculation process prioritizes the delivery person's urgency and routes that avoid traffic congestion.

[1027] Input: User intent, user emotional state, real-time traffic information

[1028] Output: Optimal route

[1029] Step 8:

[1030] The server calculates the optimal route and sends it to the device as text data, which includes specific instructions such as "Turn right at the next traffic light and your delivery address is 500 meters away."

[1031] Input: Optimal Route

[1032] Output: Text data of route directions

[1033] Step 9:

[1034] The device uses a speech synthesis engine to convert the received text data into speech and provides guidance to the user via a voice output device (smartphone or car speaker).

[1035] Input: Text data for route directions

[1036] Output: Voice guidance

[1037] In this way, the entire process from the user uttering a voice command to receiving voice guidance along an optimized route is completed.

[1038] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1039] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1040] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1041] [Fourth embodiment]

[1042] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1043] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1044] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1045] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1046] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1047] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1048] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1049] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1050] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1051] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1052] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1053] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1054] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1055] The present invention is a car navigation system that realizes a user's hands-free navigation request through voice input. This system functions mainly through cooperation between a terminal and a server.

[1056] Voice input and recognition

[1057] First, the user inputs a voice command into the device, such as "I want to turn left at the next intersection." The device is equipped with a microphone that receives this voice input. The received voice data is then sent from the device to the server.

[1058] Voice Recognition

[1059] The server analyzes the transmitted voice data using a voice recognition system and converts it into text data. For example, voice data such as "I want to turn left at the next intersection" is analyzed as text data such as "I want to turn left at the next intersection."

[1060] Intent analysis and request understanding

[1061] The server then uses a natural language processing engine to analyze the text data and understand the user's intent—in this case, the specific commands "next intersection" and "turn left"—to figure out the next route the user should take.

[1062] Real-time information acquisition and route optimization

[1063] The server also accesses the traffic information API and road information center to obtain real-time road information. This information includes current road conditions such as traffic congestion, accidents, and construction. Based on this, the server calculates the optimal route. This calculation reflects the user's instructions and suggests the optimal route to "turn left at the next intersection."

[1064] Audio output and user feedback

[1065] The calculated optimal route information is sent to the device as text data. The device then converts this text data into voice using a speech synthesis engine and provides guidance to the user. For example, voice guidance may be provided in the form of "Turn left 500 meters before the next intersection." If the user responds by saying "OK," the device again sends the voice input to the server and repeats the same process.

[1066] Specific examples

[1067] While actually driving, suppose the user says, "I want to go to a nearby gas station." The device receives this speech and sends it to the server. The server understands the intent to "find a nearby gas station" and obtains real-time information on nearby gas stations. It then suggests the optimal route and provides voice guidance to the user, such as, "Turn right at the intersection 500 meters ahead, then turn left at the next corner."

[1068] The system of this invention allows users to operate the car navigation system safely and efficiently using only their voice while driving, without using their hands. This system significantly improves user convenience and enables safer driving without distracting the driver.

[1069] The processing flow will be explained below.

[1070] Step 1:

[1071] User: Gives voice instructions to the car navigation system. For example, the user says, "I want to turn left at the next intersection."

[1072] Step 2:

[1073] Device: Receives audio input through a microphone, converts the audio data into a digital format, and prepares it for transmission to the server.

[1074] Step 3:

[1075] Terminal: Sends the received audio data to the server via network communication.

[1076] Step 4:

[1077] Server: Receives the voice data and passes it to a speech recognition engine. The speech recognition engine is used to convert the voice data into text data. For example, the text generated is "I would like to turn left at the next intersection."

[1078] Step 5:

[1079] Server: The converted text data is passed to a natural language processing engine. The natural language processing engine analyzes the text data and understands the user's intent. For example, it extracts commands such as "next intersection" and "turn left."

[1080] Step 6:

[1081] Server: After understanding the user's intent, the server accesses the traffic information API and road information center to obtain real-time road information, such as current traffic conditions and construction information.

[1082] Step 7:

[1083] Server: Calculates the optimal route based on the acquired real-time information and the user's intentions. For example, it generates specific navigation instructions such as "Turn left 500 meters before the next intersection."

[1084] Step 8:

[1085] Server: Sends calculated route information in text format to the device.

[1086] Step 9:

[1087] Terminal: Passes the received text data to a speech synthesis engine. The speech synthesis engine converts the text data into speech and provides guidance to the user. For example, it may provide a voice prompt saying, "Turn left 500 meters before the next intersection."

[1088] Step 10:

[1089] User: Listen to and follow the voice prompts. Provide further voice prompts as needed. For example, reply "Got it."

[1090] Step 11:

[1091] Terminal: Receives the user's response again and sends it to the server as voice data.

[1092] Step 12:

[1093] Server: Again performs speech recognition and natural language processing to determine if further instructions are needed. If so, return to step 1 and repeat the process.

[1094] Example 1

[1095] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1096] Conventional car navigation systems require users to operate the system manually, which increases the risk of distraction while driving. Therefore, there is a need for a hands-free method of navigating safely and efficiently while driving, improving user convenience.

[1097] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1098] In this invention, the server includes means for receiving voice input and converting the voice input into text data, means for analyzing the text data to understand the user's intention, and means for obtaining real-time traffic information and calculating the optimal route, thereby enabling the user to use navigation solely by voice while driving, without using their hands.

[1099] "Voice input" is the process of receiving a user's speech as machine-recognizable data.

[1100] "Text data" is data that expresses voice input as text information.

[1101] "User intent" refers to the actions or requests that a user intends to convey to a machine through voice input.

[1102] "Real-time traffic information" is data that indicates the current road conditions and traffic situation.

[1103] An "optimal route" is the route that most efficiently reaches the user's destination.

[1104] "Audio output" is the process of converting text data back into audio and conveying it to the user.

[1105] An "acoustic sensor" is a device that senses sound waves and converts them into a form that can be processed as audio data.

[1106] A "remote device" is a device that transmits data from a terminal to a server via the Internet or other communication means.

[1107] A "voice recognition system" is a general term for software and hardware used to convert voice data into text data.

[1108] The present invention is a car navigation system that realizes a user's hands-free navigation request through voice input. This system functions mainly through cooperation between a terminal and a server.

[1109] Voice input and recognition

[1110] First, the user inputs a voice command into the device, such as "I want to turn left at the next intersection." The device is equipped with a microphone that receives this voice input. The received voice data is sent from the device to the server. At this time, the device uses an Internet connection to encrypt the voice data according to a security protocol and send it to the server.

[1111] Voice Recognition

[1112] The server analyzes the transmitted voice data using a general-purpose voice recognition system (for example, Google Cloud Speech-to-Text) and converts it into text data. For example, the voice message "I want to turn left at the next intersection" is returned as text data saying "I want to turn left at the next intersection."

[1113] Intent analysis and request understanding

[1114] The server passes the acquired text data to a general-purpose natural language processing engine (e.g., OpenAI GPT-3) to understand the user's intention. From the text "I want to turn left at the next intersection," the server understands the action instruction "Turn left at the next intersection."

[1115] Real-time information acquisition and route optimization

[1116] The server also uses a general-purpose traffic information API (e.g., Google Maps API) to obtain real-time road information, including information on traffic congestion, accidents, and construction work. Based on this information, the server calculates the optimal route for the user's instructions. The calculation results are generated as text data.

[1117] Audio output and user feedback

[1118] The calculated optimal route information is sent to the device as text data. The device then converts this text data into voice data using a general-purpose speech synthesis engine (e.g., Amazon Polly) and provides guidance to the user. For example, the voice guidance may say, "Turn left 500 meters before the next intersection." If the user responds by saying "OK," the device again sends the voice input to the server and repeats the same process.

[1119] Specific examples

[1120] While actually driving, suppose the user says, "I want to go to a nearby gas station." The device receives this speech and sends it to the server. The server understands the intent to "find a nearby gas station" and obtains real-time information on nearby gas stations. It then suggests the optimal route and provides voice guidance to the user, such as, "Turn right at the intersection 500 meters ahead, then turn left at the next corner."

[1121] Prompt Sentence Examples

[1122] As an example of a specific prompt, you could input the following instructions to your generative AI model:

[1123] When a user says, "I want to turn left at the next intersection," please explain in detail the process of the program that analyzes their intention and calculates the next route to take. Use a general-purpose speech recognition system as the speech recognition system, a general-purpose natural language processing engine as the natural language processing engine, a general-purpose traffic information API as the traffic information API, and a general-purpose speech synthesis engine as the speech synthesis engine.

[1124] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1125] Step 1:

[1126] The user speaks a voice command, such as "I want to turn left at the next intersection," into the device's microphone, which captures this voice input, converts it into digital voice data, and sends it to the device's voice processing unit.

[1127] Input: User's voice command

[1128] Output: Digital audio data

[1129] Specific behavior:

[1130] A microphone in the terminal receives the user's voice and transmits it to the voice processing unit as digital voice data.

[1131] Step 2:

[1132] The device transmits the captured digital audio data over the internet to a server, where it encrypts the data and transmits it using a secure protocol.

[1133] Input: Digital audio data

[1134] Output: Audio data sent to the server

[1135] Specific behavior:

[1136] The device encrypts the voice data and sends it to a server via an internet connection.

[1137] Step 3:

[1138] The server sends the received voice data to a general-purpose voice recognition system (e.g., Google Cloud Speech-to-Text) and converts the voice into text data.

[1139] Input: Audio data sent to the server

[1140] Output: Text data

[1141] Specific behavior:

[1142] The server sends the voice data to a voice recognition system, which converts the digital voice data into text data.

[1143] Step 4:

[1144] The server passes the acquired text data to a general-purpose natural language processing engine (e.g., OpenAI GPT-3) to analyze the user's intent.

[1145] Input: Text data

[1146] Output: Parsed user intent

[1147] Specific behavior:

[1148] The server inputs the text data into a natural language processing engine and executes a process to extract the user's intent.

[1149] Step 5:

[1150] The server obtains real-time road information from a general-purpose traffic information API (e.g., Google Maps API) and uses this information to calculate the optimal route.

[1151] Input: Parsed user intent, real-time road information from traffic information API

[1152] Output: Text data of the optimal route

[1153] Specific behavior:

[1154] The server calculates the optimal route based on road information obtained from the traffic information API and the user's intentions, and generates it as text data.

[1155] Step 6:

[1156] The server sends the calculated optimal route as text data to the device, which converts this text data into voice data using a general-purpose speech synthesis engine (e.g., Amazon Polly).

[1157] Input: Text data of optimal route

[1158] Output: Audio data

[1159] Specific behavior:

[1160] The server transmits the text data to the terminal, and the terminal converts the text data into voice data using a voice synthesis engine.

[1161] Step 7:

[1162] The device uses a speech synthesis engine to generate voice data and plays it back to the user, for example, providing voice guidance such as "Turn left 500 meters before the next intersection."

[1163] Input: Audio data

[1164] Output: Audio output to the user

[1165] Specific behavior:

[1166] The device plays audio data through a speaker and guides the user to the optimal route.

[1167] (Application example 1)

[1168] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1169] Conventional car navigation systems require manual operation by the user, which can compromise safety while driving. Furthermore, route changes based on real-time traffic information are not made quickly, making it difficult to achieve efficient navigation. Furthermore, in autonomous vehicles, usability is poor because there is no interface that allows users to give voice instructions on destinations or route changes. There is a need for a system that can solve these issues and achieve safe and efficient autonomous driving.

[1170] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1171] In this invention, the server includes means for receiving voice input and converting the voice input into text data, means for analyzing the text data and understanding the user's intent, means for acquiring real-time traffic information and calculating an optimal route, means for outputting the calculated route information to the user by voice, means for linking the input destination and desired route to the autopilot system based on voice commands, and means for correcting the route in real time and re-navigating based on voice commands. This allows the user to specify destinations and route changes by voice without using their hands, and obtain the optimal route in real time, enabling safe and efficient automated driving.

[1172] "Voice input" refers to providing a user's spoken voice to the system as digital data.

[1173] "Text data" refers to digital information that has been converted from voice input into text.

[1174] "Real-time traffic information" refers to data showing current road and traffic conditions.

[1175] An "optimal route" refers to the most efficient and safe route calculated based on the user's specified conditions and intentions.

[1176] "Voice commands" refer to commands that a user gives to the system by voice, such as specifying a destination or instructing the system to change routes.

[1177] An "autopilot system" refers to a system that automatically drives a vehicle and allows it to reach a destination without manual operation by the user.

[1178] "Route correction" refers to the process of switching from a current route to another route.

[1179] "Navigation" refers to providing route guidance and direction to a destination.

[1180] "Server" refers to the central processing unit that analyzes voice input, obtains traffic information, and calculates routes.

[1181] A "voice recognition engine" refers to software or hardware for converting voice data into text data.

[1182] The system of the present invention is equipped with voice input, voice analysis, intent understanding, real-time information acquisition, route calculation, voice output, and linkage with an automated driving system, allowing the user to give hands-free instructions on destinations and route changes, realizing safe and efficient automated driving.

[1183] First, the user inputs a voice command into the system. This voice input is received by the device's built-in microphone and sent as digital data to the server. The server then uses a voice recognition engine to convert the voice data into text data.

[1184] The server then analyzes the converted text data and uses a natural language processing engine to understand the user's intent. For example, if a user says, "I want to go home," the system recognizes the intents "home" and "go home" and extracts the destination.

[1185] The server then uses a traffic information API to obtain real-time traffic information, collecting the latest information on congestion, accidents, road construction, etc. Based on this, it calculates the optimal route and sends that information to the device as text data.

[1186] The device uses a speech synthesis engine to convert the text data into speech, which is then fed back to the user, such as "Turn right at the next intersection." Each time the user inputs a new voice command, the system adjusts the route in real time and provides instructions to the autonomous driving system.

[1187] In this way, users can operate autonomous vehicles hands-free using only their voice.

[1188] For example, if a user says, "I want to go to a nearby gas station," the device receives the voice and sends it to the server. The server then understands the intent of "find a nearby gas station," obtains real-time information on nearby gas stations, calculates the optimal route, and provides voice guidance to the user, such as, "Turn right at the intersection 500 meters ahead, then turn left at the next corner."

[1189] Example prompts using generative AI models

[1190] "User said: 'I want to go to a nearby gas station.' Please output the generated navigation route."

[1191] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1192] Step 1:

[1193] The user issues a voice command.

[1194] For example, say, "I want to go to a nearby gas station." This voice input is captured as digital data by the device's microphone. The input is voice data, and the output is digitized voice data.

[1195] Step 2:

[1196] The terminal transmits the acquired voice data to the server.

[1197] A transmission process takes place and the server receives the audio data. The input is the digitized audio data, and the output is the same audio data received.

[1198] Step 3:

[1199] The server converts the received voice data into text data using a voice recognition engine.

[1200] Within the server, a speech recognition engine analyzes the digital voice data and generates corresponding text data. The input is voice data, and the output is text data.

[1201] Step 4:

[1202] The server analyzes the generated text data and performs natural language processing to understand the user's intent.

[1203] It uses a natural language processing engine to analyze instructions in text data and understand the user's desired destination and operation. The input is text data, and the output is data that indicates the user's intention.

[1204] Step 5:

[1205] The server accesses the traffic information API to obtain real-time traffic information.

[1206] It obtains the latest traffic data from the API and calculates the optimal route based on it. The input is the user's intention data, and the output is real-time traffic information and the calculated optimal route.

[1207] Step 6:

[1208] The server transmits the optimum route information to the terminal as text data.

[1209] A transmission process is performed and the terminal receives this information. The input is the text data of the calculated optimal route, and the output is the same text data sent to the terminal.

[1210] Step 7:

[1211] The terminal converts the received text data into voice using a voice synthesis engine and provides guidance to the user.

[1212] Text-to-speech conversion is performed, and a voice guidance message is generated: "Turn right at the intersection 500 meters ahead, then turn left at the next corner." The input is text data, and the output is voice guidance.

[1213] Step 8:

[1214] The user enters new voice commands and modifies the route as needed.

[1215] For example, issue a new command, such as "I want to stop at the cafe up ahead." The process repeats from step 1, correcting the route in real time. The input is a new voice command, and the output is a revised, optimized route and its instructions.

[1216] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1217] The present invention is a system for providing safe and efficient navigation while driving, which provides a navigation process including voice input, text conversion, user intent analysis, real-time road information acquisition, and sentiment analysis.

[1218] Voice input and recognition

[1219] First, the user speaks a voice command to the car navigation system, such as "I want to turn left at the next intersection." The device is equipped with a microphone that receives this voice input. The received voice data is then sent from the device to the server.

[1220] Speech recognition and text conversion

[1221] The server analyzes the transmitted voice data using a voice recognition engine and converts it into text data. The voice data "I want to turn left at the next intersection" is analyzed as text data "I want to turn left at the next intersection."

[1222] Intent analysis and request understanding

[1223] The server then uses a natural language processing engine to analyze the text data and understand the user's intent. By understanding specific commands such as "next intersection" and "turn left," the server knows the next route the user should take.

[1224] Emotion analysis

[1225] Furthermore, the server uses an emotion engine to recognize the user's emotions from the voice data. The emotion engine analyzes the voice tone, pitch, speed, etc. to identify the user's emotional state. For example, it determines whether the user is anxious or calm.

[1226] Real-time information acquisition and route optimization

[1227] The server accesses the traffic information API and road information center to obtain real-time road information, thereby obtaining current traffic conditions, construction information, etc. The server calculates the optimal route taking into account the obtained information, the user's intentions, and emotional state. Specific navigation instructions, such as "Turn left 500 meters before the next intersection," are generated.

[1228] Audio output and user feedback

[1229] The calculated optimal route information is sent to the device as text data. The device then converts this text data into speech using a speech synthesis engine and provides guidance to the user. For example, the device may provide a voice prompt saying, "Turn left 500 meters before the next intersection." By following this guidance, the user can achieve hands-free operation.

[1230] Specific examples

[1231] When a user says, "I want to go to a nearby gas station," the device receives the voice and sends it to the server. The server understands the user's intention and uses its emotion engine to recognize the user's sense of impatience. It then decides to guide the user to the nearest gas station via the fastest route. The voice guides the user, saying, "Turn right at the next traffic light, and the gas station is 500 meters ahead." This allows the user to reach their destination without feeling stressed.

[1232] This invention enables safe and efficient operation of car navigation systems using only voice commands without using the user's hands while driving, and provides guidance that takes the user's emotions into consideration, greatly improving the safety and comfort of driving.

[1233] The processing flow will be explained below.

[1234] Step 1:

[1235] User: Gives voice instructions to the car navigation system. For example, "I want to turn left at the next intersection."

[1236] Step 2:

[1237] Device: Collects user voice input through a microphone. Converts the voice data into a digital format and prepares it for transmission to the server.

[1238] Step 3:

[1239] Terminal: Sends the received voice data to the server via the network.

[1240] Step 4:

[1241] Server: The transmitted voice data is analyzed by a voice recognition engine and converted into text data. For example, the text data "I would like to turn left at the next intersection" is generated.

[1242] Step 5:

[1243] Server: The generated text data is passed to a natural language processing engine to understand the user's intent. For example, it identifies commands such as "turn left" at the "next intersection."

[1244] Step 6:

[1245] Server: Passes the voice data to the emotion engine and analyzes the user's emotional state. It analyzes the voice tone, pitch, speed, etc. to determine whether the user is anxious or calm.

[1246] Step 7:

[1247] Server: Based on the analyzed user's intentions and emotional state, the server accesses the traffic information API and road information center to obtain real-time road information, such as current traffic conditions, congestion, accidents, and construction information.

[1248] Step 8:

[1249] Server: Calculates the optimal route based on the acquired real-time information and the user's requests and emotional state. For example, specific navigation instructions such as "Turn left 500 meters before the next intersection" are generated.

[1250] Step 9:

[1251] Server: Sends calculated route information in text format to the device.

[1252] Step 10:

[1253] Terminal: The received text data is passed to a speech synthesis engine, which converts the text data into speech. Then, voice guidance is provided to the user. For example, it may say, "Turn left 500 meters before the next intersection."

[1254] Step 11:

[1255] User: Listens to and follows the voice prompts. For example, the user responds "Got it."

[1256] Step 12:

[1257] Terminal: The user's response is sent to the server again as voice data.

[1258] Step 13:

[1259] Server: Analyzes the user's response using a speech recognition and natural language processing engine, and optionally an emotion engine, to determine if further instructions are needed. Repeat the process as necessary.

[1260] Example 2

[1261] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1262] Conventional navigation systems can convert voice input into text, understand the user's intent, and calculate routes based on real-time road information. However, these systems often lack safety and comfort because they do not take the user's emotional state into account. The objective of this invention is to provide a navigation system that also takes the user's emotional state into account, thereby improving safety and efficiency while driving.

[1263] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving a voice input and converting the voice input into text data, means for analyzing the text data and understanding the user's intention, means for analyzing the user's emotional state from the voice data, means for acquiring real-time road information and calculating an optimal route taking into account the user's intention and emotional state, and means for outputting the calculation result to the user by voice. This enables navigation that takes into account the user's emotional state, significantly improving safety and efficiency during driving.

[1264] 1. "Voice input" is the process of capturing or recording a user's spoken voice.

[1265] 2. "Text data" refers to data that has been analyzed from voice input and converted into a string format.

[1266] 3. "User intent" refers to the commands or requests that the user makes to the system.

[1267] 4. "Emotional state" is information that expresses the user's mental and emotional state.

[1268] 5. "Real-time road information" means data on the latest traffic and road conditions.

[1269] 6. "Optimal route" refers to the most efficient and safe route calculated based on certain criteria.

[1270] 7. "Speech recognition engine" means software or hardware that analyzes voice data and converts it into text data.

[1271] 8. "Emotion Analysis Engine" means software or hardware for analyzing a user's emotional state from voice data.

[1272] 9. "Navigation system" means a system that provides users with the instructions and information they need to reach their destination.

[1273] The present invention is a system for providing safe and efficient navigation while driving, which provides a navigation process including voice input, text conversion, user intent analysis, real-time road information acquisition, and sentiment analysis.

[1274] Voice input and recognition

[1275] The user speaks a command to the car navigation system, such as "I want to turn left at the next intersection." The device is equipped with a microphone that receives this voice input. The received voice data is sent from the device to a server. The transmission is carried out over the Internet, and security protocols are used to ensure the safety of the data.

[1276] Speech recognition and text conversion

[1277] The server analyzes the transmitted voice data using a voice recognition engine (a typical example is a voice recognition API) and converts it into text data. The voice data uttered by the user, "I would like to turn left at the next intersection," is analyzed as text data saying, "I would like to turn left at the next intersection."

[1278] Intent analysis and request understanding

[1279] The server then analyzes the text data using a natural language processing engine (typically a generative AI model) to understand the user's intent, extracting specific instructions such as "next intersection" and "turn left" and figuring out the next route the user should take.

[1280] Emotion analysis

[1281] Furthermore, the server uses an emotion analysis engine to recognize the user's emotions from the voice data. The emotion analysis engine analyzes the voice tone, pitch, speed, etc. to identify the user's emotional state. For example, it determines whether the user is feeling impatient or calm.

[1282] Real-time information acquisition and route optimization

[1283] The server accesses the traffic information API and road information center to obtain real-time road information, thereby obtaining the latest traffic conditions, construction information, accident information, etc. The server calculates the optimal route taking into account the obtained information and the user's intentions and emotional state. For example, specific navigation instructions such as "Turn left 500 meters before the next intersection" are generated.

[1284] Audio output and user feedback

[1285] The calculated optimal route information is sent to the device as text data. The device then converts this text data into speech using a speech synthesis engine (a typical example is a speech synthesis API) and provides guidance to the user. For example, the device may provide a voice prompt saying, "Turn left 500 meters before the next intersection." By following this guidance, the user can achieve hands-free operation.

[1286] Specific examples

[1287] For example, if a user says, "I want to go to a nearby gas station," the device receives the voice and sends it to the server. The server understands the user's intention and uses an emotion analysis engine to recognize the user's sense of impatience. It then decides to guide the user to the nearest gas station via the fastest route. The voice guides the user, saying, "Turn right at the next traffic light, and the gas station is 500 meters ahead." This allows the user to reach their destination without feeling stressed.

[1288] Prompt Sentence Examples

[1289] An example of a prompt to be input to a generative AI model might be, "Generate appropriate navigation instructions if the user says they want to go to a nearby gas station."

[1290] Through the above specific process, the system can provide users with safe and efficient navigation.

[1291] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1292] Step 1:

[1293] The user speaks to the car navigation system, saying, "I want to turn left at the next intersection." The microphone on the device receives this voice input and records it as voice data. This voice data becomes the input for the next step.

[1294] Step 2:

[1295] The device sends the recorded audio data to the server. The transmission is over the Internet, and the data is secured by security protocols (e.g., SSL / TLS). The output of this step is the audio data sent to the server.

[1296] Step 3:

[1297] The server passes the received voice data to a voice recognition engine (e.g., a voice recognition API). The voice recognition engine analyzes the voice data and converts it into text data such as "I would like to turn left at the next intersection." This text data becomes the input for the next step.

[1298] Step 4:

[1299] The server passes the generated text data to a natural language processing engine (e.g., a generative AI model) for analysis. The natural language processing engine extracts the user's intent from the text, specifically the specific commands "next intersection" and "turn left." The results of this analysis become the input for the next step.

[1300] Step 5:

[1301] The server then passes the initial voice data to an emotion analysis engine, which analyzes the tone, pitch, and speed of the voice to identify the user's emotional state (e.g., anxious, calm, etc.). The results of this analysis are used as input for the next step.

[1302] Step 6:

[1303] Based on the analyzed user's intention and emotional state, the server accesses the traffic information API and road information center to obtain real-time road information. This obtained road information becomes the input for the next step.

[1304] Step 7:

[1305] The server calculates the optimal route based on the acquired road information, the user's intentions, and the user's emotional state. The route calculation uses an algorithm that combines the user's requests with real-time information. The result of this calculation is the input for the next step.

[1306] Step 8:

[1307] The server generates specific navigation instructions based on the optimal route information, such as "Turn left 500 meters before the next intersection." This navigation instruction becomes the input for the next step.

[1308] Step 9:

[1309] The generated navigation instructions are sent to the device as text data. The device passes this text data to a speech synthesis engine (e.g., a speech synthesis API) to convert it into speech. The output of this step is speech data.

[1310] Step 10:

[1311] The device then sends the generated voice data to the user through a speaker. For example, it may say, "Turn left 500 meters before the next intersection." By following this guidance, the user can achieve hands-free operation and reach their destination safely and efficiently.

[1312] (Application example 2)

[1313] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1314] Conventional navigation systems not only have the ability to understand user intentions and provide routes that take traffic information into account, but also have the problem of not being able to consider the emotional state of professional drivers such as delivery drivers. As a result, delivery efficiency may decrease and delivery workers' stress may increase. In the food delivery field in particular, maintaining the mental health of delivery workers is important in addition to delivery efficiency. Therefore, there is a need for a system that not only analyzes user intentions but also provides optimal routes that take emotions into account.

[1315] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving voice input and converting the voice input into text data, means for analyzing the text data and understanding the user's intention, means for analyzing the user's emotions from the voice data, means for acquiring real-time traffic information and calculating the optimal route taking into account the user's intention and emotional state, and means for outputting the calculation results to the user by voice. This enables the delivery person to select an efficient and less stressful route.

[1316] "Voice input" refers to the act of receiving a user's voice commands into a system and the system's interface.

[1317] "Text data" refers to character string data converted from voice input.

[1318] "User intent" refers to the user's goals and requests analyzed from voice input.

[1319] "Voice Data" means a digital representation of a user's voice input received by a system.

[1320] "Real-time traffic information" refers to up-to-date information about current traffic and road conditions.

[1321] An "optimal route" refers to the most efficient and safe route of travel calculated taking into account the user's intentions and emotional state.

[1322] "Means for outputting to the user by voice" refers to a method of notifying the user of calculation results and instructions using voice synthesis technology.

[1323] "Voice recording device" refers to a microphone or recording device for inputting user voice commands.

[1324] "Central processing unit" refers to a server or computer that analyzes voice commands and performs the necessary processing.

[1325] A "voice recognition engine" refers to software or a system for analyzing voice data and converting it into text data.

[1326] "Emotional state" refers to the user's psychological state and emotions analyzed from voice data.

[1327] A "delivery person" refers to a professional driver who delivers goods or food to a requested destination.

[1328] The embodiment of the present invention is configured as follows.

[1329] Generating a Program

[1330] This invention is a system that performs voice input, voice recognition, emotion analysis, acquisition of real-time traffic information, route optimization, and voice output to help delivery personnel select efficient and stress-free routes. The system is composed of elements such as a voice recording device, a central processing unit, a voice recognition engine, and an emotion analysis engine.

[1331] Processing Description

[1332] The server receives voice commands from the user using a voice recording device, such as a microphone or a smartphone. This voice data is sent to the server and converted into text data by a voice recognition engine.

[1333] The server then analyzes the converted text data using natural language processing technology to identify the user's intent, such as whether the user wants to head to a destination or take a specific route.

[1334] The server then uses an emotion analysis engine to identify the user's emotional state from the voice data, such as analyzing parameters like voice tone, pitch, and speed to determine whether the user is agitated or calm. This information is then used to optimize the route.

[1335] The server then retrieves real-time traffic information from relevant APIs and traffic information centers, including current traffic conditions, accident information, road construction information, etc.

[1336] Finally, the server calculates the optimal route based on the acquired traffic information, the user's intentions, and the user's emotional state. The calculated route information is provided to the user via a voice output device, such as a smartphone or a car speaker. At this time, text data is converted into voice using speech synthesis technology.

[1337] Specific examples

[1338] For example, if a delivery person says, "I want to get to my next delivery as quickly as possible," the voice recording device receives this speech and sends it to the server. The server first converts the speech into text data, then analyzes this data to determine the delivery person's intentions. It then uses an emotion analysis engine to recognize the delivery person's sense of impatience. It then calculates the fastest route and provides specific instructions via voice, such as, "Turn right at the next traffic light, and your delivery is 500 meters away."

[1339] Prompt Sentence Examples

[1340] If you give the voice command "I want to get to the next delivery destination as quickly as possible," and feel a sense of urgency, calculate the fastest route and provide feedback.

[1341] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1342] Step 1:

[1343] The user speaks a voice command into a voice recording device (smartphone or microphone). For example, a delivery person might say, "I want to get to the next delivery destination as soon as possible." This voice data is digitally recorded on the device.

[1344] Input: User's voice command

[1345] Output: Digitized audio data

[1346] Step 2:

[1347] The device sends the recorded audio data to a central processing unit (server), where it is converted into data packets and transferred to the server using a stable communication protocol.

[1348] Input: Digitized voice data

[1349] Output: Audio data sent to the server

[1350] Step 3:

[1351] The server converts the received voice data into text data using a voice recognition engine, which identifies phonemes from the voice waveform and converts them into corresponding text.

[1352] Input: Audio data

[1353] Output: Text data

[1354] Step 4:

[1355] The server uses a generative AI model to analyze the text data and identify the user's intent. For example, from the text "I want to get to the next delivery destination as quickly as possible," the server determines that the delivery person is looking for a fast route.

[1356] Input: Text data

[1357] Output: User intent

[1358] Step 5:

[1359] The server uses an emotion analysis engine to analyze the user's emotional state from the voice data, which analyzes the tone, pitch, speed, etc. of the voice to determine whether the user is agitated or calm.

[1360] Input: Audio data

[1361] Output: User's emotional state

[1362] Step 6:

[1363] The server uses a traffic information API to obtain real-time traffic information, including current traffic conditions, accident information, and road construction information.

[1364] Input: Traffic information API to request

[1365] Output: Real-time traffic information

[1366] Step 7:

[1367] The server integrates the user's intentions, emotional state, and real-time traffic information to calculate the optimal route using a route optimization algorithm. The calculation process prioritizes the delivery person's urgency and routes that avoid traffic congestion.

[1368] Input: User intent, user emotional state, real-time traffic information

[1369] Output: Optimal route

[1370] Step 8:

[1371] The server calculates the optimal route and sends it to the device as text data, which includes specific instructions such as "Turn right at the next traffic light and your delivery address is 500 meters away."

[1372] Input: Optimal Route

[1373] Output: Text data of route directions

[1374] Step 9:

[1375] The device uses a speech synthesis engine to convert the received text data into speech and provides guidance to the user via a voice output device (smartphone or car speaker).

[1376] Input: Text data for route directions

[1377] Output: Voice guidance

[1378] In this way, the entire process from the user uttering a voice command to receiving voice guidance along an optimized route is completed.

[1379] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1380] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1381] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1382] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1383] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1384] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1385] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1386] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1387] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1388] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1389] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1390] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1391] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1392] 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.

[1393] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1394] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1395] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1396] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1397] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1398] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1399] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1400] The following is further disclosed regarding the above embodiment.

[1401] (Claim 1)

[1402] means for receiving a voice input and converting the voice input into text data;

[1403] A means for analyzing the text data and understanding the user's intent;

[1404] A means of obtaining real-time road information and calculating the optimal route;

[1405] means for outputting the calculation result to a user by voice;

[1406] A system including:

[1407] (Claim 2)

[1408] 2. The system of claim 1, wherein the means for receiving voice input receives a user's voice command through a microphone and transmits the voice command to a server.

[1409] (Claim 3)

[1410] 2. The system of claim 1, wherein the means for converting voice data into text data uses a voice recognition engine to convert voice data into text data.

[1411] "Example 1"

[1412] (Claim 1)

[1413] means for receiving a voice input and converting the voice input into text data;

[1414] A means for analyzing the text data and understanding the user's intent;

[1415] A means of obtaining real-time traffic information and calculating the optimal route;

[1416] means for outputting the calculation result to a user by voice;

[1417] A system including:

[1418] (Claim 2)

[1419] 10. The system of claim 1, wherein the means for receiving voice input receives a user's voice command with an acoustic sensor and transmits the voice command to a remote device.

[1420] (Claim 3)

[1421] 2. The system of claim 1, wherein the means for converting voice data into text data uses a voice recognition system to convert voice data into text data.

[1422] "Application Example 1"

[1423] (Claim 1)

[1424] means for receiving a voice input and converting the voice input into text data;

[1425] A means for analyzing the text data and understanding the user's intent;

[1426] A means of obtaining real-time traffic information and calculating the optimal route;

[1427] means for outputting the calculated route information to the user by voice;

[1428] A means of linking the input destination and desired route to the autopilot system based on voice commands; and

[1429] a means for correcting and re-navigating the route in real time based on voice commands;

[1430] A system including:

[1431] (Claim 2)

[1432] 2. The system of claim 1, wherein the means for receiving voice input receives a voice command from a user through a microphone and transmits the voice command to a server.

[1433] (Claim 3)

[1434] 10. The system of claim 1, wherein the means for converting voice data into text data uses a voice recognition engine to convert voice data into text data.

[1435] "Example 2: Combining Emotion Engines"

[1436] (Claim 1)

[1437] means for receiving a voice input and converting the voice input into text data;

[1438] A means for analyzing the text data and understanding the user's intent;

[1439] means for analyzing a user's emotional state from the voice data;

[1440] means for obtaining real-time road information and calculating an optimal route taking into account the user's intentions and emotional state;

[1441] means for outputting the calculation result to a user by voice;

[1442] A system including:

[1443] (Claim 2)

[1444] 2. The system of claim 1, wherein the means for receiving voice input receives a user's voice command through a microphone and transmits the voice command to a server.

[1445] (Claim 3)

[1446] 2. The system of claim 1, wherein the means for converting voice data into text data uses a voice recognition engine to convert voice data into text data.

[1447] "Application example 2 when combining emotion engines"

[1448] (Claim 1)

[1449] means for receiving a voice input and converting the voice input into text data;

[1450] A means for analyzing the text data and understanding the user's intent;

[1451] A means for analyzing a user's emotions from voice data;

[1452] means for obtaining real-time traffic information and calculating an optimal route taking into account the user's intentions and emotional state;

[1453] means for outputting the calculation result to a user by voice;

[1454] A system including:

[1455] (Claim 2)

[1456] 10. The system of claim 1, wherein the means for receiving voice input receives the user's voice commands through a voice recording device and transmits the voice commands to a central processing unit.

[1457] (Claim 3)

[1458] 2. The system of claim 1, wherein the means for converting voice data into text data uses a voice recognition engine to convert voice data into text data. [Explanation of symbols]

[1459] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving a voice input and converting the voice input into text data; A means for analyzing the text data and understanding the user's intent; A means of obtaining real-time road information and calculating the optimal route; means for outputting the calculation result to a user by voice; A system including:

2. 2. The system of claim 1, wherein the means for receiving voice input receives a user's voice command through a microphone and transmits the voice command to a server.

3. 2. The system of claim 1, wherein the means for converting voice data into text data uses a voice recognition engine to convert voice data into text data.

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