system

Abstract

We provide the system. [Solution] A means for acquiring user voice input, A means of converting acquired voice input into text data, A means of analyzing text data to recognize user intent, Means of performing a task based on recognized intent, A means of notifying the user of the execution result by voice, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

[0006] "User" refers to the driver or person inside the vehicle who operates this system and uses voice input.

[0007] "Means for acquiring voice input" refers to devices including microphones and speech recognition devices for capturing the user's voice.

[0008] "Means of converting voice input into text data" refers to software or hardware that uses a speech recognition engine to convert voice data into text data.

[0009] "Means of analyzing text data to recognize user intent" refers to algorithms and programs that use natural language processing technology to understand user requests and intentions from text data.

[0010] "Means of performing a task" refers to programs and associated devices used to perform specific actions or operations based on the user's intent.

[0011] "Means of notifying the user of execution results via voice" refers to a speech synthesis engine and related hardware that converts text data into speech and communicates the results to the user through a speaker.

[0012] "System" refers to the entire information processing device that combines the above-mentioned means to provide a series of operations and functions. [Brief explanation of the drawing]

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

Embodiment for Carrying Out the Invention

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

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

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

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

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

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

[0021] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0034] The present invention is a system for assisting drivers who are driving a car alone. It improves driving safety and comfort by acquiring and analyzing the user's voice input and providing appropriate feedback. Specific embodiments of the present invention will be described below.

[0035] Explanation of the program's processing

[0036] 1. Acquisition of voice input

[0037] The user can give voice commands or ask questions while driving. For example, they can give voice commands such as "Check my email" or "Change to the next song."

[0038] 2. Speech Recognition

[0039] The device captures the user's voice through the microphone, and a speech recognition engine converts the voice into text data. This text data is then used for subsequent processing.

[0040] 3. Natural language understanding

[0041] The device sends the acquired text data to the server. The server uses a natural language processing engine to analyze the text data and recognize the user's intent. For example, if the instruction is "Check your email," the server recognizes the intent as "Read the email aloud."

[0042] 4. Execute the task

[0043] The server performs appropriate tasks based on the recognized user's intent. For example, it accesses the email service API to retrieve the latest email content. For route guidance, it uses the map API to calculate the route; and for changing music, it uses the music service API to change the song.

[0044] 5. Response generation

[0045] The server generates the task execution results as text data and sends it to the terminal.

[0046] 6. Voice Response

[0047] The terminal receives text data, which is then converted into speech data using a speech synthesis engine and communicated to the user via the speaker. This allows the user to obtain necessary information while concentrating on driving.

[0048] Specific example

[0049] Email confirmation

[0050] 1. The user says, "I want to check for new emails."

[0051] 2. The device acquires the user's voice and converts it into text data using a speech recognition engine.

[0052] 3. The terminal sends text data to the server.

[0053] 4. The server analyzes the text to understand the user's intent.

[0054] 5. The server accesses the email service API to retrieve the latest emails.

[0055] 6. The server generates the email content as text data and sends it to the terminal.

[0056] 7. The device converts the text data into audio data and reads aloud from the speaker, "You have a new email. The subject is 'Meeting Reminder,' and the content is 'The meeting is tomorrow at 10:00.'"

[0057] Start of directions

[0058] 1. The user says, "Tell me the way to my next destination."

[0059] 2. The device acquires the user's voice and converts it into text data using a speech recognition engine.

[0060] 3. The terminal sends text data to the server.

[0061] 4. The server analyzes the text to understand the user's intent.

[0062] 5. The server accesses the map API and calculates the optimal route.

[0063] 6. The server generates the route guidance content as text data and sends it to the terminal.

[0064] 7. The device converts the text data into audio data and reads aloud from the speaker, "Next, turn left."

[0065] Change music

[0066] 1. The user says, "Change to the next song."

[0067] 2. The device acquires the user's voice and converts it into text data using a speech recognition engine.

[0068] 3. The terminal sends text data to the server.

[0069] 4. The server analyzes the text to understand the user's intent.

[0070] 5. The server accesses the music service API and changes to the next song.

[0071] 6. The server generates a text file confirming the changes have been made and sends it to the terminal.

[0072] 7. The device converts the text data into audio data and reads aloud from the speaker, "The song has been changed."

[0073] As described above, the system of the present invention enhances driving safety and comfort by automatically executing various tasks based on the user's voice input and notifying the results by voice.

[0074] The following describes the processing flow.

[0075] Step 1:

[0076] The user says, "I want to check for new emails."

[0077] Step 2:

[0078] The device captures the user's voice using its built-in microphone.

[0079] Step 3:

[0080] The device sends voice data to the speech recognition engine, which converts it into text data.

[0081] Step 4:

[0082] The device sends the converted text data "I want to check for new emails" to the server.

[0083] Step 5:

[0084] The server receives text data and uses a natural language processing engine to analyze the user's intent.

[0085] Step 6:

[0086] The server recognizes the intention to "check email" and generates a request to access the email service API.

[0087] Step 7:

[0088] The server sends a request to the email service API to retrieve the latest email data.

[0089] Step 8:

[0090] The server analyzes the email data and generates text to be read aloud to notify the user.

[0091] Step 9:

[0092] The server sends the generated text "You have a new email. The subject is 'Meeting Reminder', and the content is 'The meeting is tomorrow at 10:00'" to the terminal.

[0093] Step 10:

[0094] The terminal receives text data, which is then sent to the speech synthesis engine to generate speech data.

[0095] Step 11:

[0096] The device plays the generated audio data through its speaker and notifies the user, "You have a new email. The subject is 'Meeting Reminder,' and the content is 'The meeting is tomorrow at 10:00.'"

[0097] (Example 1)

[0098] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0099] In recent years, there has been a growing demand for technologies that improve the safety and comfort of drivers operating vehicles alone. In particular, there is a need for systems that allow drivers to perform various operations using only their voice, without using their hands while driving. However, conventional systems have suffered from low accuracy in voice recognition and significant delays between voice input and actual task execution, making them impractical. There is a need to provide a new system that overcomes these challenges and achieves highly sensitive voice recognition and rapid task execution.

[0100] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0101] In this invention, the server includes means for acquiring voice input, means for converting the acquired voice input into text data, and means for analyzing the text data with a natural language processing engine. This makes it possible to capture the user's voice with a high-sensitivity microphone and analyze the text data with high accuracy and speed. It also includes means for executing tasks based on recognized intent and means for notifying the results by voice, and further includes means for accessing a map service API for route guidance and a music service API for changing music, thereby improving driver safety and comfort.

[0102] A "user" refers to an individual who uses the system to perform voice input.

[0103] "Means for acquiring voice input" refers to devices or technologies that capture the user's voice using a microphone and incorporate that data into the system.

[0104] "Means of converting acquired voice input into text data" refers to technologies and software that use a speech recognition engine to convert voice data into text information.

[0105] "Methods for analyzing text data to recognize user intent" refers to technologies that use a natural language processing engine to process text data and understand the content of user statements.

[0106] "Means of executing a task" refers to servers and software that perform specific operations or give instructions based on the user's intentions.

[0107] "Means of notifying the user of execution results via voice" refers to a technology that converts the results of an executed task into voice data using a speech synthesis engine and communicates it to the user through a speaker.

[0108] A "high-sensitivity microphone" refers to a microphone device that has the ability to effectively filter out noise during driving and capture clear voice input.

[0109] A "natural language processing engine" refers to software or algorithms that analyze text data, understand its context and meaning, and extract the user's intent.

[0110] A "database" refers to a system for efficiently storing and managing acquired and analyzed data.

[0111] A "map service API" refers to an interface with an external map service for providing route guidance.

[0112] A "music service API" refers to an interface with external music services for playing and modifying music.

[0113] "Text data" refers to character information converted from voice input by a speech recognition engine.

[0114] "Audio data" refers to audio information generated by a speech synthesis engine and played back to the user through a speaker.

[0115] "API" is an abbreviation for Application Programming Interface, and refers to the definitions and protocols that enable communication between different software programs.

[0116] Modes for carrying out the invention

[0117] The present invention is a system for assisting drivers who are driving a car alone. It improves driving safety and comfort by acquiring and analyzing user voice input and providing appropriate feedback. Specific embodiments of the present invention will be described below.

[0118] System Configuration

[0119] The system of the present invention consists of the following main components.

[0120] 1. Means for obtaining user voice input:

[0121] The user gives instructions and asks questions by voice while driving. Specifically, a high-sensitivity microphone installed in the vehicle is used to capture the voice. The high-sensitivity microphone effectively filters out noise during driving, ensuring clear voice input.

[0122] 2. Means for converting acquired voice input into text data:

[0123] The device captures the user's voice through the microphone and converts the voice into text data using a speech recognition engine (e.g., Microsoft® Azure® Speech SDK or Google® Cloud Speech-to-Text).

[0124] 3. Means of analyzing text data to recognize user intent:

[0125] The terminal sends the acquired text data to the server. The server analyzes the text data using a generative AI model (e.g., OpenAI® GPT-3®) to recognize the user's intent.

[0126] 4. Means of performing tasks based on recognized intentions:

[0127] The server performs the appropriate task based on the recognized user's intent. For example, to retrieve email content, it accesses the Gmail API to retrieve the latest email. For route guidance, it uses the Google Maps API to calculate the optimal route, and for changing music, it accesses the Music Services API to change the song.

[0128] 5. Means of notifying the user of the execution results by voice:

[0129] The server generates the task execution results as text data and sends it to the terminal. The terminal converts the received text data into audio data using a speech synthesis engine (e.g., Amazon Polly) and notifies the user through the speaker.

[0130] Specific example

[0131] The following are specific usage scenarios.

[0132] Email confirmation

[0133] 1. The user says, "I want to check for new emails."

[0134] 2. The device captures the user's voice and converts it into text data using Google Cloud Speech-to-Text.

[0135] 3. The terminal sends text data to the server.

[0136] 4. The server uses OpenAI GPT-3 to analyze the text and recognize the user's intent.

[0137] 5. The server accesses the Gmail API to retrieve the latest emails.

[0138] 6. The server generates the email content as text data and sends it to the terminal.

[0139] 7. The device uses Amazon Polly to convert the text data into audio data, and the speaker reads aloud, "You have a new email. The subject is 'Meeting Reminder,' and the content is 'The meeting is tomorrow at 10am.'"

[0140] Start of directions

[0141] 1. The user says, "Tell me the way to my next destination."

[0142] 2. The device captures the user's voice and converts it into text data using the Microsoft Azure Speech SDK.

[0143] 3. The terminal sends text data to the server.

[0144] 4. The server uses OpenAI GPT-3 to analyze the text and recognize the user's intent.

[0145] 5. The server accesses the Google Maps API and calculates the optimal route.

[0146] 6. The server generates the route guidance content as text data and sends it to the terminal.

[0147] 7. The device uses Amazon Polly to convert the text data into audio data and reads aloud from the speaker, "Turn left next."

[0148] Change music

[0149] 1. The user says, "Change to the next song."

[0150] 2. The device captures the user's voice and converts it into text data using Google Cloud Speech-to-Text.

[0151] 3. The terminal sends text data to the server.

[0152] 4. The server uses OpenAI GPT-3 to analyze the text and recognize the user's intent.

[0153] 5. The server accesses the Spotify API and changes to the next song.

[0154] 6. The server generates a text file confirming the changes have been made and sends it to the terminal.

[0155] 7. The device uses Amazon Polly to convert the text data into audio data and reads aloud from the speaker, "The song has been changed."

[0156] As described above, the system of the present invention automatically performs various tasks based on the user's voice input and notifies the user of the results by voice, thereby improving driving safety and comfort.

[0157] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0158] Step 1: Obtaining voice input

[0159] The user can give instructions and ask questions by voice while driving. For example, they can give voice commands such as "Check my email" or "Change to the next song." The input voice is captured by a high-sensitivity microphone installed in the car. The high-sensitivity microphone filters out noise during driving, ensuring a clear voice input. The output is the captured raw voice data.

[0160] Step 2: Speech Recognition

[0161] The device passes the captured raw audio data to a speech recognition engine such as the Microsoft Azure Speech SDK or Google Cloud Speech-to-Text. The input is the raw audio data from step 1. The speech recognition engine analyzes the audio data and converts it into corresponding text data. The output is this text data, which is used in the next processing step.

[0162] Step 3: Send text data

[0163] The terminal sends the acquired text data to the server. The input is text data. The terminal securely transfers the text data to the server via the network, where further processing continues. The output is the text data sent to the server.

[0164] Step 4: Natural Language Comprehension

[0165] The server analyzes text data using a generative AI model such as OpenAI GPT-3. The input is the text data received in step 3. The server analyzes the text data to identify the user's intent. For example, the instruction "check my email" is recognized as having the intent "read the email aloud." In terms of data processing, the text data is analyzed based on context, and the intent behind the user's request is extracted. The output is user intent information, which is used in the next task execution step.

[0166] Step 5: Execute the task

[0167] The server performs the appropriate task based on the user's intent. The input is the user's intent information from step 4. For example, to retrieve email content, it accesses the Gmail API to retrieve the latest email. For route guidance, it uses the Google Maps API to calculate the optimal route, and for changing music, it uses the Music Services API to change the song. Each API call performs data retrieval or calculation. The output is the result data of the task execution.

[0168] Step 6: Generating the response

[0169] The server generates the task execution result as text data and sends it to the terminal. The input is the task execution result data from step 5. Based on the execution result, the server generates a text message to notify the user. The output is the text data of the notification message sent to the terminal.

[0170] Step 7: Voice Response

[0171] The text data received by the device is converted into speech data by a speech synthesis engine such as Amazon Polly. The input is the text data of the notification message from step 6. The speech synthesis engine analyzes the text data and generates the corresponding natural speech. The generated speech data is notified to the user through the speaker. For example, it might read aloud, "You have a new email. The subject is 'Meeting Reminder,' and the content is 'The meeting is tomorrow at 10am.'" The output is the speech data that is played back to the user.

[0172] (Application Example 1)

[0173] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0174] In autonomous vehicles, a system is needed that allows users to efficiently perform tasks using voice commands while driving and to obtain information safely and comfortably. Conventional systems have problems such as low accuracy in voice input, slow recognition of intent, and data acquisition, which fail to adequately ensure user convenience. There is also room for improvement in acquiring real-time data from external sources and providing feedback.

[0175] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0176] In this invention, the server includes means for acquiring voice input, means for converting the acquired voice input into text data, means for analyzing the text data to recognize the user's intent, means for acquiring data from external information sources, and means for analyzing the acquired data to provide appropriate feedback to the user. This enables the user to efficiently perform tasks using voice while driving and to obtain necessary information in real time in a safe and comfortable manner.

[0177] A "user" refers to a person who uses the system.

[0178] "Voice input" refers to the act of a user providing instructions or information to a system using their voice.

[0179] "Means of acquisition" refers to methods and technologies for receiving, recording, or capturing information such as audio or data.

[0180] "Means of converting audio to text data" refers to methods and technologies for converting audio data into text-based data.

[0181] "Text data" refers to data expressed as character information.

[0182] "Means of analysis" refers to methods and techniques for analyzing acquired data and extracting specific information or patterns.

[0183] "User intent" refers to the actions a user wants to perform on the system or the information they are seeking.

[0184] "Means of executing a task" refers to methods and technologies for performing specific operations or processes based on the user's intent.

[0185] "Execution results" refer to the outcomes and information obtained after completing a task.

[0186] "External information sources" refer to information providers located outside the system. For example, these include third-party services or databases that provide weather information or news.

[0187] "Means of acquiring data" refers to the methods and technologies used to obtain necessary data from external sources.

[0188] "Means of providing feedback" refers to methods and techniques for providing users with information or the results of instructions.

[0189] "Real-time" refers to the immediate processing and provision of information at the moment an event occurs.

[0190] This invention relates to a system for autonomous vehicles that acquires user voice input, analyzes their intent, and provides appropriate feedback. This system assists users in driving safely and comfortably.

[0191] The system program is implemented using the following hardware and software.

[0192] hardware

[0193] 1. Smartphone: Captures the user's voice input and converts the voice into text data.

[0194] 2. Server: Analyzes text data and recognizes the user's intent.

[0195] 3. External information sources: Databases and third-party services that provide weather information, news, etc.

[0196] software

[0197] 1. Speech recognition engine: For example, use Google's speech recognition API to convert speech to text.

[0198] 2. Natural Language Processing Engine: For example, use Amazon Comprehend to analyze text data and recognize user intent.

[0199] 3. Data acquisition API: For example, use an API to acquire weather information.

[0200] Processing procedures and data calculations

[0201] 1. Acquisition of voice input

[0202] The user gives instructions or asks questions by voice while driving. The smartphone's microphone captures this and sends it to the voice recognition engine.

[0203] 2. Speech Recognition

[0204] The speech recognition engine converts the speech into text data. This text data is then sent to the server.

[0205] 3. Natural language understanding

[0206] The server receives text data and analyzes it using a natural language processing engine. For example, if given the instruction "Tell me today's weather," the server recognizes that it needs to retrieve weather information.

[0207] 4. Data Acquisition

[0208] The server accesses external information sources and retrieves the necessary data (e.g., weather information).

[0209] 5. Response generation

[0210] Based on the acquired data, the server generates feedback to be provided to the user. This feedback is then sent to the smartphone as text data.

[0211] 6. Voice Response

[0212] Text data is sent to a speech synthesis engine and converted into speech data. Feedback is then provided to the user through the smartphone's speaker.

[0213] Specific example

[0214] Example 1: Obtaining weather information

[0215] 1. The user says, "Tell me today's weather."

[0216] 2. The smartphone captures the audio, and the speech recognition engine converts it into text data.

[0217] 3. The server receives the text data and analyzes it using a natural language processing engine.

[0218] 4. The server accesses the weather information API to obtain the latest weather information.

[0219] 5. The server generates weather information as text data and sends it to the smartphone.

[0220] 6. The smartphone converts the text data into audio data and announces "Today's weather is sunny" through the speaker.

[0221] Example of a prompt:

[0222] When a user says, "Tell me today's weather," a smartphone application recognizes the voice and analyzes the text data using a natural language processing engine. It then calls a weather forecast API to obtain real-time weather information and notifies the user as audio data.

[0223] Thus, the system of the present invention is configured to support the user's safe and comfortable driving.

[0224] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0225] Step 1:

[0226] The user inputs instructions by voice. For example, they might say, "Tell me today's weather." This inputs the information the user wants to know or the task they want to perform as voice data into the device.

[0227] Step 2:

[0228] The device captures the user's voice data and converts it into text data using a speech recognition engine. Specifically, the smartphone's microphone captures the voice and sends it to an API that converts the voice data into text. In this case, the input is voice data and the output is text data.

[0229] Step 3:

[0230] Text data is sent from the terminal to the server. The server passes the received text data to a natural language processing engine to analyze the user's intent. A natural language processing engine (e.g., Amazon Comprehend) is used here. The input is text data, and the output is data indicating the user's intent.

[0231] Step 4:

[0232] The server retrieves data from appropriate external sources based on the user's intent. For example, to retrieve weather information, it accesses a weather information API to obtain the latest weather data. The input is data indicating the user's intent, and the output is the retrieved weather information data.

[0233] Step 5:

[0234] The server analyzes the acquired data and generates feedback to provide to the user. For example, weather information is formatted as text data such as "Today's weather is sunny." The input is the acquired information data, and the output is formatted text data to be provided to the user.

[0235] Step 6:

[0236] Formatted text data is sent from the server to the terminal. The terminal receives this data and converts it into speech data using a speech synthesis engine. Specifically, a speech synthesis engine (e.g., gTTS) is used to play the text as speech. The input is formatted text data, and the output is speech data.

[0237] Step 7:

[0238] The device plays the generated audio data through its speaker and notifies the user. This allows the user to receive necessary information in real time via voice, even while driving. The input is audio data, and the output is an audio notification to the user.

[0239] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0240] The present invention is a system for assisting drivers who are driving a car alone. It improves driving safety and comfort by acquiring and analyzing the user's voice input, recognizing the user's emotions, and providing appropriate feedback. Specific embodiments of the present invention will be described below.

[0241] Explanation of the program's processing

[0242] 1. Acquisition of voice input

[0243] The user can give voice commands or ask questions while driving. For example, they can give voice commands such as "Check my email" or "Change to the next song."

[0244] 2. Speech Recognition and Emotion Recognition

[0245] The device captures the user's voice using its built-in microphone, and a speech recognition engine converts the voice into text data. This text data is used for subsequent processing. Simultaneously, an emotion engine recognizes the user's emotions from the voice data.

[0246] 3. Natural Language Understanding and Emotion Understanding

[0247] The device sends the acquired text data and sentiment data to the server. The server uses a natural language processing engine to analyze the text data and recognize the user's intent. For example, in the case of the instruction "Check your email," the server recognizes the intention to "read the email aloud," and the sentiment engine also recognizes that the user is feeling stressed.

[0248] 4. Execute the task

[0249] The server performs appropriate tasks based on the recognized user's intentions and emotions. For example, it accesses the email service API to retrieve the latest emails to get the content of emails. For route guidance, it uses a map API to calculate the route, and for music changes, it considers the user's emotions and selects relaxing music.

[0250] 5. Response generation

[0251] The server generates the task execution results as text data and sends a message to the terminal that includes content that takes the user's feelings into consideration.

[0252] 6. Voice Response

[0253] The terminal receives text data, which is then converted into speech data using a speech synthesis engine and communicated to the user via the speaker. This allows the user to obtain necessary information while concentrating on driving.

[0254] Specific example

[0255] Email confirmation

[0256] 1. The user says, "I want to check for new emails."

[0257] 2. The device acquires the user's voice and converts it into text data using a speech recognition engine. Simultaneously, an emotion engine recognizes the user's stress level based on their tone of voice.

[0258] 3. The device sends text data and sentiment data to the server.

[0259] 4. The server analyzes the text to understand the user's intent and confirm that they are experiencing stress.

[0260] 5. The server accesses the email service API to retrieve the latest emails.

[0261] 6. The server generates and sends a text message to the terminal that reads, "You have a new email. Take care and relax. The subject is 'Meeting Reminder' and the content is 'The meeting is tomorrow at 10am'."

[0262] 7. The device converts the text data into audio data and reads aloud from the speaker: "You have a new email. Take care, relax. The subject is 'Meeting Reminder,' and the content is 'The meeting is tomorrow at 10am.'"

[0263] Start of directions

[0264] 1. The user says, "Tell me the way to my next destination."

[0265] 2. The device acquires the user's voice and converts it into text data using a speech recognition engine. Simultaneously, an emotion engine recognizes the user's emotional state.

[0266] 3. The device sends text data and sentiment data to the server.

[0267] 4. The server analyzes the text to recognize the user's intent and considers appropriate guidance that reflects their emotional state.

[0268] 5. The server accesses the map API and calculates the optimal route.

[0269] 6. The server generates the route guidance content as text data and sends it to the terminal.

[0270] 7. The device converts the text data into audio data and reads aloud from the speaker, "Please turn left next. Drive carefully."

[0271] Change music

[0272] 1. The user says, "Change to the next song."

[0273] 2. The device acquires the user's voice and converts it into text data using a speech recognition engine. Simultaneously, an emotion engine recognizes the user's emotional state.

[0274] 3. The device sends text data and sentiment data to the server.

[0275] 4. The server analyzes the text to recognize the user's intent and selects relaxing music that takes their emotional state into consideration.

[0276] 5. The server accesses the music service API and changes to the next song.

[0277] 6. The server generates a text file confirming the changes have been made and sends it to the terminal.

[0278] 7. The device converts the text data into audio data and reads aloud from the speaker, "The song has been changed, please relax and enjoy."

[0279] As described above, the system of the present invention enhances driving safety and comfort by providing appropriate responses based on the user's voice input and emotional state.

[0280] The following describes the processing flow.

[0281] Specific examples of email confirmation

[0282] Voice input acquisition and recognition

[0283] Step 1:

[0284] The user says, "I want to check for new emails."

[0285] Step 2:

[0286] The terminal captures the user's voice with the built-in microphone.

[0287] Step 3:

[0288] The terminal sends the voice data to the speech recognition engine and converts it into text data.

[0289] Step 4:

[0290] The terminal extracts the user's emotion data from the voice using the emotion engine.

[0291] Data transmission and analysis <​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​

[0301] Step 9:

[0302] The server receives the latest email data from the email service API.

[0303] Step 10:

[0304] The server analyzes the email data and generates text for reading aloud that takes into account the user's sentiment.

[0305] Step 11:

[0306] The server transmits the generated text data "There is a new email. Thank you for your hard work. Please relax. The subject is 'Meeting reminder' and the content is 'The meeting is at 10:00 tomorrow.'" to the terminal.

[0307] Voice response

[0308] Step 12:

[0309] The terminal transmits the received text data to the speech synthesis engine and converts it into voice data.

[0310] Step 13:

[0311] The terminal plays the generated voice data from the speaker and notifies the user "There is a new email. Thank you for your hard work. Please relax. The subject is 'Meeting reminder' and the content is 'The meeting is at 10:00 tomorrow.'"

[0312] Specific example of starting route guidance

[0313] Obtaining and recognizing voice input

[0314] Step 1:

[0315] The user says "Tell me the way to the next destination."

[0316] Step 2:

[0317] The device captures the user's voice using its built-in microphone.

[0318] Step 3:

[0319] The device sends voice data to the speech recognition engine, which converts it into text data.

[0320] Step 4:

[0321] The device uses an emotion engine to extract user emotion data from the voice.

[0322] Data transmission and analysis

[0323] Step 5:

[0324] The device sends the converted text data and sentiment data to the server.

[0325] Step 6:

[0326] The server receives text data and uses a natural language processing engine to analyze the user's intent.

[0327] Step 7:

[0328] The server uses an emotion engine to analyze the user's emotions and recognizes that they are in a relaxed state.

[0329] Task execution and response generation

[0330] Step 8:

[0331] The server recognizes the intention to "start directions" and sends a request to the map API.

[0332] Step 9:

[0333] The server receives route information from the map API.

[0334] Step 10:

[0335] The server analyzes route information and generates emotionally sensitive guidance text.

[0336] Step 11:

[0337] The server generates text data, "Turn left next, drive carefully," and sends it to the terminal.

[0338] Voice response

[0339] Step 12:

[0340] The terminal receives text data, which is then sent to a speech synthesis engine and converted into speech data.

[0341] Step 13:

[0342] The device generates audio data which is played through the speaker, instructing the user with messages such as, "Please turn left next. Drive carefully."

[0343] Examples of music changes

[0344] Voice input acquisition and recognition

[0345] Step 1:

[0346] The user says, "Change to the next song."

[0347] Step 2:

[0348] The device captures the user's voice using its built-in microphone.

[0349] Step 3:

[0350] The device sends voice data to the speech recognition engine, which converts it into text data.

[0351] Step 4:

[0352] The device uses an emotion engine to extract user emotion data from the voice.

[0353] Data transmission and analysis

[0354] Step 5:

[0355] The device sends the converted text data and sentiment data to the server.

[0356] Step 6:

[0357] The server receives text data and uses a natural language processing engine to analyze the user's intent.

[0358] Step 7:

[0359] The server uses an emotion engine to analyze the user's emotions and recognizes that they are in a relaxed state.

[0360] Task execution and response generation

[0361] Step 8:

[0362] The server recognizes the intention to "change the music" and sends a request to the music service API.

[0363] Step 9:

[0364] The server receives the next song data from the music service API.

[0365] Step 10:

[0366] The server analyzes the data for the next song and generates a notification text that takes emotions into consideration.

[0367] Step 11:

[0368] The server sends the generated text data "The song has been changed, please relax and enjoy" to the terminal.

[0369] Voice response

[0370] Step 12:

[0371] The terminal receives text data, which is then sent to a speech synthesis engine and converted into speech data.

[0372] Step 13:

[0373] The device plays the generated audio data through the speaker and notifies the user with the message, "The song has been changed, please relax and enjoy."

[0374] As described above, the system of the present invention enhances driving safety and comfort by providing appropriate responses based on the user's voice input and emotional state.

[0375] (Example 2)

[0376] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0377] Conventional driver assistance systems, while capable of receiving user voice commands and executing tasks, failed to adequately improve driving safety and comfort by failing to provide feedback or operations that considered the user's emotions. Furthermore, one-sided notifications and instructions that disregard the user's feelings could amplify user stress, potentially negatively impacting driving concentration and safety. To solve these problems, a system was needed that could identify not only the user's intent based on their voice but also their emotions, and provide appropriate feedback accordingly.

[0378] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring voice input, means for converting voice to text data, means for recognizing the user's intent from the text data, means for identifying emotions from the user's voice data, means for executing a task based on the user's intent and emotions, and means for notifying the user of the task execution result by voice in a manner that takes the user's emotions into consideration. This makes it possible to respond appropriately not only to the user's intent but also to their emotions, thereby improving safety and comfort while driving.

[0379] "Means for acquiring voice input" refer to microphones or voice capture devices that capture user speech in real time.

[0380] "Methods for converting audio to text data" refer to a system that uses a speech recognition engine to convert captured audio into text strings.

[0381] "Means of recognizing user intent from text data" refers to a system that uses natural language processing technology to analyze and understand user requests and instructions from converted text data.

[0382] "Methods for identifying emotions from voice data" refer to systems that use an emotion recognition engine to determine the user's emotional state from the tone and patterns of their voice.

[0383] "Means for executing a task" refer to operational means for performing relevant actions based on the analyzed user's intentions and emotions. For example, this could involve retrieving the content of an electronic message or initiating route guidance.

[0384] "Means for notifying execution results by voice" refers to a system consisting of a speech synthesis engine and a speaker for providing voice feedback to the user on the execution results of a task.

[0385] "Means for obtaining the content of electronic messages" refers to APIs and service access mechanisms that retrieve email content from various email services in response to user instructions.

[0386] A "means for initiating route guidance" refers to a system that provides the optimal route based on user instructions, utilizing map data and navigation systems.

[0387] This invention is a system for assisting drivers who are driving a car alone. It improves driving safety and comfort by acquiring and analyzing the user's voice input, recognizing the user's emotions, and providing appropriate feedback.

[0388] System Overview

[0389] This system functions through communication between the user, a terminal in the vehicle, and a central server. Specifically, the terminal is equipped with a built-in microphone to acquire voice input from the user. This voice data is converted into text data via a speech recognition engine, and the user's emotions are further identified by an emotion recognition engine. This data is sent to the server, where a natural language processing engine and an emotion analysis engine are used to understand the user's intentions and emotions and execute the task. The server then sends the execution results back to the terminal, which uses a speech synthesis engine to notify the user.

[0390] Hardware and software to be used

[0391] Terminal (in-car device): Built-in microphone, speaker

[0392] Server: High-performance processor, Internet connection

[0393] Speech recognition engine: Google Speech-to-Text API

[0394] Emotion recognition engine: IBM Watson® Tone Analyzer

[0395] Natural language processing engine: OpenAI GPT-4 (registered trademark)

[0396] Speech synthesis engine: Amazon Polly

[0397] Various APIs: Gmail API, Google Maps API, Spotify API

[0398] Specific example

[0399] Email confirmation

[0400] 1. The user says, "I want to check for new emails."

[0401] 2. The device captures the user's voice using its built-in microphone and converts it into text data using the Google Speech-to-Text API. Simultaneously, it recognizes the user's emotions using IBM Watson Tone Analyzer.

[0402] 3. The device sends text data and sentiment data to the server.

[0403] 4. The server uses GPT-4 to parse the text and confirm the intention to "read the email aloud" and whether the user is experiencing stress.

[0404] 5. The server accesses the Gmail API to retrieve the latest emails.

[0405] 6. The server generates and sends a text message to the terminal that reads, "You have a new email. Take care and relax. The subject is 'Meeting Reminder' and the content is 'The meeting is tomorrow at 10am'."

[0406] 7. The device uses Amazon Polly to convert the text data into audio data, and the speaker reads aloud, "You have a new email. Take care, relax. The subject is 'Meeting Reminder,' and the content is 'The meeting is tomorrow at 10am.'"

[0407] Start of directions

[0408] 1. The user says, "Tell me the way to my next destination."

[0409] 2. The device captures the user's voice using its built-in microphone and converts it into text data using the Google Speech-to-Text API. Simultaneously, it recognizes the user's emotions using IBM Watson Tone Analyzer.

[0410] 3. The device sends text data and sentiment data to the server.

[0411] 4. The server uses GPT-4 to analyze the user's intent and accesses the Google Maps API to calculate the optimal route.

[0412] 5. The server generates route guidance content as text data and considers guidance that includes considerations to help the user relax.

[0413] 6. The server sends this text data to the terminal.

[0414] 7. The device uses Amazon Polly to convert the message into audio data and reads it aloud from the speaker, saying, "Please turn left next. Drive carefully."

[0415] Change music

[0416] 1. The user says, "Change to the next song."

[0417] 2. The device captures the user's voice using its built-in microphone and converts it into text data using the Google Speech-to-Text API. Simultaneously, the IBM Watson Tone Analyzer recognizes the user's emotional state.

[0418] 3. The device sends text data and sentiment data to the server.

[0419] 4. The server uses GPT-4 to analyze the user's intent and accesses the Spotify API to select relaxing music.

[0420] 5. The server generates the selected items as text data and sends it to the terminal.

[0421] 6. The device uses Amazon Polly to generate audio data, and the speaker reads out, "The song has been changed, please relax and enjoy."

[0422] Through the detailed processing described above, this system can improve driving safety and comfort by responding appropriately based on the user's voice commands and emotional state.

[0423] Example of a prompt

[0424] "Please describe the process for checking new emails while driving. Provide detailed instructions using appropriate APIs and sentiment recognition."

[0425] "Please describe in detail the process for initiating route guidance to the next destination. Please include guidance that takes user emotions into consideration."

[0426] "Please explain the procedure for changing songs while driving. Clearly demonstrate the process of selecting songs based on the user's emotional state."

[0427] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0428] Step 1:

[0429] The user gives instructions or asks questions using voice. For example, they might say, "Check my email" or "Change to the next song." This voice input is the starting point for the system's processing. The input is the user's voice data, and the output is the voice data being captured by the device's microphone.

[0430] Step 2:

[0431] The device uses its built-in microphone to capture the user's voice. The captured voice data is converted into text data using the Google Speech-to-Text API. Simultaneously, the IBM Watson Tone Analyzer identifies the user's emotions. The input is the user's voice data, and the output is text data and emotion data.

[0432] Step 3:

[0433] The device sends acquired text data and sentiment data to the server. The text data is used for natural language processing, and the sentiment data is used to understand the user's psychological state. The input is text data and sentiment data, and the output is the transmission of this data to the server.

[0434] Step 4:

[0435] The server uses a natural language processing engine (GPT-4) to analyze text data and recognize the user's intent. For example, it can understand the instruction "Check your email" to mean "Read the electronic message aloud." Simultaneously, an emotion analysis engine analyzes the user's emotions, identifying states such as stress and relaxation. The input consists of text data and emotion data, while the output is data related to the user's intent and emotional state.

[0436] Step 5:

[0437] The server performs appropriate tasks based on the user's intentions and emotions. For example, it accesses an e-messaging service API to retrieve email content. If the user requests directions, it uses a map service API to calculate the optimal route; and if the user wants to change the music, it accesses a music service API to select relaxing music. The input is user intention and emotion data, and the output is the result data of the performed task.

[0438] Step 6:

[0439] The server generates text data that includes user-friendly feedback based on the task's execution results. For example, it might generate something like, "You have a new email. Take care, relax." The input is the task's result data, and the output is the feedback text data to notify the user.

[0440] Step 7:

[0441] The device receives feedback text data from the server, converts it into audio data using a speech synthesis engine (Amazon Polly), and notifies the user through the speaker. This allows the user to obtain information without relying on visual cues. The input is feedback text data, and the output is audio data.

[0442] The above outlines the processing steps from user voice input to the execution of appropriate tasks and the provision of feedback. Specific data processing and calculations are performed at each step, and the results are provided to the user.

[0443] (Application Example 2)

[0444] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0445] Conventional voice recognition systems were limited to converting user voice input into text data and performing tasks based on that data. However, because they performed tasks without considering the user's emotional state, they failed to alleviate user stress and anxiety. Therefore, there is a need for a system that eliminates the stress and inconvenience experienced by drivers and provides a more comfortable and safer driving experience.

[0446] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for analyzing voice tone and timbre to recognize the user's emotional state, means for executing a task based on the recognized intention and emotional state, and means for notifying the user of the execution result by voice. This makes it possible to execute an appropriate task while considering the user's emotional state and to provide a more comfortable and reassuring experience for the user while driving.

[0447] "Means for acquiring voice input" refers to hardware or software for capturing the voice spoken by the user.

[0448] "Means of converting to text data" refers to software or algorithms used to convert acquired voice input into string data.

[0449] "Means of recognizing user intent" refers to a natural language processing engine that analyzes converted text data to understand the content of user instructions and questions.

[0450] "Means of recognizing emotional states" refers to an emotion recognition engine that analyzes the tone and nuances of a voice to identify the user's emotions and psychological state.

[0451] "Means of executing a task" refers to software or system integration functions that execute appropriate instructions based on the user's intentions and emotional state.

[0452] "Means of notifying by voice" refers to speakers or audio systems that convert execution results into voice data using a speech synthesis engine and inform the user.

[0453] "Methods for retrieving email content and generating emotionally sensitive messages" refers to an API for accessing the content of emails requested by the user, and an algorithm for generating notifications that take the user's emotional state into consideration.

[0454] "Means of initiating directions and providing emotionally sensitive guidance" refers to a navigation system that calculates the route to the user's destination and provides driving instructions that take the user's emotional state into consideration.

[0455] This invention is a system that improves driving safety and comfort by acquiring and analyzing user voice input to recognize the user's emotions and providing appropriate feedback. The following describes specific embodiments for implementing this invention.

[0456] System Configuration

[0457] This system primarily uses the following hardware and software.

[0458] 1. Audio capture device (terminal microphone)

[0459] 2. Speech recognition engine (Google Cloud Speech-to-Text API)

[0460] 3. Emotion Recognition Engine (IBM Watson Tone Analyzer)

[0461] 4. Natural Language Processing Engine (OpenAI GPT-3)

[0462] 5. Music service API (Spotify API)

[0463] 6. Text-to-Speech API

[0464] 7. Infotainment system (in-vehicle system)

[0465] Process Overview

[0466] 1. Acquisition of voice input

[0467] The device's microphone captures the user's voice. For example, the user might say, "Change to the next song."

[0468] 2. Speech Recognition and Emotion Recognition

[0469] The device captures audio data and converts it into text data using the Google Cloud Speech-to-Text API. Simultaneously, IBM Watson Tone Analyzer is used to analyze the voice's tone and timbre to recognize the user's emotional state.

[0470] 3. Data transmission

[0471] The device sends text data and sentiment data to the cloud server.

[0472] 4. Natural Language Understanding and Task Execution

[0473] A cloud server uses OpenAI GPT-3 to analyze text data and recognize the user's intent. Simultaneously, it selects appropriate tasks based on the user's emotional state. For example, it might access the Spotify API to select music that will help the user relax.

[0474] 5. Response generation

[0475] The cloud server generates the task execution results as text data and creates a response message that includes feedback that takes the user's emotions into consideration.

[0476] 6. Voice Response

[0477] The terminal receives text data, which is then converted into speech data using a speech synthesis engine, and feedback is sent to the user through the vehicle's speakers. For example, it might announce, "The song has been changed, please relax and enjoy."

[0478] Specific example

[0479] The following shows an example of actual operation.

[0480] Change music

[0481] 1. The user says, "Change to the next song."

[0482] 2. The device acquires the user's voice and converts it into text data using a speech recognition engine (Google Cloud Speech-to-Text API). In addition, an emotion recognition engine (IBM Watson Tone Analyzer) recognizes emotions requiring relaxation from the user's tone of voice.

[0483] 3. The device sends text data and sentiment data to the cloud server.

[0484] 4. The cloud server analyzes the text, recognizes the user's intent, and selects relaxing music from the Spotify API based on their emotional state.

[0485] 5. The cloud server generates a task completion message, "The song has been changed, please relax and enjoy," and sends it to the device.

[0486] 6. The device converts the text data into audio data and notifies the user via the speaker.

[0487] Example of a prompt

[0488] Change to a new song (please select a song that will help the user relax).

[0489] In this way, the system of the present invention can automatically and appropriately respond based on the user's voice input and emotional state, thereby improving driving safety and comfort.

[0490] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0491] Step 1:

[0492] The device's microphone captures the user's voice. For example, if the user says, "Change to the next song," this voice becomes the input. The input voice is saved as digital data.

[0493] Step 2:

[0494] The device captures audio data and converts it into text data using the Google Cloud Speech-to-Text API. In this case, the input is digital audio data, and the output is the text "Change to the next song."

[0495] Step 3:

[0496] The device analyzes the voice tone and timbre of the audio simultaneously with text data using IBM Watson Tone Analyzer to recognize the user's emotional state. The input is digital audio data, and the output is an emotional state tag such as "needs to relax."

[0497] Step 4:

[0498] The device sends text data and sentiment data to the cloud server. In this step, the input is text data and sentiment state data, and the output is the action of sending them to the server.

[0499] Step 5:

[0500] The cloud server analyzes the received text data using OpenAI GPT-3 to recognize the user's intent. The input is the text data "Change to the next song," and the output is the specific instruction "Change to the next song."

[0501] Step 6:

[0502] Based on the emotional data received by the cloud server, the system performs tasks that take appropriate emotions into consideration. Using the Spotify API, it changes the song to a new one. The input is emotional data and the user's intent, and the output is specific song information such as "relaxing music."

[0503] Step 7:

[0504] A cloud server generates the task execution results as text data and creates a response message that takes the user's emotions into consideration. The input is song information and emotion data, and the output is a response message such as "The song has been changed, please relax and enjoy."

[0505] Step 8:

[0506] The device converts text data into speech data using a speech synthesis engine and notifies the user via the speaker. The input is the text data of the response message, and the output is the speech notification to the user.

[0507] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0508] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0509] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0510] [Second Embodiment]

[0511] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0512] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0513] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0514] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0515] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0516] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0517] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0518] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0519] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0520] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0521] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0522] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0523] The present invention is a system for assisting drivers who are driving a car alone. It improves driving safety and comfort by acquiring and analyzing the user's voice input and providing appropriate feedback. Specific embodiments of the present invention will be described below.

[0524] Explanation of the program's processing

[0525] 1. Acquisition of voice input

[0526] The user can give voice commands or ask questions while driving. For example, they can give voice commands such as "Check my email" or "Change to the next song."

[0527] 2. Speech Recognition

[0528] The device captures the user's voice through the microphone, and a speech recognition engine converts the voice into text data. This text data is then used for subsequent processing.

[0529] 3. Natural language understanding

[0530] The device sends the acquired text data to the server. The server uses a natural language processing engine to analyze the text data and recognize the user's intent. For example, if the instruction is "Check your email," the server recognizes the intent as "Read the email aloud."

[0531] 4. Execute the task

[0532] The server performs appropriate tasks based on the recognized user's intent. For example, it accesses the email service API to retrieve the latest email content. For route guidance, it uses the map API to calculate the route; and for changing music, it uses the music service API to change the song.

[0533] 5. Response generation

[0534] The server generates the task execution results as text data and sends it to the terminal.

[0535] 6. Voice Response

[0536] The terminal receives text data, which is then converted into speech data using a speech synthesis engine and communicated to the user via the speaker. This allows the user to obtain necessary information while concentrating on driving.

[0537] Specific example

[0538] Email confirmation

[0539] 1. The user says, "I want to check for new emails."

[0540] 2. The device acquires the user's voice and converts it into text data using a speech recognition engine.

[0541] 3. The terminal sends text data to the server.

[0542] 4. The server analyzes the text to understand the user's intent.

[0543] 5. The server accesses the email service API to retrieve the latest emails.

[0544] 6. The server generates the email content as text data and sends it to the terminal.

[0545] 7. The device converts the text data into audio data and reads aloud from the speaker, "You have a new email. The subject is 'Meeting Reminder,' and the content is 'The meeting is tomorrow at 10:00.'"

[0546] Start of directions

[0547] 1. The user says, "Tell me the way to my next destination."

[0548] 2. The device acquires the user's voice and converts it into text data using a speech recognition engine.

[0549] 3. The terminal sends text data to the server.

[0550] 4. The server analyzes the text to understand the user's intent.

[0551] 5. The server accesses the map API and calculates the optimal route.

[0552] 6. The server generates the route guidance content as text data and sends it to the terminal.

[0553] 7. The device converts the text data into audio data and reads aloud from the speaker, "Next, turn left."

[0554] Change music

[0555] 1. The user says, "Change to the next song."

[0556] 2. The device acquires the user's voice and converts it into text data using a speech recognition engine.

[0557] 3. The terminal sends text data to the server.

[0558] 4. The server analyzes the text to understand the user's intent.

[0559] 5. The server accesses the music service API and changes to the next song.

[0560] 6. The server generates a text file confirming the changes have been made and sends it to the terminal.

[0561] 7. The device converts the text data into audio data and reads aloud from the speaker, "The song has been changed."

[0562] As described above, the system of the present invention enhances driving safety and comfort by automatically executing various tasks based on the user's voice input and notifying the results by voice.

[0563] The following describes the processing flow.

[0564] Step 1:

[0565] The user says, "I want to check for new emails."

[0566] Step 2:

[0567] The device captures the user's voice using its built-in microphone.

[0568] Step 3:

[0569] The device sends voice data to the speech recognition engine, which converts it into text data.

[0570] Step 4:

[0571] The device sends the converted text data "I want to check for new emails" to the server.

[0572] Step 5:

[0573] The server receives text data and uses a natural language processing engine to analyze the user's intent.

[0574] Step 6:

[0575] The server recognizes the intention to "check email" and generates a request to access the email service API.

[0576] Step 7:

[0577] The server sends a request to the email service API to retrieve the latest email data.

[0578] Step 8:

[0579] The server analyzes the email data and generates text to be read aloud to notify the user.

[0580] Step 9:

[0581] The server sends the generated text "You have a new email. The subject is 'Meeting Reminder', and the content is 'The meeting is tomorrow at 10:00'" to the terminal.

[0582] Step 10:

[0583] The terminal receives text data, which is then sent to the speech synthesis engine to generate speech data.

[0584] Step 11:

[0585] The device plays the generated audio data through its speaker and notifies the user, "You have a new email. The subject is 'Meeting Reminder,' and the content is 'The meeting is tomorrow at 10:00.'"

[0586] (Example 1)

[0587] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0588] In recent years, there has been a growing demand for technologies that improve the safety and comfort of drivers operating vehicles alone. In particular, there is a need for systems that allow drivers to perform various operations using only their voice, without using their hands while driving. However, conventional systems have suffered from low accuracy in voice recognition and significant delays between voice input and actual task execution, making them impractical. There is a need to provide a new system that overcomes these challenges and achieves highly sensitive voice recognition and rapid task execution.

[0589] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0590] In this invention, the server includes means for acquiring voice input, means for converting the acquired voice input into text data, and means for analyzing the text data with a natural language processing engine. This makes it possible to capture the user's voice with a high-sensitivity microphone and analyze the text data with high accuracy and speed. It also includes means for executing tasks based on recognized intent and means for notifying the results by voice, and further includes means for accessing a map service API for route guidance and a music service API for changing music, thereby improving driver safety and comfort.

[0591] A "user" refers to an individual who uses the system to perform voice input.

[0592] "Means for acquiring voice input" refers to devices or technologies that capture the user's voice using a microphone and incorporate that data into the system.

[0593] "Means of converting acquired voice input into text data" refers to technologies and software that use a speech recognition engine to convert voice data into text information.

[0594] "Methods for analyzing text data to recognize user intent" refers to technologies that use a natural language processing engine to process text data and understand the content of user statements.

[0595] "Means of executing a task" refers to servers and software that perform specific operations or give instructions based on the user's intentions.

[0596] "Means of notifying the user of execution results via voice" refers to a technology that converts the results of an executed task into voice data using a speech synthesis engine and communicates it to the user through a speaker.

[0597] A "high-sensitivity microphone" refers to a microphone device that has the ability to effectively filter out noise during driving and capture clear voice input.

[0598] A "natural language processing engine" refers to software or algorithms that analyze text data, understand its context and meaning, and extract the user's intent.

[0599] A "database" refers to a system for efficiently storing and managing acquired and analyzed data.

[0600] A "map service API" refers to an interface with an external map service for providing route guidance.

[0601] A "music service API" refers to an interface with external music services for playing and modifying music.

[0602] "Text data" refers to character information converted from voice input by a speech recognition engine.

[0603] "Audio data" refers to audio information generated by a speech synthesis engine and played back to the user through a speaker.

[0604] "API" is an abbreviation for Application Programming Interface, and refers to the definitions and protocols that enable communication between different software programs.

[0605] Modes for carrying out the invention

[0606] The present invention is a system for assisting drivers who are driving a car alone. It improves driving safety and comfort by acquiring and analyzing user voice input and providing appropriate feedback. Specific embodiments of the present invention will be described below.

[0607] System Configuration

[0608] The system of the present invention consists of the following main components.

[0609] 1. Means for obtaining user voice input:

[0610] The user gives instructions and asks questions by voice while driving. Specifically, a high-sensitivity microphone installed in the vehicle is used to capture the voice. The high-sensitivity microphone effectively filters out noise during driving, ensuring clear voice input.

[0611] 2. Means for converting acquired voice input into text data:

[0612] The device captures the user's voice through the microphone and converts the voice into text data using a speech recognition engine (e.g., Microsoft Azure Speech SDK or Google Cloud Speech-to-Text).

[0613] 3. Means of analyzing text data to recognize user intent:

[0614] The device sends the acquired text data to the server. The server analyzes the text data using a generative AI model (e.g., OpenAI GPT-3) to recognize the user's intent.

[0615] 4. Means of performing tasks based on recognized intentions:

[0616] The server performs the appropriate task based on the recognized user's intent. For example, to retrieve email content, it accesses the Gmail API to retrieve the latest email. For route guidance, it uses the Google Maps API to calculate the optimal route, and for changing music, it accesses the Music Services API to change the song.

[0617] 5. Means of notifying the user of the execution results by voice:

[0618] The server generates the task execution results as text data and sends it to the terminal. The terminal converts the received text data into audio data using a speech synthesis engine (e.g., Amazon Polly) and notifies the user through the speaker.

[0619] Specific example

[0620] The following are specific usage scenarios.

[0621] Email confirmation

[0622] 1. The user says, "I want to check for new emails."

[0623] 2. The device captures the user's voice and converts it into text data using Google Cloud Speech-to-Text.

[0624] 3. The terminal sends text data to the server.

[0625] 4. The server uses OpenAI GPT-3 to analyze the text and recognize the user's intent.

[0626] 5. The server accesses the Gmail API to retrieve the latest emails.

[0627] 6. The server generates the email content as text data and sends it to the terminal.

[0628] 7. The device uses Amazon Polly to convert the text data into audio data, and the speaker reads aloud, "You have a new email. The subject is 'Meeting Reminder,' and the content is 'The meeting is tomorrow at 10am.'"

[0629] Start of directions

[0630] 1. The user says, "Tell me the way to my next destination."

[0631] 2. The device captures the user's voice and converts it into text data using the Microsoft Azure Speech SDK.

[0632] 3. The terminal sends text data to the server.

[0633] 4. The server uses OpenAI GPT-3 to analyze the text and recognize the user's intent.

[0634] 5. The server accesses the Google Maps API and calculates the optimal route.

[0635] 6. The server generates the route guidance content as text data and sends it to the terminal.

[0636] 7. The device uses Amazon Polly to convert the text data into audio data and reads aloud from the speaker, "Turn left next."

[0637] Change music

[0638] 1. The user says, "Change to the next song."

[0639] 2. The device captures the user's voice and converts it into text data using Google Cloud Speech-to-Text.

[0640] 3. The terminal sends text data to the server.

[0641] 4. The server uses OpenAI GPT-3 to analyze the text and recognize the user's intent.

[0642] 5. The server accesses the Spotify API and changes to the next song.

[0643] 6. The server generates a text file confirming the changes have been made and sends it to the terminal.

[0644] 7. The device uses Amazon Polly to convert the text data into audio data and reads aloud from the speaker, "The song has been changed."

[0645] As described above, the system of the present invention automatically performs various tasks based on the user's voice input and notifies the user of the results by voice, thereby improving driving safety and comfort.

[0646] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0647] Step 1: Obtaining voice input

[0648] The user can give instructions and ask questions by voice while driving. For example, they can give voice commands such as "Check my email" or "Change to the next song." The input voice is captured by a high-sensitivity microphone installed in the car. The high-sensitivity microphone filters out noise during driving, ensuring a clear voice input. The output is the captured raw voice data.

[0649] Step 2: Speech Recognition

[0650] The device passes the captured raw audio data to a speech recognition engine such as the Microsoft Azure Speech SDK or Google Cloud Speech-to-Text. The input is the raw audio data from step 1. The speech recognition engine analyzes the audio data and converts it into corresponding text data. The output is this text data, which is used in the next processing step.

[0651] Step 3: Send text data

[0652] The terminal sends the acquired text data to the server. The input is text data. The terminal securely transfers the text data to the server via the network, where further processing continues. The output is the text data sent to the server.

[0653] Step 4: Natural Language Comprehension

[0654] The server analyzes text data using a generative AI model such as OpenAI GPT-3. The input is the text data received in step 3. The server analyzes the text data to identify the user's intent. For example, the instruction "check my email" is recognized as having the intent "read the email aloud." In terms of data processing, the text data is analyzed based on context, and the intent behind the user's request is extracted. The output is user intent information, which is used in the next task execution step.

[0655] Step 5: Execute the task

[0656] The server performs the appropriate task based on the user's intent. The input is the user's intent information from step 4. For example, to retrieve email content, it accesses the Gmail API to retrieve the latest email. For route guidance, it uses the Google Maps API to calculate the optimal route, and for changing music, it uses the Music Services API to change the song. Each API call performs data retrieval or calculation. The output is the result data of the task execution.

[0657] Step 6: Generating the response

[0658] The server generates the task execution result as text data and sends it to the terminal. The input is the task execution result data from step 5. Based on the execution result, the server generates a text message to notify the user. The output is the text data of the notification message sent to the terminal.

[0659] Step 7: Voice Response

[0660] The text data received by the device is converted into speech data by a speech synthesis engine such as Amazon Polly. The input is the text data of the notification message from step 6. The speech synthesis engine analyzes the text data and generates the corresponding natural speech. The generated speech data is notified to the user through the speaker. For example, it might read aloud, "You have a new email. The subject is 'Meeting Reminder,' and the content is 'The meeting is tomorrow at 10am.'" The output is the speech data that is played back to the user.

[0661] (Application Example 1)

[0662] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0663] In autonomous vehicles, a system is needed that allows users to efficiently perform tasks using voice commands while driving and to obtain information safely and comfortably. Conventional systems have problems such as low accuracy in voice input, slow recognition of intent, and data acquisition, which fail to adequately ensure user convenience. There is also room for improvement in acquiring real-time data from external sources and providing feedback.

[0664] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0665] In this invention, the server includes means for acquiring voice input, means for converting the acquired voice input into text data, means for analyzing the text data to recognize the user's intent, means for acquiring data from external information sources, and means for analyzing the acquired data to provide appropriate feedback to the user. This enables the user to efficiently perform tasks using voice while driving and to obtain necessary information in real time in a safe and comfortable manner.

[0666] A "user" refers to a person who uses the system.

[0667] "Voice input" refers to the act of a user providing instructions or information to a system using their voice.

[0668] "Means of acquisition" refers to methods and technologies for receiving, recording, or capturing information such as audio or data.

[0669] "Means of converting audio to text data" refers to methods and technologies for converting audio data into text-based data.

[0670] "Text data" refers to data expressed as character information.

[0671] "Means of analysis" refers to methods and techniques for analyzing acquired data and extracting specific information or patterns.

[0672] "User intent" refers to the actions a user wants to perform on the system or the information they are seeking.

[0673] "Means of executing a task" refers to methods and technologies for performing specific operations or processes based on the user's intent.

[0674] "Execution results" refer to the outcomes and information obtained after completing a task.

[0675] "External information sources" refer to information providers located outside the system. For example, these include third-party services or databases that provide weather information or news.

[0676] "Means of acquiring data" refers to the methods and technologies used to obtain necessary data from external sources.

[0677] "Means of providing feedback" refers to methods and techniques for providing users with information or the results of instructions.

[0678] "Real-time" refers to the immediate processing and provision of information at the moment an event occurs.

[0679] This invention relates to a system for autonomous vehicles that acquires user voice input, analyzes their intent, and provides appropriate feedback. This system assists users in driving safely and comfortably.

[0680] The system program is implemented using the following hardware and software.

[0681] hardware

[0682] 1. Smartphone: Captures the user's voice input and converts the voice into text data.

[0683] 2. Server: Analyzes text data and recognizes the user's intent.

[0684] 3. External information sources: Databases and third-party services that provide weather information, news, etc.

[0685] software

[0686] 1. Speech recognition engine: For example, use Google's speech recognition API to convert speech to text.

[0687] 2. Natural Language Processing Engine: For example, use Amazon Comprehend to analyze text data and recognize user intent.

[0688] 3. Data acquisition API: For example, use an API to acquire weather information.

[0689] Processing procedures and data calculations

[0690] 1. Acquisition of voice input

[0691] The user gives instructions or asks questions by voice while driving. The smartphone's microphone captures this and sends it to the voice recognition engine.

[0692] 2. Speech Recognition

[0693] The speech recognition engine converts the speech into text data. This text data is then sent to the server.

[0694] 3. Natural language understanding

[0695] The server receives text data and analyzes it using a natural language processing engine. For example, if given the instruction "Tell me today's weather," the server recognizes that it needs to retrieve weather information.

[0696] 4. Data Acquisition

[0697] The server accesses external information sources and retrieves the necessary data (e.g., weather information).

[0698] 5. Response generation

[0699] Based on the acquired data, the server generates feedback to be provided to the user. This feedback is then sent to the smartphone as text data.

[0700] 6. Voice Response

[0701] Text data is sent to a speech synthesis engine and converted into speech data. Feedback is then provided to the user through the smartphone's speaker.

[0702] Specific example

[0703] Example 1: Obtaining weather information

[0704] 1. The user says, "Tell me today's weather."

[0705] 2. The smartphone captures the audio, and the speech recognition engine converts it into text data.

[0706] 3. The server receives the text data and analyzes it using a natural language processing engine.

[0707] 4. The server accesses the weather information API to obtain the latest weather information.

[0708] 5. The server generates weather information as text data and sends it to the smartphone.

[0709] 6. The smartphone converts the text data into audio data and announces "Today's weather is sunny" through the speaker.

[0710] Example of a prompt:

[0711] When a user says, "Tell me today's weather," a smartphone application recognizes the voice and analyzes the text data using a natural language processing engine. It then calls a weather forecast API to obtain real-time weather information and notifies the user as audio data.

[0712] Thus, the system of the present invention is configured to support the user's safe and comfortable driving.

[0713] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0714] Step 1:

[0715] The user inputs instructions by voice. For example, they might say, "Tell me today's weather." This inputs the information the user wants to know or the task they want to perform as voice data into the device.

[0716] Step 2:

[0717] The device captures the user's voice data and converts it into text data using a speech recognition engine. Specifically, the smartphone's microphone captures the voice and sends it to an API that converts the voice data into text. In this case, the input is voice data and the output is text data.

[0718] Step 3:

[0719] Text data is sent from the terminal to the server. The server passes the received text data to a natural language processing engine to analyze the user's intent. A natural language processing engine (e.g., Amazon Comprehend) is used here. The input is text data, and the output is data indicating the user's intent.

[0720] Step 4:

[0721] The server retrieves data from appropriate external sources based on the user's intent. For example, to retrieve weather information, it accesses a weather information API to obtain the latest weather data. The input is data indicating the user's intent, and the output is the retrieved weather information data.

[0722] Step 5:

[0723] The server analyzes the acquired data and generates feedback to provide to the user. For example, weather information is formatted as text data such as "Today's weather is sunny." The input is the acquired information data, and the output is formatted text data to be provided to the user.

[0724] Step 6:

[0725] Formatted text data is sent from the server to the terminal. The terminal receives this data and converts it into speech data using a speech synthesis engine. Specifically, a speech synthesis engine (e.g., gTTS) is used to play the text as speech. The input is formatted text data, and the output is speech data.

[0726] Step 7:

[0727] The device plays the generated audio data through its speaker and notifies the user. This allows the user to receive necessary information in real time via voice, even while driving. The input is audio data, and the output is an audio notification to the user.

[0728] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0729] The present invention is a system for assisting drivers who are driving a car alone. It improves driving safety and comfort by acquiring and analyzing the user's voice input, recognizing the user's emotions, and providing appropriate feedback. Specific embodiments of the present invention will be described below.

[0730] Explanation of the program's processing

[0731] 1. Acquisition of voice input

[0732] The user can give voice commands or ask questions while driving. For example, they can give voice commands such as "Check my email" or "Change to the next song."

[0733] 2. Speech Recognition and Emotion Recognition

[0734] The device captures the user's voice using its built-in microphone, and a speech recognition engine converts the voice into text data. This text data is used for subsequent processing. Simultaneously, an emotion engine recognizes the user's emotions from the voice data.

[0735] 3. Natural Language Understanding and Emotion Understanding

[0736] The device sends the acquired text data and sentiment data to the server. The server uses a natural language processing engine to analyze the text data and recognize the user's intent. For example, in the case of the instruction "Check your email," the server recognizes the intention to "read the email aloud," and the sentiment engine also recognizes that the user is feeling stressed.

[0737] 4. Execute the task

[0738] The server performs appropriate tasks based on the recognized user's intentions and emotions. For example, it accesses the email service API to retrieve the latest emails to get the content of emails. For route guidance, it uses a map API to calculate the route, and for music changes, it considers the user's emotions and selects relaxing music.

[0739] 5. Response generation

[0740] The server generates the task execution results as text data and sends a message to the terminal that includes content that takes the user's feelings into consideration.

[0741] 6. Voice Response

[0742] The terminal receives text data, which is then converted into speech data using a speech synthesis engine and communicated to the user via the speaker. This allows the user to obtain necessary information while concentrating on driving.

[0743] Specific example

[0744] Email confirmation

[0745] 1. The user says, "I want to check for new emails."

[0746] 2. The device acquires the user's voice and converts it into text data using a speech recognition engine. Simultaneously, an emotion engine recognizes the user's stress level based on their tone of voice.

[0747] 3. The device sends text data and sentiment data to the server.

[0748] 4. The server analyzes the text to understand the user's intent and confirm that they are experiencing stress.

[0749] 5. The server accesses the email service API to retrieve the latest emails.

[0750] 6. The server generates and sends a text message to the terminal that reads, "You have a new email. Take care and relax. The subject is 'Meeting Reminder' and the content is 'The meeting is tomorrow at 10am'."

[0751] 7. The device converts the text data into audio data and reads aloud from the speaker: "You have a new email. Take care, relax. The subject is 'Meeting Reminder,' and the content is 'The meeting is tomorrow at 10am.'"

[0752] Start of directions

[0753] 1. The user says, "Tell me the way to my next destination."

[0754] 2. The device acquires the user's voice and converts it into text data using a speech recognition engine. Simultaneously, an emotion engine recognizes the user's emotional state.

[0755] 3. The device sends text data and sentiment data to the server.

[0756] 4. The server analyzes the text to recognize the user's intent and considers appropriate guidance that reflects their emotional state.

[0757] 5. The server accesses the map API and calculates the optimal route.

[0758] 6. The server generates the route guidance content as text data and sends it to the terminal.

[0759] 7. The device converts the text data into audio data and reads aloud from the speaker, "Please turn left next. Drive carefully."

[0760] Change music

[0761] 1. The user says, "Change to the next song."

[0762] 2. The device acquires the user's voice and converts it into text data using a speech recognition engine. Simultaneously, an emotion engine recognizes the user's emotional state.

[0763] 3. The device sends text data and sentiment data to the server.

[0764] 4. The server analyzes the text to recognize the user's intent and selects relaxing music that takes their emotional state into consideration.

[0765] 5. The server accesses the music service API and changes to the next song.

[0766] 6. The server generates a text file confirming the changes have been made and sends it to the terminal.

[0767] 7. The device converts the text data into audio data and reads aloud from the speaker, "The song has been changed, please relax and enjoy."

[0768] As described above, the system of the present invention enhances driving safety and comfort by providing appropriate responses based on the user's voice input and emotional state.

[0769] The following describes the processing flow.

[0770] Specific examples of email confirmation

[0771] Voice input acquisition and recognition

[0772] Step 1:

[0773] The user says, "I want to check for new emails."

[0774] Step 2:

[0775] The device captures the user's voice using its built-in microphone.

[0776] Step 3:

[0777] The device sends voice data to the speech recognition engine, which converts it into text data.

[0778] Step 4:

[0779] The device uses an emotion engine to extract user emotion data from the voice.

[0780] Data transmission and analysis

[0781] Step 5:

[0782] The device sends the converted text data and sentiment data to the server.

[0783] Step 6:

[0784] The server receives text data and uses a natural language processing engine to analyze the user's intent.

[0785] Step 7:

[0786] The server uses an emotion engine to analyze the user's emotions and recognize that they are "feeling stressed."

[0787] Task execution and response generation

[0788] Step 8:

[0789] The server recognizes the intention to "check email" and sends a request to the email service API.

[0790] Step 9:

[0791] The server receives the latest email data from the email service API.

[0792] Step 10:

[0793] The server analyzes the email data and generates text for reading aloud that takes the user's emotions into consideration.

[0794] Step 11:

[0795] The server sends the generated text data "You have a new email. Take care and relax. Subject: 'Meeting Reminder', Content: 'The meeting is tomorrow at 10:00'" to the terminal.

[0796] Voice response

[0797] Step 12:

[0798] The terminal receives text data, which is then sent to a speech synthesis engine and converted into speech data.

[0799] Step 13:

[0800] The device plays the generated audio data through the speaker and notifies the user, "You have a new email. Take care and relax. The subject is 'Meeting Reminder,' and the content is 'The meeting is tomorrow at 10:00.'"

[0801] A concrete example of starting directions

[0802] Voice input acquisition and recognition

[0803] Step 1:

[0804] The user asks, "Tell me the way to my next destination."

[0805] Step 2:

[0806] The device captures the user's voice using its built-in microphone.

[0807] Step 3:

[0808] The device sends voice data to the speech recognition engine, which converts it into text data.

[0809] Step 4:

[0810] The device uses an emotion engine to extract user emotion data from the voice.

[0811] Data transmission and analysis

[0812] Step 5:

[0813] The device sends the converted text data and sentiment data to the server.

[0814] Step 6:

[0815] The server receives text data and uses a natural language processing engine to analyze the user's intent.

[0816] Step 7:

[0817] The server uses an emotion engine to analyze the user's emotions and recognizes that they are in a relaxed state.

[0818] Task execution and response generation

[0819] Step 8:

[0820] The server recognizes the intention to "start directions" and sends a request to the map API.

[0821] Step 9:

[0822] The server receives route information from the map API.

[0823] Step 10:

[0824] The server analyzes route information and generates emotionally sensitive guidance text.

[0825] Step 11:

[0826] The server generates text data, "Turn left next, drive carefully," and sends it to the terminal.

[0827] Voice response

[0828] Step 12:

[0829] The terminal receives text data, which is then sent to a speech synthesis engine and converted into speech data.

[0830] Step 13:

[0831] The device generates audio data which is played through the speaker, instructing the user with messages such as, "Please turn left next. Drive carefully."

[0832] Examples of music changes

[0833] Voice input acquisition and recognition

[0834] Step 1:

[0835] The user says, "Change to the next song."

[0836] Step 2:

[0837] The device captures the user's voice using its built-in microphone.

[0838] Step 3:

[0839] The device sends voice data to the speech recognition engine, which converts it into text data.

[0840] Step 4:

[0841] The device uses an emotion engine to extract user emotion data from the voice.

[0842] Data transmission and analysis

[0843] Step 5:

[0844] The device sends the converted text data and sentiment data to the server.

[0845] Step 6:

[0846] The server receives text data and uses a natural language processing engine to analyze the user's intent.

[0847] Step 7:

[0848] The server uses an emotion engine to analyze the user's emotions and recognizes that they are in a relaxed state.

[0849] Task execution and response generation

[0850] Step 8:

[0851] The server recognizes the intention to "change the music" and sends a request to the music service API.

[0852] Step 9:

[0853] The server receives the next song data from the music service API.

[0854] Step 10:

[0855] The server analyzes the data for the next song and generates a notification text that takes emotions into consideration.

[0856] Step 11:

[0857] The server sends the generated text data "The song has been changed, please relax and enjoy" to the terminal.

[0858] Voice response

[0859] Step 12:

[0860] The terminal receives text data, which is then sent to a speech synthesis engine and converted into speech data.

[0861] Step 13:

[0862] The device plays the generated audio data through the speaker and notifies the user with the message, "The song has been changed, please relax and enjoy."

[0863] As described above, the system of the present invention enhances driving safety and comfort by providing appropriate responses based on the user's voice input and emotional state.

[0864] (Example 2)

[0865] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0866] Conventional driver assistance systems, while capable of receiving user voice commands and executing tasks, failed to adequately improve driving safety and comfort by failing to provide feedback or operations that considered the user's emotions. Furthermore, one-sided notifications and instructions that disregard the user's feelings could amplify user stress, potentially negatively impacting driving concentration and safety. To solve these problems, a system was needed that could identify not only the user's intent based on their voice but also their emotions, and provide appropriate feedback accordingly.

[0867] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring voice input, means for converting voice to text data, means for recognizing the user's intent from the text data, means for identifying emotions from the user's voice data, means for executing a task based on the user's intent and emotions, and means for notifying the user of the task execution result by voice in a manner that takes the user's emotions into consideration. This makes it possible to respond appropriately not only to the user's intent but also to their emotions, thereby improving safety and comfort while driving.

[0868] "Means for acquiring voice input" refer to microphones or voice capture devices that capture user speech in real time.

[0869] "Methods for converting audio to text data" refer to a system that uses a speech recognition engine to convert captured audio into text strings.

[0870] "Means of recognizing user intent from text data" refers to a system that uses natural language processing technology to analyze and understand user requests and instructions from converted text data.

[0871] "Methods for identifying emotions from voice data" refer to systems that use an emotion recognition engine to determine the user's emotional state from the tone and patterns of their voice.

[0872] "Means for executing a task" refer to operational means for performing relevant actions based on the analyzed user's intentions and emotions. For example, this could involve retrieving the content of an electronic message or initiating route guidance.

[0873] "Means for notifying execution results by voice" refers to a system consisting of a speech synthesis engine and a speaker for providing voice feedback to the user on the execution results of a task.

[0874] "Means for obtaining the content of electronic messages" refers to APIs and service access mechanisms that retrieve email content from various email services in response to user instructions.

[0875] A "means for initiating route guidance" refers to a system that provides the optimal route based on user instructions, utilizing map data and navigation systems.

[0876] This invention is a system for assisting drivers who are driving a car alone. It improves driving safety and comfort by acquiring and analyzing the user's voice input, recognizing the user's emotions, and providing appropriate feedback.

[0877] System Overview

[0878] This system functions through communication between the user, a terminal in the vehicle, and a central server. Specifically, the terminal is equipped with a built-in microphone to acquire voice input from the user. This voice data is converted into text data via a speech recognition engine, and the user's emotions are further identified by an emotion recognition engine. This data is sent to the server, where a natural language processing engine and an emotion analysis engine are used to understand the user's intentions and emotions and execute the task. The server then sends the execution results back to the terminal, which uses a speech synthesis engine to notify the user.

[0879] Hardware and software to be used

[0880] Terminal (in-car device): Built-in microphone, speaker

[0881] Server: High-performance processor, Internet connection

[0882] Speech recognition engine: Google Speech-to-Text API

[0883] Emotion recognition engine: IBM Watson Tone Analyzer

[0884] Natural language processing engine: OpenAI GPT-4

[0885] Speech synthesis engine: Amazon Polly

[0886] Various APIs: Gmail API, Google Maps API, Spotify API

[0887] Specific example

[0888] Email confirmation

[0889] 1. The user says, "I want to check for new emails."

[0890] 2. The device captures the user's voice using its built-in microphone and converts it into text data using the Google Speech-to-Text API. Simultaneously, it recognizes the user's emotions using IBM Watson Tone Analyzer.

[0891] 3. The device sends text data and sentiment data to the server.

[0892] 4. The server uses GPT-4 to parse the text and confirm the intention to "read the email aloud" and whether the user is experiencing stress.

[0893] 5. The server accesses the Gmail API to retrieve the latest emails.

[0894] 6. The server generates and sends a text message to the terminal that reads, "You have a new email. Take care and relax. The subject is 'Meeting Reminder' and the content is 'The meeting is tomorrow at 10am'."

[0895] 7. The device uses Amazon Polly to convert the text data into audio data, and the speaker reads aloud, "You have a new email. Take care, relax. The subject is 'Meeting Reminder,' and the content is 'The meeting is tomorrow at 10am.'"

[0896] Start of directions

[0897] 1. The user says, "Tell me the way to my next destination."

[0898] 2. The device captures the user's voice using its built-in microphone and converts it into text data using the Google Speech-to-Text API. Simultaneously, it recognizes the user's emotions using IBM Watson Tone Analyzer.

[0899] 3. The device sends text data and sentiment data to the server.

[0900] 4. The server uses GPT-4 to analyze the user's intent and accesses the Google Maps API to calculate the optimal route.

[0901] 5. The server generates route guidance content as text data and considers guidance that includes considerations to help the user relax.

[0902] 6. The server sends this text data to the terminal.

[0903] 7. The device uses Amazon Polly to convert the message into audio data and reads it aloud from the speaker, saying, "Please turn left next. Drive carefully."

[0904] Change music

[0905] 1. The user says, "Change to the next song."

[0906] 2. The device captures the user's voice using its built-in microphone and converts it into text data using the Google Speech-to-Text API. Simultaneously, the IBM Watson Tone Analyzer recognizes the user's emotional state.

[0907] 3. The device sends text data and sentiment data to the server.

[0908] 4. The server uses GPT-4 to analyze the user's intent and accesses the Spotify API to select relaxing music.

[0909] 5. The server generates the selected items as text data and sends it to the terminal.

[0910] 6. The device uses Amazon Polly to generate audio data, and the speaker reads out, "The song has been changed, please relax and enjoy."

[0911] Through the detailed processing described above, this system can improve driving safety and comfort by responding appropriately based on the user's voice commands and emotional state.

[0912] Example of a prompt

[0913] "Please describe the process for checking new emails while driving. Provide detailed instructions using appropriate APIs and sentiment recognition."

[0914] "Please describe in detail the process for initiating route guidance to the next destination. Please include guidance that takes user emotions into consideration."

[0915] "Please explain the procedure for changing songs while driving. Clearly demonstrate the process of selecting songs based on the user's emotional state."

[0916] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0917] Step 1:

[0918] The user gives instructions or asks questions using voice. For example, they might say, "Check my email" or "Change to the next song." This voice input is the starting point for the system's processing. The input is the user's voice data, and the output is the voice data being captured by the device's microphone.

[0919] Step 2:

[0920] The device uses its built-in microphone to capture the user's voice. The captured voice data is converted into text data using the Google Speech-to-Text API. Simultaneously, the IBM Watson Tone Analyzer identifies the user's emotions. The input is the user's voice data, and the output is text data and emotion data.

[0921] Step 3:

[0922] The device sends acquired text data and sentiment data to the server. The text data is used for natural language processing, and the sentiment data is used to understand the user's psychological state. The input is text data and sentiment data, and the output is the transmission of this data to the server.

[0923] Step 4:

[0924] The server uses a natural language processing engine (GPT-4) to analyze text data and recognize the user's intent. For example, it can understand the instruction "Check your email" to mean "Read the electronic message aloud." Simultaneously, an emotion analysis engine analyzes the user's emotions, identifying states such as stress and relaxation. The input consists of text data and emotion data, while the output is data related to the user's intent and emotional state.

[0925] Step 5:

[0926] The server performs appropriate tasks based on the user's intentions and emotions. For example, it accesses an e-messaging service API to retrieve email content. If the user requests directions, it uses a map service API to calculate the optimal route; and if the user wants to change the music, it accesses a music service API to select relaxing music. The input is user intention and emotion data, and the output is the result data of the performed task.

[0927] Step 6:

[0928] The server generates text data that includes user-friendly feedback based on the task's execution results. For example, it might generate something like, "You have a new email. Take care, relax." The input is the task's result data, and the output is the feedback text data to notify the user.

[0929] Step 7:

[0930] The device receives feedback text data from the server, converts it into audio data using a speech synthesis engine (Amazon Polly), and notifies the user through the speaker. This allows the user to obtain information without relying on visual cues. The input is feedback text data, and the output is audio data.

[0931] The above outlines the processing steps from user voice input to the execution of appropriate tasks and the provision of feedback. Specific data processing and calculations are performed at each step, and the results are provided to the user.

[0932] (Application Example 2)

[0933] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0934] Conventional voice recognition systems were limited to converting user voice input into text data and performing tasks based on that data. However, because they performed tasks without considering the user's emotional state, they failed to alleviate user stress and anxiety. Therefore, there is a need for a system that eliminates the stress and inconvenience experienced by drivers and provides a more comfortable and safer driving experience.

[0935] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for analyzing voice tone and timbre to recognize the user's emotional state, means for executing a task based on the recognized intention and emotional state, and means for notifying the user of the execution result by voice. This makes it possible to execute an appropriate task while considering the user's emotional state and to provide a more comfortable and reassuring experience for the user while driving.

[0936] "Means for acquiring voice input" refers to hardware or software for capturing the voice spoken by the user.

[0937] "Means of converting to text data" refers to software or algorithms used to convert acquired voice input into string data.

[0938] "Means of recognizing user intent" refers to a natural language processing engine that analyzes converted text data to understand the content of user instructions and questions.

[0939] "Means of recognizing emotional states" refers to an emotion recognition engine that analyzes the tone and nuances of a voice to identify the user's emotions and psychological state.

[0940] "Means of executing a task" refers to software or system integration functions that execute appropriate instructions based on the user's intentions and emotional state.

[0941] "Means of notifying by voice" refers to speakers or audio systems that convert execution results into voice data using a speech synthesis engine and inform the user.

[0942] "Methods for retrieving email content and generating emotionally sensitive messages" refers to an API for accessing the content of emails requested by the user, and an algorithm for generating notifications that take the user's emotional state into consideration.

[0943] "Means of initiating directions and providing emotionally sensitive guidance" refers to a navigation system that calculates the route to the user's destination and provides driving instructions that take the user's emotional state into consideration.

[0944] This invention is a system that improves driving safety and comfort by acquiring and analyzing user voice input to recognize the user's emotions and providing appropriate feedback. The following describes specific embodiments for implementing this invention.

[0945] System Configuration

[0946] This system primarily uses the following hardware and software.

[0947] 1. Audio capture device (terminal microphone)

[0948] 2. Speech recognition engine (Google Cloud Speech-to-Text API)

[0949] 3. Emotion Recognition Engine (IBM Watson Tone Analyzer)

[0950] 4. Natural Language Processing Engine (OpenAI GPT-3)

[0951] 5. Music service API (Spotify API)

[0952] 6. Text-to-Speech API

[0953] 7. Infotainment system (in-vehicle system)

[0954] Process Overview

[0955] 1. Acquisition of voice input

[0956] The device's microphone captures the user's voice. For example, the user might say, "Change to the next song."

[0957] 2. Speech Recognition and Emotion Recognition

[0958] The device captures audio data and converts it into text data using the Google Cloud Speech-to-Text API. Simultaneously, IBM Watson Tone Analyzer is used to analyze the voice's tone and timbre to recognize the user's emotional state.

[0959] 3. Data transmission

[0960] The device sends text data and sentiment data to the cloud server.

[0961] 4. Natural Language Understanding and Task Execution

[0962] A cloud server uses OpenAI GPT-3 to analyze text data and recognize the user's intent. Simultaneously, it selects appropriate tasks based on the user's emotional state. For example, it might access the Spotify API to select music that will help the user relax.

[0963] 5. Response generation

[0964] The cloud server generates the task execution results as text data and creates a response message that includes feedback that takes the user's emotions into consideration.

[0965] 6. Voice Response

[0966] The terminal receives text data, which is then converted into speech data using a speech synthesis engine, and feedback is sent to the user through the vehicle's speakers. For example, it might announce, "The song has been changed, please relax and enjoy."

[0967] Specific example

[0968] The following shows an example of actual operation.

[0969] Change music

[0970] 1. The user says, "Change to the next song."

[0971] 2. The device acquires the user's voice and converts it into text data using a speech recognition engine (Google Cloud Speech-to-Text API). In addition, an emotion recognition engine (IBM Watson Tone Analyzer) recognizes emotions requiring relaxation from the user's tone of voice.

[0972] 3. The device sends text data and sentiment data to the cloud server.

[0973] 4. The cloud server analyzes the text, recognizes the user's intent, and selects relaxing music from the Spotify API based on their emotional state.

[0974] 5. The cloud server generates a task completion message, "The song has been changed, please relax and enjoy," and sends it to the device.

[0975] 6. The device converts the text data into audio data and notifies the user via the speaker.

[0976] Example of a prompt

[0977] Change to a new song (please select a song that will help the user relax).

[0978] In this way, the system of the present invention can automatically and appropriately respond based on the user's voice input and emotional state, thereby improving driving safety and comfort.

[0979] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0980] Step 1:

[0981] The device's microphone captures the user's voice. For example, if the user says, "Change to the next song," this voice becomes the input. The input voice is saved as digital data.

[0982] Step 2:

[0983] The device captures audio data and converts it into text data using the Google Cloud Speech-to-Text API. In this case, the input is digital audio data, and the output is the text "Change to the next song."

[0984] Step 3:

[0985] The device analyzes the voice tone and timbre of the audio simultaneously with text data using IBM Watson Tone Analyzer to recognize the user's emotional state. The input is digital audio data, and the output is an emotional state tag such as "needs to relax."

[0986] Step 4:

[0987] The device sends text data and sentiment data to the cloud server. In this step, the input is text data and sentiment state data, and the output is the action of sending them to the server.

[0988] Step 5:

[0989] The cloud server analyzes the received text data using OpenAI GPT-3 to recognize the user's intent. The input is the text data "Change to the next song," and the output is the specific instruction "Change to the next song."

[0990] Step 6:

[0991] Based on the emotional data received by the cloud server, the system performs tasks that take appropriate emotions into consideration. Using the Spotify API, it changes the song to a new one. The input is emotional data and the user's intent, and the output is specific song information such as "relaxing music."

[0992] Step 7:

[0993] A cloud server generates the task execution results as text data and creates a response message that takes the user's emotions into consideration. The input is song information and emotion data, and the output is a response message such as "The song has been changed, please relax and enjoy."

[0994] Step 8:

[0995] The device converts text data into speech data using a speech synthesis engine and notifies the user via the speaker. The input is the text data of the response message, and the output is the speech notification to the user.

[0996] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0997] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0998] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0999] [Third Embodiment]

[1000] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1001] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1002] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1003] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[1004] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1005] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1006] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1007] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1008] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1009] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1010] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1011] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[1012] The present invention is a system for assisting drivers who are driving a car alone. It improves driving safety and comfort by acquiring and analyzing the user's voice input and providing appropriate feedback. Specific embodiments of the present invention will be described below.

[1013] Explanation of the program's processing

[1014] 1. Acquisition of voice input

[1015] The user can give voice commands or ask questions while driving. For example, they can give voice commands such as "Check my email" or "Change to the next song."

[1016] 2. Speech Recognition

[1017] The device captures the user's voice through the microphone, and a speech recognition engine converts the voice into text data. This text data is then used for subsequent processing.

[1018] 3. Natural language understanding

[1019] The device sends the acquired text data to the server. The server uses a natural language processing engine to analyze the text data and recognize the user's intent. For example, if the instruction is "Check your email," the server recognizes the intent as "Read the email aloud."

[1020] 4. Execute the task

[1021] The server performs appropriate tasks based on the recognized user's intent. For example, it accesses the email service API to retrieve the latest email content. For route guidance, it uses the map API to calculate the route; and for changing music, it uses the music service API to change the song.

[1022] 5. Response generation

[1023] The server generates the task execution results as text data and sends it to the terminal.

[1024] 6. Voice Response

[1025] The terminal receives text data, which is then converted into speech data using a speech synthesis engine and communicated to the user via the speaker. This allows the user to obtain necessary information while concentrating on driving.

[1026] Specific example

[1027] Email confirmation

[1028] 1. The user says, "I want to check for new emails."

[1029] 2. The device acquires the user's voice and converts it into text data using a speech recognition engine.

[1030] 3. The terminal sends text data to the server.

[1031] 4. The server analyzes the text to understand the user's intent.

[1032] 5. The server accesses the email service API to retrieve the latest emails.

[1033] 6. The server generates the email content as text data and sends it to the terminal.

[1034] 7. The device converts the text data into audio data and reads aloud from the speaker, "You have a new email. The subject is 'Meeting Reminder,' and the content is 'The meeting is tomorrow at 10:00.'"

[1035] Start of directions

[1036] 1. The user says, "Tell me the way to my next destination."

[1037] 2. The device acquires the user's voice and converts it into text data using a speech recognition engine.

[1038] 3. The terminal sends text data to the server.

[1039] 4. The server analyzes the text to understand the user's intent.

[1040] 5. The server accesses the map API and calculates the optimal route.

[1041] 6. The server generates the route guidance content as text data and sends it to the terminal.

[1042] 7. The device converts the text data into audio data and reads aloud from the speaker, "Next, turn left."

[1043] Change music

[1044] 1. The user says, "Change to the next song."

[1045] 2. The device acquires the user's voice and converts it into text data using a speech recognition engine.

[1046] 3. The terminal sends text data to the server.

[1047] 4. The server analyzes the text to understand the user's intent.

[1048] 5. The server accesses the music service API and changes to the next song.

[1049] 6. The server generates a text file confirming the changes have been made and sends it to the terminal.

[1050] 7. The device converts the text data into audio data and reads aloud from the speaker, "The song has been changed."

[1051] As described above, the system of the present invention enhances driving safety and comfort by automatically executing various tasks based on the user's voice input and notifying the results by voice.

[1052] The following describes the processing flow.

[1053] Step 1:

[1054] The user says, "I want to check for new emails."

[1055] Step 2:

[1056] The device captures the user's voice using its built-in microphone.

[1057] Step 3:

[1058] The device sends voice data to the speech recognition engine, which converts it into text data.

[1059] Step 4:

[1060] The device sends the converted text data "I want to check for new emails" to the server.

[1061] Step 5:

[1062] The server receives text data and uses a natural language processing engine to analyze the user's intent.

[1063] Step 6:

[1064] The server recognizes the intention to "check email" and generates a request to access the email service API.

[1065] Step 7:

[1066] The server sends a request to the email service API to retrieve the latest email data.

[1067] Step 8:

[1068] The server analyzes the email data and generates text to be read aloud to notify the user.

[1069] Step 9:

[1070] The server sends the generated text "You have a new email. The subject is 'Meeting Reminder', and the content is 'The meeting is tomorrow at 10:00'" to the terminal.

[1071] Step 10:

[1072] The terminal receives text data, which is then sent to the speech synthesis engine to generate speech data.

[1073] Step 11:

[1074] The device plays the generated audio data through its speaker and notifies the user, "You have a new email. The subject is 'Meeting Reminder,' and the content is 'The meeting is tomorrow at 10:00.'"

[1075] (Example 1)

[1076] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1077] In recent years, there has been a growing demand for technologies that improve the safety and comfort of drivers operating vehicles alone. In particular, there is a need for systems that allow drivers to perform various operations using only their voice, without using their hands while driving. However, conventional systems have suffered from low accuracy in voice recognition and significant delays between voice input and actual task execution, making them impractical. There is a need to provide a new system that overcomes these challenges and achieves highly sensitive voice recognition and rapid task execution.

[1078] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1079] In this invention, the server includes means for acquiring voice input, means for converting the acquired voice input into text data, and means for analyzing the text data with a natural language processing engine. This makes it possible to capture the user's voice with a high-sensitivity microphone and analyze the text data with high accuracy and speed. It also includes means for executing tasks based on recognized intent and means for notifying the results by voice, and further includes means for accessing a map service API for route guidance and a music service API for changing music, thereby improving driver safety and comfort.

[1080] A "user" refers to an individual who uses the system to perform voice input.

[1081] "Means for acquiring voice input" refers to devices or technologies that capture the user's voice using a microphone and incorporate that data into the system.

[1082] "Means of converting acquired voice input into text data" refers to technologies and software that use a speech recognition engine to convert voice data into text information.

[1083] "Methods for analyzing text data to recognize user intent" refers to technologies that use a natural language processing engine to process text data and understand the content of user statements.

[1084] "Means of executing a task" refers to servers and software that perform specific operations or give instructions based on the user's intentions.

[1085] "Means of notifying the user of execution results via voice" refers to a technology that converts the results of an executed task into voice data using a speech synthesis engine and communicates it to the user through a speaker.

[1086] A "high-sensitivity microphone" refers to a microphone device that has the ability to effectively filter out noise during driving and capture clear voice input.

[1087] A "natural language processing engine" refers to software or algorithms that analyze text data, understand its context and meaning, and extract the user's intent.

[1088] A "database" refers to a system for efficiently storing and managing acquired and analyzed data.

[1089] A "map service API" refers to an interface with an external map service for providing route guidance.

[1090] A "music service API" refers to an interface with external music services for playing and modifying music.

[1091] "Text data" refers to character information converted from voice input by a speech recognition engine.

[1092] "Audio data" refers to audio information generated by a speech synthesis engine and played back to the user through a speaker.

[1093] "API" is an abbreviation for Application Programming Interface, and refers to the definitions and protocols that enable communication between different software programs.

[1094] Modes for carrying out the invention

[1095] The present invention is a system for assisting drivers who are driving a car alone. It improves driving safety and comfort by acquiring and analyzing user voice input and providing appropriate feedback. Specific embodiments of the present invention will be described below.

[1096] System Configuration

[1097] The system of the present invention consists of the following main components.

[1098] 1. Means for obtaining user voice input:

[1099] The user gives instructions and asks questions by voice while driving. Specifically, a high-sensitivity microphone installed in the vehicle is used to capture the voice. The high-sensitivity microphone effectively filters out noise during driving, ensuring clear voice input.

[1100] 2. Means for converting acquired voice input into text data:

[1101] The device captures the user's voice through the microphone and converts the voice into text data using a speech recognition engine (e.g., Microsoft Azure Speech SDK or Google Cloud Speech-to-Text).

[1102] 3. Means of analyzing text data to recognize user intent:

[1103] The device sends the acquired text data to the server. The server analyzes the text data using a generative AI model (e.g., OpenAI GPT-3) to recognize the user's intent.

[1104] 4. Means of performing tasks based on recognized intentions:

[1105] The server performs the appropriate task based on the recognized user's intent. For example, to retrieve email content, it accesses the Gmail API to retrieve the latest email. For route guidance, it uses the Google Maps API to calculate the optimal route, and for changing music, it accesses the Music Services API to change the song.

[1106] 5. Means of notifying the user of the execution results by voice:

[1107] The server generates the task execution results as text data and sends it to the terminal. The terminal converts the received text data into audio data using a speech synthesis engine (e.g., Amazon Polly) and notifies the user through the speaker.

[1108] Specific example

[1109] The following are specific usage scenarios.

[1110] Email confirmation

[1111] 1. The user says, "I want to check for new emails."

[1112] 2. The device captures the user's voice and converts it into text data using Google Cloud Speech-to-Text.

[1113] 3. The terminal sends text data to the server.

[1114] 4. The server uses OpenAI GPT-3 to analyze the text and recognize the user's intent.

[1115] 5. The server accesses the Gmail API to retrieve the latest emails.

[1116] 6. The server generates the email content as text data and sends it to the terminal.

[1117] 7. The device uses Amazon Polly to convert the text data into audio data, and the speaker reads aloud, "You have a new email. The subject is 'Meeting Reminder,' and the content is 'The meeting is tomorrow at 10am.'"

[1118] Start of directions

[1119] 1. The user says, "Tell me the way to my next destination."

[1120] 2. The device captures the user's voice and converts it into text data using the Microsoft Azure Speech SDK.

[1121] 3. The terminal sends text data to the server.

[1122] 4. The server uses OpenAI GPT-3 to analyze the text and recognize the user's intent.

[1123] 5. The server accesses the Google Maps API and calculates the optimal route.

[1124] 6. The server generates the route guidance content as text data and sends it to the terminal.

[1125] 7. The device uses Amazon Polly to convert the text data into audio data and reads aloud from the speaker, "Turn left next."

[1126] Change music

[1127] 1. The user says, "Change to the next song."

[1128] 2. The device captures the user's voice and converts it into text data using Google Cloud Speech-to-Text.

[1129] 3. The terminal sends text data to the server.

[1130] 4. The server uses OpenAI GPT-3 to analyze the text and recognize the user's intent.

[1131] 5. The server accesses the Spotify API and changes to the next song.

[1132] 6. The server generates a text file confirming the changes have been made and sends it to the terminal.

[1133] 7. The device uses Amazon Polly to convert the text data into audio data and reads aloud from the speaker, "The song has been changed."

[1134] As described above, the system of the present invention automatically performs various tasks based on the user's voice input and notifies the user of the results by voice, thereby improving driving safety and comfort.

[1135] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1136] Step 1: Obtaining voice input

[1137] The user can give instructions and ask questions by voice while driving. For example, they can give voice commands such as "Check my email" or "Change to the next song." The input voice is captured by a high-sensitivity microphone installed in the car. The high-sensitivity microphone filters out noise during driving, ensuring a clear voice input. The output is the captured raw voice data.

[1138] Step 2: Speech Recognition

[1139] The device passes the captured raw audio data to a speech recognition engine such as the Microsoft Azure Speech SDK or Google Cloud Speech-to-Text. The input is the raw audio data from step 1. The speech recognition engine analyzes the audio data and converts it into corresponding text data. The output is this text data, which is used in the next processing step.

[1140] Step 3: Send text data

[1141] The terminal sends the acquired text data to the server. The input is text data. The terminal securely transfers the text data to the server via the network, where further processing continues. The output is the text data sent to the server.

[1142] Step 4: Natural Language Comprehension

[1143] The server analyzes text data using a generative AI model such as OpenAI GPT-3. The input is the text data received in step 3. The server analyzes the text data to identify the user's intent. For example, the instruction "check my email" is recognized as having the intent "read the email aloud." In terms of data processing, the text data is analyzed based on context, and the intent behind the user's request is extracted. The output is user intent information, which is used in the next task execution step.

[1144] Step 5: Execute the task

[1145] The server performs the appropriate task based on the user's intent. The input is the user's intent information from step 4. For example, to retrieve email content, it accesses the Gmail API to retrieve the latest email. For route guidance, it uses the Google Maps API to calculate the optimal route, and for changing music, it uses the Music Services API to change the song. Each API call performs data retrieval or calculation. The output is the result data of the task execution.

[1146] Step 6: Generating the response

[1147] The server generates the task execution result as text data and sends it to the terminal. The input is the task execution result data from step 5. Based on the execution result, the server generates a text message to notify the user. The output is the text data of the notification message sent to the terminal.

[1148] Step 7: Voice Response

[1149] The text data received by the device is converted into speech data by a speech synthesis engine such as Amazon Polly. The input is the text data of the notification message from step 6. The speech synthesis engine analyzes the text data and generates the corresponding natural speech. The generated speech data is notified to the user through the speaker. For example, it might read aloud, "You have a new email. The subject is 'Meeting Reminder,' and the content is 'The meeting is tomorrow at 10am.'" The output is the speech data that is played back to the user.

[1150] (Application Example 1)

[1151] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1152] In autonomous vehicles, a system is needed that allows users to efficiently perform tasks using voice commands while driving and to obtain information safely and comfortably. Conventional systems have problems such as low accuracy in voice input, slow recognition of intent, and data acquisition, which fail to adequately ensure user convenience. There is also room for improvement in acquiring real-time data from external sources and providing feedback.

[1153] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1154] In this invention, the server includes means for acquiring voice input, means for converting the acquired voice input into text data, means for analyzing the text data to recognize the user's intent, means for acquiring data from external information sources, and means for analyzing the acquired data to provide appropriate feedback to the user. This enables the user to efficiently perform tasks using voice while driving and to obtain necessary information in real time in a safe and comfortable manner.

[1155] A "user" refers to a person who uses the system.

[1156] "Voice input" refers to the act of a user providing instructions or information to a system using their voice.

[1157] "Means of acquisition" refers to methods and technologies for receiving, recording, or capturing information such as audio or data.

[1158] "Means of converting audio to text data" refers to methods and technologies for converting audio data into text-based data.

[1159] "Text data" refers to data expressed as character information.

[1160] "Means of analysis" refers to methods and techniques for analyzing acquired data and extracting specific information or patterns.

[1161] "User intent" refers to the actions a user wants to perform on the system or the information they are seeking.

[1162] "Means of executing a task" refers to methods and technologies for performing specific operations or processes based on the user's intent.

[1163] "Execution results" refer to the outcomes and information obtained after completing a task.

[1164] "External information sources" refer to information providers located outside the system. For example, these include third-party services or databases that provide weather information or news.

[1165] "Means of acquiring data" refers to the methods and technologies used to obtain necessary data from external sources.

[1166] "Means of providing feedback" refers to methods and techniques for providing users with information or the results of instructions.

[1167] "Real-time" refers to the immediate processing and provision of information at the moment an event occurs.

[1168] This invention relates to a system for autonomous vehicles that acquires user voice input, analyzes their intent, and provides appropriate feedback. This system assists users in driving safely and comfortably.

[1169] The system program is implemented using the following hardware and software.

[1170] hardware

[1171] 1. Smartphone: Captures the user's voice input and converts the voice into text data.

[1172] 2. Server: Analyzes text data and recognizes the user's intent.

[1173] 3. External information sources: Databases and third-party services that provide weather information, news, etc.

[1174] software

[1175] 1. Speech recognition engine: For example, use Google's speech recognition API to convert speech to text.

[1176] 2. Natural Language Processing Engine: For example, use Amazon Comprehend to analyze text data and recognize user intent.

[1177] 3. Data acquisition API: For example, use an API to acquire weather information.

[1178] Processing procedures and data calculations

[1179] 1. Acquisition of voice input

[1180] The user gives instructions or asks questions by voice while driving. The smartphone's microphone captures this and sends it to the voice recognition engine.

[1181] 2. Speech Recognition

[1182] The speech recognition engine converts the speech into text data. This text data is then sent to the server.

[1183] 3. Natural language understanding

[1184] The server receives text data and analyzes it using a natural language processing engine. For example, if given the instruction "Tell me today's weather," the server recognizes that it needs to retrieve weather information.

[1185] 4. Data Acquisition

[1186] The server accesses external information sources and retrieves the necessary data (e.g., weather information).

[1187] 5. Response generation

[1188] Based on the acquired data, the server generates feedback to be provided to the user. This feedback is then sent to the smartphone as text data.

[1189] 6. Voice Response

[1190] Text data is sent to a speech synthesis engine and converted into speech data. Feedback is then provided to the user through the smartphone's speaker.

[1191] Specific example

[1192] Example 1: Obtaining weather information

[1193] 1. The user says, "Tell me today's weather."

[1194] 2. The smartphone captures the audio, and the speech recognition engine converts it into text data.

[1195] 3. The server receives the text data and analyzes it using a natural language processing engine.

[1196] 4. The server accesses the weather information API to obtain the latest weather information.

[1197] 5. The server generates weather information as text data and sends it to the smartphone.

[1198] 6. The smartphone converts the text data into audio data and announces "Today's weather is sunny" through the speaker.

[1199] Example of a prompt:

[1200] When a user says, "Tell me today's weather," a smartphone application recognizes the voice and analyzes the text data using a natural language processing engine. It then calls a weather forecast API to obtain real-time weather information and notifies the user as audio data.

[1201] Thus, the system of the present invention is configured to support the user's safe and comfortable driving.

[1202] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1203] Step 1:

[1204] The user inputs instructions by voice. For example, they might say, "Tell me today's weather." This inputs the information the user wants to know or the task they want to perform as voice data into the device.

[1205] Step 2:

[1206] The device captures the user's voice data and converts it into text data using a speech recognition engine. Specifically, the smartphone's microphone captures the voice and sends it to an API that converts the voice data into text. In this case, the input is voice data and the output is text data.

[1207] Step 3:

[1208] Text data is sent from the terminal to the server. The server passes the received text data to a natural language processing engine to analyze the user's intent. A natural language processing engine (e.g., Amazon Comprehend) is used here. The input is text data, and the output is data indicating the user's intent.

[1209] Step 4:

[1210] The server retrieves data from appropriate external sources based on the user's intent. For example, to retrieve weather information, it accesses a weather information API to obtain the latest weather data. The input is data indicating the user's intent, and the output is the retrieved weather information data.

[1211] Step 5:

[1212] The server analyzes the acquired data and generates feedback to provide to the user. For example, weather information is formatted as text data such as "Today's weather is sunny." The input is the acquired information data, and the output is formatted text data to be provided to the user.

[1213] Step 6:

[1214] Formatted text data is sent from the server to the terminal. The terminal receives this data and converts it into speech data using a speech synthesis engine. Specifically, a speech synthesis engine (e.g., gTTS) is used to play the text as speech. The input is formatted text data, and the output is speech data.

[1215] Step 7:

[1216] The device plays the generated audio data through its speaker and notifies the user. This allows the user to receive necessary information in real time via voice, even while driving. The input is audio data, and the output is an audio notification to the user.

[1217] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1218] The present invention is a system for assisting drivers who are driving a car alone. It improves driving safety and comfort by acquiring and analyzing the user's voice input, recognizing the user's emotions, and providing appropriate feedback. Specific embodiments of the present invention will be described below.

[1219] Explanation of the program's processing

[1220] 1. Acquisition of voice input

[1221] The user can give voice commands or ask questions while driving. For example, they can give voice commands such as "Check my email" or "Change to the next song."

[1222] 2. Speech Recognition and Emotion Recognition

[1223] The device captures the user's voice using its built-in microphone, and a speech recognition engine converts the voice into text data. This text data is used for subsequent processing. Simultaneously, an emotion engine recognizes the user's emotions from the voice data.

[1224] 3. Natural Language Understanding and Emotion Understanding

[1225] The device sends the acquired text data and sentiment data to the server. The server uses a natural language processing engine to analyze the text data and recognize the user's intent. For example, in the case of the instruction "Check your email," the server recognizes the intention to "read the email aloud," and the sentiment engine also recognizes that the user is feeling stressed.

[1226] 4. Execute the task

[1227] The server performs appropriate tasks based on the recognized user's intentions and emotions. For example, it accesses the email service API to retrieve the latest emails to get the content of emails. For route guidance, it uses a map API to calculate the route, and for music changes, it considers the user's emotions and selects relaxing music.

[1228] 5. Response generation

[1229] The server generates the task execution results as text data and sends a message to the terminal that includes content that takes the user's feelings into consideration.

[1230] 6. Voice Response

[1231] The terminal receives text data, which is then converted into speech data using a speech synthesis engine and communicated to the user via the speaker. This allows the user to obtain necessary information while concentrating on driving.

[1232] Specific example

[1233] Email confirmation

[1234] 1. The user says, "I want to check for new emails."

[1235] 2. The device acquires the user's voice and converts it into text data using a speech recognition engine. Simultaneously, an emotion engine recognizes the user's stress level based on their tone of voice.

[1236] 3. The device sends text data and sentiment data to the server.

[1237] 4. The server analyzes the text to understand the user's intent and confirm that they are experiencing stress.

[1238] 5. The server accesses the email service API to retrieve the latest emails.

[1239] 6. The server generates and sends a text message to the terminal that reads, "You have a new email. Take care and relax. The subject is 'Meeting Reminder' and the content is 'The meeting is tomorrow at 10am'."

[1240] 7. The device converts the text data into audio data and reads aloud from the speaker: "You have a new email. Take care, relax. The subject is 'Meeting Reminder,' and the content is 'The meeting is tomorrow at 10am.'"

[1241] Start of directions

[1242] 1. The user says, "Tell me the way to my next destination."

[1243] 2. The device acquires the user's voice and converts it into text data using a speech recognition engine. Simultaneously, an emotion engine recognizes the user's emotional state.

[1244] 3. The device sends text data and sentiment data to the server.

[1245] 4. The server analyzes the text to recognize the user's intent and considers appropriate guidance that reflects their emotional state.

[1246] 5. The server accesses the map API and calculates the optimal route.

[1247] 6. The server generates the route guidance content as text data and sends it to the terminal.

[1248] 7. The device converts the text data into audio data and reads aloud from the speaker, "Please turn left next. Drive carefully."

[1249] Change music

[1250] 1. The user says, "Change to the next song."

[1251] 2. The device acquires the user's voice and converts it into text data using a speech recognition engine. Simultaneously, an emotion engine recognizes the user's emotional state.

[1252] 3. The device sends text data and sentiment data to the server.

[1253] 4. The server analyzes the text to recognize the user's intent and selects relaxing music that takes their emotional state into consideration.

[1254] 5. The server accesses the music service API and changes to the next song.

[1255] 6. The server generates a text file confirming the changes have been made and sends it to the terminal.

[1256] 7. The device converts the text data into audio data and reads aloud from the speaker, "The song has been changed, please relax and enjoy."

[1257] As described above, the system of the present invention enhances driving safety and comfort by providing appropriate responses based on the user's voice input and emotional state.

[1258] The following describes the processing flow.

[1259] Specific examples of email confirmation

[1260] Voice input acquisition and recognition

[1261] Step 1:

[1262] The user says, "I want to check for new emails."

[1263] Step 2:

[1264] The device captures the user's voice using its built-in microphone.

[1265] Step 3:

[1266] The device sends voice data to the speech recognition engine, which converts it into text data.

[1267] Step 4:

[1268] The device uses an emotion engine to extract user emotion data from the voice.

[1269] Data transmission and analysis

[1270] Step 5:

[1271] The device sends the converted text data and sentiment data to the server.

[1272] Step 6:

[1273] The server receives text data and uses a natural language processing engine to analyze the user's intent.

[1274] Step 7:

[1275] The server uses an emotion engine to analyze the user's emotions and recognize that they are "feeling stressed."

[1276] Task execution and response generation

[1277] Step 8:

[1278] The server recognizes the intention to "check email" and sends a request to the email service API.

[1279] Step 9:

[1280] The server receives the latest email data from the email service API.

[1281] Step 10:

[1282] The server analyzes the email data and generates text for reading aloud that takes the user's emotions into consideration.

[1283] Step 11:

[1284] The server sends the generated text data "You have a new email. Take care and relax. Subject: 'Meeting Reminder', Content: 'The meeting is tomorrow at 10:00'" to the terminal.

[1285] Voice response

[1286] Step 12:

[1287] The terminal receives text data, which is then sent to a speech synthesis engine and converted into speech data.

[1288] Step 13:

[1289] The device plays the generated audio data through the speaker and notifies the user, "You have a new email. Take care and relax. The subject is 'Meeting Reminder,' and the content is 'The meeting is tomorrow at 10:00.'"

[1290] A concrete example of starting directions

[1291] Voice input acquisition and recognition

[1292] Step 1:

[1293] The user asks, "Tell me the way to my next destination."

[1294] Step 2:

[1295] The device captures the user's voice using its built-in microphone.

[1296] Step 3:

[1297] The device sends voice data to the speech recognition engine, which converts it into text data.

[1298] Step 4:

[1299] The device uses an emotion engine to extract user emotion data from the voice.

[1300] Data transmission and analysis

[1301] Step 5:

[1302] The device sends the converted text data and sentiment data to the server.

[1303] Step 6:

[1304] The server receives text data and uses a natural language processing engine to analyze the user's intent.

[1305] Step 7:

[1306] The server uses an emotion engine to analyze the user's emotions and recognizes that they are in a relaxed state.

[1307] Task execution and response generation

[1308] Step 8:

[1309] The server recognizes the intention to "start directions" and sends a request to the map API.

[1310] Step 9:

[1311] The server receives route information from the map API.

[1312] Step 10:

[1313] The server analyzes route information and generates emotionally sensitive guidance text.

[1314] Step 11:

[1315] The server generates text data, "Turn left next, drive carefully," and sends it to the terminal.

[1316] Voice response

[1317] Step 12:

[1318] The terminal receives text data, which is then sent to a speech synthesis engine and converted into speech data.

[1319] Step 13:

[1320] The device generates audio data which is played through the speaker, instructing the user with messages such as, "Please turn left next. Drive carefully."

[1321] Examples of music changes

[1322] Voice input acquisition and recognition

[1323] Step 1:

[1324] The user says, "Change to the next song."

[1325] Step 2:

[1326] The device captures the user's voice using its built-in microphone.

[1327] Step 3:

[1328] The device sends voice data to the speech recognition engine, which converts it into text data.

[1329] Step 4:

[1330] The device uses an emotion engine to extract user emotion data from the voice.

[1331] Data transmission and analysis

[1332] Step 5:

[1333] The device sends the converted text data and sentiment data to the server.

[1334] Step 6:

[1335] The server receives text data and uses a natural language processing engine to analyze the user's intent.

[1336] Step 7:

[1337] The server uses an emotion engine to analyze the user's emotions and recognizes that they are in a relaxed state.

[1338] Task execution and response generation

[1339] Step 8:

[1340] The server recognizes the intention to "change the music" and sends a request to the music service API.

[1341] Step 9:

[1342] The server receives the next song data from the music service API.

[1343] Step 10:

[1344] The server analyzes the data for the next song and generates a notification text that takes emotions into consideration.

[1345] Step 11:

[1346] The server sends the generated text data "The song has been changed, please relax and enjoy" to the terminal.

[1347] Voice response

[1348] Step 12:

[1349] The terminal receives text data, which is then sent to a speech synthesis engine and converted into speech data.

[1350] Step 13:

[1351] The device plays the generated audio data through the speaker and notifies the user with the message, "The song has been changed, please relax and enjoy."

[1352] As described above, the system of the present invention enhances driving safety and comfort by providing appropriate responses based on the user's voice input and emotional state.

[1353] (Example 2)

[1354] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1355] Conventional driver assistance systems, while capable of receiving user voice commands and executing tasks, failed to adequately improve driving safety and comfort by failing to provide feedback or operations that considered the user's emotions. Furthermore, one-sided notifications and instructions that disregard the user's feelings could amplify user stress, potentially negatively impacting driving concentration and safety. To solve these problems, a system was needed that could identify not only the user's intent based on their voice but also their emotions, and provide appropriate feedback accordingly.

[1356] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring voice input, means for converting voice to text data, means for recognizing the user's intent from the text data, means for identifying emotions from the user's voice data, means for executing a task based on the user's intent and emotions, and means for notifying the user of the task execution result by voice in a manner that takes the user's emotions into consideration. This makes it possible to respond appropriately not only to the user's intent but also to their emotions, thereby improving safety and comfort while driving.

[1357] "Means for acquiring voice input" refer to microphones or voice capture devices that capture user speech in real time.

[1358] "Methods for converting audio to text data" refer to a system that uses a speech recognition engine to convert captured audio into text strings.

[1359] "Means of recognizing user intent from text data" refers to a system that uses natural language processing technology to analyze and understand user requests and instructions from converted text data.

[1360] "Methods for identifying emotions from voice data" refer to systems that use an emotion recognition engine to determine the user's emotional state from the tone and patterns of their voice.

[1361] "Means for executing a task" refer to operational means for performing relevant actions based on the analyzed user's intentions and emotions. For example, this could involve retrieving the content of an electronic message or initiating route guidance.

[1362] "Means for notifying execution results by voice" refers to a system consisting of a speech synthesis engine and a speaker for providing voice feedback to the user on the execution results of a task.

[1363] "Means for obtaining the content of electronic messages" refers to APIs and service access mechanisms that retrieve email content from various email services in response to user instructions.

[1364] A "means for initiating route guidance" refers to a system that provides the optimal route based on user instructions, utilizing map data and navigation systems.

[1365] This invention is a system for assisting drivers who are driving a car alone. It improves driving safety and comfort by acquiring and analyzing the user's voice input, recognizing the user's emotions, and providing appropriate feedback.

[1366] System Overview

[1367] This system functions through communication between the user, a terminal in the vehicle, and a central server. Specifically, the terminal is equipped with a built-in microphone to acquire voice input from the user. This voice data is converted into text data via a speech recognition engine, and the user's emotions are further identified by an emotion recognition engine. This data is sent to the server, where a natural language processing engine and an emotion analysis engine are used to understand the user's intentions and emotions and execute the task. The server then sends the execution results back to the terminal, which uses a speech synthesis engine to notify the user.

[1368] Hardware and software to be used

[1369] Terminal (in-car device): Built-in microphone, speaker

[1370] Server: High-performance processor, Internet connection

[1371] Speech recognition engine: Google Speech-to-Text API

[1372] Emotion recognition engine: IBM Watson Tone Analyzer

[1373] Natural language processing engine: OpenAI GPT-4

[1374] Speech synthesis engine: Amazon Polly

[1375] Various APIs: Gmail API, Google Maps API, Spotify API

[1376] Specific example

[1377] Email confirmation

[1378] 1. The user says, "I want to check for new emails."

[1379] 2. The device captures the user's voice using its built-in microphone and converts it into text data using the Google Speech-to-Text API. Simultaneously, it recognizes the user's emotions using IBM Watson Tone Analyzer.

[1380] 3. The device sends text data and sentiment data to the server.

[1381] 4. The server uses GPT-4 to parse the text and confirm the intention to "read the email aloud" and whether the user is experiencing stress.

[1382] 5. The server accesses the Gmail API to retrieve the latest emails.

[1383] 6. The server generates and sends a text message to the terminal that reads, "You have a new email. Take care and relax. The subject is 'Meeting Reminder' and the content is 'The meeting is tomorrow at 10am'."

[1384] 7. The device uses Amazon Polly to convert the text data into audio data, and the speaker reads aloud, "You have a new email. Take care, relax. The subject is 'Meeting Reminder,' and the content is 'The meeting is tomorrow at 10am.'"

[1385] Start of directions

[1386] 1. The user says, "Tell me the way to my next destination."

[1387] 2. The device captures the user's voice using its built-in microphone and converts it into text data using the Google Speech-to-Text API. Simultaneously, it recognizes the user's emotions using IBM Watson Tone Analyzer.

[1388] 3. The device sends text data and sentiment data to the server.

[1389] 4. The server uses GPT-4 to analyze the user's intent and accesses the Google Maps API to calculate the optimal route.

[1390] 5. The server generates route guidance content as text data and considers guidance that includes considerations to help the user relax.

[1391] 6. The server sends this text data to the terminal.

[1392] 7. The device uses Amazon Polly to convert the message into audio data and reads it aloud from the speaker, saying, "Please turn left next. Drive carefully."

[1393] Change music

[1394] 1. The user says, "Change to the next song."

[1395] 2. The device captures the user's voice using its built-in microphone and converts it into text data using the Google Speech-to-Text API. Simultaneously, the IBM Watson Tone Analyzer recognizes the user's emotional state.

[1396] 3. The device sends text data and sentiment data to the server.

[1397] 4. The server uses GPT-4 to analyze the user's intent and accesses the Spotify API to select relaxing music.

[1398] 5. The server generates the selected items as text data and sends it to the terminal.

[1399] 6. The device uses Amazon Polly to generate audio data, and the speaker reads out, "The song has been changed, please relax and enjoy."

[1400] Through the detailed processing described above, this system can improve driving safety and comfort by responding appropriately based on the user's voice commands and emotional state.

[1401] Example of a prompt

[1402] "Please describe the process for checking new emails while driving. Provide detailed instructions using appropriate APIs and sentiment recognition."

[1403] "Please describe in detail the process for initiating route guidance to the next destination. Please include guidance that takes user emotions into consideration."

[1404] "Please explain the procedure for changing songs while driving. Clearly demonstrate the process of selecting songs based on the user's emotional state."

[1405] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1406] Step 1:

[1407] The user gives instructions or asks questions using voice. For example, they might say, "Check my email" or "Change to the next song." This voice input is the starting point for the system's processing. The input is the user's voice data, and the output is the voice data being captured by the device's microphone.

[1408] Step 2:

[1409] The device uses its built-in microphone to capture the user's voice. The captured voice data is converted into text data using the Google Speech-to-Text API. Simultaneously, the IBM Watson Tone Analyzer identifies the user's emotions. The input is the user's voice data, and the output is text data and emotion data.

[1410] Step 3:

[1411] The device sends acquired text data and sentiment data to the server. The text data is used for natural language processing, and the sentiment data is used to understand the user's psychological state. The input is text data and sentiment data, and the output is the transmission of this data to the server.

[1412] Step 4:

[1413] The server uses a natural language processing engine (GPT-4) to analyze text data and recognize the user's intent. For example, it can understand the instruction "Check your email" to mean "Read the electronic message aloud." Simultaneously, an emotion analysis engine analyzes the user's emotions, identifying states such as stress and relaxation. The input consists of text data and emotion data, while the output is data related to the user's intent and emotional state.

[1414] Step 5:

[1415] The server performs appropriate tasks based on the user's intentions and emotions. For example, it accesses an e-messaging service API to retrieve email content. If the user requests directions, it uses a map service API to calculate the optimal route; and if the user wants to change the music, it accesses a music service API to select relaxing music. The input is user intention and emotion data, and the output is the result data of the performed task.

[1416] Step 6:

[1417] The server generates text data that includes user-friendly feedback based on the task's execution results. For example, it might generate something like, "You have a new email. Take care, relax." The input is the task's result data, and the output is the feedback text data to notify the user.

[1418] Step 7:

[1419] The device receives feedback text data from the server, converts it into audio data using a speech synthesis engine (Amazon Polly), and notifies the user through the speaker. This allows the user to obtain information without relying on visual cues. The input is feedback text data, and the output is audio data.

[1420] The above outlines the processing steps from user voice input to the execution of appropriate tasks and the provision of feedback. Specific data processing and calculations are performed at each step, and the results are provided to the user.

[1421] (Application Example 2)

[1422] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1423] Conventional voice recognition systems were limited to converting user voice input into text data and performing tasks based on that data. However, because they performed tasks without considering the user's emotional state, they failed to alleviate user stress and anxiety. Therefore, there is a need for a system that eliminates the stress and inconvenience experienced by drivers and provides a more comfortable and safer driving experience.

[1424] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for analyzing voice tone and timbre to recognize the user's emotional state, means for executing a task based on the recognized intention and emotional state, and means for notifying the user of the execution result by voice. This makes it possible to execute an appropriate task while considering the user's emotional state and to provide a more comfortable and reassuring experience for the user while driving.

[1425] "Means for acquiring voice input" refers to hardware or software for capturing the voice spoken by the user.

[1426] "Means of converting to text data" refers to software or algorithms used to convert acquired voice input into string data.

[1427] "Means of recognizing user intent" refers to a natural language processing engine that analyzes converted text data to understand the content of user instructions and questions.

[1428] "Means of recognizing emotional states" refers to an emotion recognition engine that analyzes the tone and nuances of a voice to identify the user's emotions and psychological state.

[1429] "Means of executing a task" refers to software or system integration functions that execute appropriate instructions based on the user's intentions and emotional state.

[1430] "Means of notifying by voice" refers to speakers or audio systems that convert execution results into voice data using a speech synthesis engine and inform the user.

[1431] "Methods for retrieving email content and generating emotionally sensitive messages" refers to an API for accessing the content of emails requested by the user, and an algorithm for generating notifications that take the user's emotional state into consideration.

[1432] "Means of initiating directions and providing emotionally sensitive guidance" refers to a navigation system that calculates the route to the user's destination and provides driving instructions that take the user's emotional state into consideration.

[1433] This invention is a system that improves driving safety and comfort by acquiring and analyzing user voice input to recognize the user's emotions and providing appropriate feedback. The following describes specific embodiments for implementing this invention.

[1434] System Configuration

[1435] This system primarily uses the following hardware and software.

[1436] 1. Audio capture device (terminal microphone)

[1437] 2. Speech recognition engine (Google Cloud Speech-to-Text API)

[1438] 3. Emotion Recognition Engine (IBM Watson Tone Analyzer)

[1439] 4. Natural Language Processing Engine (OpenAI GPT-3)

[1440] 5. Music service API (Spotify API)

[1441] 6. Text-to-Speech API

[1442] 7. Infotainment system (in-vehicle system)

[1443] Process Overview

[1444] 1. Acquisition of voice input

[1445] The device's microphone captures the user's voice. For example, the user might say, "Change to the next song."

[1446] 2. Speech Recognition and Emotion Recognition

[1447] The device captures audio data and converts it into text data using the Google Cloud Speech-to-Text API. Simultaneously, IBM Watson Tone Analyzer is used to analyze the voice's tone and timbre to recognize the user's emotional state.

[1448] 3. Data transmission

[1449] The device sends text data and sentiment data to the cloud server.

[1450] 4. Natural Language Understanding and Task Execution

[1451] A cloud server uses OpenAI GPT-3 to analyze text data and recognize the user's intent. Simultaneously, it selects appropriate tasks based on the user's emotional state. For example, it might access the Spotify API to select music that will help the user relax.

[1452] 5. Response generation

[1453] The cloud server generates the task execution results as text data and creates a response message that includes feedback that takes the user's emotions into consideration.

[1454] 6. Voice Response

[1455] The terminal receives text data, which is then converted into speech data using a speech synthesis engine, and feedback is sent to the user through the vehicle's speakers. For example, it might announce, "The song has been changed, please relax and enjoy."

[1456] Specific example

[1457] The following shows an example of actual operation.

[1458] Change music

[1459] 1. The user says, "Change to the next song."

[1460] 2. The device acquires the user's voice and converts it into text data using a speech recognition engine (Google Cloud Speech-to-Text API). In addition, an emotion recognition engine (IBM Watson Tone Analyzer) recognizes emotions requiring relaxation from the user's tone of voice.

[1461] 3. The device sends text data and sentiment data to the cloud server.

[1462] 4. The cloud server analyzes the text, recognizes the user's intent, and selects relaxing music from the Spotify API based on their emotional state.

[1463] 5. The cloud server generates a task completion message, "The song has been changed, please relax and enjoy," and sends it to the device.

[1464] 6. The device converts the text data into audio data and notifies the user via the speaker.

[1465] Example of a prompt

[1466] Change to a new song (please select a song that will help the user relax).

[1467] In this way, the system of the present invention can automatically and appropriately respond based on the user's voice input and emotional state, thereby improving driving safety and comfort.

[1468] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1469] Step 1:

[1470] The device's microphone captures the user's voice. For example, if the user says, "Change to the next song," this voice becomes the input. The input voice is saved as digital data.

[1471] Step 2:

[1472] The device captures audio data and converts it into text data using the Google Cloud Speech-to-Text API. In this case, the input is digital audio data, and the output is the text "Change to the next song."

[1473] Step 3:

[1474] The device analyzes the voice tone and timbre of the audio simultaneously with text data using IBM Watson Tone Analyzer to recognize the user's emotional state. The input is digital audio data, and the output is an emotional state tag such as "needs to relax."

[1475] Step 4:

[1476] The device sends text data and sentiment data to the cloud server. In this step, the input is text data and sentiment state data, and the output is the action of sending them to the server.

[1477] Step 5:

[1478] The cloud server analyzes the received text data using OpenAI GPT-3 to recognize the user's intent. The input is the text data "Change to the next song," and the output is the specific instruction "Change to the next song."

[1479] Step 6:

[1480] Based on the emotional data received by the cloud server, the system performs tasks that take appropriate emotions into consideration. Using the Spotify API, it changes the song to a new one. The input is emotional data and the user's intent, and the output is specific song information such as "relaxing music."

[1481] Step 7:

[1482] A cloud server generates the task execution results as text data and creates a response message that takes the user's emotions into consideration. The input is song information and emotion data, and the output is a response message such as "The song has been changed, please relax and enjoy."

[1483] Step 8:

[1484] The device converts text data into speech data using a speech synthesis engine and notifies the user via the speaker. The input is the text data of the response message, and the output is the speech notification to the user.

[1485] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1486] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1487] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1488] [Fourth Embodiment]

[1489] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1490] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1491] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1492] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1493] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1494] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1495] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1496] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1497] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1498] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1499] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1500] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1501] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1502] The present invention is a system for assisting drivers who are driving a car alone. It improves driving safety and comfort by acquiring and analyzing the user's voice input and providing appropriate feedback. Specific embodiments of the present invention will be described below.

[1503] Explanation of the program's processing

[1504] 1. Acquisition of voice input

[1505] The user can give voice commands or ask questions while driving. For example, they can give voice commands such as "Check my email" or "Change to the next song."

[1506] 2. Speech Recognition

[1507] The device captures the user's voice through the microphone, and a speech recognition engine converts the voice into text data. This text data is then used for subsequent processing.

[1508] 3. Natural language understanding

[1509] The device sends the acquired text data to the server. The server uses a natural language processing engine to analyze the text data and recognize the user's intent. For example, if the instruction is "Check your email," the server recognizes the intent as "Read the email aloud."

[1510] 4. Execute the task

[1511] The server performs appropriate tasks based on the recognized user's intent. For example, it accesses the email service API to retrieve the latest email content. For route guidance, it uses the map API to calculate the route; and for changing music, it uses the music service API to change the song.

[1512] 5. Response generation

[1513] The server generates the task execution results as text data and sends it to the terminal.

[1514] 6. Voice Response

[1515] The terminal receives text data, which is then converted into speech data using a speech synthesis engine and communicated to the user via the speaker. This allows the user to obtain necessary information while concentrating on driving.

[1516] Specific example

[1517] Email confirmation

[1518] 1. The user says, "I want to check for new emails."

[1519] 2. The device acquires the user's voice and converts it into text data using a speech recognition engine.

[1520] 3. The terminal sends text data to the server.

[1521] 4. The server analyzes the text to understand the user's intent.

[1522] 5. The server accesses the email service API to retrieve the latest emails.

[1523] 6. The server generates the email content as text data and sends it to the terminal.

[1524] 7. The device converts the text data into audio data and reads aloud from the speaker, "You have a new email. The subject is 'Meeting Reminder,' and the content is 'The meeting is tomorrow at 10:00.'"

[1525] Start of directions

[1526] 1. The user says, "Tell me the way to my next destination."

[1527] 2. The device acquires the user's voice and converts it into text data using a speech recognition engine.

[1528] 3. The terminal sends text data to the server.

[1529] 4. The server analyzes the text to understand the user's intent.

[1530] 5. The server accesses the map API and calculates the optimal route.

[1531] 6. The server generates the route guidance content as text data and sends it to the terminal.

[1532] 7. The device converts the text data into audio data and reads aloud from the speaker, "Next, turn left."

[1533] Change music

[1534] 1. The user says, "Change to the next song."

[1535] 2. The device acquires the user's voice and converts it into text data using a speech recognition engine.

[1536] 3. The terminal sends text data to the server.

[1537] 4. The server analyzes the text to understand the user's intent.

[1538] 5. The server accesses the music service API and changes to the next song.

[1539] 6. The server generates a text file confirming the changes have been made and sends it to the terminal.

[1540] 7. The device converts the text data into audio data and reads aloud from the speaker, "The song has been changed."

[1541] As described above, the system of the present invention enhances driving safety and comfort by automatically executing various tasks based on the user's voice input and notifying the results by voice.

[1542] The following describes the processing flow.

[1543] Step 1:

[1544] The user says, "I want to check for new emails."

[1545] Step 2:

[1546] The device captures the user's voice using its built-in microphone.

[1547] Step 3:

[1548] The device sends voice data to the speech recognition engine, which converts it into text data.

[1549] Step 4:

[1550] The device sends the converted text data "I want to check for new emails" to the server.

[1551] Step 5:

[1552] The server receives text data and uses a natural language processing engine to analyze the user's intent.

[1553] Step 6:

[1554] The server recognizes the intention to "check email" and generates a request to access the email service API.

[1555] Step 7:

[1556] The server sends a request to the email service API to retrieve the latest email data.

[1557] Step 8:

[1558] The server analyzes the email data and generates text to be read aloud to notify the user.

[1559] Step 9:

[1560] The server sends the generated text "You have a new email. The subject is 'Meeting Reminder', and the content is 'The meeting is tomorrow at 10:00'" to the terminal.

[1561] Step 10:

[1562] The terminal receives text data, which is then sent to the speech synthesis engine to generate speech data.

[1563] Step 11:

[1564] The device plays the generated audio data through its speaker and notifies the user, "You have a new email. The subject is 'Meeting Reminder,' and the content is 'The meeting is tomorrow at 10:00.'"

[1565] (Example 1)

[1566] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1567] In recent years, there has been a growing demand for technologies that improve the safety and comfort of drivers operating vehicles alone. In particular, there is a need for systems that allow drivers to perform various operations using only their voice, without using their hands while driving. However, conventional systems have suffered from low accuracy in voice recognition and significant delays between voice input and actual task execution, making them impractical. There is a need to provide a new system that overcomes these challenges and achieves highly sensitive voice recognition and rapid task execution.

[1568] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1569] In this invention, the server includes means for acquiring voice input, means for converting the acquired voice input into text data, and means for analyzing the text data with a natural language processing engine. This makes it possible to capture the user's voice with a high-sensitivity microphone and analyze the text data with high accuracy and speed. It also includes means for executing tasks based on recognized intent and means for notifying the results by voice, and further includes means for accessing a map service API for route guidance and a music service API for changing music, thereby improving driver safety and comfort.

[1570] A "user" refers to an individual who uses the system to perform voice input.

[1571] "Means for acquiring voice input" refers to devices or technologies that capture the user's voice using a microphone and incorporate that data into the system.

[1572] "Means of converting acquired voice input into text data" refers to technologies and software that use a speech recognition engine to convert voice data into text information.

[1573] "Methods for analyzing text data to recognize user intent" refers to technologies that use a natural language processing engine to process text data and understand the content of user statements.

[1574] "Means of executing a task" refers to servers and software that perform specific operations or give instructions based on the user's intentions.

[1575] "Means of notifying the user of execution results via voice" refers to a technology that converts the results of an executed task into voice data using a speech synthesis engine and communicates it to the user through a speaker.

[1576] A "high-sensitivity microphone" refers to a microphone device that has the ability to effectively filter out noise during driving and capture clear voice input.

[1577] A "natural language processing engine" refers to software or algorithms that analyze text data, understand its context and meaning, and extract the user's intent.

[1578] A "database" refers to a system for efficiently storing and managing acquired and analyzed data.

[1579] A "map service API" refers to an interface with an external map service for providing route guidance.

[1580] A "music service API" refers to an interface with external music services for playing and modifying music.

[1581] "Text data" refers to character information converted from voice input by a speech recognition engine.

[1582] "Audio data" refers to audio information generated by a speech synthesis engine and played back to the user through a speaker.

[1583] "API" is an abbreviation for Application Programming Interface, and refers to the definitions and protocols that enable communication between different software programs.

[1584] Modes for carrying out the invention

[1585] The present invention is a system for assisting drivers who are driving a car alone. It improves driving safety and comfort by acquiring and analyzing user voice input and providing appropriate feedback. Specific embodiments of the present invention will be described below.

[1586] System Configuration

[1587] The system of the present invention consists of the following main components.

[1588] 1. Means for obtaining user voice input:

[1589] The user gives instructions and asks questions by voice while driving. Specifically, a high-sensitivity microphone installed in the vehicle is used to capture the voice. The high-sensitivity microphone effectively filters out noise during driving, ensuring clear voice input.

[1590] 2. Means for converting acquired voice input into text data:

[1591] The device captures the user's voice through the microphone and converts the voice into text data using a speech recognition engine (e.g., Microsoft Azure Speech SDK or Google Cloud Speech-to-Text).

[1592] 3. Means of analyzing text data to recognize user intent:

[1593] The device sends the acquired text data to the server. The server analyzes the text data using a generative AI model (e.g., OpenAI GPT-3) to recognize the user's intent.

[1594] 4. Means of performing tasks based on recognized intentions:

[1595] The server performs the appropriate task based on the recognized user's intent. For example, to retrieve email content, it accesses the Gmail API to retrieve the latest email. For route guidance, it uses the Google Maps API to calculate the optimal route, and for changing music, it accesses the Music Services API to change the song.

[1596] 5. Means of notifying the user of the execution results by voice:

[1597] The server generates the task execution results as text data and sends it to the terminal. The terminal converts the received text data into audio data using a speech synthesis engine (e.g., Amazon Polly) and notifies the user through the speaker.

[1598] Specific example

[1599] The following are specific usage scenarios.

[1600] Email confirmation

[1601] 1. The user says, "I want to check for new emails."

[1602] 2. The device captures the user's voice and converts it into text data using Google Cloud Speech-to-Text.

[1603] 3. The terminal sends text data to the server.

[1604] 4. The server uses OpenAI GPT-3 to analyze the text and recognize the user's intent.

[1605] 5. The server accesses the Gmail API to retrieve the latest emails.

[1606] 6. The server generates the email content as text data and sends it to the terminal.

[1607] 7. The device uses Amazon Polly to convert the text data into audio data, and the speaker reads aloud, "You have a new email. The subject is 'Meeting Reminder,' and the content is 'The meeting is tomorrow at 10am.'"

[1608] Start of directions

[1609] 1. The user says, "Tell me the way to my next destination."

[1610] 2. The device captures the user's voice and converts it into text data using the Microsoft Azure Speech SDK.

[1611] 3. The terminal sends text data to the server.

[1612] 4. The server uses OpenAI GPT-3 to analyze the text and recognize the user's intent.

[1613] 5. The server accesses the Google Maps API and calculates the optimal route.

[1614] 6. The server generates the route guidance content as text data and sends it to the terminal.

[1615] 7. The device uses Amazon Polly to convert the text data into audio data and reads aloud from the speaker, "Turn left next."

[1616] Change music

[1617] 1. The user says, "Change to the next song."

[1618] 2. The device captures the user's voice and converts it into text data using Google Cloud Speech-to-Text.

[1619] 3. The terminal sends text data to the server.

[1620] 4. The server uses OpenAI GPT-3 to analyze the text and recognize the user's intent.

[1621] 5. The server accesses the Spotify API and changes to the next song.

[1622] 6. The server generates a text file confirming the changes have been made and sends it to the terminal.

[1623] 7. The device uses Amazon Polly to convert the text data into audio data and reads aloud from the speaker, "The song has been changed."

[1624] As described above, the system of the present invention automatically performs various tasks based on the user's voice input and notifies the user of the results by voice, thereby improving driving safety and comfort.

[1625] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1626] Step 1: Obtaining voice input

[1627] The user can give instructions and ask questions by voice while driving. For example, they can give voice commands such as "Check my email" or "Change to the next song." The input voice is captured by a high-sensitivity microphone installed in the car. The high-sensitivity microphone filters out noise during driving, ensuring a clear voice input. The output is the captured raw voice data.

[1628] Step 2: Speech Recognition

[1629] The device passes the captured raw audio data to a speech recognition engine such as the Microsoft Azure Speech SDK or Google Cloud Speech-to-Text. The input is the raw audio data from step 1. The speech recognition engine analyzes the audio data and converts it into corresponding text data. The output is this text data, which is used in the next processing step.

[1630] Step 3: Send text data

[1631] The terminal sends the acquired text data to the server. The input is text data. The terminal securely transfers the text data to the server via the network, where further processing continues. The output is the text data sent to the server.

[1632] Step 4: Natural Language Comprehension

[1633] The server analyzes text data using a generative AI model such as OpenAI GPT-3. The input is the text data received in step 3. The server analyzes the text data to identify the user's intent. For example, the instruction "check my email" is recognized as having the intent "read the email aloud." In terms of data processing, the text data is analyzed based on context, and the intent behind the user's request is extracted. The output is user intent information, which is used in the next task execution step.

[1634] Step 5: Execute the task

[1635] The server performs the appropriate task based on the user's intent. The input is the user's intent information from step 4. For example, to retrieve email content, it accesses the Gmail API to retrieve the latest email. For route guidance, it uses the Google Maps API to calculate the optimal route, and for changing music, it uses the Music Services API to change the song. Each API call performs data retrieval or calculation. The output is the result data of the task execution.

[1636] Step 6: Generating the response

[1637] The server generates the task execution result as text data and sends it to the terminal. The input is the task execution result data from step 5. Based on the execution result, the server generates a text message to notify the user. The output is the text data of the notification message sent to the terminal.

[1638] Step 7: Voice Response

[1639] The text data received by the device is converted into speech data by a speech synthesis engine such as Amazon Polly. The input is the text data of the notification message from step 6. The speech synthesis engine analyzes the text data and generates the corresponding natural speech. The generated speech data is notified to the user through the speaker. For example, it might read aloud, "You have a new email. The subject is 'Meeting Reminder,' and the content is 'The meeting is tomorrow at 10am.'" The output is the speech data that is played back to the user.

[1640] (Application Example 1)

[1641] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1642] In autonomous vehicles, a system is needed that allows users to efficiently perform tasks using voice commands while driving and to obtain information safely and comfortably. Conventional systems have problems such as low accuracy in voice input, slow recognition of intent, and data acquisition, which fail to adequately ensure user convenience. There is also room for improvement in acquiring real-time data from external sources and providing feedback.

[1643] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1644] In this invention, the server includes means for acquiring voice input, means for converting the acquired voice input into text data, means for analyzing the text data to recognize the user's intent, means for acquiring data from external information sources, and means for analyzing the acquired data to provide appropriate feedback to the user. This enables the user to efficiently perform tasks using voice while driving and to obtain necessary information in real time in a safe and comfortable manner.

[1645] A "user" refers to a person who uses the system.

[1646] "Voice input" refers to the act of a user providing instructions or information to a system using their voice.

[1647] "Means of acquisition" refers to methods and technologies for receiving, recording, or capturing information such as audio or data.

[1648] "Means of converting audio to text data" refers to methods and technologies for converting audio data into text-based data.

[1649] "Text data" refers to data expressed as character information.

[1650] "Means of analysis" refers to methods and techniques for analyzing acquired data and extracting specific information or patterns.

[1651] "User intent" refers to the actions a user wants to perform on the system or the information they are seeking.

[1652] "Means of executing a task" refers to methods and technologies for performing specific operations or processes based on the user's intent.

[1653] "Execution results" refer to the outcomes and information obtained after completing a task.

[1654] "External information sources" refer to information providers located outside the system. For example, these include third-party services or databases that provide weather information or news.

[1655] "Means of acquiring data" refers to the methods and technologies used to obtain necessary data from external sources.

[1656] "Means of providing feedback" refers to methods and techniques for providing users with information or the results of instructions.

[1657] "Real-time" refers to the immediate processing and provision of information at the moment an event occurs.

[1658] This invention relates to a system for autonomous vehicles that acquires user voice input, analyzes their intent, and provides appropriate feedback. This system assists users in driving safely and comfortably.

[1659] The system program is implemented using the following hardware and software.

[1660] hardware

[1661] 1. Smartphone: Captures the user's voice input and converts the voice into text data.

[1662] 2. Server: Analyzes text data and recognizes the user's intent.

[1663] 3. External information sources: Databases and third-party services that provide weather information, news, etc.

[1664] software

[1665] 1. Speech recognition engine: For example, use Google's speech recognition API to convert speech to text.

[1666] 2. Natural Language Processing Engine: For example, use Amazon Comprehend to analyze text data and recognize user intent.

[1667] 3. Data acquisition API: For example, use an API to acquire weather information.

[1668] Processing procedures and data calculations

[1669] 1. Acquisition of voice input

[1670] The user gives instructions or asks questions by voice while driving. The smartphone's microphone captures this and sends it to the voice recognition engine.

[1671] 2. Speech Recognition

[1672] The speech recognition engine converts the speech into text data. This text data is then sent to the server.

[1673] 3. Natural language understanding

[1674] The server receives text data and analyzes it using a natural language processing engine. For example, if given the instruction "Tell me today's weather," the server recognizes that it needs to retrieve weather information.

[1675] 4. Data Acquisition

[1676] The server accesses external information sources and retrieves the necessary data (e.g., weather information).

[1677] 5. Response generation

[1678] Based on the acquired data, the server generates feedback to be provided to the user. This feedback is then sent to the smartphone as text data.

[1679] 6. Voice Response

[1680] Text data is sent to a speech synthesis engine and converted into speech data. Feedback is then provided to the user through the smartphone's speaker.

[1681] Specific example

[1682] Example 1: Obtaining weather information

[1683] 1. The user says, "Tell me today's weather."

[1684] 2. The smartphone captures the audio, and the speech recognition engine converts it into text data.

[1685] 3. The server receives the text data and analyzes it using a natural language processing engine.

[1686] 4. The server accesses the weather information API to obtain the latest weather information.

[1687] 5. The server generates weather information as text data and sends it to the smartphone.

[1688] 6. The smartphone converts the text data into audio data and announces "Today's weather is sunny" through the speaker.

[1689] Example of a prompt:

[1690] When a user says, "Tell me today's weather," a smartphone application recognizes the voice and analyzes the text data using a natural language processing engine. It then calls a weather forecast API to obtain real-time weather information and notifies the user as audio data.

[1691] Thus, the system of the present invention is configured to support the user's safe and comfortable driving.

[1692] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1693] Step 1:

[1694] The user inputs instructions by voice. For example, they might say, "Tell me today's weather." This inputs the information the user wants to know or the task they want to perform as voice data into the device.

[1695] Step 2:

[1696] The device captures the user's voice data and converts it into text data using a speech recognition engine. Specifically, the smartphone's microphone captures the voice and sends it to an API that converts the voice data into text. In this case, the input is voice data and the output is text data.

[1697] Step 3:

[1698] Text data is sent from the terminal to the server. The server passes the received text data to a natural language processing engine to analyze the user's intent. A natural language processing engine (e.g., Amazon Comprehend) is used here. The input is text data, and the output is data indicating the user's intent.

[1699] Step 4:

[1700] The server retrieves data from appropriate external sources based on the user's intent. For example, to retrieve weather information, it accesses a weather information API to obtain the latest weather data. The input is data indicating the user's intent, and the output is the retrieved weather information data.

[1701] Step 5:

[1702] The server analyzes the acquired data and generates feedback to provide to the user. For example, weather information is formatted as text data such as "Today's weather is sunny." The input is the acquired information data, and the output is formatted text data to be provided to the user.

[1703] Step 6:

[1704] Formatted text data is sent from the server to the terminal. The terminal receives this data and converts it into speech data using a speech synthesis engine. Specifically, a speech synthesis engine (e.g., gTTS) is used to play the text as speech. The input is formatted text data, and the output is speech data.

[1705] Step 7:

[1706] The device plays the generated audio data through its speaker and notifies the user. This allows the user to receive necessary information in real time via voice, even while driving. The input is audio data, and the output is an audio notification to the user.

[1707] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1708] The present invention is a system for assisting drivers who are driving a car alone. It improves driving safety and comfort by acquiring and analyzing the user's voice input, recognizing the user's emotions, and providing appropriate feedback. Specific embodiments of the present invention will be described below.

[1709] Explanation of the program's processing

[1710] 1. Acquisition of voice input

[1711] The user can give voice commands or ask questions while driving. For example, they can give voice commands such as "Check my email" or "Change to the next song."

[1712] 2. Speech Recognition and Emotion Recognition

[1713] The device captures the user's voice using its built-in microphone, and a speech recognition engine converts the voice into text data. This text data is used for subsequent processing. Simultaneously, an emotion engine recognizes the user's emotions from the voice data.

[1714] 3. Natural Language Understanding and Emotion Understanding

[1715] The device sends the acquired text data and sentiment data to the server. The server uses a natural language processing engine to analyze the text data and recognize the user's intent. For example, in the case of the instruction "Check your email," the server recognizes the intention to "read the email aloud," and the sentiment engine also recognizes that the user is feeling stressed.

[1716] 4. Execute the task

[1717] The server performs appropriate tasks based on the recognized user's intentions and emotions. For example, it accesses the email service API to retrieve the latest emails to get the content of emails. For route guidance, it uses a map API to calculate the route, and for music changes, it considers the user's emotions and selects relaxing music.

[1718] 5. Response generation

[1719] The server generates the task execution results as text data and sends a message to the terminal that includes content that takes the user's feelings into consideration.

[1720] 6. Voice Response

[1721] The terminal receives text data, which is then converted into speech data using a speech synthesis engine and communicated to the user via the speaker. This allows the user to obtain necessary information while concentrating on driving.

[1722] Specific example

[1723] Email confirmation

[1724] 1. The user says, "I want to check for new emails."

[1725] 2. The device acquires the user's voice and converts it into text data using a speech recognition engine. Simultaneously, an emotion engine recognizes the user's stress level based on their tone of voice.

[1726] 3. The device sends text data and sentiment data to the server.

[1727] 4. The server analyzes the text to understand the user's intent and confirm that they are experiencing stress.

[1728] 5. The server accesses the email service API to retrieve the latest emails.

[1729] 6. The server generates and sends a text message to the terminal that reads, "You have a new email. Take care and relax. The subject is 'Meeting Reminder' and the content is 'The meeting is tomorrow at 10am'."

[1730] 7. The device converts the text data into audio data and reads aloud from the speaker: "You have a new email. Take care, relax. The subject is 'Meeting Reminder,' and the content is 'The meeting is tomorrow at 10am.'"

[1731] Start of directions

[1732] 1. The user says, "Tell me the way to my next destination."

[1733] 2. The device acquires the user's voice and converts it into text data using a speech recognition engine. Simultaneously, an emotion engine recognizes the user's emotional state.

[1734] 3. The device sends text data and sentiment data to the server.

[1735] 4. The server analyzes the text to recognize the user's intent and considers appropriate guidance that reflects their emotional state.

[1736] 5. The server accesses the map API and calculates the optimal route.

[1737] 6. The server generates the route guidance content as text data and sends it to the terminal.

[1738] 7. The device converts the text data into audio data and reads aloud from the speaker, "Please turn left next. Drive carefully."

[1739] Change music

[1740] 1. The user says, "Change to the next song."

[1741] 2. The device acquires the user's voice and converts it into text data using a speech recognition engine. Simultaneously, an emotion engine recognizes the user's emotional state.

[1742] 3. The device sends text data and sentiment data to the server.

[1743] 4. The server analyzes the text to recognize the user's intent and selects relaxing music that takes their emotional state into consideration.

[1744] 5. The server accesses the music service API and changes to the next song.

[1745] 6. The server generates a text file confirming the changes have been made and sends it to the terminal.

[1746] 7. The device converts the text data into audio data and reads aloud from the speaker, "The song has been changed, please relax and enjoy."

[1747] As described above, the system of the present invention enhances driving safety and comfort by providing appropriate responses based on the user's voice input and emotional state.

[1748] The following describes the processing flow.

[1749] Specific examples of email confirmation

[1750] Voice input acquisition and recognition

[1751] Step 1:

[1752] The user says, "I want to check for new emails."

[1753] Step 2:

[1754] The device captures the user's voice using its built-in microphone.

[1755] Step 3:

[1756] The device sends voice data to the speech recognition engine, which converts it into text data.

[1757] Step 4:

[1758] The device uses an emotion engine to extract user emotion data from the voice.

[1759] Data transmission and analysis

[1760] Step 5:

[1761] The device sends the converted text data and sentiment data to the server.

[1762] Step 6:

[1763] The server receives text data and uses a natural language processing engine to analyze the user's intent.

[1764] Step 7:

[1765] The server uses an emotion engine to analyze the user's emotions and recognize that they are "feeling stressed."

[1766] Task execution and response generation

[1767] Step 8:

[1768] The server recognizes the intention to "check email" and sends a request to the email service API.

[1769] Step 9:

[1770] The server receives the latest email data from the email service API.

[1771] Step 10:

[1772] The server analyzes the email data and generates text for reading aloud that takes the user's emotions into consideration.

[1773] Step 11:

[1774] The server sends the generated text data "You have a new email. Take care and relax. Subject: 'Meeting Reminder', Content: 'The meeting is tomorrow at 10:00'" to the terminal.

[1775] Voice response

[1776] Step 12:

[1777] The terminal receives text data, which is then sent to a speech synthesis engine and converted into speech data.

[1778] Step 13:

[1779] The device plays the generated audio data through the speaker and notifies the user, "You have a new email. Take care and relax. The subject is 'Meeting Reminder,' and the content is 'The meeting is tomorrow at 10:00.'"

[1780] A concrete example of starting directions

[1781] Voice input acquisition and recognition

[1782] Step 1:

[1783] The user asks, "Tell me the way to my next destination."

[1784] Step 2:

[1785] The device captures the user's voice using its built-in microphone.

[1786] Step 3:

[1787] The device sends voice data to the speech recognition engine, which converts it into text data.

[1788] Step 4:

[1789] The device uses an emotion engine to extract user emotion data from the voice.

[1790] Data transmission and analysis

[1791] Step 5:

[1792] The device sends the converted text data and sentiment data to the server.

[1793] Step 6:

[1794] The server receives text data and uses a natural language processing engine to analyze the user's intent.

[1795] Step 7:

[1796] The server uses an emotion engine to analyze the user's emotions and recognizes that they are in a relaxed state.

[1797] Task execution and response generation

[1798] Step 8:

[1799] The server recognizes the intention to "start directions" and sends a request to the map API.

[1800] Step 9:

[1801] The server receives route information from the map API.

[1802] Step 10:

[1803] The server analyzes route information and generates emotionally sensitive guidance text.

[1804] Step 11:

[1805] The server generates text data, "Turn left next, drive carefully," and sends it to the terminal.

[1806] Voice response

[1807] Step 12:

[1808] The terminal receives text data, which is then sent to a speech synthesis engine and converted into speech data.

[1809] Step 13:

[1810] The device generates audio data which is played through the speaker, instructing the user with messages such as, "Please turn left next. Drive carefully."

[1811] Examples of music changes

[1812] Voice input acquisition and recognition

[1813] Step 1:

[1814] The user says, "Change to the next song."

[1815] Step 2:

[1816] The device captures the user's voice using its built-in microphone.

[1817] Step 3:

[1818] The device sends voice data to the speech recognition engine, which converts it into text data.

[1819] Step 4:

[1820] The device uses an emotion engine to extract user emotion data from the voice.

[1821] Data transmission and analysis

[1822] Step 5:

[1823] The device sends the converted text data and sentiment data to the server.

[1824] Step 6:

[1825] The server receives text data and uses a natural language processing engine to analyze the user's intent.

[1826] Step 7:

[1827] The server uses an emotion engine to analyze the user's emotions and recognizes that they are in a relaxed state.

[1828] Task execution and response generation

[1829] Step 8:

[1830] The server recognizes the intention to "change the music" and sends a request to the music service API.

[1831] Step 9:

[1832] The server receives the next song data from the music service API.

[1833] Step 10:

[1834] The server analyzes the data for the next song and generates a notification text that takes emotions into consideration.

[1835] Step 11:

[1836] The server sends the generated text data "The song has been changed, please relax and enjoy" to the terminal.

[1837] Voice response

[1838] Step 12:

[1839] The terminal receives text data, which is then sent to a speech synthesis engine and converted into speech data.

[1840] Step 13:

[1841] The device plays the generated audio data through the speaker and notifies the user with the message, "The song has been changed, please relax and enjoy."

[1842] As described above, the system of the present invention enhances driving safety and comfort by providing appropriate responses based on the user's voice input and emotional state.

[1843] (Example 2)

[1844] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1845] Conventional driver assistance systems, while capable of receiving user voice commands and executing tasks, failed to adequately improve driving safety and comfort by failing to provide feedback or operations that considered the user's emotions. Furthermore, one-sided notifications and instructions that disregard the user's feelings could amplify user stress, potentially negatively impacting driving concentration and safety. To solve these problems, a system was needed that could identify not only the user's intent based on their voice but also their emotions, and provide appropriate feedback accordingly.

[1846] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring voice input, means for converting voice to text data, means for recognizing the user's intent from the text data, means for identifying emotions from the user's voice data, means for executing a task based on the user's intent and emotions, and means for notifying the user of the task execution result by voice in a manner that takes the user's emotions into consideration. This makes it possible to respond appropriately not only to the user's intent but also to their emotions, thereby improving safety and comfort while driving.

[1847] "Means for acquiring voice input" refer to microphones or voice capture devices that capture user speech in real time.

[1848] "Methods for converting audio to text data" refer to a system that uses a speech recognition engine to convert captured audio into text strings.

[1849] "Means of recognizing user intent from text data" refers to a system that uses natural language processing technology to analyze and understand user requests and instructions from converted text data.

[1850] "Methods for identifying emotions from voice data" refer to systems that use an emotion recognition engine to determine the user's emotional state from the tone and patterns of their voice.

[1851] "Means for executing a task" refer to operational means for performing relevant actions based on the analyzed user's intentions and emotions. For example, this could involve retrieving the content of an electronic message or initiating route guidance.

[1852] "Means for notifying execution results by voice" refers to a system consisting of a speech synthesis engine and a speaker for providing voice feedback to the user on the execution results of a task.

[1853] "Means for obtaining the content of electronic messages" refers to APIs and service access mechanisms that retrieve email content from various email services in response to user instructions.

[1854] A "means for initiating route guidance" refers to a system that provides the optimal route based on user instructions, utilizing map data and navigation systems.

[1855] This invention is a system for assisting drivers who are driving a car alone. It improves driving safety and comfort by acquiring and analyzing the user's voice input, recognizing the user's emotions, and providing appropriate feedback.

[1856] System Overview

[1857] This system functions through communication between the user, a terminal in the vehicle, and a central server. Specifically, the terminal is equipped with a built-in microphone to acquire voice input from the user. This voice data is converted into text data via a speech recognition engine, and the user's emotions are further identified by an emotion recognition engine. This data is sent to the server, where a natural language processing engine and an emotion analysis engine are used to understand the user's intentions and emotions and execute the task. The server then sends the execution results back to the terminal, which uses a speech synthesis engine to notify the user.

[1858] Hardware and software to be used

[1859] Terminal (in-car device): Built-in microphone, speaker

[1860] Server: High-performance processor, Internet connection

[1861] Speech recognition engine: Google Speech-to-Text API

[1862] Emotion recognition engine: IBM Watson Tone Analyzer

[1863] Natural language processing engine: OpenAI GPT-4

[1864] Speech synthesis engine: Amazon Polly

[1865] Various APIs: Gmail API, Google Maps API, Spotify API

[1866] Specific example

[1867] Email confirmation

[1868] 1. The user says, "I want to check for new emails."

[1869] 2. The device captures the user's voice using its built-in microphone and converts it into text data using the Google Speech-to-Text API. Simultaneously, it recognizes the user's emotions using IBM Watson Tone Analyzer.

[1870] 3. The device sends text data and sentiment data to the server.

[1871] 4. The server uses GPT-4 to parse the text and confirm the intention to "read the email aloud" and whether the user is experiencing stress.

[1872] 5. The server accesses the Gmail API to retrieve the latest emails.

[1873] 6. The server generates and sends a text message to the terminal that reads, "You have a new email. Take care and relax. The subject is 'Meeting Reminder' and the content is 'The meeting is tomorrow at 10am'."

[1874] 7. The device uses Amazon Polly to convert the text data into audio data, and the speaker reads aloud, "You have a new email. Take care, relax. The subject is 'Meeting Reminder,' and the content is 'The meeting is tomorrow at 10am.'"

[1875] Start of directions

[1876] 1. The user says, "Tell me the way to my next destination."

[1877] 2. The device captures the user's voice using its built-in microphone and converts it into text data using the Google Speech-to-Text API. Simultaneously, it recognizes the user's emotions using IBM Watson Tone Analyzer.

[1878] 3. The device sends text data and sentiment data to the server.

[1879] 4. The server uses GPT-4 to analyze the user's intent and accesses the Google Maps API to calculate the optimal route.

[1880] 5. The server generates route guidance content as text data and considers guidance that includes considerations to help the user relax.

[1881] 6. The server sends this text data to the terminal.

[1882] 7. The device uses Amazon Polly to convert the message into audio data and reads it aloud from the speaker, saying, "Please turn left next. Drive carefully."

[1883] Change music

[1884] 1. The user says, "Change to the next song."

[1885] 2. The device captures the user's voice using its built-in microphone and converts it into text data using the Google Speech-to-Text API. Simultaneously, the IBM Watson Tone Analyzer recognizes the user's emotional state.

[1886] 3. The device sends text data and sentiment data to the server.

[1887] 4. The server uses GPT-4 to analyze the user's intent and accesses the Spotify API to select relaxing music.

[1888] 5. The server generates the selected items as text data and sends it to the terminal.

[1889] 6. The device uses Amazon Polly to generate audio data, and the speaker reads out, "The song has been changed, please relax and enjoy."

[1890] Through the detailed processing described above, this system can improve driving safety and comfort by responding appropriately based on the user's voice commands and emotional state.

[1891] Example of a prompt

[1892] "Please describe the process for checking new emails while driving. Provide detailed instructions using appropriate APIs and sentiment recognition."

[1893] "Please describe in detail the process for initiating route guidance to the next destination. Please include guidance that takes user emotions into consideration."

[1894] "Please explain the procedure for changing songs while driving. Clearly demonstrate the process of selecting songs based on the user's emotional state."

[1895] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1896] Step 1:

[1897] The user gives instructions or asks questions using voice. For example, they might say, "Check my email" or "Change to the next song." This voice input is the starting point for the system's processing. The input is the user's voice data, and the output is the voice data being captured by the device's microphone.

[1898] Step 2:

[1899] The device uses its built-in microphone to capture the user's voice. The captured voice data is converted into text data using the Google Speech-to-Text API. Simultaneously, the IBM Watson Tone Analyzer identifies the user's emotions. The input is the user's voice data, and the output is text data and emotion data.

[1900] Step 3:

[1901] The device sends acquired text data and sentiment data to the server. The text data is used for natural language processing, and the sentiment data is used to understand the user's psychological state. The input is text data and sentiment data, and the output is the transmission of this data to the server.

[1902] Step 4:

[1903] The server uses a natural language processing engine (GPT-4) to analyze text data and recognize the user's intent. For example, it can understand the instruction "Check your email" to mean "Read the electronic message aloud." Simultaneously, an emotion analysis engine analyzes the user's emotions, identifying states such as stress and relaxation. The input consists of text data and emotion data, while the output is data related to the user's intent and emotional state.

[1904] Step 5:

[1905] The server performs appropriate tasks based on the user's intentions and emotions. For example, it accesses an e-messaging service API to retrieve email content. If the user requests directions, it uses a map service API to calculate the optimal route; and if the user wants to change the music, it accesses a music service API to select relaxing music. The input is user intention and emotion data, and the output is the result data of the performed task.

[1906] Step 6:

[1907] The server generates text data that includes user-friendly feedback based on the task's execution results. For example, it might generate something like, "You have a new email. Take care, relax." The input is the task's result data, and the output is the feedback text data to notify the user.

[1908] Step 7:

[1909] The device receives feedback text data from the server, converts it into audio data using a speech synthesis engine (Amazon Polly), and notifies the user through the speaker. This allows the user to obtain information without relying on visual cues. The input is feedback text data, and the output is audio data.

[1910] The above outlines the processing steps from user voice input to the execution of appropriate tasks and the provision of feedback. Specific data processing and calculations are performed at each step, and the results are provided to the user.

[1911] (Application Example 2)

[1912] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1913] Conventional voice recognition systems were limited to converting user voice input into text data and performing tasks based on that data. However, because they performed tasks without considering the user's emotional state, they failed to alleviate user stress and anxiety. Therefore, there is a need for a system that eliminates the stress and inconvenience experienced by drivers and provides a more comfortable and safer driving experience.

[1914] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for analyzing voice tone and timbre to recognize the user's emotional state, means for executing a task based on the recognized intention and emotional state, and means for notifying the user of the execution result by voice. This makes it possible to execute an appropriate task while considering the user's emotional state and to provide a more comfortable and reassuring experience for the user while driving.

[1915] "Means for acquiring voice input" refers to hardware or software for capturing the voice spoken by the user.

[1916] "Means of converting to text data" refers to software or algorithms used to convert acquired voice input into string data.

[1917] "Means of recognizing user intent" refers to a natural language processing engine that analyzes converted text data to understand the content of user instructions and questions.

[1918] "Means of recognizing emotional states" refers to an emotion recognition engine that analyzes the tone and nuances of a voice to identify the user's emotions and psychological state.

[1919] "Means of executing a task" refers to software or system integration functions that execute appropriate instructions based on the user's intentions and emotional state.

[1920] "Means of notifying by voice" refers to speakers or audio systems that convert execution results into voice data using a speech synthesis engine and inform the user.

[1921] "Methods for retrieving email content and generating emotionally sensitive messages" refers to an API for accessing the content of emails requested by the user, and an algorithm for generating notifications that take the user's emotional state into consideration.

[1922] "Means of initiating directions and providing emotionally sensitive guidance" refers to a navigation system that calculates the route to the user's destination and provides driving instructions that take the user's emotional state into consideration.

[1923] This invention is a system that improves driving safety and comfort by acquiring and analyzing user voice input to recognize the user's emotions and providing appropriate feedback. The following describes specific embodiments for implementing this invention.

[1924] System Configuration

[1925] This system primarily uses the following hardware and software.

[1926] 1. Audio capture device (terminal microphone)

[1927] 2. Speech recognition engine (Google Cloud Speech-to-Text API)

[1928] 3. Emotion Recognition Engine (IBM Watson Tone Analyzer)

[1929] 4. Natural Language Processing Engine (OpenAI GPT-3)

[1930] 5. Music service API (Spotify API)

[1931] 6. Text-to-Speech API

[1932] 7. Infotainment system (in-vehicle system)

[1933] Process Overview

[1934] 1. Acquisition of voice input

[1935] The device's microphone captures the user's voice. For example, the user might say, "Change to the next song."

[1936] 2. Speech Recognition and Emotion Recognition

[1937] The device captures audio data and converts it into text data using the Google Cloud Speech-to-Text API. Simultaneously, IBM Watson Tone Analyzer is used to analyze the voice's tone and timbre to recognize the user's emotional state.

[1938] 3. Data transmission

[1939] The device sends text data and sentiment data to the cloud server.

[1940] 4. Natural Language Understanding and Task Execution

[1941] A cloud server uses OpenAI GPT-3 to analyze text data and recognize the user's intent. Simultaneously, it selects appropriate tasks based on the user's emotional state. For example, it might access the Spotify API to select music that will help the user relax.

[1942] 5. Response generation

[1943] The cloud server generates the task execution results as text data and creates a response message that includes feedback that takes the user's emotions into consideration.

[1944] 6. Voice Response

[1945] The terminal receives text data, which is then converted into speech data using a speech synthesis engine, and feedback is sent to the user through the vehicle's speakers. For example, it might announce, "The song has been changed, please relax and enjoy."

[1946] Specific example

[1947] The following shows an example of actual operation.

[1948] Change music

[1949] 1. The user says, "Change to the next song."

[1950] 2. The device acquires the user's voice and converts it into text data using a speech recognition engine (Google Cloud Speech-to-Text API). In addition, an emotion recognition engine (IBM Watson Tone Analyzer) recognizes emotions requiring relaxation from the user's tone of voice.

[1951] 3. The device sends text data and sentiment data to the cloud server.

[1952] 4. The cloud server analyzes the text, recognizes the user's intent, and selects relaxing music from the Spotify API based on their emotional state.

[1953] 5. The cloud server generates a task completion message, "The song has been changed, please relax and enjoy," and sends it to the device.

[1954] 6. The device converts the text data into audio data and notifies the user via the speaker.

[1955] Example of a prompt

[1956] Change to a new song (please select a song that will help the user relax).

[1957] In this way, the system of the present invention can automatically and appropriately respond based on the user's voice input and emotional state, thereby improving driving safety and comfort.

[1958] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1959] Step 1:

[1960] The device's microphone captures the user's voice. For example, if the user says, "Change to the next song," this voice becomes the input. The input voice is saved as digital data.

[1961] Step 2:

[1962] The device captures audio data and converts it into text data using the Google Cloud Speech-to-Text API. In this case, the input is digital audio data, and the output is the text "Change to the next song."

[1963] Step 3:

[1964] The device analyzes the voice tone and timbre of the audio simultaneously with text data using IBM Watson Tone Analyzer to recognize the user's emotional state. The input is digital audio data, and the output is an emotional state tag such as "needs to relax."

[1965] Step 4:

[1966] The device sends text data and sentiment data to the cloud server. In this step, the input is text data and sentiment state data, and the output is the action of sending them to the server.

[1967] Step 5:

[1968] The cloud server analyzes the received text data using OpenAI GPT-3 to recognize the user's intent. The input is the text data "Change to the next song," and the output is the specific instruction "Change to the next song."

[1969] Step 6:

[1970] Based on the emotional data received by the cloud server, the system performs tasks that take appropriate emotions into consideration. Using the Spotify API, it changes the song to a new one. The input is emotional data and the user's intent, and the output is specific song information such as "relaxing music."

[1971] Step 7:

[1972] A cloud server generates the task execution results as text data and creates a response message that takes the user's emotions into consideration. The input is song information and emotion data, and the output is a response message such as "The song has been changed, please relax and enjoy."

[1973] Step 8:

[1974] The device converts text data into speech data using a speech synthesis engine and notifies the user via the speaker. The input is the text data of the response message, and the output is the speech notification to the user.

[1975] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1976] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1977] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1978] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1979] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1980] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1981] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1982] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1983] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1984] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1985] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1986] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1987] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1988] 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.

[1989] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1990] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1991] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1992] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1993] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1994] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1995] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1996] The following is further disclosed regarding the embodiments described above.

[1997] (Claim 1)

[1998] A means of obtaining user voice input,

[1999] A means of converting acquired voice input into text data,

[2000] A means of analyzing text data to recognize user intent,

[2001] Means of performing a task based on recognized intent,

[2002] A means of notifying the user of the execution result by voice,

[2003] A system that includes this.

[2004] (Claim 2)

[2005] The system according to claim 1, further comprising means for obtaining the content of an email based on the recognized intent.

[2006] (Claim 3)

[2007] The system according to claim 1, further comprising means for initiating navigation based on recognized intent.

[2008] (Claim 4)

[2009] The system according to claim 1, further comprising means for changing music based on recognized intent.

[2010] "Example 1"

[2011] (Claim 1)

[2012] A means of obtaining user voice input,

[2013] A means of converting acquired voice input into text data,

[2014] A means of analyzing text data to recognize user intent,

[2015] Means of performing a task based on recognized intent,

[2016] A means of notifying the user of the execution result by voice,

[2017] A method for capturing voice input with a high-sensitivity microphone,

[2018] Methods for analyzing text data with a natural language processing engine,

[2019] A means of recording the acquisition results in a database,

[2020] A means of accessing a map service API for route guidance,

[2021] A means of accessing the music service API to change the music,

[2022] A system that includes this.

[2023] (Claim 2)

[2024] The system according to claim 1, further comprising means for obtaining the content of an email based on the recognized intent.

[2025] (Claim 3)

[2026] The system according to claim 1, further comprising means for initiating navigation based on recognized intent.

[2027] "Application Example 1"

[2028] (Claim 1)

[2029] A means of obtaining user voice input,

[2030] A means of converting acquired voice input into text data,

[2031] A means of analyzing text data to recognize user intent,

[2032] Means of performing a task based on recognized intent,

[2033] A means of notifying the user of the execution result by voice,

[2034] A means of acquiring data from external sources based on the intent of the acquired user,

[2035] A means of analyzing acquired data and providing appropriate feedback to the user,

[2036] A system that includes this.

[2037] (Claim 2)

[2038] The system according to claim 1, further comprising means for obtaining the content of an email based on the recognized intent.

[2039] (Claim 3)

[2040] The system according to claim 1, further comprising means for initiating navigation based on recognized intent.

[2041] "Example 2 of combining an emotion engine"

[2042] (Claim 1)

[2043] A means of obtaining user voice input,

[2044] A means of converting acquired voice input into text data,

[2045] A means of analyzing text data to recognize user intent,

[2046] A means of identifying emotions from user voice data,

[2047] A means of performing tasks based on the user's intentions and emotions,

[2048] A means of providing voice notifications of task completion results in a way that takes the user's emotions into consideration,

[2049] A system that includes this.

[2050] (Claim 2)

[2051] The system according to claim 1, further comprising means for obtaining the content of an electronic message based on the recognized intent.

[2052] (Claim 3)

[2053] The system according to claim 1, further comprising means for initiating route guidance based on recognized intent.

[2054] "Application example 2 when combining with an emotional engine"

[2055] (Claim 1)

[2056] A means of obtaining user voice input,

[2057] A means of converting acquired voice input into text data,

[2058] A means of analyzing text data to recognize user intent,

[2059] A means of recognizing the user's emotional state by analyzing their voice and tone,

[2060] A means of performing a task based on recognized intentions and emotional states,

[2061] A means of notifying the user of the execution result by voice,

[2062] A system that includes this.

[2063] (Claim 2)

[2064] The system according to claim 1, further comprising means for obtaining the content of an email based on recognized intent and emotional state, and for generating a message that takes the user's emotions into consideration.

[2065] (Claim 3)

[2066] The system according to claim 1, further comprising means for initiating directions based on recognized intentions and emotional states, and for providing directions that take emotional states into consideration. [Explanation of Symbols]

[2067] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of obtaining user voice input, A means of converting acquired voice input into text data, A means of analyzing text data to recognize user intent, Means of performing a task based on recognized intent, A means of notifying the user of the execution result by voice, A system that includes this.

2. The system according to claim 1, further comprising means for obtaining the content of an email based on the recognized intent.

3. The system according to claim 1, further comprising means for initiating navigation based on recognized intent.

4. The system according to claim 1, further comprising means for changing music based on recognized intent.

Images