System

Abstract

A system is provided.SOLUTION: A system comprising: sensing means for sensing conditions of a driver and a passenger; analysis means for analyzing data collected by the sensing means and predicting potential needs of the driver and the passenger; suggestion means for making suggestions to the driver and the passenger based on the needs predicted by the analysis means; and control means for receiving responses of the driver and the passenger to the suggestions and controlling operation of the system based on the responses.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In recent years, automobiles have been equipped with numerous sensors, enabling them to collect a variety of information. However, systems that utilize this information to provide added value to drivers and passengers have not yet been fully developed. Furthermore, there is a lack of concrete means to improve in-car safety and comfort, such as by assessing driver fatigue and stress and passenger satisfaction. In particular, there is no system that predicts the potential needs of the driver and passengers based on their facial expressions and conversations and makes suggestions accordingly. The present invention aims to solve these problems and improve the in-car environment to make it safer and more comfortable. [Means for solving the problem]

[0005] The present invention provides a system that includes a detection means for detecting the state of the driver and passengers, an analysis means for analyzing data collected by the detection means and predicting potential needs of the driver and passengers, a suggestion means for making suggestions to the driver and passengers based on the needs predicted by the analysis means, and a control means for receiving responses from the driver and passengers to the suggestions and controlling the operation of the system based on the responses. The detection means includes a camera for capturing facial expressions of the driver and passengers and a microphone for collecting conversations between the driver and passengers. The analysis means evaluates the emotional states of the driver and passengers using facial expression recognition technology and voice recognition technology. This provides a system that improves safety and comfort inside the vehicle.

[0006] The "detection means" refers to a device or system for detecting the state of the driver and passengers.

[0007] The "analysis means" refers to a device or system that analyzes the data collected by the detection means and predicts the potential needs of the driver and passengers.

[0008] The "suggestion means" refers to a device or system for making suggestions to the driver and passengers based on the needs predicted by the analysis means.

[0009] "Control means" means a device or system for receiving driver and passenger responses to suggestions and controlling the operation of the system based on the responses.

[0010] "Camera" refers to a device for capturing facial expressions of the driver and passengers.

[0011] A "microphone" is a device used to collect conversations between the driver and passengers.

[0012] "Facial expression recognition technology" is a technology that analyzes facial expression data captured by a camera to evaluate an individual's emotional state.

[0013] "Voice recognition technology" is a technology that analyzes voice data collected by a microphone and infers a person's psychological state from the content and tone of the conversation. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0015] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0018] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0019] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0035] MODE FOR CARRYING OUT THE INVENTION

[0036] The present invention is a system that monitors the state of a driver and passengers in real time, predicts potential needs, and makes suggestions. This system is composed of a detection means, an analysis means, a suggestion means, and a control means.

[0037] System configuration

[0038] The entire system operates through communication between a terminal installed in the vehicle and a server built on the cloud.

[0039] Device Features

[0040] The device is installed in the vehicle and is equipped with a camera and a microphone to detect the state of the driver and passengers. The camera captures the facial expressions of the driver and passengers in real time, and the microphone collects the content of their conversations. This information is used as a means of detection.

[0041] The device analyzes the collected data in real time to evaluate the safety and comfort of the vehicle, and transmits the acquired data to a cloud server at regular intervals.

[0042] Server Features

[0043] The server receives the data sent from the device and acts as an analysis means. The server performs the following processes:

[0044] 1. Facial Expression Analysis: Evaluate the emotional state (e.g., fatigue, stress, joy) of the driver and passengers based on facial expression data acquired from the camera.

[0045] 2. Voice analysis: Voice data collected by a microphone is analyzed using natural language processing, and psychological state is inferred from the content and tone of the conversation.

[0046] Based on the analysis results, the server predicts the potential needs of the driver and passengers and generates specific actions as suggestions. For example, if the server determines that the driver is tired, it will suggest rest spots.

[0047] Suggestion and Control

[0048] The analysis results received from the server are sent to the device, which then uses the device to make appropriate suggestions to the driver and passengers. For example, the following suggestions may be considered:

[0049] "You seem a little tired from driving. Would you like to take a break at the next service area?"

[0050] "I have a new music playlist. Would you like to play it?"

[0051] "We are expecting a delay in arriving at our destination. Would you like us to contact our customer?"

[0052] The user responds to these suggestions and the terminal continues to act based on the response as a means of controlling the system.

[0053] Specific examples

[0054] Example 1: Proposal for a break in your car

[0055] If the driver continues driving for a long time, the camera detects frequent rubbing of the driver's eyes, and the microphone collects yawns. Based on this, the server analyzes that the driver is highly fatigued, collects information about nearby service areas, and sends it to the device. The device then suggests to the driver, "You seem a little tired from driving. Would you like to take a break at the next service area?" If the driver agrees, the navigation system automatically sets the service area as the destination.

[0056] Example 2: Calling about being late in a commercial vehicle

[0057] If a commercial vehicle is stuck in traffic and is likely to be late for a scheduled business meeting, the terminal checks the current traffic conditions and the driver's schedule. The server analyzes this and, if it determines that there is a high possibility of a delay, generates a template for sending an automatic email. The terminal then suggests to the driver, "Your arrival at your destination is likely to be delayed. Would you like to contact your customer?" If the driver accepts, the terminal automatically sends a relevant email to the customer.

[0058] As described above, the present invention provides a safe and comfortable in-vehicle environment by monitoring the state of the driver and passengers in real time, predicting their potential needs, and making appropriate suggestions.

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] The device initializes the vehicle's various sensors and acquires information on vehicle speed, fuel level, and location, thereby collecting basic vehicle operation data.

[0062] Step 2:

[0063] The device uses a camera to capture the facial expressions of the driver and passengers, and also uses an in-car microphone to collect conversations and obtain voice data.

[0064] Step 3:

[0065] The device periodically transmits the collected sensor data, facial expression data, and voice data to the server in real time.

[0066] Step 4:

[0067] The server analyzes the received data, specifically assessing the driver and passengers' emotional state (e.g., fatigue, stress, joy) using facial expression data, and inferring their psychological state from the content and tone of their conversations using audio data.

[0068] Step 5:

[0069] Based on the analysis results, the server predicts the potential needs of the driver and passengers. For example, if it determines that the driver is tired, it generates an action plan, such as suggesting nearby rest areas.

[0070] Step 6:

[0071] The server sends the generated action plan to the device, which includes the content and timing of the proposal.

[0072] Step 7:

[0073] Based on the action plan received, the device will make suggestions to the driver and passengers. For example, it might say to the driver, "You seem a little tired while driving. Would you like to take a break at the next service area?"

[0074] Step 8:

[0075] The user (driver and passengers) responds to the proposal by choosing to accept or reject it.

[0076] Step 9:

[0077] The terminal receives the user's response and controls the system's operation based on the response. For example, if the driver agrees to take a break, the navigation system automatically sets the next service area as the destination.

[0078] Step 10:

[0079] The device records the user's feedback and responses and periodically sends them to the server, which accumulates the data.

[0080] Step 11:

[0081] The server retrains the AI ​​model based on the collected feedback data, improving the system to make more accurate suggestions. The retrained model is stored on the server side.

[0082] Step 12:

[0083] The server then uses the improved AI model in the next analysis, improving the accuracy of the system's suggestions. This cycle allows the system to continuously evolve.

[0084] Example 1

[0085] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0086] When driving a vehicle, real-time monitoring of the driver and passengers' condition is required to maintain safety and comfort. However, current systems lack the ability to effectively analyze the facial expressions and conversations of the driver and passengers, predict their potential needs, and make appropriate suggestions. This makes it difficult to quickly detect driver fatigue or stress and instruct them to take breaks or other measures at the appropriate time. There is also a lack of means to respond appropriately in situations such as traffic congestion and delays.

[0087] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0088] In this invention, the server includes a detection means for detecting the states of the driver and passengers in real time, a data transmission means for transmitting data collected by the detection means to the server, an analysis means for analyzing the data received by the server and predicting potential needs of the driver and passengers, a proposal means for making suggestions to the driver and passengers based on the needs predicted by the analysis means, and a control means for receiving responses from the driver and passengers to the proposals and controlling the operation of the system based on the responses. This makes it possible to effectively monitor the states of the driver and passengers in real time, accurately predict potential needs, and make suggestions to maintain safety and comfort.

[0089] "Detection means" refers to devices and sensors for detecting the state of the driver and passengers in real time.

[0090] "Data transmission means" refers to a communication module or protocol for transmitting data collected by the detection means to a server.

[0091] "Analysis means" refers to algorithms or software that analyze the state of the driver and passengers based on the data received by the server and predict their potential needs.

[0092] The "suggestion means" refers to a user interface or notification system for making specific suggestions to the driver and passengers based on the needs predicted by the analysis means.

[0093] "Control means" refers to hardware and / or software for receiving driver and passenger responses to suggestions and controlling the operation of the system based on those responses.

[0094] "Image capture device" refers to a camera or video device used to capture the facial expressions of the driver and passengers.

[0095] "Audio capture device" refers to a microphone or other audio collection device for capturing driver and passenger conversations.

[0096] "Image processing technology" refers to the techniques and algorithms used to analyze images captured by an imaging device and evaluate facial expressions and emotional states.

[0097] "Natural language processing" refers to a technology that converts voice data collected by a voice capture device into text and analyzes that text to evaluate psychological state and emotions.

[0098] This invention is a system that monitors the status of the driver and passengers in the vehicle in real time, predicts their potential needs, and makes appropriate suggestions. This system operates through communication between a terminal installed in the vehicle and a server built on the cloud.

[0099] System Configuration

[0100] Device configuration

[0101] The terminal is installed inside the vehicle and has a detection means for detecting the state of the driver and passengers. The detection means uses an image capture device (camera) to capture the facial expressions of the driver and passengers, and a voice capture device (microphone) to collect the content of their conversations.

[0102] The camera captures the faces of the driver and passengers in real time and uses image processing libraries such as OpenCV and dlib to extract facial features. The audio capture device collects high-quality ambient audio and converts it into text using the Google Cloud Speech-to-Text API.

[0103] The device has a data transmission means to send collected data to the server in real time, and sends the data to the server's API endpoint using an HTTP POST request. The data is encrypted in JSON format and sent securely.

[0104] Server Configuration

[0105] The server has an analysis means for analyzing the data sent from the terminal. Specifically, it performs the following processes.

[0106] 1. Facial Expression Analysis: Facial expression data acquired from a camera is analyzed to evaluate emotional states (e.g., fatigue, stress, joy). By quantifying the emotional states using Python's OpenCV library, the psychological state of the driver and passengers can be accurately evaluated.

[0107] 2. Voice analysis: Voice data acquired from the microphone is analyzed using natural language processing (NLP) technology. The voice is converted into text using the Google Cloud Speech-to-Text API, and then text analysis is performed. This allows the system to infer the user's psychological state from the content and tone of the conversation.

[0108] Based on the analysis results, the server predicts the potential needs of the driver and passengers and has a suggestion means for generating specific actions and suggestions.

[0109] Proposal submission and response

[0110] The device receives analysis results and suggestions from the server and presents them to the driver and passengers as notifications and alerts, who can then respond to the suggestions using the touchscreen or voice commands.

[0111] For example, if the device determines that the driver is tired after a long drive, it will display a prompt such as, "You seem a little tired from driving. Would you like to take a break at the next service area?" If the device has a voice assistant function, it will make similar suggestions aloud.

[0112] If the user responds by accepting the suggestion, the device will use the navigation system (e.g., Google Maps API) to set the next service area as the destination. If a delay due to traffic congestion is predicted, the device will display a prompt saying, "Arrival at the destination is expected to be delayed. Would you like to contact the customer?" and will automatically contact the customer if the user responds in favor.

[0113] Specific operation example

[0114] Example 1: Proposal for a break in your car

[0115] 1. The device uses a camera to detect frequent rubbing of the driver's eyes and a microphone to collect audio of yawning.

[0116] 2. The device sends the collected data to the server.

[0117] 3. The server analyzes the driver's fatigue level using facial expression recognition algorithms and natural language processing.

[0118] 4. The server generates suggestions to encourage the driver to take a break.

[0119] 5. The device will display a suggestion: "You seem a little tired from driving. Would you like to take a break at the next service area?"

[0120] 6. The user responds "Yes."

[0121] 7. The device sets the next service area in the navigation system and guides the driver there.

[0122] Example 2: Calling about being late in a commercial vehicle

[0123] 1. The device checks the current traffic conditions and schedule to detect congestion information.

[0124] 2. The device sends this data to the server.

[0125] 3. The server analyzes the traffic data and determines that you are likely to be late for a scheduled business meeting.

[0126] 4. The server generates the template for the automated email.

[0127] 5. The terminal displays the suggestion, "Your arrival at your destination is expected to be delayed. Would you like to contact your customer?"

[0128] 6. The user responds "Yes."

[0129] 7. The device automatically sends emails to customers using the prepared email templates.

[0130] In this way, the system of the present invention can effectively monitor the state of the driver and passengers in real time, accurately predict their potential needs, and make suggestions to maintain safety and comfort.

[0131] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0132] Step 1: Data collection

[0133] The device detects the driver and passengers' status using a camera and microphone installed in the vehicle. The camera recognizes faces in real time using the OpenCV library and extracts facial features using the dlib library. The microphone collects audio data and converts it into text using the Google Cloud Speech-to-Text API.

[0134] Input: Video and audio data of the driver and passengers

[0135] Specific operation: The camera captures the driver's facial expressions, and the microphone collects the conversation.

[0136] Output: Facial expression data (feature points) and speech text data

[0137] Step 2: Send data

[0138] The device sends the collected facial expression data and speech-to-text data to a server at regular intervals, where the data is encrypted and sent to the server's API endpoint in the cloud using an HTTP POST request.

[0139] Input: facial expression data (feature points) and voice text data

[0140] Specific operation: The data collected by the device is converted into JSON format, encrypted, and sent to the server.

[0141] Output: JSON formatted data sent to the server

[0142] Step 3: Receiving data

[0143] The server receives the JSON formatted data sent from the device, and stores it in a database for analysis.

[0144] Input: JSON format data sent from the terminal

[0145] Specific operation: The server's API analyzes the received data and extracts the necessary information.

[0146] Output: Raw data prepared for analysis

[0147] Step 4: Data analysis

[0148] The server analyzes the received data, using the OpenCV library to evaluate the emotional state (e.g., fatigue, stress, joy) of the facial expression data, and natural language processing (NLP) techniques to analyze the psychological state and conversation content of the speech and text data.

[0149] Input: Prepared raw data

[0150] Specific operations: Facial recognition algorithms are run on facial expression data to quantify emotional states, and NLP algorithms are run on voice and text data to analyze psychological states and conversation content.

[0151] Output: Analyzed emotional and psychological state data

[0152] Step 5: Proposal Generation

[0153] The server predicts the potential needs of the driver and passengers based on the analysis results, and generates appropriate suggestions based on those needs. For example, if the driver is tired, it generates suggestions to encourage them to take a break.

[0154] Input: Analyzed emotional and psychological state data

[0155] Specific behavior: Evaluate the analysis results and generate appropriate prompts or suggestions. For example, "You seem a little tired while driving. Would you like to take a break at the next service station?"

[0156] Output: Generated prompts and suggestions

[0157] Step 6: Proposal Presentation

[0158] The device displays the suggestions received from the server to the driver and passengers, and provides the suggestions through a user interface or a voice assistant.

[0159] Input: Generated prompts and suggestions

[0160] Specific operation: The device displays and presents suggestions on the LCD screen or voice assistant.

[0161] Output: Suggested prompts and suggestions for the user

[0162] Step 7: User response

[0163] The user responds to the suggestion, for example by selecting "yes" or "no" using a touchscreen or by responding with a voice command.

[0164] Input: Prompts and suggestions provided

[0165] Specific Action: The user responds with a touchscreen or voice command, for example, saying "Yes."

[0166] Output: User response data

[0167] Step 8: Take Action

[0168] The device then takes appropriate action based on the user's response, such as using the navigation system to set the destination to the nearest service area, or automatically contacting the customer using a template to inform them they'll be late.

[0169] Input: User response data

[0170] Specific operation: When the user accepts the break, the terminal will set the service area in the navigation system. When the user accepts the delay notification, the terminal will automatically send an email to the customer.

[0171] Output: Destination setting for navigation systems and automatic email transmission

[0172] In this way, the entire system works together to monitor the driver and passengers' status in real time, making it possible to make appropriate suggestions and take appropriate actions based on their potential needs.

[0173] (Application example 1)

[0174] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0175] In recent years, there has been a growing demand for systems that monitor the status of drivers and passengers in real time and propose appropriate actions to improve vehicle safety and comfort. However, existing systems rely too heavily on on-site analysis, which can result in delays and reduced accuracy in data processing. The present invention aims to solve these problems and provide a system that enables highly accurate and rapid analysis and proposals using a cloud server.

[0176] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0177] In this invention, the server includes a detection means for detecting the states of the driver and passengers, an analysis means for analyzing data collected by the detection means and predicting potential needs of the driver and passengers, a proposal means for making suggestions to the driver and passengers based on the needs predicted by the analysis means, a control means for receiving responses from the driver and passengers to the proposals and controlling the operation of the system based on the responses, and a communication means for transmitting the detected data to a cloud server, receiving the results, and providing them to the proposal means, thereby enabling highly accurate data analysis in real time and rapid proposals.

[0178] "Driver and passenger" means the person driving the vehicle and any passengers in the vehicle.

[0179] The "detection means" is a means for detecting the state of the driver and passengers, and includes devices such as a camera and a microphone.

[0180] The "analysis means" is a means for analyzing the data collected by the detection means and predicting the potential needs of the driver and passengers.

[0181] The "suggestion means" is a means for making specific suggestions to the driver and passengers based on the needs predicted by the analysis means.

[0182] "Control means" means for receiving driver and passenger responses to suggestions and controlling the operation of the system based on the responses.

[0183] The "communication means" is a means for transmitting detected data to a cloud server, receiving the results, and providing them to the proposal means.

[0184] A "cloud server" is a remote server connected via the Internet to perform data analysis and provide information.

[0185] "Facial expression recognition technology" is a technology that analyzes a person's facial expressions from images and videos to evaluate their emotional state.

[0186] "Speech recognition technology" is a technology that analyzes voice data and extracts information from its content and tone.

[0187] "Real-time" refers to the state in which data is processed and results are obtained immediately in real time.

[0188] "Potential needs" are requirements that the driver and passengers are not aware of at present, but may become necessary in the near future.

[0189] This invention is a system that monitors the status of the driver and passengers in an autonomous vehicle in real time, predicts potential needs, and makes suggestions. This system is configured using the following hardware and software.

[0190] System configuration

[0191] The entire system operates through communication between a terminal installed in the vehicle and a server built on the cloud.

[0192] Device Features

[0193] The device is installed inside the vehicle and is equipped with a camera and microphone to detect the state of the driver and passengers. In this case, the camera captures video using OpenCV and audio using pyaudio. The camera captures the facial expressions of the driver and passengers in real time, and the microphone collects the content of their conversations. This information is used as a means of detection.

[0194] The devices are equipped with a communication means to send collected data to a cloud server in real time, minimizing data delays and enabling fast and accurate analysis.

[0195] Server Features

[0196] The server receives the data sent from the device and acts as an analysis means. The server performs the following processes:

[0197] 1. Facial Expression Analysis: Evaluate the emotional state (e.g., fatigue, stress, joy) of the driver and passengers based on facial expression data acquired from the camera. Uses facial expression recognition technology.

[0198] 2. Voice analysis: Voice data collected by a microphone is analyzed using natural language processing to infer a person's psychological state from the content and tone of the conversation. Voice recognition technology is used.

[0199] Based on the analysis results, the server predicts the potential needs of the driver and passengers and generates specific actions as suggestions. For example, if the server determines that the driver is tired, it will suggest rest spots.

[0200] Suggestion and Control

[0201] The analysis results received from the server are sent to the terminal, which then makes appropriate suggestions to the driver and passengers as a suggestion means.

[0202] Examples:

[0203] 1. Suggesting a break

[0204] If the camera detects the driver rubbing their eyes frequently and the microphone picks up yawning, the server will determine that the driver is highly fatigued. It will then collect information about nearby service areas and send it to the device. The device will then suggest to the driver, "You seem a little tired from driving. Would you like to take a break at the next service area?" If the driver agrees, the navigation system will automatically set the service area as the destination.

[0205] 2. Relaxation suggestions

[0206] If the device determines that the driver is feeling stressed based on facial expression and voice data, it will suggest, "Would you like to play some relaxing music?" If the driver agrees, relaxing music will automatically play on the car's audio system.

[0207] Prompt Sentence Examples

[0208] "Based on facial expression and voice data, it seems the driver is tired. Please suggest a place to rest."

[0209] In this way, the state of the driver and passengers can be monitored in real time, potential needs can be predicted, and appropriate suggestions can be made to provide a safe and comfortable in-car environment.

[0210] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0211] Step 1:

[0212] The device uses a camera and microphone to capture the facial expressions and voices of the driver and passengers in real time. Video data from the camera and audio data from the microphone are input. This data is temporarily stored within the device.

[0213] Step 2:

[0214] The device formats and compresses the collected facial expression and audio data for transmission to the cloud server. A transmission protocol to the cloud server is executed, and the data is uploaded to the cloud server. Here, the video data is encoded into MJPEG or H.264 format, and the audio data is encoded into WAV or AAC format. The encoded data becomes the input to the cloud server.

[0215] Step 3:

[0216] The server analyzes the received facial expression and voice data. For the facial expression data, a facial expression recognition algorithm using a generative AI model is run to evaluate the emotional state of the driver and passengers. For the voice data, natural language processing and voice recognition technologies are used to infer the psychological state from the content and tone of the conversation. The results of these analyses are obtained as output from the cloud server.

[0217] Step 4:

[0218] The server predicts the potential needs of the driver and passengers based on the analysis results. Based on the predicted needs, appropriate suggestions are generated. Here, a generative AI model is used to generate prompts that suggest appropriate actions for the driver, such as taking a break or playing music. These suggestions are output from the cloud server to the device.

[0219] Step 5:

[0220] The device receives suggestions from the server and presents them to the driver and passengers. For example, a suggestion such as "You seem a little tired from driving. Would you like to take a break at the next service area?" is displayed on the display screen. The driver and passengers' responses are received via voice or touch interface.

[0221] Step 6:

[0222] The terminal receives the responses of the driver and passengers and sends them to the server. For example, if the driver responds "yes," the voice data is sent to the server. Once this data transmission is complete, the responses of the driver and passengers are recognized as inputs to the terminal.

[0223] Step 7:

[0224] The server determines the appropriate action based on the received response and sends corresponding specific instructions to the terminal. For example, if the driver agrees to take a break, the server generates an instruction to set a rest spot in the navigation system and sends it to the terminal. This instruction is output from the server to the terminal.

[0225] Step 8:

[0226] The terminal controls the system's operation based on instructions received from the server. For example, the terminal can set a service area as a destination in the navigation system, which will automatically recalculate the route, allowing the driver to head to the designated rest spot.

[0227] In this way, data is processed in real time, and appropriate suggestions and actions are made throughout the system based on the driver and passengers' conditions.

[0228] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0229] MODE FOR CARRYING OUT THE INVENTION

[0230] The present invention is a system that monitors the state of the driver and passengers in real time, predicts their potential needs, and makes suggestions. In particular, by utilizing an emotion engine, it achieves more accurate emotion recognition and personalized suggestions. This system is composed of a detection means, an analysis means, a suggestion means, a control means, and an emotion engine.

[0231] System configuration

[0232] The entire system operates through communication between a terminal installed in the vehicle and a server built on the cloud. Each component will be explained below.

[0233] Device Features

[0234] The device is installed in the vehicle and is equipped with a camera and a microphone to detect the state of the driver and passengers. The camera captures the facial expressions of the driver and passengers in real time, and the microphone collects the content of their conversations. This information is used as a means of detection.

[0235] The device analyzes the collected data in real time to evaluate the safety and comfort of the vehicle, and transmits the acquired data to a cloud server at regular intervals.

[0236] Server Features

[0237] The server receives the data sent from the device and acts as an analysis means. The server performs the following processes:

[0238] 1. Facial Expression Analysis: Evaluate the emotional state (e.g., fatigue, stress, joy) of the driver and passengers based on facial expression data acquired from the camera.

[0239] 2. Voice analysis: Voice data collected by a microphone is analyzed using natural language processing, and psychological state is inferred from the content and tone of the conversation.

[0240] Emotion Engine Functions

[0241] The emotion engine is embedded in the server and integrates facial, voice, and behavioral data to recognize user emotions with high accuracy. The emotion engine integrates information from multiple data sources to assess the complex emotional states of the driver and passengers.

[0242] Suggestion and Control

[0243] The analysis results received from the server are sent to the device, which then uses the device to make appropriate suggestions to the driver and passengers. For example, the following suggestions may be considered:

[0244] "You seem a little tired from driving. Would you like to take a break at the next service area?"

[0245] "I have a new music playlist. Would you like to play it?"

[0246] "We are expecting a delay in arriving at our destination. Would you like us to contact our customer?"

[0247] The emotion engine also recognizes the driver's emotional state, allowing for more personalized suggestions. For example, if it determines that the driver is feeling stressed, it can suggest relaxing music or display an encouraging message.

[0248] The user responds to these suggestions and the terminal continues to act based on the response as a means of controlling the system.

[0249] Specific examples

[0250] Example 1: Proposal for a break in your car

[0251] If the driver continues driving for a long time, the camera detects frequent rubbing of the driver's eyes, and the microphone collects yawns. Based on this, the emotion engine in the server analyzes that the driver is highly fatigued, collects information about nearby service areas, and sends it to the device. The device then suggests to the driver, "You seem a little tired from driving. Would you like to take a break at the next service area?" If the driver agrees, the navigation system automatically sets the service area as the destination.

[0252] Example 2: Calling about being late in a commercial vehicle

[0253] If a commercial vehicle is stuck in traffic and is likely to be late for a scheduled business meeting, the device checks the current traffic conditions and the driver's calendar. The server's emotion engine analyzes this and, if it determines that there is a high possibility of a delay, generates a template for sending an automatic email. The device suggests to the driver, "Your arrival at your destination is likely to be delayed. Would you like to contact your customer?" If the driver accepts, the device automatically sends a relevant email to the customer.

[0254] As described above, this invention provides a safe and comfortable in-car environment by monitoring the state of the driver and passengers in real time, predicting their potential needs with high accuracy, and making appropriate suggestions. The introduction of an emotion engine makes it possible to realize even more personalized services.

[0255] The processing flow will be explained below.

[0256] Step 1:

[0257] The device initializes the vehicle's various sensors, cameras, and microphones, and acquires information on vehicle speed, fuel level, and location, while also capturing the facial expressions of the driver and passengers and collecting conversations.

[0258] Step 2:

[0259] The device transmits collected sensor data (vehicle speed, remaining fuel level, location information), facial expression data, and voice data to the server in real time. The transmitted data also includes a timestamp.

[0260] Step 3:

[0261] The server analyzes the received data. First, it performs facial expression analysis and evaluates the emotional state (e.g., fatigue, stress, joy) of the driver and passengers using facial expression data obtained from the camera.

[0262] Step 4:

[0263] The server performs voice analysis, analyzing the voice data collected from the microphone using natural language processing technology to infer the psychological state from the content and tone of the conversation.

[0264] Step 5:

[0265] The server utilizes an emotion engine to integrate facial, voice, and behavioral data to recognize the user's complex emotional state, for example, detecting fatigue from facial expressions and identifying stress from voice tone.

[0266] Step 6:

[0267] The server predicts the potential needs of the driver and passengers based on the analysis results. For example, if it determines that the driver is tired, it generates an action plan to suggest the next optimal rest point.

[0268] Step 7:

[0269] The server then sends the generated action plan to the terminal, which includes the proposed action content and its timing.

[0270] Step 8:

[0271] The device will then make suggestions to the driver and passengers based on the action plan it receives. For example, it might say to the driver, "You seem a little tired from driving. Would you like to take a break at the next service area?"

[0272] Step 9:

[0273] The user responds to the suggestion, and the driver has the option to accept or reject the suggestion.

[0274] Step 10:

[0275] The terminal receives the user's response and controls the system's operation based on the response. For example, if the driver agrees to take a break, the navigation system automatically sets the next service area as the destination.

[0276] Step 11:

[0277] The terminal records the user's feedback and responses and periodically transmits them to the server, thereby accumulating data for the entire system.

[0278] Step 12:

[0279] The server retrains the AI ​​model based on the accumulated feedback data, improving the system to make even more accurate suggestions. The retrained model is stored on the server.

[0280] Step 13:

[0281] The server then uses the improved AI model in the next analysis, improving the accuracy of the system's suggestions. This cycle allows the system to continuously evolve and provide better service to drivers and passengers.

[0282] Example 2

[0283] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0284] Conventional in-vehicle monitoring systems have difficulty accurately recognizing the emotional state of the driver and passengers in real time and making personalized suggestions based on that information. Furthermore, due to the low accuracy of emotional state recognition, it was not possible to accurately predict potential needs, limiting the effectiveness of the suggestions.

[0285] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: a detection means for detecting the states of the driver and passengers; a terminal means for performing an initial analysis of the data collected by the detection means; a communication means for transmitting the results of the initial analysis to a server on the cloud; an analysis means for performing a detailed analysis in the server; an emotion engine means for integrating the results obtained by the analysis means to evaluate the emotional states of the driver and passengers; a suggestion means for making suggestions to the driver and passengers based on their emotional states; and a control means for receiving responses from the driver and passengers to the suggestions and controlling the operation of the system based on the responses. This makes it possible to recognize the emotional states of the driver and passengers with high accuracy and make appropriate suggestions in real time.

[0286] The term "detection means" refers to devices and sensors that detect the state of the driver and passengers.

[0287] "Terminal means" refers to a computer or device installed in a vehicle that performs initial analysis of data collected by the detection means.

[0288] "Communication means" refers to the interface and protocol for sending and receiving data between the terminal means and the server on the cloud.

[0289] "Analysis means" refers to an algorithm or program for analyzing in detail the initial analysis results sent from the terminal means and evaluating the emotional state with high accuracy.

[0290] "Emotion engine means" refers to a system for integrating data obtained by the analysis means and recognizing and evaluating the emotional states of the driver and passengers.

[0291] "Suggestion means" refers to a device or software for generating and presenting appropriate suggestions to the driver and passengers based on the emotional state evaluated by the emotion engine means.

[0292] "Control means" refers to a device or program for receiving responses from the driver and passengers to the suggestions generated by the suggestion means, and for continuing or changing the operation of the system based on the responses.

[0293] "Image capture device" refers to a device that uses an optical device such as a camera to capture the facial expressions and movements of the driver and passengers.

[0294] "Audio capture device" refers to a device that uses an acoustic device such as a microphone to collect conversations and voices of the driver and passengers.

[0295] "Facial expression recognition technology" refers to algorithms for analyzing facial expression data captured by an image capture device and assessing emotional state.

[0296] "Natural language processing technology" refers to algorithms that analyze voice data collected by a voice capture device and evaluate the emotional state and content of the conversation.

[0297] The present invention is a system that monitors the status of the driver and passengers in real time, predicts their potential needs, and makes appropriate suggestions. This system operates in cooperation with a terminal installed in the vehicle and a server built on the cloud. Specific embodiments for implementing this system are described below.

[0298] System configuration

[0299] The system consists of a terminal, a server, an emotion engine, a detection means, an analysis means, a proposal means, and a control means.

[0300] Device Features

[0301] The device is installed in the vehicle and is equipped with a camera and a microphone to detect the state of the driver and passengers. The camera captures the facial expressions of the driver and passengers in real time, and the microphone collects the content of their conversations. This information is used as a means of detection.

[0302] The device performs an initial analysis of the collected data and evaluates the safety and comfort of the vehicle interior. For example, the device uses facial recognition technology to identify basic facial expressions such as smile, surprise, and anger, and analyzes voice data using natural language processing technology (e.g., the Python library NLP). The results of this initial analysis are sent to a cloud server at regular intervals.

[0303] Server Features

[0304] The server receives the data sent from the device and performs detailed analysis. The server uses a deep learning model (e.g., TensorFlow or PyTorch) to perform detailed analysis of the facial image and audio data. The results of the detailed analysis are sent to the emotion engine.

[0305] Emotion Engine Functions

[0306] The emotion engine is embedded in the server and integrates detailed analysis results to assess the emotional state of the driver and passengers with high accuracy. This emotion engine integrates information from multiple data sources and recognizes complex emotional states (e.g., fatigue, stress, joy, etc.).

[0307] Suggestion and Control

[0308] The analysis results received from the server are sent to the device, which then makes appropriate suggestions to the driver and passengers. For example, if the device determines that the driver is tired, it will suggest taking a break at the next service area. If the device detects stress, it will suggest playing relaxing music.

[0309] Specific prompt examples:

[0310] "Write a natural language description for a system that detects when a driver is tired and suggests a break if they've been driving for a long time."

[0311] The user responds to these suggestions using voice commands or the touch panel, and the device receives the response and performs the next action. For example, if the user accepts the break, the navigation system automatically sets up a service area.

[0312] Specific examples

[0313] Example 1: Proposal for a break in your car

[0314] The device's camera detects frequent rubbing of the driver's eyes, and the microphone collects yawns. Based on this, the emotion engine in the server analyzes the driver's level of fatigue and determines that the driver is fatigued. The server then sends information about nearby service areas to the device, which then suggests, "You seem a little tired from driving. Would you like to take a break at the next service area?" If the user accepts, the navigation system automatically sets the service area as the destination.

[0315] Example 2: Calling about being late in a commercial vehicle

[0316] The device checks the current traffic situation and the schedule, and detects the possibility of delay due to congestion. The server's emotion engine analyzes the possibility of delay, and if the delay is determined to be high, the server generates an automatic email sending template. The device suggests, "Your arrival at your destination is expected to be delayed. Would you like to contact your customer?" If the user accepts, the device automatically sends a relevant email to the customer.

[0317] In this way, the present invention provides a safe and comfortable in-vehicle environment by monitoring the emotional states of the driver and passengers in real time with high accuracy and making appropriate suggestions.

[0318] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0319] Step 1:

[0320] The device collects data using a camera and microphone. The camera captures the facial expressions of the driver and passengers in real time, and the microphone collects conversations and audio. The input is video and audio data from inside the vehicle, and the output is the captured raw data. Specifically, the camera captures faces at 15 frames per second, and the microphone records audio every 10 seconds.

[0321] Step 2:

[0322] The data collected by the device is initially analyzed. Facial expression data is analyzed using a facial recognition algorithm (e.g., OpenCV) to identify basic emotions (e.g., smile, surprise, anger). Voice data is analyzed for conversation content and tone using the Python library NLP. The input is the raw data acquired in step 1, and the output is the analyzed basic emotional state and voice content. Specifically, the process identifies facial expressions from image data and extracts tone and content from voice data.

[0323] Step 3:

[0324] The device sends the initial analysis results to the cloud server. The analysis results are converted to JSON format and sent to the server using the HTTP POST method. The input is the analysis results obtained in step 2, and the output is the JSON data sent to the cloud server. Specifically, the initial analysis results data is generated and sent to the server via the Internet.

[0325] Step 4:

[0326] The server performs detailed analysis of the data sent from the device. It uses a deep learning model (e.g., TensorFlow or PyTorch) to perform detailed analysis of the facial image and audio data and evaluates the emotional state with high accuracy. The input is the JSON data sent in step 3, and the output is the analyzed emotional state data. Specifically, the deep learning model is used to perform detailed facial and audio recognition.

[0327] Step 5:

[0328] The emotion engine in the server integrates the detailed analysis results and evaluates the emotional state of the driver and passengers with high accuracy. The input is the emotional state data obtained in step 4, and the output is the integrated final emotional state data. Specifically, it integrates multiple data sources to identify complex emotional states.

[0329] Step 6:

[0330] The server sends the analysis results to the device. The results are converted into JSON format and sent to the device using the HTTP POST method. The input is the final emotional state data obtained in step 5, and the output is the analysis result data sent to the device. Specifically, the emotional state data is generated in JSON format and sent to the device via the Internet.

[0331] Step 7:

[0332] Based on the analysis results received by the device from the server, the device generates and presents appropriate suggestions to the driver and passengers. The suggestions are generated using a text generation model (e.g., GPT-3) algorithm. The input is the analysis result data sent in step 6, and the output is the suggestion text presented to the user. Specifically, the suggestion text is generated and notified to the user by voice or on-screen display.

[0333] Step 8:

[0334] The user responds to suggestions from the device, and the device performs the next action based on the response. For example, if the user accepts a break, the device updates the navigation system to set the next service area. The input is the user's response, and the output is the next action to be performed. Specifically, the device receives a voice command or touch input and updates the destination in the navigation system.

[0335] In this way, the entire system works together to recognize emotional states with high accuracy and make appropriate suggestions in real time, thereby providing a safe and comfortable in-car environment for the driver and passengers.

[0336] (Application example 2)

[0337] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0338] To improve the safety and comfort of drivers and passengers while driving, a system that monitors their condition in real time and makes appropriate suggestions at the appropriate time is required. However, conventional systems have been unable to accurately grasp the emotional state of the driver and passengers, making it difficult to make personalized suggestions. Furthermore, they lacked the ability to suggest appropriate breaks or entertainment based on the driver's condition, such as fatigue or stress.

[0339] To solve the above problems, it is necessary to incorporate a function that can evaluate emotional states with high accuracy and a means to make personalized suggestions.

[0340] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: detection means consisting of a camera and a microphone that detects the state of the driver and passengers; analysis means that analyzes data collected by the detection means and predicts the potential needs of the driver and passengers; proposal means that makes suggestions to the driver and passengers based on the needs predicted by the analysis means; and control means that receives responses from the driver and passengers to the proposals and controls the operation of the system based on the responses. The server also includes information provision means that uses an emotion engine to accurately evaluate the emotional states of the driver and passengers, and provides information about nearby rest spots if signs of fatigue are observed, and entertainment provision means that suggests a new music playlist if the driver and passengers are relaxed.

[0341] This makes it possible to grasp the emotional state of the driver and passengers with high accuracy in real time and make personalized suggestions, thereby providing a safe and comfortable driving environment.

[0342] 1. "Driver and passenger condition" means the physical and psychological state of the driver and passenger, including facial expressions, voice, and other physiological responses.

[0343] 2. "Detection means" refers to a device that includes an imaging device for capturing facial expressions of the driver and passengers and an audio collection device for collecting conversations.

[0344] 3. "Analysis Means" means technology that analyzes data collected by the Detection Means and predicts the potential needs and emotional states of the driver and passengers.

[0345] 4. "Proposal means" refers to a device or function that makes appropriate suggestions to the driver and passengers based on the needs predicted by the analysis means.

[0346] 5. "Control means" means a device or function for receiving responses from the driver and passengers to suggestions from the suggestion means and controlling the operation of the system based on those responses.

[0347] 6. "Emotion Engine" is a technology that integrates facial expressions, voice, and behavioral data of the driver and passengers to recognize and evaluate their emotional state with high accuracy.

[0348] 7. "Navigation and entertainment suggestion means" is a function that suggests navigation information and entertainment content based on the analysis results of the emotion engine.

[0349] 8. "Information Providing Means" means a device or function that provides information about nearby rest points when signs of fatigue are present.

[0350] 9. "Entertainment Provider" is a feature that suggests new music playlists and other entertainment content when you're relaxing.

[0351] A specific embodiment for carrying out the present invention will be described. The system of the present invention is composed of a terminal in a vehicle and a server on a cloud. The operation of the entire system is as follows.

[0352] Device configuration and functions

[0353] The terminal is installed in the vehicle and includes the following main hardware and software:

[0354] Image capture device: A camera for capturing the facial expressions of the driver and passengers in real time.

[0355] Audio collection device: Uses a microphone to collect conversations between the driver and passengers.

[0356] The terminal sends the data detected by these devices to a server on the cloud. Specifically, the camera and microphone collect signs of driver fatigue and stress and send them to the cloud server.

[0357] Server configuration and functions

[0358] The server includes the following main software and hardware components:

[0359] Emotion Engine: Integrates information from multiple data sources to accurately recognize the emotional state of the driver and passengers. Analysis is performed using facial expression and voice recognition technologies.

[0360] Software used: OpenCV, Google Cloud Speech-to-Text

[0361] Data analysis means: Data sent to the cloud server is analyzed in real time to evaluate the emotional state of the driver and passengers.

[0362] The server sends the analysis results to the device, which then makes appropriate suggestions based on the results.

[0363] Specific proposal methods and information provision

[0364] Based on the results of the emotion engine's analysis, the following specific suggestions are made:

[0365] Information provision method: If the driver frequently rubs their eyes or yawns, the emotion engine will evaluate the signs of fatigue as high. The device will suggest, "You are showing signs of fatigue. Would you like to take a break at the next service area?" If the user accepts, it will provide information about nearby rest points and set up navigation.

[0366] Entertainment provision method: If the system detects that the driver's facial expressions or voice indicate that they are relaxed, it will suggest a new music playlist. It will display a message saying, "You look relaxed. Would you like to play a new music playlist?"

[0367] Specific examples of processing procedures

[0368] For example, if a driver is driving for a long time in front of the camera and rubbing their eyes frequently, the emotion engine will analyze the driver's fatigue level and send information about nearby service areas to the device, suggesting a break. If the driver is relaxed, the system will suggest a music playlist, providing a comfortable environment.

[0369] An example of a prompt is as follows:

[0370] Create a system that suggests rest stops when the user shows signs of fatigue, suggests nearby rest areas, for example if the driver is rubbing their eyes frequently, or suggests a new music playlist when the driver is in a relaxed state.

[0371] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0372] Step 1:

[0373] The device's detection means, an imaging device and an audio collection device, capture the facial expressions and voices of the driver and passengers in real time. The input is video data from the camera and audio data from the microphone. Specifically, the device periodically collects data and sends it to a cloud server. The output is this video data and audio data.

[0374] Step 2:

[0375] The emotion engine on the server receives the video and audio data sent from the device. The input is video and audio data. The emotion engine analyzes this data using facial expression recognition technology (e.g., OpenCV) and speech recognition technology (e.g., Google Cloud Speech-to-Text). The output is the emotional state of the driver and passengers (e.g., degree of fatigue, stress, and relaxation).

[0376] Step 3:

[0377] The server's analysis means predicts the potential needs of the driver and passengers based on this emotional state. The input is emotional state data obtained from the emotion engine. Based on this, the analysis means determines needs, such as "the driver is tired" or "the driver is relaxed." The output is data related to needs.

[0378] Step 4:

[0379] The server generates appropriate suggestions based on the needs predicted by the analysis means. The input is data related to the needs. For example, if it determines that the user "needs a break," it will suggest information about nearby resting points. If it determines that the user "is relaxing," it will generate a suggestion for a new music playlist. The output is the suggestion content.

[0380] Step 5:

[0381] The server sends the suggestion to the device. The input is the suggestion from the server. The device displays it to the driver and passengers. As a specific operation, the suggestion is provided via a display device or voice assistant. The output is the state in which the suggestion is presented to the driver and passengers.

[0382] Step 6:

[0383] The user (driver and passengers) responds to this suggestion. The input is the suggestion content. Response options include, for example, "take a break" or "play music." The output is the user's response.

[0384] Step 7:

[0385] The terminal receives the user's response and controls the system's operation as a control means. The input is the user's response. Specific operations include setting navigation to a rest point or playing a music playlist. The output is the state in which the system has performed the appropriate operation.

[0386] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0387] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0388] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0389] [Second embodiment]

[0390] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0391] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0392] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0393] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0394] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0395] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0396] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0397] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0398] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0399] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0400] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0401] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0402] MODE FOR CARRYING OUT THE INVENTION

[0403] The present invention is a system that monitors the state of a driver and passengers in real time, predicts potential needs, and makes suggestions. This system is composed of a detection means, an analysis means, a suggestion means, and a control means.

[0404] System configuration

[0405] The entire system operates through communication between a terminal installed in the vehicle and a server built on the cloud.

[0406] Device Features

[0407] The device is installed in the vehicle and is equipped with a camera and a microphone to detect the state of the driver and passengers. The camera captures the facial expressions of the driver and passengers in real time, and the microphone collects the content of their conversations. This information is used as a means of detection.

[0408] The device analyzes the collected data in real time to evaluate the safety and comfort of the vehicle, and transmits the acquired data to a cloud server at regular intervals.

[0409] Server Features

[0410] The server receives the data sent from the device and acts as an analysis means. The server performs the following processes:

[0411] 1. Facial Expression Analysis: Evaluate the emotional state (e.g., fatigue, stress, joy) of the driver and passengers based on facial expression data acquired from the camera.

[0412] 2. Voice analysis: Voice data collected by a microphone is analyzed using natural language processing, and psychological state is inferred from the content and tone of the conversation.

[0413] Based on the analysis results, the server predicts the potential needs of the driver and passengers and generates specific actions as suggestions. For example, if the server determines that the driver is tired, it will suggest rest spots.

[0414] Suggestion and Control

[0415] The analysis results received from the server are sent to the device, which then uses the device to make appropriate suggestions to the driver and passengers. For example, the following suggestions may be considered:

[0416] "You seem a little tired from driving. Would you like to take a break at the next service area?"

[0417] "I have a new music playlist. Would you like to play it?"

[0418] "We are expecting a delay in arriving at our destination. Would you like us to contact our customer?"

[0419] The user responds to these suggestions and the terminal continues to act based on the response as a means of controlling the system.

[0420] Specific examples

[0421] Example 1: Proposal for a break in your car

[0422] If the driver continues driving for a long time, the camera detects frequent rubbing of the driver's eyes, and the microphone collects yawns. Based on this, the server analyzes that the driver is highly fatigued, collects information about nearby service areas, and sends it to the device. The device then suggests to the driver, "You seem a little tired from driving. Would you like to take a break at the next service area?" If the driver agrees, the navigation system automatically sets the service area as the destination.

[0423] Example 2: Calling about being late in a commercial vehicle

[0424] If a commercial vehicle is stuck in traffic and is likely to be late for a scheduled business meeting, the terminal checks the current traffic conditions and the driver's schedule. The server analyzes this and, if it determines that there is a high possibility of a delay, generates a template for sending an automatic email. The terminal then suggests to the driver, "Your arrival at your destination is likely to be delayed. Would you like to contact your customer?" If the driver accepts, the terminal automatically sends a relevant email to the customer.

[0425] As described above, the present invention provides a safe and comfortable in-vehicle environment by monitoring the state of the driver and passengers in real time, predicting their potential needs, and making appropriate suggestions.

[0426] The processing flow will be explained below.

[0427] Step 1:

[0428] The device initializes the vehicle's various sensors and acquires information on vehicle speed, fuel level, and location, thereby collecting basic vehicle operation data.

[0429] Step 2:

[0430] The device uses a camera to capture the facial expressions of the driver and passengers, and also uses an in-car microphone to collect conversations and obtain voice data.

[0431] Step 3:

[0432] The device periodically transmits the collected sensor data, facial expression data, and voice data to the server in real time.

[0433] Step 4:

[0434] The server analyzes the received data, specifically assessing the driver and passengers' emotional state (e.g., fatigue, stress, joy) using facial expression data, and inferring their psychological state from the content and tone of their conversations using audio data.

[0435] Step 5:

[0436] Based on the analysis results, the server predicts the potential needs of the driver and passengers. For example, if it determines that the driver is tired, it generates an action plan, such as suggesting nearby rest areas.

[0437] Step 6:

[0438] The server sends the generated action plan to the device, which includes the content and timing of the proposal.

[0439] Step 7:

[0440] Based on the action plan received, the device will make suggestions to the driver and passengers. For example, it might say to the driver, "You seem a little tired while driving. Would you like to take a break at the next service area?"

[0441] Step 8:

[0442] The user (driver and passengers) responds to the proposal by choosing to accept or reject it.

[0443] Step 9:

[0444] The terminal receives the user's response and controls the system's operation based on the response. For example, if the driver agrees to take a break, the navigation system automatically sets the next service area as the destination.

[0445] Step 10:

[0446] The device records the user's feedback and responses and periodically sends them to the server, which accumulates the data.

[0447] Step 11:

[0448] The server retrains the AI ​​model based on the collected feedback data, improving the system to make more accurate suggestions. The retrained model is stored on the server side.

[0449] Step 12:

[0450] The server then uses the improved AI model in the next analysis, improving the accuracy of the system's suggestions. This cycle allows the system to continuously evolve.

[0451] Example 1

[0452] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0453] When driving a vehicle, real-time monitoring of the driver and passengers' condition is required to maintain safety and comfort. However, current systems lack the ability to effectively analyze the facial expressions and conversations of the driver and passengers, predict their potential needs, and make appropriate suggestions. This makes it difficult to quickly detect driver fatigue or stress and instruct them to take breaks or other measures at the appropriate time. There is also a lack of means to respond appropriately in situations such as traffic congestion and delays.

[0454] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0455] In this invention, the server includes a detection means for detecting the states of the driver and passengers in real time, a data transmission means for transmitting data collected by the detection means to the server, an analysis means for analyzing the data received by the server and predicting potential needs of the driver and passengers, a proposal means for making suggestions to the driver and passengers based on the needs predicted by the analysis means, and a control means for receiving responses from the driver and passengers to the proposals and controlling the operation of the system based on the responses. This makes it possible to effectively monitor the states of the driver and passengers in real time, accurately predict potential needs, and make suggestions to maintain safety and comfort.

[0456] "Detection means" refers to devices and sensors for detecting the state of the driver and passengers in real time.

[0457] "Data transmission means" refers to a communication module or protocol for transmitting data collected by the detection means to a server.

[0458] "Analysis means" refers to algorithms or software that analyze the state of the driver and passengers based on the data received by the server and predict their potential needs.

[0459] The "suggestion means" refers to a user interface or notification system for making specific suggestions to the driver and passengers based on the needs predicted by the analysis means.

[0460] "Control means" refers to hardware and / or software for receiving driver and passenger responses to suggestions and controlling the operation of the system based on those responses.

[0461] "Image capture device" refers to a camera or video device used to capture the facial expressions of the driver and passengers.

[0462] "Audio capture device" refers to a microphone or other audio collection device for capturing driver and passenger conversations.

[0463] "Image processing technology" refers to the techniques and algorithms used to analyze images captured by an imaging device and evaluate facial expressions and emotional states.

[0464] "Natural language processing" refers to a technology that converts voice data collected by a voice capture device into text and analyzes that text to evaluate psychological state and emotions.

[0465] This invention is a system that monitors the status of the driver and passengers in the vehicle in real time, predicts their potential needs, and makes appropriate suggestions. This system operates through communication between a terminal installed in the vehicle and a server built on the cloud.

[0466] System Configuration

[0467] Device configuration

[0468] The terminal is installed inside the vehicle and has a detection means for detecting the state of the driver and passengers. The detection means uses an image capture device (camera) to capture the facial expressions of the driver and passengers, and a voice capture device (microphone) to collect the content of their conversations.

[0469] The camera captures the faces of the driver and passengers in real time and uses image processing libraries such as OpenCV and dlib to extract facial features. The audio capture device collects high-quality ambient audio and converts it into text using the Google Cloud Speech-to-Text API.

[0470] The device has a data transmission means to send collected data to the server in real time, and sends the data to the server's API endpoint using an HTTP POST request. The data is encrypted in JSON format and sent securely.

[0471] Server Configuration

[0472] The server has an analysis means for analyzing the data sent from the terminal. Specifically, it performs the following processes.

[0473] 1. Facial Expression Analysis: Facial expression data acquired from a camera is analyzed to evaluate emotional states (e.g., fatigue, stress, joy). By quantifying the emotional states using Python's OpenCV library, the psychological state of the driver and passengers can be accurately evaluated.

[0474] 2. Voice analysis: Voice data acquired from the microphone is analyzed using natural language processing (NLP) technology. The voice is converted into text using the Google Cloud Speech-to-Text API, and then text analysis is performed. This allows the system to infer the user's psychological state from the content and tone of the conversation.

[0475] Based on the analysis results, the server predicts the potential needs of the driver and passengers and has a suggestion means for generating specific actions and suggestions.

[0476] Proposal submission and response

[0477] The device receives analysis results and suggestions from the server and presents them to the driver and passengers as notifications and alerts, who can then respond to the suggestions using the touchscreen or voice commands.

[0478] For example, if the device determines that the driver is tired after a long drive, it will display a prompt such as, "You seem a little tired from driving. Would you like to take a break at the next service area?" If the device has a voice assistant function, it will make similar suggestions aloud.

[0479] If the user responds by accepting the suggestion, the device will use the navigation system (e.g., Google Maps API) to set the next service area as the destination. If a delay due to traffic congestion is predicted, the device will display a prompt saying, "Arrival at the destination is expected to be delayed. Would you like to contact the customer?" and will automatically contact the customer if the user responds in favor.

[0480] Specific operation example

[0481] Example 1: Proposal for a break in your car

[0482] 1. The device uses a camera to detect frequent rubbing of the driver's eyes and a microphone to collect audio of yawning.

[0483] 2. The device sends the collected data to the server.

[0484] 3. The server analyzes the driver's fatigue level using facial expression recognition algorithms and natural language processing.

[0485] 4. The server generates suggestions to encourage the driver to take a break.

[0486] 5. The device will display a suggestion: "You seem a little tired from driving. Would you like to take a break at the next service area?"

[0487] 6. The user responds "Yes."

[0488] 7. The device sets the next service area in the navigation system and guides the driver there.

[0489] Example 2: Calling about being late in a commercial vehicle

[0490] 1. The device checks the current traffic conditions and schedule to detect congestion information.

[0491] 2. The device sends this data to the server.

[0492] 3. The server analyzes the traffic data and determines that you are likely to be late for a scheduled business meeting.

[0493] 4. The server generates the template for the automated email.

[0494] 5. The terminal displays the suggestion, "Your arrival at your destination is expected to be delayed. Would you like to contact your customer?"

[0495] 6. The user responds "Yes."

[0496] 7. The device automatically sends emails to customers using the prepared email templates.

[0497] In this way, the system of the present invention can effectively monitor the state of the driver and passengers in real time, accurately predict their potential needs, and make suggestions to maintain safety and comfort.

[0498] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0499] Step 1: Data collection

[0500] The device detects the driver and passengers' status using a camera and microphone installed in the vehicle. The camera recognizes faces in real time using the OpenCV library and extracts facial features using the dlib library. The microphone collects audio data and converts it into text using the Google Cloud Speech-to-Text API.

[0501] Input: Video and audio data of the driver and passengers

[0502] Specific operation: The camera captures the driver's facial expressions, and the microphone collects the conversation.

[0503] Output: Facial expression data (feature points) and speech text data

[0504] Step 2: Send data

[0505] The device sends the collected facial expression data and speech-to-text data to a server at regular intervals, where the data is encrypted and sent to the server's API endpoint in the cloud using an HTTP POST request.

[0506] Input: facial expression data (feature points) and voice text data

[0507] Specific operation: The data collected by the device is converted into JSON format, encrypted, and sent to the server.

[0508] Output: JSON formatted data sent to the server

[0509] Step 3: Receiving data

[0510] The server receives the JSON formatted data sent from the device, and stores it in a database for analysis.

[0511] Input: JSON format data sent from the terminal

[0512] Specific operation: The server's API analyzes the received data and extracts the necessary information.

[0513] Output: Raw data prepared for analysis

[0514] Step 4: Data analysis

[0515] The server analyzes the received data, using the OpenCV library to evaluate the emotional state (e.g., fatigue, stress, joy) of the facial expression data, and natural language processing (NLP) techniques to analyze the psychological state and conversation content of the speech and text data.

[0516] Input: Prepared raw data

[0517] Specific operations: Facial recognition algorithms are run on facial expression data to quantify emotional states, and NLP algorithms are run on voice and text data to analyze psychological states and conversation content.

[0518] Output: Analyzed emotional and psychological state data

[0519] Step 5: Proposal Generation

[0520] The server predicts the potential needs of the driver and passengers based on the analysis results, and generates appropriate suggestions based on those needs. For example, if the driver is tired, it generates suggestions to encourage them to take a break.

[0521] Input: Analyzed emotional and psychological state data

[0522] Specific behavior: Evaluate the analysis results and generate appropriate prompts or suggestions. For example, "You seem a little tired while driving. Would you like to take a break at the next service station?"

[0523] Output: Generated prompts and suggestions

[0524] Step 6: Proposal Presentation

[0525] The device displays the suggestions received from the server to the driver and passengers, and provides the suggestions through a user interface or a voice assistant.

[0526] Input: Generated prompts and suggestions

[0527] Specific operation: The device displays and presents suggestions on the LCD screen or voice assistant.

[0528] Output: Suggested prompts and suggestions for the user

[0529] Step 7: User response

[0530] The user responds to the suggestion, for example by selecting "yes" or "no" using a touchscreen or by responding with a voice command.

[0531] Input: Prompts and suggestions provided

[0532] Specific Action: The user responds with a touchscreen or voice command, for example, saying "Yes."

[0533] Output: User response data

[0534] Step 8: Take Action

[0535] The device then takes appropriate action based on the user's response, such as using the navigation system to set the destination to the nearest service area, or automatically contacting the customer using a template to inform them they'll be late.

[0536] Input: User response data

[0537] Specific operation: When the user accepts the break, the terminal will set the service area in the navigation system. When the user accepts the delay notification, the terminal will automatically send an email to the customer.

[0538] Output: Destination setting for navigation systems and automatic email transmission

[0539] In this way, the entire system works together to monitor the driver and passengers' status in real time, making it possible to make appropriate suggestions and take appropriate actions based on their potential needs.

[0540] (Application example 1)

[0541] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0542] In recent years, there has been a growing demand for systems that monitor the status of drivers and passengers in real time and propose appropriate actions to improve vehicle safety and comfort. However, existing systems rely too heavily on on-site analysis, which can result in delays and reduced accuracy in data processing. The present invention aims to solve these problems and provide a system that enables highly accurate and rapid analysis and proposals using a cloud server.

[0543] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0544] In this invention, the server includes a detection means for detecting the states of the driver and passengers, an analysis means for analyzing data collected by the detection means and predicting potential needs of the driver and passengers, a proposal means for making suggestions to the driver and passengers based on the needs predicted by the analysis means, a control means for receiving responses from the driver and passengers to the proposals and controlling the operation of the system based on the responses, and a communication means for transmitting the detected data to a cloud server, receiving the results, and providing them to the proposal means, thereby enabling highly accurate data analysis in real time and rapid proposals.

[0545] "Driver and passenger" means the person driving the vehicle and any passengers in the vehicle.

[0546] The "detection means" is a means for detecting the state of the driver and passengers, and includes devices such as a camera and a microphone.

[0547] The "analysis means" is a means for analyzing the data collected by the detection means and predicting the potential needs of the driver and passengers.

[0548] The "suggestion means" is a means for making specific suggestions to the driver and passengers based on the needs predicted by the analysis means.

[0549] "Control means" means for receiving driver and passenger responses to suggestions and controlling the operation of the system based on the responses.

[0550] The "communication means" is a means for transmitting detected data to a cloud server, receiving the results, and providing them to the proposal means.

[0551] A "cloud server" is a remote server connected via the Internet to perform data analysis and provide information.

[0552] "Facial expression recognition technology" is a technology that analyzes a person's facial expressions from images and videos to evaluate their emotional state.

[0553] "Speech recognition technology" is a technology that analyzes voice data and extracts information from its content and tone.

[0554] "Real-time" refers to the state in which data is processed and results are obtained immediately in real time.

[0555] "Potential needs" are requirements that the driver and passengers are not aware of at present, but may become necessary in the near future.

[0556] This invention is a system that monitors the status of the driver and passengers in an autonomous vehicle in real time, predicts potential needs, and makes suggestions. This system is configured using the following hardware and software.

[0557] System configuration

[0558] The entire system operates through communication between a terminal installed in the vehicle and a server built on the cloud.

[0559] Device Features

[0560] The device is installed inside the vehicle and is equipped with a camera and microphone to detect the state of the driver and passengers. In this case, the camera captures video using OpenCV and audio using pyaudio. The camera captures the facial expressions of the driver and passengers in real time, and the microphone collects the content of their conversations. This information is used as a means of detection.

[0561] The devices are equipped with a communication means to send collected data to a cloud server in real time, minimizing data delays and enabling fast and accurate analysis.

[0562] Server Features

[0563] The server receives the data sent from the device and acts as an analysis means. The server performs the following processes:

[0564] 1. Facial Expression Analysis: Evaluate the emotional state (e.g., fatigue, stress, joy) of the driver and passengers based on facial expression data acquired from the camera. Uses facial expression recognition technology.

[0565] 2. Voice analysis: Voice data collected by a microphone is analyzed using natural language processing to infer a person's psychological state from the content and tone of the conversation. Voice recognition technology is used.

[0566] Based on the analysis results, the server predicts the potential needs of the driver and passengers and generates specific actions as suggestions. For example, if the server determines that the driver is tired, it will suggest rest spots.

[0567] Suggestion and Control

[0568] The analysis results received from the server are sent to the terminal, which then makes appropriate suggestions to the driver and passengers as a suggestion means.

[0569] Examples:

[0570] 1. Suggesting a break

[0571] If the camera detects the driver rubbing their eyes frequently and the microphone picks up yawning, the server will determine that the driver is highly fatigued. It will then collect information about nearby service areas and send it to the device. The device will then suggest to the driver, "You seem a little tired from driving. Would you like to take a break at the next service area?" If the driver agrees, the navigation system will automatically set the service area as the destination.

[0572] 2. Relaxation suggestions

[0573] If the device determines that the driver is feeling stressed based on facial expression and voice data, it will suggest, "Would you like to play some relaxing music?" If the driver agrees, relaxing music will automatically play on the car's audio system.

[0574] Prompt Sentence Examples

[0575] "Based on facial expression and voice data, it seems the driver is tired. Please suggest a place to rest."

[0576] In this way, the state of the driver and passengers can be monitored in real time, potential needs can be predicted, and appropriate suggestions can be made to provide a safe and comfortable in-car environment.

[0577] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0578] Step 1:

[0579] The device uses a camera and microphone to capture the facial expressions and voices of the driver and passengers in real time. Video data from the camera and audio data from the microphone are input. This data is temporarily stored within the device.

[0580] Step 2:

[0581] The device formats and compresses the collected facial expression and audio data for transmission to the cloud server. A transmission protocol to the cloud server is executed, and the data is uploaded to the cloud server. Here, the video data is encoded into MJPEG or H.264 format, and the audio data is encoded into WAV or AAC format. The encoded data becomes the input to the cloud server.

[0582] Step 3:

[0583] The server analyzes the received facial expression and voice data. For the facial expression data, a facial expression recognition algorithm using a generative AI model is run to evaluate the emotional state of the driver and passengers. For the voice data, natural language processing and voice recognition technologies are used to infer the psychological state from the content and tone of the conversation. The results of these analyses are obtained as output from the cloud server.

[0584] Step 4:

[0585] The server predicts the potential needs of the driver and passengers based on the analysis results. Based on the predicted needs, appropriate suggestions are generated. Here, a generative AI model is used to generate prompts that suggest appropriate actions for the driver, such as taking a break or playing music. These suggestions are output from the cloud server to the device.

[0586] Step 5:

[0587] The device receives suggestions from the server and presents them to the driver and passengers. For example, a suggestion such as "You seem a little tired from driving. Would you like to take a break at the next service area?" is displayed on the display screen. The driver and passengers' responses are received via voice or touch interface.

[0588] Step 6:

[0589] The terminal receives the responses of the driver and passengers and sends them to the server. For example, if the driver responds "yes," the voice data is sent to the server. Once this data transmission is complete, the responses of the driver and passengers are recognized as inputs to the terminal.

[0590] Step 7:

[0591] The server determines the appropriate action based on the received response and sends corresponding specific instructions to the terminal. For example, if the driver agrees to take a break, the server generates an instruction to set a rest spot in the navigation system and sends it to the terminal. This instruction is output from the server to the terminal.

[0592] Step 8:

[0593] The terminal controls the system's operation based on instructions received from the server. For example, the terminal can set a service area as a destination in the navigation system, which will automatically recalculate the route, allowing the driver to head to the designated rest spot.

[0594] In this way, data is processed in real time, and appropriate suggestions and actions are made throughout the system based on the driver and passengers' conditions.

[0595] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0596] MODE FOR CARRYING OUT THE INVENTION

[0597] The present invention is a system that monitors the state of the driver and passengers in real time, predicts their potential needs, and makes suggestions. In particular, by utilizing an emotion engine, it achieves more accurate emotion recognition and personalized suggestions. This system is composed of a detection means, an analysis means, a suggestion means, a control means, and an emotion engine.

[0598] System configuration

[0599] The entire system operates through communication between a terminal installed in the vehicle and a server built on the cloud. Each component will be explained below.

[0600] Device Features

[0601] The device is installed in the vehicle and is equipped with a camera and a microphone to detect the state of the driver and passengers. The camera captures the facial expressions of the driver and passengers in real time, and the microphone collects the content of their conversations. This information is used as a means of detection.

[0602] The device analyzes the collected data in real time to evaluate the safety and comfort of the vehicle, and transmits the acquired data to a cloud server at regular intervals.

[0603] Server Features

[0604] The server receives the data sent from the device and acts as an analysis means. The server performs the following processes:

[0605] 1. Facial Expression Analysis: Evaluate the emotional state (e.g., fatigue, stress, joy) of the driver and passengers based on facial expression data acquired from the camera.

[0606] 2. Voice analysis: Voice data collected by a microphone is analyzed using natural language processing, and psychological state is inferred from the content and tone of the conversation.

[0607] Emotion Engine Functions

[0608] The emotion engine is embedded in the server and integrates facial, voice, and behavioral data to recognize user emotions with high accuracy. The emotion engine integrates information from multiple data sources to assess the complex emotional states of the driver and passengers.

[0609] Suggestion and Control

[0610] The analysis results received from the server are sent to the device, which then uses the device to make appropriate suggestions to the driver and passengers. For example, the following suggestions may be considered:

[0611] "You seem a little tired from driving. Would you like to take a break at the next service area?"

[0612] "I have a new music playlist. Would you like to play it?"

[0613] "We are expecting a delay in arriving at our destination. Would you like us to contact our customer?"

[0614] The emotion engine also recognizes the driver's emotional state, allowing for more personalized suggestions. For example, if it determines that the driver is feeling stressed, it can suggest relaxing music or display an encouraging message.

[0615] The user responds to these suggestions and the terminal continues to act based on the response as a means of controlling the system.

[0616] Specific examples

[0617] Example 1: Proposal for a break in your car

[0618] If the driver continues driving for a long time, the camera detects frequent rubbing of the driver's eyes, and the microphone collects yawns. Based on this, the emotion engine in the server analyzes that the driver is highly fatigued, collects information about nearby service areas, and sends it to the device. The device then suggests to the driver, "You seem a little tired from driving. Would you like to take a break at the next service area?" If the driver agrees, the navigation system automatically sets the service area as the destination.

[0619] Example 2: Calling about being late in a commercial vehicle

[0620] If a commercial vehicle is stuck in traffic and is likely to be late for a scheduled business meeting, the device checks the current traffic conditions and the driver's calendar. The server's emotion engine analyzes this and, if it determines that there is a high possibility of a delay, generates a template for sending an automatic email. The device suggests to the driver, "Your arrival at your destination is likely to be delayed. Would you like to contact your customer?" If the driver accepts, the device automatically sends a relevant email to the customer.

[0621] As described above, this invention provides a safe and comfortable in-car environment by monitoring the state of the driver and passengers in real time, predicting their potential needs with high accuracy, and making appropriate suggestions. The introduction of an emotion engine makes it possible to realize even more personalized services.

[0622] The processing flow will be explained below.

[0623] Step 1:

[0624] The device initializes the vehicle's various sensors, cameras, and microphones, and acquires information on vehicle speed, fuel level, and location, while also capturing the facial expressions of the driver and passengers and collecting conversations.

[0625] Step 2:

[0626] The device transmits collected sensor data (vehicle speed, remaining fuel level, location information), facial expression data, and voice data to the server in real time. The transmitted data also includes a timestamp.

[0627] Step 3:

[0628] The server analyzes the received data. First, it performs facial expression analysis and evaluates the emotional state (e.g., fatigue, stress, joy) of the driver and passengers using facial expression data obtained from the camera.

[0629] Step 4:

[0630] The server performs voice analysis, analyzing the voice data collected from the microphone using natural language processing technology to infer the psychological state from the content and tone of the conversation.

[0631] Step 5:

[0632] The server utilizes an emotion engine to integrate facial, voice, and behavioral data to recognize the user's complex emotional state, for example, detecting fatigue from facial expressions and identifying stress from voice tone.

[0633] Step 6:

[0634] The server predicts the potential needs of the driver and passengers based on the analysis results. For example, if it determines that the driver is tired, it generates an action plan to suggest the next optimal rest point.

[0635] Step 7:

[0636] The server then sends the generated action plan to the terminal, which includes the proposed action content and its timing.

[0637] Step 8:

[0638] The device will then make suggestions to the driver and passengers based on the action plan it receives. For example, it might say to the driver, "You seem a little tired from driving. Would you like to take a break at the next service area?"

[0639] Step 9:

[0640] The user responds to the suggestion, and the driver has the option to accept or reject the suggestion.

[0641] Step 10:

[0642] The terminal receives the user's response and controls the system's operation based on the response. For example, if the driver agrees to take a break, the navigation system automatically sets the next service area as the destination.

[0643] Step 11:

[0644] The terminal records the user's feedback and responses and periodically transmits them to the server, thereby accumulating data for the entire system.

[0645] Step 12:

[0646] The server retrains the AI ​​model based on the accumulated feedback data, improving the system to make even more accurate suggestions. The retrained model is stored on the server.

[0647] Step 13:

[0648] The server then uses the improved AI model in the next analysis, improving the accuracy of the system's suggestions. This cycle allows the system to continuously evolve and provide better service to drivers and passengers.

[0649] Example 2

[0650] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0651] Conventional in-vehicle monitoring systems have difficulty accurately recognizing the emotional state of the driver and passengers in real time and making personalized suggestions based on that information. Furthermore, due to the low accuracy of emotional state recognition, it was not possible to accurately predict potential needs, limiting the effectiveness of the suggestions.

[0652] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: a detection means for detecting the states of the driver and passengers; a terminal means for performing an initial analysis of the data collected by the detection means; a communication means for transmitting the results of the initial analysis to a server on the cloud; an analysis means for performing a detailed analysis in the server; an emotion engine means for integrating the results obtained by the analysis means to evaluate the emotional states of the driver and passengers; a suggestion means for making suggestions to the driver and passengers based on their emotional states; and a control means for receiving responses from the driver and passengers to the suggestions and controlling the operation of the system based on the responses. This makes it possible to recognize the emotional states of the driver and passengers with high accuracy and make appropriate suggestions in real time.

[0653] The term "detection means" refers to devices and sensors that detect the state of the driver and passengers.

[0654] "Terminal means" refers to a computer or device installed in a vehicle that performs initial analysis of data collected by the detection means.

[0655] "Communication means" refers to the interface and protocol for sending and receiving data between the terminal means and the server on the cloud.

[0656] "Analysis means" refers to an algorithm or program for analyzing in detail the initial analysis results sent from the terminal means and evaluating the emotional state with high accuracy.

[0657] "Emotion engine means" refers to a system for integrating data obtained by the analysis means and recognizing and evaluating the emotional states of the driver and passengers.

[0658] "Suggestion means" refers to a device or software for generating and presenting appropriate suggestions to the driver and passengers based on the emotional state evaluated by the emotion engine means.

[0659] "Control means" refers to a device or program for receiving responses from the driver and passengers to the suggestions generated by the suggestion means, and for continuing or changing the operation of the system based on the responses.

[0660] "Image capture device" refers to a device that uses an optical device such as a camera to capture the facial expressions and movements of the driver and passengers.

[0661] "Audio capture device" refers to a device that uses an acoustic device such as a microphone to collect conversations and voices of the driver and passengers.

[0662] "Facial expression recognition technology" refers to algorithms for analyzing facial expression data captured by an image capture device and assessing emotional state.

[0663] "Natural language processing technology" refers to algorithms that analyze voice data collected by a voice capture device and evaluate the emotional state and content of the conversation.

[0664] The present invention is a system that monitors the status of the driver and passengers in real time, predicts their potential needs, and makes appropriate suggestions. This system operates in cooperation with a terminal installed in the vehicle and a server built on the cloud. Specific embodiments for implementing this system are described below.

[0665] System configuration

[0666] The system consists of a terminal, a server, an emotion engine, a detection means, an analysis means, a proposal means, and a control means.

[0667] Device Features

[0668] The device is installed in the vehicle and is equipped with a camera and a microphone to detect the state of the driver and passengers. The camera captures the facial expressions of the driver and passengers in real time, and the microphone collects the content of their conversations. This information is used as a means of detection.

[0669] The device performs an initial analysis of the collected data and evaluates the safety and comfort of the vehicle interior. For example, the device uses facial recognition technology to identify basic facial expressions such as smile, surprise, and anger, and analyzes voice data using natural language processing technology (e.g., the Python library NLP). The results of this initial analysis are sent to a cloud server at regular intervals.

[0670] Server Features

[0671] The server receives the data sent from the device and performs detailed analysis. The server uses a deep learning model (e.g., TensorFlow or PyTorch) to perform detailed analysis of the facial image and audio data. The results of the detailed analysis are sent to the emotion engine.

[0672] Emotion Engine Functions

[0673] The emotion engine is embedded in the server and integrates detailed analysis results to assess the emotional state of the driver and passengers with high accuracy. This emotion engine integrates information from multiple data sources and recognizes complex emotional states (e.g., fatigue, stress, joy, etc.).

[0674] Suggestion and Control

[0675] The analysis results received from the server are sent to the device, which then makes appropriate suggestions to the driver and passengers. For example, if the device determines that the driver is tired, it will suggest taking a break at the next service area. If the device detects stress, it will suggest playing relaxing music.

[0676] Specific prompt examples:

[0677] "Write a natural language description for a system that detects when a driver is tired and suggests a break if they've been driving for a long time."

[0678] The user responds to these suggestions using voice commands or the touch panel, and the device receives the response and performs the next action. For example, if the user accepts the break, the navigation system automatically sets up a service area.

[0679] Specific examples

[0680] Example 1: Proposal for a break in your car

[0681] The device's camera detects frequent rubbing of the driver's eyes, and the microphone collects yawns. Based on this, the emotion engine in the server analyzes the driver's level of fatigue and determines that the driver is fatigued. The server then sends information about nearby service areas to the device, which then suggests, "You seem a little tired from driving. Would you like to take a break at the next service area?" If the user accepts, the navigation system automatically sets the service area as the destination.

[0682] Example 2: Calling about being late in a commercial vehicle

[0683] The device checks the current traffic situation and the schedule, and detects the possibility of delay due to congestion. The server's emotion engine analyzes the possibility of delay, and if the delay is determined to be high, the server generates an automatic email sending template. The device suggests, "Your arrival at your destination is expected to be delayed. Would you like to contact your customer?" If the user accepts, the device automatically sends a relevant email to the customer.

[0684] In this way, the present invention provides a safe and comfortable in-vehicle environment by monitoring the emotional states of the driver and passengers in real time with high accuracy and making appropriate suggestions.

[0685] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0686] Step 1:

[0687] The device collects data using a camera and microphone. The camera captures the facial expressions of the driver and passengers in real time, and the microphone collects conversations and audio. The input is video and audio data from inside the vehicle, and the output is the captured raw data. Specifically, the camera captures faces at 15 frames per second, and the microphone records audio every 10 seconds.

[0688] Step 2:

[0689] The data collected by the device is initially analyzed. Facial expression data is analyzed using a facial recognition algorithm (e.g., OpenCV) to identify basic emotions (e.g., smile, surprise, anger). Voice data is analyzed for conversation content and tone using the Python library NLP. The input is the raw data acquired in step 1, and the output is the analyzed basic emotional state and voice content. Specifically, the process identifies facial expressions from image data and extracts tone and content from voice data.

[0690] Step 3:

[0691] The device sends the initial analysis results to the cloud server. The analysis results are converted to JSON format and sent to the server using the HTTP POST method. The input is the analysis results obtained in step 2, and the output is the JSON data sent to the cloud server. Specifically, the initial analysis results data is generated and sent to the server via the Internet.

[0692] Step 4:

[0693] The server performs detailed analysis of the data sent from the device. It uses a deep learning model (e.g., TensorFlow or PyTorch) to perform detailed analysis of the facial image and audio data and evaluates the emotional state with high accuracy. The input is the JSON data sent in step 3, and the output is the analyzed emotional state data. Specifically, the deep learning model is used to perform detailed facial and audio recognition.

[0694] Step 5:

[0695] The emotion engine in the server integrates the detailed analysis results and evaluates the emotional state of the driver and passengers with high accuracy. The input is the emotional state data obtained in step 4, and the output is the integrated final emotional state data. Specifically, it integrates multiple data sources to identify complex emotional states.

[0696] Step 6:

[0697] The server sends the analysis results to the device. The results are converted into JSON format and sent to the device using the HTTP POST method. The input is the final emotional state data obtained in step 5, and the output is the analysis result data sent to the device. Specifically, the emotional state data is generated in JSON format and sent to the device via the Internet.

[0698] Step 7:

[0699] Based on the analysis results received by the device from the server, the device generates and presents appropriate suggestions to the driver and passengers. The suggestions are generated using a text generation model (e.g., GPT-3) algorithm. The input is the analysis result data sent in step 6, and the output is the suggestion text presented to the user. Specifically, the suggestion text is generated and notified to the user by voice or on-screen display.

[0700] Step 8:

[0701] The user responds to suggestions from the device, and the device performs the next action based on the response. For example, if the user accepts a break, the device updates the navigation system to set the next service area. The input is the user's response, and the output is the next action to be performed. Specifically, the device receives a voice command or touch input and updates the destination in the navigation system.

[0702] In this way, the entire system works together to recognize emotional states with high accuracy and make appropriate suggestions in real time, thereby providing a safe and comfortable in-car environment for the driver and passengers.

[0703] (Application example 2)

[0704] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0705] To improve the safety and comfort of drivers and passengers while driving, a system that monitors their condition in real time and makes appropriate suggestions at the appropriate time is required. However, conventional systems have been unable to accurately grasp the emotional state of the driver and passengers, making it difficult to make personalized suggestions. Furthermore, they lacked the ability to suggest appropriate breaks or entertainment based on the driver's condition, such as fatigue or stress.

[0706] To solve the above problems, it is necessary to incorporate a function that can evaluate emotional states with high accuracy and a means to make personalized suggestions.

[0707] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: detection means consisting of a camera and a microphone that detects the state of the driver and passengers; analysis means that analyzes data collected by the detection means and predicts the potential needs of the driver and passengers; proposal means that makes suggestions to the driver and passengers based on the needs predicted by the analysis means; and control means that receives responses from the driver and passengers to the proposals and controls the operation of the system based on the responses. The server also includes information provision means that uses an emotion engine to accurately evaluate the emotional states of the driver and passengers, and provides information about nearby rest spots if signs of fatigue are observed, and entertainment provision means that suggests a new music playlist if the driver and passengers are relaxed.

[0708] This makes it possible to grasp the emotional state of the driver and passengers with high accuracy in real time and make personalized suggestions, thereby providing a safe and comfortable driving environment.

[0709] 1. "Driver and passenger condition" means the physical and psychological state of the driver and passenger, including facial expressions, voice, and other physiological responses.

[0710] 2. "Detection means" refers to a device that includes an imaging device for capturing facial expressions of the driver and passengers and an audio collection device for collecting conversations.

[0711] 3. "Analysis Means" means technology that analyzes data collected by the Detection Means and predicts the potential needs and emotional states of the driver and passengers.

[0712] 4. "Proposal means" refers to a device or function that makes appropriate suggestions to the driver and passengers based on the needs predicted by the analysis means.

[0713] 5. "Control means" means a device or function for receiving responses from the driver and passengers to suggestions from the suggestion means and controlling the operation of the system based on those responses.

[0714] 6. "Emotion Engine" is a technology that integrates facial expressions, voice, and behavioral data of the driver and passengers to recognize and evaluate their emotional state with high accuracy.

[0715] 7. "Navigation and entertainment suggestion means" is a function that suggests navigation information and entertainment content based on the analysis results of the emotion engine.

[0716] 8. "Information Providing Means" means a device or function that provides information about nearby rest points when signs of fatigue are present.

[0717] 9. "Entertainment Provider" is a feature that suggests new music playlists and other entertainment content when you're relaxing.

[0718] A specific embodiment for carrying out the present invention will be described. The system of the present invention is composed of a terminal in a vehicle and a server on a cloud. The operation of the entire system is as follows.

[0719] Device configuration and functions

[0720] The terminal is installed in the vehicle and includes the following main hardware and software:

[0721] Image capture device: A camera for capturing the facial expressions of the driver and passengers in real time.

[0722] Audio collection device: Uses a microphone to collect conversations between the driver and passengers.

[0723] The terminal sends the data detected by these devices to a server on the cloud. Specifically, the camera and microphone collect signs of driver fatigue and stress and send them to the cloud server.

[0724] Server configuration and functions

[0725] The server includes the following main software and hardware components:

[0726] Emotion Engine: Integrates information from multiple data sources to accurately recognize the emotional state of the driver and passengers. Analysis is performed using facial expression and voice recognition technologies.

[0727] Software used: OpenCV, Google Cloud Speech-to-Text

[0728] Data analysis means: Data sent to the cloud server is analyzed in real time to evaluate the emotional state of the driver and passengers.

[0729] The server sends the analysis results to the device, which then makes appropriate suggestions based on the results.

[0730] Specific proposal methods and information provision

[0731] Based on the results of the emotion engine's analysis, the following specific suggestions are made:

[0732] Information provision method: If the driver frequently rubs their eyes or yawns, the emotion engine will evaluate the signs of fatigue as high. The device will suggest, "You are showing signs of fatigue. Would you like to take a break at the next service area?" If the user accepts, it will provide information about nearby rest points and set up navigation.

[0733] Entertainment provision method: If the system detects that the driver's facial expressions or voice indicate that they are relaxed, it will suggest a new music playlist. It will display a message saying, "You look relaxed. Would you like to play a new music playlist?"

[0734] Specific examples of processing procedures

[0735] For example, if a driver is driving for a long time in front of the camera and rubbing their eyes frequently, the emotion engine will analyze the driver's fatigue level and send information about nearby service areas to the device, suggesting a break. If the driver is relaxed, the system will suggest a music playlist, providing a comfortable environment.

[0736] An example of a prompt is as follows:

[0737] Create a system that suggests rest stops when the user shows signs of fatigue, suggests nearby rest areas, for example if the driver is rubbing their eyes frequently, or suggests a new music playlist when the driver is in a relaxed state.

[0738] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0739] Step 1:

[0740] The device's detection means, an imaging device and an audio collection device, capture the facial expressions and voices of the driver and passengers in real time. The input is video data from the camera and audio data from the microphone. Specifically, the device periodically collects data and sends it to a cloud server. The output is this video data and audio data.

[0741] Step 2:

[0742] The emotion engine on the server receives the video and audio data sent from the device. The input is video and audio data. The emotion engine analyzes this data using facial expression recognition technology (e.g., OpenCV) and speech recognition technology (e.g., Google Cloud Speech-to-Text). The output is the emotional state of the driver and passengers (e.g., degree of fatigue, stress, and relaxation).

[0743] Step 3:

[0744] The server's analysis means predicts the potential needs of the driver and passengers based on this emotional state. The input is emotional state data obtained from the emotion engine. Based on this, the analysis means determines needs, such as "the driver is tired" or "the driver is relaxed." The output is data related to needs.

[0745] Step 4:

[0746] The server generates appropriate suggestions based on the needs predicted by the analysis means. The input is data related to the needs. For example, if it determines that the user "needs a break," it will suggest information about nearby resting points. If it determines that the user "is relaxing," it will generate a suggestion for a new music playlist. The output is the suggestion content.

[0747] Step 5:

[0748] The server sends the suggestion to the device. The input is the suggestion from the server. The device displays it to the driver and passengers. As a specific operation, the suggestion is provided via a display device or voice assistant. The output is the state in which the suggestion is presented to the driver and passengers.

[0749] Step 6:

[0750] The user (driver and passengers) responds to this suggestion. The input is the suggestion content. Response options include, for example, "take a break" or "play music." The output is the user's response.

[0751] Step 7:

[0752] The terminal receives the user's response and controls the system's operation as a control means. The input is the user's response. Specific operations include setting navigation to a rest point or playing a music playlist. The output is the state in which the system has performed the appropriate operation.

[0753] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0754] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0755] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0756] [Third embodiment]

[0757] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0758] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0759] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0760] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0761] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0762] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0763] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0764] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0765] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0766] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0767] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0768] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0769] MODE FOR CARRYING OUT THE INVENTION

[0770] The present invention is a system that monitors the state of a driver and passengers in real time, predicts potential needs, and makes suggestions. This system is composed of a detection means, an analysis means, a suggestion means, and a control means.

[0771] System configuration

[0772] The entire system operates through communication between a terminal installed in the vehicle and a server built on the cloud.

[0773] Device Features

[0774] The device is installed in the vehicle and is equipped with a camera and a microphone to detect the state of the driver and passengers. The camera captures the facial expressions of the driver and passengers in real time, and the microphone collects the content of their conversations. This information is used as a means of detection.

[0775] The device analyzes the collected data in real time to evaluate the safety and comfort of the vehicle, and transmits the acquired data to a cloud server at regular intervals.

[0776] Server Features

[0777] The server receives the data sent from the device and acts as an analysis means. The server performs the following processes:

[0778] 1. Facial Expression Analysis: Evaluate the emotional state (e.g., fatigue, stress, joy) of the driver and passengers based on facial expression data acquired from the camera.

[0779] 2. Voice analysis: Voice data collected by a microphone is analyzed using natural language processing, and psychological state is inferred from the content and tone of the conversation.

[0780] Based on the analysis results, the server predicts the potential needs of the driver and passengers and generates specific actions as suggestions. For example, if the server determines that the driver is tired, it will suggest rest spots.

[0781] Suggestion and Control

[0782] The analysis results received from the server are sent to the device, which then uses the device to make appropriate suggestions to the driver and passengers. For example, the following suggestions may be considered:

[0783] "You seem a little tired from driving. Would you like to take a break at the next service area?"

[0784] "I have a new music playlist. Would you like to play it?"

[0785] "We are expecting a delay in arriving at our destination. Would you like us to contact our customer?"

[0786] The user responds to these suggestions and the terminal continues to act based on the response as a means of controlling the system.

[0787] Specific examples

[0788] Example 1: Proposal for a break in your car

[0789] If the driver continues driving for a long time, the camera detects frequent rubbing of the driver's eyes, and the microphone collects yawns. Based on this, the server analyzes that the driver is highly fatigued, collects information about nearby service areas, and sends it to the device. The device then suggests to the driver, "You seem a little tired from driving. Would you like to take a break at the next service area?" If the driver agrees, the navigation system automatically sets the service area as the destination.

[0790] Example 2: Calling about being late in a commercial vehicle

[0791] If a commercial vehicle is stuck in traffic and is likely to be late for a scheduled business meeting, the terminal checks the current traffic conditions and the driver's schedule. The server analyzes this and, if it determines that there is a high possibility of a delay, generates a template for sending an automatic email. The terminal then suggests to the driver, "Your arrival at your destination is likely to be delayed. Would you like to contact your customer?" If the driver accepts, the terminal automatically sends a relevant email to the customer.

[0792] As described above, the present invention provides a safe and comfortable in-vehicle environment by monitoring the state of the driver and passengers in real time, predicting their potential needs, and making appropriate suggestions.

[0793] The processing flow will be explained below.

[0794] Step 1:

[0795] The device initializes the vehicle's various sensors and acquires information on vehicle speed, fuel level, and location, thereby collecting basic vehicle operation data.

[0796] Step 2:

[0797] The device uses a camera to capture the facial expressions of the driver and passengers, and also uses an in-car microphone to collect conversations and obtain voice data.

[0798] Step 3:

[0799] The device periodically transmits the collected sensor data, facial expression data, and voice data to the server in real time.

[0800] Step 4:

[0801] The server analyzes the received data, specifically assessing the driver and passengers' emotional state (e.g., fatigue, stress, joy) using facial expression data, and inferring their psychological state from the content and tone of their conversations using audio data.

[0802] Step 5:

[0803] Based on the analysis results, the server predicts the potential needs of the driver and passengers. For example, if it determines that the driver is tired, it generates an action plan, such as suggesting nearby rest areas.

[0804] Step 6:

[0805] The server sends the generated action plan to the device, which includes the content and timing of the proposal.

[0806] Step 7:

[0807] Based on the action plan received, the device will make suggestions to the driver and passengers. For example, it might say to the driver, "You seem a little tired while driving. Would you like to take a break at the next service area?"

[0808] Step 8:

[0809] The user (driver and passengers) responds to the proposal by choosing to accept or reject it.

[0810] Step 9:

[0811] The terminal receives the user's response and controls the system's operation based on the response. For example, if the driver agrees to take a break, the navigation system automatically sets the next service area as the destination.

[0812] Step 10:

[0813] The device records the user's feedback and responses and periodically sends them to the server, which accumulates the data.

[0814] Step 11:

[0815] The server retrains the AI ​​model based on the collected feedback data, improving the system to make more accurate suggestions. The retrained model is stored on the server side.

[0816] Step 12:

[0817] The server then uses the improved AI model in the next analysis, improving the accuracy of the system's suggestions. This cycle allows the system to continuously evolve.

[0818] Example 1

[0819] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0820] When driving a vehicle, real-time monitoring of the driver and passengers' condition is required to maintain safety and comfort. However, current systems lack the ability to effectively analyze the facial expressions and conversations of the driver and passengers, predict their potential needs, and make appropriate suggestions. This makes it difficult to quickly detect driver fatigue or stress and instruct them to take breaks or other measures at the appropriate time. There is also a lack of means to respond appropriately in situations such as traffic congestion and delays.

[0821] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0822] In this invention, the server includes a detection means for detecting the states of the driver and passengers in real time, a data transmission means for transmitting data collected by the detection means to the server, an analysis means for analyzing the data received by the server and predicting potential needs of the driver and passengers, a proposal means for making suggestions to the driver and passengers based on the needs predicted by the analysis means, and a control means for receiving responses from the driver and passengers to the proposals and controlling the operation of the system based on the responses. This makes it possible to effectively monitor the states of the driver and passengers in real time, accurately predict potential needs, and make suggestions to maintain safety and comfort.

[0823] "Detection means" refers to devices and sensors for detecting the state of the driver and passengers in real time.

[0824] "Data transmission means" refers to a communication module or protocol for transmitting data collected by the detection means to a server.

[0825] "Analysis means" refers to algorithms or software that analyze the state of the driver and passengers based on the data received by the server and predict their potential needs.

[0826] The "suggestion means" refers to a user interface or notification system for making specific suggestions to the driver and passengers based on the needs predicted by the analysis means.

[0827] "Control means" refers to hardware and / or software for receiving driver and passenger responses to suggestions and controlling the operation of the system based on those responses.

[0828] "Image capture device" refers to a camera or video device used to capture the facial expressions of the driver and passengers.

[0829] "Audio capture device" refers to a microphone or other audio collection device for capturing driver and passenger conversations.

[0830] "Image processing technology" refers to the techniques and algorithms used to analyze images captured by an imaging device and evaluate facial expressions and emotional states.

[0831] "Natural language processing" refers to a technology that converts voice data collected by a voice capture device into text and analyzes that text to evaluate psychological state and emotions.

[0832] This invention is a system that monitors the status of the driver and passengers in the vehicle in real time, predicts their potential needs, and makes appropriate suggestions. This system operates through communication between a terminal installed in the vehicle and a server built on the cloud.

[0833] System Configuration

[0834] Device configuration

[0835] The terminal is installed inside the vehicle and has a detection means for detecting the state of the driver and passengers. The detection means uses an image capture device (camera) to capture the facial expressions of the driver and passengers, and a voice capture device (microphone) to collect the content of their conversations.

[0836] The camera captures the faces of the driver and passengers in real time and uses image processing libraries such as OpenCV and dlib to extract facial features. The audio capture device collects high-quality ambient audio and converts it into text using the Google Cloud Speech-to-Text API.

[0837] The device has a data transmission means to send collected data to the server in real time, and sends the data to the server's API endpoint using an HTTP POST request. The data is encrypted in JSON format and sent securely.

[0838] Server Configuration

[0839] The server has an analysis means for analyzing the data sent from the terminal. Specifically, it performs the following processes.

[0840] 1. Facial Expression Analysis: Facial expression data acquired from a camera is analyzed to evaluate emotional states (e.g., fatigue, stress, joy). By quantifying the emotional states using Python's OpenCV library, the psychological state of the driver and passengers can be accurately evaluated.

[0841] 2. Voice analysis: Voice data acquired from the microphone is analyzed using natural language processing (NLP) technology. The voice is converted into text using the Google Cloud Speech-to-Text API, and then text analysis is performed. This allows the system to infer the user's psychological state from the content and tone of the conversation.

[0842] Based on the analysis results, the server predicts the potential needs of the driver and passengers and has a suggestion means for generating specific actions and suggestions.

[0843] Proposal submission and response

[0844] The device receives analysis results and suggestions from the server and presents them to the driver and passengers as notifications and alerts, who can then respond to the suggestions using the touchscreen or voice commands.

[0845] For example, if the device determines that the driver is tired after a long drive, it will display a prompt such as, "You seem a little tired from driving. Would you like to take a break at the next service area?" If the device has a voice assistant function, it will make similar suggestions aloud.

[0846] If the user responds by accepting the suggestion, the device will use the navigation system (e.g., Google Maps API) to set the next service area as the destination. If a delay due to traffic congestion is predicted, the device will display a prompt saying, "Arrival at the destination is expected to be delayed. Would you like to contact the customer?" and will automatically contact the customer if the user responds in favor.

[0847] Specific operation example

[0848] Example 1: Proposal for a break in your car

[0849] 1. The device uses a camera to detect frequent rubbing of the driver's eyes and a microphone to collect audio of yawning.

[0850] 2. The device sends the collected data to the server.

[0851] 3. The server analyzes the driver's fatigue level using facial expression recognition algorithms and natural language processing.

[0852] 4. The server generates suggestions to encourage the driver to take a break.

[0853] 5. The device will display a suggestion: "You seem a little tired from driving. Would you like to take a break at the next service area?"

[0854] 6. The user responds "Yes."

[0855] 7. The device sets the next service area in the navigation system and guides the driver there.

[0856] Example 2: Calling about being late in a commercial vehicle

[0857] 1. The device checks the current traffic conditions and schedule to detect congestion information.

[0858] 2. The device sends this data to the server.

[0859] 3. The server analyzes the traffic data and determines that you are likely to be late for a scheduled business meeting.

[0860] 4. The server generates the template for the automated email.

[0861] 5. The terminal displays the suggestion, "Your arrival at your destination is expected to be delayed. Would you like to contact your customer?"

[0862] 6. The user responds "Yes."

[0863] 7. The device automatically sends emails to customers using the prepared email templates.

[0864] In this way, the system of the present invention can effectively monitor the state of the driver and passengers in real time, accurately predict their potential needs, and make suggestions to maintain safety and comfort.

[0865] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0866] Step 1: Data collection

[0867] The device detects the driver and passengers' status using a camera and microphone installed in the vehicle. The camera recognizes faces in real time using the OpenCV library and extracts facial features using the dlib library. The microphone collects audio data and converts it into text using the Google Cloud Speech-to-Text API.

[0868] Input: Video and audio data of the driver and passengers

[0869] Specific operation: The camera captures the driver's facial expressions, and the microphone collects the conversation.

[0870] Output: Facial expression data (feature points) and speech text data

[0871] Step 2: Send data

[0872] The device sends the collected facial expression data and speech-to-text data to a server at regular intervals, where the data is encrypted and sent to the server's API endpoint in the cloud using an HTTP POST request.

[0873] Input: facial expression data (feature points) and voice text data

[0874] Specific operation: The data collected by the device is converted into JSON format, encrypted, and sent to the server.

[0875] Output: JSON formatted data sent to the server

[0876] Step 3: Receiving data

[0877] The server receives the JSON formatted data sent from the device, and stores it in a database for analysis.

[0878] Input: JSON format data sent from the terminal

[0879] Specific operation: The server's API analyzes the received data and extracts the necessary information.

[0880] Output: Raw data prepared for analysis

[0881] Step 4: Data analysis

[0882] The server analyzes the received data, using the OpenCV library to evaluate the emotional state (e.g., fatigue, stress, joy) of the facial expression data, and natural language processing (NLP) techniques to analyze the psychological state and conversation content of the speech and text data.

[0883] Input: Prepared raw data

[0884] Specific operations: Facial recognition algorithms are run on facial expression data to quantify emotional states, and NLP algorithms are run on voice and text data to analyze psychological states and conversation content.

[0885] Output: Analyzed emotional and psychological state data

[0886] Step 5: Proposal Generation

[0887] The server predicts the potential needs of the driver and passengers based on the analysis results, and generates appropriate suggestions based on those needs. For example, if the driver is tired, it generates suggestions to encourage them to take a break.

[0888] Input: Analyzed emotional and psychological state data

[0889] Specific behavior: Evaluate the analysis results and generate appropriate prompts or suggestions. For example, "You seem a little tired while driving. Would you like to take a break at the next service station?"

[0890] Output: Generated prompts and suggestions

[0891] Step 6: Proposal Presentation

[0892] The device displays the suggestions received from the server to the driver and passengers, and provides the suggestions through a user interface or a voice assistant.

[0893] Input: Generated prompts and suggestions

[0894] Specific operation: The device displays and presents suggestions on the LCD screen or voice assistant.

[0895] Output: Suggested prompts and suggestions for the user

[0896] Step 7: User response

[0897] The user responds to the suggestion, for example by selecting "yes" or "no" using a touchscreen or by responding with a voice command.

[0898] Input: Prompts and suggestions provided

[0899] Specific Action: The user responds with a touchscreen or voice command, for example, saying "Yes."

[0900] Output: User response data

[0901] Step 8: Take Action

[0902] The device then takes appropriate action based on the user's response, such as using the navigation system to set the destination to the nearest service area, or automatically contacting the customer using a template to inform them they'll be late.

[0903] Input: User response data

[0904] Specific operation: When the user accepts the break, the terminal will set the service area in the navigation system. When the user accepts the delay notification, the terminal will automatically send an email to the customer.

[0905] Output: Destination setting for navigation systems and automatic email transmission

[0906] In this way, the entire system works together to monitor the driver and passengers' status in real time, making it possible to make appropriate suggestions and take appropriate actions based on their potential needs.

[0907] (Application example 1)

[0908] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0909] In recent years, there has been a growing demand for systems that monitor the status of drivers and passengers in real time and propose appropriate actions to improve vehicle safety and comfort. However, existing systems rely too heavily on on-site analysis, which can result in delays and reduced accuracy in data processing. The present invention aims to solve these problems and provide a system that enables highly accurate and rapid analysis and proposals using a cloud server.

[0910] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0911] In this invention, the server includes a detection means for detecting the states of the driver and passengers, an analysis means for analyzing data collected by the detection means and predicting potential needs of the driver and passengers, a proposal means for making suggestions to the driver and passengers based on the needs predicted by the analysis means, a control means for receiving responses from the driver and passengers to the proposals and controlling the operation of the system based on the responses, and a communication means for transmitting the detected data to a cloud server, receiving the results, and providing them to the proposal means, thereby enabling highly accurate data analysis in real time and rapid proposals.

[0912] "Driver and passenger" means the person driving the vehicle and any passengers in the vehicle.

[0913] The "detection means" is a means for detecting the state of the driver and passengers, and includes devices such as a camera and a microphone.

[0914] The "analysis means" is a means for analyzing the data collected by the detection means and predicting the potential needs of the driver and passengers.

[0915] The "suggestion means" is a means for making specific suggestions to the driver and passengers based on the needs predicted by the analysis means.

[0916] "Control means" means for receiving driver and passenger responses to suggestions and controlling the operation of the system based on the responses.

[0917] The "communication means" is a means for transmitting detected data to a cloud server, receiving the results, and providing them to the proposal means.

[0918] A "cloud server" is a remote server connected via the Internet to perform data analysis and provide information.

[0919] "Facial expression recognition technology" is a technology that analyzes a person's facial expressions from images and videos to evaluate their emotional state.

[0920] "Speech recognition technology" is a technology that analyzes voice data and extracts information from its content and tone.

[0921] "Real-time" refers to the state in which data is processed and results are obtained immediately in real time.

[0922] "Potential needs" are requirements that the driver and passengers are not aware of at present, but may become necessary in the near future.

[0923] This invention is a system that monitors the status of the driver and passengers in an autonomous vehicle in real time, predicts potential needs, and makes suggestions. This system is configured using the following hardware and software.

[0924] System configuration

[0925] The entire system operates through communication between a terminal installed in the vehicle and a server built on the cloud.

[0926] Device Features

[0927] The device is installed inside the vehicle and is equipped with a camera and microphone to detect the state of the driver and passengers. In this case, the camera captures video using OpenCV and audio using pyaudio. The camera captures the facial expressions of the driver and passengers in real time, and the microphone collects the content of their conversations. This information is used as a means of detection.

[0928] The devices are equipped with a communication means to send collected data to a cloud server in real time, minimizing data delays and enabling fast and accurate analysis.

[0929] Server Features

[0930] The server receives the data sent from the device and acts as an analysis means. The server performs the following processes:

[0931] 1. Facial Expression Analysis: Evaluate the emotional state (e.g., fatigue, stress, joy) of the driver and passengers based on facial expression data acquired from the camera. Uses facial expression recognition technology.

[0932] 2. Voice analysis: Voice data collected by a microphone is analyzed using natural language processing to infer a person's psychological state from the content and tone of the conversation. Voice recognition technology is used.

[0933] Based on the analysis results, the server predicts the potential needs of the driver and passengers and generates specific actions as suggestions. For example, if the server determines that the driver is tired, it will suggest rest spots.

[0934] Suggestion and Control

[0935] The analysis results received from the server are sent to the terminal, which then makes appropriate suggestions to the driver and passengers as a suggestion means.

[0936] Examples:

[0937] 1. Suggesting a break

[0938] If the camera detects the driver rubbing their eyes frequently and the microphone picks up yawning, the server will determine that the driver is highly fatigued. It will then collect information about nearby service areas and send it to the device. The device will then suggest to the driver, "You seem a little tired from driving. Would you like to take a break at the next service area?" If the driver agrees, the navigation system will automatically set the service area as the destination.

[0939] 2. Relaxation suggestions

[0940] If the device determines that the driver is feeling stressed based on facial expression and voice data, it will suggest, "Would you like to play some relaxing music?" If the driver agrees, relaxing music will automatically play on the car's audio system.

[0941] Prompt Sentence Examples

[0942] "Based on facial expression and voice data, it seems the driver is tired. Please suggest a place to rest."

[0943] In this way, the state of the driver and passengers can be monitored in real time, potential needs can be predicted, and appropriate suggestions can be made to provide a safe and comfortable in-car environment.

[0944] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0945] Step 1:

[0946] The device uses a camera and microphone to capture the facial expressions and voices of the driver and passengers in real time. Video data from the camera and audio data from the microphone are input. This data is temporarily stored within the device.

[0947] Step 2:

[0948] The device formats and compresses the collected facial expression and audio data for transmission to the cloud server. A transmission protocol to the cloud server is executed, and the data is uploaded to the cloud server. Here, the video data is encoded into MJPEG or H.264 format, and the audio data is encoded into WAV or AAC format. The encoded data becomes the input to the cloud server.

[0949] Step 3:

[0950] The server analyzes the received facial expression and voice data. For the facial expression data, a facial expression recognition algorithm using a generative AI model is run to evaluate the emotional state of the driver and passengers. For the voice data, natural language processing and voice recognition technologies are used to infer the psychological state from the content and tone of the conversation. The results of these analyses are obtained as output from the cloud server.

[0951] Step 4:

[0952] The server predicts the potential needs of the driver and passengers based on the analysis results. Based on the predicted needs, appropriate suggestions are generated. Here, a generative AI model is used to generate prompts that suggest appropriate actions for the driver, such as taking a break or playing music. These suggestions are output from the cloud server to the device.

[0953] Step 5:

[0954] The device receives suggestions from the server and presents them to the driver and passengers. For example, a suggestion such as "You seem a little tired from driving. Would you like to take a break at the next service area?" is displayed on the display screen. The driver and passengers' responses are received via voice or touch interface.

[0955] Step 6:

[0956] The terminal receives the responses of the driver and passengers and sends them to the server. For example, if the driver responds "yes," the voice data is sent to the server. Once this data transmission is complete, the responses of the driver and passengers are recognized as inputs to the terminal.

[0957] Step 7:

[0958] The server determines the appropriate action based on the received response and sends corresponding specific instructions to the terminal. For example, if the driver agrees to take a break, the server generates an instruction to set a rest spot in the navigation system and sends it to the terminal. This instruction is output from the server to the terminal.

[0959] Step 8:

[0960] The terminal controls the system's operation based on instructions received from the server. For example, the terminal can set a service area as a destination in the navigation system, which will automatically recalculate the route, allowing the driver to head to the designated rest spot.

[0961] In this way, data is processed in real time, and appropriate suggestions and actions are made throughout the system based on the driver and passengers' conditions.

[0962] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0963] MODE FOR CARRYING OUT THE INVENTION

[0964] The present invention is a system that monitors the state of the driver and passengers in real time, predicts their potential needs, and makes suggestions. In particular, by utilizing an emotion engine, it achieves more accurate emotion recognition and personalized suggestions. This system is composed of a detection means, an analysis means, a suggestion means, a control means, and an emotion engine.

[0965] System configuration

[0966] The entire system operates through communication between a terminal installed in the vehicle and a server built on the cloud. Each component will be explained below.

[0967] Device Features

[0968] The device is installed in the vehicle and is equipped with a camera and a microphone to detect the state of the driver and passengers. The camera captures the facial expressions of the driver and passengers in real time, and the microphone collects the content of their conversations. This information is used as a means of detection.

[0969] The device analyzes the collected data in real time to evaluate the safety and comfort of the vehicle, and transmits the acquired data to a cloud server at regular intervals.

[0970] Server Features

[0971] The server receives the data sent from the device and acts as an analysis means. The server performs the following processes:

[0972] 1. Facial Expression Analysis: Evaluate the emotional state (e.g., fatigue, stress, joy) of the driver and passengers based on facial expression data acquired from the camera.

[0973] 2. Voice analysis: Voice data collected by a microphone is analyzed using natural language processing, and psychological state is inferred from the content and tone of the conversation.

[0974] Emotion Engine Functions

[0975] The emotion engine is embedded in the server and integrates facial, voice, and behavioral data to recognize user emotions with high accuracy. The emotion engine integrates information from multiple data sources to assess the complex emotional states of the driver and passengers.

[0976] Suggestion and Control

[0977] The analysis results received from the server are sent to the device, which then uses the device to make appropriate suggestions to the driver and passengers. For example, the following suggestions may be considered:

[0978] "You seem a little tired from driving. Would you like to take a break at the next service area?"

[0979] "I have a new music playlist. Would you like to play it?"

[0980] "We are expecting a delay in arriving at our destination. Would you like us to contact our customer?"

[0981] The emotion engine also recognizes the driver's emotional state, allowing for more personalized suggestions. For example, if it determines that the driver is feeling stressed, it can suggest relaxing music or display an encouraging message.

[0982] The user responds to these suggestions and the terminal continues to act based on the response as a means of controlling the system.

[0983] Specific examples

[0984] Example 1: Proposal for a break in your car

[0985] If the driver continues driving for a long time, the camera detects frequent rubbing of the driver's eyes, and the microphone collects yawns. Based on this, the emotion engine in the server analyzes that the driver is highly fatigued, collects information about nearby service areas, and sends it to the device. The device then suggests to the driver, "You seem a little tired from driving. Would you like to take a break at the next service area?" If the driver agrees, the navigation system automatically sets the service area as the destination.

[0986] Example 2: Calling about being late in a commercial vehicle

[0987] If a commercial vehicle is stuck in traffic and is likely to be late for a scheduled business meeting, the device checks the current traffic conditions and the driver's calendar. The server's emotion engine analyzes this and, if it determines that there is a high possibility of a delay, generates a template for sending an automatic email. The device suggests to the driver, "Your arrival at your destination is likely to be delayed. Would you like to contact your customer?" If the driver accepts, the device automatically sends a relevant email to the customer.

[0988] As described above, this invention provides a safe and comfortable in-car environment by monitoring the state of the driver and passengers in real time, predicting their potential needs with high accuracy, and making appropriate suggestions. The introduction of an emotion engine makes it possible to realize even more personalized services.

[0989] The processing flow will be explained below.

[0990] Step 1:

[0991] The device initializes the vehicle's various sensors, cameras, and microphones, and acquires information on vehicle speed, fuel level, and location, while also capturing the facial expressions of the driver and passengers and collecting conversations.

[0992] Step 2:

[0993] The device transmits collected sensor data (vehicle speed, remaining fuel level, location information), facial expression data, and voice data to the server in real time. The transmitted data also includes a timestamp.

[0994] Step 3:

[0995] The server analyzes the received data. First, it performs facial expression analysis and evaluates the emotional state (e.g., fatigue, stress, joy) of the driver and passengers using facial expression data obtained from the camera.

[0996] Step 4:

[0997] The server performs voice analysis, analyzing the voice data collected from the microphone using natural language processing technology to infer the psychological state from the content and tone of the conversation.

[0998] Step 5:

[0999] The server utilizes an emotion engine to integrate facial, voice, and behavioral data to recognize the user's complex emotional state, for example, detecting fatigue from facial expressions and identifying stress from voice tone.

[1000] Step 6:

[1001] The server predicts the potential needs of the driver and passengers based on the analysis results. For example, if it determines that the driver is tired, it generates an action plan to suggest the next optimal rest point.

[1002] Step 7:

[1003] The server then sends the generated action plan to the terminal, which includes the proposed action content and its timing.

[1004] Step 8:

[1005] The device will then make suggestions to the driver and passengers based on the action plan it receives. For example, it might say to the driver, "You seem a little tired from driving. Would you like to take a break at the next service area?"

[1006] Step 9:

[1007] The user responds to the suggestion, and the driver has the option to accept or reject the suggestion.

[1008] Step 10:

[1009] The terminal receives the user's response and controls the system's operation based on the response. For example, if the driver agrees to take a break, the navigation system automatically sets the next service area as the destination.

[1010] Step 11:

[1011] The terminal records the user's feedback and responses and periodically transmits them to the server, thereby accumulating data for the entire system.

[1012] Step 12:

[1013] The server retrains the AI ​​model based on the accumulated feedback data, improving the system to make even more accurate suggestions. The retrained model is stored on the server.

[1014] Step 13:

[1015] The server then uses the improved AI model in the next analysis, improving the accuracy of the system's suggestions. This cycle allows the system to continuously evolve and provide better service to drivers and passengers.

[1016] Example 2

[1017] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1018] Conventional in-vehicle monitoring systems have difficulty accurately recognizing the emotional state of the driver and passengers in real time and making personalized suggestions based on that information. Furthermore, due to the low accuracy of emotional state recognition, it was not possible to accurately predict potential needs, limiting the effectiveness of the suggestions.

[1019] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: a detection means for detecting the states of the driver and passengers; a terminal means for performing an initial analysis of the data collected by the detection means; a communication means for transmitting the results of the initial analysis to a server on the cloud; an analysis means for performing a detailed analysis in the server; an emotion engine means for integrating the results obtained by the analysis means to evaluate the emotional states of the driver and passengers; a suggestion means for making suggestions to the driver and passengers based on their emotional states; and a control means for receiving responses from the driver and passengers to the suggestions and controlling the operation of the system based on the responses. This makes it possible to recognize the emotional states of the driver and passengers with high accuracy and make appropriate suggestions in real time.

[1020] The term "detection means" refers to devices and sensors that detect the state of the driver and passengers.

[1021] "Terminal means" refers to a computer or device installed in a vehicle that performs initial analysis of data collected by the detection means.

[1022] "Communication means" refers to the interface and protocol for sending and receiving data between the terminal means and the server on the cloud.

[1023] "Analysis means" refers to an algorithm or program for analyzing in detail the initial analysis results sent from the terminal means and evaluating the emotional state with high accuracy.

[1024] "Emotion engine means" refers to a system for integrating data obtained by the analysis means and recognizing and evaluating the emotional states of the driver and passengers.

[1025] "Suggestion means" refers to a device or software for generating and presenting appropriate suggestions to the driver and passengers based on the emotional state evaluated by the emotion engine means.

[1026] "Control means" refers to a device or program for receiving responses from the driver and passengers to the suggestions generated by the suggestion means, and for continuing or changing the operation of the system based on the responses.

[1027] "Image capture device" refers to a device that uses an optical device such as a camera to capture the facial expressions and movements of the driver and passengers.

[1028] "Audio capture device" refers to a device that uses an acoustic device such as a microphone to collect conversations and voices of the driver and passengers.

[1029] "Facial expression recognition technology" refers to algorithms for analyzing facial expression data captured by an image capture device and assessing emotional state.

[1030] "Natural language processing technology" refers to algorithms that analyze voice data collected by a voice capture device and evaluate the emotional state and content of the conversation.

[1031] The present invention is a system that monitors the status of the driver and passengers in real time, predicts their potential needs, and makes appropriate suggestions. This system operates in cooperation with a terminal installed in the vehicle and a server built on the cloud. Specific embodiments for implementing this system are described below.

[1032] System configuration

[1033] The system consists of a terminal, a server, an emotion engine, a detection means, an analysis means, a proposal means, and a control means.

[1034] Device Features

[1035] The device is installed in the vehicle and is equipped with a camera and a microphone to detect the state of the driver and passengers. The camera captures the facial expressions of the driver and passengers in real time, and the microphone collects the content of their conversations. This information is used as a means of detection.

[1036] The device performs an initial analysis of the collected data and evaluates the safety and comfort of the vehicle interior. For example, the device uses facial recognition technology to identify basic facial expressions such as smile, surprise, and anger, and analyzes voice data using natural language processing technology (e.g., the Python library NLP). The results of this initial analysis are sent to a cloud server at regular intervals.

[1037] Server Features

[1038] The server receives the data sent from the device and performs detailed analysis. The server uses a deep learning model (e.g., TensorFlow or PyTorch) to perform detailed analysis of the facial image and audio data. The results of the detailed analysis are sent to the emotion engine.

[1039] Emotion Engine Functions

[1040] The emotion engine is embedded in the server and integrates detailed analysis results to assess the emotional state of the driver and passengers with high accuracy. This emotion engine integrates information from multiple data sources and recognizes complex emotional states (e.g., fatigue, stress, joy, etc.).

[1041] Suggestion and Control

[1042] The analysis results received from the server are sent to the device, which then makes appropriate suggestions to the driver and passengers. For example, if the device determines that the driver is tired, it will suggest taking a break at the next service area. If the device detects stress, it will suggest playing relaxing music.

[1043] Specific prompt examples:

[1044] "Write a natural language description for a system that detects when a driver is tired and suggests a break if they've been driving for a long time."

[1045] The user responds to these suggestions using voice commands or the touch panel, and the device receives the response and performs the next action. For example, if the user accepts the break, the navigation system automatically sets up a service area.

[1046] Specific examples

[1047] Example 1: Proposal for a break in your car

[1048] The device's camera detects frequent rubbing of the driver's eyes, and the microphone collects yawns. Based on this, the emotion engine in the server analyzes the driver's level of fatigue and determines that the driver is fatigued. The server then sends information about nearby service areas to the device, which then suggests, "You seem a little tired from driving. Would you like to take a break at the next service area?" If the user accepts, the navigation system automatically sets the service area as the destination.

[1049] Example 2: Calling about being late in a commercial vehicle

[1050] The device checks the current traffic situation and the schedule, and detects the possibility of delay due to congestion. The server's emotion engine analyzes the possibility of delay, and if the delay is determined to be high, the server generates an automatic email sending template. The device suggests, "Your arrival at your destination is expected to be delayed. Would you like to contact your customer?" If the user accepts, the device automatically sends a relevant email to the customer.

[1051] In this way, the present invention provides a safe and comfortable in-vehicle environment by monitoring the emotional states of the driver and passengers in real time with high accuracy and making appropriate suggestions.

[1052] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1053] Step 1:

[1054] The device collects data using a camera and microphone. The camera captures the facial expressions of the driver and passengers in real time, and the microphone collects conversations and audio. The input is video and audio data from inside the vehicle, and the output is the captured raw data. Specifically, the camera captures faces at 15 frames per second, and the microphone records audio every 10 seconds.

[1055] Step 2:

[1056] The data collected by the device is initially analyzed. Facial expression data is analyzed using a facial recognition algorithm (e.g., OpenCV) to identify basic emotions (e.g., smile, surprise, anger). Voice data is analyzed for conversation content and tone using the Python library NLP. The input is the raw data acquired in step 1, and the output is the analyzed basic emotional state and voice content. Specifically, the process identifies facial expressions from image data and extracts tone and content from voice data.

[1057] Step 3:

[1058] The device sends the initial analysis results to the cloud server. The analysis results are converted to JSON format and sent to the server using the HTTP POST method. The input is the analysis results obtained in step 2, and the output is the JSON data sent to the cloud server. Specifically, the initial analysis results data is generated and sent to the server via the Internet.

[1059] Step 4:

[1060] The server performs detailed analysis of the data sent from the device. It uses a deep learning model (e.g., TensorFlow or PyTorch) to perform detailed analysis of the facial image and audio data and evaluates the emotional state with high accuracy. The input is the JSON data sent in step 3, and the output is the analyzed emotional state data. Specifically, the deep learning model is used to perform detailed facial and audio recognition.

[1061] Step 5:

[1062] The emotion engine in the server integrates the detailed analysis results and evaluates the emotional state of the driver and passengers with high accuracy. The input is the emotional state data obtained in step 4, and the output is the integrated final emotional state data. Specifically, it integrates multiple data sources to identify complex emotional states.

[1063] Step 6:

[1064] The server sends the analysis results to the device. The results are converted into JSON format and sent to the device using the HTTP POST method. The input is the final emotional state data obtained in step 5, and the output is the analysis result data sent to the device. Specifically, the emotional state data is generated in JSON format and sent to the device via the Internet.

[1065] Step 7:

[1066] Based on the analysis results received by the device from the server, the device generates and presents appropriate suggestions to the driver and passengers. The suggestions are generated using a text generation model (e.g., GPT-3) algorithm. The input is the analysis result data sent in step 6, and the output is the suggestion text presented to the user. Specifically, the suggestion text is generated and notified to the user by voice or on-screen display.

[1067] Step 8:

[1068] The user responds to suggestions from the device, and the device performs the next action based on the response. For example, if the user accepts a break, the device updates the navigation system to set the next service area. The input is the user's response, and the output is the next action to be performed. Specifically, the device receives a voice command or touch input and updates the destination in the navigation system.

[1069] In this way, the entire system works together to recognize emotional states with high accuracy and make appropriate suggestions in real time, thereby providing a safe and comfortable in-car environment for the driver and passengers.

[1070] (Application example 2)

[1071] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1072] To improve the safety and comfort of drivers and passengers while driving, a system that monitors their condition in real time and makes appropriate suggestions at the appropriate time is required. However, conventional systems have been unable to accurately grasp the emotional state of the driver and passengers, making it difficult to make personalized suggestions. Furthermore, they lacked the ability to suggest appropriate breaks or entertainment based on the driver's condition, such as fatigue or stress.

[1073] To solve the above problems, it is necessary to incorporate a function that can evaluate emotional states with high accuracy and a means to make personalized suggestions.

[1074] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: detection means consisting of a camera and a microphone that detects the state of the driver and passengers; analysis means that analyzes data collected by the detection means and predicts the potential needs of the driver and passengers; proposal means that makes suggestions to the driver and passengers based on the needs predicted by the analysis means; and control means that receives responses from the driver and passengers to the proposals and controls the operation of the system based on the responses. The server also includes information provision means that uses an emotion engine to accurately evaluate the emotional states of the driver and passengers, and provides information about nearby rest spots if signs of fatigue are observed, and entertainment provision means that suggests a new music playlist if the driver and passengers are relaxed.

[1075] This makes it possible to grasp the emotional state of the driver and passengers with high accuracy in real time and make personalized suggestions, thereby providing a safe and comfortable driving environment.

[1076] 1. "Driver and passenger condition" means the physical and psychological state of the driver and passenger, including facial expressions, voice, and other physiological responses.

[1077] 2. "Detection means" refers to a device that includes an imaging device for capturing facial expressions of the driver and passengers and an audio collection device for collecting conversations.

[1078] 3. "Analysis Means" means technology that analyzes data collected by the Detection Means and predicts the potential needs and emotional states of the driver and passengers.

[1079] 4. "Proposal means" refers to a device or function that makes appropriate suggestions to the driver and passengers based on the needs predicted by the analysis means.

[1080] 5. "Control means" means a device or function for receiving responses from the driver and passengers to suggestions from the suggestion means and controlling the operation of the system based on those responses.

[1081] 6. "Emotion Engine" is a technology that integrates facial expressions, voice, and behavioral data of the driver and passengers to recognize and evaluate their emotional state with high accuracy.

[1082] 7. "Navigation and entertainment suggestion means" is a function that suggests navigation information and entertainment content based on the analysis results of the emotion engine.

[1083] 8. "Information Providing Means" means a device or function that provides information about nearby rest points when signs of fatigue are present.

[1084] 9. "Entertainment Provider" is a feature that suggests new music playlists and other entertainment content when you're relaxing.

[1085] A specific embodiment for carrying out the present invention will be described. The system of the present invention is composed of a terminal in a vehicle and a server on a cloud. The operation of the entire system is as follows.

[1086] Device configuration and functions

[1087] The terminal is installed in the vehicle and includes the following main hardware and software:

[1088] Image capture device: A camera for capturing the facial expressions of the driver and passengers in real time.

[1089] Audio collection device: Uses a microphone to collect conversations between the driver and passengers.

[1090] The terminal sends the data detected by these devices to a server on the cloud. Specifically, the camera and microphone collect signs of driver fatigue and stress and send them to the cloud server.

[1091] Server configuration and functions

[1092] The server includes the following main software and hardware components:

[1093] Emotion Engine: Integrates information from multiple data sources to accurately recognize the emotional state of the driver and passengers. Analysis is performed using facial expression and voice recognition technologies.

[1094] Software used: OpenCV, Google Cloud Speech-to-Text

[1095] Data analysis means: Data sent to the cloud server is analyzed in real time to evaluate the emotional state of the driver and passengers.

[1096] The server sends the analysis results to the device, which then makes appropriate suggestions based on the results.

[1097] Specific proposal methods and information provision

[1098] Based on the results of the emotion engine's analysis, the following specific suggestions are made:

[1099] Information provision method: If the driver frequently rubs their eyes or yawns, the emotion engine will evaluate the signs of fatigue as high. The device will suggest, "You are showing signs of fatigue. Would you like to take a break at the next service area?" If the user accepts, it will provide information about nearby rest points and set up navigation.

[1100] Entertainment provision method: If the system detects that the driver's facial expressions or voice indicate that they are relaxed, it will suggest a new music playlist. It will display a message saying, "You look relaxed. Would you like to play a new music playlist?"

[1101] Specific examples of processing procedures

[1102] For example, if a driver is driving for a long time in front of the camera and rubbing their eyes frequently, the emotion engine will analyze the driver's fatigue level and send information about nearby service areas to the device, suggesting a break. If the driver is relaxed, the system will suggest a music playlist, providing a comfortable environment.

[1103] An example of a prompt is as follows:

[1104] Create a system that suggests rest stops when the user shows signs of fatigue, suggests nearby rest areas, for example if the driver is rubbing their eyes frequently, or suggests a new music playlist when the driver is in a relaxed state.

[1105] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1106] Step 1:

[1107] The device's detection means, an imaging device and an audio collection device, capture the facial expressions and voices of the driver and passengers in real time. The input is video data from the camera and audio data from the microphone. Specifically, the device periodically collects data and sends it to a cloud server. The output is this video data and audio data.

[1108] Step 2:

[1109] The emotion engine on the server receives the video and audio data sent from the device. The input is video and audio data. The emotion engine analyzes this data using facial expression recognition technology (e.g., OpenCV) and speech recognition technology (e.g., Google Cloud Speech-to-Text). The output is the emotional state of the driver and passengers (e.g., degree of fatigue, stress, and relaxation).

[1110] Step 3:

[1111] The server's analysis means predicts the potential needs of the driver and passengers based on this emotional state. The input is emotional state data obtained from the emotion engine. Based on this, the analysis means determines needs, such as "the driver is tired" or "the driver is relaxed." The output is data related to needs.

[1112] Step 4:

[1113] The server generates appropriate suggestions based on the needs predicted by the analysis means. The input is data related to the needs. For example, if it determines that the user "needs a break," it will suggest information about nearby resting points. If it determines that the user "is relaxing," it will generate a suggestion for a new music playlist. The output is the suggestion content.

[1114] Step 5:

[1115] The server sends the suggestion to the device. The input is the suggestion from the server. The device displays it to the driver and passengers. As a specific operation, the suggestion is provided via a display device or voice assistant. The output is the state in which the suggestion is presented to the driver and passengers.

[1116] Step 6:

[1117] The user (driver and passengers) responds to this suggestion. The input is the suggestion content. Response options include, for example, "take a break" or "play music." The output is the user's response.

[1118] Step 7:

[1119] The terminal receives the user's response and controls the system's operation as a control means. The input is the user's response. Specific operations include setting navigation to a rest point or playing a music playlist. The output is the state in which the system has performed the appropriate operation.

[1120] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1122] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1123] [Fourth embodiment]

[1124] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1125] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1127] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1128] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1131] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1132] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1133] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1135] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1136] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1137] MODE FOR CARRYING OUT THE INVENTION

[1138] The present invention is a system that monitors the state of a driver and passengers in real time, predicts potential needs, and makes suggestions. This system is composed of a detection means, an analysis means, a suggestion means, and a control means.

[1139] System configuration

[1140] The entire system operates through communication between a terminal installed in the vehicle and a server built on the cloud.

[1141] Device Features

[1142] The device is installed in the vehicle and is equipped with a camera and a microphone to detect the state of the driver and passengers. The camera captures the facial expressions of the driver and passengers in real time, and the microphone collects the content of their conversations. This information is used as a means of detection.

[1143] The device analyzes the collected data in real time to evaluate the safety and comfort of the vehicle, and transmits the acquired data to a cloud server at regular intervals.

[1144] Server Features

[1145] The server receives the data sent from the device and acts as an analysis means. The server performs the following processes:

[1146] 1. Facial Expression Analysis: Evaluate the emotional state (e.g., fatigue, stress, joy) of the driver and passengers based on facial expression data acquired from the camera.

[1147] 2. Voice analysis: Voice data collected by a microphone is analyzed using natural language processing, and psychological state is inferred from the content and tone of the conversation.

[1148] Based on the analysis results, the server predicts the potential needs of the driver and passengers and generates specific actions as suggestions. For example, if the server determines that the driver is tired, it will suggest rest spots.

[1149] Suggestion and Control

[1150] The analysis results received from the server are sent to the device, which then uses the device to make appropriate suggestions to the driver and passengers. For example, the following suggestions may be considered:

[1151] "You seem a little tired from driving. Would you like to take a break at the next service area?"

[1152] "I have a new music playlist. Would you like to play it?"

[1153] "We are expecting a delay in arriving at our destination. Would you like us to contact our customer?"

[1154] The user responds to these suggestions and the terminal continues to act based on the response as a means of controlling the system.

[1155] Specific examples

[1156] Example 1: Proposal for a break in your car

[1157] If the driver continues driving for a long time, the camera detects frequent rubbing of the driver's eyes, and the microphone collects yawns. Based on this, the server analyzes that the driver is highly fatigued, collects information about nearby service areas, and sends it to the device. The device then suggests to the driver, "You seem a little tired from driving. Would you like to take a break at the next service area?" If the driver agrees, the navigation system automatically sets the service area as the destination.

[1158] Example 2: Calling about being late in a commercial vehicle

[1159] If a commercial vehicle is stuck in traffic and is likely to be late for a scheduled business meeting, the terminal checks the current traffic conditions and the driver's schedule. The server analyzes this and, if it determines that there is a high possibility of a delay, generates a template for sending an automatic email. The terminal then suggests to the driver, "Your arrival at your destination is likely to be delayed. Would you like to contact your customer?" If the driver accepts, the terminal automatically sends a relevant email to the customer.

[1160] As described above, the present invention provides a safe and comfortable in-vehicle environment by monitoring the state of the driver and passengers in real time, predicting their potential needs, and making appropriate suggestions.

[1161] The processing flow will be explained below.

[1162] Step 1:

[1163] The device initializes the vehicle's various sensors and acquires information on vehicle speed, fuel level, and location, thereby collecting basic vehicle operation data.

[1164] Step 2:

[1165] The device uses a camera to capture the facial expressions of the driver and passengers, and also uses an in-car microphone to collect conversations and obtain voice data.

[1166] Step 3:

[1167] The device periodically transmits the collected sensor data, facial expression data, and voice data to the server in real time.

[1168] Step 4:

[1169] The server analyzes the received data, specifically assessing the driver and passengers' emotional state (e.g., fatigue, stress, joy) using facial expression data, and inferring their psychological state from the content and tone of their conversations using audio data.

[1170] Step 5:

[1171] Based on the analysis results, the server predicts the potential needs of the driver and passengers. For example, if it determines that the driver is tired, it generates an action plan, such as suggesting nearby rest areas.

[1172] Step 6:

[1173] The server sends the generated action plan to the device, which includes the content and timing of the proposal.

[1174] Step 7:

[1175] Based on the action plan received, the device will make suggestions to the driver and passengers. For example, it might say to the driver, "You seem a little tired while driving. Would you like to take a break at the next service area?"

[1176] Step 8:

[1177] The user (driver and passengers) responds to the proposal by choosing to accept or reject it.

[1178] Step 9:

[1179] The terminal receives the user's response and controls the system's operation based on the response. For example, if the driver agrees to take a break, the navigation system automatically sets the next service area as the destination.

[1180] Step 10:

[1181] The device records the user's feedback and responses and periodically sends them to the server, which accumulates the data.

[1182] Step 11:

[1183] The server retrains the AI ​​model based on the collected feedback data, improving the system to make more accurate suggestions. The retrained model is stored on the server side.

[1184] Step 12:

[1185] The server then uses the improved AI model in the next analysis, improving the accuracy of the system's suggestions. This cycle allows the system to continuously evolve.

[1186] Example 1

[1187] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1188] When driving a vehicle, real-time monitoring of the driver and passengers' condition is required to maintain safety and comfort. However, current systems lack the ability to effectively analyze the facial expressions and conversations of the driver and passengers, predict their potential needs, and make appropriate suggestions. This makes it difficult to quickly detect driver fatigue or stress and instruct them to take breaks or other measures at the appropriate time. There is also a lack of means to respond appropriately in situations such as traffic congestion and delays.

[1189] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1190] In this invention, the server includes a detection means for detecting the states of the driver and passengers in real time, a data transmission means for transmitting data collected by the detection means to the server, an analysis means for analyzing the data received by the server and predicting potential needs of the driver and passengers, a proposal means for making suggestions to the driver and passengers based on the needs predicted by the analysis means, and a control means for receiving responses from the driver and passengers to the proposals and controlling the operation of the system based on the responses. This makes it possible to effectively monitor the states of the driver and passengers in real time, accurately predict potential needs, and make suggestions to maintain safety and comfort.

[1191] "Detection means" refers to devices and sensors for detecting the state of the driver and passengers in real time.

[1192] "Data transmission means" refers to a communication module or protocol for transmitting data collected by the detection means to a server.

[1193] "Analysis means" refers to algorithms or software that analyze the state of the driver and passengers based on the data received by the server and predict their potential needs.

[1194] The "suggestion means" refers to a user interface or notification system for making specific suggestions to the driver and passengers based on the needs predicted by the analysis means.

[1195] "Control means" refers to hardware and / or software for receiving driver and passenger responses to suggestions and controlling the operation of the system based on those responses.

[1196] "Image capture device" refers to a camera or video device used to capture the facial expressions of the driver and passengers.

[1197] "Audio capture device" refers to a microphone or other audio collection device for capturing driver and passenger conversations.

[1198] "Image processing technology" refers to the techniques and algorithms used to analyze images captured by an imaging device and evaluate facial expressions and emotional states.

[1199] "Natural language processing" refers to a technology that converts voice data collected by a voice capture device into text and analyzes that text to evaluate psychological state and emotions.

[1200] This invention is a system that monitors the status of the driver and passengers in the vehicle in real time, predicts their potential needs, and makes appropriate suggestions. This system operates through communication between a terminal installed in the vehicle and a server built on the cloud.

[1201] System Configuration

[1202] Device configuration

[1203] The terminal is installed inside the vehicle and has a detection means for detecting the state of the driver and passengers. The detection means uses an image capture device (camera) to capture the facial expressions of the driver and passengers, and a voice capture device (microphone) to collect the content of their conversations.

[1204] The camera captures the faces of the driver and passengers in real time and uses image processing libraries such as OpenCV and dlib to extract facial features. The audio capture device collects high-quality ambient audio and converts it into text using the Google Cloud Speech-to-Text API.

[1205] The device has a data transmission means to send collected data to the server in real time, and sends the data to the server's API endpoint using an HTTP POST request. The data is encrypted in JSON format and sent securely.

[1206] Server Configuration

[1207] The server has an analysis means for analyzing the data sent from the terminal. Specifically, it performs the following processes.

[1208] 1. Facial Expression Analysis: Facial expression data acquired from a camera is analyzed to evaluate emotional states (e.g., fatigue, stress, joy). By quantifying the emotional states using Python's OpenCV library, the psychological state of the driver and passengers can be accurately evaluated.

[1209] 2. Voice analysis: Voice data acquired from the microphone is analyzed using natural language processing (NLP) technology. The voice is converted into text using the Google Cloud Speech-to-Text API, and then text analysis is performed. This allows the system to infer the user's psychological state from the content and tone of the conversation.

[1210] Based on the analysis results, the server predicts the potential needs of the driver and passengers and has a suggestion means for generating specific actions and suggestions.

[1211] Proposal submission and response

[1212] The device receives analysis results and suggestions from the server and presents them to the driver and passengers as notifications and alerts, who can then respond to the suggestions using the touchscreen or voice commands.

[1213] For example, if the device determines that the driver is tired after a long drive, it will display a prompt such as, "You seem a little tired from driving. Would you like to take a break at the next service area?" If the device has a voice assistant function, it will make similar suggestions aloud.

[1214] If the user responds by accepting the suggestion, the device will use the navigation system (e.g., Google Maps API) to set the next service area as the destination. If a delay due to traffic congestion is predicted, the device will display a prompt saying, "Arrival at the destination is expected to be delayed. Would you like to contact the customer?" and will automatically contact the customer if the user responds in favor.

[1215] Specific operation example

[1216] Example 1: Proposal for a break in your car

[1217] 1. The device uses a camera to detect frequent rubbing of the driver's eyes and a microphone to collect audio of yawning.

[1218] 2. The device sends the collected data to the server.

[1219] 3. The server analyzes the driver's fatigue level using facial expression recognition algorithms and natural language processing.

[1220] 4. The server generates suggestions to encourage the driver to take a break.

[1221] 5. The device will display a suggestion: "You seem a little tired from driving. Would you like to take a break at the next service area?"

[1222] 6. The user responds "Yes."

[1223] 7. The device sets the next service area in the navigation system and guides the driver there.

[1224] Example 2: Calling about being late in a commercial vehicle

[1225] 1. The device checks the current traffic conditions and schedule to detect congestion information.

[1226] 2. The device sends this data to the server.

[1227] 3. The server analyzes the traffic data and determines that you are likely to be late for a scheduled business meeting.

[1228] 4. The server generates the template for the automated email.

[1229] 5. The terminal displays the suggestion, "Your arrival at your destination is expected to be delayed. Would you like to contact your customer?"

[1230] 6. The user responds "Yes."

[1231] 7. The device automatically sends emails to customers using the prepared email templates.

[1232] In this way, the system of the present invention can effectively monitor the state of the driver and passengers in real time, accurately predict their potential needs, and make suggestions to maintain safety and comfort.

[1233] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1234] Step 1: Data collection

[1235] The device detects the driver and passengers' status using a camera and microphone installed in the vehicle. The camera recognizes faces in real time using the OpenCV library and extracts facial features using the dlib library. The microphone collects audio data and converts it into text using the Google Cloud Speech-to-Text API.

[1236] Input: Video and audio data of the driver and passengers

[1237] Specific operation: The camera captures the driver's facial expressions, and the microphone collects the conversation.

[1238] Output: Facial expression data (feature points) and speech text data

[1239] Step 2: Send data

[1240] The device sends the collected facial expression data and speech-to-text data to a server at regular intervals, where the data is encrypted and sent to the server's API endpoint in the cloud using an HTTP POST request.

[1241] Input: facial expression data (feature points) and voice text data

[1242] Specific operation: The data collected by the device is converted into JSON format, encrypted, and sent to the server.

[1243] Output: JSON formatted data sent to the server

[1244] Step 3: Receiving data

[1245] The server receives the JSON formatted data sent from the device, and stores it in a database for analysis.

[1246] Input: JSON format data sent from the terminal

[1247] Specific operation: The server's API analyzes the received data and extracts the necessary information.

[1248] Output: Raw data prepared for analysis

[1249] Step 4: Data analysis

[1250] The server analyzes the received data, using the OpenCV library to evaluate the emotional state (e.g., fatigue, stress, joy) of the facial expression data, and natural language processing (NLP) techniques to analyze the psychological state and conversation content of the speech and text data.

[1251] Input: Prepared raw data

[1252] Specific operations: Facial recognition algorithms are run on facial expression data to quantify emotional states, and NLP algorithms are run on voice and text data to analyze psychological states and conversation content.

[1253] Output: Analyzed emotional and psychological state data

[1254] Step 5: Proposal Generation

[1255] The server predicts the potential needs of the driver and passengers based on the analysis results, and generates appropriate suggestions based on those needs. For example, if the driver is tired, it generates suggestions to encourage them to take a break.

[1256] Input: Analyzed emotional and psychological state data

[1257] Specific behavior: Evaluate the analysis results and generate appropriate prompts or suggestions. For example, "You seem a little tired while driving. Would you like to take a break at the next service station?"

[1258] Output: Generated prompts and suggestions

[1259] Step 6: Proposal Presentation

[1260] The device displays the suggestions received from the server to the driver and passengers, and provides the suggestions through a user interface or a voice assistant.

[1261] Input: Generated prompts and suggestions

[1262] Specific operation: The device displays and presents suggestions on the LCD screen or voice assistant.

[1263] Output: Suggested prompts and suggestions for the user

[1264] Step 7: User response

[1265] The user responds to the suggestion, for example by selecting "yes" or "no" using a touchscreen or by responding with a voice command.

[1266] Input: Prompts and suggestions provided

[1267] Specific Action: The user responds with a touchscreen or voice command, for example, saying "Yes."

[1268] Output: User response data

[1269] Step 8: Take Action

[1270] The device then takes appropriate action based on the user's response, such as using the navigation system to set the destination to the nearest service area, or automatically contacting the customer using a template to inform them they'll be late.

[1271] Input: User response data

[1272] Specific operation: When the user accepts the break, the terminal will set the service area in the navigation system. When the user accepts the delay notification, the terminal will automatically send an email to the customer.

[1273] Output: Destination setting for navigation systems and automatic email transmission

[1274] In this way, the entire system works together to monitor the driver and passengers' status in real time, making it possible to make appropriate suggestions and take appropriate actions based on their potential needs.

[1275] (Application example 1)

[1276] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1277] In recent years, there has been a growing demand for systems that monitor the status of drivers and passengers in real time and propose appropriate actions to improve vehicle safety and comfort. However, existing systems rely too heavily on on-site analysis, which can result in delays and reduced accuracy in data processing. The present invention aims to solve these problems and provide a system that enables highly accurate and rapid analysis and proposals using a cloud server.

[1278] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1279] In this invention, the server includes a detection means for detecting the states of the driver and passengers, an analysis means for analyzing data collected by the detection means and predicting potential needs of the driver and passengers, a proposal means for making suggestions to the driver and passengers based on the needs predicted by the analysis means, a control means for receiving responses from the driver and passengers to the proposals and controlling the operation of the system based on the responses, and a communication means for transmitting the detected data to a cloud server, receiving the results, and providing them to the proposal means, thereby enabling highly accurate data analysis in real time and rapid proposals.

[1280] "Driver and passenger" means the person driving the vehicle and any passengers in the vehicle.

[1281] The "detection means" is a means for detecting the state of the driver and passengers, and includes devices such as a camera and a microphone.

[1282] The "analysis means" is a means for analyzing the data collected by the detection means and predicting the potential needs of the driver and passengers.

[1283] The "suggestion means" is a means for making specific suggestions to the driver and passengers based on the needs predicted by the analysis means.

[1284] "Control means" means for receiving driver and passenger responses to suggestions and controlling the operation of the system based on the responses.

[1285] The "communication means" is a means for transmitting detected data to a cloud server, receiving the results, and providing them to the proposal means.

[1286] A "cloud server" is a remote server connected via the Internet to perform data analysis and provide information.

[1287] "Facial expression recognition technology" is a technology that analyzes a person's facial expressions from images and videos to evaluate their emotional state.

[1288] "Speech recognition technology" is a technology that analyzes voice data and extracts information from its content and tone.

[1289] "Real-time" refers to the state in which data is processed and results are obtained immediately in real time.

[1290] "Potential needs" are requirements that the driver and passengers are not aware of at present, but may become necessary in the near future.

[1291] This invention is a system that monitors the status of the driver and passengers in an autonomous vehicle in real time, predicts potential needs, and makes suggestions. This system is configured using the following hardware and software.

[1292] System configuration

[1293] The entire system operates through communication between a terminal installed in the vehicle and a server built on the cloud.

[1294] Device Features

[1295] The device is installed inside the vehicle and is equipped with a camera and microphone to detect the state of the driver and passengers. In this case, the camera captures video using OpenCV and audio using pyaudio. The camera captures the facial expressions of the driver and passengers in real time, and the microphone collects the content of their conversations. This information is used as a means of detection.

[1296] The devices are equipped with a communication means to send collected data to a cloud server in real time, minimizing data delays and enabling fast and accurate analysis.

[1297] Server Features

[1298] The server receives the data sent from the device and acts as an analysis means. The server performs the following processes:

[1299] 1. Facial Expression Analysis: Evaluate the emotional state (e.g., fatigue, stress, joy) of the driver and passengers based on facial expression data acquired from the camera. Uses facial expression recognition technology.

[1300] 2. Voice analysis: Voice data collected by a microphone is analyzed using natural language processing to infer a person's psychological state from the content and tone of the conversation. Voice recognition technology is used.

[1301] Based on the analysis results, the server predicts the potential needs of the driver and passengers and generates specific actions as suggestions. For example, if the server determines that the driver is tired, it will suggest rest spots.

[1302] Suggestion and Control

[1303] The analysis results received from the server are sent to the terminal, which then makes appropriate suggestions to the driver and passengers as a suggestion means.

[1304] Examples:

[1305] 1. Suggesting a break

[1306] If the camera detects the driver rubbing their eyes frequently and the microphone picks up yawning, the server will determine that the driver is highly fatigued. It will then collect information about nearby service areas and send it to the device. The device will then suggest to the driver, "You seem a little tired from driving. Would you like to take a break at the next service area?" If the driver agrees, the navigation system will automatically set the service area as the destination.

[1307] 2. Relaxation suggestions

[1308] If the device determines that the driver is feeling stressed based on facial expression and voice data, it will suggest, "Would you like to play some relaxing music?" If the driver agrees, relaxing music will automatically play on the car's audio system.

[1309] Prompt Sentence Examples

[1310] "Based on facial expression and voice data, it seems the driver is tired. Please suggest a place to rest."

[1311] In this way, the state of the driver and passengers can be monitored in real time, potential needs can be predicted, and appropriate suggestions can be made to provide a safe and comfortable in-car environment.

[1312] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1313] Step 1:

[1314] The device uses a camera and microphone to capture the facial expressions and voices of the driver and passengers in real time. Video data from the camera and audio data from the microphone are input. This data is temporarily stored within the device.

[1315] Step 2:

[1316] The device formats and compresses the collected facial expression and audio data for transmission to the cloud server. A transmission protocol to the cloud server is executed, and the data is uploaded to the cloud server. Here, the video data is encoded into MJPEG or H.264 format, and the audio data is encoded into WAV or AAC format. The encoded data becomes the input to the cloud server.

[1317] Step 3:

[1318] The server analyzes the received facial expression and voice data. For the facial expression data, a facial expression recognition algorithm using a generative AI model is run to evaluate the emotional state of the driver and passengers. For the voice data, natural language processing and voice recognition technologies are used to infer the psychological state from the content and tone of the conversation. The results of these analyses are obtained as output from the cloud server.

[1319] Step 4:

[1320] The server predicts the potential needs of the driver and passengers based on the analysis results. Based on the predicted needs, appropriate suggestions are generated. Here, a generative AI model is used to generate prompts that suggest appropriate actions for the driver, such as taking a break or playing music. These suggestions are output from the cloud server to the device.

[1321] Step 5:

[1322] The device receives suggestions from the server and presents them to the driver and passengers. For example, a suggestion such as "You seem a little tired from driving. Would you like to take a break at the next service area?" is displayed on the display screen. The driver and passengers' responses are received via voice or touch interface.

[1323] Step 6:

[1324] The terminal receives the responses of the driver and passengers and sends them to the server. For example, if the driver responds "yes," the voice data is sent to the server. Once this data transmission is complete, the responses of the driver and passengers are recognized as inputs to the terminal.

[1325] Step 7:

[1326] The server determines the appropriate action based on the received response and sends corresponding specific instructions to the terminal. For example, if the driver agrees to take a break, the server generates an instruction to set a rest spot in the navigation system and sends it to the terminal. This instruction is output from the server to the terminal.

[1327] Step 8:

[1328] The terminal controls the system's operation based on instructions received from the server. For example, the terminal can set a service area as a destination in the navigation system, which will automatically recalculate the route, allowing the driver to head to the designated rest spot.

[1329] In this way, data is processed in real time, and appropriate suggestions and actions are made throughout the system based on the driver and passengers' conditions.

[1330] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1331] MODE FOR CARRYING OUT THE INVENTION

[1332] The present invention is a system that monitors the state of the driver and passengers in real time, predicts their potential needs, and makes suggestions. In particular, by utilizing an emotion engine, it achieves more accurate emotion recognition and personalized suggestions. This system is composed of a detection means, an analysis means, a suggestion means, a control means, and an emotion engine.

[1333] System configuration

[1334] The entire system operates through communication between a terminal installed in the vehicle and a server built on the cloud. Each component will be explained below.

[1335] Device Features

[1336] The device is installed in the vehicle and is equipped with a camera and a microphone to detect the state of the driver and passengers. The camera captures the facial expressions of the driver and passengers in real time, and the microphone collects the content of their conversations. This information is used as a means of detection.

[1337] The device analyzes the collected data in real time to evaluate the safety and comfort of the vehicle, and transmits the acquired data to a cloud server at regular intervals.

[1338] Server Features

[1339] The server receives the data sent from the device and acts as an analysis means. The server performs the following processes:

[1340] 1. Facial Expression Analysis: Evaluate the emotional state (e.g., fatigue, stress, joy) of the driver and passengers based on facial expression data acquired from the camera.

[1341] 2. Voice analysis: Voice data collected by a microphone is analyzed using natural language processing, and psychological state is inferred from the content and tone of the conversation.

[1342] Emotion Engine Functions

[1343] The emotion engine is embedded in the server and integrates facial, voice, and behavioral data to recognize user emotions with high accuracy. The emotion engine integrates information from multiple data sources to assess the complex emotional states of the driver and passengers.

[1344] Suggestion and Control

[1345] The analysis results received from the server are sent to the device, which then uses the device to make appropriate suggestions to the driver and passengers. For example, the following suggestions may be considered:

[1346] "You seem a little tired from driving. Would you like to take a break at the next service area?"

[1347] "I have a new music playlist. Would you like to play it?"

[1348] "We are expecting a delay in arriving at our destination. Would you like us to contact our customer?"

[1349] The emotion engine also recognizes the driver's emotional state, allowing for more personalized suggestions. For example, if it determines that the driver is feeling stressed, it can suggest relaxing music or display an encouraging message.

[1350] The user responds to these suggestions and the terminal continues to act based on the response as a means of controlling the system.

[1351] Specific examples

[1352] Example 1: Proposal for a break in your car

[1353] If the driver continues driving for a long time, the camera detects frequent rubbing of the driver's eyes, and the microphone collects yawns. Based on this, the emotion engine in the server analyzes that the driver is highly fatigued, collects information about nearby service areas, and sends it to the device. The device then suggests to the driver, "You seem a little tired from driving. Would you like to take a break at the next service area?" If the driver agrees, the navigation system automatically sets the service area as the destination.

[1354] Example 2: Calling about being late in a commercial vehicle

[1355] If a commercial vehicle is stuck in traffic and is likely to be late for a scheduled business meeting, the device checks the current traffic conditions and the driver's calendar. The server's emotion engine analyzes this and, if it determines that there is a high possibility of a delay, generates a template for sending an automatic email. The device suggests to the driver, "Your arrival at your destination is likely to be delayed. Would you like to contact your customer?" If the driver accepts, the device automatically sends a relevant email to the customer.

[1356] As described above, this invention provides a safe and comfortable in-car environment by monitoring the state of the driver and passengers in real time, predicting their potential needs with high accuracy, and making appropriate suggestions. The introduction of an emotion engine makes it possible to realize even more personalized services.

[1357] The processing flow will be explained below.

[1358] Step 1:

[1359] The device initializes the vehicle's various sensors, cameras, and microphones, and acquires information on vehicle speed, fuel level, and location, while also capturing the facial expressions of the driver and passengers and collecting conversations.

[1360] Step 2:

[1361] The device transmits collected sensor data (vehicle speed, remaining fuel level, location information), facial expression data, and voice data to the server in real time. The transmitted data also includes a timestamp.

[1362] Step 3:

[1363] The server analyzes the received data. First, it performs facial expression analysis and evaluates the emotional state (e.g., fatigue, stress, joy) of the driver and passengers using facial expression data obtained from the camera.

[1364] Step 4:

[1365] The server performs voice analysis, analyzing the voice data collected from the microphone using natural language processing technology to infer the psychological state from the content and tone of the conversation.

[1366] Step 5:

[1367] The server utilizes an emotion engine to integrate facial, voice, and behavioral data to recognize the user's complex emotional state, for example, detecting fatigue from facial expressions and identifying stress from voice tone.

[1368] Step 6:

[1369] The server predicts the potential needs of the driver and passengers based on the analysis results. For example, if it determines that the driver is tired, it generates an action plan to suggest the next optimal rest point.

[1370] Step 7:

[1371] The server then sends the generated action plan to the terminal, which includes the proposed action content and its timing.

[1372] Step 8:

[1373] The device will then make suggestions to the driver and passengers based on the action plan it receives. For example, it might say to the driver, "You seem a little tired from driving. Would you like to take a break at the next service area?"

[1374] Step 9:

[1375] The user responds to the suggestion, and the driver has the option to accept or reject the suggestion.

[1376] Step 10:

[1377] The terminal receives the user's response and controls the system's operation based on the response. For example, if the driver agrees to take a break, the navigation system automatically sets the next service area as the destination.

[1378] Step 11:

[1379] The terminal records the user's feedback and responses and periodically transmits them to the server, thereby accumulating data for the entire system.

[1380] Step 12:

[1381] The server retrains the AI ​​model based on the accumulated feedback data, improving the system to make even more accurate suggestions. The retrained model is stored on the server.

[1382] Step 13:

[1383] The server then uses the improved AI model in the next analysis, improving the accuracy of the system's suggestions. This cycle allows the system to continuously evolve and provide better service to drivers and passengers.

[1384] Example 2

[1385] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1386] Conventional in-vehicle monitoring systems have difficulty accurately recognizing the emotional state of the driver and passengers in real time and making personalized suggestions based on that information. Furthermore, due to the low accuracy of emotional state recognition, it was not possible to accurately predict potential needs, limiting the effectiveness of the suggestions.

[1387] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: a detection means for detecting the states of the driver and passengers; a terminal means for performing an initial analysis of the data collected by the detection means; a communication means for transmitting the results of the initial analysis to a server on the cloud; an analysis means for performing a detailed analysis in the server; an emotion engine means for integrating the results obtained by the analysis means to evaluate the emotional states of the driver and passengers; a suggestion means for making suggestions to the driver and passengers based on their emotional states; and a control means for receiving responses from the driver and passengers to the suggestions and controlling the operation of the system based on the responses. This makes it possible to recognize the emotional states of the driver and passengers with high accuracy and make appropriate suggestions in real time.

[1388] The term "detection means" refers to devices and sensors that detect the state of the driver and passengers.

[1389] "Terminal means" refers to a computer or device installed in a vehicle that performs initial analysis of data collected by the detection means.

[1390] "Communication means" refers to the interface and protocol for sending and receiving data between the terminal means and the server on the cloud.

[1391] "Analysis means" refers to an algorithm or program for analyzing in detail the initial analysis results sent from the terminal means and evaluating the emotional state with high accuracy.

[1392] "Emotion engine means" refers to a system for integrating data obtained by the analysis means and recognizing and evaluating the emotional states of the driver and passengers.

[1393] "Suggestion means" refers to a device or software for generating and presenting appropriate suggestions to the driver and passengers based on the emotional state evaluated by the emotion engine means.

[1394] "Control means" refers to a device or program for receiving responses from the driver and passengers to the suggestions generated by the suggestion means, and for continuing or changing the operation of the system based on the responses.

[1395] "Image capture device" refers to a device that uses an optical device such as a camera to capture the facial expressions and movements of the driver and passengers.

[1396] "Audio capture device" refers to a device that uses an acoustic device such as a microphone to collect conversations and voices of the driver and passengers.

[1397] "Facial expression recognition technology" refers to algorithms for analyzing facial expression data captured by an image capture device and assessing emotional state.

[1398] "Natural language processing technology" refers to algorithms that analyze voice data collected by a voice capture device and evaluate the emotional state and content of the conversation.

[1399] The present invention is a system that monitors the status of the driver and passengers in real time, predicts their potential needs, and makes appropriate suggestions. This system operates in cooperation with a terminal installed in the vehicle and a server built on the cloud. Specific embodiments for implementing this system are described below.

[1400] System configuration

[1401] The system consists of a terminal, a server, an emotion engine, a detection means, an analysis means, a proposal means, and a control means.

[1402] Device Features

[1403] The device is installed in the vehicle and is equipped with a camera and a microphone to detect the state of the driver and passengers. The camera captures the facial expressions of the driver and passengers in real time, and the microphone collects the content of their conversations. This information is used as a means of detection.

[1404] The device performs an initial analysis of the collected data and evaluates the safety and comfort of the vehicle interior. For example, the device uses facial recognition technology to identify basic facial expressions such as smile, surprise, and anger, and analyzes voice data using natural language processing technology (e.g., the Python library NLP). The results of this initial analysis are sent to a cloud server at regular intervals.

[1405] Server Features

[1406] The server receives the data sent from the device and performs detailed analysis. The server uses a deep learning model (e.g., TensorFlow or PyTorch) to perform detailed analysis of the facial image and audio data. The results of the detailed analysis are sent to the emotion engine.

[1407] Emotion Engine Functions

[1408] The emotion engine is embedded in the server and integrates detailed analysis results to assess the emotional state of the driver and passengers with high accuracy. This emotion engine integrates information from multiple data sources and recognizes complex emotional states (e.g., fatigue, stress, joy, etc.).

[1409] Suggestion and Control

[1410] The analysis results received from the server are sent to the device, which then makes appropriate suggestions to the driver and passengers. For example, if the device determines that the driver is tired, it will suggest taking a break at the next service area. If the device detects stress, it will suggest playing relaxing music.

[1411] Specific prompt examples:

[1412] "Write a natural language description for a system that detects when a driver is tired and suggests a break if they've been driving for a long time."

[1413] The user responds to these suggestions using voice commands or the touch panel, and the device receives the response and performs the next action. For example, if the user accepts the break, the navigation system automatically sets up a service area.

[1414] Specific examples

[1415] Example 1: Proposal for a break in your car

[1416] The device's camera detects frequent rubbing of the driver's eyes, and the microphone collects yawns. Based on this, the emotion engine in the server analyzes the driver's level of fatigue and determines that the driver is fatigued. The server then sends information about nearby service areas to the device, which then suggests, "You seem a little tired from driving. Would you like to take a break at the next service area?" If the user accepts, the navigation system automatically sets the service area as the destination.

[1417] Example 2: Calling about being late in a commercial vehicle

[1418] The device checks the current traffic situation and the schedule, and detects the possibility of delay due to congestion. The server's emotion engine analyzes the possibility of delay, and if the delay is determined to be high, the server generates an automatic email sending template. The device suggests, "Your arrival at your destination is expected to be delayed. Would you like to contact your customer?" If the user accepts, the device automatically sends a relevant email to the customer.

[1419] In this way, the present invention provides a safe and comfortable in-vehicle environment by monitoring the emotional states of the driver and passengers in real time with high accuracy and making appropriate suggestions.

[1420] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1421] Step 1:

[1422] The device collects data using a camera and microphone. The camera captures the facial expressions of the driver and passengers in real time, and the microphone collects conversations and audio. The input is video and audio data from inside the vehicle, and the output is the captured raw data. Specifically, the camera captures faces at 15 frames per second, and the microphone records audio every 10 seconds.

[1423] Step 2:

[1424] The data collected by the device is initially analyzed. Facial expression data is analyzed using a facial recognition algorithm (e.g., OpenCV) to identify basic emotions (e.g., smile, surprise, anger). Voice data is analyzed for conversation content and tone using the Python library NLP. The input is the raw data acquired in step 1, and the output is the analyzed basic emotional state and voice content. Specifically, the process identifies facial expressions from image data and extracts tone and content from voice data.

[1425] Step 3:

[1426] The device sends the initial analysis results to the cloud server. The analysis results are converted to JSON format and sent to the server using the HTTP POST method. The input is the analysis results obtained in step 2, and the output is the JSON data sent to the cloud server. Specifically, the initial analysis results data is generated and sent to the server via the Internet.

[1427] Step 4:

[1428] The server performs detailed analysis of the data sent from the device. It uses a deep learning model (e.g., TensorFlow or PyTorch) to perform detailed analysis of the facial image and audio data and evaluates the emotional state with high accuracy. The input is the JSON data sent in step 3, and the output is the analyzed emotional state data. Specifically, the deep learning model is used to perform detailed facial and audio recognition.

[1429] Step 5:

[1430] The emotion engine in the server integrates the detailed analysis results and evaluates the emotional state of the driver and passengers with high accuracy. The input is the emotional state data obtained in step 4, and the output is the integrated final emotional state data. Specifically, it integrates multiple data sources to identify complex emotional states.

[1431] Step 6:

[1432] The server sends the analysis results to the device. The results are converted into JSON format and sent to the device using the HTTP POST method. The input is the final emotional state data obtained in step 5, and the output is the analysis result data sent to the device. Specifically, the emotional state data is generated in JSON format and sent to the device via the Internet.

[1433] Step 7:

[1434] Based on the analysis results received by the device from the server, the device generates and presents appropriate suggestions to the driver and passengers. The suggestions are generated using a text generation model (e.g., GPT-3) algorithm. The input is the analysis result data sent in step 6, and the output is the suggestion text presented to the user. Specifically, the suggestion text is generated and notified to the user by voice or on-screen display.

[1435] Step 8:

[1436] The user responds to suggestions from the device, and the device performs the next action based on the response. For example, if the user accepts a break, the device updates the navigation system to set the next service area. The input is the user's response, and the output is the next action to be performed. Specifically, the device receives a voice command or touch input and updates the destination in the navigation system.

[1437] In this way, the entire system works together to recognize emotional states with high accuracy and make appropriate suggestions in real time, thereby providing a safe and comfortable in-car environment for the driver and passengers.

[1438] (Application example 2)

[1439] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1440] To improve the safety and comfort of drivers and passengers while driving, a system that monitors their condition in real time and makes appropriate suggestions at the appropriate time is required. However, conventional systems have been unable to accurately grasp the emotional state of the driver and passengers, making it difficult to make personalized suggestions. Furthermore, they lacked the ability to suggest appropriate breaks or entertainment based on the driver's condition, such as fatigue or stress.

[1441] To solve the above problems, it is necessary to incorporate a function that can evaluate emotional states with high accuracy and a means to make personalized suggestions.

[1442] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: detection means consisting of a camera and a microphone that detects the state of the driver and passengers; analysis means that analyzes data collected by the detection means and predicts the potential needs of the driver and passengers; proposal means that makes suggestions to the driver and passengers based on the needs predicted by the analysis means; and control means that receives responses from the driver and passengers to the proposals and controls the operation of the system based on the responses. The server also includes information provision means that uses an emotion engine to accurately evaluate the emotional states of the driver and passengers, and provides information about nearby rest spots if signs of fatigue are observed, and entertainment provision means that suggests a new music playlist if the driver and passengers are relaxed.

[1443] This makes it possible to grasp the emotional state of the driver and passengers with high accuracy in real time and make personalized suggestions, thereby providing a safe and comfortable driving environment.

[1444] 1. "Driver and passenger condition" means the physical and psychological state of the driver and passenger, including facial expressions, voice, and other physiological responses.

[1445] 2. "Detection means" refers to a device that includes an imaging device for capturing facial expressions of the driver and passengers and an audio collection device for collecting conversations.

[1446] 3. "Analysis Means" means technology that analyzes data collected by the Detection Means and predicts the potential needs and emotional states of the driver and passengers.

[1447] 4. "Proposal means" refers to a device or function that makes appropriate suggestions to the driver and passengers based on the needs predicted by the analysis means.

[1448] 5. "Control means" means a device or function for receiving responses from the driver and passengers to suggestions from the suggestion means and controlling the operation of the system based on those responses.

[1449] 6. "Emotion Engine" is a technology that integrates facial expressions, voice, and behavioral data of the driver and passengers to recognize and evaluate their emotional state with high accuracy.

[1450] 7. "Navigation and entertainment suggestion means" is a function that suggests navigation information and entertainment content based on the analysis results of the emotion engine.

[1451] 8. "Information Providing Means" means a device or function that provides information about nearby rest points when signs of fatigue are present.

[1452] 9. "Entertainment Provider" is a feature that suggests new music playlists and other entertainment content when you're relaxing.

[1453] A specific embodiment for carrying out the present invention will be described. The system of the present invention is composed of a terminal in a vehicle and a server on a cloud. The operation of the entire system is as follows.

[1454] Device configuration and functions

[1455] The terminal is installed in the vehicle and includes the following main hardware and software:

[1456] Image capture device: A camera for capturing the facial expressions of the driver and passengers in real time.

[1457] Audio collection device: Uses a microphone to collect conversations between the driver and passengers.

[1458] The terminal sends the data detected by these devices to a server on the cloud. Specifically, the camera and microphone collect signs of driver fatigue and stress and send them to the cloud server.

[1459] Server configuration and functions

[1460] The server includes the following main software and hardware components:

[1461] Emotion Engine: Integrates information from multiple data sources to accurately recognize the emotional state of the driver and passengers. Analysis is performed using facial expression and voice recognition technologies.

[1462] Software used: OpenCV, Google Cloud Speech-to-Text

[1463] Data analysis means: Data sent to the cloud server is analyzed in real time to evaluate the emotional state of the driver and passengers.

[1464] The server sends the analysis results to the device, which then makes appropriate suggestions based on the results.

[1465] Specific proposal methods and information provision

[1466] Based on the results of the emotion engine's analysis, the following specific suggestions are made:

[1467] Information provision method: If the driver frequently rubs their eyes or yawns, the emotion engine will evaluate the signs of fatigue as high. The device will suggest, "You are showing signs of fatigue. Would you like to take a break at the next service area?" If the user accepts, it will provide information about nearby rest points and set up navigation.

[1468] Entertainment provision method: If the system detects that the driver's facial expressions or voice indicate that they are relaxed, it will suggest a new music playlist. It will display a message saying, "You look relaxed. Would you like to play a new music playlist?"

[1469] Specific examples of processing procedures

[1470] For example, if a driver is driving for a long time in front of the camera and rubbing their eyes frequently, the emotion engine will analyze the driver's fatigue level and send information about nearby service areas to the device, suggesting a break. If the driver is relaxed, the system will suggest a music playlist, providing a comfortable environment.

[1471] An example of a prompt is as follows:

[1472] Create a system that suggests rest stops when the user shows signs of fatigue, suggests nearby rest areas, for example if the driver is rubbing their eyes frequently, or suggests a new music playlist when the driver is in a relaxed state.

[1473] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1474] Step 1:

[1475] The device's detection means, an imaging device and an audio collection device, capture the facial expressions and voices of the driver and passengers in real time. The input is video data from the camera and audio data from the microphone. Specifically, the device periodically collects data and sends it to a cloud server. The output is this video data and audio data.

[1476] Step 2:

[1477] The emotion engine on the server receives the video and audio data sent from the device. The input is video and audio data. The emotion engine analyzes this data using facial expression recognition technology (e.g., OpenCV) and speech recognition technology (e.g., Google Cloud Speech-to-Text). The output is the emotional state of the driver and passengers (e.g., degree of fatigue, stress, and relaxation).

[1478] Step 3:

[1479] The server's analysis means predicts the potential needs of the driver and passengers based on this emotional state. The input is emotional state data obtained from the emotion engine. Based on this, the analysis means determines needs, such as "the driver is tired" or "the driver is relaxed." The output is data related to needs.

[1480] Step 4:

[1481] The server generates appropriate suggestions based on the needs predicted by the analysis means. The input is data related to the needs. For example, if it determines that the user "needs a break," it will suggest information about nearby resting points. If it determines that the user "is relaxing," it will generate a suggestion for a new music playlist. The output is the suggestion content.

[1482] Step 5:

[1483] The server sends the suggestion to the device. The input is the suggestion from the server. The device displays it to the driver and passengers. As a specific operation, the suggestion is provided via a display device or voice assistant. The output is the state in which the suggestion is presented to the driver and passengers.

[1484] Step 6:

[1485] The user (driver and passengers) responds to this suggestion. The input is the suggestion content. Response options include, for example, "take a break" or "play music." The output is the user's response.

[1486] Step 7:

[1487] The terminal receives the user's response and controls the system's operation as a control means. The input is the user's response. Specific operations include setting navigation to a rest point or playing a music playlist. The output is the state in which the system has performed the appropriate operation.

[1488] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1489] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1490] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1491] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1492] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1493] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1494] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1495] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1496] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1497] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1498] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1499] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1500] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1501] 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.

[1502] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1503] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1504] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1505] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1506] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1507] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1508] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1509] The following is further disclosed regarding the above embodiment.

[1510] (Claim 1)

[1511] detection means for detecting the state of the driver and passengers;

[1512] an analysis means for analyzing the data collected by the detection means and predicting potential needs of the driver and passengers;

[1513] suggestion means for making suggestions to the driver and passengers based on the needs predicted by the analysis means;

[1514] control means for receiving driver and passenger responses to the suggestions and for controlling operation of the system based on the responses;

[1515] A system including:

[1516] (Claim 2)

[1517] 2. The system of claim 1, wherein the detecting means includes a camera for capturing facial expressions of the driver and passengers and a microphone for collecting speech of the driver and passengers.

[1518] (Claim 3)

[1519] 2. The system of claim 1, wherein the analysis means assesses the emotional state of the driver and passengers using facial expression and voice recognition techniques.

[1520] "Example 1"

[1521] (Claim 1)

[1522] a detection means for detecting the state of the driver and passengers in real time;

[1523] data transmission means for transmitting the data collected by the detection means to a server;

[1524] an analysis means for analyzing the data received by the server and predicting potential needs of the driver and passengers;

[1525] a suggestion means for making suggestions to the driver and passengers based on the needs predicted by the analysis means;

[1526] control means for receiving driver and passenger responses to the suggestions and for controlling operation of the system based on the responses;

[1527] A system including:

[1528] (Claim 2)

[1529] 2. The system according to claim 1, wherein the detection means includes an imaging device for capturing facial expressions of the driver and passengers, and an audio capture device for collecting conversations between the driver and passengers.

[1530] (Claim 3)

[1531] 2. The system of claim 1, wherein the analysis means uses image processing techniques and natural language processing to assess the emotional state of the driver and passengers.

[1532] "Application Example 1"

[1533] (Claim 1)

[1534] detection means for detecting the state of the driver and passengers;

[1535] an analysis means for analyzing the data collected by the detection means and predicting potential needs of the driver and passengers;

[1536] suggestion means for making suggestions to the driver and passengers based on the needs predicted by the analysis means;

[1537] control means for receiving driver and passenger responses to the suggestions and for controlling operation of the system based on the responses;

[1538] a communication means for transmitting the detected data to a cloud server, receiving the results, and providing the results to the suggestion means;

[1539] A system including:

[1540] (Claim 2)

[1541] 2. The system of claim 1, wherein the detecting means includes a camera for capturing facial expressions of the driver and passengers and a microphone for collecting speech of the driver and passengers.

[1542] (Claim 3)

[1543] 2. The system of claim 1, wherein the analysis means assesses the emotional state of the driver and passengers using facial expression and voice recognition techniques.

[1544] "Example 2: Combining Emotion Engines"

[1545] (Claim 1)

[1546] detection means for detecting the state of the driver and passengers;

[1547] terminal means for initial analysis of the data collected by the detection means;

[1548] a communication means for transmitting the initial analysis results to a server on the cloud;

[1549] an analysis means for performing detailed analysis in the server;

[1550] emotion engine means for integrating the results obtained by the analysis means to evaluate the emotional states of the driver and passengers;

[1551] suggestion means for making suggestions to the driver and passengers based on their emotional state;

[1552] control means for receiving driver and passenger responses to the suggestions and for controlling operation of the system based on the responses;

[1553] A system including:

[1554] (Claim 2)

[1555] 2. The system of claim 1, wherein the detecting means includes an image capture device for capturing facial expressions of the driver and passengers, and an audio capture device for collecting conversations of the driver and passengers.

[1556] (Claim 3)

[1557] The system according to claim 1, characterized in that the analysis means evaluates the emotional states of the driver and passengers using facial expression recognition technology and natural language processing technology, and integrates the results to recognize the emotional states with high accuracy.

[1558] "Application example 2 when combining emotion engines"

[1559] (Claim 1)

[1560] detection means for detecting the state of the driver and passengers;

[1561] an analysis means for analyzing the data collected by the detection means and predicting potential needs of the driver and passengers;

[1562] suggestion means for making suggestions to the driver and passengers based on the needs predicted by the analysis means;

[1563] control means for receiving driver and passenger responses to the suggestions and for controlling operation of the system based on the responses;

[1564] an emotion engine that assesses the emotional state of the driver and passengers with high accuracy;

[1565] means for providing personalized navigation and entertainment suggestions based on the analysis results of the emotion engine;

[1566] an information providing means for providing information about nearby rest points when signs of fatigue are observed;

[1567] An entertainment solution to suggest new music playlists when you're relaxing;

[1568] A system including:

[1569] (Claim 2)

[1570] 2. The system of claim 1, wherein the detecting means includes an imaging device for capturing facial expressions of the driver and passengers, and an audio collecting device for collecting conversations of the driver and passengers.

[1571] (Claim 3)

[1572] 2. The system of claim 1, wherein the analysis means assesses the emotional state of the driver and passengers using facial expression and voice recognition techniques. [Explanation of symbols]

[1573] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. detection means for detecting the state of the driver and passengers; an analysis means for analyzing the data collected by the detection means and predicting potential needs of the driver and passengers; suggestion means for making suggestions to the driver and passengers based on the needs predicted by the analysis means; control means for receiving driver and passenger responses to the suggestions and for controlling operation of the system based on the responses; A system including:

2. 2. The system of claim 1, wherein the detecting means includes a camera for capturing facial expressions of the driver and passengers and a microphone for collecting speech of the driver and passengers.

3. 2. The system of claim 1, wherein the analyzing means assesses the emotional state of the driver and passengers using facial expression and voice recognition techniques.

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