System

Abstract

A system is provided.SOLUTION: A system comprising: means for capturing facial expression data of a user obtained from a smart device; means for capturing speech data of the user; means for obtaining location information; means for collecting state data of a vehicle from vehicle sensors; means for analyzing the data and predicting potential needs of the user; and means for suggesting appropriate actions to the user based on the prediction.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] Many modern automobiles are equipped with sensors that can collect various types of information. However, systems that can predict a user's potential needs based on the collected information and automatically suggest appropriate responses are still in the development stage. If this issue is left unaddressed, driving safety may not be ensured and a comfortable environment for the user may not be provided. In particular, it is difficult to appropriately detect and respond to a user's fatigue or stress during long drives or when traveling in a commercial vehicle. Furthermore, commercial vehicles lack systems that notify the user in a timely manner that they will be late for a business meeting or other important schedule. The purpose of this invention is to solve these issues and provide a safer and more comfortable in-vehicle environment. [Means for solving the problem]

[0005] The present invention provides a system including means for capturing a user's facial expression data acquired from a smart device, means for capturing the user's conversation data, means for acquiring location information, means for collecting vehicle status data from vehicle sensors, means for analyzing the data and predicting the user's potential needs, and means for suggesting appropriate actions to the user based on the prediction. This system comprehensively analyzes the user's facial expression, conversation, location, and vehicle status data to detect the user's fatigue and stress and suggest appropriate breaks. Furthermore, in the case of commercial vehicles, this data can be linked with the user's business negotiation schedule information to notify the parties involved in the negotiation of estimated arrival times and delays. This improves safety and provides a comfortable and efficient in-vehicle environment for the user.

[0006] A "smart device" is an electronic device for capturing or collecting information about a user, and examples include smartphones, cameras, microphones, etc.

[0007] "Facial expression data" is digital data relating to the user's facial expressions acquired through an in-car camera, and is used to read the user's emotional state.

[0008] "Conversation data" is voice data relating to the content of a user's speech acquired through an in-vehicle microphone, and is used to analyze the user's words and the content of the conversation.

[0009] "Location Information" means data regarding the current geographic location of a vehicle obtained through a location information system such as a GPS.

[0010] "Vehicle Sensor" means a sensor used to measure the performance or condition of a vehicle, such as vehicle speed, fuel level, or engine condition.

[0011] "Vehicle status data" refers to data relating to the performance and status of a vehicle collected from vehicle sensors.

[0012] "Analysis" refers to the technical procedures used to process collected data and evaluate the potential needs and conditions of users.

[0013] "Latent needs" are needs or desires that are not explicitly requested by the user but are predicted based on the situation or state.

[0014] "Prediction" means estimating the future state and needs of users from the analysis results.

[0015] An "action" is a specific behavior or measure that the system suggests to or takes for the user.

[0016] The "means for suggesting a break" is a function that detects the user's fatigue or stress state and notifies or advises the user to take an appropriate break.

[0017] "Business negotiation schedule information" is data relating to the time and location of business negotiations and meetings that the user is scheduled to attend.

[0018] "Notification" refers to the means by which the system communicates information to a user or interested party, and includes, for example, voice notification, email, message, etc. [Brief explanation of the drawings]

[0019] [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

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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."

[0040] ---

[0041] The system of the present invention provides a safe and comfortable in-car environment by predicting the user's potential needs based on data acquired from smart devices and suggesting appropriate actions. This system is composed of the following main components: a sensing module, a data analysis module, and an interaction module.

[0042] Sensing Module

[0043] The device collects data using cameras, microphones, GPS, and vehicle sensors installed in the vehicle. Specifically, the camera captures the user's facial expression data, the microphone records conversation data inside the vehicle, the GPS obtains current location information, and the vehicle sensors collect status data such as vehicle speed, fuel level, and engine condition.

[0044] Data Analysis Module

[0045] The collected data is sent to a server where it is comprehensively analyzed. The server first analyzes facial expression data obtained from the camera to identify the user's emotional state (for example, fatigue, stress, joy, etc.). Next, it performs audio analysis of the conversation data obtained from the microphone to extract information needed to understand the content and context of the user's conversation. It also analyzes GPS data and vehicle status data to evaluate the current vehicle location and status. It then comprehensively evaluates this data and uses an AI model to predict the user's potential needs.

[0046] Interaction Module

[0047] Based on predicted needs, the system suggests appropriate actions to the user. For example, if a user is on a long drive and facial expression analysis reveals signs of fatigue, the system can suggest, by voice, "Wouldn't it be time to take a break?" In addition, for commercial vehicles, the system can obtain the user's business meeting schedule information and, if a delay due to traffic congestion is predicted, notify relevant parties that "the vehicle will be delayed due to traffic congestion."

[0048] Specific examples

[0049] Specific examples of the present invention are shown below.

[0050] Example 1: Proposal for rest breaks in a private car

[0051] Consider a scenario in which a user is on a long drive. The device captures the user's facial expressions using an in-car camera, and the collected facial data is sent to the server. The server analyzes the facial data and determines that the user is tired. The system then suggests by voice, "Shouldn't you take a break now?" This allows the user to reduce fatigue and continue driving safely.

[0052] Example 2: Notification system in commercial vehicles

[0053] Consider a scenario in which a user is traveling for a business meeting. The device uses GPS to determine the user's current location and simultaneously obtains the business meeting schedule information. The server analyzes this data and determines that a traffic jam has occurred. If a delay is predicted, the system notifies the relevant parties by email or message that "there will be a delay due to traffic congestion." This allows timely information sharing with the business partner, ensuring smooth communication.

[0054] As described above, the system of the present invention comprehensively analyzes data from a variety of sensors, accurately predicts the user's potential needs, and suggests appropriate actions, thereby realizing a safe and comfortable in-car environment.

[0055] The processing flow will be explained below.

[0056] ---

[0057] Step 1: Data collection

[0058] The device collects data using cameras, microphones, GPS, and vehicle sensors installed in the vehicle. Specifically, the camera captures the user's facial expressions, the microphone records conversations inside the vehicle, the GPS obtains the current location, and the vehicle sensors collect data such as the vehicle's speed, fuel level, and engine status.

[0059] ---

[0060] Step 2: Send data

[0061] The device sends the collected data (facial expression data, conversation data, location information, vehicle status data) to a server, where it is processed in real time, enabling rapid analysis.

[0062] ---

[0063] Step 3: Analyzing facial expression data

[0064] The server receives the camera data and uses an AI model to analyze the user's facial expressions. The facial expression data identifies the user's emotional state (fatigue, stress, joy, etc.). The results of this analysis are used in the next prediction step.

[0065] ---

[0066] Step 4: Analyzing the conversation data

[0067] The server receives the microphone data and uses a voice analysis algorithm to analyze the conversation, extracting important keywords and context, which are used to understand the user's needs and requests.

[0068] ---

[0069] Step 5: Analyzing location and vehicle data

[0070] The server receives and analyzes GPS data and vehicle sensor data to determine the current vehicle location and status, allowing traffic conditions and vehicle status to be tracked in real time.

[0071] ---

[0072] Step 6: Comprehensive analysis and prediction

[0073] The server comprehensively evaluates facial expression data, conversation data, location information, and vehicle data, and then uses an AI model to predict the user's potential needs. For example, if it determines that the user is tired, it will suggest an appropriate break.

[0074] ---

[0075] Step 7: Select an action

[0076] The server selects the optimal action for the user based on the prediction results, such as suggesting a break to the user or providing traffic information.

[0077] ---

[0078] Step 8: Take Action

[0079] The device receives the action from the server and executes it for the user, such as suggesting "Should we take a break now?" or sending an email to notify relevant parties of a delay.

[0080] ---

[0081] Step 9: Gather feedback

[0082] The device collects user reactions and feedback and sends it to the server, which improves the system's analysis accuracy and the quality of its suggestions.

[0083] ---

[0084] This trend will enable users to continue driving safely and comfortably while receiving appropriate support from AI.

[0085] Example 1

[0086] 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."

[0087] To make the driving environment safer and more comfortable for modern vehicles, systems that monitor the user's condition and vehicle status in real time and suggest appropriate actions based on that information are required. However, conventional systems have had difficulty efficiently collecting and analyzing the user's facial expression data, conversation data, location information, and vehicle status data, and suggesting appropriate actions. Furthermore, it has been difficult to predict the user's potential needs based on the results of advanced analysis.

[0088] 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.

[0089] In this invention, the server includes means for capturing user facial expression data acquired from the smart device, means for capturing user conversation data, means for acquiring location information, means for collecting vehicle status data from vehicle sensors, means for transmitting the data to the server, means for analyzing the data in the server and identifying the user's emotional state, the content of the conversation, and the vehicle's location and status, means for predicting the user's potential needs using a generative AI model based on the analysis results, and means for suggesting appropriate actions to the user based on the predictions. This makes it possible to monitor the user's driving environment in real time and suggest appropriate actions at the right time.

[0090] ---

[0091] A "smart device" is an electronic device used to acquire a user's facial expression data, conversation data, location information, and vehicle status data.

[0092] "Facial expression data" is data about a user's facial expressions captured using a device such as a camera.

[0093] "Conversation data" refers to data about the content of conversations between users in a vehicle, collected using a device such as a microphone.

[0094] "Location Information" is data regarding the current geographic location of a vehicle obtained using a location measuring device such as a GPS.

[0095] "Vehicle sensor" refers generally to sensing devices installed to collect information such as vehicle speed, fuel level, and engine condition.

[0096] The "potential needs of the user" are requests and desires regarding actions and services that the user may require, which are predicted based on the analysis results.

[0097] A "server" is a computer system that receives the collected data, analyzes it, and suggests appropriate actions to the user.

[0098] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to analyze data and predict users' potential needs.

[0099] "Action suggestions" refers to guidelines and advice such as break suggestions and notifications provided to users by the system.

[0100] "Real-time analysis" is an analysis method that can quickly process collected data and obtain results immediately.

[0101] A "commercial vehicle" is a vehicle used for business or commercial travel.

[0102] "Schedule information" is time-related information such as the user's plans and appointments.

[0103] "Delay notification" refers to informing relevant parties when arrival is delayed due to traffic conditions or other reasons.

[0104] ---

[0105] The system of the present invention provides a safe and comfortable in-car environment by predicting the user's potential needs and suggesting appropriate actions based on data acquired from smart devices. This system is composed of the following main components: a sensing module, a data analysis module, and an interaction module.

[0106] Sensing Module

[0107] The device collects data using cameras, microphones, GPS, and vehicle sensors installed in the vehicle. Specifically, the camera captures the user's facial expressions, and the microphone records conversations inside the vehicle. GPS obtains the vehicle's current location, and vehicle sensors collect data such as speed, fuel level, and engine status. This data is collected in real time and sent to a server via batch processing or real-time streaming.

[0108] Data Analysis Module

[0109] The server analyzes the received data to determine the user's emotional state, the content of the conversation, and the vehicle's location and status. Specifically, it performs the following processes:

[0110] 1. The server analyzes the collected image data and identifies the user's emotional state (e.g., fatigue, stress, joy, etc.) from their facial expressions.

[0111] 2. The server converts the voice data into text and analyzes the conversation content and context.

[0112] 3. The server evaluates GPS data and vehicle sensor data to determine the current vehicle location and status.

[0113] Based on these analysis results, the server uses a generative AI model to predict the user's potential needs, for example, if the user's facial expression shows signs of fatigue, it predicts that they should take a break.

[0114] Interaction Module

[0115] Based on predicted needs, the system will suggest appropriate actions to the user. For example, if it determines that a user is tired after a long drive, it will make a voice suggestion saying, "Would you like to take a break now?" In the case of commercial vehicles, the system will analyze the user's business meeting schedule information and current traffic information, and if a delay is predicted, it will send a notification to relevant parties saying, "You will be delayed due to traffic congestion."

[0116] Specific examples

[0117] The following are specific examples of the present invention:

[0118] Example 1: Proposal for rest breaks in a private car

[0119] 1. When the user is on a long drive, the device captures the user's facial expressions using the in-car camera.

[0120] 2. The captured facial expression data is sent to the server.

[0121] 3. The server uses an AI model to analyze whether the user is tired.

[0122] 4. If fatigue is detected, the system will suggest to the user via voice, "Would you like to take a break now?"

[0123] Example 2: Notification system in commercial vehicles

[0124] 1. When a user is traveling for a business meeting, the device uses GPS to obtain the user's current location and collects business meeting schedule information.

[0125] 2. The collected data is sent to the server.

[0126] 3. The server analyzes the traffic data and determines that a traffic jam is occurring.

[0127] 4. If a delay is predicted, the system will notify relevant parties via email or message that "there will be a delay due to traffic congestion."

[0128] Prompt Sentence Examples

[0129] "Please explain a scenario where you want to detect fatigue from facial expression data of a user during a long drive and suggest a break."

[0130] "Describe a scenario in which a user heading to a business meeting is predicted to be delayed based on their current location and traffic information, and relevant parties are notified."

[0131] As described above, the present invention makes it possible to comprehensively analyze data from multiple sensors, accurately predict the potential needs of a user, and make the driving environment for the user safer and more comfortable.

[0132] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0133] ---

[0134] Step 1: Data collection

[0135] Input: Data from in-car cameras, microphones, GPS, and vehicle sensors

[0136] Output: Collected facial expression data, conversation data, location information, vehicle status data

[0137] The device captures the user's facial expressions using a camera installed inside the vehicle, records conversations inside the vehicle using a microphone, and obtains the current location information using GPS. It also collects data such as speed, fuel level, and engine status from vehicle sensors. This data is sensed in real time and sent to a server via batch processing or real-time streaming as needed.

[0138] Step 2: Send data

[0139] Input: Collected facial expression data, conversation data, location information, vehicle status data

[0140] Output: Data sent to the server

[0141] The device sends the collected data to the server periodically or in real time. Camera image data is sent in a fixed batch format, audio data is streamed in real time, and GPS data and vehicle status data are also sent in packets periodically.

[0142] Step 3: Facial Expression Analysis

[0143] Input: Facial expression data sent to the server

[0144] Output: User's emotional state

[0145] The server analyzes the received image data and uses facial recognition technology to identify the user's emotional state from their facial expressions. For example, it uses an image analysis algorithm to determine whether the user is smiling, tired, angry, etc. The analysis results in identifying the user's emotional state.

[0146] Step 4: Audio analysis

[0147] Input: Conversation data sent to the server

[0148] Output: Analyzed conversation

[0149] The server uses speech recognition technology to convert the conversation into text. The server analyzes the collected voice data using speech recognition software and generates text information to understand the content and context of the conversation. The analysis results identify the content and context of the conversation.

[0150] Step 5: Analyze location and vehicle status

[0151] Input: Location information sent to the server, vehicle sensor data

[0152] Output: Current vehicle position and status

[0153] The server analyzes the collected GPS data and vehicle sensor data. It identifies the vehicle's current location from the GPS data and evaluates the vehicle sensor data for speed, fuel level, engine status, etc. As a result of the analysis, it identifies the vehicle's current location and status.

[0154] Step 6: Anticipate potential needs

[0155] Input: Analyzed facial expression data, conversation data, location information, vehicle status data

[0156] Output: User's potential needs

[0157] The server integrates all the analyzed data and uses a generative AI model to predict the user's potential needs. For example, if the user's facial expression shows signs of fatigue, it predicts that a break is necessary. As a result of this analysis, the server identifies the user's potential needs.

[0158] Step 7: Action proposals

[0159] Input: predicted potential user needs

[0160] Output: Suggested action for the user

[0161] The system will suggest appropriate actions to the user based on predicted needs. For example, if it determines that the user is tired after a long drive, it will make a voice suggestion such as, "Wouldn't it be time to take a break?". Also, if there is traffic congestion based on business meeting schedule information, it will send a notification to the relevant parties saying, "You will be delayed due to traffic congestion."

[0162] Through the above processing steps, this system monitors the user's driving environment in real time and suggests appropriate actions at the right time, thereby providing a safe and comfortable in-car environment.

[0163] (Application example 1)

[0164] 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."

[0165] Autonomous vehicles are required to accurately predict passengers' potential needs and provide a safe and comfortable in-car environment. Conventional technologies have difficulty assessing passenger fatigue and stress levels in real time, and lack a means to suggest rest at the appropriate time. Furthermore, there are insufficient methods for sharing information in a timely manner in the event of delays due to long driving times or traffic congestion. Therefore, a system is needed to achieve a higher level of passenger safety and comfort.

[0166] 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.

[0167] In this invention, the server includes means for capturing user facial expression data acquired from a smart device, means for capturing user conversation data, means for acquiring location information, means for collecting vehicle status data from vehicle sensors, means for analyzing the data and predicting the user's potential needs, means for suggesting appropriate actions to the user based on the prediction, means for assessing the user's fatigue state and sending a notification in real time, and means for displaying information via a head-mounted display. This makes it possible to monitor passenger fatigue and stress in real time and suggest breaks at appropriate times. In addition, in the case of commercial vehicles, if a delay is predicted, a notification can be sent by email, allowing relevant parties in a business negotiation to be informed of the estimated arrival time and delay in a timely manner.

[0168] A "smart device" is an electronic device that is equipped with an internet connection and a wide variety of sensors and is capable of acquiring and processing data.

[0169] "Facial expression data" is digital data relating to a user's facial expressions that is captured using an image capture device such as a camera.

[0170] "Conversation data" is audio data relating to the content of a user's speech or conversation, which is acquired using an audio capture device such as a microphone.

[0171] "Location information" is data about the current location of a vehicle or user, obtained using a location measurement device such as a GPS.

[0172] A "vehicle sensor" is a sensor device for collecting status data such as vehicle speed, fuel level, engine status, etc.

[0173] "Real-time" refers to data acquisition and processing occurring immediately, without delay.

[0174] A "head-mounted display" is a display device worn by a user on the head, which displays information within the user's field of vision.

[0175] The "fatigue state" refers to a state that indicates the user's physical or mental fatigue, particularly one that is affected by prolonged activity or stress.

[0176] A "notification" is a message or alert sent to inform a user of important information.

[0177] "Relaxation content" refers to content such as music, video, or guidance provided to relieve the user's fatigue and stress.

[0178] "Business meeting schedule information" is information about the time, location, and participants of business meetings and conferences.

[0179] "Email" means a digital letter or message sent or received over the Internet.

[0180] The system of this invention predicts potential user needs and proposes appropriate actions to provide a safe and comfortable environment in an autonomous vehicle by analyzing data collected from smart devices and related sensors. The main components of the system are a sensing module, a data analysis module, and an interaction module.

[0181] Sensing Module

[0182] The device collects data using cameras, microphones, GPS, and vehicle sensors installed in the vehicle. Specifically, the camera captures the user's facial expression data, the microphone records conversation data in the vehicle, the GPS obtains current location information, and the vehicle sensors collect status data such as vehicle speed and engine condition.

[0183] Data Analysis Module

[0184] The collected data is sent to a server where it is comprehensively analyzed. The server first analyzes facial expression data obtained from the camera to identify the user's emotional state (for example, fatigue, stress, joy, etc.). Next, it performs audio analysis of the conversation data obtained from the microphone to extract information needed to understand the content and context of the user's conversation. It also analyzes GPS data and vehicle status data to evaluate the current vehicle location and status. It then comprehensively evaluates this data and uses an AI model to predict the user's potential needs.

[0185] Interaction Module

[0186] The system suggests appropriate actions to the user based on predicted needs. For example, if a user is on a long drive and facial expression analysis reveals signs of fatigue, the system can suggest audibly, "Shouldn't it be time to take a break?" Information such as traffic congestion and estimated arrival times at the destination can also be displayed via a head-mounted display.

[0187] Example

[0188] Example 1: Fatigue suggestions for long-distance drivers

[0189] The system analyzes video captured by an in-car camera in real time and evaluates the user's level of fatigue based on facial expression data. If fatigue exceeds a certain threshold, the system displays a message on the head-mounted display saying, "You are feeling drowsy. You are 10 minutes away from a rest area." If the user has enabled the email notification option, the system also sends a notification by email.

[0190] Example 2: Schedule management for commercial vehicles

[0191] Based on GPS and business meeting schedule information, the system notifies the user and business meeting participants of delays in the event of traffic congestion. The system displays a message on the head-mounted display saying, "Due to traffic congestion, you will be delayed from the scheduled arrival time," and can be configured to notify relevant parties by email.

[0192] Example prompt

[0193] "Create a head-mounted display application that monitors passenger status in real time and detects fatigue and stress."

[0194] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0195] Step 1:

[0196] Acquiring sensing data

[0197] The device collects data using cameras, microphones, GPS, and vehicle sensors installed inside the vehicle. Inputs include facial expression data from the camera, conversation data from the microphone, location information from GPS, and vehicle status data from vehicle sensors (speed, fuel level, engine status, etc.). After acquiring this data, it sends it to a server.

[0198] Step 2:

[0199] Analysis of facial expression data

[0200] The server analyzes the facial expression data sent from the camera. The input is image data containing the user's facial expressions. The server uses facial landmark detection technology (e.g., the dlib library) to identify the user's emotional state (fatigue, stress, joy, etc.). The output is an evaluation of these emotional states.

[0201] Step 3:

[0202] Analysis of conversation data

[0203] The server performs speech analysis on the conversation data sent from the microphone. The input is the user's conversation voice data. This is converted into text data using speech recognition technology, and information needed to understand the content and context is extracted. The output is the text data of the conversation content and the analysis results.

[0204] Step 4:

[0205] Location and vehicle status analysis

[0206] The server analyzes the location information sent from the GPS and data from the vehicle sensors. The input is location information and vehicle status data. This is used to evaluate the current vehicle location and status. The output is detailed information about the vehicle's current location and operating status.

[0207] Step 5:

[0208] Comprehensive evaluation and needs forecast

[0209] The server comprehensively evaluates the facial expression data evaluation results, conversation analysis results, location information, and vehicle status data, and uses an AI model to predict the user's potential needs. The input is data from various analysis results. Based on this, it identifies when the user is tired or has been driving for a long time. The output is a prediction of the user's potential needs and status.

[0210] Step 6:

[0211] Action suggestions and notifications

[0212] The server proposes appropriate actions to the user based on the predicted needs. For example, if fatigue is detected, it generates a voice suggestion such as "Shouldn't you take a break now?". Furthermore, if the user is wearing a head-mounted display, it also provides relaxation content and traffic information to be displayed on the display. The input is the predicted needs, and the output is the action suggestion and notification content.

[0213] Step 7:

[0214] Sending notifications

[0215] The server sends email notifications to the appropriate parties based on the notification options set by the user. For example, if a commercial vehicle is predicted to be delayed, an email is sent to the relevant parties saying, "Due to traffic congestion, the vehicle will be delayed from the scheduled arrival time." The input is the data and email address information for the delay notification, and the output is the email sent.

[0216] 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.

[0217] ---

[0218] The system of the present invention provides a safe and comfortable in-car environment by predicting the user's potential needs based on data acquired from smart devices and suggesting appropriate actions. This system is composed of the following main components: a sensing module, a data analysis module, an interaction module, and an emotion engine.

[0219] Sensing Module

[0220] The device collects data using cameras, microphones, GPS, and vehicle sensors installed in the vehicle. Specifically, the camera captures the user's facial expression data, the microphone records conversation data inside the vehicle, the GPS obtains current location information, and the vehicle sensors collect status data such as vehicle speed, fuel level, and engine condition.

[0221] Data Analysis Module

[0222] The collected data is sent to a server where it is comprehensively analyzed. The server first receives facial expression and conversation data captured by the camera and uses an emotion engine to analyze the user's emotional state. For example, it can identify whether the user is tired, stressed, happy, etc. Next, it receives GPS data and vehicle status data to ascertain the location and condition of the vehicle. It then comprehensively evaluates this data and uses an AI model to predict the user's potential needs.

[0223] Interaction Module

[0224] Based on the prediction results, the system will suggest optimal actions to the user. For example, if the user is on a long drive and facial expression analysis and emotion recognition indicate fatigue, the emotion engine can use voice prompts such as, "Shouldn't it be time to take a break?" The system can also suggest appropriate music or media based on the user's emotional state, making the drive more comfortable.

[0225] Specific examples

[0226] Specific examples of the present invention are shown below.

[0227] Example 1: Proposal for rest breaks in a private car

[0228] Consider a scenario in which a user is on a long drive. The device uses an in-car camera to capture the user's facial expression data, and the collected data and conversation data are sent to the server. The server analyzes this data using an emotion engine, and if it determines that the user is tired, the system will suggest by voice, "Shouldn't you take a break now?" This allows the user to reduce fatigue and continue driving safely.

[0229] Example 2: Notification system in commercial vehicles

[0230] Consider a scenario in which a user is traveling for a business meeting. The device uses GPS to determine the user's current location and simultaneously obtains the business meeting schedule information. The server analyzes this data and determines that a traffic jam has occurred. If a delay is predicted, the emotion engine generates an appropriate notification based on the user's emotional state, and notifies relevant parties by email or message that "there will be a delay due to traffic congestion." This allows timely information sharing with business partners, ensuring smooth communication.

[0231] Example 3: Media suggestions based on emotional state

[0232] If the user feels stressed while driving, the device uses a camera and microphone to collect facial and speech data. The server analyzes this data with an emotion engine and identifies the user as feeling stressed. Based on the results, the system suggests relaxing music or podcasts to reduce the user's stress.

[0233] As described above, the system of the present invention uses an emotion engine to analyze the user's emotion data and make more accurate predictions and suggestions, thereby realizing a safe and comfortable in-car environment.

[0234] The processing flow will be explained below.

[0235] ---

[0236] Step 1: Data collection

[0237] The device uses a camera installed inside the vehicle to capture the user's facial expressions, a microphone installed inside the vehicle to record conversations, a GPS module to obtain current location information, and vehicle sensors to collect data such as vehicle speed, fuel level, and engine status.

[0238] ---

[0239] Step 2: Send data

[0240] The device transmits the collected data (facial expression data, conversation data, location information, vehicle status data) to the server in real time, allowing the server to receive the data in a timely manner and begin analysis.

[0241] ---

[0242] Step 3: Analyzing facial expression and conversation data

[0243] The server receives the camera and microphone data and analyzes the user's facial expression and speech data using an emotion engine, which determines the user's current emotional state (e.g., fatigue, stress, joy, etc.) from these data.

[0244] ---

[0245] Step 4: Analyzing location and vehicle data

[0246] The server receives GPS data and vehicle sensor data and analyzes it to determine the current vehicle location and status, thereby understanding the user's riding situation and environment in real time.

[0247] ---

[0248] Step 5: Comprehensive analysis and prediction

[0249] The server integrates facial expression data, conversation data, location information, and vehicle data, and uses AI models and an emotion engine to predict the user's potential needs. For example, if it determines that the user is tired, it determines the appropriate action based on the prediction.

[0250] ---

[0251] Step 6: Select an action

[0252] The server then selects the optimal action based on the prediction results. Specifically, if it determines that the user is tired, it will suggest taking a break. It may also recommend media that will help the user relax depending on the user's emotional state.

[0253] ---

[0254] Step 7: Take Action

[0255] The device then executes suggestions based on the action instructions received from the server. For example, it may issue a voice message saying, "Would you like to take a break?" or play appropriate music. In the case of commercial vehicles, it may also notify relevant parties of estimated arrival times or delays via email or message.

[0256] ---

[0257] Step 8: Gather feedback

[0258] The device collects the user's reactions and feedback after making a suggestion and sends it to the server, which can then use the feedback data to improve the accuracy of analysis and the quality of the suggestions.

[0259] ---

[0260] Through this step, the system of the present invention comprehensively analyzes data from various sensors, accurately predicts the user's potential needs, and suggests appropriate actions to provide a safe and comfortable in-car environment.

[0261] Example 2

[0262] 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."

[0263] In conventional in-car environments, it has been difficult to grasp the user's status and needs in real time and provide appropriate suggestions and actions. Furthermore, during long driving periods or in stressful environments, reduced safety and comfort have become an issue. The present invention aims to solve these problems and provide a safer and more comfortable in-car environment.

[0264] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0265] In this invention, the server includes means for capturing user facial expression data acquired from the smart device, means for capturing user conversation data, means for acquiring location information, means for collecting vehicle situation data, means including an emotion engine for analyzing the data and estimating the user's emotional state, means for comprehensively evaluating the data and predicting the user's potential needs using a generative AI model, and means for suggesting appropriate actions to the user based on the prediction, thereby enabling the server to grasp the user's state in real time and realize a safe and comfortable in-car environment.

[0266] A "smart device" is an electronic device that has a camera, microphone, GPS, and various sensors that are used to collect data about a user.

[0267] "Facial Expression Data" refers to information that captures and stores in digital form a user's facial expressions.

[0268] "Conversation data" refers to information that has been recorded and stored in digital format, including the user's speaking voice and conversation content.

[0269] "Location information" is data indicating the current geographic coordinates of a vehicle obtained by GPS.

[0270] "Vehicle status data" is data relating to the operating and mechanical conditions of a vehicle, such as speed, fuel level, engine condition, etc.

[0271] An "emotion engine" is software or an algorithm that analyzes a user's facial expressions and conversation data to estimate their emotional state.

[0272] A "generative AI model" is an artificial intelligence model used to predict users' potential needs, and is a system that analyzes multiple data sets to learn patterns.

[0273] "Action suggestions" refers to suggesting actions or options to the user based on the analysis results, and are done through methods such as voice guidance or screen displays.

[0274] The system of the present invention provides a safe and comfortable in-car environment by predicting the user's potential needs based on data acquired from smart devices and suggesting appropriate actions. This system is composed of the following main components: a sensing module, a data analysis module, an interaction module, and an emotion engine.

[0275] Sensing Module

[0276] The device collects data using cameras, microphones, GPS, and vehicle sensors installed in the vehicle. Specifically, each sensor operates as follows:

[0277] The camera captures the user's facial expression data, for example, to detect whether the user is smiling or looking stern.

[0278] The microphone records conversation data inside the car, including the content and tone of what the user is saying.

[0279] GPS obtains current location information and tracks the vehicle's route and geographical location.

[0280] Vehicle sensors collect data on vehicle speed, fuel level, engine status, etc. For example, they can provide low fuel warnings and detect abnormal engine conditions.

[0281] Data Analysis Module

[0282] The collected data is sent to a server where it is analyzed and processed in the following steps:

[0283] The facial expression data and conversation data are received, and an emotion engine is used to analyze the user's emotional state, for example, determining that "the user is tired" or "the user is stressed."

[0284] It also receives GPS data and vehicle status data and analyzes the location and vehicle condition, such as "You are currently on the highway" or "You are low on fuel."

[0285] All data is integrated and a generative AI model is used to predict the user's potential needs, such as "the user needs a break" or "the user needs relaxing music."

[0286] Interaction Module

[0287] The system will suggest the best action to the user based on the prediction results from the server. Specific examples include:

[0288] If the system determines that the user has been driving for a long time and is tired, it will suggest through voice, "Would you like to take a break now?"

[0289] If the user is feeling stressed, it suggests relaxing music or podcasts.

[0290] If a traffic jam occurs and a user is likely to be late for a business meeting, the system notifies relevant parties by email or message that "the user will be late due to traffic congestion."

[0291] Specific examples

[0292] Specific examples of the present invention are shown below.

[0293] Example 1: Proposal for rest breaks in a private car

[0294] Consider a scenario where a user is driving for a long time. The device captures the user's facial expression data using an in-car camera and transmits it to a server along with conversation data.

[0295] The server analyzes this data using an emotion engine, and if it determines that the user is tired, the system will suggest audibly, "Wouldn't it be time to take a break?"

[0296] This reduces the user's fatigue and allows them to continue driving safely.

[0297] Example 2: Notification system in commercial vehicles

[0298] Imagine a scenario where a user is traveling for a business meeting. The device uses GPS to determine the user's current location and simultaneously obtains the business meeting schedule information.

[0299] The server analyzes this data and determines if a traffic jam is occurring. If a delay is predicted, the system notifies relevant parties by email or message that "the trip will be delayed due to traffic congestion."

[0300] This allows information to be shared with business partners in a timely manner, ensuring smooth communication.

[0301] Example 3: Media suggestions based on emotional state

[0302] If the user feels stressed while driving, the device will use the camera and microphone to collect the user's facial expression and speech data.

[0303] The server analyzes this data using an emotion engine and determines whether the user is feeling stressed.

[0304] Based on the results, the system suggests relaxing music and podcasts to help reduce stress for users.

[0305] Prompt Sentence Examples

[0306] "The user is on a long drive. The device collects facial expression and conversation data from the in-car camera and sends it to the server. The server uses its emotion engine to analyze that the user is tired. What will the system suggest?"

[0307] "A user is traveling to a business meeting. The device uses GPS to obtain the user's current location and business meeting schedule information. The server analyzes traffic conditions and predicts a delay. How will the system notify the relevant parties?"

[0308] The above is a detailed description of the embodiment of the invention. This system accurately analyzes the user's emotional data and makes appropriate suggestions to create a safe and comfortable in-car environment.

[0309] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0310] Detailed explanation of the processing steps

[0311] Step 1:

[0312] The device collects user data using sensors installed inside the vehicle.

[0313] Input: User facial expressions, conversational audio, vehicle status, and location information.

[0314] Data processing: Facial expressions are captured with a camera and converted into digital data, conversations are recorded with a microphone, location is recorded with GPS, and driving status is acquired from vehicle sensors. Facial expression data is saved as still images or videos, and conversation data is saved as an audio file.

[0315] Output: Digital facial expression data, conversation data, location information, and vehicle status data.

[0316] Step 2:

[0317] The terminal transmits the collected data to the server.

[0318] Input: Digital facial expression data, conversation data, location information, and vehicle status data.

[0319] Data calculation: Various data is packetized according to the communication protocol and sent to the server via the network.

[0320] Output: Data packets sent to the server.

[0321] Step 3:

[0322] The server receives the transmitted data and begins analyzing it.

[0323] Input: Facial expression data, conversation data, location information, and vehicle state data received as data packets.

[0324] Data processing: Reconstructing received data and returning it to its individual data format. For example, image data can be restored as facial expression data, and audio files can be restored as conversation data.

[0325] Output: Reconstructed data (facial expression data, conversation data, location information, vehicle state data).

[0326] Step 4:

[0327] The server uses an emotion engine to analyze the user's emotional state.

[0328] Input: Facial expression and speech data.

[0329] Data Computation: Analyzes facial expression data and applies emotion recognition algorithms to estimate the user's emotional state (e.g., joy, anger, sadness, happiness, stress, fatigue). Speech data is also subjected to speech recognition and emotion analysis to extract emotions from the content and tone of spoken words.

[0330] Output: User's emotional state data.

[0331] Step 5:

[0332] The server analyzes the location information and vehicle status data.

[0333] Input: GPS location and vehicle status data (speed, fuel level, engine status).

[0334] Data calculation: Location information is compared with map data to confirm the vehicle's current location and driving route. Vehicle status is analyzed by analyzing speed and fuel level to understand driving conditions.

[0335] Output: Current location and vehicle status analysis results.

[0336] Step 6:

[0337] The server integrates all collected data and uses a generative AI model to predict the user's potential needs.

[0338] Input: User emotional state data, current location information, and vehicle state analysis results.

[0339] Data calculation: Generative AI models are used to comprehensively evaluate various data and predict future user needs and behavior.

[0340] Output: User's potential needs (e.g. need for rest, suitable type of music, etc.).

[0341] Step 7:

[0342] The system suggests appropriate actions to the user based on the prediction results.

[0343] Input: The user's potential needs.

[0344] Data Calculation: Selecting appropriate actions based on user needs and generating interactions with the user.

[0345] Output: A voice announcement, a screen display, or a notification message to the user. For example, saying "Would you like to take a break?", recommending relaxing music, or notifying relevant parties when a delay is expected.

[0346] The above is the processing flow of this system's program. Each step incorporates specific operations, and the final output is obtained through a series of data processing and calculations based on the input data.

[0347] (Application example 2)

[0348] 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."

[0349] Conventional vehicle security systems often lack speed and accuracy in detecting suspicious individuals and issuing warnings. They also lack the ability to predict users' potential needs, making it difficult to provide a comfortable in-vehicle environment while improving vehicle safety. Therefore, there is a need for systems that can detect human emotions and behaviors in real time in conjunction with more advanced data analysis technology and provide appropriate action.

[0350] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0351] In this invention, the server includes means for capturing facial expression data of a user acquired from a smart device, means for capturing conversation data of the user, means for acquiring location information, means for collecting vehicle status data from vehicle sensors, means for analyzing the data and predicting potential needs of the user, means for suggesting appropriate actions to the user based on the prediction, means for detecting suspicious behavior and issuing a warning, and means for transmitting an alert when suspicious behavior is detected. This makes it possible to enhance safety in the vehicle, quickly detect suspicious behavior and notify the user, and provide a comfortable in-vehicle environment.

[0352] A "smart device" is a device that is equipped with sensors such as a camera, microphone, and GPS and can acquire data.

[0353] "Facial expression data" is facial expression information of a user captured by a camera.

[0354] "Conversation data" is information about voices inside the vehicle that are recorded by a microphone.

[0355] "Location Information" means geographic location data obtained from a GPS.

[0356] "Vehicle sensors" are various sensors for detecting the state of the vehicle, including speed, fuel level, engine status, etc.

[0357] "Data analysis" is the process of comprehensively analyzing various types of acquired data and extracting useful information.

[0358] "Potential needs" are requests or desires that the user has not explicitly stated but that may be needed.

[0359] "Appropriate actions" are specific actions or countermeasures suggested to the user based on the analysis results.

[0360] "Suspicious behavior" is a suspicious pattern of behavior that differs from normal behavior and may pose a security risk.

[0361] An "alert" is a warning message issued when suspicious activity is detected.

[0362] An "alert" is a message sent to notify users and other relevant parties when suspicious behavior or anomalies are detected.

[0363] The system of the present invention analyzes data acquired from smart devices in real time, detects suspicious behavior, and proposes and executes appropriate actions to improve safety and comfort in vehicles. The present invention includes the following main components and their respective operating procedures:

[0364] Sensing Module

[0365] The device collects data using cameras, microphones, GPS, and vehicle sensors installed in the vehicle. Specifically, the camera captures the user's facial expression data, the microphone records conversation data inside the vehicle, the GPS obtains current location information, and the vehicle sensors collect status data such as vehicle speed, fuel level, and engine condition.

[0366] Data Analysis Module

[0367] The server receives the various collected data and performs a comprehensive analysis. First, the server receives facial expression data and conversation data captured by the camera and uses an emotion engine to analyze the user's emotional state. For example, it identifies whether the user is tired, stressed, happy, etc. Next, it receives GPS data and vehicle status data to understand the location and condition of the vehicle. It then comprehensively evaluates this data and uses an AI model to predict the user's potential needs.

[0368] Interaction Module

[0369] Based on the prediction results, the system suggests optimal actions to the user and executes them at the appropriate time. For example, if the user is on a long drive and facial expression analysis and emotion recognition indicate fatigue, the system will suggest audibly, "Shouldn't it be time to take a break?" The system can also suggest appropriate music or media based on the user's emotional state, making the drive more comfortable.

[0370] Security Features

[0371] The system includes a means for detecting suspicious behavior and issuing a warning, as well as a means for sending an alert when suspicious behavior is detected. It uses cameras and various sensors to monitor the environment around the vehicle, and if, for example, a suspicious person approaches the vehicle, it will sound an alarm and send an alert to the owner, thereby improving vehicle safety.

[0372] Hardware and software used

[0373] The system uses cameras (e.g., USB cameras), microphones, GPS sensors, and a variety of other sensors in the vehicle. Data analysis is performed using an AI model and emotion engine running on a server. Specifically, it uses Python and OpenCV libraries to perform real-time person detection and send alerts.

[0374] Specific examples

[0375] 1. Proposal for resting in private cars

[0376] Consider a scenario in which a user is on a long drive. The device uses an in-car camera to capture the user's facial expression data, and the collected data and conversation data are sent to the server. The server analyzes this data using an emotion engine, and if it determines that the user is tired, the system will suggest by voice, "Shouldn't you take a break now?" This allows the user to reduce fatigue and continue driving safely.

[0377] 2. Notification systems for commercial vehicles

[0378] Consider a scenario in which a user is traveling for a business meeting. The device uses GPS to determine the user's current location and simultaneously obtains the business meeting schedule information. The server analyzes this data and determines that a traffic jam has occurred. If a delay is predicted, the emotion engine generates an appropriate notification based on the user's emotional state, and notifies relevant parties by email or message that "there will be a delay due to traffic congestion." This allows timely information sharing with business partners, ensuring smooth communication.

[0379] 3. Media suggestions based on emotional state

[0380] If the user feels stressed while driving, the device uses a camera and microphone to collect facial and speech data. The server analyzes this data with an emotion engine and identifies the user as feeling stressed. Based on the results, the system suggests relaxing music or podcasts to reduce the user's stress.

[0381] 4. Suspicious person detection and alert sending

[0382] If a suspicious person approaches the vehicle, the camera captures their behavior and analyzes it in real time. If the server determines that the behavior is suspicious, the system will sound an alarm and simultaneously send an alert to the owner. This allows for a quick response and improves vehicle safety.

[0383] Example prompts to input to a generative AI model:

[0384] "Write a Python program that detects suspicious people approaching a vehicle and sends an alert. The program should capture camera footage in real time and send an alert to a specified email address as soon as a person is detected."

[0385] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0386] Step 1:

[0387] The terminal acquires the data.

[0388] It uses a camera to capture the user's facial expression data and a microphone to collect conversation data. It obtains location information from GPS and collects data such as speed, fuel level, and engine status from vehicle sensors. The inputs here are camera images, audio data, location information, and vehicle status data, which are collected in real time by each sensor. The output is the collected raw data.

[0389] Step 2:

[0390] The terminal transmits the collected data to the server.

[0391] The collected raw data is preprocessed, compressed or encrypted, and sent to the server. The input here is the raw data obtained in step 1, which is preprocessed to ensure the reliability and security of the data. The output is a notification of completion of transmission.

[0392] Step 3:

[0393] The server analyzes the received data.

[0394] Facial expression data from the camera is analyzed using image processing algorithms (e.g., OpenCV) to extract the user's emotional state. Voice data is analyzed using natural language processing (NLP) technology to understand the content of the conversation. Location information and vehicle state data are used to understand the user's movement status and the vehicle's condition. The input is preprocessed data, and the output is the analysis results.

[0395] Step 4:

[0396] The server predicts the user's potential needs based on the data analysis results.

[0397] The AI ​​model is used to comprehensively evaluate the user's emotional state, conversation content, location information, and vehicle status data to predict whether the user has a specific need. In this step, a generative AI model is used, with the input being the analysis results and the output being the user's potential need data.

[0398] Step 5:

[0399] The server suggests appropriate actions based on your needs.

[0400] Based on the predicted needs, the system suggests the user to take a break, change the music, or take other actions. The input is the user's potential needs data, and the output is the suggestion content. The suggestion is notified to the user through audio output or a display screen.

[0401] Step 6:

[0402] The terminal monitors the area around the vehicle.

[0403] The environment around the vehicle is monitored using cameras and various sensors. Camera footage is analyzed to detect suspicious behavior (e.g., a person approaching the vehicle). The input is video data around the vehicle, and the output is the detection result of suspicious behavior.

[0404] Step 7:

[0405] The server takes action when suspicious behavior is detected.

[0406] If suspicious behavior is detected, countermeasures are implemented, such as sounding an alarm or sending an alert email to the owner. The input is the result of detecting suspicious behavior, and the output is the specific response action, such as sounding an alarm or sending an alert email.

[0407] Example prompts to input to a generative AI model:

[0408] "Write a Python program that detects suspicious people approaching a vehicle and sends an alert. The program should capture camera footage in real time and send an alert to a specified email address as soon as a person is detected."

[0409] 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.

[0410] 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.

[0411] 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.

[0412] [Second embodiment]

[0413] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0414] 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.

[0415] 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).

[0416] 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.

[0417] 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.

[0418] 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).

[0419] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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.

[0424] 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."

[0425] ---

[0426] The system of the present invention provides a safe and comfortable in-car environment by predicting the user's potential needs based on data acquired from smart devices and suggesting appropriate actions. This system is composed of the following main components: a sensing module, a data analysis module, and an interaction module.

[0427] Sensing Module

[0428] The device collects data using cameras, microphones, GPS, and vehicle sensors installed in the vehicle. Specifically, the camera captures the user's facial expression data, the microphone records conversation data inside the vehicle, the GPS obtains current location information, and the vehicle sensors collect status data such as vehicle speed, fuel level, and engine condition.

[0429] Data Analysis Module

[0430] The collected data is sent to a server where it is comprehensively analyzed. The server first analyzes facial expression data obtained from the camera to identify the user's emotional state (for example, fatigue, stress, joy, etc.). Next, it performs audio analysis of the conversation data obtained from the microphone to extract information needed to understand the content and context of the user's conversation. It also analyzes GPS data and vehicle status data to evaluate the current vehicle location and status. It then comprehensively evaluates this data and uses an AI model to predict the user's potential needs.

[0431] Interaction Module

[0432] Based on predicted needs, the system suggests appropriate actions to the user. For example, if a user is on a long drive and facial expression analysis reveals signs of fatigue, the system can suggest, by voice, "Wouldn't it be time to take a break?" In addition, for commercial vehicles, the system can obtain the user's business meeting schedule information and, if a delay due to traffic congestion is predicted, notify relevant parties that "the vehicle will be delayed due to traffic congestion."

[0433] Specific examples

[0434] Specific examples of the present invention are given below.

[0435] Example 1: Proposal for rest breaks in a private car

[0436] Consider a scenario in which a user is on a long drive. The device captures the user's facial expressions using an in-car camera, and the collected facial data is sent to the server. The server analyzes the facial data and determines that the user is tired. The system then suggests by voice, "Shouldn't you take a break now?" This allows the user to reduce fatigue and continue driving safely.

[0437] Example 2: Notification system in commercial vehicles

[0438] Consider a scenario in which a user is traveling for a business meeting. The device uses GPS to determine the user's current location and simultaneously obtains the business meeting schedule information. The server analyzes this data and determines that a traffic jam has occurred. If a delay is predicted, the system notifies the relevant parties by email or message that "there will be a delay due to traffic congestion." This allows timely information sharing with the business partner, ensuring smooth communication.

[0439] As described above, the system of the present invention comprehensively analyzes data from a variety of sensors, accurately predicts the user's potential needs, and suggests appropriate actions, thereby realizing a safe and comfortable in-car environment.

[0440] The processing flow will be explained below.

[0441] ---

[0442] Step 1: Data collection

[0443] The device collects data using cameras, microphones, GPS, and vehicle sensors installed in the vehicle. Specifically, the camera captures the user's facial expressions, the microphone records conversations inside the vehicle, the GPS obtains the current location, and the vehicle sensors collect data such as the vehicle's speed, fuel level, and engine status.

[0444] ---

[0445] Step 2: Send data

[0446] The device sends the collected data (facial expression data, conversation data, location information, vehicle status data) to a server, where it is processed in real time, enabling rapid analysis.

[0447] ---

[0448] Step 3: Analyzing facial expression data

[0449] The server receives the camera data and uses an AI model to analyze the user's facial expressions. The facial expression data identifies the user's emotional state (fatigue, stress, joy, etc.). The results of this analysis are used in the next prediction step.

[0450] ---

[0451] Step 4: Analyzing the conversation data

[0452] The server receives the microphone data and uses a voice analysis algorithm to analyze the conversation, extracting important keywords and context, which are used to understand the user's needs and requests.

[0453] ---

[0454] Step 5: Analyzing location and vehicle data

[0455] The server receives and analyzes GPS data and vehicle sensor data to determine the current vehicle location and status, allowing traffic conditions and vehicle status to be tracked in real time.

[0456] ---

[0457] Step 6: Comprehensive analysis and prediction

[0458] The server comprehensively evaluates facial expression data, conversation data, location information, and vehicle data, and then uses an AI model to predict the user's potential needs. For example, if it determines that the user is tired, it will suggest an appropriate break.

[0459] ---

[0460] Step 7: Select an action

[0461] The server selects the optimal action for the user based on the prediction results, such as suggesting a break to the user or providing traffic information.

[0462] ---

[0463] Step 8: Take Action

[0464] The device executes the action received from the server for the user, such as suggesting "Should we take a break now?" or sending an email to notify relevant parties of a delay.

[0465] ---

[0466] Step 9: Gather feedback

[0467] The device collects user reactions and feedback and sends it to the server, which improves the system's analysis accuracy and the quality of its suggestions.

[0468] ---

[0469] This trend will enable users to continue driving safely and comfortably while receiving appropriate support from AI.

[0470] Example 1

[0471] 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."

[0472] To make the driving environment safer and more comfortable for modern vehicles, systems that monitor the user's condition and vehicle status in real time and suggest appropriate actions based on that information are required. However, conventional systems have had difficulty efficiently collecting and analyzing the user's facial expression data, conversation data, location information, and vehicle status data, and suggesting appropriate actions. Furthermore, it has been difficult to predict the user's potential needs based on the results of advanced analysis.

[0473] 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.

[0474] In this invention, the server includes means for capturing user facial expression data acquired from the smart device, means for capturing user conversation data, means for acquiring location information, means for collecting vehicle status data from vehicle sensors, means for transmitting the data to the server, means for analyzing the data in the server and identifying the user's emotional state, the content of the conversation, and the vehicle's location and status, means for predicting the user's potential needs using a generative AI model based on the analysis results, and means for suggesting appropriate actions to the user based on the predictions. This makes it possible to monitor the user's driving environment in real time and suggest appropriate actions at the right time.

[0475] ---

[0476] A "smart device" is an electronic device used to acquire a user's facial expression data, conversation data, location information, and vehicle status data.

[0477] "Facial expression data" is data about a user's facial expressions captured using a device such as a camera.

[0478] "Conversation data" refers to data about the content of conversations between users in a vehicle, collected using a device such as a microphone.

[0479] "Location Information" is data regarding the current geographic location of a vehicle obtained using a location measuring device such as a GPS.

[0480] "Vehicle sensor" refers generally to sensing devices installed to collect information such as vehicle speed, fuel level, and engine condition.

[0481] The "potential needs of the user" are requests and desires regarding actions and services that the user may require, which are predicted based on the analysis results.

[0482] A "server" is a computer system that receives the collected data, analyzes it, and suggests appropriate actions to the user.

[0483] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to analyze data and predict users' potential needs.

[0484] "Action suggestions" refers to guidelines and advice such as break suggestions and notifications provided to users by the system.

[0485] "Real-time analysis" is an analysis method that can quickly process collected data and obtain results immediately.

[0486] A "commercial vehicle" is a vehicle used for business or commercial travel.

[0487] "Schedule information" is time-related information such as the user's plans and appointments.

[0488] "Delay notification" refers to informing relevant parties when arrival is delayed due to traffic conditions or other reasons.

[0489] ---

[0490] The system of the present invention provides a safe and comfortable in-car environment by predicting the user's potential needs and suggesting appropriate actions based on data acquired from smart devices. This system is composed of the following main components: a sensing module, a data analysis module, and an interaction module.

[0491] Sensing Module

[0492] The device collects data using cameras, microphones, GPS, and vehicle sensors installed in the vehicle. Specifically, the camera captures the user's facial expressions, and the microphone records conversations inside the vehicle. GPS obtains the vehicle's current location, and vehicle sensors collect data such as speed, fuel level, and engine status. This data is collected in real time and sent to a server via batch processing or real-time streaming.

[0493] Data Analysis Module

[0494] The server analyzes the received data to determine the user's emotional state, the content of the conversation, and the vehicle's location and status. Specifically, it performs the following processes:

[0495] 1. The server analyzes the collected image data and identifies the user's emotional state (e.g., fatigue, stress, joy, etc.) from their facial expressions.

[0496] 2. The server converts the voice data into text and analyzes the conversation content and context.

[0497] 3. The server evaluates GPS data and vehicle sensor data to determine the current vehicle location and status.

[0498] Based on these analysis results, the server uses a generative AI model to predict the user's potential needs, for example, if the user's facial expression shows signs of fatigue, it predicts that they should take a break.

[0499] Interaction Module

[0500] Based on predicted needs, the system will suggest appropriate actions to the user. For example, if it determines that a user is tired after a long drive, it will make a voice suggestion saying, "Would you like to take a break now?" In the case of commercial vehicles, the system will analyze the user's business meeting schedule information and current traffic information, and if a delay is predicted, it will send a notification to relevant parties saying, "You will be delayed due to traffic congestion."

[0501] Specific examples

[0502] The following are specific examples of the present invention:

[0503] Example 1: Proposal for rest breaks in a private car

[0504] 1. When the user is on a long drive, the device captures the user's facial expressions using the in-car camera.

[0505] 2. The captured facial expression data is sent to the server.

[0506] 3. The server uses an AI model to analyze whether the user is tired.

[0507] 4. If fatigue is detected, the system will suggest to the user via voice, "Would you like to take a break now?"

[0508] Example 2: Notification system in commercial vehicles

[0509] 1. When a user is traveling for a business meeting, the device uses GPS to obtain the user's current location and collects business meeting schedule information.

[0510] 2. The collected data is sent to the server.

[0511] 3. The server analyzes the traffic data and determines that a traffic jam is occurring.

[0512] 4. If a delay is predicted, the system will notify relevant parties via email or message that "there will be a delay due to traffic congestion."

[0513] Prompt Sentence Examples

[0514] "Please explain a scenario where you want to detect fatigue from facial expression data of a user during a long drive and suggest a break."

[0515] "Describe a scenario in which a user heading to a business meeting is predicted to be delayed based on their current location and traffic information, and relevant parties are notified."

[0516] As described above, the present invention makes it possible to comprehensively analyze data from multiple sensors, accurately predict the potential needs of a user, and make the driving environment for the user safer and more comfortable.

[0517] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0518] ---

[0519] Step 1: Data collection

[0520] Input: Data from in-car cameras, microphones, GPS, and vehicle sensors

[0521] Output: Collected facial expression data, conversation data, location information, vehicle status data

[0522] The device captures the user's facial expressions using a camera installed inside the vehicle, records conversations inside the vehicle using a microphone, and obtains the current location information using GPS. It also collects data such as speed, fuel level, and engine status from vehicle sensors. This data is sensed in real time and sent to a server via batch processing or real-time streaming as needed.

[0523] Step 2: Send data

[0524] Input: Collected facial expression data, conversation data, location information, vehicle status data

[0525] Output: Data sent to the server

[0526] The device sends the collected data to the server periodically or in real time. Camera image data is sent in a fixed batch format, audio data is streamed in real time, and GPS data and vehicle status data are also sent in packets periodically.

[0527] Step 3: Facial Expression Analysis

[0528] Input: Facial expression data sent to the server

[0529] Output: User's emotional state

[0530] The server analyzes the received image data and uses facial recognition technology to identify the user's emotional state from their facial expressions. For example, it uses an image analysis algorithm to determine whether the user is smiling, tired, angry, etc. The analysis results in identifying the user's emotional state.

[0531] Step 4: Audio analysis

[0532] Input: Conversation data sent to the server

[0533] Output: Analyzed conversation

[0534] The server uses speech recognition technology to convert the conversation into text. The server analyzes the collected voice data using speech recognition software and generates text information to understand the content and context of the conversation. The analysis results identify the content and context of the conversation.

[0535] Step 5: Analyze location and vehicle status

[0536] Input: Location information sent to the server, vehicle sensor data

[0537] Output: Current vehicle position and status

[0538] The server analyzes the collected GPS data and vehicle sensor data. It identifies the vehicle's current location from the GPS data and evaluates the vehicle sensor data for speed, fuel level, engine status, etc. As a result of the analysis, it identifies the vehicle's current location and status.

[0539] Step 6: Anticipate potential needs

[0540] Input: Analyzed facial expression data, conversation data, location information, vehicle status data

[0541] Output: User's potential needs

[0542] The server integrates all the analyzed data and uses a generative AI model to predict the user's potential needs. For example, if the user's facial expression shows signs of fatigue, it predicts that a break is necessary. As a result of this analysis, the server identifies the user's potential needs.

[0543] Step 7: Action proposals

[0544] Input: predicted potential user needs

[0545] Output: Suggested action for the user

[0546] The system will suggest appropriate actions to the user based on predicted needs. For example, if it determines that the user is tired after a long drive, it will make a voice suggestion such as, "Wouldn't it be time to take a break?". Also, if there is traffic congestion based on business meeting schedule information, it will send a notification to the relevant parties saying, "You will be delayed due to traffic congestion."

[0547] Through the above processing steps, this system monitors the user's driving environment in real time and suggests appropriate actions at the right time, thereby providing a safe and comfortable in-car environment.

[0548] (Application example 1)

[0549] 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."

[0550] Autonomous vehicles are required to accurately predict passengers' potential needs and provide a safe and comfortable in-car environment. Conventional technologies have difficulty assessing passenger fatigue and stress levels in real time, and lack a means to suggest rest at the appropriate time. Furthermore, there are insufficient methods for sharing information in a timely manner in the event of delays due to long driving times or traffic congestion. Therefore, a system is needed to achieve a higher level of passenger safety and comfort.

[0551] 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.

[0552] In this invention, the server includes means for capturing user facial expression data acquired from a smart device, means for capturing user conversation data, means for acquiring location information, means for collecting vehicle status data from vehicle sensors, means for analyzing the data and predicting the user's potential needs, means for suggesting appropriate actions to the user based on the prediction, means for assessing the user's fatigue state and sending a notification in real time, and means for displaying information via a head-mounted display. This makes it possible to monitor passenger fatigue and stress in real time and suggest breaks at appropriate times. In addition, in the case of commercial vehicles, if a delay is predicted, a notification can be sent by email, allowing relevant parties in a business negotiation to be informed of the estimated arrival time and delay in a timely manner.

[0553] A "smart device" is an electronic device that is equipped with an internet connection and a wide variety of sensors and is capable of acquiring and processing data.

[0554] "Facial expression data" is digital data relating to a user's facial expressions that is captured using an image capture device such as a camera.

[0555] "Conversation data" is audio data relating to the content of a user's speech or conversation, which is acquired using an audio capture device such as a microphone.

[0556] "Location information" is data about the current location of a vehicle or user, obtained using a location measurement device such as a GPS.

[0557] A "vehicle sensor" is a sensor device for collecting status data such as vehicle speed, fuel level, engine status, etc.

[0558] "Real-time" refers to data acquisition and processing occurring immediately, without delay.

[0559] A "head-mounted display" is a display device worn by a user on the head, which displays information within the user's field of vision.

[0560] The "fatigue state" refers to a state that indicates the user's physical or mental fatigue, particularly one that is affected by prolonged activity or stress.

[0561] A "notification" is a message or alert sent to inform a user of important information.

[0562] "Relaxation content" refers to content such as music, video, or guidance provided to relieve the user's fatigue and stress.

[0563] "Business meeting schedule information" is information about the time, location, and participants of business meetings and conferences.

[0564] "Email" means a digital letter or message sent or received over the Internet.

[0565] The system of this invention predicts potential user needs and proposes appropriate actions to provide a safe and comfortable environment in an autonomous vehicle by analyzing data collected from smart devices and related sensors. The main components of the system are a sensing module, a data analysis module, and an interaction module.

[0566] Sensing Module

[0567] The device collects data using cameras, microphones, GPS, and vehicle sensors installed in the vehicle. Specifically, the camera captures the user's facial expression data, the microphone records conversation data in the vehicle, the GPS obtains current location information, and the vehicle sensors collect status data such as vehicle speed and engine condition.

[0568] Data Analysis Module

[0569] The collected data is sent to a server where it is comprehensively analyzed. The server first analyzes facial expression data obtained from the camera to identify the user's emotional state (for example, fatigue, stress, joy, etc.). Next, it performs audio analysis of the conversation data obtained from the microphone to extract information needed to understand the content and context of the user's conversation. It also analyzes GPS data and vehicle status data to evaluate the current vehicle location and status. It then comprehensively evaluates this data and uses an AI model to predict the user's potential needs.

[0570] Interaction Module

[0571] The system suggests appropriate actions to the user based on predicted needs. For example, if a user is on a long drive and facial expression analysis reveals signs of fatigue, the system can suggest audibly, "Shouldn't it be time to take a break?" Information such as traffic congestion and estimated arrival times at the destination can also be displayed via a head-mounted display.

[0572] Example

[0573] Example 1: Fatigue suggestions for long-distance drivers

[0574] The system analyzes video captured by an in-car camera in real time and evaluates the user's level of fatigue based on facial expression data. If fatigue exceeds a certain threshold, the system displays a message on the head-mounted display saying, "You are feeling drowsy. You are 10 minutes away from a rest area." If the user has enabled the email notification option, the system also sends a notification by email.

[0575] Example 2: Schedule management for commercial vehicles

[0576] Based on GPS and business meeting schedule information, the system notifies the user and business meeting participants of delays in the event of traffic congestion. The system displays a message on the head-mounted display saying, "Due to traffic congestion, you will be delayed from the scheduled arrival time," and can be configured to notify relevant parties by email.

[0577] Example prompt

[0578] "Create a head-mounted display application that monitors passenger status in real time and detects fatigue and stress."

[0579] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0580] Step 1:

[0581] Acquiring sensing data

[0582] The device collects data using cameras, microphones, GPS, and vehicle sensors installed inside the vehicle. Inputs include facial expression data from the camera, conversation data from the microphone, location information from GPS, and vehicle status data from vehicle sensors (speed, fuel level, engine status, etc.). After acquiring this data, it sends it to a server.

[0583] Step 2:

[0584] Analysis of facial expression data

[0585] The server analyzes the facial expression data sent from the camera. The input is image data containing the user's facial expressions. The server uses facial landmark detection technology (e.g., the dlib library) to identify the user's emotional state (fatigue, stress, joy, etc.). The output is an evaluation of these emotional states.

[0586] Step 3:

[0587] Conversation data analysis

[0588] The server performs speech analysis on the conversation data sent from the microphone. The input is the user's conversation voice data. This is converted into text data using speech recognition technology, and information needed to understand the content and context is extracted. The output is the text data of the conversation content and the analysis results.

[0589] Step 4:

[0590] Location and vehicle status analysis

[0591] The server analyzes the location information sent from the GPS and data from the vehicle sensors. The input is location information and vehicle status data. This is used to evaluate the current vehicle location and status. The output is detailed information about the vehicle's current location and operating status.

[0592] Step 5:

[0593] Comprehensive evaluation and needs forecast

[0594] The server comprehensively evaluates the facial expression data evaluation results, conversation analysis results, location information, and vehicle status data, and uses an AI model to predict the user's potential needs. The input is data from various analysis results. Based on this, it identifies when the user is tired or has been driving for a long time. The output is a prediction of the user's potential needs and status.

[0595] Step 6:

[0596] Action suggestions and notifications

[0597] The server proposes appropriate actions to the user based on the predicted needs. For example, if fatigue is detected, it generates a voice suggestion such as "Shouldn't you take a break now?". Furthermore, if the user is wearing a head-mounted display, it also provides relaxation content and traffic information to be displayed on the display. The input is the predicted needs, and the output is the action suggestion and notification content.

[0598] Step 7:

[0599] Sending notifications

[0600] The server sends email notifications to the appropriate parties based on the notification options set by the user. For example, if a commercial vehicle is predicted to be delayed, an email is sent to the relevant parties saying, "Due to traffic congestion, the vehicle will be delayed from the scheduled arrival time." The input is the data and email address information for the delay notification, and the output is the email sent.

[0601] 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.

[0602] ---

[0603] The system of the present invention provides a safe and comfortable in-car environment by predicting the user's potential needs based on data acquired from smart devices and suggesting appropriate actions. This system is composed of the following main components: a sensing module, a data analysis module, an interaction module, and an emotion engine.

[0604] Sensing Module

[0605] The device collects data using cameras, microphones, GPS, and vehicle sensors installed in the vehicle. Specifically, the camera captures the user's facial expression data, the microphone records conversation data inside the vehicle, the GPS obtains current location information, and the vehicle sensors collect status data such as vehicle speed, fuel level, and engine condition.

[0606] Data Analysis Module

[0607] The collected data is sent to a server where it is comprehensively analyzed. The server first receives facial expression and conversation data captured by the camera and uses an emotion engine to analyze the user's emotional state. For example, it can identify whether the user is tired, stressed, happy, etc. Next, it receives GPS data and vehicle status data to ascertain the location and condition of the vehicle. It then comprehensively evaluates this data and uses an AI model to predict the user's potential needs.

[0608] Interaction Module

[0609] Based on the prediction results, the system will suggest optimal actions to the user. For example, if the user is on a long drive and facial expression analysis and emotion recognition indicate fatigue, the emotion engine can use voice prompts such as, "Shouldn't it be time to take a break?" The system can also suggest appropriate music or media based on the user's emotional state, making the drive more comfortable.

[0610] Specific examples

[0611] Specific examples of the present invention are given below.

[0612] Example 1: Proposal for rest breaks in a private car

[0613] Consider a scenario in which a user is on a long drive. The device uses an in-car camera to capture the user's facial expression data, and the collected data and conversation data are sent to the server. The server analyzes this data using an emotion engine, and if it determines that the user is tired, the system will suggest by voice, "Shouldn't you take a break now?" This allows the user to reduce fatigue and continue driving safely.

[0614] Example 2: Notification system in commercial vehicles

[0615] Consider a scenario in which a user is traveling for a business meeting. The device uses GPS to determine the user's current location and simultaneously obtains the business meeting schedule information. The server analyzes this data and determines that a traffic jam has occurred. If a delay is predicted, the emotion engine generates an appropriate notification based on the user's emotional state, and notifies relevant parties by email or message that "there will be a delay due to traffic congestion." This allows timely information sharing with business partners, ensuring smooth communication.

[0616] Example 3: Media suggestions based on emotional state

[0617] If the user feels stressed while driving, the device uses a camera and microphone to collect facial expression and speech data. The server analyzes this data with an emotion engine and identifies the user as feeling stressed. Based on the results, the system suggests relaxing music or podcasts to reduce the user's stress.

[0618] As described above, the system of the present invention uses an emotion engine to analyze the user's emotion data and make more accurate predictions and suggestions, thereby realizing a safe and comfortable in-car environment.

[0619] The processing flow will be explained below.

[0620] ---

[0621] Step 1: Data collection

[0622] The device uses a camera installed inside the vehicle to capture the user's facial expressions, a microphone installed inside the vehicle to record conversations, a GPS module to obtain current location information, and vehicle sensors to collect data such as vehicle speed, fuel level, and engine status.

[0623] ---

[0624] Step 2: Send data

[0625] The device transmits the collected data (facial expression data, conversation data, location information, vehicle status data) to the server in real time, allowing the server to receive the data in a timely manner and begin analysis.

[0626] ---

[0627] Step 3: Analyzing facial expression and conversation data

[0628] The server receives the camera and microphone data and analyzes the user's facial expression and speech data using an emotion engine, which determines the user's current emotional state (e.g., fatigue, stress, joy, etc.) from the data.

[0629] ---

[0630] Step 4: Analyzing location and vehicle data

[0631] The server receives GPS data and vehicle sensor data and analyzes it to determine the current vehicle location and status, thereby understanding the user's riding situation and environment in real time.

[0632] ---

[0633] Step 5: Comprehensive analysis and prediction

[0634] The server integrates facial expression data, conversation data, location information, and vehicle data, and uses AI models and an emotion engine to predict the user's potential needs. For example, if it determines that the user is tired, it determines the appropriate action based on the prediction.

[0635] ---

[0636] Step 6: Select an action

[0637] The server then selects the optimal action based on the prediction results. Specifically, if it determines that the user is tired, it will suggest taking a break. It may also recommend media that will help the user relax depending on the user's emotional state.

[0638] ---

[0639] Step 7: Take Action

[0640] The device then executes suggestions based on the action instructions received from the server. For example, it may issue a voice message saying, "Would you like to take a break?" or play appropriate music. In the case of commercial vehicles, it may also notify relevant parties of estimated arrival times or delays via email or message.

[0641] ---

[0642] Step 8: Gather feedback

[0643] The device collects the user's reactions and feedback after making a suggestion and sends it to the server, which can then use the feedback data to improve the accuracy of analysis and the quality of the suggestions.

[0644] ---

[0645] Through this step, the system of the present invention comprehensively analyzes data from various sensors, accurately predicts the user's potential needs, and suggests appropriate actions to provide a safe and comfortable in-car environment.

[0646] Example 2

[0647] 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."

[0648] In conventional in-car environments, it has been difficult to grasp the user's status and needs in real time and provide appropriate suggestions and actions. Furthermore, during long driving periods or in stressful environments, reduced safety and comfort have become an issue. The present invention aims to solve these problems and provide a safer and more comfortable in-car environment.

[0649] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0650] In this invention, the server includes means for capturing user facial expression data acquired from the smart device, means for capturing user conversation data, means for acquiring location information, means for collecting vehicle situation data, means including an emotion engine for analyzing the data and estimating the user's emotional state, means for comprehensively evaluating the data and predicting the user's potential needs using a generative AI model, and means for suggesting appropriate actions to the user based on the prediction, thereby enabling the server to grasp the user's state in real time and realize a safe and comfortable in-car environment.

[0651] A "smart device" is an electronic device that has a camera, microphone, GPS, and various sensors that are used to collect data about a user.

[0652] "Facial Expression Data" refers to information that captures and stores in digital form a user's facial expressions.

[0653] "Conversation data" refers to information that has been recorded and stored in digital format, including the user's speaking voice and conversation content.

[0654] "Location information" is data indicating the current geographic coordinates of a vehicle obtained by GPS.

[0655] "Vehicle status data" is data relating to the operating and mechanical conditions of a vehicle, such as speed, fuel level, engine condition, etc.

[0656] An "emotion engine" is software or an algorithm that analyzes a user's facial expressions and conversation data to estimate their emotional state.

[0657] A "generative AI model" is an artificial intelligence model used to predict users' potential needs, and is a system that analyzes multiple data sets to learn patterns.

[0658] "Action suggestions" are suggestions of actions or options to the user based on the analysis results, and are done through methods such as voice guidance or screen displays.

[0659] The system of the present invention provides a safe and comfortable in-car environment by predicting the user's potential needs based on data acquired from smart devices and suggesting appropriate actions. This system is composed of the following main components: a sensing module, a data analysis module, an interaction module, and an emotion engine.

[0660] Sensing Module

[0661] The device collects data using cameras, microphones, GPS, and vehicle sensors installed in the vehicle. Specifically, each sensor operates as follows:

[0662] The camera captures the user's facial expression data, for example, to detect whether the user is smiling or looking stern.

[0663] The microphone records conversation data inside the car, including the content and tone of what the user is saying.

[0664] GPS obtains current location information and tracks the vehicle's route and geographical location.

[0665] Vehicle sensors collect data on vehicle speed, fuel level, engine status, etc. For example, they can provide low fuel warnings and detect abnormal engine conditions.

[0666] Data Analysis Module

[0667] The collected data is sent to a server where it is analyzed and processed in the following steps:

[0668] The facial expression data and conversation data are received, and an emotion engine is used to analyze the user's emotional state, for example, determining that "the user is tired" or "the user is stressed."

[0669] It also receives GPS data and vehicle status data and analyzes the location and vehicle condition, such as "You are currently on the highway" or "You are low on fuel."

[0670] All data is integrated and a generative AI model is used to predict the user's potential needs, such as "the user needs a break" or "the user needs relaxing music."

[0671] Interaction Module

[0672] The system will suggest the best action to the user based on the prediction results from the server. Specific examples include:

[0673] If the system determines that the user has been driving for a long time and is tired, it will suggest through voice, "Would you like to take a break now?"

[0674] If the user is feeling stressed, it suggests relaxing music or podcasts.

[0675] If a traffic jam occurs and a user is likely to be late for a business meeting, the system notifies relevant parties by email or message that "the user will be late due to traffic congestion."

[0676] Specific examples

[0677] Specific examples of the present invention are given below.

[0678] Example 1: Proposal for rest breaks in a private car

[0679] Consider a scenario where a user is driving for a long time. The device captures the user's facial expression data using an in-car camera and transmits it to a server along with conversation data.

[0680] The server analyzes this data using an emotion engine, and if it determines that the user is tired, the system will suggest audibly, "Wouldn't it be time to take a break?"

[0681] This reduces the user's fatigue and allows them to continue driving safely.

[0682] Example 2: Notification system in commercial vehicles

[0683] Imagine a scenario where a user is traveling for a business meeting. The device uses GPS to determine the user's current location and simultaneously obtains the business meeting schedule information.

[0684] The server analyzes this data and determines if a traffic jam is occurring. If a delay is predicted, the system notifies relevant parties by email or message that "the trip will be delayed due to traffic congestion."

[0685] This allows information to be shared with business partners in a timely manner, ensuring smooth communication.

[0686] Example 3: Media suggestions based on emotional state

[0687] If the user feels stressed while driving, the device will use the camera and microphone to collect the user's facial expression and speech data.

[0688] The server analyzes this data using an emotion engine and determines whether the user is feeling stressed.

[0689] Based on the results, the system suggests relaxing music and podcasts to help reduce stress for users.

[0690] Prompt Sentence Examples

[0691] "The user is on a long drive. The device collects facial expression and conversation data from the in-car camera and sends it to the server. The server uses its emotion engine to analyze that the user is tired. What will the system suggest?"

[0692] "A user is traveling to a business meeting. The device uses GPS to obtain the user's current location and business meeting schedule information. The server analyzes traffic conditions and predicts a delay. How will the system notify the relevant parties?"

[0693] The above is a detailed description of the embodiment of the invention. This system accurately analyzes the user's emotional data and makes appropriate suggestions to create a safe and comfortable in-car environment.

[0694] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0695] Detailed explanation of the processing steps

[0696] Step 1:

[0697] The device collects user data using sensors installed inside the vehicle.

[0698] Input: User facial expressions, conversational audio, vehicle status, and location information.

[0699] Data processing: Facial expressions are captured with a camera and converted into digital data, conversations are recorded with a microphone, location is recorded with GPS, and driving status is acquired from vehicle sensors. Facial expression data is saved as still images or videos, and conversation data is saved as an audio file.

[0700] Output: Digital facial expression data, conversation data, location information, and vehicle status data.

[0701] Step 2:

[0702] The terminal transmits the collected data to the server.

[0703] Input: Digital facial expression data, conversation data, location information, and vehicle status data.

[0704] Data calculation: Various data is packetized according to the communication protocol and sent to the server via the network.

[0705] Output: Data packets sent to the server.

[0706] Step 3:

[0707] The server receives the transmitted data and begins analyzing it.

[0708] Input: Facial expression data, conversation data, location information, and vehicle state data received as data packets.

[0709] Data processing: Reconstructing received data and returning it to its individual data format. For example, image data can be restored as facial expression data, and audio files can be restored as conversation data.

[0710] Output: Reconstructed data (facial expression data, conversation data, location information, vehicle state data).

[0711] Step 4:

[0712] The server uses an emotion engine to analyze the user's emotional state.

[0713] Input: Facial expression and speech data.

[0714] Data Computation: Analyzes facial expression data and applies emotion recognition algorithms to estimate the user's emotional state (e.g., joy, anger, sadness, happiness, stress, fatigue). Speech data is also subjected to speech recognition and emotion analysis to extract emotions from the content and tone of spoken words.

[0715] Output: User's emotional state data.

[0716] Step 5:

[0717] The server analyzes the location information and vehicle status data.

[0718] Input: GPS location and vehicle status data (speed, fuel level, engine status).

[0719] Data calculation: Location information is compared with map data to confirm the vehicle's current location and driving route. Vehicle status is analyzed by analyzing speed and fuel level to understand driving conditions.

[0720] Output: Current location and vehicle status analysis results.

[0721] Step 6:

[0722] The server integrates all collected data and uses a generative AI model to predict the user's potential needs.

[0723] Input: User emotional state data, current location information, and vehicle state analysis results.

[0724] Data calculation: Generative AI models are used to comprehensively evaluate various data and predict future user needs and behavior.

[0725] Output: User's potential needs (e.g. need for rest, suitable type of music, etc.).

[0726] Step 7:

[0727] The system suggests appropriate actions to the user based on the prediction results.

[0728] Input: The user's potential needs.

[0729] Data Calculation: Selecting appropriate actions based on user needs and generating interactions with the user.

[0730] Output: A voice announcement, a screen display, or a notification message to the user. For example, saying "Would you like to take a break?", recommending relaxing music, or notifying relevant parties when a delay is expected.

[0731] The above is the processing flow of this system's program. Each step incorporates specific operations, and the final output is obtained through a series of data processing and calculations based on the input data.

[0732] (Application example 2)

[0733] 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."

[0734] Conventional vehicle security systems often lack speed and accuracy in detecting suspicious individuals and issuing warnings. They also lack the ability to predict users' potential needs, making it difficult to provide a comfortable in-vehicle environment while improving vehicle safety. Therefore, there is a need for systems that can detect human emotions and behaviors in real time in conjunction with more advanced data analysis technology and provide appropriate action.

[0735] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0736] In this invention, the server includes means for capturing facial expression data of a user acquired from a smart device, means for capturing conversation data of the user, means for acquiring location information, means for collecting vehicle status data from vehicle sensors, means for analyzing the data and predicting potential needs of the user, means for suggesting appropriate actions to the user based on the prediction, means for detecting suspicious behavior and issuing a warning, and means for transmitting an alert when suspicious behavior is detected. This makes it possible to enhance safety in the vehicle, quickly detect suspicious behavior and notify the user, and provide a comfortable in-vehicle environment.

[0737] A "smart device" is a device that is equipped with sensors such as a camera, microphone, and GPS and can acquire data.

[0738] "Facial expression data" is facial expression information of a user captured by a camera.

[0739] "Conversation data" is information about voices inside the vehicle that are recorded by a microphone.

[0740] "Location Information" means geographic location data obtained from a GPS.

[0741] "Vehicle sensors" are various sensors for detecting the state of the vehicle, including speed, fuel level, engine status, etc.

[0742] "Data analysis" is the process of comprehensively analyzing various types of acquired data and extracting useful information.

[0743] "Potential needs" are requests or desires that the user has not explicitly stated but that may be needed.

[0744] "Appropriate actions" are specific actions or countermeasures suggested to the user based on the analysis results.

[0745] "Suspicious behavior" is a suspicious pattern of behavior that differs from normal behavior and may pose a security risk.

[0746] An "alert" is a warning message issued when suspicious activity is detected.

[0747] An "alert" is a message sent to notify users and other relevant parties when suspicious behavior or anomalies are detected.

[0748] The system of the present invention analyzes data acquired from smart devices in real time, detects suspicious behavior, and proposes and executes appropriate actions to improve safety and comfort in vehicles. The present invention includes the following main components and their respective operating procedures:

[0749] Sensing Module

[0750] The device collects data using cameras, microphones, GPS, and vehicle sensors installed in the vehicle. Specifically, the camera captures the user's facial expression data, the microphone records conversation data inside the vehicle, the GPS obtains current location information, and the vehicle sensors collect status data such as vehicle speed, fuel level, and engine condition.

[0751] Data Analysis Module

[0752] The server receives the various collected data and performs a comprehensive analysis. First, the server receives facial expression data and conversation data captured by the camera and uses an emotion engine to analyze the user's emotional state. For example, it identifies whether the user is tired, stressed, happy, etc. Next, it receives GPS data and vehicle status data to understand the location and condition of the vehicle. It then comprehensively evaluates this data and uses an AI model to predict the user's potential needs.

[0753] Interaction Module

[0754] Based on the prediction results, the system suggests optimal actions to the user and executes them at the appropriate time. For example, if the user is on a long drive and facial expression analysis and emotion recognition indicate fatigue, the system will suggest audibly, "Shouldn't it be time to take a break?" The system can also suggest appropriate music or media based on the user's emotional state, making the drive more comfortable.

[0755] Security Features

[0756] The system includes a means for detecting suspicious behavior and issuing a warning, as well as a means for sending an alert when suspicious behavior is detected. It uses cameras and various sensors to monitor the environment around the vehicle, and if, for example, a suspicious person approaches the vehicle, it will sound an alarm and send an alert to the owner, thereby improving vehicle safety.

[0757] Hardware and software used

[0758] The system uses cameras (e.g., USB cameras), microphones, GPS sensors, and a variety of other sensors in the vehicle. Data analysis is performed using an AI model and emotion engine running on a server. Specifically, it uses Python and OpenCV libraries to perform real-time person detection and send alerts.

[0759] Specific examples

[0760] 1. Proposal for resting in private cars

[0761] Consider a scenario in which a user is on a long drive. The device uses an in-car camera to capture the user's facial expression data, and the collected data and conversation data are sent to the server. The server analyzes this data using an emotion engine, and if it determines that the user is tired, the system will suggest by voice, "Shouldn't you take a break now?" This allows the user to reduce fatigue and continue driving safely.

[0762] 2. Notification systems for commercial vehicles

[0763] Consider a scenario in which a user is traveling for a business meeting. The device uses GPS to determine the user's current location and simultaneously obtains the business meeting schedule information. The server analyzes this data and determines that a traffic jam has occurred. If a delay is predicted, the emotion engine generates an appropriate notification based on the user's emotional state, and notifies relevant parties by email or message that "there will be a delay due to traffic congestion." This allows timely information sharing with business partners, ensuring smooth communication.

[0764] 3. Media suggestions based on emotional state

[0765] If the user feels stressed while driving, the device uses a camera and microphone to collect facial and speech data. The server analyzes this data with an emotion engine and identifies the user as feeling stressed. Based on the results, the system suggests relaxing music or podcasts to reduce the user's stress.

[0766] 4. Suspicious person detection and alert sending

[0767] If a suspicious person approaches the vehicle, the camera captures their behavior and analyzes it in real time. If the server determines that the behavior is suspicious, the system will sound an alarm and simultaneously send an alert to the owner. This allows for a quick response and improves vehicle safety.

[0768] Example prompts to input to a generative AI model:

[0769] "Write a Python program that detects suspicious people approaching a vehicle and sends an alert. The program should capture camera footage in real time and send an alert to a specified email address as soon as a person is detected."

[0770] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0771] Step 1:

[0772] The terminal acquires the data.

[0773] It uses a camera to capture the user's facial expression data and a microphone to collect conversation data. It obtains location information from GPS and collects data such as speed, fuel level, and engine status from vehicle sensors. The inputs here are camera images, audio data, location information, and vehicle status data, which are collected in real time by each sensor. The output is the collected raw data.

[0774] Step 2:

[0775] The terminal transmits the collected data to the server.

[0776] The collected raw data is preprocessed, compressed or encrypted, and sent to the server. The input here is the raw data obtained in step 1, which is preprocessed to ensure the reliability and security of the data. The output is a notification of completion of transmission.

[0777] Step 3:

[0778] The server analyzes the received data.

[0779] Facial expression data from the camera is analyzed using image processing algorithms (e.g., OpenCV) to extract the user's emotional state. Voice data is analyzed using natural language processing (NLP) technology to understand the content of the conversation. Location information and vehicle state data are used to understand the user's movement status and the vehicle's condition. The input is preprocessed data, and the output is the analysis results.

[0780] Step 4:

[0781] The server predicts the user's potential needs based on the data analysis results.

[0782] The AI ​​model is used to comprehensively evaluate the user's emotional state, conversation content, location information, and vehicle status data to predict whether the user has a specific need. In this step, a generative AI model is used, with the input being the analysis results and the output being the user's potential need data.

[0783] Step 5:

[0784] The server suggests appropriate actions based on your needs.

[0785] Based on the predicted needs, the system suggests the user to take a break, change the music, or take other actions. The input is the user's potential needs data, and the output is the suggestion content. The suggestion is notified to the user through audio output or a display screen.

[0786] Step 6:

[0787] The terminal monitors the area around the vehicle.

[0788] The environment around the vehicle is monitored using cameras and various sensors. Camera footage is analyzed to detect suspicious behavior (e.g., a person approaching the vehicle). The input is video data around the vehicle, and the output is the detection result of suspicious behavior.

[0789] Step 7:

[0790] The server takes action when suspicious behavior is detected.

[0791] If suspicious behavior is detected, countermeasures are implemented, such as sounding an alarm or sending an alert email to the owner. The input is the result of detecting suspicious behavior, and the output is the specific response action, such as sounding an alarm or sending an alert email.

[0792] Example prompts to input to a generative AI model:

[0793] "Write a Python program that detects suspicious people approaching a vehicle and sends an alert. The program should capture camera footage in real time and send an alert to a specified email address as soon as a person is detected."

[0794] 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.

[0795] 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.

[0796] 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.

[0797] [Third embodiment]

[0798] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0799] 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.

[0800] 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).

[0801] 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.

[0802] 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.

[0803] 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).

[0804] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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."

[0810] ---

[0811] The system of the present invention provides a safe and comfortable in-car environment by predicting the user's potential needs based on data acquired from smart devices and suggesting appropriate actions. This system is composed of the following main components: a sensing module, a data analysis module, and an interaction module.

[0812] Sensing Module

[0813] The device collects data using cameras, microphones, GPS, and vehicle sensors installed in the vehicle. Specifically, the camera captures the user's facial expression data, the microphone records conversation data inside the vehicle, the GPS obtains current location information, and the vehicle sensors collect status data such as vehicle speed, fuel level, and engine condition.

[0814] Data Analysis Module

[0815] The collected data is sent to a server where it is comprehensively analyzed. The server first analyzes facial expression data obtained from the camera to identify the user's emotional state (for example, fatigue, stress, joy, etc.). Next, it performs audio analysis of the conversation data obtained from the microphone to extract information needed to understand the content and context of the user's conversation. It also analyzes GPS data and vehicle status data to evaluate the current vehicle location and status. It then comprehensively evaluates this data and uses an AI model to predict the user's potential needs.

[0816] Interaction Module

[0817] Based on predicted needs, the system suggests appropriate actions to the user. For example, if a user is on a long drive and facial expression analysis reveals signs of fatigue, the system can suggest, by voice, "Wouldn't it be time to take a break?" In addition, for commercial vehicles, the system can obtain the user's business meeting schedule information and, if a delay due to traffic congestion is predicted, notify relevant parties that "the vehicle will be delayed due to traffic congestion."

[0818] Specific examples

[0819] Specific examples of the present invention are given below.

[0820] Example 1: Proposal for rest breaks in a private car

[0821] Consider a scenario in which a user is on a long drive. The device captures the user's facial expressions using an in-car camera, and the collected facial data is sent to the server. The server analyzes the facial data and determines that the user is tired. The system then suggests by voice, "Shouldn't you take a break now?" This allows the user to reduce fatigue and continue driving safely.

[0822] Example 2: Notification system in commercial vehicles

[0823] Consider a scenario in which a user is traveling for a business meeting. The device uses GPS to determine the user's current location and simultaneously obtains the business meeting schedule information. The server analyzes this data and determines that a traffic jam has occurred. If a delay is predicted, the system notifies the relevant parties by email or message that "there will be a delay due to traffic congestion." This allows timely information sharing with the business partner, ensuring smooth communication.

[0824] As described above, the system of the present invention comprehensively analyzes data from a variety of sensors, accurately predicts the user's potential needs, and suggests appropriate actions, thereby realizing a safe and comfortable in-car environment.

[0825] The processing flow will be explained below.

[0826] ---

[0827] Step 1: Data collection

[0828] The device collects data using cameras, microphones, GPS, and vehicle sensors installed in the vehicle. Specifically, the camera captures the user's facial expressions, the microphone records conversations inside the vehicle, the GPS obtains the current location, and the vehicle sensors collect data such as the vehicle's speed, fuel level, and engine status.

[0829] ---

[0830] Step 2: Send data

[0831] The device sends the collected data (facial expression data, conversation data, location information, vehicle status data) to a server, where it is processed in real time, enabling rapid analysis.

[0832] ---

[0833] Step 3: Analyzing facial expression data

[0834] The server receives the camera data and uses an AI model to analyze the user's facial expressions. The facial expression data identifies the user's emotional state (fatigue, stress, joy, etc.). The results of this analysis are used in the next prediction step.

[0835] ---

[0836] Step 4: Analyzing the conversation data

[0837] The server receives the microphone data and uses a voice analysis algorithm to analyze the conversation, extracting important keywords and context, which are used to understand the user's needs and requests.

[0838] ---

[0839] Step 5: Analyzing location and vehicle data

[0840] The server receives and analyzes GPS data and vehicle sensor data to determine the current vehicle location and status, allowing traffic conditions and vehicle status to be tracked in real time.

[0841] ---

[0842] Step 6: Comprehensive analysis and prediction

[0843] The server comprehensively evaluates facial expression data, conversation data, location information, and vehicle data, and then uses an AI model to predict the user's potential needs. For example, if it determines that the user is tired, it will suggest an appropriate break.

[0844] ---

[0845] Step 7: Select an action

[0846] The server selects the optimal action for the user based on the prediction results, such as suggesting a break to the user or providing traffic information.

[0847] ---

[0848] Step 8: Take Action

[0849] The device executes the action received from the server for the user, such as suggesting "Should we take a break now?" or sending an email to notify relevant parties of a delay.

[0850] ---

[0851] Step 9: Gather feedback

[0852] The device collects user reactions and feedback and sends it to the server, which improves the system's analysis accuracy and the quality of its suggestions.

[0853] ---

[0854] This trend will enable users to continue driving safely and comfortably while receiving appropriate support from AI.

[0855] Example 1

[0856] 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."

[0857] To make the driving environment safer and more comfortable for modern vehicles, systems that monitor the user's condition and vehicle status in real time and suggest appropriate actions based on that information are required. However, conventional systems have had difficulty efficiently collecting and analyzing the user's facial expression data, conversation data, location information, and vehicle status data, and suggesting appropriate actions. Furthermore, it has been difficult to predict the user's potential needs based on the results of advanced analysis.

[0858] 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.

[0859] In this invention, the server includes means for capturing user facial expression data acquired from the smart device, means for capturing user conversation data, means for acquiring location information, means for collecting vehicle status data from vehicle sensors, means for transmitting the data to the server, means for analyzing the data in the server and identifying the user's emotional state, the content of the conversation, and the vehicle's location and status, means for predicting the user's potential needs using a generative AI model based on the analysis results, and means for suggesting appropriate actions to the user based on the predictions. This makes it possible to monitor the user's driving environment in real time and suggest appropriate actions at the right time.

[0860] ---

[0861] A "smart device" is an electronic device used to acquire a user's facial expression data, conversation data, location information, and vehicle status data.

[0862] "Facial expression data" is data about a user's facial expressions captured using a device such as a camera.

[0863] "Conversation data" refers to data about the content of conversations between users in a vehicle, collected using a device such as a microphone.

[0864] "Location Information" is data regarding the current geographic location of a vehicle obtained using a location measuring device such as a GPS.

[0865] "Vehicle sensor" refers generally to sensing devices installed to collect information such as vehicle speed, fuel level, and engine condition.

[0866] The "potential needs of the user" are requests and desires regarding actions and services that the user may require, which are predicted based on the analysis results.

[0867] A "server" is a computer system that receives the collected data, analyzes it, and suggests appropriate actions to the user.

[0868] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to analyze data and predict users' potential needs.

[0869] "Action suggestions" refers to guidelines and advice such as break suggestions and notifications provided to users by the system.

[0870] "Real-time analysis" is an analysis method that can quickly process collected data and obtain results immediately.

[0871] A "commercial vehicle" is a vehicle used for business or commercial travel.

[0872] "Schedule information" is time-related information such as the user's plans and appointments.

[0873] "Delay notification" refers to informing relevant parties when arrival is delayed due to traffic conditions or other reasons.

[0874] ---

[0875] The system of the present invention provides a safe and comfortable in-car environment by predicting the user's potential needs and suggesting appropriate actions based on data acquired from smart devices. This system is composed of the following main components: a sensing module, a data analysis module, and an interaction module.

[0876] Sensing Module

[0877] The device collects data using cameras, microphones, GPS, and vehicle sensors installed in the vehicle. Specifically, the camera captures the user's facial expressions, and the microphone records conversations inside the vehicle. GPS obtains the vehicle's current location, and vehicle sensors collect data such as speed, fuel level, and engine status. This data is collected in real time and sent to a server via batch processing or real-time streaming.

[0878] Data Analysis Module

[0879] The server analyzes the received data to determine the user's emotional state, the content of the conversation, and the vehicle's location and status. Specifically, it performs the following processes:

[0880] 1. The server analyzes the collected image data and identifies the user's emotional state (e.g., fatigue, stress, joy, etc.) from their facial expressions.

[0881] 2. The server converts the voice data into text and analyzes the conversation content and context.

[0882] 3. The server evaluates GPS data and vehicle sensor data to determine the current vehicle location and status.

[0883] Based on these analysis results, the server uses a generative AI model to predict the user's potential needs, for example, if the user's facial expression shows signs of fatigue, it predicts that they should take a break.

[0884] Interaction Module

[0885] Based on predicted needs, the system will suggest appropriate actions to the user. For example, if it determines that a user is tired after a long drive, it will make a voice suggestion saying, "Would you like to take a break now?" In the case of commercial vehicles, the system will analyze the user's business meeting schedule information and current traffic information, and if a delay is predicted, it will send a notification to relevant parties saying, "You will be delayed due to traffic congestion."

[0886] Specific examples

[0887] The following are specific examples of the present invention:

[0888] Example 1: Proposal for rest breaks in a private car

[0889] 1. When the user is on a long drive, the device captures the user's facial expressions using the in-car camera.

[0890] 2. The captured facial expression data is sent to the server.

[0891] 3. The server uses an AI model to analyze whether the user is tired.

[0892] 4. If fatigue is detected, the system will suggest to the user via voice, "Would you like to take a break now?"

[0893] Example 2: Notification system in commercial vehicles

[0894] 1. When a user is traveling for a business meeting, the device uses GPS to obtain the user's current location and collects business meeting schedule information.

[0895] 2. The collected data is sent to the server.

[0896] 3. The server analyzes the traffic data and determines that a traffic jam is occurring.

[0897] 4. If a delay is predicted, the system will notify relevant parties via email or message that "there will be a delay due to traffic congestion."

[0898] Prompt Sentence Examples

[0899] "Please explain a scenario where you want to detect fatigue from facial expression data of a user during a long drive and suggest a break."

[0900] "Describe a scenario in which a user heading to a business meeting is predicted to be delayed based on their current location and traffic information, and relevant parties are notified."

[0901] As described above, the present invention makes it possible to comprehensively analyze data from multiple sensors, accurately predict the potential needs of a user, and make the driving environment for the user safer and more comfortable.

[0902] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0903] ---

[0904] Step 1: Data collection

[0905] Input: Data from in-car cameras, microphones, GPS, and vehicle sensors

[0906] Output: Collected facial expression data, conversation data, location information, vehicle status data

[0907] The device captures the user's facial expressions using a camera installed inside the vehicle, records conversations inside the vehicle using a microphone, and obtains the current location information using GPS. It also collects data such as speed, fuel level, and engine status from vehicle sensors. This data is sensed in real time and sent to a server via batch processing or real-time streaming as needed.

[0908] Step 2: Send data

[0909] Input: Collected facial expression data, conversation data, location information, vehicle status data

[0910] Output: Data sent to the server

[0911] The device sends the collected data to the server periodically or in real time. Camera image data is sent in a fixed batch format, audio data is streamed in real time, and GPS data and vehicle status data are also sent in packets periodically.

[0912] Step 3: Facial Expression Analysis

[0913] Input: Facial expression data sent to the server

[0914] Output: User's emotional state

[0915] The server analyzes the received image data and uses facial recognition technology to identify the user's emotional state from their facial expressions. For example, it uses an image analysis algorithm to determine whether the user is smiling, tired, angry, etc. The analysis results in identifying the user's emotional state.

[0916] Step 4: Audio analysis

[0917] Input: Conversation data sent to the server

[0918] Output: Analyzed conversation

[0919] The server uses speech recognition technology to convert the conversation into text. The server analyzes the collected voice data using speech recognition software and generates text information to understand the content and context of the conversation. The analysis results identify the content and context of the conversation.

[0920] Step 5: Analyze location and vehicle status

[0921] Input: Location information sent to the server, vehicle sensor data

[0922] Output: Current vehicle position and status

[0923] The server analyzes the collected GPS data and vehicle sensor data. It identifies the vehicle's current location from the GPS data and evaluates the vehicle sensor data for speed, fuel level, engine status, etc. As a result of the analysis, it identifies the vehicle's current location and status.

[0924] Step 6: Anticipate potential needs

[0925] Input: Analyzed facial expression data, conversation data, location information, vehicle status data

[0926] Output: User's potential needs

[0927] The server integrates all the analyzed data and uses a generative AI model to predict the user's potential needs. For example, if the user's facial expression shows signs of fatigue, it predicts that a break is necessary. As a result of this analysis, the server identifies the user's potential needs.

[0928] Step 7: Action proposals

[0929] Input: predicted potential user needs

[0930] Output: Suggested action for the user

[0931] The system will suggest appropriate actions to the user based on predicted needs. For example, if it determines that the user is tired after a long drive, it will make a voice suggestion such as, "Wouldn't it be time to take a break?". Also, if there is traffic congestion based on business meeting schedule information, it will send a notification to the relevant parties saying, "You will be delayed due to traffic congestion."

[0932] Through the above processing steps, this system monitors the user's driving environment in real time and suggests appropriate actions at the right time, thereby providing a safe and comfortable in-car environment.

[0933] (Application example 1)

[0934] 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."

[0935] Autonomous vehicles are required to accurately predict passengers' potential needs and provide a safe and comfortable in-car environment. Conventional technologies have difficulty assessing passenger fatigue and stress levels in real time, and lack a means to suggest rest at the appropriate time. Furthermore, there are insufficient methods for sharing information in a timely manner in the event of delays due to long driving times or traffic congestion. Therefore, a system is needed to achieve a higher level of passenger safety and comfort.

[0936] 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.

[0937] In this invention, the server includes means for capturing user facial expression data acquired from a smart device, means for capturing user conversation data, means for acquiring location information, means for collecting vehicle status data from vehicle sensors, means for analyzing the data and predicting the user's potential needs, means for suggesting appropriate actions to the user based on the prediction, means for assessing the user's fatigue state and sending a notification in real time, and means for displaying information via a head-mounted display. This makes it possible to monitor passenger fatigue and stress in real time and suggest breaks at appropriate times. In addition, in the case of commercial vehicles, if a delay is predicted, a notification can be sent by email, allowing relevant parties in a business negotiation to be informed of the estimated arrival time and delay in a timely manner.

[0938] A "smart device" is an electronic device that is equipped with an internet connection and a wide variety of sensors and is capable of acquiring and processing data.

[0939] "Facial expression data" is digital data relating to a user's facial expressions that is captured using an image capture device such as a camera.

[0940] "Conversation data" is audio data relating to the content of a user's speech or conversation, which is acquired using an audio capture device such as a microphone.

[0941] "Location information" is data about the current location of a vehicle or user, obtained using a location measurement device such as a GPS.

[0942] A "vehicle sensor" is a sensor device for collecting status data such as vehicle speed, fuel level, engine status, etc.

[0943] "Real-time" refers to data acquisition and processing occurring immediately, without delay.

[0944] A "head-mounted display" is a display device worn by a user on the head, which displays information within the user's field of vision.

[0945] The "fatigue state" refers to a state that indicates the user's physical or mental fatigue, particularly one that is affected by prolonged activity or stress.

[0946] A "notification" is a message or alert sent to inform a user of important information.

[0947] "Relaxation content" refers to content such as music, video, or guidance provided to relieve the user's fatigue and stress.

[0948] "Business meeting schedule information" is information about the time, location, and participants of business meetings and conferences.

[0949] "Email" means a digital letter or message sent or received over the Internet.

[0950] The system of this invention predicts potential user needs and proposes appropriate actions to provide a safe and comfortable environment in an autonomous vehicle by analyzing data collected from smart devices and related sensors. The main components of the system are a sensing module, a data analysis module, and an interaction module.

[0951] Sensing Module

[0952] The device collects data using cameras, microphones, GPS, and vehicle sensors installed in the vehicle. Specifically, the camera captures the user's facial expression data, the microphone records conversation data in the vehicle, the GPS obtains current location information, and the vehicle sensors collect status data such as vehicle speed and engine condition.

[0953] Data Analysis Module

[0954] The collected data is sent to a server where it is comprehensively analyzed. The server first analyzes facial expression data obtained from the camera to identify the user's emotional state (for example, fatigue, stress, joy, etc.). Next, it performs audio analysis of the conversation data obtained from the microphone to extract information needed to understand the content and context of the user's conversation. It also analyzes GPS data and vehicle status data to evaluate the current vehicle location and status. It then comprehensively evaluates this data and uses an AI model to predict the user's potential needs.

[0955] Interaction Module

[0956] The system suggests appropriate actions to the user based on predicted needs. For example, if a user is on a long drive and facial expression analysis reveals signs of fatigue, the system can suggest audibly, "Shouldn't it be time to take a break?" Information such as traffic congestion and estimated arrival times at the destination can also be displayed via a head-mounted display.

[0957] Example

[0958] Example 1: Fatigue suggestions for long-distance drivers

[0959] The system analyzes video captured by an in-car camera in real time and evaluates the user's level of fatigue based on facial expression data. If fatigue exceeds a certain threshold, the system displays a message on the head-mounted display saying, "You are feeling drowsy. You are 10 minutes away from a rest area." If the user has enabled the email notification option, the system also sends a notification by email.

[0960] Example 2: Schedule management for commercial vehicles

[0961] Based on GPS and business meeting schedule information, the system notifies the user and business meeting participants of delays in the event of traffic congestion. The system displays a message on the head-mounted display saying, "Due to traffic congestion, you will be delayed from the scheduled arrival time," and can be configured to notify relevant parties by email.

[0962] Example prompt

[0963] "Create a head-mounted display application that monitors passenger status in real time and detects fatigue and stress."

[0964] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0965] Step 1:

[0966] Acquiring sensing data

[0967] The device collects data using cameras, microphones, GPS, and vehicle sensors installed inside the vehicle. Inputs include facial expression data from the camera, conversation data from the microphone, location information from GPS, and vehicle status data from vehicle sensors (speed, fuel level, engine status, etc.). After acquiring this data, it sends it to a server.

[0968] Step 2:

[0969] Analysis of facial expression data

[0970] The server analyzes the facial expression data sent from the camera. The input is image data containing the user's facial expressions. The server uses facial landmark detection technology (e.g., the dlib library) to identify the user's emotional state (fatigue, stress, joy, etc.). The output is an evaluation of these emotional states.

[0971] Step 3:

[0972] Conversation data analysis

[0973] The server performs speech analysis on the conversation data sent from the microphone. The input is the user's conversation voice data. This is converted into text data using speech recognition technology, and information needed to understand the content and context is extracted. The output is the text data of the conversation content and the analysis results.

[0974] Step 4:

[0975] Location and vehicle status analysis

[0976] The server analyzes the location information sent from the GPS and data from the vehicle sensors. The input is location information and vehicle status data. This is used to evaluate the current vehicle location and status. The output is detailed information about the vehicle's current location and operating status.

[0977] Step 5:

[0978] Comprehensive evaluation and needs forecast

[0979] The server comprehensively evaluates the facial expression data evaluation results, conversation analysis results, location information, and vehicle status data, and uses an AI model to predict the user's potential needs. The input is data from various analysis results. Based on this, it identifies when the user is tired or has been driving for a long time. The output is a prediction of the user's potential needs and status.

[0980] Step 6:

[0981] Action suggestions and notifications

[0982] The server proposes appropriate actions to the user based on the predicted needs. For example, if fatigue is detected, it generates a voice suggestion such as "Shouldn't you take a break now?". Furthermore, if the user is wearing a head-mounted display, it also provides relaxation content and traffic information to be displayed on the display. The input is the predicted needs, and the output is the action suggestion and notification content.

[0983] Step 7:

[0984] Sending notifications

[0985] The server sends email notifications to the appropriate parties based on the notification options set by the user. For example, if a commercial vehicle is predicted to be delayed, an email is sent to the relevant parties saying, "Due to traffic congestion, the vehicle will be delayed from the scheduled arrival time." The input is the data and email address information for the delay notification, and the output is the email sent.

[0986] 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.

[0987] ---

[0988] The system of the present invention provides a safe and comfortable in-car environment by predicting the user's potential needs based on data acquired from smart devices and suggesting appropriate actions. This system is composed of the following main components: a sensing module, a data analysis module, an interaction module, and an emotion engine.

[0989] Sensing Module

[0990] The device collects data using cameras, microphones, GPS, and vehicle sensors installed in the vehicle. Specifically, the camera captures the user's facial expression data, the microphone records conversation data inside the vehicle, the GPS obtains current location information, and the vehicle sensors collect status data such as vehicle speed, fuel level, and engine condition.

[0991] Data Analysis Module

[0992] The collected data is sent to a server where it is comprehensively analyzed. The server first receives facial expression and conversation data captured by the camera and uses an emotion engine to analyze the user's emotional state. For example, it can identify whether the user is tired, stressed, happy, etc. Next, it receives GPS data and vehicle status data to ascertain the location and condition of the vehicle. It then comprehensively evaluates this data and uses an AI model to predict the user's potential needs.

[0993] Interaction Module

[0994] Based on the prediction results, the system will suggest optimal actions to the user. For example, if the user is on a long drive and facial expression analysis and emotion recognition indicate fatigue, the emotion engine can use voice prompts such as, "Shouldn't it be time to take a break?" The system can also suggest appropriate music or media based on the user's emotional state, making the drive more comfortable.

[0995] Specific examples

[0996] Specific examples of the present invention are given below.

[0997] Example 1: Proposal for rest breaks in a private car

[0998] Consider a scenario in which a user is on a long drive. The device uses an in-car camera to capture the user's facial expression data, and the collected data and conversation data are sent to the server. The server analyzes this data using an emotion engine, and if it determines that the user is tired, the system will suggest by voice, "Shouldn't you take a break now?" This allows the user to reduce fatigue and continue driving safely.

[0999] Example 2: Notification system in commercial vehicles

[1000] Consider a scenario in which a user is traveling for a business meeting. The device uses GPS to determine the user's current location and simultaneously obtains the business meeting schedule information. The server analyzes this data and determines that a traffic jam has occurred. If a delay is predicted, the emotion engine generates an appropriate notification based on the user's emotional state, and notifies relevant parties by email or message that "there will be a delay due to traffic congestion." This allows timely information sharing with business partners, ensuring smooth communication.

[1001] Example 3: Media suggestions based on emotional state

[1002] If the user feels stressed while driving, the device uses a camera and microphone to collect facial expression and speech data. The server analyzes this data with an emotion engine and identifies the user as feeling stressed. Based on the results, the system suggests relaxing music or podcasts to reduce the user's stress.

[1003] As described above, the system of the present invention uses an emotion engine to analyze the user's emotion data and make more accurate predictions and suggestions, thereby realizing a safe and comfortable in-car environment.

[1004] The processing flow will be explained below.

[1005] ---

[1006] Step 1: Data collection

[1007] The device uses a camera installed inside the vehicle to capture the user's facial expressions, a microphone installed inside the vehicle to record conversations, a GPS module to obtain current location information, and vehicle sensors to collect data such as vehicle speed, fuel level, and engine status.

[1008] ---

[1009] Step 2: Send data

[1010] The device transmits the collected data (facial expression data, conversation data, location information, vehicle status data) to the server in real time, allowing the server to receive the data in a timely manner and begin analysis.

[1011] ---

[1012] Step 3: Analyzing facial expression and conversation data

[1013] The server receives the camera and microphone data and analyzes the user's facial expression and speech data using an emotion engine, which determines the user's current emotional state (e.g., fatigue, stress, joy, etc.) from the data.

[1014] ---

[1015] Step 4: Analyzing location and vehicle data

[1016] The server receives GPS data and vehicle sensor data and analyzes it to determine the current vehicle location and status, thereby understanding the user's riding situation and environment in real time.

[1017] ---

[1018] Step 5: Comprehensive analysis and prediction

[1019] The server integrates facial expression data, conversation data, location information, and vehicle data, and uses AI models and an emotion engine to predict the user's potential needs. For example, if it determines that the user is tired, it determines the appropriate action based on the prediction.

[1020] ---

[1021] Step 6: Select an action

[1022] The server then selects the optimal action based on the prediction results. Specifically, if it determines that the user is tired, it will suggest taking a break. It may also recommend media that will help the user relax depending on the user's emotional state.

[1023] ---

[1024] Step 7: Take Action

[1025] The device then executes suggestions based on the action instructions received from the server. For example, it may issue a voice message saying, "Would you like to take a break?" or play appropriate music. In the case of commercial vehicles, it may also notify relevant parties of estimated arrival times or delays via email or message.

[1026] ---

[1027] Step 8: Gather feedback

[1028] The device collects the user's reactions and feedback after making a suggestion and sends it to the server, which can then use the feedback data to improve the accuracy of analysis and the quality of the suggestions.

[1029] ---

[1030] Through this step, the system of the present invention comprehensively analyzes data from various sensors, accurately predicts the user's potential needs, and suggests appropriate actions to provide a safe and comfortable in-car environment.

[1031] Example 2

[1032] 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."

[1033] In conventional in-car environments, it has been difficult to grasp the user's status and needs in real time and provide appropriate suggestions and actions. Furthermore, during long driving periods or in stressful environments, reduced safety and comfort have become an issue. The present invention aims to solve these problems and provide a safer and more comfortable in-car environment.

[1034] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1035] In this invention, the server includes means for capturing user facial expression data acquired from the smart device, means for capturing user conversation data, means for acquiring location information, means for collecting vehicle situation data, means including an emotion engine for analyzing the data and estimating the user's emotional state, means for comprehensively evaluating the data and predicting the user's potential needs using a generative AI model, and means for suggesting appropriate actions to the user based on the prediction, thereby enabling the server to grasp the user's state in real time and realize a safe and comfortable in-car environment.

[1036] A "smart device" is an electronic device that has a camera, microphone, GPS, and various sensors that are used to collect data about a user.

[1037] "Facial Expression Data" refers to information that captures and stores in digital form a user's facial expressions.

[1038] "Conversation data" refers to information that has been recorded and stored in digital format, including the user's speaking voice and conversation content.

[1039] "Location information" is data indicating the current geographic coordinates of a vehicle obtained by GPS.

[1040] "Vehicle status data" is data relating to the operating and mechanical conditions of a vehicle, such as speed, fuel level, engine condition, etc.

[1041] An "emotion engine" is software or an algorithm that analyzes a user's facial expressions and conversation data to estimate their emotional state.

[1042] A "generative AI model" is an artificial intelligence model used to predict users' potential needs, and is a system that analyzes multiple data sets to learn patterns.

[1043] "Action suggestions" are suggestions of actions or options to the user based on the analysis results, and are done through methods such as voice guidance or screen displays.

[1044] The system of the present invention provides a safe and comfortable in-car environment by predicting the user's potential needs based on data acquired from smart devices and suggesting appropriate actions. This system is composed of the following main components: a sensing module, a data analysis module, an interaction module, and an emotion engine.

[1045] Sensing Module

[1046] The device collects data using cameras, microphones, GPS, and vehicle sensors installed in the vehicle. Specifically, each sensor operates as follows:

[1047] The camera captures the user's facial expression data, for example, to detect whether the user is smiling or looking stern.

[1048] The microphone records conversation data inside the car, including the content and tone of what the user is saying.

[1049] GPS obtains current location information and tracks the vehicle's route and geographical location.

[1050] Vehicle sensors collect data on vehicle speed, fuel level, engine status, etc. For example, they can provide low fuel warnings and detect abnormal engine conditions.

[1051] Data Analysis Module

[1052] The collected data is sent to a server where it is analyzed and processed in the following steps:

[1053] The facial expression data and conversation data are received, and an emotion engine is used to analyze the user's emotional state, for example, determining that "the user is tired" or "the user is stressed."

[1054] It also receives GPS data and vehicle status data and analyzes the location and vehicle condition, such as "You are currently on the highway" or "You are low on fuel."

[1055] All data is integrated and a generative AI model is used to predict the user's potential needs, such as "the user needs a break" or "the user needs relaxing music."

[1056] Interaction Module

[1057] The system will suggest the best action to the user based on the prediction results from the server. Specific examples include:

[1058] If the system determines that the user has been driving for a long time and is tired, it will suggest through voice, "Would you like to take a break now?"

[1059] If the user is feeling stressed, it suggests relaxing music or podcasts.

[1060] If a traffic jam occurs and a user is likely to be late for a business meeting, the system notifies relevant parties by email or message that "the user will be late due to traffic congestion."

[1061] Specific examples

[1062] Specific examples of the present invention are given below.

[1063] Example 1: Proposal for rest breaks in a private car

[1064] Consider a scenario where a user is driving for a long time. The device captures the user's facial expression data using an in-car camera and transmits it to a server along with conversation data.

[1065] The server analyzes this data using an emotion engine, and if it determines that the user is tired, the system will suggest audibly, "Wouldn't it be time to take a break?"

[1066] This reduces the user's fatigue and allows them to continue driving safely.

[1067] Example 2: Notification system in commercial vehicles

[1068] Imagine a scenario where a user is traveling for a business meeting. The device uses GPS to determine the user's current location and simultaneously obtains the business meeting schedule information.

[1069] The server analyzes this data and determines if a traffic jam is occurring. If a delay is predicted, the system notifies relevant parties by email or message that "the trip will be delayed due to traffic congestion."

[1070] This allows information to be shared with business partners in a timely manner, ensuring smooth communication.

[1071] Example 3: Media suggestions based on emotional state

[1072] If the user feels stressed while driving, the device will use the camera and microphone to collect the user's facial expression and speech data.

[1073] The server analyzes this data using an emotion engine and determines whether the user is feeling stressed.

[1074] Based on the results, the system suggests relaxing music and podcasts to help reduce stress for users.

[1075] Prompt Sentence Examples

[1076] "The user is on a long drive. The device collects facial expression and conversation data from the in-car camera and sends it to the server. The server uses its emotion engine to analyze that the user is tired. What will the system suggest?"

[1077] "A user is traveling to a business meeting. The device uses GPS to obtain the user's current location and business meeting schedule information. The server analyzes traffic conditions and predicts a delay. How will the system notify the relevant parties?"

[1078] The above is a detailed description of the embodiment of the invention. This system accurately analyzes the user's emotional data and makes appropriate suggestions to create a safe and comfortable in-car environment.

[1079] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1080] Detailed explanation of the processing steps

[1081] Step 1:

[1082] The device collects user data using sensors installed inside the vehicle.

[1083] Input: User facial expressions, conversational audio, vehicle status, and location information.

[1084] Data processing: Facial expressions are captured with a camera and converted into digital data, conversations are recorded with a microphone, location is recorded with GPS, and driving status is acquired from vehicle sensors. Facial expression data is saved as still images or videos, and conversation data is saved as an audio file.

[1085] Output: Digital facial expression data, conversation data, location information, and vehicle status data.

[1086] Step 2:

[1087] The terminal transmits the collected data to the server.

[1088] Input: Digital facial expression data, conversation data, location information, and vehicle status data.

[1089] Data calculation: Various data is packetized according to the communication protocol and sent to the server via the network.

[1090] Output: Data packets sent to the server.

[1091] Step 3:

[1092] The server receives the transmitted data and begins analyzing it.

[1093] Input: Facial expression data, conversation data, location information, and vehicle state data received as data packets.

[1094] Data processing: Reconstructing received data and returning it to its individual data format. For example, image data can be restored as facial expression data, and audio files can be restored as conversation data.

[1095] Output: Reconstructed data (facial expression data, conversation data, location information, vehicle state data).

[1096] Step 4:

[1097] The server uses an emotion engine to analyze the user's emotional state.

[1098] Input: Facial expression and speech data.

[1099] Data Computation: Analyzes facial expression data and applies emotion recognition algorithms to estimate the user's emotional state (e.g., joy, anger, sadness, happiness, stress, fatigue). Speech data is also subjected to speech recognition and emotion analysis to extract emotions from the content and tone of spoken words.

[1100] Output: User's emotional state data.

[1101] Step 5:

[1102] The server analyzes the location information and vehicle status data.

[1103] Input: GPS location and vehicle status data (speed, fuel level, engine status).

[1104] Data calculation: Location information is compared with map data to confirm the vehicle's current location and driving route. Vehicle status is analyzed by analyzing speed and fuel level to understand driving conditions.

[1105] Output: Current location and vehicle status analysis results.

[1106] Step 6:

[1107] The server integrates all collected data and uses a generative AI model to predict the user's potential needs.

[1108] Input: User emotional state data, current location information, and vehicle state analysis results.

[1109] Data calculation: Generative AI models are used to comprehensively evaluate various data and predict future user needs and behavior.

[1110] Output: User's potential needs (e.g. need for rest, suitable type of music, etc.).

[1111] Step 7:

[1112] The system suggests appropriate actions to the user based on the prediction results.

[1113] Input: The user's potential needs.

[1114] Data Calculation: Selecting appropriate actions based on user needs and generating interactions with the user.

[1115] Output: A voice announcement, a screen display, or a notification message to the user. For example, saying "Would you like to take a break?", recommending relaxing music, or notifying relevant parties when a delay is expected.

[1116] The above is the processing flow of this system's program. Each step incorporates specific operations, and the final output is obtained through a series of data processing and calculations based on the input data.

[1117] (Application example 2)

[1118] 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."

[1119] Conventional vehicle security systems often lack speed and accuracy in detecting suspicious individuals and issuing warnings. They also lack the ability to predict users' potential needs, making it difficult to provide a comfortable in-vehicle environment while improving vehicle safety. Therefore, there is a need for systems that can detect human emotions and behaviors in real time in conjunction with more advanced data analysis technology and provide appropriate action.

[1120] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1121] In this invention, the server includes means for capturing facial expression data of a user acquired from a smart device, means for capturing conversation data of the user, means for acquiring location information, means for collecting vehicle status data from vehicle sensors, means for analyzing the data and predicting potential needs of the user, means for suggesting appropriate actions to the user based on the prediction, means for detecting suspicious behavior and issuing a warning, and means for transmitting an alert when suspicious behavior is detected. This makes it possible to enhance safety in the vehicle, quickly detect suspicious behavior and notify the user, and provide a comfortable in-vehicle environment.

[1122] A "smart device" is a device that is equipped with sensors such as a camera, microphone, and GPS and can acquire data.

[1123] "Facial expression data" is facial expression information of a user captured by a camera.

[1124] "Conversation data" is voice information recorded inside the vehicle by a microphone.

[1125] "Location Information" means geographic location data obtained from a GPS.

[1126] "Vehicle sensors" are various sensors for detecting the state of the vehicle, including speed, fuel level, engine status, etc.

[1127] "Data analysis" is the process of comprehensively analyzing various types of acquired data and extracting useful information.

[1128] "Potential needs" are requests or desires that the user has not explicitly stated but that may be needed.

[1129] "Appropriate actions" are specific actions or countermeasures suggested to the user based on the analysis results.

[1130] "Suspicious behavior" is a suspicious pattern of behavior that differs from normal behavior and may pose a security risk.

[1131] An "alert" is a warning message issued when suspicious activity is detected.

[1132] An "alert" is a message sent to notify users and other relevant parties when suspicious behavior or anomalies are detected.

[1133] The system of the present invention analyzes data acquired from smart devices in real time, detects suspicious behavior, and proposes and executes appropriate actions to improve safety and comfort in vehicles. The present invention includes the following main components and their respective operating procedures:

[1134] Sensing Module

[1135] The device collects data using cameras, microphones, GPS, and vehicle sensors installed in the vehicle. Specifically, the camera captures the user's facial expression data, the microphone records conversation data inside the vehicle, the GPS obtains current location information, and the vehicle sensors collect status data such as vehicle speed, fuel level, and engine condition.

[1136] Data Analysis Module

[1137] The server receives the various collected data and performs a comprehensive analysis. First, the server receives facial expression data and conversation data captured by the camera and uses an emotion engine to analyze the user's emotional state. For example, it identifies whether the user is tired, stressed, happy, etc. Next, it receives GPS data and vehicle status data to understand the location and condition of the vehicle. It then comprehensively evaluates this data and uses an AI model to predict the user's potential needs.

[1138] Interaction Module

[1139] Based on the prediction results, the system suggests optimal actions to the user and executes them at the appropriate time. For example, if the user is on a long drive and facial expression analysis and emotion recognition indicate fatigue, the system will suggest audibly, "Shouldn't it be time to take a break?" The system can also suggest appropriate music or media based on the user's emotional state, making the drive more comfortable.

[1140] Security Features

[1141] The system includes a means for detecting suspicious behavior and issuing a warning, as well as a means for sending an alert when suspicious behavior is detected. It uses cameras and various sensors to monitor the environment around the vehicle, and if, for example, a suspicious person approaches the vehicle, it will sound an alarm and send an alert to the owner, thereby improving vehicle safety.

[1142] Hardware and software used

[1143] The system uses cameras (e.g., USB cameras), microphones, GPS sensors, and a variety of other sensors in the vehicle. Data analysis is performed using an AI model and emotion engine running on a server. Specifically, it uses Python and OpenCV libraries to perform real-time person detection and send alerts.

[1144] Specific examples

[1145] 1. Proposal for resting in private cars

[1146] Consider a scenario in which a user is on a long drive. The device uses an in-car camera to capture the user's facial expression data, and the collected data and conversation data are sent to the server. The server analyzes this data using an emotion engine, and if it determines that the user is tired, the system will suggest by voice, "Shouldn't you take a break now?" This allows the user to reduce fatigue and continue driving safely.

[1147] 2. Notification systems for commercial vehicles

[1148] Consider a scenario in which a user is traveling for a business meeting. The device uses GPS to determine the user's current location and simultaneously obtains the business meeting schedule information. The server analyzes this data and determines that a traffic jam has occurred. If a delay is predicted, the emotion engine generates an appropriate notification based on the user's emotional state, and notifies relevant parties by email or message that "there will be a delay due to traffic congestion." This allows timely information sharing with business partners, ensuring smooth communication.

[1149] 3. Media suggestions based on emotional state

[1150] If the user feels stressed while driving, the device uses a camera and microphone to collect facial expression and speech data. The server analyzes this data with an emotion engine and identifies the user as feeling stressed. Based on the results, the system suggests relaxing music or podcasts to reduce the user's stress.

[1151] 4. Suspicious person detection and alert sending

[1152] If a suspicious person approaches the vehicle, the camera captures their behavior and analyzes it in real time. If the server determines that the behavior is suspicious, the system will sound an alarm and simultaneously send an alert to the owner. This allows for a quick response and improves vehicle safety.

[1153] Example prompts to input to a generative AI model:

[1154] "Write a Python program that detects suspicious people approaching a vehicle and sends an alert. The program should capture camera footage in real time and send an alert to a specified email address as soon as a person is detected."

[1155] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1156] Step 1:

[1157] The terminal acquires the data.

[1158] It uses a camera to capture the user's facial expression data and a microphone to collect conversation data. It obtains location information from GPS and collects data such as speed, fuel level, and engine status from vehicle sensors. The inputs here are camera images, audio data, location information, and vehicle status data, which are collected in real time by each sensor. The output is the collected raw data.

[1159] Step 2:

[1160] The terminal transmits the collected data to the server.

[1161] The collected raw data is preprocessed, compressed or encrypted, and sent to the server. The input here is the raw data obtained in step 1, which is preprocessed to ensure the reliability and security of the data. The output is a notification of completion of transmission.

[1162] Step 3:

[1163] The server analyzes the received data.

[1164] Facial expression data from the camera is analyzed using image processing algorithms (e.g., OpenCV) to extract the user's emotional state. Voice data is analyzed using natural language processing (NLP) technology to understand the content of the conversation. Location information and vehicle state data are used to understand the user's movement status and the vehicle's condition. The input is preprocessed data, and the output is the analysis results.

[1165] Step 4:

[1166] The server predicts the user's potential needs based on the data analysis results.

[1167] The AI ​​model is used to comprehensively evaluate the user's emotional state, conversation content, location information, and vehicle status data to predict whether the user has a specific need. In this step, a generative AI model is used, with the input being the analysis results and the output being the user's potential need data.

[1168] Step 5:

[1169] The server suggests appropriate actions based on your needs.

[1170] Based on the predicted needs, the system suggests the user to take a break, change the music, or take other actions. The input is the user's potential needs data, and the output is the suggestion content. The suggestion is notified to the user through audio output or a display screen.

[1171] Step 6:

[1172] The terminal monitors the area around the vehicle.

[1173] The environment around the vehicle is monitored using cameras and various sensors. Camera footage is analyzed to detect suspicious behavior (e.g., a person approaching the vehicle). The input is video data around the vehicle, and the output is the detection result of suspicious behavior.

[1174] Step 7:

[1175] The server takes action when suspicious behavior is detected.

[1176] If suspicious behavior is detected, countermeasures are implemented, such as sounding an alarm or sending an alert email to the owner. The input is the result of detecting suspicious behavior, and the output is the specific response action, such as sounding an alarm or sending an alert email.

[1177] Example prompts to input to a generative AI model:

[1178] "Write a Python program that detects suspicious people approaching a vehicle and sends an alert. The program should capture camera footage in real time and send an alert to a specified email address as soon as a person is detected."

[1179] 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.

[1180] 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.

[1181] 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.

[1182] [Fourth embodiment]

[1183] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1184] 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.

[1185] 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).

[1186] 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.

[1187] 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.

[1188] 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).

[1189] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1190] 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.

[1191] 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.

[1192] 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.

[1193] 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.

[1194] 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.

[1195] 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."

[1196] ---

[1197] The system of the present invention provides a safe and comfortable in-car environment by predicting the user's potential needs based on data acquired from smart devices and suggesting appropriate actions. This system is composed of the following main components: a sensing module, a data analysis module, and an interaction module.

[1198] Sensing Module

[1199] The device collects data using cameras, microphones, GPS, and vehicle sensors installed in the vehicle. Specifically, the camera captures the user's facial expression data, the microphone records conversation data inside the vehicle, the GPS obtains current location information, and the vehicle sensors collect status data such as vehicle speed, fuel level, and engine condition.

[1200] Data Analysis Module

[1201] The collected data is sent to a server where it is comprehensively analyzed. The server first analyzes facial expression data obtained from the camera to identify the user's emotional state (for example, fatigue, stress, joy, etc.). Next, it performs audio analysis of the conversation data obtained from the microphone to extract information needed to understand the content and context of the user's conversation. It also analyzes GPS data and vehicle status data to evaluate the current vehicle location and status. It then comprehensively evaluates this data and uses an AI model to predict the user's potential needs.

[1202] Interaction Module

[1203] Based on predicted needs, the system suggests appropriate actions to the user. For example, if a user is on a long drive and facial expression analysis reveals signs of fatigue, the system can suggest, by voice, "Wouldn't it be time to take a break?" In addition, for commercial vehicles, the system can obtain the user's business meeting schedule information and, if a delay due to traffic congestion is predicted, notify relevant parties that "the vehicle will be delayed due to traffic congestion."

[1204] Specific examples

[1205] Specific examples of the present invention are shown below.

[1206] Example 1: Proposal for rest breaks in a private car

[1207] Consider a scenario in which a user is on a long drive. The device captures the user's facial expressions using an in-car camera, and the collected facial data is sent to the server. The server analyzes the facial data and determines that the user is tired. The system then suggests by voice, "Shouldn't you take a break now?" This allows the user to reduce fatigue and continue driving safely.

[1208] Example 2: Notification system in commercial vehicles

[1209] Consider a scenario in which a user is traveling for a business meeting. The device uses GPS to determine the user's current location and simultaneously obtains the business meeting schedule information. The server analyzes this data and determines that a traffic jam has occurred. If a delay is predicted, the system notifies the relevant parties by email or message that "there will be a delay due to traffic congestion." This allows timely information sharing with the business partner, ensuring smooth communication.

[1210] As described above, the system of the present invention comprehensively analyzes data from a variety of sensors, accurately predicts the user's potential needs, and suggests appropriate actions, thereby realizing a safe and comfortable in-car environment.

[1211] The processing flow will be explained below.

[1212] ---

[1213] Step 1: Data collection

[1214] The device collects data using cameras, microphones, GPS, and vehicle sensors installed in the vehicle. Specifically, the camera captures the user's facial expressions, the microphone records conversations inside the vehicle, the GPS obtains the current location, and the vehicle sensors collect data such as the vehicle's speed, fuel level, and engine status.

[1215] ---

[1216] Step 2: Send data

[1217] The device sends the collected data (facial expression data, conversation data, location information, vehicle status data) to a server, where it is processed in real time, enabling rapid analysis.

[1218] ---

[1219] Step 3: Analyzing facial expression data

[1220] The server receives the camera data and uses an AI model to analyze the user's facial expressions. The facial expression data identifies the user's emotional state (fatigue, stress, joy, etc.). The results of this analysis are used in the next prediction step.

[1221] ---

[1222] Step 4: Analyzing the conversation data

[1223] The server receives the microphone data and uses a voice analysis algorithm to analyze the conversation, extracting important keywords and context, which are used to understand the user's needs and requests.

[1224] ---

[1225] Step 5: Analyzing location and vehicle data

[1226] The server receives and analyzes GPS data and vehicle sensor data to determine the current vehicle location and status, allowing traffic conditions and vehicle status to be tracked in real time.

[1227] ---

[1228] Step 6: Comprehensive analysis and prediction

[1229] The server comprehensively evaluates facial expression data, conversation data, location information, and vehicle data, and then uses an AI model to predict the user's potential needs. For example, if it determines that the user is tired, it will suggest an appropriate break.

[1230] ---

[1231] Step 7: Select an action

[1232] The server selects the optimal action for the user based on the prediction results, such as suggesting a break to the user or providing traffic information.

[1233] ---

[1234] Step 8: Take Action

[1235] The device receives the action from the server and executes it for the user, such as suggesting "Should we take a break now?" or sending an email to notify relevant parties of a delay.

[1236] ---

[1237] Step 9: Gather feedback

[1238] The device collects user reactions and feedback and sends it to the server, which improves the system's analysis accuracy and the quality of its suggestions.

[1239] ---

[1240] This trend will enable users to continue driving safely and comfortably while receiving appropriate support from AI.

[1241] Example 1

[1242] 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."

[1243] To make the driving environment safer and more comfortable for modern vehicles, systems that monitor the user's condition and vehicle status in real time and suggest appropriate actions based on that information are required. However, conventional systems have had difficulty efficiently collecting and analyzing the user's facial expression data, conversation data, location information, and vehicle status data, and suggesting appropriate actions. Furthermore, it has been difficult to predict the user's potential needs based on the results of advanced analysis.

[1244] 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.

[1245] In this invention, the server includes means for capturing user facial expression data acquired from the smart device, means for capturing user conversation data, means for acquiring location information, means for collecting vehicle status data from vehicle sensors, means for transmitting the data to the server, means for analyzing the data in the server and identifying the user's emotional state, the content of the conversation, and the vehicle's location and status, means for predicting the user's potential needs using a generative AI model based on the analysis results, and means for suggesting appropriate actions to the user based on the predictions. This makes it possible to monitor the user's driving environment in real time and suggest appropriate actions at the right time.

[1246] ---

[1247] A "smart device" is an electronic device used to acquire a user's facial expression data, conversation data, location information, and vehicle status data.

[1248] "Facial expression data" is data about a user's facial expressions captured using a device such as a camera.

[1249] "Conversation data" refers to data about the content of conversations between users in a vehicle, collected using a device such as a microphone.

[1250] "Location Information" is data regarding the current geographic location of a vehicle obtained using a location measuring device such as a GPS.

[1251] "Vehicle sensor" refers generally to sensing devices installed to collect information such as vehicle speed, fuel level, and engine condition.

[1252] The "potential needs of the user" are requests and desires regarding actions and services that the user may require, which are predicted based on the analysis results.

[1253] A "server" is a computer system that receives the collected data, analyzes it, and suggests appropriate actions to the user.

[1254] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to analyze data and predict users' potential needs.

[1255] "Action suggestions" refers to guidelines and advice such as break suggestions and notifications provided to users by the system.

[1256] "Real-time analysis" is an analysis method that can quickly process collected data and obtain results immediately.

[1257] A "commercial vehicle" is a vehicle used for business or commercial travel.

[1258] "Schedule information" is time-related information such as the user's plans and appointments.

[1259] "Delay notification" refers to informing relevant parties when arrival is delayed due to traffic conditions or other reasons.

[1260] ---

[1261] The system of the present invention provides a safe and comfortable in-car environment by predicting the user's potential needs and suggesting appropriate actions based on data acquired from smart devices. This system is composed of the following main components: a sensing module, a data analysis module, and an interaction module.

[1262] Sensing Module

[1263] The device collects data using cameras, microphones, GPS, and vehicle sensors installed in the vehicle. Specifically, the camera captures the user's facial expressions, and the microphone records conversations inside the vehicle. GPS obtains the vehicle's current location, and vehicle sensors collect data such as speed, fuel level, and engine status. This data is collected in real time and sent to a server via batch processing or real-time streaming.

[1264] Data Analysis Module

[1265] The server analyzes the received data to determine the user's emotional state, the content of the conversation, and the vehicle's location and status. Specifically, it performs the following processes:

[1266] 1. The server analyzes the collected image data and identifies the user's emotional state (e.g., fatigue, stress, joy, etc.) from their facial expressions.

[1267] 2. The server converts the voice data into text and analyzes the conversation content and context.

[1268] 3. The server evaluates GPS data and vehicle sensor data to determine the current vehicle location and status.

[1269] Based on these analysis results, the server uses a generative AI model to predict the user's potential needs, for example, if the user's facial expression shows signs of fatigue, it predicts that they should take a break.

[1270] Interaction Module

[1271] Based on predicted needs, the system will suggest appropriate actions to the user. For example, if it determines that a user is tired after a long drive, it will make a voice suggestion saying, "Would you like to take a break now?" In the case of commercial vehicles, the system will analyze the user's business meeting schedule information and current traffic information, and if a delay is predicted, it will send a notification to relevant parties saying, "You will be delayed due to traffic congestion."

[1272] Specific examples

[1273] The following are specific examples of the present invention:

[1274] Example 1: Proposal for rest breaks in a private car

[1275] 1. When the user is on a long drive, the device captures the user's facial expressions using the in-car camera.

[1276] 2. The captured facial expression data is sent to the server.

[1277] 3. The server uses an AI model to analyze whether the user is tired.

[1278] 4. If fatigue is detected, the system will suggest to the user via voice, "Would you like to take a break now?"

[1279] Example 2: Notification system in commercial vehicles

[1280] 1. When a user is traveling for a business meeting, the device uses GPS to obtain the user's current location and collects business meeting schedule information.

[1281] 2. The collected data is sent to the server.

[1282] 3. The server analyzes the traffic data and determines that a traffic jam is occurring.

[1283] 4. If a delay is predicted, the system will notify relevant parties via email or message that "there will be a delay due to traffic congestion."

[1284] Prompt Sentence Examples

[1285] "Please explain a scenario where you want to detect fatigue from facial expression data of a user during a long drive and suggest a break."

[1286] "Describe a scenario in which a user heading to a business meeting is predicted to be delayed based on their current location and traffic information, and relevant parties are notified."

[1287] As described above, the present invention makes it possible to comprehensively analyze data from multiple sensors, accurately predict the potential needs of a user, and make the driving environment for the user safer and more comfortable.

[1288] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1289] ---

[1290] Step 1: Data collection

[1291] Input: Data from in-car cameras, microphones, GPS, and vehicle sensors

[1292] Output: Collected facial expression data, conversation data, location information, vehicle status data

[1293] The device captures the user's facial expressions using a camera installed inside the vehicle, records conversations inside the vehicle using a microphone, and obtains the current location information using GPS. It also collects data such as speed, fuel level, and engine status from vehicle sensors. This data is sensed in real time and sent to a server via batch processing or real-time streaming as needed.

[1294] Step 2: Send data

[1295] Input: Collected facial expression data, conversation data, location information, vehicle status data

[1296] Output: Data sent to the server

[1297] The device sends the collected data to the server periodically or in real time. Camera image data is sent in a fixed batch format, audio data is streamed in real time, and GPS data and vehicle status data are also sent in packets periodically.

[1298] Step 3: Facial Expression Analysis

[1299] Input: Facial expression data sent to the server

[1300] Output: User's emotional state

[1301] The server analyzes the received image data and uses facial recognition technology to identify the user's emotional state from their facial expressions. For example, it uses an image analysis algorithm to determine whether the user is smiling, tired, angry, etc. The analysis results in identifying the user's emotional state.

[1302] Step 4: Audio analysis

[1303] Input: Conversation data sent to the server

[1304] Output: Analyzed conversation

[1305] The server uses speech recognition technology to convert the conversation into text. The server analyzes the collected voice data using speech recognition software and generates text information to understand the content and context of the conversation. The analysis results identify the content and context of the conversation.

[1306] Step 5: Analyze location and vehicle status

[1307] Input: Location information sent to the server, vehicle sensor data

[1308] Output: Current vehicle position and status

[1309] The server analyzes the collected GPS data and vehicle sensor data. It identifies the vehicle's current location from the GPS data and evaluates the vehicle sensor data for speed, fuel level, engine status, etc. As a result of the analysis, it identifies the vehicle's current location and status.

[1310] Step 6: Anticipate potential needs

[1311] Input: Analyzed facial expression data, conversation data, location information, vehicle status data

[1312] Output: User's potential needs

[1313] The server integrates all the analyzed data and uses a generative AI model to predict the user's potential needs. For example, if the user's facial expression shows signs of fatigue, it predicts that a break is necessary. As a result of this analysis, the server identifies the user's potential needs.

[1314] Step 7: Action proposals

[1315] Input: predicted potential user needs

[1316] Output: Suggested action for the user

[1317] The system will suggest appropriate actions to the user based on predicted needs. For example, if it determines that the user is tired after a long drive, it will make a voice suggestion such as, "Wouldn't it be time to take a break?". Also, if there is traffic congestion based on business meeting schedule information, it will send a notification to the relevant parties saying, "You will be delayed due to traffic congestion."

[1318] Through the above processing steps, this system monitors the user's driving environment in real time and suggests appropriate actions at the right time, thereby providing a safe and comfortable in-car environment.

[1319] (Application example 1)

[1320] 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."

[1321] Autonomous vehicles are required to accurately predict passengers' potential needs and provide a safe and comfortable in-car environment. Conventional technologies have difficulty assessing passenger fatigue and stress levels in real time, and lack a means to suggest rest at the appropriate time. Furthermore, there are insufficient methods for sharing information in a timely manner in the event of delays due to long driving times or traffic congestion. Therefore, a system is needed to achieve a higher level of passenger safety and comfort.

[1322] 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.

[1323] In this invention, the server includes means for capturing user facial expression data acquired from a smart device, means for capturing user conversation data, means for acquiring location information, means for collecting vehicle status data from vehicle sensors, means for analyzing the data and predicting the user's potential needs, means for suggesting appropriate actions to the user based on the prediction, means for assessing the user's fatigue state and sending a notification in real time, and means for displaying information via a head-mounted display. This makes it possible to monitor passenger fatigue and stress in real time and suggest breaks at appropriate times. In addition, in the case of commercial vehicles, if a delay is predicted, a notification can be sent by email, allowing relevant parties in a business negotiation to be informed of the estimated arrival time and delay in a timely manner.

[1324] A "smart device" is an electronic device that is equipped with an internet connection and a wide variety of sensors and is capable of acquiring and processing data.

[1325] "Facial expression data" is digital data relating to a user's facial expressions that is captured using an image capture device such as a camera.

[1326] "Conversation data" is audio data relating to the content of a user's speech or conversation, which is acquired using an audio capture device such as a microphone.

[1327] "Location information" is data about the current location of a vehicle or user, obtained using a location measurement device such as a GPS.

[1328] A "vehicle sensor" is a sensor device for collecting status data such as vehicle speed, fuel level, engine status, etc.

[1329] "Real-time" refers to data acquisition and processing occurring immediately, without delay.

[1330] A "head-mounted display" is a display device worn by a user on the head, which displays information within the user's field of vision.

[1331] The "fatigue state" refers to a state that indicates the user's physical or mental fatigue, particularly one that is affected by prolonged activity or stress.

[1332] A "notification" is a message or alert sent to inform a user of important information.

[1333] "Relaxation content" refers to content such as music, video, or guidance provided to relieve the user's fatigue and stress.

[1334] "Business meeting schedule information" is information about the time, location, and participants of business meetings and conferences.

[1335] "Email" means a digital letter or message sent or received over the Internet.

[1336] The system of this invention predicts potential user needs and proposes appropriate actions to provide a safe and comfortable environment in an autonomous vehicle by analyzing data collected from smart devices and related sensors. The main components of the system are a sensing module, a data analysis module, and an interaction module.

[1337] Sensing Module

[1338] The device collects data using cameras, microphones, GPS, and vehicle sensors installed in the vehicle. Specifically, the camera captures the user's facial expression data, the microphone records conversation data in the vehicle, the GPS obtains current location information, and the vehicle sensors collect status data such as vehicle speed and engine condition.

[1339] Data Analysis Module

[1340] The collected data is sent to a server where it is comprehensively analyzed. The server first analyzes facial expression data obtained from the camera to identify the user's emotional state (for example, fatigue, stress, joy, etc.). Next, it performs audio analysis of the conversation data obtained from the microphone to extract information needed to understand the content and context of the user's conversation. It also analyzes GPS data and vehicle status data to evaluate the current vehicle location and status. It then comprehensively evaluates this data and uses an AI model to predict the user's potential needs.

[1341] Interaction Module

[1342] The system suggests appropriate actions to the user based on predicted needs. For example, if a user is on a long drive and facial expression analysis reveals signs of fatigue, the system can suggest audibly, "Shouldn't it be time to take a break?" Information such as traffic congestion and estimated arrival times at the destination can also be displayed via a head-mounted display.

[1343] Example

[1344] Example 1: Fatigue suggestions for long-distance drivers

[1345] The system analyzes video captured by an in-car camera in real time and evaluates the user's level of fatigue based on facial expression data. If fatigue exceeds a certain threshold, the system displays a message on the head-mounted display saying, "You are feeling drowsy. You are 10 minutes away from a rest area." If the user has enabled the email notification option, the system also sends a notification by email.

[1346] Example 2: Schedule management for commercial vehicles

[1347] Based on GPS and business meeting schedule information, the system notifies the user and business meeting participants of delays in the event of traffic congestion. The system displays a message on the head-mounted display saying, "Due to traffic congestion, you will be delayed from the scheduled arrival time," and can be configured to notify relevant parties by email.

[1348] Example prompt

[1349] "Create a head-mounted display application that monitors passenger status in real time and detects fatigue and stress."

[1350] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1351] Step 1:

[1352] Acquiring sensing data

[1353] The device collects data using cameras, microphones, GPS, and vehicle sensors installed inside the vehicle. Inputs include facial expression data from the camera, conversation data from the microphone, location information from GPS, and vehicle status data from vehicle sensors (speed, fuel level, engine status, etc.). After acquiring this data, it sends it to a server.

[1354] Step 2:

[1355] Analysis of facial expression data

[1356] The server analyzes the facial expression data sent from the camera. The input is image data containing the user's facial expressions. The server uses facial landmark detection technology (e.g., the dlib library) to identify the user's emotional state (fatigue, stress, joy, etc.). The output is an evaluation of these emotional states.

[1357] Step 3:

[1358] Conversation data analysis

[1359] The server performs speech analysis on the conversation data sent from the microphone. The input is the user's conversation voice data. This is converted into text data using speech recognition technology, and information needed to understand the content and context is extracted. The output is the text data of the conversation content and the analysis results.

[1360] Step 4:

[1361] Location and vehicle status analysis

[1362] The server analyzes the location information sent from the GPS and data from the vehicle sensors. The input is location information and vehicle status data. This is used to evaluate the current vehicle location and status. The output is detailed information about the vehicle's current location and operating status.

[1363] Step 5:

[1364] Comprehensive evaluation and needs forecast

[1365] The server comprehensively evaluates the facial expression data evaluation results, conversation analysis results, location information, and vehicle status data, and uses an AI model to predict the user's potential needs. The input is data from various analysis results. Based on this, it identifies when the user is tired or has been driving for a long time. The output is a prediction of the user's potential needs and status.

[1366] Step 6:

[1367] Action suggestions and notifications

[1368] The server proposes appropriate actions to the user based on the predicted needs. For example, if fatigue is detected, it generates a voice suggestion such as "Shouldn't you take a break now?". Furthermore, if the user is wearing a head-mounted display, it also provides relaxation content and traffic information to be displayed on the display. The input is the predicted needs, and the output is the action suggestion and notification content.

[1369] Step 7:

[1370] Sending notifications

[1371] The server sends email notifications to the appropriate parties based on the notification options set by the user. For example, if a commercial vehicle is predicted to be delayed, an email is sent to the relevant parties saying, "Due to traffic congestion, the vehicle will be delayed from the scheduled arrival time." The input is the data and email address information for the delay notification, and the output is the email sent.

[1372] 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.

[1373] ---

[1374] The system of the present invention provides a safe and comfortable in-car environment by predicting the user's potential needs based on data acquired from smart devices and suggesting appropriate actions. This system is composed of the following main components: a sensing module, a data analysis module, an interaction module, and an emotion engine.

[1375] Sensing Module

[1376] The device collects data using cameras, microphones, GPS, and vehicle sensors installed in the vehicle. Specifically, the camera captures the user's facial expression data, the microphone records conversation data inside the vehicle, the GPS obtains current location information, and the vehicle sensors collect status data such as vehicle speed, fuel level, and engine condition.

[1377] Data Analysis Module

[1378] The collected data is sent to a server where it is comprehensively analyzed. The server first receives facial expression and conversation data captured by the camera and uses an emotion engine to analyze the user's emotional state. For example, it can identify whether the user is tired, stressed, happy, etc. Next, it receives GPS data and vehicle status data to ascertain the location and condition of the vehicle. It then comprehensively evaluates this data and uses an AI model to predict the user's potential needs.

[1379] Interaction Module

[1380] Based on the prediction results, the system will suggest optimal actions to the user. For example, if the user is on a long drive and facial expression analysis and emotion recognition indicate fatigue, the emotion engine can use voice prompts such as, "Shouldn't it be time to take a break?" The system can also suggest appropriate music or media based on the user's emotional state, making the drive more comfortable.

[1381] Specific examples

[1382] Specific examples of the present invention are given below.

[1383] Example 1: Proposal for rest breaks in a private car

[1384] Consider a scenario in which a user is on a long drive. The device uses an in-car camera to capture the user's facial expression data, and the collected data and conversation data are sent to the server. The server analyzes this data using an emotion engine, and if it determines that the user is tired, the system will suggest by voice, "Shouldn't you take a break now?" This allows the user to reduce fatigue and continue driving safely.

[1385] Example 2: Notification system in commercial vehicles

[1386] Consider a scenario in which a user is traveling for a business meeting. The device uses GPS to determine the user's current location and simultaneously obtains the business meeting schedule information. The server analyzes this data and determines that a traffic jam has occurred. If a delay is predicted, the emotion engine generates an appropriate notification based on the user's emotional state, and notifies relevant parties by email or message that "there will be a delay due to traffic congestion." This allows timely information sharing with business partners, ensuring smooth communication.

[1387] Example 3: Media suggestions based on emotional state

[1388] If the user feels stressed while driving, the device uses a camera and microphone to collect facial expression and speech data. The server analyzes this data with an emotion engine and identifies the user as feeling stressed. Based on the results, the system suggests relaxing music or podcasts to reduce the user's stress.

[1389] As described above, the system of the present invention uses an emotion engine to analyze the user's emotion data and make more accurate predictions and suggestions, thereby realizing a safe and comfortable in-car environment.

[1390] The processing flow will be explained below.

[1391] ---

[1392] Step 1: Data collection

[1393] The device uses a camera installed inside the vehicle to capture the user's facial expressions, a microphone installed inside the vehicle to record conversations, a GPS module to obtain current location information, and vehicle sensors to collect data such as vehicle speed, fuel level, and engine status.

[1394] ---

[1395] Step 2: Send data

[1396] The device transmits the collected data (facial expression data, conversation data, location information, vehicle status data) to the server in real time, allowing the server to receive the data in a timely manner and begin analysis.

[1397] ---

[1398] Step 3: Analyzing facial expression and conversation data

[1399] The server receives the camera and microphone data and analyzes the user's facial expression and speech data using an emotion engine, which determines the user's current emotional state (e.g., fatigue, stress, joy, etc.) from the data.

[1400] ---

[1401] Step 4: Analyzing location and vehicle data

[1402] The server receives GPS data and vehicle sensor data and analyzes it to determine the current vehicle location and status, thereby understanding the user's riding situation and environment in real time.

[1403] ---

[1404] Step 5: Comprehensive analysis and prediction

[1405] The server integrates facial expression data, conversation data, location information, and vehicle data, and uses AI models and an emotion engine to predict the user's potential needs. For example, if it determines that the user is tired, it determines the appropriate action based on the prediction.

[1406] ---

[1407] Step 6: Select an action

[1408] The server then selects the optimal action based on the prediction results. Specifically, if it determines that the user is tired, it will suggest taking a break. It may also recommend media that will help the user relax depending on the user's emotional state.

[1409] ---

[1410] Step 7: Take Action

[1411] The device then executes suggestions based on the action instructions received from the server. For example, it may issue a voice message saying, "Would you like to take a break?" or play appropriate music. In the case of commercial vehicles, it may also notify relevant parties of estimated arrival times or delays via email or message.

[1412] ---

[1413] Step 8: Gather feedback

[1414] The device collects the user's reactions and feedback after making a suggestion and sends it to the server, which can then use the feedback data to improve the accuracy of analysis and the quality of the suggestions.

[1415] ---

[1416] Through this step, the system of the present invention comprehensively analyzes data from various sensors, accurately predicts the user's potential needs, and suggests appropriate actions to provide a safe and comfortable in-car environment.

[1417] Example 2

[1418] 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."

[1419] In conventional in-car environments, it has been difficult to grasp the user's status and needs in real time and provide appropriate suggestions and actions. Furthermore, during long driving periods or in stressful environments, reduced safety and comfort have become an issue. The present invention aims to solve these problems and provide a safer and more comfortable in-car environment.

[1420] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1421] In this invention, the server includes means for capturing user facial expression data acquired from the smart device, means for capturing user conversation data, means for acquiring location information, means for collecting vehicle situation data, means including an emotion engine for analyzing the data and estimating the user's emotional state, means for comprehensively evaluating the data and predicting the user's potential needs using a generative AI model, and means for suggesting appropriate actions to the user based on the prediction, thereby enabling the server to grasp the user's state in real time and realize a safe and comfortable in-car environment.

[1422] A "smart device" is an electronic device that has a camera, microphone, GPS, and various sensors that are used to collect data about a user.

[1423] "Facial Expression Data" refers to information that captures and stores in digital form a user's facial expressions.

[1424] "Conversation data" refers to information that has been recorded and stored in digital format, including the user's speaking voice and conversation content.

[1425] "Location information" is data indicating the current geographic coordinates of a vehicle obtained by GPS.

[1426] "Vehicle status data" is data relating to the operating and mechanical conditions of a vehicle, such as speed, fuel level, engine condition, etc.

[1427] An "emotion engine" is software or an algorithm that analyzes a user's facial expressions and conversation data to estimate their emotional state.

[1428] A "generative AI model" is an artificial intelligence model used to predict users' potential needs, and is a system that analyzes multiple data sets to learn patterns.

[1429] "Action suggestions" are suggestions of actions or options to the user based on the analysis results, and are done through methods such as voice guidance or screen displays.

[1430] The system of the present invention provides a safe and comfortable in-car environment by predicting the user's potential needs based on data acquired from smart devices and suggesting appropriate actions. This system is composed of the following main components: a sensing module, a data analysis module, an interaction module, and an emotion engine.

[1431] Sensing Module

[1432] The device collects data using cameras, microphones, GPS, and vehicle sensors installed in the vehicle. Specifically, each sensor operates as follows:

[1433] The camera captures the user's facial expression data, for example, to detect whether the user is smiling or looking stern.

[1434] The microphone records conversation data inside the car, including the content and tone of what the user is saying.

[1435] GPS obtains current location information and tracks the vehicle's route and geographical location.

[1436] Vehicle sensors collect data on vehicle speed, fuel level, engine status, etc. For example, they can provide low fuel warnings and detect abnormal engine conditions.

[1437] Data Analysis Module

[1438] The collected data is sent to a server where it is analyzed and processed in the following steps:

[1439] The facial expression data and conversation data are received, and an emotion engine is used to analyze the user's emotional state, for example, determining that "the user is tired" or "the user is stressed."

[1440] It also receives GPS data and vehicle status data and analyzes the location and vehicle condition, such as "You are currently on the highway" or "You are low on fuel."

[1441] All data is integrated and a generative AI model is used to predict the user's potential needs, such as "the user needs a break" or "the user needs relaxing music."

[1442] Interaction Module

[1443] The system will suggest the best action to the user based on the prediction results from the server. Specific examples include:

[1444] If the system determines that the user has been driving for a long time and is tired, it will suggest through voice, "Would you like to take a break now?"

[1445] If the user is feeling stressed, it suggests relaxing music or podcasts.

[1446] If a traffic jam occurs and a user is likely to be late for a business meeting, the system notifies relevant parties by email or message that "the user will be late due to traffic congestion."

[1447] Specific examples

[1448] Specific examples of the present invention are given below.

[1449] Example 1: Proposal for rest breaks in a private car

[1450] Consider a scenario where a user is driving for a long time. The device captures the user's facial expression data using an in-car camera and transmits it to a server along with conversation data.

[1451] The server analyzes this data using an emotion engine, and if it determines that the user is tired, the system will suggest audibly, "Wouldn't it be time to take a break?"

[1452] This reduces the user's fatigue and allows them to continue driving safely.

[1453] Example 2: Notification system in commercial vehicles

[1454] Imagine a scenario where a user is traveling for a business meeting. The device uses GPS to determine the user's current location and simultaneously obtains the business meeting schedule information.

[1455] The server analyzes this data and determines if a traffic jam is occurring. If a delay is predicted, the system notifies relevant parties by email or message that "the trip will be delayed due to traffic congestion."

[1456] This allows information to be shared with business partners in a timely manner, ensuring smooth communication.

[1457] Example 3: Media suggestions based on emotional state

[1458] If the user feels stressed while driving, the device will use the camera and microphone to collect the user's facial expression and speech data.

[1459] The server analyzes this data using an emotion engine and determines whether the user is feeling stressed.

[1460] Based on the results, the system suggests relaxing music and podcasts to help reduce stress for users.

[1461] Prompt Sentence Examples

[1462] "The user is on a long drive. The device collects facial expression and conversation data from the in-car camera and sends it to the server. The server uses its emotion engine to analyze that the user is tired. What will the system suggest?"

[1463] "A user is traveling to a business meeting. The device uses GPS to obtain the user's current location and business meeting schedule information. The server analyzes traffic conditions and predicts a delay. How will the system notify the relevant parties?"

[1464] The above is a detailed description of the embodiment of the invention. This system accurately analyzes the user's emotional data and makes appropriate suggestions to create a safe and comfortable in-car environment.

[1465] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1466] Detailed explanation of the processing steps

[1467] Step 1:

[1468] The device collects user data using sensors installed inside the vehicle.

[1469] Input: User facial expressions, conversational audio, vehicle status, and location information.

[1470] Data processing: Facial expressions are captured with a camera and converted into digital data, conversations are recorded with a microphone, location is recorded with GPS, and driving status is acquired from vehicle sensors. Facial expression data is saved as still images or videos, and conversation data is saved as an audio file.

[1471] Output: Digital facial expression data, conversation data, location information, and vehicle status data.

[1472] Step 2:

[1473] The terminal transmits the collected data to the server.

[1474] Input: Digital facial expression data, conversation data, location information, and vehicle status data.

[1475] Data calculation: Various data is packetized according to the communication protocol and sent to the server via the network.

[1476] Output: Data packets sent to the server.

[1477] Step 3:

[1478] The server receives the transmitted data and begins analyzing it.

[1479] Input: Facial expression data, conversation data, location information, and vehicle state data received as data packets.

[1480] Data processing: Reconstructing received data and returning it to its individual data format. For example, image data can be restored as facial expression data, and audio files can be restored as conversation data.

[1481] Output: Reconstructed data (facial expression data, conversation data, location information, vehicle state data).

[1482] Step 4:

[1483] The server uses an emotion engine to analyze the user's emotional state.

[1484] Input: Facial expression and speech data.

[1485] Data Computation: Analyzes facial expression data and applies emotion recognition algorithms to estimate the user's emotional state (e.g., joy, anger, sadness, happiness, stress, fatigue). Speech data is also subjected to speech recognition and emotion analysis to extract emotions from the content and tone of spoken words.

[1486] Output: User's emotional state data.

[1487] Step 5:

[1488] The server analyzes the location information and vehicle status data.

[1489] Input: GPS location and vehicle status data (speed, fuel level, engine status).

[1490] Data calculation: Location information is compared with map data to confirm the vehicle's current location and driving route. Vehicle status is analyzed by analyzing speed and fuel level to understand driving conditions.

[1491] Output: Current location and vehicle status analysis results.

[1492] Step 6:

[1493] The server integrates all collected data and uses a generative AI model to predict the user's potential needs.

[1494] Input: User emotional state data, current location information, and vehicle state analysis results.

[1495] Data calculation: Generative AI models are used to comprehensively evaluate various data and predict future user needs and behavior.

[1496] Output: User's potential needs (e.g. need for rest, suitable type of music, etc.).

[1497] Step 7:

[1498] The system suggests appropriate actions to the user based on the prediction results.

[1499] Input: The user's potential needs.

[1500] Data Calculation: Selecting appropriate actions based on user needs and generating interactions with the user.

[1501] Output: A voice announcement, a screen display, or a notification message to the user. For example, saying "Would you like to take a break?", recommending relaxing music, or notifying relevant parties when a delay is expected.

[1502] The above is the processing flow of this system's program. Each step incorporates specific operations, and the final output is obtained through a series of data processing and calculations based on the input data.

[1503] (Application example 2)

[1504] 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."

[1505] Conventional vehicle security systems often lack speed and accuracy in detecting suspicious individuals and issuing warnings. They also lack the ability to predict users' potential needs, making it difficult to provide a comfortable in-vehicle environment while improving vehicle safety. Therefore, there is a need for systems that can detect human emotions and behaviors in real time in conjunction with more advanced data analysis technology and provide appropriate action.

[1506] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1507] In this invention, the server includes means for capturing facial expression data of a user acquired from a smart device, means for capturing conversation data of the user, means for acquiring location information, means for collecting vehicle status data from vehicle sensors, means for analyzing the data and predicting potential needs of the user, means for suggesting appropriate actions to the user based on the prediction, means for detecting suspicious behavior and issuing a warning, and means for transmitting an alert when suspicious behavior is detected. This makes it possible to enhance safety in the vehicle, quickly detect suspicious behavior and notify the user, and provide a comfortable in-vehicle environment.

[1508] A "smart device" is a device that is equipped with sensors such as a camera, microphone, and GPS and can acquire data.

[1509] "Facial expression data" is facial expression information of a user captured by a camera.

[1510] "Conversation data" is voice information recorded inside the vehicle by a microphone.

[1511] "Location Information" means geographic location data obtained from a GPS.

[1512] "Vehicle sensors" are various sensors for detecting the state of the vehicle, including speed, fuel level, engine status, etc.

[1513] "Data analysis" is the process of comprehensively analyzing various types of acquired data and extracting useful information.

[1514] "Potential needs" are requests or desires that the user has not explicitly stated but that may be needed.

[1515] "Appropriate actions" are specific actions or countermeasures suggested to the user based on the analysis results.

[1516] "Suspicious behavior" is a suspicious pattern of behavior that differs from normal behavior and may pose a security risk.

[1517] An "alert" is a warning message issued when suspicious activity is detected.

[1518] An "alert" is a message sent to notify users and other relevant parties when suspicious behavior or anomalies are detected.

[1519] The system of the present invention analyzes data acquired from smart devices in real time, detects suspicious behavior, and proposes and executes appropriate actions to improve safety and comfort in vehicles. The present invention includes the following main components and their respective operating procedures:

[1520] Sensing Module

[1521] The device collects data using cameras, microphones, GPS, and vehicle sensors installed in the vehicle. Specifically, the camera captures the user's facial expression data, the microphone records conversation data inside the vehicle, the GPS obtains current location information, and the vehicle sensors collect status data such as vehicle speed, fuel level, and engine condition.

[1522] Data Analysis Module

[1523] The server receives the various collected data and performs a comprehensive analysis. First, the server receives facial expression data and conversation data captured by the camera and uses an emotion engine to analyze the user's emotional state. For example, it identifies whether the user is tired, stressed, happy, etc. Next, it receives GPS data and vehicle status data to understand the location and condition of the vehicle. It then comprehensively evaluates this data and uses an AI model to predict the user's potential needs.

[1524] Interaction Module

[1525] Based on the prediction results, the system suggests optimal actions to the user and executes them at the appropriate time. For example, if the user is on a long drive and facial expression analysis and emotion recognition indicate fatigue, the system will suggest audibly, "Shouldn't it be time to take a break?" The system can also suggest appropriate music or media based on the user's emotional state, making the drive more comfortable.

[1526] Security Features

[1527] The system includes a means for detecting suspicious behavior and issuing a warning, as well as a means for sending an alert when suspicious behavior is detected. It uses cameras and various sensors to monitor the environment around the vehicle, and if, for example, a suspicious person approaches the vehicle, it will sound an alarm and send an alert to the owner, thereby improving vehicle safety.

[1528] Hardware and software used

[1529] The system uses cameras (e.g., USB cameras), microphones, GPS sensors, and a variety of other sensors in the vehicle. Data analysis is performed using an AI model and emotion engine running on a server. Specifically, it uses Python and OpenCV libraries to perform real-time person detection and send alerts.

[1530] Specific examples

[1531] 1. Proposal for resting in private cars

[1532] Consider a scenario in which a user is on a long drive. The device uses an in-car camera to capture the user's facial expression data, and the collected data and conversation data are sent to the server. The server analyzes this data using an emotion engine, and if it determines that the user is tired, the system will suggest by voice, "Shouldn't you take a break now?" This allows the user to reduce fatigue and continue driving safely.

[1533] 2. Notification systems for commercial vehicles

[1534] Consider a scenario in which a user is traveling for a business meeting. The device uses GPS to determine the user's current location and simultaneously obtains the business meeting schedule information. The server analyzes this data and determines that a traffic jam has occurred. If a delay is predicted, the emotion engine generates an appropriate notification based on the user's emotional state, and notifies relevant parties by email or message that "there will be a delay due to traffic congestion." This allows timely information sharing with business partners, ensuring smooth communication.

[1535] 3. Media suggestions based on emotional state

[1536] If the user feels stressed while driving, the device uses a camera and microphone to collect facial expression and speech data. The server analyzes this data with an emotion engine and identifies the user as feeling stressed. Based on the results, the system suggests relaxing music or podcasts to reduce the user's stress.

[1537] 4. Suspicious person detection and alert sending

[1538] If a suspicious person approaches the vehicle, the camera captures their behavior and analyzes it in real time. If the server determines that the behavior is suspicious, the system will sound an alarm and simultaneously send an alert to the owner. This allows for a quick response and improves vehicle safety.

[1539] Example prompts to input to a generative AI model:

[1540] "Write a Python program that detects suspicious people approaching a vehicle and sends an alert. The program should capture camera footage in real time and send an alert to a specified email address as soon as a person is detected."

[1541] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1542] Step 1:

[1543] The terminal acquires the data.

[1544] It uses a camera to capture the user's facial expression data and a microphone to collect conversation data. It obtains location information from GPS and collects data such as speed, fuel level, and engine status from vehicle sensors. The inputs here are camera images, audio data, location information, and vehicle status data, which are collected in real time by each sensor. The output is the collected raw data.

[1545] Step 2:

[1546] The terminal transmits the collected data to the server.

[1547] The collected raw data is preprocessed, compressed or encrypted, and sent to the server. The input here is the raw data obtained in step 1, which is preprocessed to ensure the reliability and security of the data. The output is a notification of completion of transmission.

[1548] Step 3:

[1549] The server analyzes the received data.

[1550] Facial expression data from the camera is analyzed using image processing algorithms (e.g., OpenCV) to extract the user's emotional state. Voice data is analyzed using natural language processing (NLP) technology to understand the content of the conversation. Location information and vehicle state data are used to understand the user's movement status and the vehicle's condition. The input is preprocessed data, and the output is the analysis results.

[1551] Step 4:

[1552] The server predicts the user's potential needs based on the data analysis results.

[1553] The AI ​​model is used to comprehensively evaluate the user's emotional state, conversation content, location information, and vehicle status data to predict whether the user has a specific need. In this step, a generative AI model is used, with the input being the analysis results and the output being the user's potential need data.

[1554] Step 5:

[1555] The server suggests appropriate actions based on your needs.

[1556] Based on the predicted needs, the system suggests the user to take a break, change the music, or take other actions. The input is the user's potential needs data, and the output is the suggestion content. The suggestion is notified to the user through audio output or a display screen.

[1557] Step 6:

[1558] The terminal monitors the area around the vehicle.

[1559] The environment around the vehicle is monitored using cameras and various sensors. Camera footage is analyzed to detect suspicious behavior (e.g., a person approaching the vehicle). The input is video data around the vehicle, and the output is the detection result of suspicious behavior.

[1560] Step 7:

[1561] The server takes action when suspicious behavior is detected.

[1562] If suspicious behavior is detected, countermeasures are implemented, such as sounding an alarm or sending an alert email to the owner. The input is the result of detecting suspicious behavior, and the output is the specific response action, such as sounding an alarm or sending an alert email.

[1563] Example prompts to input to a generative AI model:

[1564] "Write a Python program that detects suspicious people approaching a vehicle and sends an alert. The program should capture camera footage in real time and send an alert to a specified email address as soon as a person is detected."

[1565] 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.

[1566] 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.

[1567] 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.

[1568] 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.

[1569] 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.

[1570] 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.

[1571] 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).

[1572] 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.

[1573] 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."

[1574] 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.

[1575] 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).

[1576] 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.

[1577] 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.

[1578] 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.

[1579] 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.

[1580] 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.

[1581] 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.

[1582] 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.

[1583] 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.

[1584] 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.

[1585] 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.

[1586] The following is further disclosed regarding the above embodiment.

[1587] (Claim 1)

[1588] means for capturing facial expression data of a user obtained from a smart device;

[1589] means for capturing user conversation data;

[1590] A means for acquiring location information;

[1591] means for collecting vehicle condition data from vehicle sensors;

[1592] A means for analyzing the data and predicting potential needs of users;

[1593] means for suggesting appropriate actions to a user based on said prediction;

[1594] A system including:

[1595] (Claim 2)

[1596] A means for analyzing user facial expression data, conversation data, location information, and vehicle status data in real time;

[1597] A means for suggesting a break to the user based on the analysis results;

[1598] 10. The system of claim 1, comprising:

[1599] (Claim 3)

[1600] In the commercial vehicle, a means for analyzing the user's business negotiation schedule information and the data and notifying parties involved in the business negotiation of an expected arrival date or a delay;

[1601] 10. The system of claim 1, comprising:

[1602] "Example 1"

[1603] ---

[1604] (Claim 1)

[1605] means for capturing facial expression data of a user obtained from a smart device;

[1606] means for capturing user conversation data;

[1607] A means for acquiring location information;

[1608] means for collecting vehicle condition data from vehicle sensors;

[1609] means for transmitting said data to a server;

[1610] means for analyzing the data at the server to determine the user's emotional state, the content of the conversation, and the location and status of the vehicle;

[1611] A means of predicting users' potential needs using a generative AI model based on the analysis results;

[1612] means for suggesting appropriate actions to a user based on said prediction;

[1613] A system including:

[1614] (Claim 2)

[1615] The system is equipped with a server that analyzes the user's facial expression data, conversation data, location information, and vehicle status data in real time.

[1616] A means for suggesting a break to the user based on the analysis results;

[1617] 10. The system of claim 1, comprising:

[1618] (Claim 3)

[1619] In the commercial vehicle, means for analyzing the user's schedule information and the data, and notifying relevant parties of the estimated arrival time or delay if a delay is predicted;

[1620] 10. The system of claim 1, comprising:

[1621] "Application Example 1"

[1622] (Claim 1)

[1623] means for capturing facial expression data of a user obtained from a smart device;

[1624] means for capturing user conversation data;

[1625] A means for acquiring location information;

[1626] means for collecting vehicle condition data from vehicle sensors;

[1627] A means for analyzing the data and predicting potential needs of users;

[1628] means for suggesting appropriate actions to a user based on said prediction;

[1629] means for assessing a user's fatigue state and sending notifications in real time;

[1630] means for displaying information via a head mounted display;

[1631] A system including:

[1632] (Claim 2)

[1633] A means for analyzing user facial expression data, conversation data, location information, and vehicle status data in real time;

[1634] A means for suggesting a break to the user based on the analysis results;

[1635] A means for suggesting a break or presenting relaxation content when the user's fatigue state exceeds a certain threshold;

[1636] 10. The system of claim 1, comprising:

[1637] (Claim 3)

[1638] In the commercial vehicle, a means for analyzing the user's business negotiation schedule information and the data and notifying parties involved in the business negotiation of an expected arrival date or a delay;

[1639] A means of sending notifications via email if the user has enabled the email notification option;

[1640] 10. The system of claim 1, comprising:

[1641] "Example 2: Combining Emotion Engines"

[1642] (Claim 1)

[1643] means for capturing facial expression data of a user obtained from a smart device;

[1644] means for capturing user conversation data;

[1645] A means for acquiring location information;

[1646] means for collecting vehicle status data;

[1647] means including an emotion engine for analyzing said data and inferring an emotional state of a user;

[1648] A means for comprehensively evaluating the data and predicting potential needs of users using a generative AI model;

[1649] means for suggesting appropriate actions to a user based on said prediction;

[1650] A system including:

[1651] (Claim 2)

[1652] A means for analyzing user facial expression data, conversation data, location information, and vehicle status data in real time;

[1653] A means for suggesting a break to the user based on the analysis results;

[1654] 10. The system of claim 1, comprising:

[1655] (Claim 3)

[1656] means for analyzing said data in conjunction with user schedule information in a commercial vehicle and notifying schedule participants of an estimated arrival time or delay;

[1657] 10. The system of claim 1, comprising:

[1658] "Application example 2 when combining emotion engines"

[1659] (Claim 1)

[1660] means for capturing facial expression data of a user obtained from a smart device;

[1661] means for capturing user conversation data;

[1662] A means for acquiring location information;

[1663] means for collecting vehicle condition data from vehicle sensors;

[1664] A means for analyzing the data and predicting potential needs of users;

[1665] means for suggesting appropriate actions to a user based on said prediction;

[1666] A means of detecting and alerting on suspicious activity;

[1667] a means of sending alerts when suspicious activity is detected;

[1668] A system including:

[1669] (Claim 2)

[1670] A means for analyzing user facial expression data, conversation data, location information, and vehicle status data in real time;

[1671] A means for suggesting a break to the user based on the analysis results;

[1672] means for analyzing the video data from the camera to detect suspicious individuals;

[1673] A means for sending a predetermined message when a suspicious person is detected;

[1674] 10. The system of claim 1, comprising:

[1675] (Claim 3)

[1676] In the commercial vehicle, a means for analyzing the user's business negotiation schedule information and the data and notifying parties involved in the business negotiation of an expected arrival date or a delay;

[1677] A means for detecting suspicious individuals and taking appropriate action, including an alert sending / warning function;

[1678] 10. The system of claim 1, comprising: [Explanation of symbols]

[1679] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for capturing facial expression data of a user obtained from a smart device; means for capturing user conversation data; A means for acquiring location information; means for collecting vehicle condition data from vehicle sensors; A means for analyzing the data and predicting potential needs of users; means for suggesting appropriate actions to a user based on said prediction; A system including:

2. A means for analyzing user facial expression data, conversation data, location information, and vehicle status data in real time; A means for suggesting a break to the user based on the analysis results; The system of claim 1 , comprising:

3. In the commercial vehicle, a means for analyzing the user's business negotiation schedule information and the data and notifying parties involved in the business negotiation of an expected arrival date or a delay; The system of claim 1 , comprising:

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