system

Abstract

We provide the system. [Solution] A means of collecting data using multiple sensors and cameras to monitor the movements of a person requiring care in real time, A means for processing collected data and detecting falls and abnormal movements, A means of sending a notification and an alert to emergency contacts when an anomaly is detected, A means including a natural language processing unit for generating natural conversations with care recipients, A means of operating home appliances based on voice commands entered by a person requiring care, A means of immediately notifying pre-set emergency contacts in the event of an emergency, A system that includes this.

Description

Technical Field

[0004]

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the living environment of care recipients, it is difficult to improve the situation where appropriate care is insufficient, especially to enable single elderly people to live independently with peace of mind. In addition, it is necessary to solve the problems that it is impossible to respond promptly when a fall or abnormal behavior occurs, and there are limited ways to eliminate daily loneliness and mental anxiety.

Means for Solving the Problems

[0005] This invention provides a system that uses multiple sensors and cameras to collect data in real time to monitor the movements of care recipients, and processes this data to detect falls and abnormal movements. When an abnormality is detected, it sends a notification and an alert to emergency contacts. Furthermore, it is equipped with a natural language processing unit to generate natural conversations with care recipients and has the function of operating home appliances based on voice-input commands. This enables a rapid response in emergencies, reduces feelings of loneliness through daily conversations, and supports a safe, secure, and independent life.

[0006] A "sensor" is a device that measures physical or chemical properties and detects changes in them.

[0007] A "camera" is a device used to record or transmit images, and is particularly used to acquire visual data.

[0008] "Data" refers to a form of information representation that can be processed by a computer.

[0009] "Falling over" is the phenomenon in which an object or living being loses its balance and falls to the ground, changing its position.

[0010] "Abnormal behavior" refers to movements or actions that deviate from normal behavioral patterns, often indicating an unexpected situation.

[0011] "Notification" is the process of information transmission carried out to convey specific information.

[0012] An "alert" is a warning or notification issued to draw attention to something.

[0013] "Natural language processing" is a technology that uses computers to process, understand, and generate human language.

[0014] A "voice command" is an instruction spoken by a human being, which is input for a machine or software to recognize and process.

[0015] "Home appliances" refer to electrical products used within a household and are devices that are operated to provide functions according to convenience.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [[ID=​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiment for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (for example, hard disks), or magnetic tapes, and the like.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention relates to a robotic system for supporting the lives of those requiring care, and is implemented using a terminal equipped with multiple sensors and a camera. This terminal can constantly monitor the movements of the person requiring care and collect data in real time. The data is transmitted to a server and analyzed through image processing and abnormal movement detection algorithms. When the server detects abnormal movement, it immediately sends a notification to emergency contacts.

[0038] Furthermore, the device is equipped with a natural language processing unit, enabling smooth voice interaction with those requiring care. User voice input is converted to text, processed, and then output as an appropriate response. This helps to alleviate feelings of loneliness in daily life.

[0039] Furthermore, users can control household appliances using voice commands. The terminal generates appropriate control signals based on the voice recognition results and sends commands to the corresponding appliances.

[0040] For example, if a user gives a voice command such as "Turn on the lights," the terminal converts the voice into text and sends signals to control home appliances such as air conditioners and lights. All data exchange and command transmission are based on secure communication protocols, protecting user privacy.

[0041] Thus, the system according to the present invention provides comprehensive support to enable those requiring care to live independently safely and comfortably, and also aims to reduce the burden of caregiving.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The device uses built-in multi-sensors and a 360-degree camera to collect data in real time in order to monitor the movements of the person requiring care.

[0045] Step 2:

[0046] The device compresses the collected data and sends it to the server via a secure communication protocol.

[0047] Step 3:

[0048] The server applies image processing algorithms to the received data to analyze the movements of the person requiring care.

[0049] Step 4:

[0050] The server detects abnormal behavior, such as a fall or movement that deviates from normal, and determines whether an emergency alert is necessary.

[0051] Step 5:

[0052] If the server detects an anomaly, it will send an alert notification to the designated emergency contact.

[0053] Step 6:

[0054] When a user wants to have a natural conversation, the device receives voice input and converts it into text data.

[0055] Step 7:

[0056] The terminal passes the converted text to a natural language processing module, which then generates an appropriate response.

[0057] Step 8:

[0058] The terminal outputs the generated response as speech using a speech synthesis module and responds to the user.

[0059] Step 9:

[0060] When a user operates a home appliance using voice commands, the terminal analyzes the commands and generates corresponding control signals.

[0061] Step 10:

[0062] The terminal transmits control signals to home appliances and operates the appliances based on user requests.

[0063] (Example 1)

[0064] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0065] To enhance the safety and comfort of the living environment for those requiring care, real-time monitoring of their movements and health status is necessary. Furthermore, early detection and prompt notification of abnormal movements, as well as smooth communication to alleviate feelings of isolation among those requiring care, are required. Conventional systems have been unable to adequately address these challenges, highlighting the need for a more comprehensive and effective support system.

[0066] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0067] In this invention, the server includes means for collecting information using multiple detection and imaging devices for monitoring the movements and health information of care recipients in real time, means for encrypting the collected information and transmitting it to a remote analysis device via communication, and means for processing the collected information and detecting falls and abnormal behavior. This ensures safety in the living environment of care recipients and enables prompt response and communication support.

[0068] A "person requiring care" refers to an individual who needs continuous and appropriate support or care in their daily life.

[0069] A "detection device" refers to an instrument used to measure physical or physiological parameters and acquire that data.

[0070] An "imaging device" refers to equipment used to record video or image data.

[0071] "Information" refers to the data and records of events that are acquired, including motion data, health data, and audio data.

[0072] "Encryption" refers to the process of transforming information to securely protect data and prevent unauthorized access.

[0073] "Communication" refers to the activity of transmitting data from one point to another.

[0074] A "remote analysis device" refers to a device used to receive and analyze data from a remote location.

[0075] A "natural language processing system" refers to a device or algorithm that enables a machine to understand and process human language.

[0076] "Household appliances" refer to electrical equipment and appliances used in daily life, including lighting, televisions, and air conditioners.

[0077] "Dialogue fluency" refers to the degree to which a conversation flows naturally without interruption.

[0078] To implement this invention, a system is constructed with the aim of improving the safety and comfort of the living environment for those requiring care. The specific form of this system is shown below.

[0079] The server is located remotely and receives and analyzes information transmitted from terminals. To achieve advanced data processing, the server is recommended to use machine learning libraries. Specifically, Deep Learning libraries are used as general-purpose libraries for analyzing motion and video information. Using these libraries, falls and abnormal movements of care recipients can be detected with high accuracy. Furthermore, to ensure privacy and protect data confidentiality, the server applies encryption technology to the collected information.

[0080] The terminal is installed near the person requiring care. By attaching multiple sensors and imaging devices, it acquires information on movement, activity, and health in real time. This allows the terminal to continuously transmit necessary information to the server, ensuring stable communication. It utilizes speech recognition technology and has an automated voice dialogue function. A natural language processing library is used for speech analysis to achieve smooth dialogue.

[0081] Users can input commands by voice. For example, if a user gives a command such as "Turn on the lights," the terminal interprets this command and sends a signal to a household appliance to control it.

[0082] As a concrete example, if a care recipient says "Turn on the TV" to the device, it converts the voice into text, generates the appropriate signal, and operates the TV. An example of an input prompt for the generating AI model might be, "Please explain in detail the functions of the robot care system for the elderly. In particular, please describe in detail the voice recognition and abnormal motion detection functions."

[0083] Thus, the system according to the present invention can provide comprehensive support to enable those requiring care to live a safe and comfortable life, and can reduce the burden of caregiving.

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] The terminal uses multiple sensors and imaging devices to collect movement and health information of the person requiring care. Inputs include the person's body temperature, heart rate, location data, and movement images. To accurately collect this data and transmit it to the server in real time, individual data from each sensor is integrated into a single data packet. Additionally, location sensors are used to determine the person's current location and collect movement history data.

[0087] Step 2:

[0088] The server receives data sent from the terminal and begins analysis. The input is an integrated data packet sent from the terminal. The server processes the data using machine learning algorithms, particularly analyzing behavioral patterns to detect abnormal behavior. For example, if an abnormal behavioral pattern such as a fall is detected, the server identifies the timing and location of its occurrence. Deep learning technology is used in processing this data to perform anomaly detection with high accuracy.

[0089] Step 3:

[0090] The server immediately sends a notification to registered emergency contacts if an anomaly is detected. The input is the anomaly detection alert data. The server generates an alert message and sends the notification to relevant parties via SMS or email through a secure communication protocol. This notification includes the location and time the anomaly occurred, and the estimated risk level.

[0091] Step 4:

[0092] The terminal receives voice commands from the person requiring care and converts the voice into text. The input is the voice signal of the person requiring care. The terminal uses a voice analysis engine to convert the voice into a string and performs natural language processing to understand the command. For example, if the command "Turn on the TV" is entered, the terminal generates the corresponding command signal.

[0093] Step 5:

[0094] The terminal controls household appliances based on the results of voice analysis. The input is a control command generated by the voice analysis. The terminal sends a signal to the corresponding household appliance to execute its action. For example, in the case of lighting, in response to the command "Turn on the light," a signal to turn on the light is generated and sent from the terminal, thereby operating the light.

[0095] (Application Example 1)

[0096] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0097] To ensure worker safety and improve work efficiency within a factory, it is necessary to monitor operations in real time, detect abnormalities and hazards early, and take appropriate action. However, currently, many factories have not implemented such advanced monitoring and interaction systems, leading to the risk of accidents due to human error or negligence. This invention aims to solve these problems and provide a safer and more efficient work environment.

[0098] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0099] In this invention, the server includes means for collecting information using multiple detection and imaging devices for monitoring human movements in real time, means for processing the collected information and detecting dangerous actions, and means for issuing warnings and sending alerts to administrators when an abnormality is detected. This makes it possible to ensure the safety of workers in the factory while streamlining the operation of equipment based on voice-input instructions.

[0100] A "detection device" is a sensor or device used to detect the movements and states of people or objects in real time.

[0101] A "recording device" refers to a camera or similar device used to record still images or videos.

[0102] "Information" refers to all data acquired through detection devices and imaging devices.

[0103] "Processing" refers to the manipulation of data to analyze collected information and derive useful judgments and actions.

[0104] A "dangerous action" is an action that could potentially pose a risk to normal work or behavior.

[0105] A "warning" is a notification or alert issued when there is an abnormality in the operation or state of something.

[0106] "Administrator" refers to a person or group responsible for monitoring, operating, and troubleshooting a system.

[0107] "Voice-inputted instructions" refer to commands or orders spoken by workers or other personnel via a microphone or similar device.

[0108] "Equipment" refers to devices or equipment used to perform specific tasks or operations.

[0109] The system that implements this application is intended to ensure safety and improve work efficiency within a factory. The main components of the system include multiple detection devices, cameras, a server, terminals, and a control device that responds to voice commands.

[0110] The server plays a central role in data collection and analysis. It receives and processes real-time data transmitted from detection and imaging devices. It uses TENSORFLOW® to analyze motion and image data, detecting abnormal or dangerous behavior. Furthermore, it uses ROS (Robot Operating System) to control robots and equipment.

[0111] The terminal functions as an interface with the worker. It receives voice input from the worker, converts it to text using Rasa, and generates an appropriate response. The generated response is played back as audio, and further generates equipment control signals as needed.

[0112] Users can operate equipment by issuing voice commands in natural language. This interface allows for efficient operation of equipment and systems even when workers are unavailable. Furthermore, voice alerts enable quick response in the event of detected anomalies or hazards.

[0113] As a concrete example, consider a forklift operating in a factory. The server monitors the movements of workers near the forklift in real time and immediately sends an alert if a worker approaches incorrectly. When a worker instructs the forklift to "carry the next material," the terminal recognizes this and sends a command to the forklift.

[0114] Examples of prompts to input into a generative AI model:

[0115] "Design a robotic system to improve safety management and efficiency within the factory. It should use sensors to monitor worker movements and predict hazards. Include an interface that allows machine operation via voice commands."

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] The server receives data in real time from multiple detection and imaging devices. The input consists of motion data and image data, which are fed into a model pre-trained with TensorFlow to predict abnormal or dangerous behavior. The output is the result of determining whether the behavior is normal or abnormal.

[0119] Step 2:

[0120] Based on the TensorFlow analysis results, the server generates a warning message if an anomaly is detected and sends an alert to the administrator or the corresponding terminal. The input is the anomaly detection result from the previous step, and the output is the specific warning content and notification. This is sent as an email or voice message.

[0121] Step 3:

[0122] The user inputs voice commands into the terminal. The input is a voice command from the worker, which the terminal uses Rasa to convert into text format and parse as an executable command. The output is the parsed text command.

[0123] Step 4:

[0124] The terminal sends control signals to the relevant equipment based on commands parsed by Rasa. Inputs are text-based commands, and outputs are operational commands for the equipment. Examples include commands to operate a forklift or to shut down equipment.

[0125] Step 5:

[0126] The device transmits voice feedback to the user. The device generates feedback regarding the operation results and whether or not there are any abnormalities, and informs the user of this feedback via voice. The input is the operation results of the device and abnormality notifications from the server, and the output is voice-based feedback.

[0127] This series of steps enables users to monitor the factory environment in real time and perform efficient tasks using voice commands.

[0128] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0129] This invention relates to a robotic system that supports the daily lives of those requiring care. It incorporates an emotion engine to recognize emotions and provide appropriate responses based on the results. The terminal is equipped with multiple sensors, a 360-degree camera, a microphone, and a speaker to monitor the movements and voice of the person requiring care in real time. The emotion engine then analyzes the user's voice and facial expressions to evaluate their emotional state.

[0130] The server receives sensor and camera data, as well as audio data, transmitted from the terminal and processes it in real time. The emotion engine uses this data to detect changes in the user's mood. As a result, the robot is programmed to provide considerate responses that are in line with the user's emotions, in addition to normal voice responses.

[0131] For example, if signs of anxiety or stress are detected in the user's voice during a normal conversation, the device will make a voice suggestion such as, "Would you like to take a short break?" Also, if anxiety is detected through facial expression analysis, the device will ask in a gentle tone, "Is there anything I can help you with?" If the abnormal emotional state exceeds a threshold, the server will send an alert to a pre-configured emergency contact, enabling a quick response.

[0132] This system makes it possible to reduce not only the physical but also the psychological burden on those requiring care, creating a more comfortable and safe living environment. The introduction of the emotional engine is key to providing a more humane dialogue experience and enabling flexible advice and support tailored to individual needs.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] The device collects voice and facial expression data of the person requiring care in real time using multiple sensors and a camera. At this stage, the voice is captured by a microphone.

[0136] Step 2:

[0137] The device temporarily stores the collected data and performs preprocessing to pass it to the emotion engine. The audio data undergoes noise reduction processing.

[0138] Step 3:

[0139] The emotion engine within the device analyzes voice and facial expression data to determine the user's emotional state. The algorithms used here utilize feature extraction and pattern recognition technologies.

[0140] Step 4:

[0141] The server receives emotional state information sent from the terminal and updates the current conversation context. This allows for a deeper understanding of the interaction with the user.

[0142] Step 5:

[0143] The device generates an appropriate response based on the results determined by the emotion engine via a natural language processing unit and responds to the user in voice using a speech synthesis module.

[0144] Step 6:

[0145] If the device detects an abnormal emotional state, it will take measures to send an alert to emergency contacts via the server. At the same time, a description of the situation and location information will also be sent.

[0146] Step 7:

[0147] If the user gives new instructions by voice, the device will accept voice again and repeat the process from step 1. This ensures continuous monitoring and appropriate interaction.

[0148] (Example 2)

[0149] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0150] Improving the quality of life for those requiring care, and especially reducing their psychological burden, is crucial. However, conventional support systems have struggled to accurately recognize users' emotional states and to provide natural, humane dialogue that responds to those emotions. Furthermore, there has been a lack of effective mechanisms for quickly detecting abnormal emotional states and providing emergency responses.

[0151] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0152] In this invention, the server includes means for using multiple detection devices to monitor the movements and voice of the person requiring care in real time and collect data; means for analyzing and evaluating the emotional state based on the user's voice and facial expressions using an emotion engine; and means for generating an appropriate response according to the analyzed emotional state and communicating it to the user through a voice output device. This enables flexible and appropriate support for the psychological health of the person requiring care. Furthermore, by utilizing a generative AI model, natural and human-like dialogue can be achieved, and a rapid response can be made in emergencies.

[0153] A "detection device" is a device that includes multiple sensors and cameras used to acquire the movements and voices of a person requiring care in real time.

[0154] An "emotion engine" is a software component that analyzes a user's voice and facial expression data to evaluate their emotional state.

[0155] A "generative AI model" is an artificial intelligence model used to generate natural and human-like responses based on the results of user emotion analysis.

[0156] A "voice output device" is a device that provides the response generated by the server to the user as voice.

[0157] An "emergency contact" is a contact person who has been pre-configured to receive notifications in case of an emergency.

[0158] An "abnormal state" refers to a situation where a user's emotions exceed the normal range, requiring special attention.

[0159] This invention provides a device for supporting the lives of people requiring care, comprising a terminal equipped with multiple detection devices, a server for emotion analysis, and a generative AI model that enables natural and flexible dialogue.

[0160] The device monitors movement and sound in real time. Equipped with multiple sensors, a 360-degree camera, and a microphone, it has the capability to capture the actions and voice of the person requiring care. For example, it can record the user's voice and facial expressions during everyday conversations and transmit that data to a server.

[0161] The server receives data sent from the terminal and performs data analysis. Using an emotion engine, it analyzes voice and facial expression data to evaluate the user's emotional state. In this process, the server utilizes a generative AI model to generate natural-sounding dialogue based on the analysis results. Examples of prompts could include, "How would a gentle voice assistant respond when the user is tired?" or "Please provide examples of considerate dialogue for someone feeling anxious."

[0162] The generated response is delivered to the user through the terminal's voice output device. For example, if the user feels anxious, a gentle voice message will say, "Is there anything I can do to help?" This system reduces the psychological burden on those requiring care while enabling a rapid emergency response. As a result, it can provide a safer and more comfortable living environment for those requiring care.

[0163] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0164] Step 1:

[0165] The device collects the movements and voice of the person requiring care in real time.

[0166] The system uses multiple sensors, a 360-degree camera, and a microphone as inputs. These devices detect the actions, tone of voice, and facial expressions of the person requiring care. The output includes motion data, audio data, and video data. Specifically, the microphone records voice, and the camera captures facial expressions.

[0167] Step 2:

[0168] The device sends the collected data to the server.

[0169] The input consists of motion data, audio data, and video data acquired in Step 1. This data is transmitted to the server via a low-latency network. The output is the data arriving at the server and ready for analysis. Specifically, the data is compressed into a predetermined format and transferred to the server via the network.

[0170] Step 3:

[0171] The server analyzes the data it receives.

[0172] The input consists of motion data, audio data, and video data sent from the terminal. The server uses an emotion engine to analyze this data and evaluate the user's emotional state. The output is the evaluation result of the user's emotional state. Specifically, it analyzes the tone of voice using speech recognition technology and reads facial expressions using image analysis technology.

[0173] Step 4:

[0174] The server generates an appropriate response based on the emotional state.

[0175] The evaluation results of the emotional state obtained in step 3 are used as input. A generative AI model is used to generate a natural language response through a prompt sentence based on the evaluation results. The output is a appropriately structured response sentence. Specifically, the generative AI model takes the emotional evaluation into consideration and generates sentences using natural and human-like phrasing.

[0176] Step 5:

[0177] The terminal notifies the user of the response it has generated.

[0178] The system receives the response text generated in step 4 as input. This response is played back as audio through the audio output device. The output is the voice response being communicated to the care recipient. Specifically, the speaker delivers the generated voice response to the user.

[0179] (Application Example 2)

[0180] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0181] In modern factory work, emotional states such as stress and anxiety can affect worker efficiency and safety, posing a significant challenge. Conventional systems have struggled to evaluate emotional states in the work environment in real time and address them appropriately, leading to concerns about decreased work efficiency and safety. This invention aims to solve these problems by providing a system that uses emotion recognition technology to understand workers' emotional states and provide appropriate feedback.

[0182] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0183] In this invention, the server includes a computing device for emotion recognition, means for generating commands to provide breaks or advice according to the worker's emotional state, and communication means for notifying a safety manager when a specific emotional state exceeds a threshold. This makes it possible to evaluate the emotional state of workers in the work environment in real time and respond quickly as needed.

[0184] A "detector" is a general term for sensor devices used to detect the actions or states of an object in real time.

[0185] "Shooting equipment" refers to camera equipment such as 360-degree cameras, which are devices used to acquire video information of the surroundings.

[0186] "Information" refers to data and observation results acquired by sensors and imaging devices, and is the subject of processing and analysis.

[0187] A "computational device" is a computer device used to perform various analyses and evaluate emotional states based on collected information.

[0188] "Dialogue" refers to the act of verbal communication that takes place between care recipients and workers, and is a form of communication that allows for the exchange of opinions in a natural flow.

[0189] "Electrical appliances" is a general term for electrical devices used in daily life that can be operated by voice commands.

[0190] "Communication methods" is a general term for network technologies and equipment used to transmit alerts and notifications to remote locations.

[0191] A "command" is a message or instruction that the system generates in response to an emotional state and conveys to the worker as advice or a warning.

[0192] An "alert" is an alert or notification issued by a system when it detects an anomaly, and it is information that prompts emergency response.

[0193] The system of this invention is designed to monitor the emotional state of workers in the work environment, enabling them to perform their duties efficiently and safely. The system collects motion information and image information of workers using sensors and cameras. The collected information is transmitted to a server and analyzed by a computing unit that performs emotion recognition. Based on this analysis, the system evaluates the worker's emotional state in real time and generates commands appropriate to the situation. Specifically, if stress or anxiety is detected, a command prompting a break is sent to the worker via voice or visual message. If a specific emotional state exceeds a threshold, a notification is sent to the safety manager via communication means, enabling a quick response.

[0194] The hardware uses smart glasses (e.g., Google Glass®), and the software uses an AI model (e.g., Hume AI) responsible for emotion recognition. A server integrates this information and provides feedback to the worker. The AI ​​model takes voice and image data as input, analyzes emotional states, and generates appropriate feedback.

[0195] As a concrete example, consider a situation where a worker in a workshop begins to feel stressed one day. This state is detected by smart glasses, and a command is sent to the worker saying, "You need a short break." This information is also sent to the safety manager, who takes swift action to support the worker. An example of a prompt sentence to input into the generating AI model is, "Please tell me how to analyze emotional changes during work."

[0196] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0197] Step 1:

[0198] The terminal uses sensors and cameras mounted on smart glasses to collect real-time information on the worker's movements and images. The input for this step is raw data from the sensors and cameras, while the output is formatted data for transmission to the server. The terminal appropriately formats this data and sends it to the server via a secure communication protocol.

[0199] Step 2:

[0200] The server inputs the motion information and image information received from the terminal into an emotion recognition AI model to analyze the worker's emotional state. The input for this step is formatted data sent from the terminal, and the output is an evaluation result indicating the emotional state. The server uses the AI ​​model to extract features of the emotional state and performs data calculations to determine signs of stress and anxiety.

[0201] Step 3:

[0202] The server generates feedback for the worker based on the analysis results. Specifically, if the worker is feeling stressed, it generates a message such as, "You need a short break." The input for this step is the analysis results from the emotion recognition AI model, and the output is a specific feedback message for the worker. The server uses appropriate natural language processing techniques to create text-based feedback and sends it to the terminal.

[0203] Step 4:

[0204] The terminal conveys feedback messages sent from the server to the worker either verbally or visually. The input for this step is the feedback message from the server, and the output is the notification to the worker. The terminal converts text messages into speech using speech synthesis technology, providing information in a way that is easily understandable to the worker.

[0205] Step 5:

[0206] The server sends an alert to the safety manager if a specific emotional state exceeds a pre-set threshold. The input for this step is the result of an AI model's evaluation of the emotional state, and the output is an alert notification to the safety manager. Depending on the urgency, the server sends the alert via email or text message to prompt a quick response.

[0207] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0208] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0209] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0210] [Second Embodiment]

[0211] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0212] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0213] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0214] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0215] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0216] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0217] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0218] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0219] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0220] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0221] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0222] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0223] This invention relates to a robotic system for supporting the lives of those requiring care, and is implemented using a terminal equipped with multiple sensors and a camera. This terminal can constantly monitor the movements of the person requiring care and collect data in real time. The data is transmitted to a server and analyzed through image processing and abnormal movement detection algorithms. When the server detects abnormal movement, it immediately sends a notification to emergency contacts.

[0224] Furthermore, the device is equipped with a natural language processing unit, enabling smooth voice interaction with those requiring care. User voice input is converted to text, processed, and then output as an appropriate response. This helps to alleviate feelings of loneliness in daily life.

[0225] Furthermore, users can control household appliances using voice commands. The terminal generates appropriate control signals based on the voice recognition results and sends commands to the corresponding appliances.

[0226] For example, if a user gives a voice command such as "Turn on the lights," the terminal converts the voice into text and sends signals to control home appliances such as air conditioners and lights. All data exchange and command transmission are based on secure communication protocols, protecting user privacy.

[0227] Thus, the system according to the present invention provides comprehensive support to enable those requiring care to live independently safely and comfortably, and also aims to reduce the burden of caregiving.

[0228] The following describes the processing flow.

[0229] Step 1:

[0230] The device uses built-in multi-sensors and a 360-degree camera to collect data in real time in order to monitor the movements of the person requiring care.

[0231] Step 2:

[0232] The device compresses the collected data and sends it to the server via a secure communication protocol.

[0233] Step 3:

[0234] The server applies image processing algorithms to the received data to analyze the movements of the person requiring care.

[0235] Step 4:

[0236] The server detects abnormal behavior, such as a fall or movement that deviates from normal, and determines whether an emergency alert is necessary.

[0237] Step 5:

[0238] If the server detects an anomaly, it will send an alert notification to the designated emergency contact.

[0239] Step 6:

[0240] When a user wants to have a natural conversation, the device receives voice input and converts it into text data.

[0241] Step 7:

[0242] The terminal passes the converted text to a natural language processing module, which then generates an appropriate response.

[0243] Step 8:

[0244] The terminal outputs the generated response as speech using a speech synthesis module and responds to the user.

[0245] Step 9:

[0246] When a user operates a home appliance using voice commands, the terminal analyzes the commands and generates corresponding control signals.

[0247] Step 10:

[0248] The terminal transmits control signals to home appliances and operates the appliances based on user requests.

[0249] (Example 1)

[0250] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0251] To enhance the safety and comfort of the living environment for those requiring care, real-time monitoring of their movements and health status is necessary. Furthermore, early detection and prompt notification of abnormal movements, as well as smooth communication to alleviate feelings of isolation among those requiring care, are required. Conventional systems have been unable to adequately address these challenges, highlighting the need for a more comprehensive and effective support system.

[0252] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0253] In this invention, the server includes means for collecting information using multiple detection and imaging devices for monitoring the movements and health information of care recipients in real time, means for encrypting the collected information and transmitting it to a remote analysis device via communication, and means for processing the collected information and detecting falls and abnormal behavior. This ensures safety in the living environment of care recipients and enables prompt response and communication support.

[0254] A "person requiring care" refers to an individual who needs continuous and appropriate support or care in their daily life.

[0255] A "detection device" refers to an instrument used to measure physical or physiological parameters and acquire that data.

[0256] An "imaging device" refers to equipment used to record video or image data.

[0257] "Information" refers to the data and records of events that are acquired, including motion data, health data, and audio data.

[0258] "Encryption" refers to the process of transforming information to securely protect data and prevent unauthorized access.

[0259] "Communication" refers to the activity of transmitting data from one point to another.

[0260] A "remote analysis device" refers to a device used to receive and analyze data from a remote location.

[0261] A "natural language processing system" refers to a device or algorithm that enables a machine to understand and process human language.

[0262] "Household appliances" refer to electrical equipment and appliances used in daily life, including lighting, televisions, and air conditioners.

[0263] "Dialogue fluency" refers to the degree to which a conversation flows naturally without interruption.

[0264] To implement this invention, a system is constructed with the aim of improving the safety and comfort of the living environment for those requiring care. The specific form of this system is shown below.

[0265] The server is located remotely and receives and analyzes information transmitted from terminals. To achieve advanced data processing, the server is recommended to use machine learning libraries. Specifically, Deep Learning libraries are used as general-purpose libraries for analyzing motion and video information. Using these libraries, falls and abnormal movements of care recipients can be detected with high accuracy. Furthermore, to ensure privacy and protect data confidentiality, the server applies encryption technology to the collected information.

[0266] The terminal is installed near the person requiring care. By attaching multiple sensors and imaging devices, it acquires information on movement, activity, and health in real time. This allows the terminal to continuously transmit necessary information to the server, ensuring stable communication. It utilizes speech recognition technology and has an automated voice dialogue function. A natural language processing library is used for speech analysis to achieve smooth dialogue.

[0267] Users can input commands by voice. For example, if a user gives a command such as "Turn on the lights," the terminal interprets this command and sends a signal to a household appliance to control it.

[0268] As a concrete example, if a care recipient says "Turn on the TV" to the device, it converts the voice into text, generates the appropriate signal, and operates the TV. An example of an input prompt for the generating AI model might be, "Please explain in detail the functions of the robot care system for the elderly. In particular, please describe in detail the voice recognition and abnormal motion detection functions."

[0269] Thus, the system according to the present invention can provide comprehensive support to enable those requiring care to live a safe and comfortable life, and can reduce the burden of caregiving.

[0270] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0271] Step 1:

[0272] The terminal uses multiple sensors and imaging devices to collect movement and health information of the person requiring care. Inputs include the person's body temperature, heart rate, location data, and movement images. To accurately collect this data and transmit it to the server in real time, individual data from each sensor is integrated into a single data packet. Additionally, location sensors are used to determine the person's current location and collect movement history data.

[0273] Step 2:

[0274] The server receives data sent from the terminal and begins analysis. The input is an integrated data packet sent from the terminal. The server processes the data using machine learning algorithms, particularly analyzing behavioral patterns to detect abnormal behavior. For example, if an abnormal behavioral pattern such as a fall is detected, the server identifies the timing and location of its occurrence. Deep learning technology is used in processing this data to perform anomaly detection with high accuracy.

[0275] Step 3:

[0276] The server immediately sends a notification to registered emergency contacts if an anomaly is detected. The input is the anomaly detection alert data. The server generates an alert message and sends the notification to relevant parties via SMS or email through a secure communication protocol. This notification includes the location and time the anomaly occurred, and the estimated risk level.

[0277] Step 4:

[0278] The terminal receives voice commands from the person requiring care and converts the voice into text. The input is the voice signal of the person requiring care. The terminal uses a voice analysis engine to convert the voice into a string and performs natural language processing to understand the command. For example, if the command "Turn on the TV" is entered, the terminal generates the corresponding command signal.

[0279] Step 5:

[0280] The terminal controls household appliances based on the results of voice analysis. The input is a control command generated by the voice analysis. The terminal sends a signal to the corresponding household appliance to execute its action. For example, in the case of lighting, in response to the command "Turn on the light," a signal to turn on the light is generated and sent from the terminal, thereby operating the light.

[0281] (Application Example 1)

[0282] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0283] In order to ensure the safety of workers in the factory and improve work efficiency, it is necessary to monitor operations in real time, detect abnormalities and dangers at an early stage, and take appropriate actions. However, at present, such advanced monitoring and interaction systems have not been introduced in many factories, and there is a risk of accidents due to human errors and carelessness. The purpose of this invention is to solve these problems and provide a safer and more efficient working environment.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0285] In this invention, the server includes means for collecting information using a plurality of detection devices and imaging devices for monitoring human actions in real time, means for processing the collected information and detecting dangerous actions, and means for transmitting a warning and sending an alert to the administrator when an abnormality is detected. As a result, while ensuring the safety of workers in the factory, it is possible to improve the efficiency of operating equipment based on instructions input by voice.

[0286] The "detection device" is a sensor or device for sensing the actions and states of people and objects in real time.

[0287] The "imaging device" is a camera or similar device for recording still images and moving images.

[0288] The "information" refers to all data obtained through the detection device and the imaging device.

[0289] The "processing" is a data operation for analyzing the collected information and deriving useful judgments and actions.

[0290] A "dangerous action" is an action that could potentially pose a risk to normal work or behavior.

[0291] A "warning" is a notification or alert issued when there is an abnormality in the operation or state of something.

[0292] "Administrator" refers to a person or group responsible for monitoring, operating, and troubleshooting a system.

[0293] "Voice-inputted instructions" refer to commands or orders spoken by workers or other personnel via a microphone or similar device.

[0294] "Equipment" refers to devices or equipment used to perform specific tasks or operations.

[0295] The system that implements this application is intended to ensure safety and improve work efficiency within a factory. The main components of the system include multiple detection devices, cameras, a server, terminals, and a control device that responds to voice commands.

[0296] The server plays a central role in data collection and analysis. It receives and processes real-time data transmitted from detection and imaging devices. It uses TensorFlow to analyze motion and image data, detecting abnormal or dangerous behavior. Furthermore, it uses ROS (Robot Operating System) to control robots and equipment.

[0297] The terminal functions as an interface with the worker. It receives voice input from the worker, converts it to text using Rasa, and generates an appropriate response. The generated response is played back as audio, and further generates equipment control signals as needed.

[0298] Users can operate equipment by issuing voice commands in natural language. This interface allows for efficient operation of equipment and systems even when workers are unavailable. Furthermore, voice alerts enable quick response in the event of detected anomalies or hazards.

[0299] As a specific example, consider the case where a forklift installed in a factory is in motion. The server monitors the movements of workers near the forklift in real time and issues an alert immediately if there is an accidental approach. When a worker instructs the forklift to "carry the next material," the terminal recognizes this and sends a command to the forklift.

[0300] Example of a prompt sentence to be input into the generative AI model:

[0301] "Please design a robot system for enhancing safety management and efficiency within a factory. Monitor the movements of workers with sensors and predict risks. Include an interface that enables machine operation via voice commands."

[0302] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0303] Step 1:

[0304] The server receives data in real time from a plurality of detection devices and imaging devices. The inputs are motion data and image data, which are input into a model pre-trained with TensorFlow to predict abnormal and dangerous operations. The output is a judgment result as to whether the operation is normal or abnormal.

[0305] Step 2:

[0306] Based on the analysis result of TensorFlow, when the server detects an abnormality, it generates a warning message and sends an alert to the administrator or the corresponding terminal. The input is the abnormality judgment result from the previous step, and the output is the specific warning content or notification. This is sent as an email or a voice message.

[0307] Step 3:

[0308] The user inputs voice commands into the terminal. The input is a voice command from the worker, which the terminal uses Rasa to convert into text format and parse as an executable command. The output is the parsed text command.

[0309] Step 4:

[0310] The terminal sends control signals to the relevant equipment based on commands parsed by Rasa. Inputs are text-based commands, and outputs are operational commands for the equipment. Examples include commands to operate a forklift or to shut down equipment.

[0311] Step 5:

[0312] The device transmits voice feedback to the user. The device generates feedback regarding the operation results and whether or not there are any abnormalities, and informs the user of this feedback via voice. The input is the operation results of the device and abnormality notifications from the server, and the output is voice-based feedback.

[0313] This series of steps enables users to monitor the factory environment in real time and perform efficient tasks using voice commands.

[0314] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0315] This invention relates to a robotic system that supports the daily lives of those requiring care. It incorporates an emotion engine to recognize emotions and provide appropriate responses based on the results. The terminal is equipped with multiple sensors, a 360-degree camera, a microphone, and a speaker to monitor the movements and voice of the person requiring care in real time. The emotion engine then analyzes the user's voice and facial expressions to evaluate their emotional state.

[0316] The server receives sensor and camera data, as well as audio data, transmitted from the terminal and processes it in real time. The emotion engine uses this data to detect changes in the user's mood. As a result, the robot is programmed to provide considerate responses that are in line with the user's emotions, in addition to normal voice responses.

[0317] For example, if signs of anxiety or stress are detected in the user's voice during a normal conversation, the device will make a voice suggestion such as, "Would you like to take a short break?" Also, if anxiety is detected through facial expression analysis, the device will ask in a gentle tone, "Is there anything I can help you with?" If the abnormal emotional state exceeds a threshold, the server will send an alert to a pre-configured emergency contact, enabling a quick response.

[0318] This system makes it possible to reduce not only the physical but also the psychological burden on those requiring care, creating a more comfortable and safe living environment. The introduction of the emotional engine is key to providing a more humane dialogue experience and enabling flexible advice and support tailored to individual needs.

[0319] The following describes the processing flow.

[0320] Step 1:

[0321] The device collects voice and facial expression data of the person requiring care in real time using multiple sensors and a camera. At this stage, the voice is captured by a microphone.

[0322] Step 2:

[0323] The device temporarily stores the collected data and performs preprocessing to pass it to the emotion engine. The audio data undergoes noise reduction processing.

[0324] Step 3:

[0325] The emotion engine within the device analyzes voice and facial expression data to determine the user's emotional state. The algorithms used here utilize feature extraction and pattern recognition technologies.

[0326] Step 4:

[0327] The server receives emotional state information sent from the terminal and updates the current conversation context. This allows for a deeper understanding of the interaction with the user.

[0328] Step 5:

[0329] The device generates an appropriate response based on the results determined by the emotion engine via a natural language processing unit and responds to the user in voice using a speech synthesis module.

[0330] Step 6:

[0331] If the device detects an abnormal emotional state, it will take measures to send an alert to emergency contacts via the server. At the same time, a description of the situation and location information will also be sent.

[0332] Step 7:

[0333] If the user gives new instructions by voice, the device will accept voice again and repeat the process from step 1. This ensures continuous monitoring and appropriate interaction.

[0334] (Example 2)

[0335] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0336] Improving the quality of life for those requiring care, and especially reducing their psychological burden, is crucial. However, conventional support systems have struggled to accurately recognize users' emotional states and to provide natural, humane dialogue that responds to those emotions. Furthermore, there has been a lack of effective mechanisms for quickly detecting abnormal emotional states and providing emergency responses.

[0337] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0338] In this invention, the server includes means for using multiple detection devices to monitor the movements and voice of the person requiring care in real time and collect data; means for analyzing and evaluating the emotional state based on the user's voice and facial expressions using an emotion engine; and means for generating an appropriate response according to the analyzed emotional state and communicating it to the user through a voice output device. This enables flexible and appropriate support for the psychological health of the person requiring care. Furthermore, by utilizing a generative AI model, natural and human-like dialogue can be achieved, and a rapid response can be made in emergencies.

[0339] A "detection device" is a device that includes multiple sensors and cameras used to acquire the movements and voices of a person requiring care in real time.

[0340] An "emotion engine" is a software component that analyzes a user's voice and facial expression data to evaluate their emotional state.

[0341] A "generative AI model" is an artificial intelligence model used to generate natural and human-like responses based on the results of user emotion analysis.

[0342] A "voice output device" is a device that provides the response generated by the server to the user as voice.

[0343] An "emergency contact" is a contact person who has been pre-configured to receive notifications in case of an emergency.

[0344] An "abnormal state" refers to a situation where a user's emotions exceed the normal range, requiring special attention.

[0345] This invention provides a device for supporting the lives of people requiring care, comprising a terminal equipped with multiple detection devices, a server for emotion analysis, and a generative AI model that enables natural and flexible dialogue.

[0346] The device monitors movement and sound in real time. Equipped with multiple sensors, a 360-degree camera, and a microphone, it has the capability to capture the actions and voice of the person requiring care. For example, it can record the user's voice and facial expressions during everyday conversations and transmit that data to a server.

[0347] The server receives data sent from the terminal and performs data analysis. Using an emotion engine, it analyzes voice and facial expression data to evaluate the user's emotional state. In this process, the server utilizes a generative AI model to generate natural-sounding dialogue based on the analysis results. Examples of prompts could include, "How would a gentle voice assistant respond when the user is tired?" or "Please provide examples of considerate dialogue for someone feeling anxious."

[0348] The generated response is delivered to the user through the terminal's voice output device. For example, if the user feels anxious, a gentle voice message will say, "Is there anything I can do to help?" This system reduces the psychological burden on those requiring care while enabling a rapid emergency response. As a result, it can provide a safer and more comfortable living environment for those requiring care.

[0349] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0350] Step 1:

[0351] The device collects the movements and voice of the person requiring care in real time.

[0352] The system uses multiple sensors, a 360-degree camera, and a microphone as inputs. These devices detect the actions, tone of voice, and facial expressions of the person requiring care. The output includes motion data, audio data, and video data. Specifically, the microphone records voice, and the camera captures facial expressions.

[0353] Step 2:

[0354] The device sends the collected data to the server.

[0355] The input consists of motion data, audio data, and video data acquired in Step 1. This data is transmitted to the server via a low-latency network. The output is the data arriving at the server and ready for analysis. Specifically, the data is compressed into a predetermined format and transferred to the server via the network.

[0356] Step 3:

[0357] The server analyzes the data it receives.

[0358] The input consists of motion data, audio data, and video data sent from the terminal. The server uses an emotion engine to analyze this data and evaluate the user's emotional state. The output is the evaluation result of the user's emotional state. Specifically, it analyzes the tone of voice using speech recognition technology and reads facial expressions using image analysis technology.

[0359] Step 4:

[0360] The server generates an appropriate response based on the emotional state.

[0361] The evaluation results of the emotional state obtained in step 3 are used as input. A generative AI model is used to generate a natural language response through a prompt sentence based on the evaluation results. The output is a appropriately structured response sentence. Specifically, the generative AI model takes the emotional evaluation into consideration and generates sentences using natural and human-like phrasing.

[0362] Step 5:

[0363] The terminal notifies the user of the response it has generated.

[0364] The system receives the response text generated in step 4 as input. This response is played back as audio through the audio output device. The output is the voice response being communicated to the care recipient. Specifically, the speaker delivers the generated voice response to the user.

[0365] (Application Example 2)

[0366] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0367] In modern factory work, emotional states such as stress and anxiety can affect worker efficiency and safety, posing a significant challenge. Conventional systems have struggled to evaluate emotional states in the work environment in real time and address them appropriately, leading to concerns about decreased work efficiency and safety. This invention aims to solve these problems by providing a system that uses emotion recognition technology to understand workers' emotional states and provide appropriate feedback.

[0368] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0369] In this invention, the server includes a computing device for emotion recognition, means for generating commands to provide breaks or advice according to the worker's emotional state, and communication means for notifying a safety manager when a specific emotional state exceeds a threshold. This makes it possible to evaluate the emotional state of workers in the work environment in real time and respond quickly as needed.

[0370] A "detector" is a general term for sensor devices used to detect the actions or states of an object in real time.

[0371] "Shooting equipment" refers to camera equipment such as 360-degree cameras, which are devices used to acquire video information of the surroundings.

[0372] "Information" refers to data and observation results acquired by sensors and imaging devices, and is the subject of processing and analysis.

[0373] A "computational device" is a computer device used to perform various analyses and evaluate emotional states based on collected information.

[0374] "Dialogue" refers to the act of verbal communication that takes place between care recipients and workers, and is a form of communication that allows for the exchange of opinions in a natural flow.

[0375] "Electrical appliances" is a general term for electrical devices used in daily life that can be operated by voice commands.

[0376] "Communication methods" is a general term for network technologies and equipment used to transmit alerts and notifications to remote locations.

[0377] A "command" is a message or instruction that the system generates in response to an emotional state and conveys to the worker as advice or a warning.

[0378] An "alert" is an alert or notification issued by a system when it detects an anomaly, and it is information that prompts emergency response.

[0379] The system of this invention is designed to monitor the emotional state of workers in the work environment, enabling them to perform their duties efficiently and safely. The system collects motion information and image information of workers using sensors and cameras. The collected information is transmitted to a server and analyzed by a computing unit that performs emotion recognition. Based on this analysis, the system evaluates the worker's emotional state in real time and generates commands appropriate to the situation. Specifically, if stress or anxiety is detected, a command prompting a break is sent to the worker via voice or visual message. If a specific emotional state exceeds a threshold, a notification is sent to the safety manager via communication means, enabling a quick response.

[0380] The hardware uses smart glasses (e.g., Google Glass), and the software uses an AI model (e.g., Hume AI) responsible for emotion recognition. A server integrates this information and provides feedback to the worker. The AI ​​model takes voice and image data as input, analyzes emotional states, and generates appropriate feedback.

[0381] As a concrete example, consider a situation where a worker in a workshop begins to feel stressed one day. This state is detected by smart glasses, and a command is sent to the worker saying, "You need a short break." This information is also sent to the safety manager, who takes swift action to support the worker. An example of a prompt sentence to input into the generating AI model is, "Please tell me how to analyze emotional changes during work."

[0382] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0383] Step 1:

[0384] The terminal uses sensors and cameras mounted on smart glasses to collect real-time information on the worker's movements and images. The input for this step is raw data from the sensors and cameras, while the output is formatted data for transmission to the server. The terminal appropriately formats this data and sends it to the server via a secure communication protocol.

[0385] Step 2:

[0386] The server inputs the motion information and image information received from the terminal into an emotion recognition AI model to analyze the worker's emotional state. The input for this step is formatted data sent from the terminal, and the output is an evaluation result indicating the emotional state. The server uses the AI ​​model to extract features of the emotional state and performs data calculations to determine signs of stress and anxiety.

[0387] Step 3:

[0388] The server generates feedback for the worker based on the analysis results. Specifically, if the worker is feeling stressed, it generates a message such as, "You need a short break." The input for this step is the analysis results from the emotion recognition AI model, and the output is a specific feedback message for the worker. The server uses appropriate natural language processing techniques to create text-based feedback and sends it to the terminal.

[0389] Step 4:

[0390] The terminal conveys feedback messages sent from the server to the worker either verbally or visually. The input for this step is the feedback message from the server, and the output is the notification to the worker. The terminal converts text messages into speech using speech synthesis technology, providing information in a way that is easily understandable to the worker.

[0391] Step 5:

[0392] The server sends an alert to the safety manager if a specific emotional state exceeds a pre-set threshold. The input for this step is the result of an AI model's evaluation of the emotional state, and the output is an alert notification to the safety manager. Depending on the urgency, the server sends the alert via email or text message to prompt a quick response.

[0393] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0394] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0395] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0396] [Third Embodiment]

[0397] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0398] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0399] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0400] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0401] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0402] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0403] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0404] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0405] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0406] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0407] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0408] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0409] This invention relates to a robotic system for supporting the lives of those requiring care, and is implemented using a terminal equipped with multiple sensors and a camera. This terminal can constantly monitor the movements of the person requiring care and collect data in real time. The data is transmitted to a server and analyzed through image processing and abnormal movement detection algorithms. When the server detects abnormal movement, it immediately sends a notification to emergency contacts.

[0410] Furthermore, the device is equipped with a natural language processing unit, enabling smooth voice interaction with those requiring care. User voice input is converted to text, processed, and then output as an appropriate response. This helps to alleviate feelings of loneliness in daily life.

[0411] Furthermore, users can control household appliances using voice commands. The terminal generates appropriate control signals based on the voice recognition results and sends commands to the corresponding appliances.

[0412] For example, if a user gives a voice command such as "Turn on the lights," the terminal converts the voice into text and sends signals to control home appliances such as air conditioners and lights. All data exchange and command transmission are based on secure communication protocols, protecting user privacy.

[0413] Thus, the system according to the present invention provides comprehensive support to enable those requiring care to live independently safely and comfortably, and also aims to reduce the burden of caregiving.

[0414] The following describes the processing flow.

[0415] Step 1:

[0416] The device uses built-in multi-sensors and a 360-degree camera to collect data in real time in order to monitor the movements of the person requiring care.

[0417] Step 2:

[0418] The device compresses the collected data and sends it to the server via a secure communication protocol.

[0419] Step 3:

[0420] The server applies image processing algorithms to the received data to analyze the movements of the person requiring care.

[0421] Step 4:

[0422] The server detects abnormal behavior, such as a fall or movement that deviates from normal, and determines whether an emergency alert is necessary.

[0423] Step 5:

[0424] If the server detects an anomaly, it will send an alert notification to the designated emergency contact.

[0425] Step 6:

[0426] When a user wants to have a natural conversation, the device receives voice input and converts it into text data.

[0427] Step 7:

[0428] The terminal passes the converted text to a natural language processing module, which then generates an appropriate response.

[0429] Step 8:

[0430] The terminal outputs the generated response as speech using a speech synthesis module and responds to the user.

[0431] Step 9:

[0432] When a user operates a home appliance using voice commands, the terminal analyzes the commands and generates corresponding control signals.

[0433] Step 10:

[0434] The terminal transmits control signals to home appliances and operates the appliances based on user requests.

[0435] (Example 1)

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

[0437] To enhance the safety and comfort of the living environment for those requiring care, real-time monitoring of their movements and health status is necessary. Furthermore, early detection and prompt notification of abnormal movements, as well as smooth communication to alleviate feelings of isolation among those requiring care, are required. Conventional systems have been unable to adequately address these challenges, highlighting the need for a more comprehensive and effective support system.

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

[0439] In this invention, the server includes means for collecting information using multiple detection and imaging devices for monitoring the movements and health information of care recipients in real time, means for encrypting the collected information and transmitting it to a remote analysis device via communication, and means for processing the collected information and detecting falls and abnormal behavior. This ensures safety in the living environment of care recipients and enables prompt response and communication support.

[0440] A "person requiring care" refers to an individual who needs continuous and appropriate support or care in their daily life.

[0441] A "detection device" refers to an instrument used to measure physical or physiological parameters and acquire that data.

[0442] An "imaging device" refers to equipment used to record video or image data.

[0443] "Information" refers to the data and records of events that are acquired, including motion data, health data, and audio data.

[0444] "Encryption" refers to the process of transforming information to securely protect data and prevent unauthorized access.

[0445] "Communication" refers to the activity of transmitting data from one point to another.

[0446] A "remote analysis device" refers to a device used to receive and analyze data from a remote location.

[0447] A "natural language processing system" refers to a device or algorithm that enables a machine to understand and process human language.

[0448] "Household appliances" refer to electrical equipment and appliances used in daily life, including lighting, televisions, and air conditioners.

[0449] "Dialogue fluency" refers to the degree to which a conversation flows naturally without interruption.

[0450] To implement this invention, a system is constructed with the aim of improving the safety and comfort of the living environment for those requiring care. The specific form of this system is shown below.

[0451] The server is located remotely and receives and analyzes information transmitted from terminals. To achieve advanced data processing, the server is recommended to use machine learning libraries. Specifically, Deep Learning libraries are used as general-purpose libraries for analyzing motion and video information. Using these libraries, falls and abnormal movements of care recipients can be detected with high accuracy. Furthermore, to ensure privacy and protect data confidentiality, the server applies encryption technology to the collected information.

[0452] The terminal is installed near the person requiring care. By attaching multiple sensors and imaging devices, it acquires information on movement, activity, and health in real time. This allows the terminal to continuously transmit necessary information to the server, ensuring stable communication. It utilizes speech recognition technology and has an automated voice dialogue function. A natural language processing library is used for speech analysis to achieve smooth dialogue.

[0453] Users can input commands by voice. For example, if a user gives a command such as "Turn on the lights," the terminal interprets this command and sends a signal to a household appliance to control it.

[0454] As a concrete example, if a care recipient says "Turn on the TV" to the device, it converts the voice into text, generates the appropriate signal, and operates the TV. An example of an input prompt for the generating AI model might be, "Please explain in detail the functions of the robot care system for the elderly. In particular, please describe in detail the voice recognition and abnormal motion detection functions."

[0455] Thus, the system according to the present invention can provide comprehensive support to enable those requiring care to live a safe and comfortable life, and can reduce the burden of caregiving.

[0456] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0457] Step 1:

[0458] The terminal uses multiple sensors and imaging devices to collect movement and health information of the person requiring care. Inputs include the person's body temperature, heart rate, location data, and movement images. To accurately collect this data and transmit it to the server in real time, individual data from each sensor is integrated into a single data packet. Additionally, location sensors are used to determine the person's current location and collect movement history data.

[0459] Step 2:

[0460] The server receives data sent from the terminal and begins analysis. The input is an integrated data packet sent from the terminal. The server processes the data using machine learning algorithms, particularly analyzing behavioral patterns to detect abnormal behavior. For example, if an abnormal behavioral pattern such as a fall is detected, the server identifies the timing and location of its occurrence. Deep learning technology is used in processing this data to perform anomaly detection with high accuracy.

[0461] Step 3:

[0462] The server immediately sends a notification to registered emergency contacts if an anomaly is detected. The input is the anomaly detection alert data. The server generates an alert message and sends the notification to relevant parties via SMS or email through a secure communication protocol. This notification includes the location and time the anomaly occurred, and the estimated risk level.

[0463] Step 4:

[0464] The terminal receives voice commands from the person requiring care and converts the voice into text. The input is the voice signal of the person requiring care. The terminal uses a voice analysis engine to convert the voice into a string and performs natural language processing to understand the command. For example, if the command "Turn on the TV" is entered, the terminal generates the corresponding command signal.

[0465] Step 5:

[0466] The terminal controls household appliances based on the results of voice analysis. The input is a control command generated by the voice analysis. The terminal sends a signal to the corresponding household appliance to execute its action. For example, in the case of lighting, in response to the command "Turn on the light," a signal to turn on the light is generated and sent from the terminal, thereby operating the light.

[0467] (Application Example 1)

[0468] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0469] To ensure worker safety and improve work efficiency within a factory, it is necessary to monitor operations in real time, detect abnormalities and hazards early, and take appropriate action. However, currently, many factories have not implemented such advanced monitoring and interaction systems, leading to the risk of accidents due to human error or negligence. This invention aims to solve these problems and provide a safer and more efficient work environment.

[0470] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0471] In this invention, the server includes means for collecting information using multiple detection and imaging devices for monitoring human movements in real time, means for processing the collected information and detecting dangerous actions, and means for issuing warnings and sending alerts to administrators when an abnormality is detected. This makes it possible to ensure the safety of workers in the factory while streamlining the operation of equipment based on voice-input instructions.

[0472] A "detection device" is a sensor or device used to detect the movements and states of people or objects in real time.

[0473] A "recording device" refers to a camera or similar device used to record still images or videos.

[0474] "Information" refers to all data acquired through detection devices and imaging devices.

[0475] "Processing" refers to the manipulation of data to analyze collected information and derive useful judgments and actions.

[0476] A "dangerous action" is an action that could potentially pose a risk to normal work or behavior.

[0477] A "warning" is a notification or alert issued when there is an abnormality in the operation or state of something.

[0478] "Administrator" refers to a person or group responsible for monitoring, operating, and troubleshooting a system.

[0479] "Voice-inputted instructions" refer to commands or orders spoken by workers or other personnel via a microphone or similar device.

[0480] "Equipment" refers to devices or equipment used to perform specific tasks or operations.

[0481] The system that implements this application is intended to ensure safety and improve work efficiency within a factory. The main components of the system include multiple detection devices, cameras, a server, terminals, and a control device that responds to voice commands.

[0482] The server plays a central role in data collection and analysis. It receives and processes real-time data transmitted from detection and imaging devices. It uses TensorFlow to analyze motion and image data, detecting abnormal or dangerous behavior. Furthermore, it uses ROS (Robot Operating System) to control robots and equipment.

[0483] The terminal functions as an interface with the worker. It receives voice input from the worker, converts it to text using Rasa, and generates an appropriate response. The generated response is played back as audio, and further generates equipment control signals as needed.

[0484] Users can operate equipment by issuing voice commands in natural language. This interface allows for efficient operation of equipment and systems even when workers are unavailable. Furthermore, voice alerts enable quick response in the event of detected anomalies or hazards.

[0485] As a concrete example, consider a forklift operating in a factory. The server monitors the movements of workers near the forklift in real time and immediately sends an alert if a worker approaches incorrectly. When a worker instructs the forklift to "carry the next material," the terminal recognizes this and sends a command to the forklift.

[0486] Examples of prompts to input into a generative AI model:

[0487] "Design a robotic system to improve safety management and efficiency within the factory. It should use sensors to monitor worker movements and predict hazards. Include an interface that allows machine operation via voice commands."

[0488] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0489] Step 1:

[0490] The server receives data in real time from multiple detection and imaging devices. The input consists of motion data and image data, which are fed into a model pre-trained with TensorFlow to predict abnormal or dangerous behavior. The output is the result of determining whether the behavior is normal or abnormal.

[0491] Step 2:

[0492] Based on the TensorFlow analysis results, the server generates a warning message if an anomaly is detected and sends an alert to the administrator or the corresponding terminal. The input is the anomaly detection result from the previous step, and the output is the specific warning content and notification. This is sent as an email or voice message.

[0493] Step 3:

[0494] The user inputs voice commands into the terminal. The input is a voice command from the worker, which the terminal uses Rasa to convert into text format and parse as an executable command. The output is the parsed text command.

[0495] Step 4:

[0496] The terminal sends control signals to the relevant equipment based on commands parsed by Rasa. Inputs are text-based commands, and outputs are operational commands for the equipment. Examples include commands to operate a forklift or to shut down equipment.

[0497] Step 5:

[0498] The device transmits voice feedback to the user. The device generates feedback regarding the operation results and whether or not there are any abnormalities, and informs the user of this feedback via voice. The input is the operation results of the device and abnormality notifications from the server, and the output is voice-based feedback.

[0499] This series of steps enables users to monitor the factory environment in real time and perform efficient tasks using voice commands.

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

[0501] This invention relates to a robotic system that supports the daily lives of those requiring care. It incorporates an emotion engine to recognize emotions and provide appropriate responses based on the results. The terminal is equipped with multiple sensors, a 360-degree camera, a microphone, and a speaker to monitor the movements and voice of the person requiring care in real time. The emotion engine then analyzes the user's voice and facial expressions to evaluate their emotional state.

[0502] The server receives sensor and camera data, as well as audio data, transmitted from the terminal and processes it in real time. The emotion engine uses this data to detect changes in the user's mood. As a result, the robot is programmed to provide considerate responses that are in line with the user's emotions, in addition to normal voice responses.

[0503] For example, if signs of anxiety or stress are detected in the user's voice during a normal conversation, the device will make a voice suggestion such as, "Would you like to take a short break?" Also, if anxiety is detected through facial expression analysis, the device will ask in a gentle tone, "Is there anything I can help you with?" If the abnormal emotional state exceeds a threshold, the server will send an alert to a pre-configured emergency contact, enabling a quick response.

[0504] This system makes it possible to reduce not only the physical but also the psychological burden on those requiring care, creating a more comfortable and safe living environment. The introduction of the emotional engine is key to providing a more humane dialogue experience and enabling flexible advice and support tailored to individual needs.

[0505] The following describes the processing flow.

[0506] Step 1:

[0507] The device collects voice and facial expression data of the person requiring care in real time using multiple sensors and a camera. At this stage, the voice is captured by a microphone.

[0508] Step 2:

[0509] The device temporarily stores the collected data and performs preprocessing to pass it to the emotion engine. The audio data undergoes noise reduction processing.

[0510] Step 3:

[0511] The emotion engine within the device analyzes voice and facial expression data to determine the user's emotional state. The algorithms used here utilize feature extraction and pattern recognition technologies.

[0512] Step 4:

[0513] The server receives emotional state information sent from the terminal and updates the current conversation context. This allows for a deeper understanding of the interaction with the user.

[0514] Step 5:

[0515] The device generates an appropriate response based on the results determined by the emotion engine via a natural language processing unit and responds to the user in voice using a speech synthesis module.

[0516] Step 6:

[0517] If the device detects an abnormal emotional state, it will take measures to send an alert to emergency contacts via the server. At the same time, a description of the situation and location information will also be sent.

[0518] Step 7:

[0519] If the user gives new instructions by voice, the device will accept voice again and repeat the process from step 1. This ensures continuous monitoring and appropriate interaction.

[0520] (Example 2)

[0521] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0522] Improving the quality of life for those requiring care, and especially reducing their psychological burden, is crucial. However, conventional support systems have struggled to accurately recognize users' emotional states and to provide natural, humane dialogue that responds to those emotions. Furthermore, there has been a lack of effective mechanisms for quickly detecting abnormal emotional states and providing emergency responses.

[0523] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0524] In this invention, the server includes means for using multiple detection devices to monitor the movements and voice of the person requiring care in real time and collect data; means for analyzing and evaluating the emotional state based on the user's voice and facial expressions using an emotion engine; and means for generating an appropriate response according to the analyzed emotional state and communicating it to the user through a voice output device. This enables flexible and appropriate support for the psychological health of the person requiring care. Furthermore, by utilizing a generative AI model, natural and human-like dialogue can be achieved, and a rapid response can be made in emergencies.

[0525] A "detection device" is a device that includes multiple sensors and cameras used to acquire the movements and voices of a person requiring care in real time.

[0526] An "emotion engine" is a software component that analyzes a user's voice and facial expression data to evaluate their emotional state.

[0527] A "generative AI model" is an artificial intelligence model used to generate natural and human-like responses based on the results of user emotion analysis.

[0528] A "voice output device" is a device that provides the response generated by the server to the user as voice.

[0529] An "emergency contact" is a contact person who has been pre-configured to receive notifications in case of an emergency.

[0530] An "abnormal state" refers to a situation where a user's emotions exceed the normal range, requiring special attention.

[0531] This invention provides a device for supporting the lives of people requiring care, comprising a terminal equipped with multiple detection devices, a server for emotion analysis, and a generative AI model that enables natural and flexible dialogue.

[0532] The device monitors movement and sound in real time. Equipped with multiple sensors, a 360-degree camera, and a microphone, it has the capability to capture the actions and voice of the person requiring care. For example, it can record the user's voice and facial expressions during everyday conversations and transmit that data to a server.

[0533] The server receives data sent from the terminal and performs data analysis. Using an emotion engine, it analyzes voice and facial expression data to evaluate the user's emotional state. In this process, the server utilizes a generative AI model to generate natural-sounding dialogue based on the analysis results. Examples of prompts could include, "How would a gentle voice assistant respond when the user is tired?" or "Please provide examples of considerate dialogue for someone feeling anxious."

[0534] The generated response is delivered to the user through the terminal's voice output device. For example, if the user feels anxious, a gentle voice message will say, "Is there anything I can do to help?" This system reduces the psychological burden on those requiring care while enabling a rapid emergency response. As a result, it can provide a safer and more comfortable living environment for those requiring care.

[0535] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0536] Step 1:

[0537] The device collects the movements and voice of the person requiring care in real time.

[0538] The system uses multiple sensors, a 360-degree camera, and a microphone as inputs. These devices detect the actions, tone of voice, and facial expressions of the person requiring care. The output includes motion data, audio data, and video data. Specifically, the microphone records voice, and the camera captures facial expressions.

[0539] Step 2:

[0540] The device sends the collected data to the server.

[0541] The input consists of motion data, audio data, and video data acquired in Step 1. This data is transmitted to the server via a low-latency network. The output is the data arriving at the server and ready for analysis. Specifically, the data is compressed into a predetermined format and transferred to the server via the network.

[0542] Step 3:

[0543] The server analyzes the data it receives.

[0544] The input consists of motion data, audio data, and video data sent from the terminal. The server uses an emotion engine to analyze this data and evaluate the user's emotional state. The output is the evaluation result of the user's emotional state. Specifically, it analyzes the tone of voice using speech recognition technology and reads facial expressions using image analysis technology.

[0545] Step 4:

[0546] The server generates an appropriate response based on the emotional state.

[0547] The evaluation results of the emotional state obtained in step 3 are used as input. A generative AI model is used to generate a natural language response through a prompt sentence based on the evaluation results. The output is a appropriately structured response sentence. Specifically, the generative AI model takes the emotional evaluation into consideration and generates sentences using natural and human-like phrasing.

[0548] Step 5:

[0549] The terminal notifies the user of the response it has generated.

[0550] The system receives the response text generated in step 4 as input. This response is played back as audio through the audio output device. The output is the voice response being communicated to the care recipient. Specifically, the speaker delivers the generated voice response to the user.

[0551] (Application Example 2)

[0552] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0553] In modern factory work, emotional states such as stress and anxiety can affect worker efficiency and safety, posing a significant challenge. Conventional systems have struggled to evaluate emotional states in the work environment in real time and address them appropriately, leading to concerns about decreased work efficiency and safety. This invention aims to solve these problems by providing a system that uses emotion recognition technology to understand workers' emotional states and provide appropriate feedback.

[0554] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0555] In this invention, the server includes a computing device for emotion recognition, means for generating commands to provide breaks or advice according to the worker's emotional state, and communication means for notifying a safety manager when a specific emotional state exceeds a threshold. This makes it possible to evaluate the emotional state of workers in the work environment in real time and respond quickly as needed.

[0556] A "detector" is a general term for sensor devices used to detect the actions or states of an object in real time.

[0557] "Shooting equipment" refers to camera equipment such as 360-degree cameras, which are devices used to acquire video information of the surroundings.

[0558] "Information" refers to data and observation results acquired by sensors and imaging devices, and is the subject of processing and analysis.

[0559] A "computational device" is a computer device used to perform various analyses and evaluate emotional states based on collected information.

[0560] "Dialogue" refers to the act of verbal communication that takes place between care recipients and workers, and is a form of communication that allows for the exchange of opinions in a natural flow.

[0561] "Electrical appliances" is a general term for electrical devices used in daily life that can be operated by voice commands.

[0562] "Communication methods" is a general term for network technologies and equipment used to transmit alerts and notifications to remote locations.

[0563] A "command" is a message or instruction that the system generates in response to an emotional state and conveys to the worker as advice or a warning.

[0564] An "alert" is an alert or notification issued by a system when it detects an anomaly, and it is information that prompts emergency response.

[0565] The system of this invention is designed to monitor the emotional state of workers in the work environment, enabling them to perform their duties efficiently and safely. The system collects motion information and image information of workers using sensors and cameras. The collected information is transmitted to a server and analyzed by a computing unit that performs emotion recognition. Based on this analysis, the system evaluates the worker's emotional state in real time and generates commands appropriate to the situation. Specifically, if stress or anxiety is detected, a command prompting a break is sent to the worker via voice or visual message. If a specific emotional state exceeds a threshold, a notification is sent to the safety manager via communication means, enabling a quick response.

[0566] The hardware uses smart glasses (e.g., Google Glass), and the software uses an AI model (e.g., Hume AI) responsible for emotion recognition. A server integrates this information and provides feedback to the worker. The AI ​​model takes voice and image data as input, analyzes emotional states, and generates appropriate feedback.

[0567] As a concrete example, consider a situation where a worker in a workshop begins to feel stressed one day. This state is detected by smart glasses, and a command is sent to the worker saying, "You need a short break." This information is also sent to the safety manager, who takes swift action to support the worker. An example of a prompt sentence to input into the generating AI model is, "Please tell me how to analyze emotional changes during work."

[0568] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0569] Step 1:

[0570] The terminal uses sensors and cameras mounted on smart glasses to collect real-time information on the worker's movements and images. The input for this step is raw data from the sensors and cameras, while the output is formatted data for transmission to the server. The terminal appropriately formats this data and sends it to the server via a secure communication protocol.

[0571] Step 2:

[0572] The server inputs the motion information and image information received from the terminal into an emotion recognition AI model to analyze the worker's emotional state. The input for this step is formatted data sent from the terminal, and the output is an evaluation result indicating the emotional state. The server uses the AI ​​model to extract features of the emotional state and performs data calculations to determine signs of stress and anxiety.

[0573] Step 3:

[0574] The server generates feedback for the worker based on the analysis results. Specifically, if the worker is feeling stressed, it generates a message such as, "You need a short break." The input for this step is the analysis results from the emotion recognition AI model, and the output is a specific feedback message for the worker. The server uses appropriate natural language processing techniques to create text-based feedback and sends it to the terminal.

[0575] Step 4:

[0576] The terminal conveys feedback messages sent from the server to the worker either verbally or visually. The input for this step is the feedback message from the server, and the output is the notification to the worker. The terminal converts text messages into speech using speech synthesis technology, providing information in a way that is easily understandable to the worker.

[0577] Step 5:

[0578] The server sends an alert to the safety manager if a specific emotional state exceeds a pre-set threshold. The input for this step is the result of an AI model's evaluation of the emotional state, and the output is an alert notification to the safety manager. Depending on the urgency, the server sends the alert via email or text message to prompt a quick response.

[0579] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0580] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0581] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0582] [Fourth Embodiment]

[0583] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0584] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0585] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0586] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0587] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0588] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0589] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0590] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0591] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0592] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0593] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0594] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0595] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0596] This invention relates to a robotic system for supporting the lives of those requiring care, and is implemented using a terminal equipped with multiple sensors and a camera. This terminal can constantly monitor the movements of the person requiring care and collect data in real time. The data is transmitted to a server and analyzed through image processing and abnormal movement detection algorithms. When the server detects abnormal movement, it immediately sends a notification to emergency contacts.

[0597] Furthermore, the device is equipped with a natural language processing unit, enabling smooth voice interaction with those requiring care. User voice input is converted to text, processed, and then output as an appropriate response. This helps to alleviate feelings of loneliness in daily life.

[0598] Furthermore, users can control household appliances using voice commands. The terminal generates appropriate control signals based on the voice recognition results and sends commands to the corresponding appliances.

[0599] For example, if a user gives a voice command such as "Turn on the lights," the terminal converts the voice into text and sends signals to control home appliances such as air conditioners and lights. All data exchange and command transmission are based on secure communication protocols, protecting user privacy.

[0600] Thus, the system according to the present invention provides comprehensive support to enable those requiring care to live independently safely and comfortably, and also aims to reduce the burden of caregiving.

[0601] The following describes the processing flow.

[0602] Step 1:

[0603] The device uses built-in multi-sensors and a 360-degree camera to collect data in real time in order to monitor the movements of the person requiring care.

[0604] Step 2:

[0605] The device compresses the collected data and sends it to the server via a secure communication protocol.

[0606] Step 3:

[0607] The server applies image processing algorithms to the received data to analyze the movements of the person requiring care.

[0608] Step 4:

[0609] The server detects abnormal behavior, such as a fall or movement that deviates from normal, and determines whether an emergency alert is necessary.

[0610] Step 5:

[0611] If the server detects an anomaly, it will send an alert notification to the designated emergency contact.

[0612] Step 6:

[0613] When a user wants to have a natural conversation, the device receives voice input and converts it into text data.

[0614] Step 7:

[0615] The terminal passes the converted text to a natural language processing module, which then generates an appropriate response.

[0616] Step 8:

[0617] The terminal outputs the generated response as speech using a speech synthesis module and responds to the user.

[0618] Step 9:

[0619] When a user operates a home appliance using voice commands, the terminal analyzes the commands and generates corresponding control signals.

[0620] Step 10:

[0621] The terminal transmits control signals to home appliances and operates the appliances based on user requests.

[0622] (Example 1)

[0623] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0624] To enhance the safety and comfort of the living environment for those requiring care, real-time monitoring of their movements and health status is necessary. Furthermore, early detection and prompt notification of abnormal movements, as well as smooth communication to alleviate feelings of isolation among those requiring care, are required. Conventional systems have been unable to adequately address these challenges, highlighting the need for a more comprehensive and effective support system.

[0625] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0626] In this invention, the server includes means for collecting information using multiple detection and imaging devices for monitoring the movements and health information of care recipients in real time, means for encrypting the collected information and transmitting it to a remote analysis device via communication, and means for processing the collected information and detecting falls and abnormal behavior. This ensures safety in the living environment of care recipients and enables prompt response and communication support.

[0627] A "person requiring care" refers to an individual who needs continuous and appropriate support or care in their daily life.

[0628] A "detection device" refers to an instrument used to measure physical or physiological parameters and acquire that data.

[0629] An "imaging device" refers to equipment used to record video or image data.

[0630] "Information" refers to the data and records of events that are acquired, including motion data, health data, and audio data.

[0631] "Encryption" refers to the process of transforming information to securely protect data and prevent unauthorized access.

[0632] "Communication" refers to the activity of transmitting data from one point to another.

[0633] A "remote analysis device" refers to a device used to receive and analyze data from a remote location.

[0634] A "natural language processing system" refers to a device or algorithm that enables a machine to understand and process human language.

[0635] "Household appliances" refer to electrical equipment and appliances used in daily life, including lighting, televisions, and air conditioners.

[0636] "Dialogue fluency" refers to the degree to which a conversation flows naturally without interruption.

[0637] To implement this invention, a system is constructed with the aim of improving the safety and comfort of the living environment for those requiring care. The specific form of this system is shown below.

[0638] The server is located remotely and receives and analyzes information transmitted from terminals. To achieve advanced data processing, the server is recommended to use machine learning libraries. Specifically, Deep Learning libraries are used as general-purpose libraries for analyzing motion and video information. Using these libraries, falls and abnormal movements of care recipients can be detected with high accuracy. Furthermore, to ensure privacy and protect data confidentiality, the server applies encryption technology to the collected information.

[0639] The terminal is installed near the person requiring care. By attaching multiple sensors and imaging devices, it acquires information on movement, activity, and health in real time. This allows the terminal to continuously transmit necessary information to the server, ensuring stable communication. It utilizes speech recognition technology and has an automated voice dialogue function. A natural language processing library is used for speech analysis to achieve smooth dialogue.

[0640] Users can input commands by voice. For example, if a user gives a command such as "Turn on the lights," the terminal interprets this command and sends a signal to a household appliance to control it.

[0641] As a concrete example, if a care recipient says "Turn on the TV" to the device, it converts the voice into text, generates the appropriate signal, and operates the TV. An example of an input prompt for the generating AI model might be, "Please explain in detail the functions of the robot care system for the elderly. In particular, please describe in detail the voice recognition and abnormal motion detection functions."

[0642] Thus, the system according to the present invention can provide comprehensive support to enable those requiring care to live a safe and comfortable life, and can reduce the burden of caregiving.

[0643] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0644] Step 1:

[0645] The terminal uses multiple sensors and imaging devices to collect movement and health information of the person requiring care. Inputs include the person's body temperature, heart rate, location data, and movement images. To accurately collect this data and transmit it to the server in real time, individual data from each sensor is integrated into a single data packet. Additionally, location sensors are used to determine the person's current location and collect movement history data.

[0646] Step 2:

[0647] The server receives data sent from the terminal and begins analysis. The input is an integrated data packet sent from the terminal. The server processes the data using machine learning algorithms, particularly analyzing behavioral patterns to detect abnormal behavior. For example, if an abnormal behavioral pattern such as a fall is detected, the server identifies the timing and location of its occurrence. Deep learning technology is used in processing this data to perform anomaly detection with high accuracy.

[0648] Step 3:

[0649] The server immediately sends a notification to registered emergency contacts if an anomaly is detected. The input is the anomaly detection alert data. The server generates an alert message and sends the notification to relevant parties via SMS or email through a secure communication protocol. This notification includes the location and time the anomaly occurred, and the estimated risk level.

[0650] Step 4:

[0651] The terminal receives voice commands from the person requiring care and converts the voice into text. The input is the voice signal of the person requiring care. The terminal uses a voice analysis engine to convert the voice into a string and performs natural language processing to understand the command. For example, if the command "Turn on the TV" is entered, the terminal generates the corresponding command signal.

[0652] Step 5:

[0653] The terminal controls household appliances based on the results of voice analysis. The input is a control command generated by the voice analysis. The terminal sends a signal to the corresponding household appliance to execute its action. For example, in the case of lighting, in response to the command "Turn on the light," a signal to turn on the light is generated and sent from the terminal, thereby operating the light.

[0654] (Application Example 1)

[0655] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0656] To ensure worker safety and improve work efficiency within a factory, it is necessary to monitor operations in real time, detect abnormalities and hazards early, and take appropriate action. However, currently, many factories have not implemented such advanced monitoring and interaction systems, leading to the risk of accidents due to human error or negligence. This invention aims to solve these problems and provide a safer and more efficient work environment.

[0657] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0658] In this invention, the server includes means for collecting information using multiple detection and imaging devices for monitoring human movements in real time, means for processing the collected information and detecting dangerous actions, and means for issuing warnings and sending alerts to administrators when an abnormality is detected. This makes it possible to ensure the safety of workers in the factory while streamlining the operation of equipment based on voice-input instructions.

[0659] A "detection device" is a sensor or device used to detect the movements and states of people or objects in real time.

[0660] A "recording device" refers to a camera or similar device used to record still images or videos.

[0661] "Information" refers to all data acquired through detection devices and imaging devices.

[0662] "Processing" refers to the manipulation of data to analyze collected information and derive useful judgments and actions.

[0663] A "dangerous action" is an action that could potentially pose a risk to normal work or behavior.

[0664] A "warning" is a notification or alert issued when there is an abnormality in the operation or state of something.

[0665] "Administrator" refers to a person or group responsible for monitoring, operating, and troubleshooting a system.

[0666] "Voice-inputted instructions" refer to commands or orders spoken by workers or other personnel via a microphone or similar device.

[0667] "Equipment" refers to devices or equipment used to perform specific tasks or operations.

[0668] The system that implements this application is intended to ensure safety and improve work efficiency within a factory. The main components of the system include multiple detection devices, cameras, a server, terminals, and a control device that responds to voice commands.

[0669] The server plays a central role in data collection and analysis. It receives and processes real-time data transmitted from detection and imaging devices. It uses TensorFlow to analyze motion and image data, detecting abnormal or dangerous behavior. Furthermore, it uses ROS (Robot Operating System) to control robots and equipment.

[0670] The terminal functions as an interface with the worker. It receives voice input from the worker, converts it to text using Rasa, and generates an appropriate response. The generated response is played back as audio, and further generates equipment control signals as needed.

[0671] Users can operate equipment by issuing voice commands in natural language. This interface allows for efficient operation of equipment and systems even when workers are unavailable. Furthermore, voice alerts enable quick response in the event of detected anomalies or hazards.

[0672] As a concrete example, consider a forklift operating in a factory. The server monitors the movements of workers near the forklift in real time and immediately sends an alert if a worker approaches incorrectly. When a worker instructs the forklift to "carry the next material," the terminal recognizes this and sends a command to the forklift.

[0673] Examples of prompts to input into a generative AI model:

[0674] "Design a robotic system to improve safety management and efficiency within the factory. It should use sensors to monitor worker movements and predict hazards. Include an interface that allows machine operation via voice commands."

[0675] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0676] Step 1:

[0677] The server receives data in real time from multiple detection and imaging devices. The input consists of motion data and image data, which are fed into a model pre-trained with TensorFlow to predict abnormal or dangerous behavior. The output is the result of determining whether the behavior is normal or abnormal.

[0678] Step 2:

[0679] Based on the TensorFlow analysis results, the server generates a warning message if an anomaly is detected and sends an alert to the administrator or the corresponding terminal. The input is the anomaly detection result from the previous step, and the output is the specific warning content and notification. This is sent as an email or voice message.

[0680] Step 3:

[0681] The user inputs voice commands into the terminal. The input is a voice command from the worker, which the terminal uses Rasa to convert into text format and parse as an executable command. The output is the parsed text command.

[0682] Step 4:

[0683] The terminal sends control signals to the relevant equipment based on commands parsed by Rasa. Inputs are text-based commands, and outputs are operational commands for the equipment. Examples include commands to operate a forklift or to shut down equipment.

[0684] Step 5:

[0685] The device transmits voice feedback to the user. The device generates feedback regarding the operation results and whether or not there are any abnormalities, and informs the user of this feedback via voice. The input is the operation results of the device and abnormality notifications from the server, and the output is voice-based feedback.

[0686] This series of steps enables users to monitor the factory environment in real time and perform efficient tasks using voice commands.

[0687] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0688] This invention relates to a robotic system that supports the daily lives of those requiring care. It incorporates an emotion engine to recognize emotions and provide appropriate responses based on the results. The terminal is equipped with multiple sensors, a 360-degree camera, a microphone, and a speaker to monitor the movements and voice of the person requiring care in real time. The emotion engine then analyzes the user's voice and facial expressions to evaluate their emotional state.

[0689] The server receives sensor and camera data, as well as audio data, transmitted from the terminal and processes it in real time. The emotion engine uses this data to detect changes in the user's mood. As a result, the robot is programmed to provide considerate responses that are in line with the user's emotions, in addition to normal voice responses.

[0690] For example, if signs of anxiety or stress are detected in the user's voice during a normal conversation, the device will make a voice suggestion such as, "Would you like to take a short break?" Also, if anxiety is detected through facial expression analysis, the device will ask in a gentle tone, "Is there anything I can help you with?" If the abnormal emotional state exceeds a threshold, the server will send an alert to a pre-configured emergency contact, enabling a quick response.

[0691] This system makes it possible to reduce not only the physical but also the psychological burden on those requiring care, creating a more comfortable and safe living environment. The introduction of the emotional engine is key to providing a more humane dialogue experience and enabling flexible advice and support tailored to individual needs.

[0692] The following describes the processing flow.

[0693] Step 1:

[0694] The device collects voice and facial expression data of the person requiring care in real time using multiple sensors and a camera. At this stage, the voice is captured by a microphone.

[0695] Step 2:

[0696] The device temporarily stores the collected data and performs preprocessing to pass it to the emotion engine. The audio data undergoes noise reduction processing.

[0697] Step 3:

[0698] The emotion engine within the device analyzes voice and facial expression data to determine the user's emotional state. The algorithms used here utilize feature extraction and pattern recognition technologies.

[0699] Step 4:

[0700] The server receives emotional state information sent from the terminal and updates the current conversation context. This allows for a deeper understanding of the interaction with the user.

[0701] Step 5:

[0702] The device generates an appropriate response based on the results determined by the emotion engine via a natural language processing unit and responds to the user in voice using a speech synthesis module.

[0703] Step 6:

[0704] If the device detects an abnormal emotional state, it will take measures to send an alert to emergency contacts via the server. At the same time, a description of the situation and location information will also be sent.

[0705] Step 7:

[0706] If the user gives new instructions by voice, the device will accept voice again and repeat the process from step 1. This ensures continuous monitoring and appropriate interaction.

[0707] (Example 2)

[0708] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0709] Improving the quality of life for those requiring care, and especially reducing their psychological burden, is crucial. However, conventional support systems have struggled to accurately recognize users' emotional states and to provide natural, humane dialogue that responds to those emotions. Furthermore, there has been a lack of effective mechanisms for quickly detecting abnormal emotional states and providing emergency responses.

[0710] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0711] In this invention, the server includes means for using multiple detection devices to monitor the movements and voice of the person requiring care in real time and collect data; means for analyzing and evaluating the emotional state based on the user's voice and facial expressions using an emotion engine; and means for generating an appropriate response according to the analyzed emotional state and communicating it to the user through a voice output device. This enables flexible and appropriate support for the psychological health of the person requiring care. Furthermore, by utilizing a generative AI model, natural and human-like dialogue can be achieved, and a rapid response can be made in emergencies.

[0712] A "detection device" is a device that includes multiple sensors and cameras used to acquire the movements and voices of a person requiring care in real time.

[0713] An "emotion engine" is a software component that analyzes a user's voice and facial expression data to evaluate their emotional state.

[0714] A "generative AI model" is an artificial intelligence model used to generate natural and human-like responses based on the results of user emotion analysis.

[0715] A "voice output device" is a device that provides the response generated by the server to the user as voice.

[0716] An "emergency contact" is a contact person who has been pre-configured to receive notifications in case of an emergency.

[0717] An "abnormal state" refers to a situation where a user's emotions exceed the normal range, requiring special attention.

[0718] This invention provides a device for supporting the lives of people requiring care, comprising a terminal equipped with multiple detection devices, a server for emotion analysis, and a generative AI model that enables natural and flexible dialogue.

[0719] The device monitors movement and sound in real time. Equipped with multiple sensors, a 360-degree camera, and a microphone, it has the capability to capture the actions and voice of the person requiring care. For example, it can record the user's voice and facial expressions during everyday conversations and transmit that data to a server.

[0720] The server receives data sent from the terminal and performs data analysis. Using an emotion engine, it analyzes voice and facial expression data to evaluate the user's emotional state. In this process, the server utilizes a generative AI model to generate natural-sounding dialogue based on the analysis results. Examples of prompts could include, "How would a gentle voice assistant respond when the user is tired?" or "Please provide examples of considerate dialogue for someone feeling anxious."

[0721] The generated response is delivered to the user through the terminal's voice output device. For example, if the user feels anxious, a gentle voice message will say, "Is there anything I can do to help?" This system reduces the psychological burden on those requiring care while enabling a rapid emergency response. As a result, it can provide a safer and more comfortable living environment for those requiring care.

[0722] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0723] Step 1:

[0724] The device collects the movements and voice of the person requiring care in real time.

[0725] The system uses multiple sensors, a 360-degree camera, and a microphone as inputs. These devices detect the actions, tone of voice, and facial expressions of the person requiring care. The output includes motion data, audio data, and video data. Specifically, the microphone records voice, and the camera captures facial expressions.

[0726] Step 2:

[0727] The device sends the collected data to the server.

[0728] The input consists of motion data, audio data, and video data acquired in Step 1. This data is transmitted to the server via a low-latency network. The output is the data arriving at the server and ready for analysis. Specifically, the data is compressed into a predetermined format and transferred to the server via the network.

[0729] Step 3:

[0730] The server analyzes the data it receives.

[0731] The input consists of motion data, audio data, and video data sent from the terminal. The server uses an emotion engine to analyze this data and evaluate the user's emotional state. The output is the evaluation result of the user's emotional state. Specifically, it analyzes the tone of voice using speech recognition technology and reads facial expressions using image analysis technology.

[0732] Step 4:

[0733] The server generates an appropriate response based on the emotional state.

[0734] The evaluation results of the emotional state obtained in step 3 are used as input. A generative AI model is used to generate a natural language response through a prompt sentence based on the evaluation results. The output is a appropriately structured response sentence. Specifically, the generative AI model takes the emotional evaluation into consideration and generates sentences using natural and human-like phrasing.

[0735] Step 5:

[0736] The terminal notifies the user of the response it has generated.

[0737] The system receives the response text generated in step 4 as input. This response is played back as audio through the audio output device. The output is the voice response being communicated to the care recipient. Specifically, the speaker delivers the generated voice response to the user.

[0738] (Application Example 2)

[0739] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0740] In modern factory work, emotional states such as stress and anxiety can affect worker efficiency and safety, posing a significant challenge. Conventional systems have struggled to evaluate emotional states in the work environment in real time and address them appropriately, leading to concerns about decreased work efficiency and safety. This invention aims to solve these problems by providing a system that uses emotion recognition technology to understand workers' emotional states and provide appropriate feedback.

[0741] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0742] In this invention, the server includes a computing device for emotion recognition, means for generating commands to provide breaks or advice according to the worker's emotional state, and communication means for notifying a safety manager when a specific emotional state exceeds a threshold. This makes it possible to evaluate the emotional state of workers in the work environment in real time and respond quickly as needed.

[0743] A "detector" is a general term for sensor devices used to detect the actions or states of an object in real time.

[0744] "Shooting equipment" refers to camera equipment such as 360-degree cameras, which are devices used to acquire video information of the surroundings.

[0745] "Information" refers to data and observation results acquired by sensors and imaging devices, and is the subject of processing and analysis.

[0746] A "computational device" is a computer device used to perform various analyses and evaluate emotional states based on collected information.

[0747] "Dialogue" refers to the act of verbal communication that takes place between care recipients and workers, and is a form of communication that allows for the exchange of opinions in a natural flow.

[0748] "Electrical appliances" is a general term for electrical devices used in daily life that can be operated by voice commands.

[0749] "Communication methods" is a general term for network technologies and equipment used to transmit alerts and notifications to remote locations.

[0750] A "command" is a message or instruction that the system generates in response to an emotional state and conveys to the worker as advice or a warning.

[0751] An "alert" is an alert or notification issued by a system when it detects an anomaly, and it is information that prompts emergency response.

[0752] The system of this invention is designed to monitor the emotional state of workers in the work environment, enabling them to perform their duties efficiently and safely. The system collects motion information and image information of workers using sensors and cameras. The collected information is transmitted to a server and analyzed by a computing unit that performs emotion recognition. Based on this analysis, the system evaluates the worker's emotional state in real time and generates commands appropriate to the situation. Specifically, if stress or anxiety is detected, a command prompting a break is sent to the worker via voice or visual message. If a specific emotional state exceeds a threshold, a notification is sent to the safety manager via communication means, enabling a quick response.

[0753] The hardware uses smart glasses (e.g., Google Glass), and the software uses an AI model (e.g., Hume AI) responsible for emotion recognition. A server integrates this information and provides feedback to the worker. The AI ​​model takes voice and image data as input, analyzes emotional states, and generates appropriate feedback.

[0754] As a concrete example, consider a situation where a worker in a workshop begins to feel stressed one day. This state is detected by smart glasses, and a command is sent to the worker saying, "You need a short break." This information is also sent to the safety manager, who takes swift action to support the worker. An example of a prompt sentence to input into the generating AI model is, "Please tell me how to analyze emotional changes during work."

[0755] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0756] Step 1:

[0757] The terminal uses sensors and cameras mounted on smart glasses to collect real-time information on the worker's movements and images. The input for this step is raw data from the sensors and cameras, while the output is formatted data for transmission to the server. The terminal appropriately formats this data and sends it to the server via a secure communication protocol.

[0758] Step 2:

[0759] The server inputs the motion information and image information received from the terminal into an emotion recognition AI model to analyze the worker's emotional state. The input for this step is formatted data sent from the terminal, and the output is an evaluation result indicating the emotional state. The server uses the AI ​​model to extract features of the emotional state and performs data calculations to determine signs of stress and anxiety.

[0760] Step 3:

[0761] The server generates feedback for the worker based on the analysis results. Specifically, if the worker is feeling stressed, it generates a message such as, "You need a short break." The input for this step is the analysis results from the emotion recognition AI model, and the output is a specific feedback message for the worker. The server uses appropriate natural language processing techniques to create text-based feedback and sends it to the terminal.

[0762] Step 4:

[0763] The terminal conveys feedback messages sent from the server to the worker either verbally or visually. The input for this step is the feedback message from the server, and the output is the notification to the worker. The terminal converts text messages into speech using speech synthesis technology, providing information in a way that is easily understandable to the worker.

[0764] Step 5:

[0765] The server sends an alert to the safety manager if a specific emotional state exceeds a pre-set threshold. The input for this step is the result of an AI model's evaluation of the emotional state, and the output is an alert notification to the safety manager. Depending on the urgency, the server sends the alert via email or text message to prompt a quick response.

[0766] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0767] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0768] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0769] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0770] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0771] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0772] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0773] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0774] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0775] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0776] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0777] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0778] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0779] 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.

[0780] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0781] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0782] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0783] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0784] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0785] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0786] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0787] The following is further disclosed regarding the embodiments described above.

[0788] (Claim 1)

[0789] A means of collecting data using multiple sensors and cameras to monitor the movements of a person requiring care in real time,

[0790] A means for processing collected data and detecting falls and abnormal movements,

[0791] A means of sending a notification and an alert to emergency contacts when an anomaly is detected,

[0792] A means including a natural language processing unit for generating natural conversations with care recipients,

[0793] A means of operating home appliances based on voice commands entered by a person requiring care,

[0794] A means of immediately notifying pre-set emergency contacts in the event of an emergency,

[0795] A system that includes this.

[0796] (Claim 2)

[0797] The system according to claim 1, which converts voice input into text data, generates an appropriate response, and outputs it back as voice, in order to enable natural voice dialogue with a person requiring care.

[0798] (Claim 3)

[0799] The system according to claim 1, which processes motion data and image data in real time when detecting falls or abnormal movements of a person requiring care.

[0800] "Example 1"

[0801] (Claim 1)

[0802] A means for collecting information using multiple detection and imaging devices to monitor the movements and health information of a person requiring care in real time,

[0803] A means for encrypting the collected information and transmitting it to a remote analysis device via communication,

[0804] A means of processing collected information and detecting falls and abnormal behavior,

[0805] A means of sending a notification to pre-configured contacts when an anomaly is detected,

[0806] Means including a natural language processing device for generating natural dialogue with a person requiring care,

[0807] A means of controlling household appliances based on commands entered by the person requiring care via voice,

[0808] A means of outputting responses as audio information and maintaining the fluency of the dialogue,

[0809] A system that includes this.

[0810] (Claim 2)

[0811] The system according to claim 1, which converts speech input into text information, generates an appropriate response, and outputs it again as speech information.

[0812] (Claim 3)

[0813] The system according to claim 1, which processes motion information and video information in real time when detecting falls or abnormal behavior.

[0814] "Application Example 1"

[0815] (Claim 1)

[0816] A means of collecting information using multiple detection and imaging devices for monitoring human movements in real time,

[0817] A means for processing collected information and detecting dangerous behavior,

[0818] A means of issuing a warning and sending an alert to the administrator when an anomaly is detected,

[0819] A means including a language processing unit for generating natural conversations with people,

[0820] A means of operating a device based on voice input instructions,

[0821] A means of immediately notifying pre-set contacts in an emergency,

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, which converts voice input into text information, generates an appropriate response, and outputs it again as voice, in order to enable natural voice interaction with people.

[0825] (Claim 3)

[0826] The system according to claim 1, which processes motion information and image information in real time when detecting dangerous human behavior.

[0827] "Example 2 of combining an emotion engine"

[0828] (Claim 1)

[0829] A means of monitoring the movements and voice of a person requiring care in real time and using multiple detection devices to collect data,

[0830] A means of analyzing and evaluating the emotional state of a user based on their voice and facial expressions using an emotion engine,

[0831] A means of generating an appropriate response according to the analyzed emotional state and conveying it to the user through an audio output device,

[0832] A means of quickly notifying emergency contacts if the user's emotional state is deemed abnormal,

[0833] A means of operating electrical equipment based on instructions entered by a person requiring care via voice,

[0834] To make dialogue more natural and human-like using an emotion engine, a means including a generative AI model that utilizes prompt sentences,

[0835] A system that includes this.

[0836] (Claim 2)

[0837] The system according to claim 1, which converts voice input into text data, generates a response based on the emotional state obtained from the analysis, and outputs it again as voice, in order to enable natural voice dialogue with a person requiring care.

[0838] (Claim 3)

[0839] The system according to claim 1, which, when detecting changes in the emotions of a person requiring care, processes them in real time based on motion data, voice data, and image data, and utilizes a generative AI model.

[0840] "Application example 2 when combining with an emotional engine"

[0841] (Claim 1)

[0842] A means of collecting information using multiple sensors and cameras to monitor the movements of a person requiring care in real time,

[0843] A means for processing collected information and detecting falls or abnormal movements,

[0844] A means of transmitting a communication and sending an alert to emergency contacts when an anomaly is detected,

[0845] A means including a natural language processing device for generating natural dialogue with a person requiring care,

[0846] A means of operating electrical appliances based on voice commands entered by a person requiring care,

[0847] A computing device for performing emotion recognition,

[0848] A means for generating instructions that provide breaks and advice according to the emotional state of the worker,

[0849] A communication method that notifies the safety manager when a specific emotional state exceeds a threshold,

[0850] A system that includes this.

[0851] (Claim 2)

[0852] The system according to claim 1, which converts voice input into text information, generates an appropriate response, and outputs it back as voice, in order to enable natural voice dialogue with a person requiring care.

[0853] (Claim 3)

[0854] The system according to claim 1, which processes motion information and image information in real time when detecting falls or abnormal movements of a person requiring care. [Explanation of symbols]

[0855] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting data using multiple sensors and cameras to monitor the movements of a person requiring care in real time, A means for processing collected data and detecting falls and abnormal movements, A means of sending a notification and an alert to emergency contacts when an anomaly is detected, A means including a natural language processing unit for generating natural conversations with care recipients, A means of operating home appliances based on voice commands entered by a person requiring care, A means of immediately notifying pre-set emergency contacts in the event of an emergency, A system that includes this.

2. The system according to claim 1, which converts voice input into text data, generates an appropriate response, and outputs it again as voice, in order to enable natural voice dialogue with a person requiring care.

3. The system according to claim 1, which processes motion data and image data in real time when detecting falls or abnormal movements of a person requiring care.

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