system

Abstract

Provide a system. 【Solution means】 Means for generating conversation content using a generation model, Means for collecting biological data using sensors and cameras, Means for analyzing the collected biological data to detect behavioral patterns different from normal, Means for automatically reporting when an abnormality is determined, Means for obtaining biological data in cooperation with an external device, Means for converting the generated conversation content into voice and outputting it, Means for analyzing biological data to monitor the health status and notifying information when an abnormality is detected, A system including the above.

Description

Technical Field

[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, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] There is a need to reduce the sense of loneliness and health risks that the elderly feel when living alone, especially the risk of sudden physical changes and solitary death, and to provide an environment in which they can live a safe and fulfilling life. However, many current monitoring systems lack the ability to conduct daily communication with the elderly, and there is also a problem that even if an abnormality is detected, the system for promptly reporting it to the relevant parties is not established.

Means for Solving the Problems

[0005] This invention provides a robot with a dialogue mechanism using a generative model, enabling the robot to serve as a daily conversational partner for the elderly. Furthermore, it provides technology to collect biometric information of the elderly using sensors and cameras and to detect patterns that are different from the normal. In addition, by incorporating a function to automatically send notifications when an abnormality is detected, the system ensures the safety of the elderly and enables a rapid response.

[0006] A "generative model" is a computational model that uses artificial intelligence to generate data based on input information, and has the ability to create natural dialogue and text.

[0007] A "sensor" is a device that can measure physical or biological changes and process that information as an electronic signal. In this invention, it is used to collect data such as heart rate and body temperature.

[0008] A "camera" is a device that captures visual information as digital data and records or analyzes it, and is used to monitor the behavioral patterns of elderly people.

[0009] "Biometric data" refers to information about an individual's life activities, such as heart rate, body temperature, and body movements, and is collected through sensors and cameras.

[0010] "Automatic notification mechanisms" refer to processes or functions that, when a system detects an anomaly, send an emergency notification to pre-configured contacts without human intervention. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3]This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, when an emotion engine is combined. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface that includes a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] This invention describes an embodiment of a system that uses an interactive robot based on a generative model to monitor the lives of elderly people, alleviate feelings of loneliness through conversation, and ensure their safety.

[0033] This system functions through mutual communication between the server and the terminal. The server runs a generative model, generating conversation scripts using seasonal information and event data. For example, in spring, it generates natural-sounding conversation such as, "It's warm today, and the flowers have started to bloom." The generated script is sent to the terminal as digital data.

[0034] The terminal provides interaction with the elderly in the form of a robot. Using its built-in speech synthesis engine, the terminal converts conversation scripts received from the server into speech and outputs it to the elderly user. The terminal also features a microphone, sensors, and a camera for daily interaction and observation of the elderly. It receives the user's voice input using the microphone and transmits it to the server as text data in real time.

[0035] The server analyzes the text data sent by the user and generates the content of the next conversation. For example, if a user says, "I've been having trouble walking lately," the server will understand the context and generate a corresponding question such as, "That's worrying. Which foot hurts?"

[0036] The device also uses built-in sensors to acquire biometric data such as heart rate and body temperature, and motion sensors and cameras to collect behavioral patterns of elderly individuals. This data is sent to a server and compared with past data to detect abnormal conditions.

[0037] If the server detects an anomaly, such as a sudden drop in the user's heart rate or prolonged inactivity, it will automatically notify pre-registered contacts. An emergency notification, including specific details of the anomaly and location information, will be sent via email or SMS.

[0038] This allows elderly people to alleviate feelings of loneliness while also ensuring a system that can respond quickly in the event of an emergency.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The server inputs information based on seasons and events into a generative model and generates a conversation script. The generated script is then sent to the terminal.

[0042] Step 2:

[0043] The terminal receives the conversation script from the server, converts it into speech data using a speech synthesis engine, and outputs it to the user.

[0044] Step 3:

[0045] The user responds with voice to the audio output by the device.

[0046] Step 4:

[0047] The terminal receives the user's voice via a microphone, converts it into text data using a speech recognition system, and sends it to the server.

[0048] Step 5:

[0049] The server analyzes the received text data and generates the necessary conversation script. The generated script is then sent back to the terminal.

[0050] Step 6:

[0051] The device periodically acquires the user's biometric data from sensors and sends it to the server.

[0052] Step 7:

[0053] The server analyzes the biometric data in real time to determine whether or not there are any abnormalities.

[0054] Step 8:

[0055] If an anomaly is detected, the server will automatically send a notification to pre-registered contacts. The notification will include details of the anomaly and the time it was detected.

[0056] (Example 1)

[0057] 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."

[0058] Elderly people face feelings of loneliness, and changes in their health often go undetected. Therefore, it is essential to support the lives of the elderly, improve their quality of life, and detect abnormal situations early to ensure appropriate responses.

[0059] 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.

[0060] In this invention, the server includes means for generating conversation content using a generative model, means for converting voice information into text and analyzing user input information, and means for acquiring biometric information using sensors and imaging devices and recognizing unusual behavioral patterns. This makes it possible to alleviate feelings of loneliness among the elderly while enabling early detection of abnormal situations and rapid response.

[0061] A "generative model" is an artificial intelligence algorithm used to generate new data or content based on specific input data.

[0062] "Means for generating conversation content" refers to a function that uses a generative model to create text used in interactions with users.

[0063] "Means of converting audio information into text" refers to technology that converts audio data collected from users into text data.

[0064] "Means of analyzing user input information" refers to an analytical process that understands user utterances and text data and derives appropriate responses.

[0065] "Means for acquiring biological information using sensors and imaging devices" refers to monitoring devices and related devices used to collect biological data such as body temperature and heart rate.

[0066] "Means of recognizing behavioral patterns" refers to technologies that detect changes in user behavior and health status based on collected data.

[0067] "A means of automatically notifying when an anomaly is detected" refers to a function that sends information to a pre-designated contact when the system determines that an anomaly has occurred with the user.

[0068] This system works by having a server and terminals work together to support the lives of the elderly. A specific embodiment of the invention is described below.

[0069] The server is equipped with a high-performance generative AI model and generates conversation content with the user based on prompts. Examples of prompts include "How was your day?" and "How have you been feeling lately?". The server uses these prompts to generate a script to start a conversation, facilitating a natural and meaningful dialogue with the user. The generated conversation script is sent to the terminal as digital data.

[0070] The device has a built-in speech synthesis engine that converts conversation scripts received from the server into speech. This allows the device to speak to the elderly user. Furthermore, the device has a voice input function that recognizes the user's speech and sends it to the server in real time, maintaining the continuity of the conversation.

[0071] Sensors and cameras are built into the terminal to acquire biometric information such as the user's heart rate and body temperature. This data is sent to a server, which analyzes the user's behavior patterns to detect unusual situations. For example, if a user says, "I'm not feeling well today," the server takes that statement into consideration, immediately analyzes the user's health condition, and generates follow-up questions such as, "How are you feeling unwell?"

[0072] If the server detects an abnormality in the user's health status, the system automatically notifies pre-configured contacts. This includes emergency contact via email or SMS, allowing family members and caregivers to be quickly informed of the situation.

[0073] This invention makes it possible to alleviate feelings of loneliness among the elderly while also enabling rapid response to changes in their health. In particular, by generating conversations using seasonal information and event data, it improves the quality of everyday conversations while enabling early response to abnormal situations.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The user initiates a conversation with the device. At this point, they input a prompt such as, "How did you spend your day?" The device sends this prompt to its speech synthesis engine, converts it into speech, and then speaks this message to the user. This gives the user a starting point for a conversation.

[0077] Step 2:

[0078] The user's response is input as audio data via the device's microphone. The device uses speech recognition technology to convert this audio data into text data. The converted text data is sent to a server and prepared for further analysis.

[0079] Step 3:

[0080] The server receives text data sent from the terminal. Based on this input, a generative AI model understands the context and generates the next conversation script corresponding to the user's words. For example, if the user says, "I took a walk in the park," the server uses the generative AI model to output a response such as, "That's nice, what did you see?"

[0081] Step 4:

[0082] The new conversation script generated by the server is sent to the terminal as digital data. The terminal then sends this data back to the speech synthesis engine, which converts it into speech that the user hears. This continues the flow of dialogue between the user and the terminal.

[0083] Step 5:

[0084] The device utilizes built-in sensors and imaging equipment to measure the user's biometric information, such as heart rate and body temperature. This data is captured by the device as sensor input, converted into digital data, and then transmitted to a server.

[0085] Step 6:

[0086] The server analyzes the acquired biometric information and checks for any differences from normal behavioral patterns. It performs data calculations and determines abnormalities, for example, if the heart rate is lower than normal or the body temperature is abnormally high. Based on the analysis results, notification data is generated if necessary.

[0087] Step 7:

[0088] If an anomaly is detected, the server generates an emergency notification and automatically sends it to pre-configured contacts. This may be done via email or SMS. The notification may include details of the anomaly and, if necessary, the user's current location. This allows for a rapid response.

[0089] (Application Example 1)

[0090] 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."

[0091] Alleviating feelings of loneliness and ensuring safety in the lives of the elderly are important challenges in modern society. In particular, monitoring health status and responding quickly to emergencies are essential for improving quality of life. However, current methods make it difficult to implement these effectively.

[0092] 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.

[0093] In this invention, the server includes means for generating dialogue content using a generative model, means for acquiring biometric data in cooperation with an external device, and means for converting the generated dialogue content into speech and outputting it. This enables natural dialogue with elderly people, real-time monitoring of their health status, and rapid notification in the event of an abnormality.

[0094] A "generative model" is an algorithm for automatically generating dialogue content in a human-understandable format based on input information.

[0095] "Dialogue content" refers to the set of information exchanged between humans and computers, generated by a generative model.

[0096] A "sensor" is a device that detects a physical quantity, converts it into an electrical signal, and outputs it.

[0097] A "camera" is a photographic device that captures image information from its surroundings and stores and processes it as digital data.

[0098] "Biometric data" refers to numerical information that indicates the physiological state of the human body, such as heart rate and body temperature.

[0099] "Behavioral patterns" refer to a portion of the information that indicates the tendencies of an individual's actions and activities in their daily life.

[0100] An "abnormality" is an event that deviates from the normal state or expected behavior.

[0101] "Notification" refers to the act of automatically sending a notification to designated recipients in the event of an anomaly.

[0102] An "external device" is an electronic device connected to the system from an external source, with which the system interacts.

[0103] "Converting to speech" is the process of outputting text information as audio.

[0104] "Health status" refers to the state of an individual's current level of physical and mental health.

[0105] An "emergency notice" is a message intended to draw immediate attention beyond normal communication methods.

[0106] This invention provides a method for specifically implementing an interactive robot system to support the lives of the elderly. The system mainly consists of a server and a terminal.

[0107] The server has the functionality to generate conversation content with elderly people using a generative AI model. The generative model creates natural and friendly conversation scripts while taking into account seasonal and event information. In this process, the prompt "Generate a conversation scenario for when the user has forgotten their medication. Create a response that gently reminds the user." is used.

[0108] The terminal functions as a robot and handles interactions with the elderly. It has a built-in speech synthesis engine that converts conversation scripts received from the server into speech and outputs it. The terminal also collects voice and biometric data from the elderly using a microphone, sensors, and a camera. Specifically, it acquires heart rate, body temperature, and patterns of daily activities.

[0109] This biometric data is transmitted to a server in real time, and anomaly detection algorithms are used to analyze any abnormalities in the data. If an abnormality is detected, for example, if the heart rate is unstable or if there is a significant deviation from the normal behavior pattern, the server automatically notifies pre-registered contacts.

[0110] Specifically, the server converts speech to text using a speech recognition API (e.g., Amazon Transcribe) and outputs the speech using a speech synthesis API such as Google Cloud Text-to-Speech. It also connects to an external device, a fitness tracker, via Bluetooth to acquire biometric data.

[0111] A concrete example of this invention is a scenario where, to prevent users from forgetting to take their medication after breakfast, a robot kindly asks, "Did you enjoy your breakfast? Have you forgotten your medication?" In this way, the system aims to alleviate feelings of loneliness and improve the quality of life while supporting the user's health and safety.

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The server receives user input and provides a prompt to the generative AI model. The prompt used is: "Generate a conversation scenario for when the user has forgotten their medication. Create a gentle reminder response for the user." Based on this input, the generative AI model generates a dialogue script, and the server outputs the result as text data.

[0115] Step 2:

[0116] The server sends the generated dialogue script to the speech synthesis engine. The input text data is converted into speech data using speech synthesis technology and sent to the terminal. The specific operation uses software APIs such as Google Cloud Text-to-Speech.

[0117] Step 3:

[0118] The terminal outputs the received audio data through its speaker, providing interaction with the user. The user speaks their response through the microphone. The terminal receives this audio input and sends it to the server for further processing. This process utilizes available speech recognition technology.

[0119] Step 4:

[0120] The server converts the user's voice input into text data using a speech recognition API. It analyzes the input voice data, converts its content into text, and then sends it to a generative AI model to generate the next dialogue. This step utilizes an API such as Amazon Transcribe.

[0121] Step 5:

[0122] The device collects sensor and camera information to monitor the user's health. This biometric data is received from an external device via Bluetooth and transmitted to a server. Inputs include heart rate, body temperature, and motion detection data.

[0123] Step 6:

[0124] The server analyzes the transmitted biometric data using an anomaly detection algorithm. It compares the input data with normal data and immediately generates a notification if an anomaly is detected. Specifically, it sends a notification via email or SMS to the registered contacts.

[0125] Step 7:

[0126] The server completes the anomaly notification and saves the event as a log. This allows it to be referenced later as part of the user's health management history. This saved data will be used for future improvements and monitoring.

[0127] 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.

[0128] This invention is a system that supports the lives of the elderly and enables personalized responses based on their emotions through an interactive robot using a generative model and an emotion engine. By recognizing and analyzing the user's emotions, this system improves the quality of dialogue and provides more effective safety and comfort.

[0129] The system primarily consists of three main components: a server, a terminal, and an emotion engine. The server generates conversation scripts using a generative model and adjusts the content according to the user's emotional state. The generative model can incorporate seasonal and event information, as well as topics that will interest the user. For example, if the system detects that the user is feeling down, it can prioritize generating uplifting conversation content.

[0130] The terminal is a device that interacts directly with the user. The emotion engine is built into the terminal and acquires and analyzes the user's voice and facial expression data. Emotion analysis uses elements extracted from voice tone, changes in facial expression, and biometric data. This allows the system to estimate the user's current emotional state in real time and send that information to the server.

[0131] If a user says something like, "I've been feeling down lately," the emotion engine analyzes their voice tone and biometric data to determine that they are in a low emotional state. Based on this emotional information, the server adjusts the conversation and generates suggestions such as, "How about getting some fresh air?" or "Shall we listen to your favorite music together?"

[0132] The device also continuously acquires biometric data using its built-in sensors and transmits it to a server. The server analyzes this data and monitors for any unusual behavioral patterns. If an abnormality is detected and the user's emotional state is deemed unstable, an automatic notification is sent to pre-registered contacts.

[0133] In this way, the system of the present invention supports the daily lives of the elderly, enables emotionally responsive care, and provides a safe and secure environment.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] The server uses a generative model to create conversational scripts based on seasons and events. The generated scripts are then sent to the terminal.

[0137] Step 2:

[0138] The terminal receives the conversation script from the server, converts it into speech using a speech synthesis engine, and outputs it to the user.

[0139] Step 3:

[0140] The user responds verbally to the audio output from the device.

[0141] Step 4:

[0142] The device captures the user's voice with a microphone and uses an emotion engine to analyze the tone of the voice and estimate the user's emotions.

[0143] Step 5:

[0144] The emotion engine captures the user's facial expressions with a camera and analyzes them along with biometric data to determine their emotional state. Specifically, it detects whether or not the user is smiling, eye movements, and changes in heart rate.

[0145] Step 6:

[0146] The terminal sends the results of the emotion engine's analysis to the server. The server takes the user's emotional state into consideration, dynamically adjusts the dialogue content, and generates a new conversation script.

[0147] Step 7:

[0148] The server sends the generated conversation script to the terminal and continues the dialogue. If necessary, it incorporates suggestions to soothe the user's emotions into the script.

[0149] Step 8:

[0150] The device continuously acquires the user's biometric data through sensors and monitors for any abnormalities. The data is then transmitted to a server.

[0151] Step 9:

[0152] The server analyzes the received biometric data and detects deviations from normal patterns. If an anomaly is detected and the emotional state is determined to be unstable, an automatic notification is triggered.

[0153] Step 10:

[0154] A notification containing information about abnormalities and emotional states is automatically sent via email or SMS to pre-registered emergency contacts.

[0155] (Example 2)

[0156] 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".

[0157] There is a need for systems that can improve mental health care by understanding the emotional state of users, including the elderly, in real time and providing appropriate dialogue in their daily lives. Furthermore, a challenge is to detect abnormalities early based on the user's health and emotional state and enable appropriate responses.

[0158] 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.

[0159] In this invention, the server includes means for generating dialogue content based on emotional state using a generative model, means for estimating the user's emotional state in real time using voice and facial expression data, and means for continuously collecting biometric data using sensors and detecting unusual behavioral patterns. This enables dialogue that is attentive to the user's emotions, as well as early detection of abnormalities in health or behavior and prompt response.

[0160] A "generative model" is an algorithm or program that generates new data or content based on input information.

[0161] "Emotional state" is an indicator that shows the user's psychological and emotional condition, and is determined by analyzing voice and facial expression data.

[0162] "Dialogue content" refers to the sentences and phrases used in communication with the user, and it changes according to the user's interests and emotional state.

[0163] "Voice and facial expression data" refers to a collection of raw data used to identify emotions, such as the tone of the user's voice and facial expressions.

[0164] "Real-time estimation" means that the process from data acquisition to analysis is performed instantaneously, and results can be obtained immediately.

[0165] A "sensor" is a device that acquires information from the environment and users, and collects biometric data.

[0166] "Biometric data" refers to various types of data that indicate the user's physical condition, such as heart rate and body temperature.

[0167] A "behavioral pattern" refers to a sequence of actions and tendencies in a user's daily life, and serves as a criterion for detecting unusual behavior.

[0168] The system of this invention enables personalized responses by understanding the user's emotional state and generating corresponding dialogue. The system mainly consists of three main elements: a server, a terminal, and an emotion engine.

[0169] The server utilizes a generative AI model to generate dialogue based on the user's emotional state. This generative model incorporates commonly used natural language processing techniques, enabling dynamic content generation that takes into account, for example, seasonal and event information. Based on the emotional information transmitted from the terminal, the server can adjust prompts to include topics that will interest the user. For example, it can generate prompts such as, "You seem a little down lately, would you like to talk about something?" tailored to the user's mood.

[0170] The terminal is a device for interacting with the user and has a built-in camera and microphone. It acquires the user's voice and facial expression data in real time and supplies this data to the emotion engine. The emotion engine contains a program for analyzing voice tone and facial expression changes, and is responsible for analyzing the user's emotional state in real time and sending it to the server.

[0171] A sensor built into the device is used to collect biometric data. This sensor constantly monitors the user's biometric data and transmits it to a server. Based on this data, the server detects unusual behavior to identify user anomalies early and automatically notifies pre-registered contacts as needed.

[0172] The user follows the prompts suggested by the system to carry out everyday conversations. For example, if the user mutters, "I'm so tired today," the system immediately performs sentiment analysis and provides a personalized suggestion such as, "Why don't you take a break and listen to your favorite music?" This allows the user to receive emotional support and a safe and comfortable environment.

[0173] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0174] Step 1:

[0175] The device captures the user's voice and facial expressions in real time using its camera and microphone. This input data includes the user's voice tone and changes in facial expressions. This data is sent to an emotion engine. This engine estimates the user's emotional state by using algorithms to analyze voice tone and facial expression changes. The output of this step is a label or score indicating the user's emotion.

[0176] Step 2:

[0177] The device sends the analyzed emotional state to the server. This data transfer is usually done via Wi-Fi or Bluetooth. The server stores the received emotional data in a database for use in the next step. The output of this step is the emotional data recorded on the server side.

[0178] Step 3:

[0179] The server uses stored sentiment data to generate appropriate dialogue through a generative AI model. The model combines sentiment data with seasonal and event information to create prompts that will interest the user. For example, if the user is tired, a suggestion such as "Why don't you take a break and listen to your favorite music?" might be generated. The output of this step is the generated prompt.

[0180] Step 4:

[0181] The terminal presents the user with prompt messages received from the server. The interaction with the user is reproduced in natural-sounding speech using a speech synthesis engine. When the user speaks, the audio is again captured and analyzed by the emotion engine. The input for this step is the prompt messages sent from the server, and the output is the specific conversation content presented to the user.

[0182] Step 5:

[0183] The server analyzes biometric data continuously transmitted from the device to detect unusual behavioral patterns. This biometric data includes heart rate and body temperature, and is analyzed using statistical methods and machine learning algorithms. If an anomaly is detected, an automatic notification is sent to pre-registered contacts. The output of this step is an alert indicating that an anomaly has been detected.

[0184] (Application Example 2)

[0185] 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 device 14 will be referred to as the "terminal."

[0186] Loneliness and anxiety are often serious problems in the lives of the elderly. Furthermore, if emotional instability in daily life is not addressed appropriately, it can negatively impact their psychological health. To address these challenges, there is a need for a system that can monitor the emotional state of the elderly in real time and provide dialogue tailored to their emotions.

[0187] 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.

[0188] In this invention, the server includes means for generating dialogue content using a generative model, means for collecting biometric information using sensors and imaging devices, and means for analyzing the user's voice and biometric information to estimate their emotional state in real time. This makes it possible to quickly and accurately analyze the user's emotional state and provide appropriate dialogue content that meets their individual needs.

[0189] A "generative model" is an algorithm or method that generates new information or content based on given data or information.

[0190] "Dialogue content" refers to the specific details of the communication exchanged between the user and the system.

[0191] A "sensor" is a device that measures physical or chemical information and outputs it as an electronic signal.

[0192] A "photography device" is a device used to record the user's appearance and actions as image data.

[0193] "Biometric information" refers to data about the user's physiological state, including heart rate and skin temperature.

[0194] "Emotional state" refers to the psychological or emotional state that a user is experiencing at a particular point in time.

[0195] "Real-time estimation" means performing analysis and making decisions instantly the moment data is input.

[0196] "Individual needs" refers to situations where different demands or requirements exist for each specific user, and a corresponding approach is required.

[0197] To realize this system, the server, terminal, and emotion analysis engine work together. First, the terminal is a crucial device that directly interacts with the user, and the emotion analysis engine is integrated into it. Specifically, it collects the user's voice and facial expression data using voice input devices and camera devices. This collected biometric information is analyzed by the emotion analysis engine. Based on the analysis results, the user's emotional state is estimated in real time, and this information is transmitted to the server.

[0198] The server dynamically generates dialogue content using a generative AI model based on the user's emotional state and biometric information from sensors. Seasonal and event information is also considered to select topics that are more likely to interest the user. For example, if a user says, "I'm lonely today," the server uses the generative AI model to generate a suggestion such as, "How about starting a new hobby today?" This is achieved by using the prompt message, "The user says they are lonely today. Please offer them some encouraging words. For example, use warm words that suggest a new hobby."

[0199] Furthermore, if an abnormal behavioral pattern is detected, the server automatically notifies pre-registered contacts. This system allows users to always feel secure while also informing family members and caregivers of any risks.

[0200] In this way, systems based on generative models and emotion engines support the improvement of the quality of life for the elderly by providing users with emotionally resonant conversations and care.

[0201] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0202] Step 1:

[0203] The device collects the user's voice and facial expression data. This input includes voice data acquired by a voice input device and image data acquired by a camera. This data is sent to an emotion analysis engine for emotion analysis. Specifically, the device's microphone and camera operate simultaneously to capture the user's speech and the changes in their facial expressions during that time.

[0204] Step 2:

[0205] The emotion analysis engine analyzes the received audio and image data to estimate the user's emotional state. The analysis process applies voice tone analysis and facial expression recognition algorithms. As output, the user's emotional state at that moment is sent to the server in real time. Specifically, an emotion score (e.g., joy, anger, sadness) is generated.

[0206] Step 3:

[0207] The server receives emotional state information as input and generates dialogue content using a generative AI model. Based on the input, and taking into account seasonal and event information, the generative model designs the optimal conversation content according to the prompt. The generated content takes the user's emotions into consideration and constructs the most appropriate response.

[0208] Step 4:

[0209] The server sends the generated dialogue to the terminal, which then relays it to the user. The outputted dialogue is delivered to the user as either audio or text. The terminal's speaker and display are used in this process.

[0210] Step 5:

[0211] The server also constantly monitors biometric data from sensors to detect unusual behavioral patterns. The input is real-time biometric data, and if an anomaly is detected, an alert is automatically sent to registered contacts. Specifically, if the system detects a sudden change in heart rate or an abnormal rise in body temperature, it immediately sends a warning message.

[0212] 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.

[0213] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0214] 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.

[0215] [Second Embodiment]

[0216] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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".

[0228] This invention describes an embodiment of a system that uses an interactive robot based on a generative model to monitor the lives of elderly people, alleviate feelings of loneliness through conversation, and ensure their safety.

[0229] This system functions through mutual communication between the server and the terminal. The server runs a generative model, generating conversation scripts using seasonal information and event data. For example, in spring, it generates natural-sounding conversation such as, "It's warm today, and the flowers have started to bloom." The generated script is sent to the terminal as digital data.

[0230] The terminal provides interaction with the elderly in the form of a robot. Using its built-in speech synthesis engine, the terminal converts conversation scripts received from the server into speech and outputs it to the elderly user. The terminal also features a microphone, sensors, and a camera for daily interaction and observation of the elderly. It receives the user's voice input using the microphone and transmits it to the server as text data in real time.

[0231] The server analyzes the text data sent by the user and generates the content of the next conversation. For example, if a user says, "I've been having trouble walking lately," the server will understand the context and generate a corresponding question such as, "That's worrying. Which foot hurts?"

[0232] The device also uses built-in sensors to acquire biometric data such as heart rate and body temperature, and motion sensors and cameras to collect behavioral patterns of elderly individuals. This data is sent to a server and compared with past data to detect abnormal conditions.

[0233] If the server detects an anomaly, such as a sudden drop in the user's heart rate or prolonged inactivity, it will automatically notify pre-registered contacts. An emergency notification, including specific details of the anomaly and location information, will be sent via email or SMS.

[0234] This allows elderly people to alleviate feelings of loneliness while also ensuring a system that can respond quickly in the event of an emergency.

[0235] The following describes the processing flow.

[0236] Step 1:

[0237] The server inputs information based on seasons and events into a generative model and generates a conversation script. The generated script is then sent to the terminal.

[0238] Step 2:

[0239] The terminal receives the conversation script from the server, converts it into speech data using a speech synthesis engine, and outputs it to the user.

[0240] Step 3:

[0241] The user responds with voice to the audio output by the device.

[0242] Step 4:

[0243] The terminal receives the user's voice via a microphone, converts it into text data using a speech recognition system, and sends it to the server.

[0244] Step 5:

[0245] The server analyzes the received text data and generates the necessary conversation script. The generated script is then sent back to the terminal.

[0246] Step 6:

[0247] The device periodically acquires the user's biometric data from sensors and sends it to the server.

[0248] Step 7:

[0249] The server analyzes the biometric data in real time to determine whether or not there are any abnormalities.

[0250] Step 8:

[0251] If an anomaly is detected, the server will automatically send a notification to pre-registered contacts. The notification will include details of the anomaly and the time it was detected.

[0252] (Example 1)

[0253] 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".

[0254] Elderly people face feelings of loneliness, and changes in their health often go undetected. Therefore, it is essential to support the lives of the elderly, improve their quality of life, and detect abnormal situations early to ensure appropriate responses.

[0255] 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.

[0256] In this invention, the server includes means for generating conversation content using a generative model, means for converting voice information into text and analyzing user input information, and means for acquiring biometric information using sensors and imaging devices and recognizing unusual behavioral patterns. This makes it possible to alleviate feelings of loneliness among the elderly while enabling early detection of abnormal situations and rapid response.

[0257] A "generative model" is an artificial intelligence algorithm used to generate new data or content based on specific input data.

[0258] "Means for generating conversation content" refers to a function that uses a generative model to create text used in interactions with users.

[0259] "Means of converting audio information into text" refers to technology that converts audio data collected from users into text data.

[0260] "Means of analyzing user input information" refers to an analytical process that understands user utterances and text data and derives appropriate responses.

[0261] "Means for acquiring biological information using sensors and imaging devices" refers to monitoring devices and related devices used to collect biological data such as body temperature and heart rate.

[0262] "Means of recognizing behavioral patterns" refers to technologies that detect changes in user behavior and health status based on collected data.

[0263] "A means of automatically notifying when an anomaly is detected" refers to a function that sends information to a pre-designated contact when the system determines that an anomaly has occurred with the user.

[0264] This system works by having a server and terminals work together to support the lives of the elderly. A specific embodiment of the invention is described below.

[0265] The server is equipped with a high-performance generative AI model and generates conversation content with the user based on prompts. Examples of prompts include "How was your day?" and "How have you been feeling lately?". The server uses these prompts to generate a script to start a conversation, facilitating a natural and meaningful dialogue with the user. The generated conversation script is sent to the terminal as digital data.

[0266] The device has a built-in speech synthesis engine that converts conversation scripts received from the server into speech. This allows the device to speak to the elderly user. Furthermore, the device has a voice input function that recognizes the user's speech and sends it to the server in real time, maintaining the continuity of the conversation.

[0267] Sensors and cameras are built into the terminal to acquire biometric information such as the user's heart rate and body temperature. This data is sent to a server, which analyzes the user's behavior patterns to detect unusual situations. For example, if a user says, "I'm not feeling well today," the server takes that statement into consideration, immediately analyzes the user's health condition, and generates follow-up questions such as, "How are you feeling unwell?"

[0268] If the server detects an abnormality in the user's health status, the system automatically notifies pre-configured contacts. This includes emergency contact via email or SMS, allowing family members and caregivers to be quickly informed of the situation.

[0269] This invention makes it possible to alleviate feelings of loneliness among the elderly while also enabling rapid response to changes in their health. In particular, by generating conversations using seasonal information and event data, it improves the quality of everyday conversations while enabling early response to abnormal situations.

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

[0271] Step 1:

[0272] The user initiates a conversation with the device. At this point, they input a prompt such as, "How did you spend your day?" The device sends this prompt to its speech synthesis engine, converts it into speech, and then speaks this message to the user. This gives the user a starting point for a conversation.

[0273] Step 2:

[0274] The user's response is input as audio data via the device's microphone. The device uses speech recognition technology to convert this audio data into text data. The converted text data is sent to a server and prepared for further analysis.

[0275] Step 3:

[0276] The server receives text data sent from the terminal. Based on this input, a generative AI model understands the context and generates the next conversation script corresponding to the user's words. For example, if the user says, "I took a walk in the park," the server uses the generative AI model to output a response such as, "That's nice, what did you see?"

[0277] Step 4:

[0278] The new conversation script generated by the server is transmitted to the terminal as digital data. The terminal sends this data back into the speech synthesis engine, converts it into speech, and plays it for the user. In this way, the flow of the conversation between the user and the terminal continues.

[0279] Step 5:

[0280] The terminal utilizes the built-in sensors and imaging device to measure biometric information such as the user's heart rate and body temperature. These data are captured by the terminal as sensor inputs, converted into digital data, and then transmitted to the server.

[0281] Step 6:

[0282] The server analyzes the acquired biometric information and checks for differences from normal behavior patterns. It performs data calculations and determines it as abnormal when, for example, the heart rate is lower than normal or the body temperature is abnormally high. Based on the analysis results, data for notification is generated if necessary.

[0283] Step 7:

[0284] If an abnormality is detected, the server generates an emergency notification and automatically sends it to the pre-set contacts. Email or SMS is used for this. The notification may include the details of the abnormality and, if necessary, the user's current location. This enables a prompt response.

[0285] (Application Example 1)

[0286] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0287] Alleviating feelings of loneliness and ensuring safety in the lives of the elderly are important challenges in modern society. In particular, monitoring health status and responding quickly to emergencies are essential for improving quality of life. However, current methods make it difficult to implement these effectively.

[0288] 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.

[0289] In this invention, the server includes means for generating dialogue content using a generative model, means for acquiring biometric data in cooperation with an external device, and means for converting the generated dialogue content into speech and outputting it. This enables natural dialogue with elderly people, real-time monitoring of their health status, and rapid notification in the event of an abnormality.

[0290] A "generative model" is an algorithm for automatically generating dialogue content in a human-understandable format based on input information.

[0291] "Dialogue content" refers to the set of information exchanged between humans and computers, generated by a generative model.

[0292] A "sensor" is a device that detects a physical quantity, converts it into an electrical signal, and outputs it.

[0293] A "camera" is a photographic device that captures image information from its surroundings and stores and processes it as digital data.

[0294] "Biometric data" refers to numerical information that indicates the physiological state of the human body, such as heart rate and body temperature.

[0295] "Behavioral patterns" refer to a portion of the information that indicates the tendencies of an individual's actions and activities in their daily life.

[0296] An "abnormality" is an event that deviates from the normal state or expected behavior.

[0297] "Notification" refers to the act of automatically sending a notification to designated recipients in the event of an anomaly.

[0298] An "external device" is an electronic device connected to the system from an external source, with which the system interacts.

[0299] "Converting to speech" is the process of outputting text information as audio.

[0300] "Health status" refers to the state of an individual's current level of physical and mental health.

[0301] An "emergency notice" is a message intended to draw immediate attention beyond normal communication methods.

[0302] This invention provides a method for specifically implementing an interactive robot system to support the lives of the elderly. The system mainly consists of a server and a terminal.

[0303] The server has the functionality to generate conversation content with elderly people using a generative AI model. The generative model creates natural and friendly conversation scripts while taking into account seasonal and event information. In this process, the prompt "Generate a conversation scenario for when the user has forgotten their medication. Create a response that gently reminds the user." is used.

[0304] The terminal functions as a robot and handles interactions with the elderly. It has a built-in speech synthesis engine that converts conversation scripts received from the server into speech and outputs it. The terminal also collects voice and biometric data from the elderly using a microphone, sensors, and a camera. Specifically, it acquires heart rate, body temperature, and patterns of daily activities.

[0305] These biological data are sent to the server in real time, and the abnormalities in the data are analyzed using an anomaly detection algorithm. If an abnormality is detected, for example, when the heart rate is unstable or when there is a significant deviation from the normal behavior pattern, the server automatically reports it to the pre-registered contact.

[0306] Specifically, the server converts the voice to text using a speech recognition API (e.g., Amazon Transcribe) and outputs the voice using a speech synthesis API such as Google Cloud Text-to-Speech. It also connects to the fitness tracker, which is an external device, via Bluetooth to obtain biological data.

[0307] As a specific example of this invention, in order to prevent forgetting to take medicine after breakfast, there is a scene where a robot kindly asks, "Was your breakfast delicious? Didn't you forget to take your medicine?" In this way, a system is realized that alleviates loneliness while supporting the health and safety of users and improves the quality of life.

[0308] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0309] Step 1:

[0310] The server receives the user's input and inputs the prompt text into the generation AI model. As the prompt text, "Please generate a conversation scenario when the user forgets to take medicine. Please create a response that kindly reminds the user." is used. Based on this input, the generation AI model generates a dialogue script, and the server outputs the result as text data.

[0311] Step 2:

[0312] The server sends the generated dialogue script to the speech synthesis engine. The input text data is converted into speech data using speech synthesis technology and sent to the terminal. The specific operation uses software APIs such as Google Cloud Text-to-Speech.

[0313] Step 3:

[0314] The terminal outputs the received audio data through its speaker, providing interaction with the user. The user speaks their response through the microphone. The terminal receives this audio input and sends it to the server for further processing. This process utilizes available speech recognition technology.

[0315] Step 4:

[0316] The server converts the user's voice input into text data using a speech recognition API. It analyzes the input voice data, converts its content into text, and then sends it to a generative AI model to generate the next dialogue. This step utilizes an API such as Amazon Transcribe.

[0317] Step 5:

[0318] The device collects sensor and camera information to monitor the user's health. This biometric data is received from an external device via Bluetooth and transmitted to a server. Inputs include heart rate, body temperature, and motion detection data.

[0319] Step 6:

[0320] The server analyzes the transmitted biometric data using an anomaly detection algorithm. It compares the input data with normal data and immediately generates a notification if an anomaly is detected. Specifically, it sends a notification via email or SMS to the registered contacts.

[0321] Step 7:

[0322] The server completes the anomaly notification and saves the event as a log. This allows it to be referenced later as part of the user's health management history. This saved data will be used for future improvements and monitoring.

[0323] 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.

[0324] This invention is a system that supports the lives of the elderly and enables personalized responses based on their emotions through an interactive robot using a generative model and an emotion engine. By recognizing and analyzing the user's emotions, this system improves the quality of dialogue and provides more effective safety and comfort.

[0325] The system primarily consists of three main components: a server, a terminal, and an emotion engine. The server generates conversation scripts using a generative model and adjusts the content according to the user's emotional state. The generative model can incorporate seasonal and event information, as well as topics that will interest the user. For example, if the system detects that the user is feeling down, it can prioritize generating uplifting conversation content.

[0326] The terminal is a device that interacts directly with the user. The emotion engine is built into the terminal and acquires and analyzes the user's voice and facial expression data. Emotion analysis uses elements extracted from voice tone, changes in facial expression, and biometric data. This allows the system to estimate the user's current emotional state in real time and send that information to the server.

[0327] If a user says something like, "I've been feeling down lately," the emotion engine analyzes their voice tone and biometric data to determine that they are in a low emotional state. Based on this emotional information, the server adjusts the conversation and generates suggestions such as, "How about getting some fresh air?" or "Shall we listen to your favorite music together?"

[0328] The device also continuously acquires biometric data using its built-in sensors and transmits it to a server. The server analyzes this data and monitors for any unusual behavioral patterns. If an abnormality is detected and the user's emotional state is deemed unstable, an automatic notification is sent to pre-registered contacts.

[0329] In this way, the system of the present invention supports the daily lives of the elderly, enables emotionally responsive care, and provides a safe and secure environment.

[0330] The following describes the processing flow.

[0331] Step 1:

[0332] The server uses a generative model to create conversational scripts based on seasons and events. The generated scripts are then sent to the terminal.

[0333] Step 2:

[0334] The terminal receives the conversation script from the server, converts it into speech using a speech synthesis engine, and outputs it to the user.

[0335] Step 3:

[0336] The user responds verbally to the audio output from the device.

[0337] Step 4:

[0338] The device captures the user's voice with a microphone and uses an emotion engine to analyze the tone of the voice and estimate the user's emotions.

[0339] Step 5:

[0340] The emotion engine captures the user's facial expressions with a camera and analyzes them along with biometric data to determine their emotional state. Specifically, it detects whether or not the user is smiling, eye movements, and changes in heart rate.

[0341] Step 6:

[0342] The terminal sends the results of the emotion engine's analysis to the server. The server takes the user's emotional state into consideration, dynamically adjusts the dialogue content, and generates a new conversation script.

[0343] Step 7:

[0344] The server sends the generated conversation script to the terminal and continues the dialogue. If necessary, it incorporates suggestions to soothe the user's emotions into the script.

[0345] Step 8:

[0346] The device continuously acquires the user's biometric data through sensors and monitors for any abnormalities. The data is then transmitted to a server.

[0347] Step 9:

[0348] The server analyzes the received biometric data and detects deviations from normal patterns. If an anomaly is detected and the emotional state is determined to be unstable, an automatic notification is triggered.

[0349] Step 10:

[0350] A notification containing information about abnormalities and emotional states is automatically sent via email or SMS to pre-registered emergency contacts.

[0351] (Example 2)

[0352] 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".

[0353] There is a need for systems that can improve mental health care by understanding the emotional state of users, including the elderly, in real time and providing appropriate dialogue in their daily lives. Furthermore, a challenge is to detect abnormalities early based on the user's health and emotional state and enable appropriate responses.

[0354] 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.

[0355] In this invention, the server includes means for generating dialogue content based on emotional state using a generative model, means for estimating the user's emotional state in real time using voice and facial expression data, and means for continuously collecting biometric data using sensors and detecting unusual behavioral patterns. This enables dialogue that is attentive to the user's emotions, as well as early detection of abnormalities in health or behavior and prompt response.

[0356] A "generative model" is an algorithm or program that generates new data or content based on input information.

[0357] "Emotional state" is an indicator that shows the user's psychological and emotional condition, and is determined by analyzing voice and facial expression data.

[0358] "Dialogue content" refers to the sentences and phrases used in communication with the user, and it changes according to the user's interests and emotional state.

[0359] "Voice and facial expression data" refers to a collection of raw data used to identify emotions, such as the tone of the user's voice and facial expressions.

[0360] "Real-time estimation" means that the process from data acquisition to analysis is performed instantaneously, and results can be obtained immediately.

[0361] A "sensor" is a device that acquires information from the environment and users, and collects biometric data.

[0362] "Biometric data" refers to various types of data that indicate the user's physical condition, such as heart rate and body temperature.

[0363] A "behavioral pattern" refers to a sequence of actions and tendencies in a user's daily life, and serves as a criterion for detecting unusual behavior.

[0364] The system of this invention enables personalized responses by understanding the user's emotional state and generating corresponding dialogue. The system mainly consists of three main elements: a server, a terminal, and an emotion engine.

[0365] The server utilizes a generative AI model to generate dialogue based on the user's emotional state. This generative model incorporates commonly used natural language processing techniques, enabling dynamic content generation that takes into account, for example, seasonal and event information. Based on the emotional information transmitted from the terminal, the server can adjust prompts to include topics that will interest the user. For example, it can generate prompts such as, "You seem a little down lately, would you like to talk about something?" tailored to the user's mood.

[0366] The terminal is a device for interacting with the user and has a built-in camera and microphone. It acquires the user's voice and facial expression data in real time and supplies this data to the emotion engine. The emotion engine contains a program for analyzing voice tone and facial expression changes, and is responsible for analyzing the user's emotional state in real time and sending it to the server.

[0367] A sensor built into the device is used to collect biometric data. This sensor constantly monitors the user's biometric data and transmits it to a server. Based on this data, the server detects unusual behavior to identify user anomalies early and automatically notifies pre-registered contacts as needed.

[0368] The user follows the prompts suggested by the system to carry out everyday conversations. For example, if the user mutters, "I'm so tired today," the system immediately performs sentiment analysis and provides a personalized suggestion such as, "Why don't you take a break and listen to your favorite music?" This allows the user to receive emotional support and a safe and comfortable environment.

[0369] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0370] Step 1:

[0371] The device captures the user's voice and facial expressions in real time using its camera and microphone. This input data includes the user's voice tone and changes in facial expressions. This data is sent to an emotion engine. This engine estimates the user's emotional state by using algorithms to analyze voice tone and facial expression changes. The output of this step is a label or score indicating the user's emotion.

[0372] Step 2:

[0373] The device sends the analyzed emotional state to the server. This data transfer is usually done via Wi-Fi or Bluetooth. The server stores the received emotional data in a database for use in the next step. The output of this step is the emotional data recorded on the server side.

[0374] Step 3:

[0375] The server uses stored sentiment data to generate appropriate dialogue through a generative AI model. The model combines sentiment data with seasonal and event information to create prompts that will interest the user. For example, if the user is tired, a suggestion such as "Why don't you take a break and listen to your favorite music?" might be generated. The output of this step is the generated prompt.

[0376] Step 4:

[0377] The terminal presents the user with prompt messages received from the server. The interaction with the user is reproduced in natural-sounding speech using a speech synthesis engine. When the user speaks, the audio is again captured and analyzed by the emotion engine. The input for this step is the prompt messages sent from the server, and the output is the specific conversation content presented to the user.

[0378] Step 5:

[0379] The server analyzes biometric data continuously transmitted from the device to detect unusual behavioral patterns. This biometric data includes heart rate and body temperature, and is analyzed using statistical methods and machine learning algorithms. If an anomaly is detected, an automatic notification is sent to pre-registered contacts. The output of this step is an alert indicating that an anomaly has been detected.

[0380] (Application Example 2)

[0381] 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."

[0382] Loneliness and anxiety are often serious problems in the lives of the elderly. Furthermore, if emotional instability in daily life is not addressed appropriately, it can negatively impact their psychological health. To address these challenges, there is a need for a system that can monitor the emotional state of the elderly in real time and provide dialogue tailored to their emotions.

[0383] 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.

[0384] In this invention, the server includes means for generating dialogue content using a generative model, means for collecting biometric information using sensors and imaging devices, and means for analyzing the user's voice and biometric information to estimate their emotional state in real time. This makes it possible to quickly and accurately analyze the user's emotional state and provide appropriate dialogue content that meets their individual needs.

[0385] A "generative model" is an algorithm or method that generates new information or content based on given data or information.

[0386] "Dialogue content" refers to the specific details of the communication exchanged between the user and the system.

[0387] A "sensor" is a device that measures physical or chemical information and outputs it as an electronic signal.

[0388] A "photography device" is a device used to record the user's appearance and actions as image data.

[0389] "Biometric information" refers to data about the user's physiological state, including heart rate and skin temperature.

[0390] "Emotional state" refers to the psychological or emotional state that a user is experiencing at a particular point in time.

[0391] "Real-time estimation" means performing analysis and making decisions instantly the moment data is input.

[0392] "Individual needs" refers to situations where different demands or requirements exist for each specific user, and a corresponding approach is required.

[0393] To realize this system, the server, terminal, and emotion analysis engine work together. First, the terminal is a crucial device that directly interacts with the user, and the emotion analysis engine is integrated into it. Specifically, it collects the user's voice and facial expression data using voice input devices and camera devices. This collected biometric information is analyzed by the emotion analysis engine. Based on the analysis results, the user's emotional state is estimated in real time, and this information is transmitted to the server.

[0394] The server dynamically generates dialogue content using a generative AI model based on the user's emotional state and biometric information from sensors. Seasonal and event information is also considered to select topics that are more likely to interest the user. For example, if a user says, "I'm lonely today," the server uses the generative AI model to generate a suggestion such as, "How about starting a new hobby today?" This is achieved by using the prompt message, "The user says they are lonely today. Please offer them some encouraging words. For example, use warm words that suggest a new hobby."

[0395] Furthermore, if an abnormal behavioral pattern is detected, the server automatically notifies pre-registered contacts. This system allows users to always feel secure while also informing family members and caregivers of any risks.

[0396] In this way, systems based on generative models and emotion engines support the improvement of the quality of life for the elderly by providing users with emotionally resonant conversations and care.

[0397] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0398] Step 1:

[0399] The device collects the user's voice and facial expression data. This input includes voice data acquired by a voice input device and image data acquired by a camera. This data is sent to an emotion analysis engine for emotion analysis. Specifically, the device's microphone and camera operate simultaneously to capture the user's speech and the changes in their facial expressions during that time.

[0400] Step 2:

[0401] The emotion analysis engine analyzes the received audio and image data to estimate the user's emotional state. The analysis process applies voice tone analysis and facial expression recognition algorithms. As output, the user's emotional state at that moment is sent to the server in real time. Specifically, an emotion score (e.g., joy, anger, sadness) is generated.

[0402] Step 3:

[0403] The server receives emotional state information as input and generates dialogue content using a generative AI model. Based on the input, and taking into account seasonal and event information, the generative model designs the optimal conversation content according to the prompt. The generated content takes the user's emotions into consideration and constructs the most appropriate response.

[0404] Step 4:

[0405] The server sends the generated dialogue to the terminal, which then relays it to the user. The outputted dialogue is delivered to the user as either audio or text. The terminal's speaker and display are used in this process.

[0406] Step 5:

[0407] The server also constantly monitors biometric data from sensors to detect unusual behavioral patterns. The input is real-time biometric data, and if an anomaly is detected, an alert is automatically sent to registered contacts. Specifically, if the system detects a sudden change in heart rate or an abnormal rise in body temperature, it immediately sends a warning message.

[0408] 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.

[0409] 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 the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0410] 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.

[0411] [Third Embodiment]

[0412] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0413] 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.

[0414] 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).

[0415] 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.

[0416] 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.

[0417] 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).

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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".

[0424] This invention describes an embodiment of a system that uses an interactive robot based on a generative model to monitor the lives of elderly people, alleviate feelings of loneliness through conversation, and ensure their safety.

[0425] This system functions through mutual communication between the server and the terminal. The server runs a generative model, generating conversation scripts using seasonal information and event data. For example, in spring, it generates natural-sounding conversation such as, "It's warm today, and the flowers have started to bloom." The generated script is sent to the terminal as digital data.

[0426] The terminal provides interaction with the elderly in the form of a robot. Using its built-in speech synthesis engine, the terminal converts conversation scripts received from the server into speech and outputs it to the elderly user. The terminal also features a microphone, sensors, and a camera for daily interaction and observation of the elderly. It receives the user's voice input using the microphone and transmits it to the server as text data in real time.

[0427] The server analyzes the text data sent by the user and generates the content of the next conversation. For example, if a user says, "I've been having trouble walking lately," the server will understand the context and generate a corresponding question such as, "That's worrying. Which foot hurts?"

[0428] The device also uses built-in sensors to acquire biometric data such as heart rate and body temperature, and motion sensors and cameras to collect behavioral patterns of elderly individuals. This data is sent to a server and compared with past data to detect abnormal conditions.

[0429] If the server detects an anomaly, such as a sudden drop in the user's heart rate or prolonged inactivity, it will automatically notify pre-registered contacts. An emergency notification, including specific details of the anomaly and location information, will be sent via email or SMS.

[0430] This allows elderly people to alleviate feelings of loneliness while also ensuring a system that can respond quickly in the event of an emergency.

[0431] The following describes the processing flow.

[0432] Step 1:

[0433] The server inputs information based on seasons and events into a generative model and generates a conversation script. The generated script is then sent to the terminal.

[0434] Step 2:

[0435] The terminal receives the conversation script from the server, converts it into speech data using a speech synthesis engine, and outputs it to the user.

[0436] Step 3:

[0437] The user responds with voice to the audio output by the device.

[0438] Step 4:

[0439] The terminal receives the user's voice via a microphone, converts it into text data using a speech recognition system, and sends it to the server.

[0440] Step 5:

[0441] The server analyzes the received text data and generates the necessary conversation script. The generated script is then sent back to the terminal.

[0442] Step 6:

[0443] The device periodically acquires the user's biometric data from sensors and sends it to the server.

[0444] Step 7:

[0445] The server analyzes the biometric data in real time to determine whether or not there are any abnormalities.

[0446] Step 8:

[0447] If an anomaly is detected, the server will automatically send a notification to pre-registered contacts. The notification will include details of the anomaly and the time it was detected.

[0448] (Example 1)

[0449] 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."

[0450] Elderly people face feelings of loneliness, and changes in their health often go undetected. Therefore, it is essential to support the lives of the elderly, improve their quality of life, and detect abnormal situations early to ensure appropriate responses.

[0451] 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.

[0452] In this invention, the server includes means for generating conversation content using a generative model, means for converting voice information into text and analyzing user input information, and means for acquiring biometric information using sensors and imaging devices and recognizing unusual behavioral patterns. This makes it possible to alleviate feelings of loneliness among the elderly while enabling early detection of abnormal situations and rapid response.

[0453] A "generative model" is an artificial intelligence algorithm used to generate new data or content based on specific input data.

[0454] "Means for generating conversation content" refers to a function that uses a generative model to create text used in interactions with users.

[0455] "Means of converting audio information into text" refers to technology that converts audio data collected from users into text data.

[0456] "Means of analyzing user input information" refers to an analytical process that understands user utterances and text data and derives appropriate responses.

[0457] "Means for acquiring biological information using sensors and imaging devices" refers to monitoring devices and related devices used to collect biological data such as body temperature and heart rate.

[0458] "Means of recognizing behavioral patterns" refers to technologies that detect changes in user behavior and health status based on collected data.

[0459] "A means of automatically notifying when an anomaly is detected" refers to a function that sends information to a pre-designated contact when the system determines that an anomaly has occurred with the user.

[0460] This system works by having a server and terminals work together to support the lives of the elderly. A specific embodiment of the invention is described below.

[0461] The server is equipped with a high-performance generative AI model and generates conversation content with the user based on prompts. Examples of prompts include "How was your day?" and "How have you been feeling lately?". The server uses these prompts to generate a script to start a conversation, facilitating a natural and meaningful dialogue with the user. The generated conversation script is sent to the terminal as digital data.

[0462] The device has a built-in speech synthesis engine that converts conversation scripts received from the server into speech. This allows the device to speak to the elderly user. Furthermore, the device has a voice input function that recognizes the user's speech and sends it to the server in real time, maintaining the continuity of the conversation.

[0463] Sensors and cameras are built into the terminal to acquire biometric information such as the user's heart rate and body temperature. This data is sent to a server, which analyzes the user's behavior patterns to detect unusual situations. For example, if a user says, "I'm not feeling well today," the server takes that statement into consideration, immediately analyzes the user's health condition, and generates follow-up questions such as, "How are you feeling unwell?"

[0464] If the server detects an abnormality in the user's health status, the system automatically notifies pre-configured contacts. This includes emergency contact via email or SMS, allowing family members and caregivers to be quickly informed of the situation.

[0465] This invention makes it possible to alleviate feelings of loneliness among the elderly while also enabling rapid response to changes in their health. In particular, by generating conversations using seasonal information and event data, it improves the quality of everyday conversations while enabling early response to abnormal situations.

[0466] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0467] Step 1:

[0468] The user initiates a conversation with the device. At this point, they input a prompt such as, "How did you spend your day?" The device sends this prompt to its speech synthesis engine, converts it into speech, and then speaks this message to the user. This gives the user a starting point for a conversation.

[0469] Step 2:

[0470] The user's response is input as audio data via the device's microphone. The device uses speech recognition technology to convert this audio data into text data. The converted text data is sent to a server and prepared for further analysis.

[0471] Step 3:

[0472] The server receives text data sent from the terminal. Based on this input, a generative AI model understands the context and generates the next conversation script corresponding to the user's words. For example, if the user says, "I took a walk in the park," the server uses the generative AI model to output a response such as, "That's nice, what did you see?"

[0473] Step 4:

[0474] The new conversation script generated by the server is sent to the terminal as digital data. The terminal then sends this data back to the speech synthesis engine, which converts it into speech that the user hears. This continues the flow of dialogue between the user and the terminal.

[0475] Step 5:

[0476] The device utilizes built-in sensors and imaging equipment to measure the user's biometric information, such as heart rate and body temperature. This data is captured by the device as sensor input, converted into digital data, and then transmitted to a server.

[0477] Step 6:

[0478] The server analyzes the acquired biometric information and checks for any differences from normal behavioral patterns. It performs data calculations and determines abnormalities, for example, if the heart rate is lower than normal or the body temperature is abnormally high. Based on the analysis results, notification data is generated if necessary.

[0479] Step 7:

[0480] If an anomaly is detected, the server generates an emergency notification and automatically sends it to pre-configured contacts. This may be done via email or SMS. The notification may include details of the anomaly and, if necessary, the user's current location. This allows for a rapid response.

[0481] (Application Example 1)

[0482] 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."

[0483] Alleviating feelings of loneliness and ensuring safety in the lives of the elderly are important challenges in modern society. In particular, monitoring health status and responding quickly to emergencies are essential for improving quality of life. However, current methods make it difficult to implement these effectively.

[0484] 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.

[0485] In this invention, the server includes means for generating dialogue content using a generative model, means for acquiring biometric data in cooperation with an external device, and means for converting the generated dialogue content into speech and outputting it. This enables natural dialogue with elderly people, real-time monitoring of their health status, and rapid notification in the event of an abnormality.

[0486] A "generative model" is an algorithm for automatically generating dialogue content in a human-understandable format based on input information.

[0487] "Dialogue content" refers to the set of information exchanged between humans and computers, generated by a generative model.

[0488] A "sensor" is a device that detects a physical quantity, converts it into an electrical signal, and outputs it.

[0489] A "camera" is a photographic device that captures image information from its surroundings and stores and processes it as digital data.

[0490] "Biometric data" refers to numerical information that indicates the physiological state of the human body, such as heart rate and body temperature.

[0491] "Behavioral patterns" refer to a portion of the information that indicates the tendencies of an individual's actions and activities in their daily life.

[0492] An "abnormality" is an event that deviates from the normal state or expected behavior.

[0493] "Notification" refers to the act of automatically sending a notification to designated recipients in the event of an anomaly.

[0494] An "external device" is an electronic device connected to the system from an external source, with which the system interacts.

[0495] "Converting to speech" is the process of outputting text information as audio.

[0496] "Health status" refers to the state of an individual's current level of physical and mental health.

[0497] An "emergency notice" is a message intended to draw immediate attention beyond normal communication methods.

[0498] This invention provides a method for specifically implementing an interactive robot system to support the lives of the elderly. The system mainly consists of a server and a terminal.

[0499] The server has the functionality to generate conversation content with elderly people using a generative AI model. The generative model creates natural and friendly conversation scripts while taking into account seasonal and event information. In this process, the prompt "Generate a conversation scenario for when the user has forgotten their medication. Create a response that gently reminds the user." is used.

[0500] The terminal functions as a robot and handles interactions with the elderly. It has a built-in speech synthesis engine that converts conversation scripts received from the server into speech and outputs it. The terminal also collects voice and biometric data from the elderly using a microphone, sensors, and a camera. Specifically, it acquires heart rate, body temperature, and patterns of daily activities.

[0501] This biometric data is transmitted to a server in real time, and anomaly detection algorithms are used to analyze any abnormalities in the data. If an abnormality is detected, for example, if the heart rate is unstable or if there is a significant deviation from the normal behavior pattern, the server automatically notifies pre-registered contacts.

[0502] Specifically, the server converts speech to text using a speech recognition API (e.g., Amazon Transcribe) and outputs the speech using a speech synthesis API such as Google Cloud Text-to-Speech. It also connects to an external device, a fitness tracker, via Bluetooth to acquire biometric data.

[0503] A concrete example of this invention is a scenario where, to prevent users from forgetting to take their medication after breakfast, a robot kindly asks, "Did you enjoy your breakfast? Have you forgotten your medication?" In this way, the system aims to alleviate feelings of loneliness and improve the quality of life while supporting the user's health and safety.

[0504] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0505] Step 1:

[0506] The server receives user input and provides a prompt to the generative AI model. The prompt used is: "Generate a conversation scenario for when the user has forgotten their medication. Create a gentle reminder response for the user." Based on this input, the generative AI model generates a dialogue script, and the server outputs the result as text data.

[0507] Step 2:

[0508] The server sends the generated dialogue script to the speech synthesis engine. The input text data is converted into speech data using speech synthesis technology and sent to the terminal. The specific operation uses software APIs such as Google Cloud Text-to-Speech.

[0509] Step 3:

[0510] The terminal outputs the received audio data through its speaker, providing interaction with the user. The user speaks their response through the microphone. The terminal receives this audio input and sends it to the server for further processing. This process utilizes available speech recognition technology.

[0511] Step 4:

[0512] The server converts the user's voice input into text data using a speech recognition API. It analyzes the input voice data, converts its content into text, and then sends it to a generative AI model to generate the next dialogue. This step utilizes an API such as Amazon Transcribe.

[0513] Step 5:

[0514] The device collects sensor and camera information to monitor the user's health. This biometric data is received from an external device via Bluetooth and transmitted to a server. Inputs include heart rate, body temperature, and motion detection data.

[0515] Step 6:

[0516] The server analyzes the transmitted biometric data using an anomaly detection algorithm. It compares the input data with normal data and immediately generates a notification if an anomaly is detected. Specifically, it sends a notification via email or SMS to the registered contacts.

[0517] Step 7:

[0518] The server completes the anomaly notification and saves the event as a log. This allows it to be referenced later as part of the user's health management history. This saved data will be used for future improvements and monitoring.

[0519] 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.

[0520] This invention is a system that supports the lives of the elderly and enables personalized responses based on their emotions through an interactive robot using a generative model and an emotion engine. By recognizing and analyzing the user's emotions, this system improves the quality of dialogue and provides more effective safety and comfort.

[0521] The system primarily consists of three main components: a server, a terminal, and an emotion engine. The server generates conversation scripts using a generative model and adjusts the content according to the user's emotional state. The generative model can incorporate seasonal and event information, as well as topics that will interest the user. For example, if the system detects that the user is feeling down, it can prioritize generating uplifting conversation content.

[0522] The terminal is a device that interacts directly with the user. The emotion engine is built into the terminal and acquires and analyzes the user's voice and facial expression data. Emotion analysis uses elements extracted from voice tone, changes in facial expression, and biometric data. This allows the system to estimate the user's current emotional state in real time and send that information to the server.

[0523] If a user says something like, "I've been feeling down lately," the emotion engine analyzes their voice tone and biometric data to determine that they are in a low emotional state. Based on this emotional information, the server adjusts the conversation and generates suggestions such as, "How about getting some fresh air?" or "Shall we listen to your favorite music together?"

[0524] The device also continuously acquires biometric data using its built-in sensors and transmits it to a server. The server analyzes this data and monitors for any unusual behavioral patterns. If an abnormality is detected and the user's emotional state is deemed unstable, an automatic notification is sent to pre-registered contacts.

[0525] In this way, the system of the present invention supports the daily lives of the elderly, enables emotionally responsive care, and provides a safe and secure environment.

[0526] The following describes the processing flow.

[0527] Step 1:

[0528] The server uses a generative model to create conversational scripts based on seasons and events. The generated scripts are then sent to the terminal.

[0529] Step 2:

[0530] The terminal receives the conversation script from the server, converts it into speech using a speech synthesis engine, and outputs it to the user.

[0531] Step 3:

[0532] The user responds verbally to the audio output from the device.

[0533] Step 4:

[0534] The device captures the user's voice with a microphone and uses an emotion engine to analyze the tone of the voice and estimate the user's emotions.

[0535] Step 5:

[0536] The emotion engine captures the user's facial expressions with a camera and analyzes them along with biometric data to determine their emotional state. Specifically, it detects whether or not the user is smiling, eye movements, and changes in heart rate.

[0537] Step 6:

[0538] The terminal sends the results of the emotion engine's analysis to the server. The server takes the user's emotional state into consideration, dynamically adjusts the dialogue content, and generates a new conversation script.

[0539] Step 7:

[0540] The server sends the generated conversation script to the terminal and continues the dialogue. If necessary, it incorporates suggestions to soothe the user's emotions into the script.

[0541] Step 8:

[0542] The device continuously acquires the user's biometric data through sensors and monitors for any abnormalities. The data is then transmitted to a server.

[0543] Step 9:

[0544] The server analyzes the received biometric data and detects deviations from normal patterns. If an anomaly is detected and the emotional state is determined to be unstable, an automatic notification is triggered.

[0545] Step 10:

[0546] A notification containing information about abnormalities and emotional states is automatically sent via email or SMS to pre-registered emergency contacts.

[0547] (Example 2)

[0548] 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."

[0549] There is a need for systems that can improve mental health care by understanding the emotional state of users, including the elderly, in real time and providing appropriate dialogue in their daily lives. Furthermore, a challenge is to detect abnormalities early based on the user's health and emotional state and enable appropriate responses.

[0550] 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.

[0551] In this invention, the server includes means for generating dialogue content based on emotional state using a generative model, means for estimating the user's emotional state in real time using voice and facial expression data, and means for continuously collecting biometric data using sensors and detecting unusual behavioral patterns. This enables dialogue that is attentive to the user's emotions, as well as early detection of abnormalities in health or behavior and prompt response.

[0552] A "generative model" is an algorithm or program that generates new data or content based on input information.

[0553] "Emotional state" is an indicator that shows the user's psychological and emotional condition, and is determined by analyzing voice and facial expression data.

[0554] "Dialogue content" refers to the sentences and phrases used in communication with the user, and it changes according to the user's interests and emotional state.

[0555] "Voice and facial expression data" refers to a collection of raw data used to identify emotions, such as the tone of the user's voice and facial expressions.

[0556] "Real-time estimation" means that the process from data acquisition to analysis is performed instantaneously, and results can be obtained immediately.

[0557] A "sensor" is a device that acquires information from the environment and users, and collects biometric data.

[0558] "Biometric data" refers to various types of data that indicate the user's physical condition, such as heart rate and body temperature.

[0559] A "behavioral pattern" refers to a sequence of actions and tendencies in a user's daily life, and serves as a criterion for detecting unusual behavior.

[0560] The system of this invention enables personalized responses by understanding the user's emotional state and generating corresponding dialogue. The system mainly consists of three main elements: a server, a terminal, and an emotion engine.

[0561] The server utilizes a generative AI model to generate dialogue based on the user's emotional state. This generative model incorporates commonly used natural language processing techniques, enabling dynamic content generation that takes into account, for example, seasonal and event information. Based on the emotional information transmitted from the terminal, the server can adjust prompts to include topics that will interest the user. For example, it can generate prompts such as, "You seem a little down lately, would you like to talk about something?" tailored to the user's mood.

[0562] The terminal is a device for interacting with the user and has a built-in camera and microphone. It acquires the user's voice and facial expression data in real time and supplies this data to the emotion engine. The emotion engine contains a program for analyzing voice tone and facial expression changes, and is responsible for analyzing the user's emotional state in real time and sending it to the server.

[0563] A sensor built into the device is used to collect biometric data. This sensor constantly monitors the user's biometric data and transmits it to a server. Based on this data, the server detects unusual behavior to identify user anomalies early and automatically notifies pre-registered contacts as needed.

[0564] The user follows the prompts suggested by the system to carry out everyday conversations. For example, if the user mutters, "I'm so tired today," the system immediately performs sentiment analysis and provides a personalized suggestion such as, "Why don't you take a break and listen to your favorite music?" This allows the user to receive emotional support and a safe and comfortable environment.

[0565] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0566] Step 1:

[0567] The device captures the user's voice and facial expressions in real time using its camera and microphone. This input data includes the user's voice tone and changes in facial expressions. This data is sent to an emotion engine. This engine estimates the user's emotional state by using algorithms to analyze voice tone and facial expression changes. The output of this step is a label or score indicating the user's emotion.

[0568] Step 2:

[0569] The device sends the analyzed emotional state to the server. This data transfer is usually done via Wi-Fi or Bluetooth. The server stores the received emotional data in a database for use in the next step. The output of this step is the emotional data recorded on the server side.

[0570] Step 3:

[0571] The server uses stored sentiment data to generate appropriate dialogue through a generative AI model. The model combines sentiment data with seasonal and event information to create prompts that will interest the user. For example, if the user is tired, a suggestion such as "Why don't you take a break and listen to your favorite music?" might be generated. The output of this step is the generated prompt.

[0572] Step 4:

[0573] The terminal presents the user with prompt messages received from the server. The interaction with the user is reproduced in natural-sounding speech using a speech synthesis engine. When the user speaks, the audio is again captured and analyzed by the emotion engine. The input for this step is the prompt messages sent from the server, and the output is the specific conversation content presented to the user.

[0574] Step 5:

[0575] The server analyzes biometric data continuously transmitted from the device to detect unusual behavioral patterns. This biometric data includes heart rate and body temperature, and is analyzed using statistical methods and machine learning algorithms. If an anomaly is detected, an automatic notification is sent to pre-registered contacts. The output of this step is an alert indicating that an anomaly has been detected.

[0576] (Application Example 2)

[0577] 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."

[0578] Loneliness and anxiety are often serious problems in the lives of the elderly. Furthermore, if emotional instability in daily life is not addressed appropriately, it can negatively impact their psychological health. To address these challenges, there is a need for a system that can monitor the emotional state of the elderly in real time and provide dialogue tailored to their emotions.

[0579] 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.

[0580] In this invention, the server includes means for generating dialogue content using a generative model, means for collecting biometric information using sensors and imaging devices, and means for analyzing the user's voice and biometric information to estimate their emotional state in real time. This makes it possible to quickly and accurately analyze the user's emotional state and provide appropriate dialogue content that meets their individual needs.

[0581] A "generative model" is an algorithm or method that generates new information or content based on given data or information.

[0582] "Dialogue content" refers to the specific details of the communication exchanged between the user and the system.

[0583] A "sensor" is a device that measures physical or chemical information and outputs it as an electronic signal.

[0584] A "photography device" is a device used to record the user's appearance and actions as image data.

[0585] "Biometric information" refers to data about the user's physiological state, including heart rate and skin temperature.

[0586] "Emotional state" refers to the psychological or emotional state that a user is experiencing at a particular point in time.

[0587] "Real-time estimation" means performing analysis and making decisions instantly the moment data is input.

[0588] "Individual needs" refers to situations where different demands or requirements exist for each specific user, and a corresponding approach is required.

[0589] To realize this system, the server, terminal, and emotion analysis engine work together. First, the terminal is a crucial device that directly interacts with the user, and the emotion analysis engine is integrated into it. Specifically, it collects the user's voice and facial expression data using voice input devices and camera devices. This collected biometric information is analyzed by the emotion analysis engine. Based on the analysis results, the user's emotional state is estimated in real time, and this information is transmitted to the server.

[0590] The server dynamically generates dialogue content using a generative AI model based on the user's emotional state and biometric information from sensors. Seasonal and event information is also considered to select topics that are more likely to interest the user. For example, if a user says, "I'm lonely today," the server uses the generative AI model to generate a suggestion such as, "How about starting a new hobby today?" This is achieved by using the prompt message, "The user says they are lonely today. Please offer them some encouraging words. For example, use warm words that suggest a new hobby."

[0591] Furthermore, if an abnormal behavioral pattern is detected, the server automatically notifies pre-registered contacts. This system allows users to always feel secure while also informing family members and caregivers of any risks.

[0592] In this way, systems based on generative models and emotion engines support the improvement of the quality of life for the elderly by providing users with emotionally resonant conversations and care.

[0593] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0594] Step 1:

[0595] The device collects the user's voice and facial expression data. This input includes voice data acquired by a voice input device and image data acquired by a camera. This data is sent to an emotion analysis engine for emotion analysis. Specifically, the device's microphone and camera operate simultaneously to capture the user's speech and the changes in their facial expressions during that time.

[0596] Step 2:

[0597] The emotion analysis engine analyzes the received audio and image data to estimate the user's emotional state. The analysis process applies voice tone analysis and facial expression recognition algorithms. As output, the user's emotional state at that moment is sent to the server in real time. Specifically, an emotion score (e.g., joy, anger, sadness) is generated.

[0598] Step 3:

[0599] The server receives emotional state information as input and generates dialogue content using a generative AI model. Based on the input, and taking into account seasonal and event information, the generative model designs the optimal conversation content according to the prompt. The generated content takes the user's emotions into consideration and constructs the most appropriate response.

[0600] Step 4:

[0601] The server sends the generated dialogue to the terminal, which then relays it to the user. The outputted dialogue is delivered to the user as either audio or text. The terminal's speaker and display are used in this process.

[0602] Step 5:

[0603] The server also constantly monitors biometric data from sensors to detect unusual behavioral patterns. The input is real-time biometric data, and if an anomaly is detected, an alert is automatically sent to registered contacts. Specifically, if the system detects a sudden change in heart rate or an abnormal rise in body temperature, it immediately sends a warning message.

[0604] 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.

[0605] 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 the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0606] 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.

[0607] [Fourth Embodiment]

[0608] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0609] 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.

[0610] 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).

[0611] 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.

[0612] 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.

[0613] 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).

[0614] 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.

[0615] 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.

[0616] 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.

[0617] 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.

[0618] 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.

[0619] 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.

[0620] 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".

[0621] This invention describes an embodiment of a system that uses an interactive robot based on a generative model to monitor the lives of elderly people, alleviate feelings of loneliness through conversation, and ensure their safety.

[0622] This system functions through mutual communication between the server and the terminal. The server runs a generative model, generating conversation scripts using seasonal information and event data. For example, in spring, it generates natural-sounding conversation such as, "It's warm today, and the flowers have started to bloom." The generated script is sent to the terminal as digital data.

[0623] The terminal provides interaction with the elderly in the form of a robot. Using its built-in speech synthesis engine, the terminal converts conversation scripts received from the server into speech and outputs it to the elderly user. The terminal also features a microphone, sensors, and a camera for daily interaction and observation of the elderly. It receives the user's voice input using the microphone and transmits it to the server as text data in real time.

[0624] The server analyzes the text data sent by the user and generates the content of the next conversation. For example, if a user says, "I've been having trouble walking lately," the server will understand the context and generate a corresponding question such as, "That's worrying. Which foot hurts?"

[0625] The device also uses built-in sensors to acquire biometric data such as heart rate and body temperature, and motion sensors and cameras to collect behavioral patterns of elderly individuals. This data is sent to a server and compared with past data to detect abnormal conditions.

[0626] If the server detects an anomaly, such as a sudden drop in the user's heart rate or prolonged inactivity, it will automatically notify pre-registered contacts. An emergency notification, including specific details of the anomaly and location information, will be sent via email or SMS.

[0627] This allows elderly people to alleviate feelings of loneliness while also ensuring a system that can respond quickly in the event of an emergency.

[0628] The following describes the processing flow.

[0629] Step 1:

[0630] The server inputs information based on seasons and events into a generative model and generates a conversation script. The generated script is then sent to the terminal.

[0631] Step 2:

[0632] The terminal receives the conversation script from the server, converts it into speech data using a speech synthesis engine, and outputs it to the user.

[0633] Step 3:

[0634] The user responds with voice to the audio output by the device.

[0635] Step 4:

[0636] The terminal receives the user's voice via a microphone, converts it into text data using a speech recognition system, and sends it to the server.

[0637] Step 5:

[0638] The server analyzes the received text data and generates the necessary conversation script. The generated script is then sent back to the terminal.

[0639] Step 6:

[0640] The device periodically acquires the user's biometric data from sensors and sends it to the server.

[0641] Step 7:

[0642] The server analyzes the biometric data in real time to determine whether or not there are any abnormalities.

[0643] Step 8:

[0644] If an anomaly is detected, the server will automatically send a notification to pre-registered contacts. The notification will include details of the anomaly and the time it was detected.

[0645] (Example 1)

[0646] 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".

[0647] Elderly people face feelings of loneliness, and changes in their health often go undetected. Therefore, it is essential to support the lives of the elderly, improve their quality of life, and detect abnormal situations early to ensure appropriate responses.

[0648] 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.

[0649] In this invention, the server includes means for generating conversation content using a generative model, means for converting voice information into text and analyzing user input information, and means for acquiring biometric information using sensors and imaging devices and recognizing unusual behavioral patterns. This makes it possible to alleviate feelings of loneliness among the elderly while enabling early detection of abnormal situations and rapid response.

[0650] A "generative model" is an artificial intelligence algorithm used to generate new data or content based on specific input data.

[0651] "Means for generating conversation content" refers to a function that uses a generative model to create text used in interactions with users.

[0652] "Means of converting audio information into text" refers to technology that converts audio data collected from users into text data.

[0653] "Means of analyzing user input information" refers to an analytical process that understands user utterances and text data and derives appropriate responses.

[0654] "Means for acquiring biological information using sensors and imaging devices" refers to monitoring devices and related devices used to collect biological data such as body temperature and heart rate.

[0655] "Means of recognizing behavioral patterns" refers to technologies that detect changes in user behavior and health status based on collected data.

[0656] "A means of automatically notifying when an anomaly is detected" refers to a function that sends information to a pre-designated contact when the system determines that an anomaly has occurred with the user.

[0657] This system works by having a server and terminals work together to support the lives of the elderly. A specific embodiment of the invention is described below.

[0658] The server is equipped with a high-performance generative AI model and generates conversation content with the user based on prompts. Examples of prompts include "How was your day?" and "How have you been feeling lately?". The server uses these prompts to generate a script to start a conversation, facilitating a natural and meaningful dialogue with the user. The generated conversation script is sent to the terminal as digital data.

[0659] The device has a built-in speech synthesis engine that converts conversation scripts received from the server into speech. This allows the device to speak to the elderly user. Furthermore, the device has a voice input function that recognizes the user's speech and sends it to the server in real time, maintaining the continuity of the conversation.

[0660] Sensors and cameras are built into the terminal to acquire biometric information such as the user's heart rate and body temperature. This data is sent to a server, which analyzes the user's behavior patterns to detect unusual situations. For example, if a user says, "I'm not feeling well today," the server takes that statement into consideration, immediately analyzes the user's health condition, and generates follow-up questions such as, "How are you feeling unwell?"

[0661] If the server detects an abnormality in the user's health status, the system automatically notifies pre-configured contacts. This includes emergency contact via email or SMS, allowing family members and caregivers to be quickly informed of the situation.

[0662] This invention makes it possible to alleviate feelings of loneliness among the elderly while also enabling rapid response to changes in their health. In particular, by generating conversations using seasonal information and event data, it improves the quality of everyday conversations while enabling early response to abnormal situations.

[0663] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0664] Step 1:

[0665] The user initiates a conversation with the device. At this point, they input a prompt such as, "How did you spend your day?" The device sends this prompt to its speech synthesis engine, converts it into speech, and then speaks this message to the user. This gives the user a starting point for a conversation.

[0666] Step 2:

[0667] The user's response is input as audio data via the device's microphone. The device uses speech recognition technology to convert this audio data into text data. The converted text data is sent to a server and prepared for further analysis.

[0668] Step 3:

[0669] The server receives text data sent from the terminal. Based on this input, a generative AI model understands the context and generates the next conversation script corresponding to the user's words. For example, if the user says, "I took a walk in the park," the server uses the generative AI model to output a response such as, "That's nice, what did you see?"

[0670] Step 4:

[0671] The new conversation script generated by the server is sent to the terminal as digital data. The terminal then sends this data back to the speech synthesis engine, which converts it into speech that the user hears. This continues the flow of dialogue between the user and the terminal.

[0672] Step 5:

[0673] The device utilizes built-in sensors and imaging equipment to measure the user's biometric information, such as heart rate and body temperature. This data is captured by the device as sensor input, converted into digital data, and then transmitted to a server.

[0674] Step 6:

[0675] The server analyzes the acquired biometric information and checks for any differences from normal behavioral patterns. It performs data calculations and determines abnormalities, for example, if the heart rate is lower than normal or the body temperature is abnormally high. Based on the analysis results, notification data is generated if necessary.

[0676] Step 7:

[0677] If an anomaly is detected, the server generates an emergency notification and automatically sends it to pre-configured contacts. This may be done via email or SMS. The notification may include details of the anomaly and, if necessary, the user's current location. This allows for a rapid response.

[0678] (Application Example 1)

[0679] 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".

[0680] Alleviating feelings of loneliness and ensuring safety in the lives of the elderly are important challenges in modern society. In particular, monitoring health status and responding quickly to emergencies are essential for improving quality of life. However, current methods make it difficult to implement these effectively.

[0681] 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.

[0682] In this invention, the server includes means for generating dialogue content using a generative model, means for acquiring biometric data in cooperation with an external device, and means for converting the generated dialogue content into speech and outputting it. This enables natural dialogue with elderly people, real-time monitoring of their health status, and rapid notification in the event of an abnormality.

[0683] A "generative model" is an algorithm for automatically generating dialogue content in a human-understandable format based on input information.

[0684] "Dialogue content" refers to the set of information exchanged between humans and computers, generated by a generative model.

[0685] A "sensor" is a device that detects a physical quantity, converts it into an electrical signal, and outputs it.

[0686] A "camera" is a photographic device that captures image information from its surroundings and stores and processes it as digital data.

[0687] "Biometric data" refers to numerical information that indicates the physiological state of the human body, such as heart rate and body temperature.

[0688] "Behavioral patterns" refer to a portion of the information that indicates the tendencies of an individual's actions and activities in their daily life.

[0689] An "abnormality" is an event that deviates from the normal state or expected behavior.

[0690] "Notification" refers to the act of automatically sending a notification to designated recipients in the event of an anomaly.

[0691] An "external device" is an electronic device connected to the system from an external source, with which the system interacts.

[0692] "Converting to speech" is the process of outputting text information as audio.

[0693] "Health status" refers to the state of an individual's current level of physical and mental health.

[0694] An "emergency notice" is a message intended to draw immediate attention beyond normal communication methods.

[0695] This invention provides a method for specifically implementing an interactive robot system to support the lives of the elderly. The system mainly consists of a server and a terminal.

[0696] The server has the functionality to generate conversation content with elderly people using a generative AI model. The generative model creates natural and friendly conversation scripts while taking into account seasonal and event information. In this process, the prompt "Generate a conversation scenario for when the user has forgotten their medication. Create a response that gently reminds the user." is used.

[0697] The terminal functions as a robot and handles interactions with the elderly. It has a built-in speech synthesis engine that converts conversation scripts received from the server into speech and outputs it. The terminal also collects voice and biometric data from the elderly using a microphone, sensors, and a camera. Specifically, it acquires heart rate, body temperature, and patterns of daily activities.

[0698] This biometric data is transmitted to a server in real time, and anomaly detection algorithms are used to analyze any abnormalities in the data. If an abnormality is detected, for example, if the heart rate is unstable or if there is a significant deviation from the normal behavior pattern, the server automatically notifies pre-registered contacts.

[0699] Specifically, the server converts speech to text using a speech recognition API (e.g., Amazon Transcribe) and outputs the speech using a speech synthesis API such as Google Cloud Text-to-Speech. It also connects to an external device, a fitness tracker, via Bluetooth to acquire biometric data.

[0700] A concrete example of this invention is a scenario where, to prevent users from forgetting to take their medication after breakfast, a robot kindly asks, "Did you enjoy your breakfast? Have you forgotten your medication?" In this way, the system aims to alleviate feelings of loneliness and improve the quality of life while supporting the user's health and safety.

[0701] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0702] Step 1:

[0703] The server receives user input and provides a prompt to the generative AI model. The prompt used is: "Generate a conversation scenario for when the user has forgotten their medication. Create a gentle reminder response for the user." Based on this input, the generative AI model generates a dialogue script, and the server outputs the result as text data.

[0704] Step 2:

[0705] The server sends the generated dialogue script to the speech synthesis engine. The input text data is converted into speech data using speech synthesis technology and sent to the terminal. The specific operation uses software APIs such as Google Cloud Text-to-Speech.

[0706] Step 3:

[0707] The terminal outputs the received audio data through its speaker, providing interaction with the user. The user speaks their response through the microphone. The terminal receives this audio input and sends it to the server for further processing. This process utilizes available speech recognition technology.

[0708] Step 4:

[0709] The server converts the user's voice input into text data using a speech recognition API. It analyzes the input voice data, converts its content into text, and then sends it to a generative AI model to generate the next dialogue. This step utilizes an API such as Amazon Transcribe.

[0710] Step 5:

[0711] The device collects sensor and camera information to monitor the user's health. This biometric data is received from an external device via Bluetooth and transmitted to a server. Inputs include heart rate, body temperature, and motion detection data.

[0712] Step 6:

[0713] The server analyzes the transmitted biometric data using an anomaly detection algorithm. It compares the input data with normal data and immediately generates a notification if an anomaly is detected. Specifically, it sends a notification via email or SMS to the registered contacts.

[0714] Step 7:

[0715] The server completes the anomaly notification and saves the event as a log. This allows it to be referenced later as part of the user's health management history. This saved data will be used for future improvements and monitoring.

[0716] 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.

[0717] This invention is a system that supports the lives of the elderly and enables personalized responses based on their emotions through an interactive robot using a generative model and an emotion engine. By recognizing and analyzing the user's emotions, this system improves the quality of dialogue and provides more effective safety and comfort.

[0718] The system primarily consists of three main components: a server, a terminal, and an emotion engine. The server generates conversation scripts using a generative model and adjusts the content according to the user's emotional state. The generative model can incorporate seasonal and event information, as well as topics that will interest the user. For example, if the system detects that the user is feeling down, it can prioritize generating uplifting conversation content.

[0719] The terminal is a device that interacts directly with the user. The emotion engine is built into the terminal and acquires and analyzes the user's voice and facial expression data. Emotion analysis uses elements extracted from voice tone, changes in facial expression, and biometric data. This allows the system to estimate the user's current emotional state in real time and send that information to the server.

[0720] If a user says something like, "I've been feeling down lately," the emotion engine analyzes their voice tone and biometric data to determine that they are in a low emotional state. Based on this emotional information, the server adjusts the conversation and generates suggestions such as, "How about getting some fresh air?" or "Shall we listen to your favorite music together?"

[0721] The device also continuously acquires biometric data using its built-in sensors and transmits it to a server. The server analyzes this data and monitors for any unusual behavioral patterns. If an abnormality is detected and the user's emotional state is deemed unstable, an automatic notification is sent to pre-registered contacts.

[0722] In this way, the system of the present invention supports the daily lives of the elderly, enables emotionally responsive care, and provides a safe and secure environment.

[0723] The following describes the processing flow.

[0724] Step 1:

[0725] The server uses a generative model to create conversational scripts based on seasons and events. The generated scripts are then sent to the terminal.

[0726] Step 2:

[0727] The terminal receives the conversation script from the server, converts it into speech using a speech synthesis engine, and outputs it to the user.

[0728] Step 3:

[0729] The user responds verbally to the audio output from the device.

[0730] Step 4:

[0731] The device captures the user's voice with a microphone and uses an emotion engine to analyze the tone of the voice and estimate the user's emotions.

[0732] Step 5:

[0733] The emotion engine captures the user's facial expressions with a camera and analyzes them along with biometric data to determine their emotional state. Specifically, it detects whether or not the user is smiling, eye movements, and changes in heart rate.

[0734] Step 6:

[0735] The terminal sends the results of the emotion engine's analysis to the server. The server takes the user's emotional state into consideration, dynamically adjusts the dialogue content, and generates a new conversation script.

[0736] Step 7:

[0737] The server sends the generated conversation script to the terminal and continues the dialogue. If necessary, it incorporates suggestions to soothe the user's emotions into the script.

[0738] Step 8:

[0739] The device continuously acquires the user's biometric data through sensors and monitors for any abnormalities. The data is then transmitted to a server.

[0740] Step 9:

[0741] The server analyzes the received biometric data and detects deviations from normal patterns. If an anomaly is detected and the emotional state is determined to be unstable, an automatic notification is triggered.

[0742] Step 10:

[0743] A notification containing information about abnormalities and emotional states is automatically sent via email or SMS to pre-registered emergency contacts.

[0744] (Example 2)

[0745] 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".

[0746] There is a need for systems that can improve mental health care by understanding the emotional state of users, including the elderly, in real time and providing appropriate dialogue in their daily lives. Furthermore, a challenge is to detect abnormalities early based on the user's health and emotional state and enable appropriate responses.

[0747] 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.

[0748] In this invention, the server includes means for generating dialogue content based on emotional state using a generative model, means for estimating the user's emotional state in real time using voice and facial expression data, and means for continuously collecting biometric data using sensors and detecting unusual behavioral patterns. This enables dialogue that is attentive to the user's emotions, as well as early detection of abnormalities in health or behavior and prompt response.

[0749] A "generative model" is an algorithm or program that generates new data or content based on input information.

[0750] "Emotional state" is an indicator that shows the user's psychological and emotional condition, and is determined by analyzing voice and facial expression data.

[0751] "Dialogue content" refers to the sentences and phrases used in communication with the user, and it changes according to the user's interests and emotional state.

[0752] "Voice and facial expression data" refers to a collection of raw data used to identify emotions, such as the tone of the user's voice and facial expressions.

[0753] "Real-time estimation" means that the process from data acquisition to analysis is performed instantaneously, and results can be obtained immediately.

[0754] A "sensor" is a device that acquires information from the environment and users, and collects biometric data.

[0755] "Biometric data" refers to various types of data that indicate the user's physical condition, such as heart rate and body temperature.

[0756] A "behavioral pattern" refers to a sequence of actions and tendencies in a user's daily life, and serves as a criterion for detecting unusual behavior.

[0757] The system of this invention enables personalized responses by understanding the user's emotional state and generating corresponding dialogue. The system mainly consists of three main elements: a server, a terminal, and an emotion engine.

[0758] The server utilizes a generative AI model to generate dialogue based on the user's emotional state. This generative model incorporates commonly used natural language processing techniques, enabling dynamic content generation that takes into account, for example, seasonal and event information. Based on the emotional information transmitted from the terminal, the server can adjust prompts to include topics that will interest the user. For example, it can generate prompts such as, "You seem a little down lately, would you like to talk about something?" tailored to the user's mood.

[0759] The terminal is a device for interacting with the user and has a built-in camera and microphone. It acquires the user's voice and facial expression data in real time and supplies this data to the emotion engine. The emotion engine contains a program for analyzing voice tone and facial expression changes, and is responsible for analyzing the user's emotional state in real time and sending it to the server.

[0760] A sensor built into the device is used to collect biometric data. This sensor constantly monitors the user's biometric data and transmits it to a server. Based on this data, the server detects unusual behavior to identify user anomalies early and automatically notifies pre-registered contacts as needed.

[0761] The user follows the prompts suggested by the system to carry out everyday conversations. For example, if the user mutters, "I'm so tired today," the system immediately performs sentiment analysis and provides a personalized suggestion such as, "Why don't you take a break and listen to your favorite music?" This allows the user to receive emotional support and a safe and comfortable environment.

[0762] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0763] Step 1:

[0764] The device captures the user's voice and facial expressions in real time using its camera and microphone. This input data includes the user's voice tone and changes in facial expressions. This data is sent to an emotion engine. This engine estimates the user's emotional state by using algorithms to analyze voice tone and facial expression changes. The output of this step is a label or score indicating the user's emotion.

[0765] Step 2:

[0766] The device sends the analyzed emotional state to the server. This data transfer is usually done via Wi-Fi or Bluetooth. The server stores the received emotional data in a database for use in the next step. The output of this step is the emotional data recorded on the server side.

[0767] Step 3:

[0768] The server uses stored sentiment data to generate appropriate dialogue through a generative AI model. The model combines sentiment data with seasonal and event information to create prompts that will interest the user. For example, if the user is tired, a suggestion such as "Why don't you take a break and listen to your favorite music?" might be generated. The output of this step is the generated prompt.

[0769] Step 4:

[0770] The terminal presents the user with prompt messages received from the server. The interaction with the user is reproduced in natural-sounding speech using a speech synthesis engine. When the user speaks, the audio is again captured and analyzed by the emotion engine. The input for this step is the prompt messages sent from the server, and the output is the specific conversation content presented to the user.

[0771] Step 5:

[0772] The server analyzes biometric data continuously transmitted from the device to detect unusual behavioral patterns. This biometric data includes heart rate and body temperature, and is analyzed using statistical methods and machine learning algorithms. If an anomaly is detected, an automatic notification is sent to pre-registered contacts. The output of this step is an alert indicating that an anomaly has been detected.

[0773] (Application Example 2)

[0774] 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".

[0775] Loneliness and anxiety are often serious problems in the lives of the elderly. Furthermore, if emotional instability in daily life is not addressed appropriately, it can negatively impact their psychological health. To address these challenges, there is a need for a system that can monitor the emotional state of the elderly in real time and provide dialogue tailored to their emotions.

[0776] 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.

[0777] In this invention, the server includes means for generating dialogue content using a generative model, means for collecting biometric information using sensors and imaging devices, and means for analyzing the user's voice and biometric information to estimate their emotional state in real time. This makes it possible to quickly and accurately analyze the user's emotional state and provide appropriate dialogue content that meets their individual needs.

[0778] A "generative model" is an algorithm or method that generates new information or content based on given data or information.

[0779] "Dialogue content" refers to the specific details of the communication exchanged between the user and the system.

[0780] A "sensor" is a device that measures physical or chemical information and outputs it as an electronic signal.

[0781] A "photography device" is a device used to record the user's appearance and actions as image data.

[0782] "Biometric information" refers to data about the user's physiological state, including heart rate and skin temperature.

[0783] "Emotional state" refers to the psychological or emotional state that a user is experiencing at a particular point in time.

[0784] "Real-time estimation" means performing analysis and making decisions instantly the moment data is input.

[0785] "Individual needs" refers to situations where different demands or requirements exist for each specific user, and a corresponding approach is required.

[0786] To realize this system, the server, terminal, and emotion analysis engine work together. First, the terminal is a crucial device that directly interacts with the user, and the emotion analysis engine is integrated into it. Specifically, it collects the user's voice and facial expression data using voice input devices and camera devices. This collected biometric information is analyzed by the emotion analysis engine. Based on the analysis results, the user's emotional state is estimated in real time, and this information is transmitted to the server.

[0787] The server dynamically generates dialogue content using a generative AI model based on the user's emotional state and biometric information from sensors. Seasonal and event information is also considered to select topics that are more likely to interest the user. For example, if a user says, "I'm lonely today," the server uses the generative AI model to generate a suggestion such as, "How about starting a new hobby today?" This is achieved by using the prompt message, "The user says they are lonely today. Please offer them some encouraging words. For example, use warm words that suggest a new hobby."

[0788] Furthermore, if an abnormal behavioral pattern is detected, the server automatically notifies pre-registered contacts. This system allows users to always feel secure while also informing family members and caregivers of any risks.

[0789] In this way, systems based on generative models and emotion engines support the improvement of the quality of life for the elderly by providing users with emotionally resonant conversations and care.

[0790] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0791] Step 1:

[0792] The device collects the user's voice and facial expression data. This input includes voice data acquired by a voice input device and image data acquired by a camera. This data is sent to an emotion analysis engine for emotion analysis. Specifically, the device's microphone and camera operate simultaneously to capture the user's speech and the changes in their facial expressions during that time.

[0793] Step 2:

[0794] The emotion analysis engine analyzes the received audio and image data to estimate the user's emotional state. The analysis process applies voice tone analysis and facial expression recognition algorithms. As output, the user's emotional state at that moment is sent to the server in real time. Specifically, an emotion score (e.g., joy, anger, sadness) is generated.

[0795] Step 3:

[0796] The server receives emotional state information as input and generates dialogue content using a generative AI model. Based on the input, and taking into account seasonal and event information, the generative model designs the optimal conversation content according to the prompt. The generated content takes the user's emotions into consideration and constructs the most appropriate response.

[0797] Step 4:

[0798] The server sends the generated dialogue to the terminal, which then relays it to the user. The outputted dialogue is delivered to the user as either audio or text. The terminal's speaker and display are used in this process.

[0799] Step 5:

[0800] The server also constantly monitors biometric data from sensors to detect unusual behavioral patterns. The input is real-time biometric data, and if an anomaly is detected, an alert is automatically sent to registered contacts. Specifically, if the system detects a sudden change in heart rate or an abnormal rise in body temperature, it immediately sends a warning message.

[0801] 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.

[0802] 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 the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0803] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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."

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0822] The following is further disclosed regarding the embodiments described above.

[0823] (Claim 1)

[0824] A means of generating dialogue content using a generative model,

[0825] A means for collecting biometric data using sensors and cameras,

[0826] A means for analyzing collected biometric data to detect behavioral patterns that are different from the norm,

[0827] A means of automatically sending a notification when an abnormality is detected,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, wherein the generative model dynamically generates dialogue content based on seasonal and event information.

[0831] (Claim 3)

[0832] The system according to claim 1, which models the user's health status by analyzing biometric data and detects deviations from that pattern in real time.

[0833] "Example 1"

[0834] (Claim 1)

[0835] A means of generating conversation content using a generative model,

[0836] A means of converting audio information into text and analyzing user input information,

[0837] A dynamic content generation means for generating the next conversation content based on the utterance,

[0838] A means for acquiring biological information using sensors and imaging devices and recognizing unusual behavioral patterns,

[0839] A means of automatically notifying when an anomaly is detected,

[0840] A system that includes this.

[0841] (Claim 2)

[0842] The system according to claim 1, wherein the generative model dynamically generates conversation content based on time of day and event information.

[0843] (Claim 3)

[0844] The system according to claim 1, which models the user's physical condition by analyzing biometric information and immediately recognizes deviations from the standard.

[0845] "Application Example 1"

[0846] (Claim 1)

[0847] A means of generating dialogue content using a generative model,

[0848] A means for collecting biometric data using sensors and cameras,

[0849] A means for analyzing collected biometric data to detect behavioral patterns that are different from the norm,

[0850] A means of automatically sending a notification when an abnormality is detected,

[0851] A means of acquiring biometric data in conjunction with an external device,

[0852] A means for converting the generated dialogue content into speech and outputting it,

[0853] A means of analyzing biometric data to monitor health status and notifying information when an anomaly is detected,

[0854] A system that includes this.

[0855] (Claim 2)

[0856] The system according to claim 1, wherein the generative model dynamically generates dialogue content based on seasonal and event information.

[0857] (Claim 3)

[0858] The system according to claim 1, which analyzes biometric data to model the user's health status, detects deviations from that pattern in real time, and sends emergency notifications through pre-registered contact methods.

[0859] "Example 2 of combining an emotion engine"

[0860] (Claim 1)

[0861] A means of generating dialogue content based on emotional states using a generative model,

[0862] A means for estimating a user's emotional state in real time using voice and facial expression data,

[0863] A means for continuously collecting biometric data using sensors and detecting behavioral patterns that are different from the norm,

[0864] A means of automatically sending notifications to pre-registered contacts when an anomaly is detected,

[0865] A system that includes this.

[0866] (Claim 2)

[0867] The system according to claim 1, wherein the generative model dynamically generates dialogue content using seasonal and event information along with emotional information.

[0868] (Claim 3)

[0869] The system according to claim 1, which integrates and analyzes biometric data and emotional data to monitor the user's health and emotional state and detects deviations from those patterns in real time.

[0870] "Application example 2 when combining with an emotional engine"

[0871] (Claim 1)

[0872] A means of generating dialogue content using a generative model,

[0873] A means for collecting biological information using sensors and imaging devices,

[0874] A means for analyzing collected biometric information to detect behavioral patterns that are different from the norm,

[0875] A means of automatically sending a notification when an abnormality is detected,

[0876] A means of analyzing a user's voice and biometric information to estimate their emotional state in real time,

[0877] A means of adjusting the content of the dialogue and generating suggestions according to the emotional state,

[0878] A system that includes this.

[0879] (Claim 2)

[0880] The system according to claim 1, wherein the generative model dynamically generates dialogue content based on seasonal and event information.

[0881] (Claim 3)

[0882] The system according to claim 1, which models the user's health status by analyzing biometric information and detects deviations from that pattern in real time. [Explanation of symbols]

[0883] 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 generating dialogue content using a generative model, A means for collecting biometric data using sensors and cameras, A means for analyzing collected biometric data to detect behavioral patterns that are different from the norm, A means of automatically sending a notification when an abnormality is detected, A means of acquiring biometric data in conjunction with an external device, A means for converting the generated dialogue content into speech and outputting it, A means of analyzing biometric data to monitor health status and notifying information when an anomaly is detected, A system that includes this.

2. The system according to claim 1, wherein the generative model dynamically generates dialogue content based on seasonal and event information.

3. The system according to claim 1, which analyzes biometric data to model the user's health status, detects deviations from that pattern in real time, and sends emergency notifications through pre-registered contact methods.

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