system

Abstract

We provide the system. [Solution] A means of acquiring voice conversations with elderly people, A conversion means for converting acquired voice conversation into text data, An analysis means for analyzing the aforementioned text data and evaluating the accuracy of pronunciation and the degree of understanding of the conversation content, A notification mechanism that detects anomalies based on analysis results and notifies the user or designated contact, A storage means for saving the aforementioned analysis results to a database, A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot character; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In an aging society, it is important to routinely check the health status of the elderly living in remote areas. In particular, in order to grasp the signs of diseases such as cerebral infarction for which early detection is important, it is necessary to routinely monitor changes in pronunciation and the content of conversations. However, when there is no direct caregiver, it is not easy to achieve this, and there is also a problem that it is a heavy burden to set aside time individually every day. 【Means for Solving the Problems】 【0005】 This invention is a system that acquires voice conversations, converts them into text data, and analyzes that text data to evaluate the accuracy of pronunciation and the degree of comprehension of the conversation. Furthermore, based on the analysis results, it detects abnormalities in health status and notifies the user or pre-set contacts. The data is stored daily and, by comparing it with past conversation history, makes it easier to grasp trends in abnormalities. This makes it possible to detect changes in the health status of elderly people early and take necessary actions quickly. 【0006】 "Means of acquisition" refers to a device or function for acquiring voice conversations with elderly people as digital data. 【0007】 "Conversion means" refers to a device or function for converting acquired audio data into text format. 【0008】 "Analysis means" refers to a device or function that analyzes text data and evaluates the accuracy of pronunciation and the degree of comprehension of conversation content. 【0009】 "Notification means" refers to a device or function that detects anomalies based on analysis results and issues warnings or notifications to the user or designated contact person as necessary. 【0010】 "Storage means" refers to a device or function that records analysis results and conversation history data and stores them in a way that allows them to be referenced later. [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 the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] 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 labeled 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 labeled 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 labeled 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 labeled communication I / F (Interface) is an interface including 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】 To implement this invention, it is first necessary to install an application for acquiring voice conversations on a smartphone or dedicated device used by the elderly. By launching this application, the user starts a voice conversation every morning by following the on-screen guide. 【0033】 Terminal role 【0034】 The device picks up the user's speech using a microphone and acquires it as audio data. Then, using speech recognition technology, it converts the acquired audio into text data in real time. In this process, a unique machine learning model is used to obtain highly accurate results with reduced noise. 【0035】 Server Role 【0036】 Once text data is generated, it is sent from the terminal to the server. The server is responsible for analyzing the received text data. This analysis incorporates natural language processing algorithms to evaluate the user's pronunciation and speaking style, determining the accuracy of pronunciation and the appropriateness of grammar. Furthermore, the analysis results are compared with the user's previously saved conversation history, and if an anomaly is detected, an analysis report is generated. 【0037】 Feedback and notifications 【0038】 Once the analysis report is generated, the server sends the results to the terminal. The terminal displays the results in an easy-to-understand format for the user. For example, if changes in pronunciation or a decline in comprehension are detected, the terminal will provide a message such as, "There are some changes in your speaking style today. Please take care of your health." Furthermore, if a more serious abnormality is detected, the system can automatically send warnings or notifications to contacts specified by the user in advance (e.g., family or medical institutions). 【0039】 For example, if a user says, "I'm planning to go for a walk today," but their pronunciation is unclear, the server will compare it with past history and detect that something is different. In this case, the device will display a message saying, "A slight change in pronunciation was observed today. Please check your health status as a precaution." 【0040】 In this way, this invention provides support for understanding daily health conditions and facilitating early medical intervention. 【0041】 The following describes the processing flow. 【0042】 Step 1: 【0043】 The user launches the application on their smartphone or dedicated device and begins a voice conversation with the chatbot. The app then asks the user, "Good morning, what are your plans for today?" 【0044】 Step 2: 【0045】 The device captures the user's speech through the microphone and acquires it as audio data. At this time, a noise reduction filter is applied to ensure clear audio data. 【0046】 Step 3: 【0047】 The device sends the acquired audio data to a speech recognition engine, which converts it into text data in real time. This process utilizes a pre-trained model to improve accuracy. 【0048】 Step 4: 【0049】 The converted text data is sent from the device to a server in the cloud. Here, data security is ensured through encryption of the communication. 【0050】 Step 5: 【0051】 The server analyzes the received text data. Natural language processing techniques are used to evaluate pronunciation clarity and conversational content. Specifically, algorithms are applied to detect grammatical errors and pronunciation changes. 【0052】 Step 6: 【0053】 The server detects anomalies based on the analysis results and generates an anomaly report as needed. This report includes information about the anomalies found and their severity. 【0054】 Step 7: 【0055】 The analysis results are sent from the server to the terminal. The terminal then notifies the user of the results. For example, if an anomaly is detected, a warning message such as, "A slight change in your pronunciation this morning has been detected. Please take care of yourself," is displayed. 【0056】 Step 8: 【0057】 Based on serious anomalies or user-defined conditions, the server automatically notifies pre-registered family members and medical institutions. This enables a rapid response. 【0058】 Step 9: 【0059】 The server stores all analysis results and the resulting decision information in a database, accumulating it as part of the user's long-term health history. This information is useful for subsequent analysis and the development of care plans. 【0060】 (Example 1) 【0061】 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." 【0062】 There is a need to provide a system that can detect changes in the health status of elderly people early through voice data and enable timely medical intervention. However, conventional methods have the problem of being insufficient in monitoring health changes through everyday conversations and responding quickly if abnormalities are detected. 【0063】 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. 【0064】 In this invention, the server includes an acquisition unit means for acquiring voice information, a conversion unit means for converting the voice information into string data, and an analysis unit means for evaluating the string data and analyzing voice features using a generation AI model. This enables real-time health monitoring through everyday conversations. 【0065】 "Audio information" refers to data that records sounds, such as a user's voice, as digital signals. 【0066】 An "acquisition unit" is a device that captures audio information and receives that data. 【0067】 "String data" refers to text-based information obtained by converting audio information. 【0068】 A "conversion unit" is a device that performs the process of converting audio information into string data. 【0069】 A "generative AI model" is an artificial intelligence algorithm used to analyze the characteristics of speech with high accuracy. 【0070】 The "analysis unit" is a device that uses a generative AI model to evaluate string data and analyze the finer details of speech. 【0071】 A "message unit" is a device that detects anomalies based on analysis results and sends appropriate response messages as needed. 【0072】 "History information" refers to data that summarizes past conversations and analysis results. 【0073】 A "storage unit" is a device used to store analysis results and historical information in a database or similar format. 【0074】 "Anomaly detection" is an analytical process that compares the current analysis results with past historical information to detect conditions that are different from the normal state. 【0075】 This invention is designed to monitor the health status of elderly people through their daily conversations and to detect abnormalities early. The system consists of a terminal and a server for acquiring and analyzing voice information. 【0076】 Terminal configuration and operation 【0077】 The user uses a device with a dedicated application installed. The device includes an acquisition unit that uses a microphone to capture the user's voice information. This voice information is converted into text data through a speech recognition engine utilizing a generative AI model. This conversion process employs noise cancellation technology and software to improve the accuracy of speech recognition. 【0078】 Server configuration and operation 【0079】 The converted string data is sent to the server. The server contains an analysis unit that evaluates the string data and analyzes speech features. This analysis unit uses natural language processing (NLP) techniques to evaluate the user's pronunciation and grammatical structure. The analysis results are compared with historical information, and if any anomalies are detected, a message unit notifies the user. The server securely manages the information and provides immediate feedback of the results, enabling daily monitoring of the user's health status. 【0080】 Examples of specific cases and prompt statements 【0081】 For example, if a user says "I'm going to the park today" and their pronunciation is different from usual, the analysis unit will identify the change and send a message to the terminal saying, "A change in pronunciation has been detected. Please check your health status." An example of a prompt message would be, "Please create a program to detect changes in health status from the everyday conversations of elderly people." 【0082】 In this way, this invention enables the monitoring of the user's daily health status through voice conversations and provides support for rapid and effective medical assistance. 【0083】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0084】 Step 1: 【0085】 The user launches an application on their device and begins a voice conversation. The device uses its microphone to acquire the user's voice information in real time. The input is analog audio waveform data, and the output is digital audio information. This conversion utilizes digital signal processing and noise reduction techniques. 【0086】 Step 2: 【0087】 The device processes the acquired digital audio information and converts it into string data using a generative AI model. The input is digital audio information, and the output is the corresponding string data. In this process, the speech recognition engine analyzes phonemes and generates highly accurate text data. 【0088】 Step 3: 【0089】 When string data is generated, the terminal sends it to the server. The server evaluates the received string data using a parsing unit. The input is string data, and the output is evaluation data including the parsing results. The server utilizes natural language processing techniques to analyze the grammar, content, and pronunciation characteristics of the text. 【0090】 Step 4: 【0091】 The server compares the analysis results with historical information stored within the server. The input consists of the current analysis results and past historical data, while the output is judgment data regarding the presence or absence of anomalies. This judgment is made by identifying deviations from normal patterns using an anomaly detection algorithm. 【0092】 Step 5: 【0093】 When an anomaly is detected, the server generates a notification using a message unit. The input is the anomaly detection data, and the output is the notification message. The notification is sent to the user's terminal and displays a message such as, "There is a change in pronunciation. Please pay attention to your health." In the case of a serious anomaly, a notification is also automatically sent to the configured emergency contact. 【0094】 (Application Example 1) 【0095】 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." 【0096】 There is a lack of systems in place to regularly monitor the health status of the elderly and to respond quickly and appropriately when abnormalities are detected. Therefore, there is a need for a system that can detect changes in speech and pronunciation abnormalities early and provide the necessary support. 【0097】 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. 【0098】 In this invention, the server includes an acquisition means for acquiring voice conversations, a conversion means, an analysis means, a notification means, a storage means, and a management means. This makes it possible to accurately evaluate the content of conversations of elderly people and to quickly notify and respond when an anomaly is detected. 【0099】 The "acquisition method" refers to a mechanism that has the function of capturing the voice conversations of elderly people in real time. 【0100】 "Conversion means" refers to a function for converting acquired audio data into text data. 【0101】 The "analysis means" is a mechanism that executes algorithms to evaluate the accuracy of pronunciation and the content of conversation based on the converted text data. 【0102】 A "notification system" is a system that reports to the user or a designated contact when an anomaly is detected based on the analysis results. 【0103】 The "storage mechanism" refers to a memory management system for storing analysis results and conversation history in a database. 【0104】 "Management measures" refer to functions that enable immediate notification via smart devices when an anomaly is detected, and to take appropriate action according to the risk level. 【0105】 The system implementing this invention is designed for daily use by elderly individuals. Users engage in daily conversations with the system using a smartphone or a dedicated smart device. The device captures the user's speech in real time using a high-precision microphone. This audio data is converted into text data using the Google® Cloud Speech-to-Text API. 【0106】 The acquired text data is sent to the server, where natural language processing is performed using Python's NLTK (Natural Language Toolkit). The server analyzes the accuracy of the user's pronunciation and their comprehension of the conversation, comparing it with past conversation history to identify anomalies. This analysis process continuously monitors the user's speech characteristics and detects any changes. 【0107】 If an anomaly is detected, the server uses the Twilio API to immediately send a notification to the user or designated contacts. The notification includes both audio and text, along with information to prompt a quick response if necessary. This allows users and their families to take early action regarding the health of elderly individuals. 【0108】 For example, if a user says "I'm not feeling well this morning" in an unusual tone, the system immediately detects this change and sends a notification to family members saying, "There may be a change in the user's health. Please check on them." This can be continuously improved by using the generative AI model GPT-3® to input prompts such as "Please tell me how to handle situations where the user's speech is different from usual." 【0109】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0110】 Step 1: 【0111】 The terminal captures the speech of elderly individuals using a high-precision microphone. The input is an audio signal, and the output is a digital recording of that audio. In this step, signal processing is performed to reduce noise and prepare data suitable for language processing. 【0112】 Step 2: 【0113】 The device converts audio signals into text data using the Google Cloud Speech-to-Text API. The input is digital audio data, and the output is the converted text data. This conversion uses a speech recognition algorithm to accurately convert the user's speech into text information. 【0114】 Step 3: 【0115】 The terminal sends the converted text to the server. The input is text data, and the output is data transfer to the server. In this step, encryption protocols are used to ensure that the data is transmitted securely. 【0116】 Step 4: 【0117】 The server performs natural language processing on the received text data using Python's NLTK. The input is text data, and the output is the accuracy of the analyzed pronunciation and the characteristics of the language structure. This analysis utilizes a language model and processes data to detect changes in the user's speaking style and pronunciation. 【0118】 Step 5: 【0119】 The server compares the analysis results with existing conversation history to detect anomalies. The input consists of the analysis results and historical data, while the output is an anomaly detection flag and its level. Statistical methods are used in this comparison to quantify deviations from normal speech. 【0120】 Step 6: 【0121】 When an anomaly is detected, the server uses the Twilio API to send a notification. The input is the flag and level of the anomaly detection, and the output is a notification message to the user and their family. This notification includes both audio and text, and an alert is automatically generated based on the relevant risk level. 【0122】 Step 7: 【0123】 The user receives a notification and takes action to check their health status. The input is a notification sent from the server, and the output is the user checking their status or consulting a medical institution. In this step, a generative AI model is used to enable a rapid response by referring to prompts such as "What should I do next if information about the user's health status is available?" 【0124】 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. 【0125】 This invention provides a system for elderly individuals to record their daily health status via voice, and by incorporating an emotion engine into this system, it becomes possible to understand the user's emotional state. This system is implemented through an application that runs on a smartphone or a dedicated device. 【0126】 Terminal role 【0127】 The user uses the application every morning or as needed, following on-screen instructions to initiate a voice conversation. The device accepts the voice input and acquires the voice data. The acquired voice data is immediately converted into text data by a conversion device. This conversion process uses speech recognition technology, which can identify even subtle differences in pronunciation. 【0128】 Server Role 【0129】 The converted text data is sent from the terminal to the server. The server receives this data and evaluates the accuracy of pronunciation and the level of understanding of the conversation through an analysis tool. The analysis tool incorporates a natural language processing algorithm to detect grammatical errors and unnatural speech patterns in particular. It then compares this data with past conversation history to determine if there are any abnormalities. 【0130】 Furthermore, the emotion engine uses both the audio data itself and the analyzed text data to identify the user's emotions. This allows for a deeper understanding of the user's health by considering the possibility that they may be experiencing stress or anxiety. 【0131】 Feedback and notifications 【0132】 The results analyzed by the server are sent to the user's device as feedback. If a specific anomaly is detected, the system can alert the user via voice or on-screen messages. For example, a message such as, "There has been a slight change in your conversation content and emotional state today. We recommend you take a rest," might be displayed. Furthermore, if a serious anomaly or emotional state fluctuation is detected, notifications will also be sent to the user's registered family members and medical institutions. 【0133】 For example, if a user says, "I'm feeling a little down today," the emotion engine will determine the emotional state from the audio, and the server will generate analytical information based on that. If an abnormality is detected, the system will provide advice to the user through the terminal and prompt them to notify a medical institution if necessary. 【0134】 In this way, by incorporating an emotion engine, it becomes possible to support users not only from a health perspective but also from an emotional one. 【0135】 The following describes the processing flow. 【0136】 Step 1: 【0137】 The user launches the application on their smartphone or dedicated device and initiates a voice conversation to record their daily status. At this point, the app prompts the user with, "Good morning, please tell me how you're feeling and how you're feeling today." 【0138】 Step 2: 【0139】 The device captures the user's speech using a microphone and acquires it as audio data. At this point, noise cancellation technology is used to eliminate external noise and improve the quality of the audio data. 【0140】 Step 3: 【0141】 The acquired audio data is converted into text data by the device's built-in speech recognition engine. This text conversion process aims to accurately reflect the user's pronunciation and uses high-precision speech recognition technology. 【0142】 Step 4: 【0143】 The device sends the converted text data to the server. The data is encrypted before transmission, ensuring privacy. 【0144】 Step 5: 【0145】 The server analyzes the received text data and evaluates the accuracy of pronunciation and comprehension of the content. This analysis uses natural language processing algorithms to evaluate grammatical correctness and content continuity. 【0146】 Step 6: 【0147】 The emotion engine independently analyzes voice data and infers the user's emotional state. This engine specifically analyzes voice tone, speaking speed, and emphasis patterns to determine the emotional state. 【0148】 Step 7: 【0149】 The server combines the analysis results and feedback from the emotion engine to generate a comprehensive report for the user. This report includes advice on health and emotional state. 【0150】 Step 8: 【0151】 The device receives the report and provides visual or audio feedback to the user. For example, it might display a message such as, "You seem to be feeling a little breathless today. It would be good to take some time to relax." 【0152】 Step 9: 【0153】 If a significant health or emotional abnormality is detected, the server will notify pre-registered family members or medical institutions. This notification allows for prompt follow-up. 【0154】 Step 10: 【0155】 The server stores analytical data and emotional states in a database, building a long-term history of the user's health and emotions. This information is useful for the user themselves and medical professionals to understand their condition. 【0156】 (Example 2) 【0157】 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". 【0158】 When elderly individuals record their daily health status via voice, there is a need for a system that can accurately assess not only the accuracy of pronunciation and comprehension of conversations, but also their emotional state, enabling early detection and response to abnormalities. Furthermore, conventional systems lack sufficient information regarding emotional changes, making them inadequate for monitoring the overall health of users. This issue is particularly urgent in modern society, given the importance of daily health management for the elderly and other individuals requiring special consideration. 【0159】 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. 【0160】 In this invention, the server includes acquisition means for acquiring voice data from the user, conversion means for converting the acquired voice data into text data, analysis means for analyzing the text data and evaluating the accuracy of pronunciation and the degree of understanding of the conversation, emotion identification means for identifying the emotional state based on the analysis results and voice data, notification means for detecting abnormalities based on the analysis and emotion identification results and notifying the user or a designated contact, and storage means for storing the analysis and emotion identification results in a memory area. This enables comprehensive support from an emotional perspective as well as monitoring the user's health status. 【0161】 "Acquisition means" refers to a function that receives voice data from the user and acquires it in a format that can be processed within the system. 【0162】 A "conversion means" is a function for converting acquired audio data into analyzable text data. 【0163】 "Analysis means" refers to a function that evaluates the accuracy of pronunciation and the degree of comprehension of conversation content based on text data. 【0164】 "Emotion identification means" refers to a function that identifies the user's emotional state based on analyzed text and audio data. 【0165】 "Notification means" refers to a function that detects anomalies based on analysis results and emotion recognition results, and transmits information to the user or designated contact. 【0166】 "Storage means" refers to a function for storing the results of acquisition, analysis, and emotion identification in a recording area. 【0167】 This invention is a system that allows users to record their daily health status via voice and comprehensively understand their health and emotional state based on that content. The system mainly consists of a terminal and a server. 【0168】 Device configuration and role 【0169】 The terminal's role is to acquire voice data from the user. Specifically, a smartphone or a dedicated mobile device is used. These devices have built-in microphones, making it possible to record everyday conversations in high quality. The user launches the application on the terminal and begins voice input. For example, if the user says, "I feel a little better today," that voice is captured by the terminal. The terminal uses speech recognition technology to convert the voice data into text data. For this, speech recognition software such as the Google Speech-to-Text API can be used. 【0170】 Server Configuration and Roles 【0171】 The server receives text data sent from the terminal and analyzes it. This analysis requires natural language processing (NLP) technology, and a commonly used NLP library (e.g., spaCy) is applied. The server then uses an emotion recognition engine to identify the user's emotions from the audio data and the analyzed text data. This uses an emotion analysis tool (e.g., IBM Watson® Tone Analyzer). The analysis results are stored in memory and compared with past data to form the basis for notifying the user of anomalies or risks. 【0172】 Specific examples and prompt statements 【0173】 As a concrete example, suppose a user says, "It's raining today and I'm feeling a little down." In this case, the emotion engine can identify the feeling of sadness, and the server can analyze the result and provide feedback to the user such as, "Try to do something relaxing today." 【0174】 Example prompts for a generative AI model: 【0175】 "Analyze the user's voice input to determine their emotional state and provide appropriate advice. Example: 'It's raining today, and I'm feeling a bit down.'" 【0176】 In this way, the system is able to support users' daily health management by providing them with appropriate and timely feedback that matches their emotional state. 【0177】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0178】 Step 1: 【0179】 The user launches an application on their smartphone or a dedicated device and records their health status using voice. The user's voice is captured through a microphone. The input is the user's raw voice data, which the device acquires in real time. 【0180】 Step 2: 【0181】 The device uses speech recognition technology to convert the acquired audio data into text data. Specifically, it utilizes the Google Speech-to-Text API, among others, to output the audio as text. In this step, the audio data is the input and the text data is the output. As the device generates the text data, the analysis of the conversation content can proceed. 【0182】 Step 3: 【0183】 The terminal sends text data to the server via the internet. Encryption technology is applied during transmission to protect user privacy. Here, the input is the text data converted by the terminal, and the output is the data securely sent to the server. 【0184】 Step 4: 【0185】 The server analyzes the received text data using a natural language processing library (e.g., spaCy). The analysis process includes grammatical structure analysis and verification of pronunciation accuracy. The input is the text data sent to the server, and the output is the analyzed text result. The server then proceeds with further processing based on this analysis result. 【0186】 Step 5: 【0187】 The server uses the parsed text data to identify the user's emotional state using an emotion engine (e.g., IBM Watson Tone Analyzer). In this process, the input is parsed text data, and the output is an emotional state label. The server then confirms the information regarding the user's emotions. 【0188】 Step 6: 【0189】 The server integrates the results of analysis and sentiment recognition, and notifies the user or designated contacts if an anomaly is detected. Push notifications and email may be used as notification methods. The input consists of various analysis results generated within the server, and the output consists of specific advice and warning messages. 【0190】 Step 7: 【0191】 The server ultimately stores all analysis and sentiment recognition results in memory. This storage allows past information to be used as foundational data for future analyses. The input consists of all completed analysis and sentiment recognition data, and the output is permanently stored record data. 【0192】 (Application Example 2) 【0193】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal". 【0194】 In the lives of older adults, it is crucial to appropriately understand their daily health and emotional states and provide necessary care. However, current systems struggle to efficiently monitor the emotional states of older adults and provide appropriate feedback based on that information. This situation could lead to overlooking potential health risks, highlighting the need for a more effective monitoring system. 【0195】 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. 【0196】 In this invention, the server includes an emotion analysis means for identifying emotional states and generating care-oriented feedback based on the analysis results, and an external notification means for notifying specific parties or organizations when an abnormality is detected. This makes it possible to accurately grasp the emotional state of elderly people and provide appropriate advice and notifications. 【0197】 "Acquisition method" refers to a function for capturing and recording voice conversations with elderly people on a device. 【0198】 The "conversion means" refers to a function that converts acquired voice conversations into text data using speech recognition technology. 【0199】 "Analysis means" refers to a function that evaluates the grammatical and pronunciation accuracy of text data and analyzes the level of comprehension of the conversation. 【0200】 A "notification method" is a function that transmits information to the user or a designated contact when an anomaly is detected based on the analysis results. 【0201】 "Storage method" refers to a function for securely storing analysis results and user conversation history in a database. 【0202】 "Emotional analysis means" refers to a function that identifies the emotional state of elderly people from voice data and generates appropriate feedback based on the analysis results. 【0203】 An "external notification mechanism" is a function that notifies specific parties or organizations when an anomaly is detected. 【0204】 The system for realizing this application consists of a voice recording device used daily by elderly people and a server system that works in conjunction with it. 【0205】 The terminal's role is to detect voice conversations with elderly individuals, collect voice data, and then convert it into text data using speech recognition technology. The specific technology used is the speech_recognition library, and the Google Speech-to-Text API is responsible for the text conversion. 【0206】 Next, the server uses natural language processing techniques to evaluate the accuracy of pronunciation and comprehension of the conversation based on the text data sent from the terminal. The text data is then analyzed using sentiment analysis tools and used to determine the emotional state of the elderly person. This analysis is expected to utilize a text analysis library. 【0207】 The analysis results are stored in a database, and if an anomaly is detected, a warning is sent to the user or designated contacts via a notification function. Furthermore, feedback provided by the sentiment analysis system is displayed to the user as visual or audio advice. For example, if speech recognition detects "I'm feeling a little down today," the sentiment analysis system analyzes the emotion from the speech, and the server generates advice based on that, such as "Relax and take a break." 【0208】 In this invention, by operating a generative AI model, specific instructions such as "Please tell me what the elderly person said about how they felt today. Analyze their statements, determine whether their emotions are positive or negative, and provide helpful feedback for care" can be used as prompts for determining emotional states. These prompts are used to improve the quality of detailed analysis and feedback. 【0209】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0210】 Step 1: 【0211】 The device uses a microphone to capture the elderly person's voice as input. It then saves the acquired voice data to an internal buffer. This voice data serves as the basis for subsequent processing. 【0212】 Step 2: 【0213】 The device analyzes the acquired audio data using the Speech Recognition library and converts it into text data. This converted text data is then output. In this process, the speech recognition algorithm analyzes the waveform information of the audio and identifies language-related nuances and pronunciations. 【0214】 Step 3: 【0215】 The server receives text data sent from the terminal as input. It then analyzes the text using natural language processing technology and evaluates the accuracy of pronunciation and the level of understanding of the conversation. The output includes the analysis results and, if necessary, whether or not anomalies have been detected. The server also performs grammar checks and key phrase recognition to identify unnatural expressions and unusual vocabulary. 【0216】 Step 4: 【0217】 The server applies sentiment analysis techniques to the analyzed text data to identify emotional states. The input is the analyzed text data, and the output generates sentiment scores and state reports. This process uses a sentiment analysis model to evaluate emotional words and phrases within the text, assigning them to sentiment categories such as positive and negative. 【0218】 Step 5: 【0219】 The server generates specific feedback and advice for older adults based on their emotional state and analysis results. This involves following prompts using a generative AI model to form contextually relevant feedback sentences. The output is feedback text recommending ways to improve the older adult's emotional state and promote relaxation. 【0220】 Step 6: 【0221】 If an anomaly is detected, the server will notify the designated contact using an external notification method. The input for this notification is the anomaly detection result, and the output is a warning message. The server will refer to pre-registered contact data and send a warning message as needed. 【0222】 Step 7: 【0223】 The server saves all analysis results and feedback to a database. The input is the analysis and feedback data, and the output is the updated database. This saving process continuously accumulates the user's conversation history and emotional state, which can then be used for future analyses. 【0224】 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. 【0225】 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. 【0226】 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. 【0227】 [Second Embodiment] 【0228】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0229】 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. 【0230】 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). 【0231】 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. 【0232】 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. 【0233】 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). 【0234】 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. 【0235】 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. 【0236】 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. 【0237】 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. 【0238】 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. 【0239】 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". 【0240】 To implement this invention, it is first necessary to install an application for acquiring voice conversations on a smartphone or dedicated device used by the elderly. By launching this application, the user starts a voice conversation every morning by following the on-screen guide. 【0241】 Terminal role 【0242】 The device picks up the user's speech using a microphone and acquires it as audio data. Then, using speech recognition technology, it converts the acquired audio into text data in real time. In this process, a unique machine learning model is used to obtain highly accurate results with reduced noise. 【0243】 Server Role 【0244】 Once text data is generated, it is sent from the terminal to the server. The server is responsible for analyzing the received text data. This analysis incorporates natural language processing algorithms to evaluate the user's pronunciation and speaking style, determining the accuracy of pronunciation and the appropriateness of grammar. Furthermore, the analysis results are compared with the user's previously saved conversation history, and if an anomaly is detected, an analysis report is generated. 【0245】 Feedback and notifications 【0246】 Once the analysis report is generated, the server sends the results to the terminal. The terminal displays the results in an easy-to-understand format for the user. For example, if changes in pronunciation or a decline in comprehension are detected, the terminal will provide a message such as, "There are some changes in your speaking style today. Please take care of your health." Furthermore, if a more serious abnormality is detected, the system can automatically send warnings or notifications to contacts specified by the user in advance (e.g., family or medical institutions). 【0247】 For example, if a user says, "I'm planning to go for a walk today," but their pronunciation is unclear, the server will compare it with past history and detect that something is different. In this case, the device will display a message saying, "A slight change in pronunciation was observed today. Please check your health status as a precaution." 【0248】 In this way, this invention provides support for understanding daily health conditions and facilitating early medical intervention. 【0249】 The following describes the processing flow. 【0250】 Step 1: 【0251】 The user launches the application on their smartphone or dedicated device and begins a voice conversation with the chatbot. The app then asks the user, "Good morning, what are your plans for today?" 【0252】 Step 2: 【0253】 The device captures the user's speech through the microphone and acquires it as audio data. At this time, a noise reduction filter is applied to ensure clear audio data. 【0254】 Step 3: 【0255】 The device sends the acquired audio data to a speech recognition engine, which converts it into text data in real time. This process utilizes a pre-trained model to improve accuracy. 【0256】 Step 4: 【0257】 The converted text data is sent from the device to a server in the cloud. Here, data security is ensured through encryption of the communication. 【0258】 Step 5: 【0259】 The server analyzes the received text data. Natural language processing techniques are used to evaluate pronunciation clarity and conversational content. Specifically, algorithms are applied to detect grammatical errors and pronunciation changes. 【0260】 Step 6: 【0261】 The server detects anomalies based on the analysis results and generates an anomaly report as needed. This report includes information about the anomalies found and their severity. 【0262】 Step 7: 【0263】 The analysis results are sent from the server to the terminal. The terminal then notifies the user of the results. For example, if an anomaly is detected, a warning message such as, "A slight change in your pronunciation this morning has been detected. Please take care of yourself," is displayed. 【0264】 Step 8: 【0265】 Based on serious anomalies or user-defined conditions, the server automatically notifies pre-registered family members and medical institutions. This enables a rapid response. 【0266】 Step 9: 【0267】 The server stores all analysis results and the resulting decision information in a database, accumulating it as part of the user's long-term health history. This information is useful for subsequent analysis and the development of care plans. 【0268】 (Example 1) 【0269】 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". 【0270】 There is a need to provide a system that can detect changes in the health status of elderly people early through voice data and enable timely medical intervention. However, conventional methods have the problem of being insufficient in monitoring health changes through everyday conversations and responding quickly if abnormalities are detected. 【0271】 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. 【0272】 In this invention, the server includes an acquisition unit means for acquiring voice information, a conversion unit means for converting the voice information into string data, and an analysis unit means for evaluating the string data and analyzing voice features using a generation AI model. This enables real-time health monitoring through everyday conversations. 【0273】 "Audio information" refers to data that records sounds, such as a user's voice, as digital signals. 【0274】 An "acquisition unit" is a device that captures audio information and receives that data. 【0275】 "String data" refers to text-based information obtained by converting audio information. 【0276】 A "conversion unit" is a device that performs the process of converting audio information into string data. 【0277】 A "generative AI model" is an artificial intelligence algorithm used to analyze the characteristics of speech with high accuracy. 【0278】 The "analysis unit" is a device that uses a generative AI model to evaluate string data and analyze the finer details of speech. 【0279】 A "message unit" is a device that detects anomalies based on analysis results and sends appropriate response messages as needed. 【0280】 "History information" refers to data that summarizes past conversations and analysis results. 【0281】 A "storage unit" is a device used to store analysis results and historical information in a database or similar format. 【0282】 "Anomaly detection" is an analytical process that compares the current analysis results with past historical information to detect conditions that are different from the normal state. 【0283】 This invention is configured for the purpose of monitoring the health status through the daily conversations of the elderly and detecting abnormalities at an early stage. The system consists of a terminal and a server for acquiring and analyzing voice information. 【0284】 Configuration and Operation of the Terminal 【0285】 The user uses a terminal installed with a dedicated application. The terminal includes an acquisition unit that acquires the user's voice information using a microphone. This voice information is converted into string data through a voice recognition engine utilizing a generative AI model. This conversion process utilizes noise cancellation technology and uses software to improve the accuracy of voice recognition. 【0286】 Configuration and Operation of the Server 【0287】 The converted string data is transmitted to the server. The server includes an analysis unit that evaluates the string data and analyzes voice features. This analysis unit utilizes natural language processing (NLP) technology to evaluate the user's pronunciation and grammar structure. The analysis results are compared with the historical information, and if there are abnormalities, notifications are sent by the message unit. The server securely manages the information and provides immediate feedback on the results, thereby realizing the monitoring of the daily health status. 【0288】 Examples of Specific Cases and Prompt Sentences 【0289】 As a specific example, when the user says "I will go to the park today" and the pronunciation is different from normal, the analysis unit identifies the change and sends a message to the terminal saying "A change in pronunciation has been recognized. Please check your health status." An example of a prompt sentence is "Please create a program for detecting changes in the health status from the daily conversations of the elderly." 【0290】 In this way, this invention enables the monitoring of the user's daily health status through voice conversations and provides support for rapid and effective medical assistance. 【0291】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0292】 Step 1: 【0293】 The user launches an application on their device and begins a voice conversation. The device uses its microphone to acquire the user's voice information in real time. The input is analog audio waveform data, and the output is digital audio information. This conversion utilizes digital signal processing and noise reduction techniques. 【0294】 Step 2: 【0295】 The device processes the acquired digital audio information and converts it into string data using a generative AI model. The input is digital audio information, and the output is the corresponding string data. In this process, the speech recognition engine analyzes phonemes and generates highly accurate text data. 【0296】 Step 3: 【0297】 When string data is generated, the terminal sends it to the server. The server evaluates the received string data using a parsing unit. The input is string data, and the output is evaluation data including the parsing results. The server utilizes natural language processing techniques to analyze the grammar, content, and pronunciation characteristics of the text. 【0298】 Step 4: 【0299】 The server compares the analysis results with historical information stored within the server. The input consists of the current analysis results and past historical data, while the output is judgment data regarding the presence or absence of anomalies. This judgment is made by identifying deviations from normal patterns using an anomaly detection algorithm. 【0300】 Step 5: 【0301】 When an abnormality is detected, the server generates a notification using the message unit. The input is abnormality determination data, and the output is a notification message. The notification is sent to the user's terminal, and a message such as "There is a change in pronunciation. Please pay attention to your health." is displayed. Also, in the event of a serious abnormality, a notification is automatically sent to the set emergency contact. 【0302】 (Application Example 1) 【0303】 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". 【0304】 There is a lack of a mechanism to routinely grasp the health status of the elderly and to be able to respond quickly and appropriately when an abnormality is discovered. For this reason, there is a need for a system that can detect changes in speech and abnormalities in pronunciation at an early stage and provide the necessary support. 【0305】 The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0306】 In this invention, the server includes an acquisition means for acquiring a voice conversation, a conversion means, an analysis means, a notification means, a storage means, and a management means. Thereby, it becomes possible to accurately evaluate the conversation content of the elderly and to notify and respond quickly when an abnormality is detected. 【0307】 The "acquisition means" is a mechanism having a function of capturing the voice conversation of the elderly in real time. 【0308】 The "conversion means" is a function for converting the acquired voice data into text data. 【0309】 The "analysis means" is a mechanism that executes algorithms to evaluate the accuracy of pronunciation and the content of conversation based on the converted text data. 【0310】 A "notification system" is a system that reports to the user or a designated contact when an anomaly is detected based on the analysis results. 【0311】 The "storage mechanism" refers to a memory management system for storing analysis results and conversation history in a database. 【0312】 "Management measures" refer to functions that enable immediate notification via smart devices when an anomaly is detected, and to take appropriate action according to the risk level. 【0313】 The system implementing this invention is designed for daily use by elderly individuals. Users engage in daily conversations with the system using a smartphone or a dedicated smart device. The device captures the user's speech in real time using a high-precision microphone. This audio data is converted into text data using the Google Cloud Speech-to-Text API. 【0314】 The acquired text data is sent to the server, where natural language processing is performed using Python's NLTK (Natural Language Toolkit). The server analyzes the accuracy of the user's pronunciation and their comprehension of the conversation, comparing it with past conversation history to identify anomalies. This analysis process continuously monitors the user's speech characteristics and detects any changes. 【0315】 If an anomaly is detected, the server uses the Twilio API to immediately send a notification to the user or designated contacts. The notification includes both audio and text, along with information to prompt a quick response if necessary. This allows users and their families to take early action regarding the health of elderly individuals. 【0316】 For example, if a user says "I'm not feeling well this morning" in an unusual tone, the system immediately detects this change and sends a notification to family members saying, "There may be a change in the user's health. Please check on them." This can be continuously improved by using the generative AI model GPT-3 to input prompts such as "Please tell me how to handle situations where the user's speech is unusual." 【0317】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0318】 Step 1: 【0319】 The terminal captures the speech of elderly individuals using a high-precision microphone. The input is an audio signal, and the output is a digital recording of that audio. In this step, signal processing is performed to reduce noise and prepare data suitable for language processing. 【0320】 Step 2: 【0321】 The device converts audio signals into text data using the Google Cloud Speech-to-Text API. The input is digital audio data, and the output is the converted text data. This conversion uses a speech recognition algorithm to accurately convert the user's speech into text information. 【0322】 Step 3: 【0323】 The terminal sends the converted text to the server. The input is text data, and the output is data transfer to the server. In this step, encryption protocols are used to ensure that the data is transmitted securely. 【0324】 Step 4: 【0325】 The server performs natural language processing on the received text data using Python's NLTK. The input is text data, and the output is the accuracy of the analyzed pronunciation and the characteristics of the language structure. This analysis utilizes a language model and processes data to detect changes in the user's speaking style and pronunciation. 【0326】 Step 5: 【0327】 The server compares the analysis results with existing conversation history to detect anomalies. The input consists of the analysis results and historical data, while the output is an anomaly detection flag and its level. Statistical methods are used in this comparison to quantify deviations from normal speech. 【0328】 Step 6: 【0329】 When an anomaly is detected, the server uses the Twilio API to send a notification. The input is the flag and level of the anomaly detection, and the output is a notification message to the user and their family. This notification includes both audio and text, and an alert is automatically generated based on the relevant risk level. 【0330】 Step 7: 【0331】 The user receives a notification and takes action to check their health status. The input is a notification sent from the server, and the output is the user checking their status or consulting a medical institution. In this step, a generative AI model is used to enable a rapid response by referring to prompts such as "What should I do next if information about the user's health status is available?" 【0332】 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. 【0333】 This invention provides a system for elderly individuals to record their daily health status via voice, and by incorporating an emotion engine into this system, it becomes possible to understand the user's emotional state. This system is implemented through an application that runs on a smartphone or a dedicated device. 【0334】 Terminal role 【0335】 The user uses the application every morning or as needed, following on-screen instructions to initiate a voice conversation. The device accepts the voice input and acquires the voice data. The acquired voice data is immediately converted into text data by a conversion device. This conversion process uses speech recognition technology, which can identify even subtle differences in pronunciation. 【0336】 Server Role 【0337】 The converted text data is sent from the terminal to the server. The server receives this data and evaluates the accuracy of pronunciation and the level of understanding of the conversation through an analysis tool. The analysis tool incorporates a natural language processing algorithm to detect grammatical errors and unnatural speech patterns in particular. It then compares this data with past conversation history to determine if there are any abnormalities. 【0338】 Furthermore, the emotion engine uses both the audio data itself and the analyzed text data to identify the user's emotions. This allows for a deeper understanding of the user's health by considering the possibility that they may be experiencing stress or anxiety. 【0339】 Feedback and notifications 【0340】 The results analyzed by the server are sent to the user's device as feedback. If a specific anomaly is detected, the system can alert the user via voice or on-screen messages. For example, a message such as, "There has been a slight change in your conversation content and emotional state today. We recommend you take a rest," might be displayed. Furthermore, if a serious anomaly or emotional state fluctuation is detected, notifications will also be sent to the user's registered family members and medical institutions. 【0341】 For example, if a user says, "I'm feeling a little down today," the emotion engine will determine the emotional state from the audio, and the server will generate analytical information based on that. If an abnormality is detected, the system will provide advice to the user through the terminal and prompt them to notify a medical institution if necessary. 【0342】 In this way, by incorporating an emotion engine, it becomes possible to support users not only from a health perspective but also from an emotional one. 【0343】 The following describes the processing flow. 【0344】 Step 1: 【0345】 The user launches the application on their smartphone or dedicated device and initiates a voice conversation to record their daily status. At this point, the app prompts the user with, "Good morning, please tell me how you're feeling and how you're feeling today." 【0346】 Step 2: 【0347】 The device captures the user's speech using a microphone and acquires it as audio data. At this point, noise cancellation technology is used to eliminate external noise and improve the quality of the audio data. 【0348】 Step 3: 【0349】 The acquired audio data is converted into text data by the device's built-in speech recognition engine. This text conversion process aims to accurately reflect the user's pronunciation and uses high-precision speech recognition technology. 【0350】 Step 4: 【0351】 The device sends the converted text data to the server. The data is encrypted before transmission, ensuring privacy. 【0352】 Step 5: 【0353】 The server analyzes the received text data and evaluates the accuracy of pronunciation and comprehension of the content. This analysis uses natural language processing algorithms to evaluate grammatical correctness and content continuity. 【0354】 Step 6: 【0355】 The emotion engine independently analyzes voice data and infers the user's emotional state. This engine specifically analyzes voice tone, speaking speed, and emphasis patterns to determine the emotional state. 【0356】 Step 7: 【0357】 The server combines the analysis results and feedback from the emotion engine to generate a comprehensive report for the user. This report includes advice on health and emotional state. 【0358】 Step 8: 【0359】 The device receives the report and provides visual or audio feedback to the user. For example, it might display a message such as, "You seem to be feeling a little breathless today. It would be good to take some time to relax." 【0360】 Step 9: 【0361】 If a significant health or emotional abnormality is detected, the server will notify pre-registered family members or medical institutions. This notification allows for prompt follow-up. 【0362】 Step 10: 【0363】 The server stores analytical data and emotional states in a database, building a long-term history of the user's health and emotions. This information is useful for the user themselves and medical professionals to understand their condition. 【0364】 (Example 2) 【0365】 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". 【0366】 When elderly individuals record their daily health status via voice, there is a need for a system that can accurately assess not only the accuracy of pronunciation and comprehension of conversations, but also their emotional state, enabling early detection and response to abnormalities. Furthermore, conventional systems lack sufficient information regarding emotional changes, making them inadequate for monitoring the overall health of users. This issue is particularly urgent in modern society, given the importance of daily health management for the elderly and other individuals requiring special consideration. 【0367】 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. 【0368】 In this invention, the server includes acquisition means for acquiring voice data from the user, conversion means for converting the acquired voice data into text data, analysis means for analyzing the text data and evaluating the accuracy of pronunciation and the degree of understanding of the conversation, emotion identification means for identifying the emotional state based on the analysis results and voice data, notification means for detecting abnormalities based on the analysis and emotion identification results and notifying the user or a designated contact, and storage means for storing the analysis and emotion identification results in a memory area. This enables comprehensive support from an emotional perspective as well as monitoring the user's health status. 【0369】 "Acquisition means" refers to a function that receives voice data from the user and acquires it in a format that can be processed within the system. 【0370】 A "conversion means" is a function for converting acquired audio data into analyzable text data. 【0371】 "Analysis means" refers to a function that evaluates the accuracy of pronunciation and the degree of comprehension of conversation content based on text data. 【0372】 "Emotion identification means" refers to a function that identifies the user's emotional state based on analyzed text and audio data. 【0373】 "Notification means" refers to a function that detects anomalies based on analysis results and emotion recognition results, and transmits information to the user or designated contact. 【0374】 "Storage means" refers to a function for storing the results of acquisition, analysis, and emotion identification in a recording area. 【0375】 This invention is a system that allows users to record their daily health status via voice and comprehensively understand their health and emotional state based on that content. The system mainly consists of a terminal and a server. 【0376】 Device configuration and role 【0377】 The terminal's role is to acquire voice data from the user. Specifically, a smartphone or a dedicated mobile device is used. These devices have built-in microphones, making it possible to record everyday conversations in high quality. The user launches the application on the terminal and begins voice input. For example, if the user says, "I feel a little better today," that voice is captured by the terminal. The terminal uses speech recognition technology to convert the voice data into text data. For this, speech recognition software such as the Google Speech-to-Text API can be used. 【0378】 Server Configuration and Roles 【0379】 The server receives text data sent from the terminal and analyzes it. This analysis requires natural language processing (NLP) technology, and a commonly used NLP library (e.g., spaCy) is applied. The server then uses an emotion recognition engine to identify the user's emotions from the audio data and the analyzed text data. This is done using an emotion analysis tool (e.g., IBM Watson Tone Analyzer). The analysis results are stored in memory and compared with past data to form the basis for notifying the user of anomalies or risks. 【0380】 Specific examples and prompt statements 【0381】 As a concrete example, suppose a user says, "It's raining today and I'm feeling a little down." In this case, the emotion engine can identify the feeling of sadness, and the server can analyze the result and provide feedback to the user such as, "Try to do something relaxing today." 【0382】 Example prompts for a generative AI model: 【0383】 "Analyze the user's voice input to determine their emotional state and provide appropriate advice. Example: 'It's raining today, and I'm feeling a bit down.'" 【0384】 In this way, the system is able to support users' daily health management by providing them with appropriate and timely feedback that matches their emotional state. 【0385】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0386】 Step 1: 【0387】 The user launches an application on their smartphone or a dedicated device and records their health status using voice. The user's voice is captured through a microphone. The input is the user's raw voice data, which the device acquires in real time. 【0388】 Step 2: 【0389】 The device uses speech recognition technology to convert the acquired audio data into text data. Specifically, it utilizes the Google Speech-to-Text API, among others, to output the audio as text. In this step, the audio data is the input and the text data is the output. As the device generates the text data, the analysis of the conversation content can proceed. 【0390】 Step 3: 【0391】 The terminal sends text data to the server via the internet. Encryption technology is applied during transmission to protect user privacy. Here, the input is the text data converted by the terminal, and the output is the data securely sent to the server. 【0392】 Step 4: 【0393】 The server analyzes the received text data using a natural language processing library (e.g., spaCy). The analysis process includes grammatical structure analysis and verification of pronunciation accuracy. The input is the text data sent to the server, and the output is the analyzed text result. The server then proceeds with further processing based on this analysis result. 【0394】 Step 5: 【0395】 The server uses the parsed text data to identify the user's emotional state using an emotion engine (e.g., IBM Watson Tone Analyzer). In this process, the input is parsed text data, and the output is an emotional state label. The server then confirms the information regarding the user's emotions. 【0396】 Step 6: 【0397】 The server integrates the results of analysis and sentiment recognition, and notifies the user or designated contacts if an anomaly is detected. Push notifications and email may be used as notification methods. The input consists of various analysis results generated within the server, and the output consists of specific advice and warning messages. 【0398】 Step 7: 【0399】 The server ultimately stores all analysis and sentiment recognition results in memory. This storage allows past information to be used as foundational data for future analyses. The input consists of all completed analysis and sentiment recognition data, and the output is permanently stored record data. 【0400】 (Application Example 2) 【0401】 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 as the "terminal". 【0402】 In the lives of older adults, it is crucial to appropriately understand their daily health and emotional states and provide necessary care. However, current systems struggle to efficiently monitor the emotional states of older adults and provide appropriate feedback based on that information. This situation could lead to overlooking potential health risks, highlighting the need for a more effective monitoring system. 【0403】 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. 【0404】 In this invention, the server includes an emotion analysis means for identifying emotional states and generating care-oriented feedback based on the analysis results, and an external notification means for notifying specific parties or organizations when an abnormality is detected. This makes it possible to accurately grasp the emotional state of elderly people and provide appropriate advice and notifications. 【0405】 "Acquisition method" refers to a function for capturing and recording voice conversations with elderly people on a device. 【0406】 The "conversion means" refers to a function that converts acquired voice conversations into text data using speech recognition technology. 【0407】 "Analysis means" refers to a function that evaluates the grammatical and pronunciation accuracy of text data and analyzes the level of comprehension of the conversation. 【0408】 A "notification method" is a function that transmits information to the user or a designated contact when an anomaly is detected based on the analysis results. 【0409】 "Storage method" refers to a function for securely storing analysis results and user conversation history in a database. 【0410】 "Emotional analysis means" refers to a function that identifies the emotional state of elderly people from voice data and generates appropriate feedback based on the analysis results. 【0411】 An "external notification mechanism" is a function that notifies specific parties or organizations when an anomaly is detected. 【0412】 The system for realizing this application consists of a voice recording device used daily by elderly people and a server system that works in conjunction with it. 【0413】 The terminal's role is to detect voice conversations with elderly individuals, collect voice data, and then convert it into text data using speech recognition technology. The specific technology used is the speech_recognition library, and the Google Speech-to-Text API is responsible for the text conversion. 【0414】 Next, the server uses natural language processing techniques to evaluate the accuracy of pronunciation and comprehension of the conversation based on the text data sent from the terminal. The text data is then analyzed using sentiment analysis tools and used to determine the emotional state of the elderly person. This analysis is expected to utilize a text analysis library. 【0415】 The analysis results are stored in a database, and if an anomaly is detected, a warning is sent to the user or designated contacts via a notification function. Furthermore, feedback provided by the sentiment analysis system is displayed to the user as visual or audio advice. For example, if speech recognition detects "I'm feeling a little down today," the sentiment analysis system analyzes the emotion from the speech, and the server generates advice based on that, such as "Relax and take a break." 【0416】 In this invention, by operating a generative AI model, specific instructions such as "Please tell me what the elderly person said about how they felt today. Analyze their statements, determine whether their emotions are positive or negative, and provide helpful feedback for care" can be used as prompts for determining emotional states. These prompts are used to improve the quality of detailed analysis and feedback. 【0417】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0418】 Step 1: 【0419】 The device uses a microphone to capture the elderly person's voice as input. It then saves the acquired voice data to an internal buffer. This voice data serves as the basis for subsequent processing. 【0420】 Step 2: 【0421】 The device analyzes the acquired audio data using the Speech Recognition library and converts it into text data. This converted text data is then output. In this process, the speech recognition algorithm analyzes the waveform information of the audio and identifies language-related nuances and pronunciations. 【0422】 Step 3: 【0423】 The server receives text data sent from the terminal as input. It then analyzes the text using natural language processing technology and evaluates the accuracy of pronunciation and the level of understanding of the conversation. The output includes the analysis results and, if necessary, whether or not anomalies have been detected. The server also performs grammar checks and key phrase recognition to identify unnatural expressions and unusual vocabulary. 【0424】 Step 4: 【0425】 The server applies sentiment analysis techniques to the analyzed text data to identify emotional states. The input is the analyzed text data, and the output generates sentiment scores and state reports. This process uses a sentiment analysis model to evaluate emotional words and phrases within the text, assigning them to sentiment categories such as positive and negative. 【0426】 Step 5: 【0427】 The server generates specific feedback and advice for older adults based on their emotional state and analysis results. This involves following prompts using a generative AI model to form contextually relevant feedback sentences. The output is feedback text recommending ways to improve the older adult's emotional state and promote relaxation. 【0428】 Step 6: 【0429】 If an anomaly is detected, the server will notify the designated contact using an external notification method. The input for this notification is the anomaly detection result, and the output is a warning message. The server will refer to pre-registered contact data and send a warning message as needed. 【0430】 Step 7: 【0431】 The server saves all analysis results and feedback to a database. The input is the analysis and feedback data, and the output is the updated database. This saving process continuously accumulates the user's conversation history and emotional state, which can then be used for future analyses. 【0432】 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. 【0433】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0434】 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. 【0435】 [Third Embodiment] 【0436】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0437】 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. 【0438】 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). 【0439】 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. 【0440】 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. 【0441】 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). 【0442】 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. 【0443】 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. 【0444】 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. 【0445】 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. 【0446】 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. 【0447】 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". 【0448】 To implement this invention, it is first necessary to install an application for acquiring voice conversations on a smartphone or dedicated device used by the elderly. By launching this application, the user starts a voice conversation every morning by following the on-screen guide. 【0449】 Terminal role 【0450】 The device picks up the user's speech using a microphone and acquires it as audio data. Then, using speech recognition technology, it converts the acquired audio into text data in real time. In this process, a unique machine learning model is used to obtain highly accurate results with reduced noise. 【0451】 Server Role 【0452】 Once text data is generated, it is sent from the terminal to the server. The server is responsible for analyzing the received text data. This analysis incorporates natural language processing algorithms to evaluate the user's pronunciation and speaking style, determining the accuracy of pronunciation and the appropriateness of grammar. Furthermore, the analysis results are compared with the user's previously saved conversation history, and if an anomaly is detected, an analysis report is generated. 【0453】 Feedback and notifications 【0454】 Once the analysis report is generated, the server sends the results to the terminal. The terminal displays the results in an easy-to-understand format for the user. For example, if changes in pronunciation or a decline in comprehension are detected, the terminal will provide a message such as, "There are some changes in your speaking style today. Please take care of your health." Furthermore, if a more serious abnormality is detected, the system can automatically send warnings or notifications to contacts specified by the user in advance (e.g., family or medical institutions). 【0455】 For example, if a user says, "I'm planning to go for a walk today," but their pronunciation is unclear, the server will compare it with past history and detect that something is different. In this case, the device will display a message saying, "A slight change in pronunciation was observed today. Please check your health status as a precaution." 【0456】 In this way, this invention provides support for understanding daily health conditions and facilitating early medical intervention. 【0457】 The following describes the processing flow. 【0458】 Step 1: 【0459】 The user launches the application on their smartphone or dedicated device and begins a voice conversation with the chatbot. The app then asks the user, "Good morning, what are your plans for today?" 【0460】 Step 2: 【0461】 The device captures the user's speech through the microphone and acquires it as audio data. At this time, a noise reduction filter is applied to ensure clear audio data. 【0462】 Step 3: 【0463】 The device sends the acquired audio data to a speech recognition engine, which converts it into text data in real time. This process utilizes a pre-trained model to improve accuracy. 【0464】 Step 4: 【0465】 The converted text data is sent from the device to a server in the cloud. Here, data security is ensured through encryption of the communication. 【0466】 Step 5: 【0467】 The server analyzes the received text data. Natural language processing techniques are used to evaluate pronunciation clarity and conversational content. Specifically, algorithms are applied to detect grammatical errors and pronunciation changes. 【0468】 Step 6: 【0469】 The server detects anomalies based on the analysis results and generates an anomaly report as needed. This report includes information about the anomalies found and their severity. 【0470】 Step 7: 【0471】 The analysis results are sent from the server to the terminal. The terminal then notifies the user of the results. For example, if an anomaly is detected, a warning message such as, "A slight change in your pronunciation this morning has been detected. Please take care of yourself," is displayed. 【0472】 Step 8: 【0473】 Based on serious anomalies or user-defined conditions, the server automatically notifies pre-registered family members and medical institutions. This enables a rapid response. 【0474】 Step 9: 【0475】 The server stores all analysis results and the resulting decision information in a database, accumulating it as part of the user's long-term health history. This information is useful for subsequent analysis and the development of care plans. 【0476】 (Example 1) 【0477】 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." 【0478】 There is a need to provide a system that can detect changes in the health status of elderly people early through voice data and enable timely medical intervention. However, conventional methods have the problem of being insufficient in monitoring health changes through everyday conversations and responding quickly if abnormalities are detected. 【0479】 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. 【0480】 In this invention, the server includes an acquisition unit means for acquiring voice information, a conversion unit means for converting the voice information into string data, and an analysis unit means for evaluating the string data and analyzing voice features using a generation AI model. This enables real-time health monitoring through everyday conversations. 【0481】 "Audio information" refers to data that records sounds, such as a user's voice, as digital signals. 【0482】 An "acquisition unit" is a device that captures audio information and receives that data. 【0483】 "String data" refers to text-based information obtained by converting audio information. 【0484】 A "conversion unit" is a device that performs the process of converting audio information into string data. 【0485】 A "generative AI model" is an artificial intelligence algorithm used to analyze the characteristics of speech with high accuracy. 【0486】 The "analysis unit" is a device that uses a generative AI model to evaluate string data and analyze the finer details of speech. 【0487】 A "message unit" is a device that detects anomalies based on analysis results and sends appropriate response messages as needed. 【0488】 "History information" refers to data that summarizes past conversations and analysis results. 【0489】 A "storage unit" is a device used to store analysis results and historical information in a database or similar format. 【0490】 "Anomaly detection" is an analytical process that compares the current analysis results with past historical information to detect conditions that are different from the normal state. 【0491】 This invention is designed to monitor the health status of elderly people through their daily conversations and to detect abnormalities early. The system consists of a terminal and a server for acquiring and analyzing voice information. 【0492】 Terminal configuration and operation 【0493】 The user uses a device with a dedicated application installed. The device includes an acquisition unit that uses a microphone to capture the user's voice information. This voice information is converted into text data through a speech recognition engine utilizing a generative AI model. This conversion process employs noise cancellation technology and software to improve the accuracy of speech recognition. 【0494】 Server configuration and operation 【0495】 The converted string data is sent to the server. The server contains an analysis unit that evaluates the string data and analyzes speech features. This analysis unit uses natural language processing (NLP) techniques to evaluate the user's pronunciation and grammatical structure. The analysis results are compared with historical information, and if any anomalies are detected, a message unit notifies the user. The server securely manages the information and provides immediate feedback of the results, enabling daily monitoring of the user's health status. 【0496】 Examples of specific cases and prompt statements 【0497】 For example, if a user says "I'm going to the park today" and their pronunciation is different from usual, the analysis unit will identify the change and send a message to the terminal saying, "A change in pronunciation has been detected. Please check your health status." An example of a prompt message would be, "Please create a program to detect changes in health status from the everyday conversations of elderly people." 【0498】 In this way, this invention enables the monitoring of the user's daily health status through voice conversations and provides support for rapid and effective medical assistance. 【0499】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0500】 Step 1: 【0501】 The user launches an application on their device and begins a voice conversation. The device uses its microphone to acquire the user's voice information in real time. The input is analog audio waveform data, and the output is digital audio information. This conversion utilizes digital signal processing and noise reduction techniques. 【0502】 Step 2: 【0503】 The device processes the acquired digital audio information and converts it into string data using a generative AI model. The input is digital audio information, and the output is the corresponding string data. In this process, the speech recognition engine analyzes phonemes and generates highly accurate text data. 【0504】 Step 3: 【0505】 When string data is generated, the terminal sends it to the server. The server evaluates the received string data using a parsing unit. The input is string data, and the output is evaluation data including the parsing results. The server utilizes natural language processing techniques to analyze the grammar, content, and pronunciation characteristics of the text. 【0506】 Step 4: 【0507】 The server compares the analysis results with historical information stored within the server. The input consists of the current analysis results and past historical data, while the output is judgment data regarding the presence or absence of anomalies. This judgment is made by identifying deviations from normal patterns using an anomaly detection algorithm. 【0508】 Step 5: 【0509】 When an anomaly is detected, the server generates a notification using a message unit. The input is the anomaly detection data, and the output is the notification message. The notification is sent to the user's terminal and displays a message such as, "There is a change in pronunciation. Please pay attention to your health." In the case of a serious anomaly, a notification is also automatically sent to the configured emergency contact. 【0510】 (Application Example 1) 【0511】 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." 【0512】 There is a lack of systems in place to regularly monitor the health status of the elderly and to respond quickly and appropriately when abnormalities are detected. Therefore, there is a need for a system that can detect changes in speech and pronunciation abnormalities early and provide the necessary support. 【0513】 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. 【0514】 In this invention, the server includes an acquisition means for acquiring voice conversations, a conversion means, an analysis means, a notification means, a storage means, and a management means. This makes it possible to accurately evaluate the content of conversations of elderly people and to quickly notify and respond when an anomaly is detected. 【0515】 The "acquisition method" refers to a mechanism that has the function of capturing the voice conversations of elderly people in real time. 【0516】 "Conversion means" refers to a function for converting acquired audio data into text data. 【0517】 The "analysis means" is a mechanism that executes algorithms to evaluate the accuracy of pronunciation and the content of conversation based on the converted text data. 【0518】 A "notification system" is a system that reports to the user or a designated contact when an anomaly is detected based on the analysis results. 【0519】 The "storage mechanism" refers to a memory management system for storing analysis results and conversation history in a database. 【0520】 "Management measures" refer to functions that enable immediate notification via smart devices when an anomaly is detected, and to take appropriate action according to the risk level. 【0521】 The system implementing this invention is designed for daily use by elderly individuals. Users engage in daily conversations with the system using a smartphone or a dedicated smart device. The device captures the user's speech in real time using a high-precision microphone. This audio data is converted into text data using the Google Cloud Speech-to-Text API. 【0522】 The acquired text data is sent to the server, where natural language processing is performed using Python's NLTK (Natural Language Toolkit). The server analyzes the accuracy of the user's pronunciation and their comprehension of the conversation, comparing it with past conversation history to identify anomalies. This analysis process continuously monitors the user's speech characteristics and detects any changes. 【0523】 If an anomaly is detected, the server uses the Twilio API to immediately send a notification to the user or designated contacts. The notification includes both audio and text, along with information to prompt a quick response if necessary. This allows users and their families to take early action regarding the health of elderly individuals. 【0524】 For example, if a user says "I'm not feeling well this morning" in an unusual tone, the system immediately detects this change and sends a notification to family members saying, "There may be a change in the user's health. Please check on them." This can be continuously improved by using the generative AI model GPT-3 to input prompts such as "Please tell me how to handle situations where the user's speech is unusual." 【0525】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0526】 Step 1: 【0527】 The terminal captures the speech of elderly individuals using a high-precision microphone. The input is an audio signal, and the output is a digital recording of that audio. In this step, signal processing is performed to reduce noise and prepare data suitable for language processing. 【0528】 Step 2: 【0529】 The device converts audio signals into text data using the Google Cloud Speech-to-Text API. The input is digital audio data, and the output is the converted text data. This conversion uses a speech recognition algorithm to accurately convert the user's speech into text information. 【0530】 Step 3: 【0531】 The terminal sends the converted text to the server. The input is text data, and the output is data transfer to the server. In this step, encryption protocols are used to ensure that the data is transmitted securely. 【0532】 Step 4: 【0533】 The server performs natural language processing on the received text data using Python's NLTK. The input is text data, and the output is the accuracy of the analyzed pronunciation and the characteristics of the language structure. This analysis utilizes a language model and processes data to detect changes in the user's speaking style and pronunciation. 【0534】 Step 5: 【0535】 The server compares the analysis results with existing conversation history to detect anomalies. The input consists of the analysis results and historical data, while the output is an anomaly detection flag and its level. Statistical methods are used in this comparison to quantify deviations from normal speech. 【0536】 Step 6: 【0537】 When an anomaly is detected, the server uses the Twilio API to send a notification. The input is the flag and level of the anomaly detection, and the output is a notification message to the user and their family. This notification includes both audio and text, and an alert is automatically generated based on the relevant risk level. 【0538】 Step 7: 【0539】 The user receives a notification and takes action to check their health status. The input is a notification sent from the server, and the output is the user checking their status or consulting a medical institution. In this step, a generative AI model is used to enable a rapid response by referring to prompts such as "What should I do next if information about the user's health status is available?" 【0540】 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. 【0541】 This invention provides a system for elderly individuals to record their daily health status via voice, and by incorporating an emotion engine into this system, it becomes possible to understand the user's emotional state. This system is implemented through an application that runs on a smartphone or a dedicated device. 【0542】 Terminal role 【0543】 The user uses the application every morning or as needed, following on-screen instructions to initiate a voice conversation. The device accepts the voice input and acquires the voice data. The acquired voice data is immediately converted into text data by a conversion device. This conversion process uses speech recognition technology, which can identify even subtle differences in pronunciation. 【0544】 Server Role 【0545】 The converted text data is sent from the terminal to the server. The server receives this data and evaluates the accuracy of pronunciation and the level of understanding of the conversation through an analysis tool. The analysis tool incorporates a natural language processing algorithm to detect grammatical errors and unnatural speech patterns in particular. It then compares this data with past conversation history to determine if there are any abnormalities. 【0546】 Furthermore, the emotion engine uses both the audio data itself and the analyzed text data to identify the user's emotions. This allows for a deeper understanding of the user's health by considering the possibility that they may be experiencing stress or anxiety. 【0547】 Feedback and notifications 【0548】 The results analyzed by the server are sent to the user's device as feedback. If a specific anomaly is detected, the system can alert the user via voice or on-screen messages. For example, a message such as, "There has been a slight change in your conversation content and emotional state today. We recommend you take a rest," might be displayed. Furthermore, if a serious anomaly or emotional state fluctuation is detected, notifications will also be sent to the user's registered family members and medical institutions. 【0549】 For example, if a user says, "I'm feeling a little down today," the emotion engine will determine the emotional state from the audio, and the server will generate analytical information based on that. If an abnormality is detected, the system will provide advice to the user through the terminal and prompt them to notify a medical institution if necessary. 【0550】 In this way, by incorporating an emotion engine, it becomes possible to support users not only from a health perspective but also from an emotional one. 【0551】 The following describes the processing flow. 【0552】 Step 1: 【0553】 The user launches the application on their smartphone or dedicated device and initiates a voice conversation to record their daily status. At this point, the app prompts the user with, "Good morning, please tell me how you're feeling and how you're feeling today." 【0554】 Step 2: 【0555】 The device captures the user's speech using a microphone and acquires it as audio data. At this point, noise cancellation technology is used to eliminate external noise and improve the quality of the audio data. 【0556】 Step 3: 【0557】 The acquired audio data is converted into text data by the device's built-in speech recognition engine. This text conversion process aims to accurately reflect the user's pronunciation and uses high-precision speech recognition technology. 【0558】 Step 4: 【0559】 The device sends the converted text data to the server. The data is encrypted before transmission, ensuring privacy. 【0560】 Step 5: 【0561】 The server analyzes the received text data and evaluates the accuracy of pronunciation and comprehension of the content. This analysis uses natural language processing algorithms to evaluate grammatical correctness and content continuity. 【0562】 Step 6: 【0563】 The emotion engine independently analyzes voice data and infers the user's emotional state. This engine specifically analyzes voice tone, speaking speed, and emphasis patterns to determine the emotional state. 【0564】 Step 7: 【0565】 The server combines the analysis results and feedback from the emotion engine to generate a comprehensive report for the user. This report includes advice on health and emotional state. 【0566】 Step 8: 【0567】 The device receives the report and provides visual or audio feedback to the user. For example, it might display a message such as, "You seem to be feeling a little breathless today. It would be good to take some time to relax." 【0568】 Step 9: 【0569】 If a significant health or emotional abnormality is detected, the server will notify pre-registered family members or medical institutions. This notification allows for prompt follow-up. 【0570】 Step 10: 【0571】 The server stores analytical data and emotional states in a database, building a long-term history of the user's health and emotions. This information is useful for the user themselves and medical professionals to understand their condition. 【0572】 (Example 2) 【0573】 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." 【0574】 When elderly individuals record their daily health status via voice, there is a need for a system that can accurately assess not only the accuracy of pronunciation and comprehension of conversations, but also their emotional state, enabling early detection and response to abnormalities. Furthermore, conventional systems lack sufficient information regarding emotional changes, making them inadequate for monitoring the overall health of users. This issue is particularly urgent in modern society, given the importance of daily health management for the elderly and other individuals requiring special consideration. 【0575】 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. 【0576】 In this invention, the server includes acquisition means for acquiring voice data from the user, conversion means for converting the acquired voice data into text data, analysis means for analyzing the text data and evaluating the accuracy of pronunciation and the degree of understanding of the conversation, emotion identification means for identifying the emotional state based on the analysis results and voice data, notification means for detecting abnormalities based on the analysis and emotion identification results and notifying the user or a designated contact, and storage means for storing the analysis and emotion identification results in a memory area. This enables comprehensive support from an emotional perspective as well as monitoring the user's health status. 【0577】 "Acquisition means" refers to a function that receives voice data from the user and acquires it in a format that can be processed within the system. 【0578】 A "conversion means" is a function for converting acquired audio data into analyzable text data. 【0579】 "Analysis means" refers to a function that evaluates the accuracy of pronunciation and the degree of comprehension of conversation content based on text data. 【0580】 "Emotion identification means" refers to a function that identifies the user's emotional state based on analyzed text and audio data. 【0581】 "Notification means" refers to a function that detects anomalies based on analysis results and emotion recognition results, and transmits information to the user or designated contact. 【0582】 "Storage means" refers to a function for storing the results of acquisition, analysis, and emotion identification in a recording area. 【0583】 This invention is a system that allows users to record their daily health status via voice and comprehensively understand their health and emotional state based on that content. The system mainly consists of a terminal and a server. 【0584】 Device configuration and role 【0585】 The terminal's role is to acquire voice data from the user. Specifically, a smartphone or a dedicated mobile device is used. These devices have built-in microphones, making it possible to record everyday conversations in high quality. The user launches the application on the terminal and begins voice input. For example, if the user says, "I feel a little better today," that voice is captured by the terminal. The terminal uses speech recognition technology to convert the voice data into text data. For this, speech recognition software such as the Google Speech-to-Text API can be used. 【0586】 Server Configuration and Roles 【0587】 The server receives text data sent from the terminal and analyzes it. This analysis requires natural language processing (NLP) technology, and a commonly used NLP library (e.g., spaCy) is applied. The server then uses an emotion recognition engine to identify the user's emotions from the audio data and the analyzed text data. This is done using an emotion analysis tool (e.g., IBM Watson Tone Analyzer). The analysis results are stored in memory and compared with past data to form the basis for notifying the user of anomalies or risks. 【0588】 Specific examples and prompt statements 【0589】 As a concrete example, suppose a user says, "It's raining today and I'm feeling a little down." In this case, the emotion engine can identify the feeling of sadness, and the server can analyze the result and provide feedback to the user such as, "Try to do something relaxing today." 【0590】 Example prompts for a generative AI model: 【0591】 "Analyze the user's voice input to determine their emotional state and provide appropriate advice. Example: 'It's raining today, and I'm feeling a bit down.'" 【0592】 In this way, the system is able to support users' daily health management by providing them with appropriate and timely feedback that matches their emotional state. 【0593】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0594】 Step 1: 【0595】 The user launches an application on their smartphone or a dedicated device and records their health status using voice. The user's voice is captured through a microphone. The input is the user's raw voice data, which the device acquires in real time. 【0596】 Step 2: 【0597】 The device uses speech recognition technology to convert the acquired audio data into text data. Specifically, it utilizes the Google Speech-to-Text API, among others, to output the audio as text. In this step, the audio data is the input and the text data is the output. As the device generates the text data, the analysis of the conversation content can proceed. 【0598】 Step 3: 【0599】 The terminal sends text data to the server via the internet. Encryption technology is applied during transmission to protect user privacy. Here, the input is the text data converted by the terminal, and the output is the data securely sent to the server. 【0600】 Step 4: 【0601】 The server analyzes the received text data using a natural language processing library (e.g., spaCy). The analysis process includes grammatical structure analysis and verification of pronunciation accuracy. The input is the text data sent to the server, and the output is the analyzed text result. The server then proceeds with further processing based on this analysis result. 【0602】 Step 5: 【0603】 The server uses the parsed text data to identify the user's emotional state using an emotion engine (e.g., IBM Watson Tone Analyzer). In this process, the input is parsed text data, and the output is an emotional state label. The server then confirms the information regarding the user's emotions. 【0604】 Step 6: 【0605】 The server integrates the results of analysis and sentiment recognition, and notifies the user or designated contacts if an anomaly is detected. Push notifications and email may be used as notification methods. The input consists of various analysis results generated within the server, and the output consists of specific advice and warning messages. 【0606】 Step 7: 【0607】 The server ultimately stores all analysis and sentiment recognition results in memory. This storage allows past information to be used as foundational data for future analyses. The input consists of all completed analysis and sentiment recognition data, and the output is permanently stored record data. 【0608】 (Application Example 2) 【0609】 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." 【0610】 In the lives of older adults, it is crucial to appropriately understand their daily health and emotional states and provide necessary care. However, current systems struggle to efficiently monitor the emotional states of older adults and provide appropriate feedback based on that information. This situation could lead to overlooking potential health risks, highlighting the need for a more effective monitoring system. 【0611】 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. 【0612】 In this invention, the server includes an emotion analysis means for identifying emotional states and generating care-oriented feedback based on the analysis results, and an external notification means for notifying specific parties or organizations when an abnormality is detected. This makes it possible to accurately grasp the emotional state of elderly people and provide appropriate advice and notifications. 【0613】 "Acquisition method" refers to a function for capturing and recording voice conversations with elderly people on a device. 【0614】 The "conversion means" refers to a function that converts acquired voice conversations into text data using speech recognition technology. 【0615】 "Analysis means" refers to a function that evaluates the grammatical and pronunciation accuracy of text data and analyzes the level of comprehension of the conversation. 【0616】 A "notification method" is a function that transmits information to the user or a designated contact when an anomaly is detected based on the analysis results. 【0617】 "Storage method" refers to a function for securely storing analysis results and user conversation history in a database. 【0618】 "Emotional analysis means" refers to a function that identifies the emotional state of elderly people from voice data and generates appropriate feedback based on the analysis results. 【0619】 An "external notification mechanism" is a function that notifies specific parties or organizations when an anomaly is detected. 【0620】 The system for realizing this application consists of a voice recording device used daily by elderly people and a server system that works in conjunction with it. 【0621】 The terminal's role is to detect voice conversations with elderly individuals, collect voice data, and then convert it into text data using speech recognition technology. The specific technology used is the speech_recognition library, and the Google Speech-to-Text API is responsible for the text conversion. 【0622】 Next, the server uses natural language processing techniques to evaluate the accuracy of pronunciation and comprehension of the conversation based on the text data sent from the terminal. The text data is then analyzed using sentiment analysis tools and used to determine the emotional state of the elderly person. This analysis is expected to utilize a text analysis library. 【0623】 The analysis results are stored in a database, and if an anomaly is detected, a warning is sent to the user or designated contacts via a notification function. Furthermore, feedback provided by the sentiment analysis system is displayed to the user as visual or audio advice. For example, if speech recognition detects "I'm feeling a little down today," the sentiment analysis system analyzes the emotion from the speech, and the server generates advice based on that, such as "Relax and take a break." 【0624】 In this invention, by operating a generative AI model, specific instructions such as "Please tell me what the elderly person said about how they felt today. Analyze their statements, determine whether their emotions are positive or negative, and provide helpful feedback for care" can be used as prompts for determining emotional states. These prompts are used to improve the quality of detailed analysis and feedback. 【0625】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0626】 Step 1: 【0627】 The device uses a microphone to capture the elderly person's voice as input. It then saves the acquired voice data to an internal buffer. This voice data serves as the basis for subsequent processing. 【0628】 Step 2: 【0629】 The device analyzes the acquired audio data using the Speech Recognition library and converts it into text data. This converted text data is then output. In this process, the speech recognition algorithm analyzes the waveform information of the audio and identifies language-related nuances and pronunciations. 【0630】 Step 3: 【0631】 The server receives text data sent from the terminal as input. It then analyzes the text using natural language processing technology and evaluates the accuracy of pronunciation and the level of understanding of the conversation. The output includes the analysis results and, if necessary, whether or not anomalies have been detected. The server also performs grammar checks and key phrase recognition to identify unnatural expressions and unusual vocabulary. 【0632】 Step 4: 【0633】 The server applies sentiment analysis techniques to the analyzed text data to identify emotional states. The input is the analyzed text data, and the output generates sentiment scores and state reports. This process uses a sentiment analysis model to evaluate emotional words and phrases within the text, assigning them to sentiment categories such as positive and negative. 【0634】 Step 5: 【0635】 The server generates specific feedback and advice for older adults based on their emotional state and analysis results. This involves following prompts using a generative AI model to form contextually relevant feedback sentences. The output is feedback text recommending ways to improve the older adult's emotional state and promote relaxation. 【0636】 Step 6: 【0637】 If an anomaly is detected, the server will notify the designated contact using an external notification method. The input for this notification is the anomaly detection result, and the output is a warning message. The server will refer to pre-registered contact data and send a warning message as needed. 【0638】 Step 7: 【0639】 The server saves all analysis results and feedback to a database. The input is the analysis and feedback data, and the output is the updated database. This saving process continuously accumulates the user's conversation history and emotional state, which can then be used for future analyses. 【0640】 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. 【0641】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0642】 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. 【0643】 [Fourth Embodiment] 【0644】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0645】 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. 【0646】 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). 【0647】 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. 【0648】 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. 【0649】 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). 【0650】 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. 【0651】 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. 【0652】 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. 【0653】 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. 【0654】 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. 【0655】 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. 【0656】 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". 【0657】 To implement this invention, it is first necessary to install an application for acquiring voice conversations on a smartphone or dedicated device used by the elderly. By launching this application, the user starts a voice conversation every morning by following the on-screen guide. 【0658】 Terminal role 【0659】 The device picks up the user's speech using a microphone and acquires it as audio data. Then, using speech recognition technology, it converts the acquired audio into text data in real time. In this process, a unique machine learning model is used to obtain highly accurate results with reduced noise. 【0660】 Server Role 【0661】 Once text data is generated, it is sent from the terminal to the server. The server is responsible for analyzing the received text data. This analysis incorporates natural language processing algorithms to evaluate the user's pronunciation and speaking style, determining the accuracy of pronunciation and the appropriateness of grammar. Furthermore, the analysis results are compared with the user's previously saved conversation history, and if an anomaly is detected, an analysis report is generated. 【0662】 Feedback and notifications 【0663】 Once the analysis report is generated, the server sends the results to the terminal. The terminal displays the results in an easy-to-understand format for the user. For example, if changes in pronunciation or a decline in comprehension are detected, the terminal will provide a message such as, "There are some changes in your speaking style today. Please take care of your health." Furthermore, if a more serious abnormality is detected, the system can automatically send warnings or notifications to contacts specified by the user in advance (e.g., family or medical institutions). 【0664】 For example, if a user says, "I'm planning to go for a walk today," but their pronunciation is unclear, the server will compare it with past history and detect that something is different. In this case, the device will display a message saying, "A slight change in pronunciation was observed today. Please check your health status as a precaution." 【0665】 In this way, this invention provides support for understanding daily health conditions and facilitating early medical intervention. 【0666】 The following describes the processing flow. 【0667】 Step 1: 【0668】 The user launches the application on their smartphone or dedicated device and begins a voice conversation with the chatbot. The app then asks the user, "Good morning, what are your plans for today?" 【0669】 Step 2: 【0670】 The device captures the user's speech through the microphone and acquires it as audio data. At this time, a noise reduction filter is applied to ensure clear audio data. 【0671】 Step 3: 【0672】 The device sends the acquired audio data to a speech recognition engine, which converts it into text data in real time. This process utilizes a pre-trained model to improve accuracy. 【0673】 Step 4: 【0674】 The converted text data is sent from the device to a server in the cloud. Here, data security is ensured through encryption of the communication. 【0675】 Step 5: 【0676】 The server analyzes the received text data. Natural language processing techniques are used to evaluate pronunciation clarity and conversational content. Specifically, algorithms are applied to detect grammatical errors and pronunciation changes. 【0677】 Step 6: 【0678】 The server detects anomalies based on the analysis results and generates an anomaly report as needed. This report includes information about the anomalies found and their severity. 【0679】 Step 7: 【0680】 The analysis results are sent from the server to the terminal. The terminal then notifies the user of the results. For example, if an anomaly is detected, a warning message such as, "A slight change in your pronunciation this morning has been detected. Please take care of yourself," is displayed. 【0681】 Step 8: 【0682】 Based on serious anomalies or user-defined conditions, the server automatically notifies pre-registered family members and medical institutions. This enables a rapid response. 【0683】 Step 9: 【0684】 The server stores all analysis results and the resulting decision information in a database, accumulating it as part of the user's long-term health history. This information is useful for subsequent analysis and the development of care plans. 【0685】 (Example 1) 【0686】 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". 【0687】 There is a need to provide a system that can detect changes in the health status of elderly people early through voice data and enable timely medical intervention. However, conventional methods have the problem of being insufficient in monitoring health changes through everyday conversations and responding quickly if abnormalities are detected. 【0688】 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. 【0689】 In this invention, the server includes an acquisition unit means for acquiring voice information, a conversion unit means for converting the voice information into string data, and an analysis unit means for evaluating the string data and analyzing voice features using a generation AI model. This enables real-time health monitoring through everyday conversations. 【0690】 "Audio information" refers to data that records sounds, such as a user's voice, as digital signals. 【0691】 An "acquisition unit" is a device that captures audio information and receives that data. 【0692】 "String data" refers to text-based information obtained by converting audio information. 【0693】 A "conversion unit" is a device that performs the process of converting audio information into string data. 【0694】 A "generative AI model" is an artificial intelligence algorithm used to analyze the characteristics of speech with high accuracy. 【0695】 The "analysis unit" is a device that uses a generative AI model to evaluate string data and analyze the finer details of speech. 【0696】 A "message unit" is a device that detects anomalies based on analysis results and sends appropriate response messages as needed. 【0697】 "History information" refers to data that summarizes past conversations and analysis results. 【0698】 A "storage unit" is a device used to store analysis results and historical information in a database or similar format. 【0699】 "Anomaly detection" is an analytical process that compares the current analysis results with past historical information to detect conditions that are different from the normal state. 【0700】 This invention is designed to monitor the health status of elderly people through their daily conversations and to detect abnormalities early. The system consists of a terminal and a server for acquiring and analyzing voice information. 【0701】 Terminal configuration and operation 【0702】 The user uses a device with a dedicated application installed. The device includes an acquisition unit that uses a microphone to capture the user's voice information. This voice information is converted into text data through a speech recognition engine utilizing a generative AI model. This conversion process employs noise cancellation technology and software to improve the accuracy of speech recognition. 【0703】 Server configuration and operation 【0704】 The converted string data is sent to the server. The server contains an analysis unit that evaluates the string data and analyzes speech features. This analysis unit uses natural language processing (NLP) techniques to evaluate the user's pronunciation and grammatical structure. The analysis results are compared with historical information, and if any anomalies are detected, a message unit notifies the user. The server securely manages the information and provides immediate feedback of the results, enabling daily monitoring of the user's health status. 【0705】 Examples of specific cases and prompt statements 【0706】 For example, if a user says "I'm going to the park today" and their pronunciation is different from usual, the analysis unit will identify the change and send a message to the terminal saying, "A change in pronunciation has been detected. Please check your health status." An example of a prompt message would be, "Please create a program to detect changes in health status from the everyday conversations of elderly people." 【0707】 In this way, this invention enables the monitoring of the user's daily health status through voice conversations and provides support for rapid and effective medical assistance. 【0708】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0709】 Step 1: 【0710】 The user launches an application on their device and begins a voice conversation. The device uses its microphone to acquire the user's voice information in real time. The input is analog audio waveform data, and the output is digital audio information. This conversion utilizes digital signal processing and noise reduction techniques. 【0711】 Step 2: 【0712】 The device processes the acquired digital audio information and converts it into string data using a generative AI model. The input is digital audio information, and the output is the corresponding string data. In this process, the speech recognition engine analyzes phonemes and generates highly accurate text data. 【0713】 Step 3: 【0714】 When string data is generated, the terminal sends it to the server. The server evaluates the received string data using a parsing unit. The input is string data, and the output is evaluation data including the parsing results. The server utilizes natural language processing techniques to analyze the grammar, content, and pronunciation characteristics of the text. 【0715】 Step 4: 【0716】 The server compares the analysis results with historical information stored within the server. The input consists of the current analysis results and past historical data, while the output is judgment data regarding the presence or absence of anomalies. This judgment is made by identifying deviations from normal patterns using an anomaly detection algorithm. 【0717】 Step 5: 【0718】 When an anomaly is detected, the server generates a notification using a message unit. The input is the anomaly detection data, and the output is the notification message. The notification is sent to the user's terminal and displays a message such as, "There is a change in pronunciation. Please pay attention to your health." In the case of a serious anomaly, a notification is also automatically sent to the configured emergency contact. 【0719】 (Application Example 1) 【0720】 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". 【0721】 There is a lack of systems in place to regularly monitor the health status of the elderly and to respond quickly and appropriately when abnormalities are detected. Therefore, there is a need for a system that can detect changes in speech and pronunciation abnormalities early and provide the necessary support. 【0722】 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. 【0723】 In this invention, the server includes an acquisition means for acquiring voice conversations, a conversion means, an analysis means, a notification means, a storage means, and a management means. This makes it possible to accurately evaluate the content of conversations of elderly people and to quickly notify and respond when an anomaly is detected. 【0724】 The "acquisition method" refers to a mechanism that has the function of capturing the voice conversations of elderly people in real time. 【0725】 "Conversion means" refers to a function for converting acquired audio data into text data. 【0726】 The "analysis means" is a mechanism that executes algorithms to evaluate the accuracy of pronunciation and the content of conversation based on the converted text data. 【0727】 A "notification system" is a system that reports to the user or a designated contact when an anomaly is detected based on the analysis results. 【0728】 The "storage mechanism" refers to a memory management system for storing analysis results and conversation history in a database. 【0729】 "Management measures" refer to functions that enable immediate notification via smart devices when an anomaly is detected, and to take appropriate action according to the risk level. 【0730】 The system implementing this invention is designed for daily use by elderly individuals. Users engage in daily conversations with the system using a smartphone or a dedicated smart device. The device captures the user's speech in real time using a high-precision microphone. This audio data is converted into text data using the Google Cloud Speech-to-Text API. 【0731】 The acquired text data is sent to the server, where natural language processing is performed using Python's NLTK (Natural Language Toolkit). The server analyzes the accuracy of the user's pronunciation and their comprehension of the conversation, comparing it with past conversation history to identify anomalies. This analysis process continuously monitors the user's speech characteristics and detects any changes. 【0732】 If an anomaly is detected, the server uses the Twilio API to immediately send a notification to the user or designated contacts. The notification includes both audio and text, along with information to prompt a quick response if necessary. This allows users and their families to take early action regarding the health of elderly individuals. 【0733】 For example, if a user says "I'm not feeling well this morning" in an unusual tone, the system immediately detects this change and sends a notification to family members saying, "There may be a change in the user's health. Please check on them." This can be continuously improved by using the generative AI model GPT-3 to input prompts such as "Please tell me how to handle situations where the user's speech is unusual." 【0734】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0735】 Step 1: 【0736】 The terminal captures the speech of elderly individuals using a high-precision microphone. The input is an audio signal, and the output is a digital recording of that audio. In this step, signal processing is performed to reduce noise and prepare data suitable for language processing. 【0737】 Step 2: 【0738】 The device converts audio signals into text data using the Google Cloud Speech-to-Text API. The input is digital audio data, and the output is the converted text data. This conversion uses a speech recognition algorithm to accurately convert the user's speech into text information. 【0739】 Step 3: 【0740】 The terminal sends the converted text to the server. The input is text data, and the output is data transfer to the server. In this step, encryption protocols are used to ensure that the data is transmitted securely. 【0741】 Step 4: 【0742】 The server performs natural language processing on the received text data using Python's NLTK. The input is text data, and the output is the accuracy of the analyzed pronunciation and the characteristics of the language structure. This analysis utilizes a language model and processes data to detect changes in the user's speaking style and pronunciation. 【0743】 Step 5: 【0744】 The server compares the analysis results with existing conversation history to detect anomalies. The input consists of the analysis results and historical data, while the output is an anomaly detection flag and its level. Statistical methods are used in this comparison to quantify deviations from normal speech. 【0745】 Step 6: 【0746】 When an anomaly is detected, the server uses the Twilio API to send a notification. The input is the flag and level of the anomaly detection, and the output is a notification message to the user and their family. This notification includes both audio and text, and an alert is automatically generated based on the relevant risk level. 【0747】 Step 7: 【0748】 The user receives a notification and takes action to check their health status. The input is a notification sent from the server, and the output is the user checking their status or consulting a medical institution. In this step, a generative AI model is used to enable a rapid response by referring to prompts such as "What should I do next if information about the user's health status is available?" 【0749】 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. 【0750】 This invention provides a system for elderly individuals to record their daily health status via voice, and by incorporating an emotion engine into this system, it becomes possible to understand the user's emotional state. This system is implemented through an application that runs on a smartphone or a dedicated device. 【0751】 Terminal role 【0752】 The user uses the application every morning or as needed, following on-screen instructions to initiate a voice conversation. The device accepts the voice input and acquires the voice data. The acquired voice data is immediately converted into text data by a conversion device. This conversion process uses speech recognition technology, which can identify even subtle differences in pronunciation. 【0753】 Server Role 【0754】 The converted text data is sent from the terminal to the server. The server receives this data and evaluates the accuracy of pronunciation and the level of understanding of the conversation through an analysis tool. The analysis tool incorporates a natural language processing algorithm to detect grammatical errors and unnatural speech patterns in particular. It then compares this data with past conversation history to determine if there are any abnormalities. 【0755】 Furthermore, the emotion engine uses both the audio data itself and the analyzed text data to identify the user's emotions. This allows for a deeper understanding of the user's health by considering the possibility that they may be experiencing stress or anxiety. 【0756】 Feedback and notifications 【0757】 The results analyzed by the server are sent to the user's device as feedback. If a specific anomaly is detected, the system can alert the user via voice or on-screen messages. For example, a message such as, "There has been a slight change in your conversation content and emotional state today. We recommend you take a rest," might be displayed. Furthermore, if a serious anomaly or emotional state fluctuation is detected, notifications will also be sent to the user's registered family members and medical institutions. 【0758】 For example, if a user says, "I'm feeling a little down today," the emotion engine will determine the emotional state from the audio, and the server will generate analytical information based on that. If an abnormality is detected, the system will provide advice to the user through the terminal and prompt them to notify a medical institution if necessary. 【0759】 In this way, by incorporating an emotion engine, it becomes possible to support users not only from a health perspective but also from an emotional one. 【0760】 The following describes the processing flow. 【0761】 Step 1: 【0762】 The user launches the application on their smartphone or dedicated device and initiates a voice conversation to record their daily status. At this point, the app prompts the user with, "Good morning, please tell me how you're feeling and how you're feeling today." 【0763】 Step 2: 【0764】 The device captures the user's speech using a microphone and acquires it as audio data. At this point, noise cancellation technology is used to eliminate external noise and improve the quality of the audio data. 【0765】 Step 3: 【0766】 The acquired audio data is converted into text data by the device's built-in speech recognition engine. This text conversion process aims to accurately reflect the user's pronunciation and uses high-precision speech recognition technology. 【0767】 Step 4: 【0768】 The device sends the converted text data to the server. The data is encrypted before transmission, ensuring privacy. 【0769】 Step 5: 【0770】 The server analyzes the received text data and evaluates the accuracy of pronunciation and comprehension of the content. This analysis uses natural language processing algorithms to evaluate grammatical correctness and content continuity. 【0771】 Step 6: 【0772】 The emotion engine independently analyzes voice data and infers the user's emotional state. This engine specifically analyzes voice tone, speaking speed, and emphasis patterns to determine the emotional state. 【0773】 Step 7: 【0774】 The server combines the analysis results and feedback from the emotion engine to generate a comprehensive report for the user. This report includes advice on health and emotional state. 【0775】 Step 8: 【0776】 The device receives the report and provides visual or audio feedback to the user. For example, it might display a message such as, "You seem to be feeling a little breathless today. It would be good to take some time to relax." 【0777】 Step 9: 【0778】 If a significant health or emotional abnormality is detected, the server will notify pre-registered family members or medical institutions. This notification allows for prompt follow-up. 【0779】 Step 10: 【0780】 The server stores analytical data and emotional states in a database, building a long-term history of the user's health and emotions. This information is useful for the user themselves and medical professionals to understand their condition. 【0781】 (Example 2) 【0782】 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". 【0783】 When elderly individuals record their daily health status via voice, there is a need for a system that can accurately assess not only the accuracy of pronunciation and comprehension of conversations, but also their emotional state, enabling early detection and response to abnormalities. Furthermore, conventional systems lack sufficient information regarding emotional changes, making them inadequate for monitoring the overall health of users. This issue is particularly urgent in modern society, given the importance of daily health management for the elderly and other individuals requiring special consideration. 【0784】 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. 【0785】 In this invention, the server includes acquisition means for acquiring voice data from the user, conversion means for converting the acquired voice data into text data, analysis means for analyzing the text data and evaluating the accuracy of pronunciation and the degree of understanding of the conversation, emotion identification means for identifying the emotional state based on the analysis results and voice data, notification means for detecting abnormalities based on the analysis and emotion identification results and notifying the user or a designated contact, and storage means for storing the analysis and emotion identification results in a memory area. This enables comprehensive support from an emotional perspective as well as monitoring the user's health status. 【0786】 "Acquisition means" refers to a function that receives voice data from the user and acquires it in a format that can be processed within the system. 【0787】 A "conversion means" is a function for converting acquired audio data into analyzable text data. 【0788】 "Analysis means" refers to a function that evaluates the accuracy of pronunciation and the degree of comprehension of conversation content based on text data. 【0789】 "Emotion identification means" refers to a function that identifies the user's emotional state based on analyzed text and audio data. 【0790】 "Notification means" refers to a function that detects anomalies based on analysis results and emotion recognition results, and transmits information to the user or designated contact. 【0791】 "Storage means" refers to a function for storing the results of acquisition, analysis, and emotion identification in a recording area. 【0792】 This invention is a system that allows users to record their daily health status via voice and comprehensively understand their health and emotional state based on that content. The system mainly consists of a terminal and a server. 【0793】 Device configuration and role 【0794】 The terminal's role is to acquire voice data from the user. Specifically, a smartphone or a dedicated mobile device is used. These devices have built-in microphones, making it possible to record everyday conversations in high quality. The user launches the application on the terminal and begins voice input. For example, if the user says, "I feel a little better today," that voice is captured by the terminal. The terminal uses speech recognition technology to convert the voice data into text data. For this, speech recognition software such as the Google Speech-to-Text API can be used. 【0795】 Server Configuration and Roles 【0796】 The server receives text data sent from the terminal and analyzes it. This analysis requires natural language processing (NLP) technology, and a commonly used NLP library (e.g., spaCy) is applied. The server then uses an emotion recognition engine to identify the user's emotions from the audio data and the analyzed text data. This is done using an emotion analysis tool (e.g., IBM Watson Tone Analyzer). The analysis results are stored in memory and compared with past data to form the basis for notifying the user of anomalies or risks. 【0797】 Specific examples and prompt statements 【0798】 As a concrete example, suppose a user says, "It's raining today and I'm feeling a little down." In this case, the emotion engine can identify the feeling of sadness, and the server can analyze the result and provide feedback to the user such as, "Try to do something relaxing today." 【0799】 Example prompts for a generative AI model: 【0800】 "Analyze the user's voice input to determine their emotional state and provide appropriate advice. Example: 'It's raining today, and I'm feeling a bit down.'" 【0801】 In this way, the system is able to support users' daily health management by providing them with appropriate and timely feedback that matches their emotional state. 【0802】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0803】 Step 1: 【0804】 The user launches an application on their smartphone or a dedicated device and records their health status using voice. The user's voice is captured through a microphone. The input is the user's raw voice data, which the device acquires in real time. 【0805】 Step 2: 【0806】 The device uses speech recognition technology to convert the acquired audio data into text data. Specifically, it utilizes the Google Speech-to-Text API, among others, to output the audio as text. In this step, the audio data is the input and the text data is the output. As the device generates the text data, the analysis of the conversation content can proceed. 【0807】 Step 3: 【0808】 The terminal sends text data to the server via the internet. Encryption technology is applied during transmission to protect user privacy. Here, the input is the text data converted by the terminal, and the output is the data securely sent to the server. 【0809】 Step 4: 【0810】 The server analyzes the received text data using a natural language processing library (e.g., spaCy). The analysis process includes grammatical structure analysis and verification of pronunciation accuracy. The input is the text data sent to the server, and the output is the analyzed text result. The server then proceeds with further processing based on this analysis result. 【0811】 Step 5: 【0812】 The server uses the parsed text data to identify the user's emotional state using an emotion engine (e.g., IBM Watson Tone Analyzer). In this process, the input is parsed text data, and the output is an emotional state label. The server then confirms the information regarding the user's emotions. 【0813】 Step 6: 【0814】 The server integrates the results of analysis and sentiment recognition, and notifies the user or designated contacts if an anomaly is detected. Push notifications and email may be used as notification methods. The input consists of various analysis results generated within the server, and the output consists of specific advice and warning messages. 【0815】 Step 7: 【0816】 The server ultimately stores all analysis and sentiment recognition results in memory. This storage allows past information to be used as foundational data for future analyses. The input consists of all completed analysis and sentiment recognition data, and the output is permanently stored record data. 【0817】 (Application Example 2) 【0818】 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". 【0819】 In the lives of older adults, it is crucial to appropriately understand their daily health and emotional states and provide necessary care. However, current systems struggle to efficiently monitor the emotional states of older adults and provide appropriate feedback based on that information. This situation could lead to overlooking potential health risks, highlighting the need for a more effective monitoring system. 【0820】 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. 【0821】 In this invention, the server includes an emotion analysis means for identifying emotional states and generating care-oriented feedback based on the analysis results, and an external notification means for notifying specific parties or organizations when an abnormality is detected. This makes it possible to accurately grasp the emotional state of elderly people and provide appropriate advice and notifications. 【0822】 "Acquisition method" refers to a function for capturing and recording voice conversations with elderly people on a device. 【0823】 The "conversion means" refers to a function that converts acquired voice conversations into text data using speech recognition technology. 【0824】 "Analysis means" refers to a function that evaluates the grammatical and pronunciation accuracy of text data and analyzes the level of comprehension of the conversation. 【0825】 A "notification method" is a function that transmits information to the user or a designated contact when an anomaly is detected based on the analysis results. 【0826】 "Storage method" refers to a function for securely storing analysis results and user conversation history in a database. 【0827】 "Emotional analysis means" refers to a function that identifies the emotional state of elderly people from voice data and generates appropriate feedback based on the analysis results. 【0828】 An "external notification mechanism" is a function that notifies specific parties or organizations when an anomaly is detected. 【0829】 The system for realizing this application consists of a voice recording device used daily by elderly people and a server system that works in conjunction with it. 【0830】 The terminal's role is to detect voice conversations with elderly individuals, collect voice data, and then convert it into text data using speech recognition technology. The specific technology used is the speech_recognition library, and the Google Speech-to-Text API is responsible for the text conversion. 【0831】 Next, the server uses natural language processing techniques to evaluate the accuracy of pronunciation and comprehension of the conversation based on the text data sent from the terminal. The text data is then analyzed using sentiment analysis tools and used to determine the emotional state of the elderly person. This analysis is expected to utilize a text analysis library. 【0832】 The analysis results are stored in a database, and if an anomaly is detected, a warning is sent to the user or designated contacts via a notification function. Furthermore, feedback provided by the sentiment analysis system is displayed to the user as visual or audio advice. For example, if speech recognition detects "I'm feeling a little down today," the sentiment analysis system analyzes the emotion from the speech, and the server generates advice based on that, such as "Relax and take a break." 【0833】 In this invention, by operating a generative AI model, specific instructions such as "Please tell me what the elderly person said about how they felt today. Analyze their statements, determine whether their emotions are positive or negative, and provide helpful feedback for care" can be used as prompts for determining emotional states. These prompts are used to improve the quality of detailed analysis and feedback. 【0834】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0835】 Step 1: 【0836】 The device uses a microphone to capture the elderly person's voice as input. It then saves the acquired voice data to an internal buffer. This voice data serves as the basis for subsequent processing. 【0837】 Step 2: 【0838】 The device analyzes the acquired audio data using the Speech Recognition library and converts it into text data. This converted text data is then output. In this process, the speech recognition algorithm analyzes the waveform information of the audio and identifies language-related nuances and pronunciations. 【0839】 Step 3: 【0840】 The server receives text data sent from the terminal as input. It then analyzes the text using natural language processing technology and evaluates the accuracy of pronunciation and the level of understanding of the conversation. The output includes the analysis results and, if necessary, whether or not anomalies have been detected. The server also performs grammar checks and key phrase recognition to identify unnatural expressions and unusual vocabulary. 【0841】 Step 4: 【0842】 The server applies sentiment analysis techniques to the analyzed text data to identify emotional states. The input is the analyzed text data, and the output generates sentiment scores and state reports. This process uses a sentiment analysis model to evaluate emotional words and phrases within the text, assigning them to sentiment categories such as positive and negative. 【0843】 Step 5: 【0844】 The server generates specific feedback and advice for older adults based on their emotional state and analysis results. This involves following prompts using a generative AI model to form contextually relevant feedback sentences. The output is feedback text recommending ways to improve the older adult's emotional state and promote relaxation. 【0845】 Step 6: 【0846】 If an anomaly is detected, the server will notify the designated contact using an external notification method. The input for this notification is the anomaly detection result, and the output is a warning message. The server will refer to pre-registered contact data and send a warning message as needed. 【0847】 Step 7: 【0848】 The server saves all analysis results and feedback to a database. The input is the analysis and feedback data, and the output is the updated database. This saving process continuously accumulates the user's conversation history and emotional state, which can then be used for future analyses. 【0849】 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. 【0850】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0851】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0852】 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. 【0853】 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. 【0854】 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. 【0855】 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. 【0856】 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. 【0857】 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." 【0858】 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. 【0859】 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. 【0860】 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. 【0861】 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. 【0862】 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. 【0863】 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. 【0864】 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 this memory. 【0865】 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. 【0866】 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. 【0867】 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. 【0868】 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. 【0869】 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. 【0870】 The following is further disclosed regarding the embodiments described above. 【0871】 (Claim 1) 【0872】 A means of acquiring voice conversations with elderly people, 【0873】 A conversion means for converting acquired voice conversation into text data, 【0874】 An analysis means for analyzing the aforementioned text data and evaluating the accuracy of pronunciation and the degree of understanding of the conversation content, 【0875】 A notification mechanism that detects anomalies based on analysis results and notifies the user or designated contact, 【0876】 A storage means for saving the aforementioned analysis results to a database, 【0877】 A system that includes this. 【0878】 (Claim 2) 【0879】 The system according to claim 1, wherein the analysis means includes means for determining an anomaly by comparing it with past conversation history. 【0880】 (Claim 3) 【0881】 The system according to claim 1, wherein the notification means includes means for setting a risk level according to the level of abnormality and notifying a medical institution as necessary. 【0882】 "Example 1" 【0883】 (Claim 1) 【0884】 An acquisition unit that acquires audio information, 【0885】 A conversion unit that converts the aforementioned audio information into string data, 【0886】 An analysis unit that evaluates the string data and analyzes its speech features using a generative AI model, 【0887】 A message unit that detects anomalies based on the analysis results and sends an instruction message to the recipient, 【0888】 A storage unit that stores the aforementioned analysis results and historical information in a storage device, 【0889】 A system that includes this. 【0890】 (Claim 2) 【0891】 The system according to claim 1, wherein the analysis unit performs an anomaly determination by comparing it with historical information. 【0892】 (Claim 3) 【0893】 The system according to claim 1, wherein the message unit sets a risk level according to the degree of abnormality and transmits a message to a support facility as necessary. 【0894】 "Application Example 1" 【0895】 (Claim 1) 【0896】 A means of acquiring voice conversations with elderly people, 【0897】 A conversion means for converting acquired voice conversation into text data, 【0898】 An analysis means for analyzing the aforementioned text data and evaluating the accuracy of pronunciation and the degree of understanding of the conversation content, 【0899】 A notification mechanism that detects anomalies based on analysis results and notifies the user or designated contact, 【0900】 A storage means for saving the aforementioned analysis results to a database, 【0901】 A management system that immediately notifies via a smart device when an anomaly is detected and takes action according to the risk level, 【0902】 A system that includes this. 【0903】 (Claim 2) 【0904】 The system according to claim 1, wherein the analysis means includes means for determining anomalies by comparing them with the speaker's past history and monitoring continuous speech characteristics. 【0905】 (Claim 3) 【0906】 The system according to claim 1, wherein the notification means includes a means that, when an abnormality is detected, provides a combined notification by voice and text, and prompts advice to others as necessary. 【0907】 "Example 2 of combining an emotion engine" 【0908】 (Claim 1) 【0909】 A means for acquiring voice data from users, 【0910】 A conversion means for converting acquired audio data into text data, 【0911】 An analysis means for analyzing the aforementioned character data and evaluating the accuracy of pronunciation and the degree of understanding of the conversation content, 【0912】 An emotion identification means for identifying emotional states based on analysis results and audio data, 【0913】 A notification mechanism that detects anomalies based on analysis and sentiment recognition results and notifies the user or designated contact person, 【0914】 A storage means for storing the analysis and emotion identification results in a memory area, 【0915】 A system that includes this. 【0916】 (Claim 2) 【0917】 The system according to claim 1, wherein the analysis means includes means for determining an anomaly by comparing it with past conversation history. 【0918】 (Claim 3) 【0919】 The system according to claim 1, wherein the notification means includes means for setting a risk level according to the level of abnormality and notifying a medical institution as necessary. 【0920】 "Application example 2 when combining with an emotional engine" 【0921】 (Claim 1) 【0922】 A means of acquiring voice conversations with elderly people, 【0923】 A conversion means for converting acquired voice conversation into text data, 【0924】 An analysis means for analyzing the aforementioned text data and evaluating the accuracy of pronunciation and the degree of understanding of the conversation content, 【0925】 A notification mechanism that detects anomalies based on analysis results and notifies the user or designated contact, 【0926】 A storage means for saving the aforementioned analysis results to a database, 【0927】 An emotion analysis means for identifying emotional states and generating care-related feedback based on the analysis results, 【0928】 An external notification mechanism that notifies specific parties or organizations when an anomaly is detected, 【0929】 A system that includes this. 【0930】 (Claim 2) 【0931】 The system according to claim 1, wherein the analysis means includes means for determining an anomaly by comparing it with past conversation history. 【0932】 (Claim 3) 【0933】 The system according to claim 1, wherein the notification means includes means for setting a risk level according to the level of abnormality and notifying a medical institution as necessary. [Explanation of symbols] 【0934】 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

[Claim 1] A means of acquiring voice conversations with elderly people, A conversion means for converting acquired voice conversation into text data, An analysis means for analyzing the aforementioned text data and evaluating the accuracy of pronunciation and the degree of understanding of the conversation content, A notification mechanism that detects anomalies based on analysis results and notifies the user or designated contact, A storage means for saving the aforementioned analysis results to a database, A system that includes this. [Claim 2] The system according to claim 1, wherein the analysis means includes means for determining an anomaly by comparing it with past conversation history. [Claim 3] The system according to claim 1, wherein the notification means includes means for setting a risk level according to the level of abnormality and notifying a medical institution as necessary.

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