system

Abstract

We provide the system. [Solution] A means of collecting user voice data via voice input and obtaining conversation data, A means for analyzing the aforementioned conversation data using natural language processing and extracting indicators of health status, A means of evaluating health status based on extracted indicators and generating alerts according to the evaluation results, A means for notifying a third party of the evaluation results and generated alerts using appropriate communication technology, One method involves uploading the evaluation results to a cloud server and providing them as a report by visualizing the data. A means of engaging in friendly conversation with users using artificial intelligence and providing topics that alleviate feelings of loneliness, 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 persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In an aging society, there is a problem that it is difficult to detect and address the sense of loneliness and health problems faced by the elderly in their daily lives at an early stage. In particular, health problems such as dementia and depression are difficult to detect in the initial stage, and the consequences may have a significant impact on the quality of life of the elderly. There are also social problems such as an increased burden on medical staff and an increase in medical costs.

Means for Solving the Problems

[0005] This invention provides a system for understanding the health status of elderly people by collecting and analyzing their daily conversations via voice input. By analyzing the voice data using natural language processing technology, indicators of health status are extracted, enabling early detection of abnormalities based on the evaluation. The evaluation results are notified to healthcare professionals and family members, facilitating prompt responses and supporting the health management of the elderly. Furthermore, through interaction with an AI agent, it provides a means to alleviate feelings of loneliness among the elderly and maintain their mental health.

[0006] "Voice input" refers to a method of acquiring voice data and providing voice instructions to an electronic device.

[0007] "Conversation data" refers to a collection of information obtained from the user's voice input and converted into text format.

[0008] "Natural language processing" is a computer technology that analyzes and understands human language and processes information based on that analysis.

[0009] "Health status indicators" are standard data or characteristics used to indicate a user's health status.

[0010] An "alert" is a notification sent to relevant parties as a warning when an anomaly is detected.

[0011] A "third party" refers to any person other than the user themselves, and includes, but is not limited to, medical professionals and family members.

[0012] A "communication method" refers to a means or protocol for sharing information with other devices or people.

[0013] A "report" is a document containing organized information that is provided to third parties on a regular basis.

[0014] An "AI agent" is a program that operates using artificial intelligence technology and provides information and support through dialogue with users.

[0015] "Interests and concerns" refer to topics and areas that users are particularly involved in and want to know about.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

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

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

[0019] In the following embodiments, a 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), etc.

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

[0021] In the following embodiments, a 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, etc.

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

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

[0024] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0037] This invention is a system that evaluates the health status of elderly people by acquiring their daily conversations from voice input and analyzing that data. Elderly people can speak naturally using a dedicated terminal, and their voice is converted to text in real time.

[0038] Device: This device uses speech recognition technology to capture the everyday conversations of elderly people and converts the audio data into text data. This allows elderly people to continue conversations smoothly without feeling any inconvenience.

[0039] Server: Receives text data acquired from terminals and analyzes it using natural language processing (NLP) technology. The analysis extracts keywords indicating emotions and health status from the conversation content, and this information is then processed statistically and evaluated using evaluation algorithms to become health indicators. For example, if an elderly person mentions specific symptoms such as "I haven't been able to sleep lately" or "I have no appetite," these words are used as keywords to determine if there is a possibility of a health problem.

[0040] Based on the analysis results, the health status is assessed, and if an abnormality is detected, the server promptly generates an alert. This alert is sent to healthcare professionals, and necessary actions are taken.

[0041] The server also has a function to periodically compile information on the health status of elderly individuals and provide it to their families as a report. The report includes details of health changes and lifestyle inferred from conversations, making it easier for families to understand the elderly person's situation.

[0042] Interaction with the Agent: The device is powered by an artificial intelligence agent that provides engaging topics for seniors. Users can interact with the agent based on their interests and receive advice related to daily life and health in the process. For example, the AI ​​may ask questions about the weather or hobbies, and seniors can respond accordingly, resulting in a relaxed conversation.

[0043] By implementing this invention, health management for the elderly can become more efficient and effective, and in addition, feelings of loneliness can be reduced.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The device starts voice input when the user begins speaking and collects voice data in real time. It then uses speech recognition technology to convert the voice into text format.

[0047] Step 2:

[0048] The device sends the converted text data to the server using a secure protocol. The data is encrypted during transmission for privacy protection.

[0049] Step 3:

[0050] The server processes the received text data using a natural language processing (NLP) engine. The analysis extracts keywords and phrases related to health status from the conversation content.

[0051] Step 4:

[0052] The server uses the extracted keywords to assess health status. It processes the data based on a health assessment algorithm to determine normal and abnormal values.

[0053] Step 5:

[0054] The server generates an alert if an anomaly is detected based on the evaluation results. The generated alert is sent to healthcare professionals via email or app.

[0055] Step 6:

[0056] The server periodically generates health status reports based on conversation data and sends them to the family. The reports include information about health status and changes in lifestyle.

[0057] Step 7:

[0058] The AI ​​agent installed in the device provides topics tailored to the user's interests and concerns, and asks appropriate questions to the elderly to facilitate continuous interaction with them.

[0059] Step 8:

[0060] Through conversations with the AI ​​agent, users can freely express their thoughts on everyday events and health-related matters, and continue the dialogue in an enjoyable way.

[0061] (Example 1)

[0062] 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."

[0063] In modern society, managing the health of the elderly is a crucial issue, but monitoring their health through natural conversations in daily life and providing timely medical intervention is difficult. Furthermore, many elderly people experience feelings of loneliness, and methods to alleviate this are needed. Considering these challenges, a system is required to safely and efficiently manage the health of the elderly and improve their quality of life.

[0064] 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.

[0065] In this invention, the server includes means for collecting user acoustic data using acoustic input and acquiring dialogue data, means for analyzing the dialogue data using natural language processing and extracting indicators of health status, and means for evaluating the health status based on the extracted indicators and generating warnings according to the evaluation results. This makes it possible to monitor the health status of elderly people in real time through natural conversations in their daily lives and to quickly notify medical professionals when necessary. Furthermore, the use of an intelligent agent in dialogue can also help reduce feelings of loneliness.

[0066] "Audio input" refers to a technology that converts voice and sound into digital data, and its role is to capture the user's voice and incorporate it into the system.

[0067] "Dialogue data" refers to information obtained from conversations with users, recorded in a structured format, and used for analyzing the content of their statements.

[0068] "Natural language processing" is a technology that enables computers to understand human language and recognize its meaning. It is a process of scrutinizing the grammar and content of text data to extract meaning.

[0069] "Health status indicators" are data points and keywords used to evaluate the user's health, which are used to determine abnormalities or situations requiring attention.

[0070] A "warning" is a notification issued by the system when it detects an abnormality in a person's health condition, and is generated to quickly communicate this information to healthcare professionals and caregivers.

[0071] An "intelligent agent" is a program that uses artificial intelligence technology to interact with the user and provide information based on their interests and concerns, contributing to the promotion of dialogue and the reduction of feelings of loneliness.

[0072] "Text data" refers to data in text format that has been converted to record speech in an analyzable form, and is used in natural language processing.

[0073] The following describes embodiments for carrying out this invention. The system aims to monitor the health status of elderly people and improve their quality of life. The system mainly includes acoustic input, analysis of conversational data, assessment of health status, generation and notification of warnings, and interaction by an intelligent agent.

[0074] Terminal:

[0075] The terminal is equipped with a high-performance microphone to acquire the voice of elderly people using acoustic input. The acquired voice is converted into text data using speech recognition technology. For this purpose, open-source speech recognition systems are used as the speech recognition software. This text data converted from the voice is sent to the server as conversation data.

[0076] server:

[0077] The server processes the received text data using natural language processing tools to extract indicators that may reflect the user's health status. This natural language processing utilizes open-source natural language processing libraries, among others. Based on these indicators, the server evaluates the user's health status and, if necessary, generates warnings and notifies others. Furthermore, by utilizing an intelligent agent, the server provides topics tailored to the interests and concerns of the elderly through everyday conversations. An open-source AI chatbot platform is used for the intelligent agent. This allows the elderly to enjoy conversations in a relaxed state, contributing to a reduction in feelings of loneliness.

[0078] User:

[0079] Elderly users can interact with an intelligent agent through the device. For example, if an elderly person says to the device, "I haven't been sleeping well lately," the content is converted into text data and sent to the server. The server analyzes the results, which are then used as an indicator of their health status, and warnings are sent to healthcare professionals as needed.

[0080] Examples of specific cases and prompt statements:

[0081] For example, if an elderly person asks the device, "What's the weather like today?", the intelligent agent might respond, "It's sunny today. It might be nice to go for a walk outside." This expands the conversation and improves the user's quality of life.

[0082] An example of a prompt for a generative AI model might be: "From this statement, select words that could potentially affect the user's health and provide advice based on them."

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The device acquires audio data from the elderly person's surroundings via a microphone. The input is audio from the external environment, and this audio is transmitted to the speech recognition engine in real time. The speech recognition engine performs data processing, analyzing the audio data and converting it into corresponding text. The output is text data.

[0086] Step 2:

[0087] The server receives text data sent from the terminal. The input is text data in string format, and the server uses a natural language processing library to perform grammatical interpretation and semantic analysis. This process identifies keywords related to emotions and behaviors, and the output is the analyzed emotion data and a keyword list.

[0088] Step 3:

[0089] The server inputs the analyzed sentiment data and keyword list into an evaluation algorithm. This algorithm performs data calculations that quantify the health status of elderly individuals based on the keywords and generate warnings if abnormal values ​​are detected. The output is a health evaluation score and, if necessary, a warning message.

[0090] Step 4:

[0091] The server securely stores the generated health assessment scores and warning messages and notifies healthcare professionals and family members using appropriate communication methods. The input is the score and warning message generated in the previous step, and the output is the notification message delivered to healthcare professionals and family members.

[0092] Step 5:

[0093] Users engage in everyday conversations through an intelligent agent. The agent provides appropriate topics and advice based on user input. Input is the user's speech, and output is the agent's response message. This interaction engages the interest of elderly individuals and provides a relaxing environment.

[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] In elderly care, a key challenge is to promptly and accurately monitor the health status of the elderly and, when necessary, to quickly inform medical professionals and family members. Furthermore, providing means of communication that allow the elderly to live their daily lives with peace of mind and without feeling isolated is also crucial.

[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 means for collecting user voice data via voice input and acquiring conversation data, means for analyzing the conversation data using natural language processing and extracting indicators of health status, and means for uploading the evaluation results to a cloud server and providing the data as a report by visualizing it. This makes it easier to monitor the daily health of the elderly, and enables healthcare professionals and family members to detect abnormalities early and intervene. In addition, friendly dialogue using artificial intelligence enables psychological support for the elderly.

[0099] "Voice input" is a technology that converts a user's verbal instructions or everyday conversations into digital data.

[0100] "Conversation data" refers to a collection of information that is converted from voice input into text and then analyzed.

[0101] "Natural language processing" is a technology that enables computers to analyze, understand, and generate language that humans use on a daily basis.

[0102] "Health status indicators" are indicators that express a user's health status using numerical values ​​or categories.

[0103] An "alert" is a warning message issued to prompt a quick response when an abnormality is detected in the user's health condition.

[0104] A "report" is a document that summarizes the results of an assessment of the user's health status and is used to provide information to relevant parties.

[0105] Artificial intelligence is a technology that gives machines the ability to think, learn, and improve themselves like humans.

[0106] "Friendly dialogue" refers to a form of communication in which the machine responds appropriately so that users can converse naturally and with peace of mind.

[0107] A "cloud server" is a remote computing service that stores and processes data via the internet.

[0108] "Visualization" is a technique for representing data in an easily understandable format.

[0109] The system implementing this invention mainly consists of a voice input terminal, a server, and a cloud infrastructure.

[0110] The device utilizes speech recognition software to capture the elderly person's voice in real time. Specifically, it uses the Google® Cloud Speech-to-Text API to convert the user's voice into text data. This allows the elderly person to have a natural conversation while recording the content in digital format.

[0111] The server receives text data acquired via cloud services and analyzes it using natural language processing technologies such as Amazon Comprehend. The server extracts relevant keywords related to emotions and health status from the conversation and uses them to derive indicators of the health status of older adults. Based on these indicators, it assesses the health status and generates alerts if problems are detected.

[0112] Furthermore, the server uploads the evaluation results to a cloud server to visualize the data and provides it to family members and healthcare professionals as regular reports. These reports allow stakeholders to understand the health status of the elderly and take prompt action as needed.

[0113] Furthermore, the device engages in natural conversations with elderly individuals through an AI-powered conversational agent. The agent aims to provide a sense of security by offering topics based on the elderly person's interests and concerns.

[0114] As a concrete example, if an elderly person speaks to the device and says, "I haven't been able to sleep lately," the AI ​​agent will provide advice on how to improve sleep. This information is then analyzed on a server, and if necessary, the system is set up to automatically notify family members.

[0115] An example of a prompt sentence would be, "Please provide a conversation example to determine how older adults are feeling these days and what health challenges they are facing."

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

[0117] Step 1:

[0118] The device inputs the user's voice via a microphone and converts it into digital data. The input is raw voice data, and the output is digital voice data. This data is then sent to a speech recognition API.

[0119] Step 2:

[0120] The device uses the Google Cloud Speech-to-Text API to convert digital audio data into text data. The input is digital audio data, and the output is text data. It calls the API and performs the conversion process.

[0121] Step 3:

[0122] The server receives text data on the cloud. The input is text data, and the server stores this data in a queue for analysis.

[0123] Step 4:

[0124] The server uses Amazon Comprehend to perform natural language processing on text data. The input is text data, and the output is keyword and metric data indicating emotions and health status. It analyzes the text and extracts the necessary information.

[0125] Step 5:

[0126] The server evaluates the health status of elderly individuals based on extracted indicator data and generates alerts if abnormalities are detected. The input is indicator data, and the output is health assessment data and alert data. The server executes an evaluation algorithm and performs the alert generation operation.

[0127] Step 6:

[0128] The server records evaluation results and alerts in a database and uploads them to the cloud server. Inputs are health evaluation data and alert data, and output is the recorded data. It performs write and upload operations.

[0129] Step 7:

[0130] The server periodically compiles evaluation results into a report and notifies relevant parties. The input is recorded data, and the output is report data. It performs data aggregation and report generation operations.

[0131] Step 8:

[0132] The device uses an AI agent to converse with elderly individuals, providing topics based on their interests and concerns. Input is the elderly person's responses and statements, and output is the generated conversation content. The user interacts with the agent.

[0133] 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.

[0134] This invention provides a system that evaluates the health status and emotions of elderly people from voice data obtained through their daily conversations. The system collects the user's voice input in real time and analyzes this data to understand both the user's health and emotions.

[0135] Device: To acquire audio data, the microphone activates when the user begins speaking, converting the speech into text. The device is equipped with high-precision speech recognition technology, enabling the user to continue the conversation naturally.

[0136] Server: Text data received from the terminal is analyzed by a natural language processing (NLP) engine and an emotion engine. The NLP engine identifies keywords and phrases indicating health status, and by adding the emotion engine's identification of emotional status to this, a more sophisticated health assessment can be performed. Based on this combined assessment, if an abnormality in health status or a change in emotion is detected, an appropriate alert is generated and notified to healthcare professionals or family members.

[0137] For example, if a user says, "I've been very tired lately and a little anxious," the NLP engine extracts the keywords "tired" and "anxiety," while the emotion engine identifies feelings of "anxiety" and "sadness" from the tone and content of the speech. This allows the system to provide an assessment that takes into account not only physical health but also emotional aspects.

[0138] Reporting and Interaction: The system regularly generates reports on the user's health and emotions and provides them to family members and healthcare professionals. These reports include the user's overall health status, emotional trends, and suggestions for lifestyle improvements based on these trends. The AI ​​agent encourages ongoing dialogue with the user by engaging their interests through conversation and suggesting recommended behaviors and lifestyle habits.

[0139] This embodiment of the invention enables comprehensive monitoring of the health and emotions of elderly individuals, allowing for prompt responses and helping to reduce feelings of loneliness and anxiety.

[0140] The following describes the processing flow.

[0141] Step 1:

[0142] The device uses a microphone to collect the voices that elderly people speak on a daily basis and converts them into text using a speech recognition system. This process is performed in real time, allowing elderly people to continue conversations naturally.

[0143] Step 2:

[0144] The device sends the converted text data to the server using a secure protocol. During this process, the data is encrypted, ensuring privacy is protected.

[0145] Step 3:

[0146] The server feeds the received text data into a natural language processing (NLP) engine to extract health-related keywords and phrases. This creates indicators that show specific health conditions.

[0147] Step 4:

[0148] The server then uses an emotion engine to analyze the user's emotional state from the text data. This engine identifies emotions from the content and tone of the conversation and classifies them as positive, negative, or neutral.

[0149] Step 5:

[0150] The server integrates the health assessment results from the NLP engine with the emotion analysis results from the emotion engine. This allows for a comprehensive understanding of the user's overall health and emotional state.

[0151] Step 6:

[0152] The server generates an alert if an anomaly is detected or if a persistent change in emotions is observed. The alert is promptly notified to healthcare workers and family members so that necessary actions can be taken.

[0153] Step 7:

[0154] The server generates regular health and emotional reports based on the analysis results. These reports include specific advice and trend analyses to promote a healthy lifestyle and are sent to family members and healthcare professionals.

[0155] Step 8:

[0156] The AI ​​agent installed in the device presents conversation topics to the user based on their interests and concerns. Through these conversations, users can gain a sense of security and receive emotional and healthy support.

[0157] (Example 2)

[0158] 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".

[0159] In modern society, there is a need to appropriately monitor and promptly respond to the physical and mental health of the elderly. However, conventional methods make it difficult to accurately grasp the emotional state and daily changes of the elderly, and it is difficult to provide effective support to alleviate feelings of isolation and anxiety. Therefore, there is a need for a system that comprehensively evaluates the health and emotions of the elderly through voice data and enables appropriate intervention.

[0160] 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.

[0161] In this invention, the server includes means for collecting user speech data using voice input and obtaining a language record; means for analyzing the language record via natural language processing and identifying indicators indicating health status; and means for evaluating the health status and emotional status based on the extracted indicators and generating a warning signal accordingly. This enables real-time evaluation of the health status and emotional status of elderly people and allows for quick and accurate responses.

[0162] "Voice input" refers to an input method for acquiring voice data, and includes a device for detecting sound, such as a microphone.

[0163] "Users" refers to individuals who use this system, including, in particular, those targeted for health and emotional monitoring, especially the elderly.

[0164] "Speech data" refers to voice information obtained from the user, and includes text data generated based on that voice information.

[0165] "Language recording" refers to text-formatted data obtained by analyzing audio data, and it records the content of the user's conversation.

[0166] "Natural language processing" is a technique used by computers to analyze text data and has the ability to interpret the grammar and meaning of various languages.

[0167] An "indicator" refers to standard data or information used to evaluate a user's health status.

[0168] A "warning signal" is notification data generated when an abnormality is detected in the user's health condition, and it is a signal intended to prompt relevant parties to take prompt action.

[0169] "Communication methods" refer to the infrastructure and technologies used to transmit information such as evaluation results and warnings to external parties, and include email, applications, and other digital communications.

[0170] "Emotional state" is an indicator that quantifies the user's psychological state and represents a specific emotion (such as anxiety, happiness, or sadness).

[0171] This invention provides a system for comprehensively evaluating a user's health and emotional state using voice data. The following describes a specific implementation of this system.

[0172] Acquisition of audio data

[0173] When a user begins a conversation, the device uses its built-in microphone to acquire voice data. This device runs high-precision speech recognition software, such as "Speech Recognition Engine X." The voice data is converted to text in real time.

[0174] Data Analysis

[0175] Text data generated on the terminal is sent to the server via encrypted communication. On the server, a natural language processing (NLP) engine first receives and analyzes this data. The "Natural Language Processing Engine Y" is used for analysis, extracting keywords and phrases that indicate health status.

[0176] Next, the server uses an emotion engine to identify the user's emotional state from the tone and context of the text. For example, the "Emotion Analysis Engine Z" is useful in this process. This allows the user's physical and emotional state to be evaluated.

[0177] Notifications and reports

[0178] The server immediately generates a warning signal if it detects any abnormalities in the user's health or emotional state. This information is then communicated to healthcare professionals or family members via means such as email or mobile applications.

[0179] The system also periodically generates reports based on these evaluation results and distributes them to all relevant stakeholders. These reports detail the user's health trends, emotional changes, and recommended lifestyle improvements.

[0180] Specific examples and prompt statements

[0181] For example, if a user says, "Recently, I've been experiencing pain when I breathe," the device can capture this utterance, analyze it on the server, and generate an alert prompting appropriate action. An example of a prompt to input to the generating AI model would be, "Based on conversational data of elderly people, analyze the current situation and generate suggestions for improvement."

[0182] This system allows for comprehensive, real-time management of the health and emotions of the elderly, thereby enhancing support for care and medical services.

[0183] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0184] Step 1:

[0185] The device acquires voice data through the microphone when the user begins a conversation. When the voice input reaches the device's sensor, voice recognition software converts it into text data. Specifically, a filtering process is performed to remove noise, resulting in clear text from the voice signal. The input is the user's voice data, and the output is the converted text data.

[0186] Step 2:

[0187] The terminal sends the text data converted from the voice to the server. At this stage, the text data is encrypted to enhance security. Specifically, the terminal uses the Internet Protocol to packetize the data and forward it to the server's receiving module. The input is filtered text data, and the output is secure transmission data to the server.

[0188] Step 3:

[0189] The server passes the received text data to a natural language processing engine. The engine extracts health check keywords, performs contextual analysis, and generates health status indicators. Specifically, a keyword matching algorithm is applied. The input is the text data sent to the server, and the output is the extracted health indicators.

[0190] Step 4:

[0191] The server then uses an emotion engine to analyze the emotional state of the text. Positive and negative emotions are identified during this process. For example, emotion words such as "happy" and "anxious" are referenced from the database. The input is text data containing health indicators, and the output is the analyzed emotional state.

[0192] Step 5:

[0193] If an abnormality is detected in the user's health or emotional state, the server generates an alert. The warning signal is then prepared to notify family members or healthcare professionals. Specifically, an email sending queue is processed based on a configured list. The input is the evaluation data that detected the anomaly, and the output is the generated notification alert.

[0194] Step 6:

[0195] The server generates periodic reports, recording changes in users' health and emotions. These reports include trend data and improvement suggestions. Specifically, historical data is extracted from the database and graphed using analytical software. The input is regularly updated evaluation data, and the output is the generated report.

[0196] (Application Example 2)

[0197] 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".

[0198] In the daily lives of the elderly, there is a challenge in efficiently monitoring changes in their health and emotions, detecting abnormalities early, and taking appropriate action. Furthermore, there is a need to alleviate feelings of loneliness and anxiety through continuous dialogue based on the user's interests and concerns.

[0199] 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.

[0200] In this invention, the server includes means for collecting user voice data via voice input and acquiring conversation data; means for analyzing the conversation data using natural language processing and extracting indicators of health status; and means for evaluating the health status based on the extracted indicators and information including emotional state, and generating alerts according to the evaluation results. This enables comprehensive monitoring of the health and emotions of elderly people on a daily basis and allows for prompt responses.

[0201] "Voice input" is a technology that collects and processes the voice spoken by the user in digital format.

[0202] "Conversation data" refers to text-formatted data obtained from voice input, and is used to analyze the user's intentions and state of mind.

[0203] "Natural language processing" is a technology that enables computers to understand and analyze natural language. It involves converting audio data into text data and then analyzing its meaning.

[0204] "Health status indicators" are specific keywords or phrases that indicate the user's physical and mental health, and are extracted using natural language processing.

[0205] "Emotional state" refers to a numerical or categorical representation of a user's emotions, and is the user's mood or feelings inferred from the tone and content of their conversation.

[0206] A "generative AI model" is a system that uses machine learning algorithms to learn patterns from data and provides generative content and recommendations.

[0207] "Feedback" refers to information provided to users based on analysis results and evaluations, intended to encourage user behavior.

[0208] The system of this invention evaluates a user's health status and emotions in real time through their everyday conversations. To implement this invention, a terminal for collecting voice data, a server for analyzing the voice data, and a generative AI model are mainly required.

[0209] A suitable device is one with voice input capabilities, such as a smartphone, where the microphone automatically activates when the user begins speaking. This device uses the Google Cloud Speech-to-Text API to convert the speech into text data. The converted text data is immediately sent to a server in the cloud.

[0210] The server uses a natural language processing engine, such as Amazon Comprehend, to analyze the transmitted text data and extract indicators of health status and emotional states. Microsoft® Azure® Sentiment Analysis API complements this sentiment analysis. Generative AI models are further utilized to generate proactive suggestions for the user. Based on historical data and general health information, the AI ​​models provide feedback on recommended behaviors and lifestyles to the user.

[0211] As a concrete example, suppose a user says to their smartphone, "My back has been hurting since last night, and I couldn't sleep well." In this case, the system identifies the health indicator "back pain" and the possible anxiety of "not being able to sleep," and based on this, it starts the process of sending alerts to family members or healthcare professionals. The AI ​​may also suggest simple stretches to the user.

[0212] An example of a prompt sentence to input into a generative AI model is: "Explain how to evaluate the user's health and emotions based on text data extracted from their voice input, and generate appropriate alerts."

[0213] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0214] Step 1:

[0215] The device activates the microphone when it detects user speech. It collects audio data in real time and converts it to text data using the Google Cloud Speech-to-Text API. The input for this step is the user's voice, and the output is the converted text data. Speech recognition technology is used to convert the audio signal into a stable text format.

[0216] Step 2:

[0217] The server receives text data sent from the terminal. Next, it uses Amazon Comprehend to perform natural language processing, extracting keywords from the text data that indicate health status and emotions. The input for this step is the received text data, and the output is the extracted keywords and information about emotions. The text content is then analyzed, and data processing is performed to measure important health indicators and emotional states.

[0218] Step 3:

[0219] The server uses Microsoft Azure's Sentiment Analysis API to evaluate the emotional state of the extracted keywords. This identifies changes in the user's mood and potential fluctuations in their mental state. The input for this step is the previously extracted set of keywords, and the output is the emotional evaluation associated with those keywords. The sentiment analysis engine quantifies the tone of the text.

[0220] Step 4:

[0221] The server utilizes a generative AI model to generate behavioral guidance and lifestyle suggestions based on the user's health and emotions. The input for this step is evaluated health indicators and emotional states, while the output is specific actions and advice suggested by the AI. Model learning is used to generate appropriate feedback from personal data and similar past cases.

[0222] Step 5:

[0223] The server compiles the assessment results and generated actions, and notifies family members and healthcare professionals as needed. The inputs for this step are the user's assessment results and suggested actions, and the output is notification data. An appropriate communication method is selected to send information reflecting the user's current status to third parties in a timely manner.

[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] This invention is a system that evaluates the health status of elderly people by acquiring their daily conversations from voice input and analyzing that data. Elderly people can speak naturally using a dedicated terminal, and their voice is converted to text in real time.

[0241] Device: This device uses speech recognition technology to capture the everyday conversations of elderly people and converts the audio data into text data. This allows elderly people to continue conversations smoothly without feeling any inconvenience.

[0242] Server: Receives text data acquired from terminals and analyzes it using natural language processing (NLP) technology. The analysis extracts keywords indicating emotions and health status from the conversation content, and this information is then processed statistically and evaluated using evaluation algorithms to become health indicators. For example, if an elderly person mentions specific symptoms such as "I haven't been able to sleep lately" or "I have no appetite," these words are used as keywords to determine if there is a possibility of a health problem.

[0243] Based on the analysis results, the health status is assessed, and if an abnormality is detected, the server promptly generates an alert. This alert is sent to healthcare professionals, and necessary actions are taken.

[0244] The server also has a function to periodically compile information on the health status of elderly individuals and provide it to their families as a report. The report includes details of health changes and lifestyle inferred from conversations, making it easier for families to understand the elderly person's situation.

[0245] Interaction with the Agent: The device is powered by an artificial intelligence agent that provides engaging topics for seniors. Users can interact with the agent based on their interests and receive advice related to daily life and health in the process. For example, the AI ​​may ask questions about the weather or hobbies, and seniors can respond accordingly, resulting in a relaxed conversation.

[0246] By implementing this invention, health management for the elderly can become more efficient and effective, and in addition, feelings of loneliness can be reduced.

[0247] The following describes the processing flow.

[0248] Step 1:

[0249] The device starts voice input when the user begins speaking and collects voice data in real time. It then uses speech recognition technology to convert the voice into text format.

[0250] Step 2:

[0251] The device sends the converted text data to the server using a secure protocol. The data is encrypted during transmission for privacy protection.

[0252] Step 3:

[0253] The server processes the received text data using a natural language processing (NLP) engine. The analysis extracts keywords and phrases related to health status from the conversation content.

[0254] Step 4:

[0255] The server uses the extracted keywords to assess health status. It processes the data based on a health assessment algorithm to determine normal and abnormal values.

[0256] Step 5:

[0257] The server generates an alert if an anomaly is detected based on the evaluation results. The generated alert is sent to healthcare professionals via email or app.

[0258] Step 6:

[0259] The server periodically generates health status reports based on conversation data and sends them to the family. The reports include information about health status and changes in lifestyle.

[0260] Step 7:

[0261] The AI ​​agent installed in the device provides topics tailored to the user's interests and concerns, and asks appropriate questions to the elderly to facilitate continuous interaction with them.

[0262] Step 8:

[0263] Through conversations with the AI ​​agent, users can freely express their thoughts on everyday events and health-related matters, and continue the dialogue in an enjoyable way.

[0264] (Example 1)

[0265] 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."

[0266] In modern society, managing the health of the elderly is a crucial issue, but monitoring their health through natural conversations in daily life and providing timely medical intervention is difficult. Furthermore, many elderly people experience feelings of loneliness, and methods to alleviate this are needed. Considering these challenges, a system is required to safely and efficiently manage the health of the elderly and improve their quality of life.

[0267] 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.

[0268] In this invention, the server includes means for collecting user acoustic data using acoustic input and acquiring dialogue data, means for analyzing the dialogue data using natural language processing and extracting indicators of health status, and means for evaluating the health status based on the extracted indicators and generating warnings according to the evaluation results. This makes it possible to monitor the health status of elderly people in real time through natural conversations in their daily lives and to quickly notify medical professionals when necessary. Furthermore, the use of an intelligent agent in dialogue can also help reduce feelings of loneliness.

[0269] "Audio input" refers to a technology that converts voice and sound into digital data, and its role is to capture the user's voice and incorporate it into the system.

[0270] "Dialogue data" refers to information obtained from conversations with users, recorded in a structured format, and used for analyzing the content of their statements.

[0271] "Natural language processing" is a technology that enables computers to understand human language and recognize its meaning. It is a process of scrutinizing the grammar and content of text data to extract meaning.

[0272] "Health status indicators" are data points and keywords used to evaluate the user's health, which are used to determine abnormalities or situations requiring attention.

[0273] A "warning" is a notification issued by the system when it detects an abnormality in a person's health condition, and is generated to quickly communicate this information to healthcare professionals and caregivers.

[0274] An "intelligent agent" is a program that uses artificial intelligence technology to interact with the user and provide information based on their interests and concerns, contributing to the promotion of dialogue and the reduction of feelings of loneliness.

[0275] "Text data" refers to data in text format that has been converted to record speech in an analyzable form, and is used in natural language processing.

[0276] The following describes embodiments for carrying out this invention. The system aims to monitor the health status of elderly people and improve their quality of life. The system mainly includes acoustic input, analysis of conversational data, assessment of health status, generation and notification of warnings, and interaction by an intelligent agent.

[0277] Terminal:

[0278] The terminal is equipped with a high-performance microphone to acquire the voice of elderly people using acoustic input. The acquired voice is converted into text data using speech recognition technology. For this purpose, open-source speech recognition systems are used as the speech recognition software. This text data converted from the voice is sent to the server as conversation data.

[0279] server:

[0280] The server processes the received character data using a natural language analysis tool and extracts indicators that may indicate the health status. For this natural language analysis, an "open-source natural language processing library" or the like is used. Based on the indicators, the health status of the user is evaluated, and a warning is generated and notified to others if necessary. Also, by utilizing an intelligent agent, topics corresponding to the interests and concerns of the elderly are provided through daily conversations. An "open-source AI chatbot platform" is used for the intelligent agent. As a result, the elderly can enjoy conversations in a relaxed state, contributing to the reduction of loneliness.

[0281] User:

[0282] The elderly user, who is the user, can interact with the intelligent agent through the terminal. For example, when the elderly person says to the terminal "I often can't sleep well recently", the content is converted into character data and sent to the server. The result analyzed by the server is treated as an indicator of the health status, and a warning is sent to medical staff if necessary.

[0283] Specific examples and example prompt sentences:

[0284] As a specific example, when the elderly person asks the terminal "What's the weather like today?", the intelligent agent replies "It's sunny today. It might be nice to go for a walk outside". As a result, the conversation expands further, improving the quality of life of the user.

[0285] As an example of a prompt sentence for the generation AI model, an instruction such as "Select words from this utterance content that may affect the health status and provide advice based on them." can be considered.

[0286] The flow of the specific process in Example 1 will be described using FIG. 11.

[0287] Step 1:

[0288] The device acquires audio data from the elderly person's surroundings via a microphone. The input is audio from the external environment, and this audio is transmitted to the speech recognition engine in real time. The speech recognition engine performs data processing, analyzing the audio data and converting it into corresponding text. The output is text data.

[0289] Step 2:

[0290] The server receives text data sent from the terminal. The input is text data in string format, and the server uses a natural language processing library to perform grammatical interpretation and semantic analysis. This process identifies keywords related to emotions and behaviors, and the output is the analyzed emotion data and a keyword list.

[0291] Step 3:

[0292] The server inputs the analyzed sentiment data and keyword list into an evaluation algorithm. This algorithm performs data calculations that quantify the health status of elderly individuals based on the keywords and generate warnings if abnormal values ​​are detected. The output is a health evaluation score and, if necessary, a warning message.

[0293] Step 4:

[0294] The server securely stores the generated health assessment scores and warning messages and notifies healthcare professionals and family members using appropriate communication methods. The input is the score and warning message generated in the previous step, and the output is the notification message delivered to healthcare professionals and family members.

[0295] Step 5:

[0296] Users engage in everyday conversations through an intelligent agent. The agent provides appropriate topics and advice based on user input. Input is the user's speech, and output is the agent's response message. This interaction engages the interest of elderly individuals and provides a relaxing environment.

[0297] (Application Example 1)

[0298] 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 glasses 214 will be referred to as the "terminal."

[0299] In elderly care, a key challenge is to promptly and accurately monitor the health status of the elderly and, when necessary, to quickly inform medical professionals and family members. Furthermore, providing means of communication that allow the elderly to live their daily lives with peace of mind and without feeling isolated is also crucial.

[0300] 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.

[0301] In this invention, the server includes means for collecting user voice data via voice input and acquiring conversation data, means for analyzing the conversation data using natural language processing and extracting indicators of health status, and means for uploading the evaluation results to a cloud server and providing the data as a report by visualizing it. This makes it easier to monitor the daily health of the elderly, and enables healthcare professionals and family members to detect abnormalities early and intervene. In addition, friendly dialogue using artificial intelligence enables psychological support for the elderly.

[0302] "Voice input" is a technology that converts a user's verbal instructions or everyday conversations into digital data.

[0303] "Conversation data" refers to a collection of information that is converted from voice input into text and then analyzed.

[0304] "Natural language processing" is a technology that enables computers to analyze, understand, and generate language that humans use on a daily basis.

[0305] The "health status indicator" refers to an indicator that expresses the user's health status in numerical values or categories.

[0306] An "alert" is warning information issued to prompt a prompt response when an abnormality is detected in the user's health status.

[0307] A "report" is a document that summarizes the evaluation results regarding the user's health status and is used to provide information to relevant parties.

[0308] "Artificial intelligence" is a technology that enables machines to think, learn, and self-improve like humans.

[0309] "Interact friendly" is a form of communication in which the machine responds appropriately so that the user can talk comfortably and naturally.

[0310] A "cloud server" refers to a remote computing service for storing and processing data via the Internet.

[0311] "Visualization" is a technique for presenting data in an easily understandable form.

[0312] The system for implementing this invention mainly consists of a voice input terminal, a server, and a cloud infrastructure.

[0313] The terminal uses speech recognition software to acquire the voices of the elderly in real time. Specifically, the Google Cloud Speech-to-Text API is used to convert the user's voice into text data. As a result, the elderly can record the content in digital form while having a natural conversation.

[0314] The server receives text data acquired via cloud services and analyzes it using natural language processing technologies such as Amazon Comprehend. The server extracts relevant keywords related to emotions and health status from the conversation and uses them to derive indicators of the health status of older adults. Based on these indicators, it assesses the health status and generates alerts if problems are detected.

[0315] Furthermore, the server uploads the evaluation results to a cloud server to visualize the data and provides it to family members and healthcare professionals as regular reports. These reports allow stakeholders to understand the health status of the elderly and take prompt action as needed.

[0316] Furthermore, the device engages in natural conversations with elderly individuals through an AI-powered conversational agent. The agent aims to provide a sense of security by offering topics based on the elderly person's interests and concerns.

[0317] As a concrete example, if an elderly person speaks to the device and says, "I haven't been able to sleep lately," the AI ​​agent will provide advice on how to improve sleep. This information is then analyzed on a server, and if necessary, the system is set up to automatically notify family members.

[0318] An example of a prompt sentence would be, "Please provide a conversation example to determine how older adults are feeling these days and what health challenges they are facing."

[0319] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0320] Step 1:

[0321] The device inputs the user's voice via a microphone and converts it into digital data. The input is raw voice data, and the output is digital voice data. This data is then sent to a speech recognition API.

[0322] Step 2:

[0323] The device uses the Google Cloud Speech-to-Text API to convert digital audio data into text data. The input is digital audio data, and the output is text data. It calls the API and performs the conversion process.

[0324] Step 3:

[0325] The server receives text data on the cloud. The input is text data, and the server stores this data in a queue for analysis.

[0326] Step 4:

[0327] The server uses Amazon Comprehend to perform natural language processing on text data. The input is text data, and the output is keyword and metric data indicating emotions and health status. It analyzes the text and extracts the necessary information.

[0328] Step 5:

[0329] The server evaluates the health status of elderly individuals based on extracted indicator data and generates alerts if abnormalities are detected. The input is indicator data, and the output is health assessment data and alert data. The server executes an evaluation algorithm and performs the alert generation operation.

[0330] Step 6:

[0331] The server records evaluation results and alerts in a database and uploads them to the cloud server. Inputs are health evaluation data and alert data, and output is the recorded data. It performs write and upload operations.

[0332] Step 7:

[0333] The server periodically compiles evaluation results into a report and notifies relevant parties. The input is recorded data, and the output is report data. It performs data aggregation and report generation operations.

[0334] Step 8:

[0335] The device uses an AI agent to converse with elderly individuals, providing topics based on their interests and concerns. Input is the elderly person's responses and statements, and output is the generated conversation content. The user interacts with the agent.

[0336] 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.

[0337] This invention provides a system that evaluates the health status and emotions of elderly people from voice data obtained through their daily conversations. The system collects the user's voice input in real time and analyzes this data to understand both the user's health and emotions.

[0338] Device: To acquire audio data, the microphone activates when the user begins speaking, converting the speech into text. The device is equipped with high-precision speech recognition technology, enabling the user to continue the conversation naturally.

[0339] Server: Text data received from the terminal is analyzed by a natural language processing (NLP) engine and an emotion engine. The NLP engine identifies keywords and phrases indicating health status, and by adding the emotion engine's identification of emotional status to this, a more sophisticated health assessment can be performed. Based on this combined assessment, if an abnormality in health status or a change in emotion is detected, an appropriate alert is generated and notified to healthcare professionals or family members.

[0340] For example, if a user says, "I've been very tired lately and a little anxious," the NLP engine extracts the keywords "tired" and "anxiety," while the emotion engine identifies feelings of "anxiety" and "sadness" from the tone and content of the speech. This allows the system to provide an assessment that takes into account not only physical health but also emotional aspects.

[0341] Reporting and Interaction: The system regularly generates reports on the user's health and emotions and provides them to family members and healthcare professionals. These reports include the user's overall health status, emotional trends, and suggestions for lifestyle improvements based on these trends. The AI ​​agent encourages ongoing dialogue with the user by engaging their interests through conversation and suggesting recommended behaviors and lifestyle habits.

[0342] This embodiment of the invention enables comprehensive monitoring of the health and emotions of elderly individuals, allowing for prompt responses and helping to reduce feelings of loneliness and anxiety.

[0343] The following describes the processing flow.

[0344] Step 1:

[0345] The device uses a microphone to collect the voices that elderly people speak on a daily basis and converts them into text using a speech recognition system. This process is performed in real time, allowing elderly people to continue conversations naturally.

[0346] Step 2:

[0347] The device sends the converted text data to the server using a secure protocol. During this process, the data is encrypted, ensuring privacy is protected.

[0348] Step 3:

[0349] The server feeds the received text data into a natural language processing (NLP) engine to extract health-related keywords and phrases. This creates indicators that show specific health conditions.

[0350] Step 4:

[0351] The server then uses an emotion engine to analyze the user's emotional state from the text data. This engine identifies emotions from the content and tone of the conversation and classifies them as positive, negative, or neutral.

[0352] Step 5:

[0353] The server integrates the health assessment results from the NLP engine with the emotion analysis results from the emotion engine. This allows for a comprehensive understanding of the user's overall health and emotional state.

[0354] Step 6:

[0355] The server generates an alert if an anomaly is detected or if a persistent change in emotions is observed. The alert is promptly notified to healthcare workers and family members so that necessary actions can be taken.

[0356] Step 7:

[0357] The server generates regular health and emotional reports based on the analysis results. These reports include specific advice and trend analyses to promote a healthy lifestyle and are sent to family members and healthcare professionals.

[0358] Step 8:

[0359] The AI ​​agent installed in the device presents conversation topics to the user based on their interests and concerns. Through these conversations, users can gain a sense of security and receive emotional and healthy support.

[0360] (Example 2)

[0361] 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".

[0362] In modern society, there is a need to appropriately monitor and promptly respond to the physical and mental health of the elderly. However, conventional methods make it difficult to accurately grasp the emotional state and daily changes of the elderly, and it is difficult to provide effective support to alleviate feelings of isolation and anxiety. Therefore, there is a need for a system that comprehensively evaluates the health and emotions of the elderly through voice data and enables appropriate intervention.

[0363] 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.

[0364] In this invention, the server includes means for collecting user speech data using voice input and obtaining a language record; means for analyzing the language record via natural language processing and identifying indicators indicating health status; and means for evaluating the health status and emotional status based on the extracted indicators and generating a warning signal accordingly. This enables real-time evaluation of the health status and emotional status of elderly people and allows for quick and accurate responses.

[0365] "Voice input" refers to an input method for acquiring voice data, and includes a device for detecting sound, such as a microphone.

[0366] "Users" refers to individuals who use this system, including, in particular, those targeted for health and emotional monitoring, especially the elderly.

[0367] "Speech data" refers to voice information obtained from the user, and includes text data generated based on that voice information.

[0368] "Language recording" refers to text-formatted data obtained by analyzing audio data, and it records the content of the user's conversation.

[0369] "Natural language processing" is a technique used by computers to analyze text data and has the ability to interpret the grammar and meaning of various languages.

[0370] An "indicator" refers to standard data or information used to evaluate a user's health status.

[0371] A "warning signal" is notification data generated when an abnormality is detected in the user's health condition, and it is a signal intended to prompt relevant parties to take prompt action.

[0372] "Communication methods" refer to the infrastructure and technologies used to transmit information such as evaluation results and warnings to external parties, and include email, applications, and other digital communications.

[0373] "Emotional state" is an indicator that quantifies the user's psychological state and represents a specific emotion (such as anxiety, happiness, or sadness).

[0374] This invention provides a system for comprehensively evaluating a user's health and emotional state using voice data. The following describes a specific implementation of this system.

[0375] Acquisition of audio data

[0376] When a user begins a conversation, the device uses its built-in microphone to acquire voice data. This device runs high-precision speech recognition software, such as "Speech Recognition Engine X." The voice data is converted to text in real time.

[0377] Data Analysis

[0378] Text data generated on the terminal is sent to the server via encrypted communication. On the server, a natural language processing (NLP) engine first receives and analyzes this data. The "Natural Language Processing Engine Y" is used for analysis, extracting keywords and phrases that indicate health status.

[0379] Next, the server uses an emotion engine to identify the user's emotional state from the tone and context of the text. For example, the "Emotion Analysis Engine Z" is useful in this process. This allows the user's physical and emotional state to be evaluated.

[0380] Notifications and reports

[0381] The server immediately generates a warning signal if it detects any abnormalities in the user's health or emotional state. This information is then communicated to healthcare professionals or family members via means such as email or mobile applications.

[0382] The system also periodically generates reports based on these evaluation results and distributes them to all relevant stakeholders. These reports detail the user's health trends, emotional changes, and recommended lifestyle improvements.

[0383] Specific examples and prompt statements

[0384] For example, if a user says, "Recently, I've been experiencing pain when I breathe," the device can capture this utterance, analyze it on the server, and generate an alert prompting appropriate action. An example of a prompt to input to the generating AI model would be, "Based on conversational data of elderly people, analyze the current situation and generate suggestions for improvement."

[0385] This system allows for comprehensive, real-time management of the health and emotions of the elderly, thereby enhancing support for care and medical services.

[0386] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0387] Step 1:

[0388] The device acquires voice data through the microphone when the user begins a conversation. When the voice input reaches the device's sensor, voice recognition software converts it into text data. Specifically, a filtering process is performed to remove noise, resulting in clear text from the voice signal. The input is the user's voice data, and the output is the converted text data.

[0389] Step 2:

[0390] The terminal sends the text data converted from the voice to the server. At this stage, the text data is encrypted to enhance security. Specifically, the terminal uses the Internet Protocol to packetize the data and forward it to the server's receiving module. The input is filtered text data, and the output is secure transmission data to the server.

[0391] Step 3:

[0392] The server passes the received text data to a natural language processing engine. The engine extracts health check keywords, performs contextual analysis, and generates health status indicators. Specifically, a keyword matching algorithm is applied. The input is the text data sent to the server, and the output is the extracted health indicators.

[0393] Step 4:

[0394] The server then uses an emotion engine to analyze the emotional state of the text. Positive and negative emotions are identified during this process. For example, emotion words such as "happy" and "anxious" are referenced from the database. The input is text data containing health indicators, and the output is the analyzed emotional state.

[0395] Step 5:

[0396] If an abnormality is detected in the user's health or emotional state, the server generates an alert. The warning signal is then prepared to notify family members or healthcare professionals. Specifically, an email sending queue is processed based on a configured list. The input is the evaluation data that detected the anomaly, and the output is the generated notification alert.

[0397] Step 6:

[0398] The server generates periodic reports, recording changes in users' health and emotions. These reports include trend data and improvement suggestions. Specifically, historical data is extracted from the database and graphed using analytical software. The input is regularly updated evaluation data, and the output is the generated report.

[0399] (Application Example 2)

[0400] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0401] In the daily lives of the elderly, there is a challenge in efficiently monitoring changes in their health and emotions, detecting abnormalities early, and taking appropriate action. Furthermore, there is a need to alleviate feelings of loneliness and anxiety through continuous dialogue based on the user's interests and concerns.

[0402] 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.

[0403] In this invention, the server includes means for collecting user voice data via voice input and acquiring conversation data; means for analyzing the conversation data using natural language processing and extracting indicators of health status; and means for evaluating the health status based on the extracted indicators and information including emotional state, and generating alerts according to the evaluation results. This enables comprehensive monitoring of the health and emotions of elderly people on a daily basis and allows for prompt responses.

[0404] "Voice input" is a technology that collects and processes the voice spoken by the user in digital format.

[0405] "Conversation data" refers to text-formatted data obtained from voice input, and is used to analyze the user's intentions and state of mind.

[0406] "Natural language processing" is a technology that enables computers to understand and analyze natural language. It involves converting audio data into text data and then analyzing its meaning.

[0407] "Health status indicators" are specific keywords or phrases that indicate the user's physical and mental health, and are extracted using natural language processing.

[0408] "Emotional state" refers to a numerical or categorical representation of a user's emotions, and is the user's mood or feelings inferred from the tone and content of their conversation.

[0409] A "generative AI model" is a system that uses machine learning algorithms to learn patterns from data and provides generative content and recommendations.

[0410] "Feedback" refers to information provided to users based on analysis results and evaluations, intended to encourage user behavior.

[0411] The system of this invention evaluates a user's health status and emotions in real time through their everyday conversations. To implement this invention, a terminal for collecting voice data, a server for analyzing the voice data, and a generative AI model are mainly required.

[0412] A suitable device is one with voice input capabilities, such as a smartphone, where the microphone automatically activates when the user begins speaking. This device uses the Google Cloud Speech-to-Text API to convert the speech into text data. The converted text data is immediately sent to a server in the cloud.

[0413] The server uses a natural language processing engine, such as Amazon Comprehend, to analyze the transmitted text data and extract indicators of health status and emotional states. Microsoft Azure's Sentiment Analysis API complements this sentiment analysis. Generative AI models are further leveraged to generate proactive suggestions for the user. Based on historical data and general health information, the AI ​​models provide feedback on recommended behaviors and lifestyles to the user.

[0414] As a concrete example, suppose a user says to their smartphone, "My back has been hurting since last night, and I couldn't sleep well." In this case, the system identifies the health indicator "back pain" and the possible anxiety of "not being able to sleep," and based on this, it starts the process of sending alerts to family members or healthcare professionals. The AI ​​may also suggest simple stretches to the user.

[0415] An example of a prompt sentence to input into a generative AI model is: "Explain how to evaluate the user's health and emotions based on text data extracted from their voice input, and generate appropriate alerts."

[0416] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0417] Step 1:

[0418] The device activates the microphone when it detects user speech. It collects audio data in real time and converts it to text data using the Google Cloud Speech-to-Text API. The input for this step is the user's voice, and the output is the converted text data. Speech recognition technology is used to convert the audio signal into a stable text format.

[0419] Step 2:

[0420] The server receives text data sent from the terminal. Next, it uses Amazon Comprehend to perform natural language processing, extracting keywords from the text data that indicate health status and emotions. The input for this step is the received text data, and the output is the extracted keywords and information about emotions. The text content is then analyzed, and data processing is performed to measure important health indicators and emotional states.

[0421] Step 3:

[0422] The server uses Microsoft Azure's Sentiment Analysis API to evaluate the emotional state of the extracted keywords. This identifies changes in the user's mood and potential fluctuations in their mental state. The input for this step is the previously extracted set of keywords, and the output is the emotional evaluation associated with those keywords. The sentiment analysis engine quantifies the tone of the text.

[0423] Step 4:

[0424] The server utilizes a generative AI model to generate behavioral guidance and lifestyle suggestions based on the user's health and emotions. The input for this step is evaluated health indicators and emotional states, while the output is specific actions and advice suggested by the AI. Model learning is used to generate appropriate feedback from personal data and similar past cases.

[0425] Step 5:

[0426] The server compiles the assessment results and generated actions, and notifies family members and healthcare professionals as needed. The inputs for this step are the user's assessment results and suggested actions, and the output is notification data. An appropriate communication method is selected to send information reflecting the user's current status to third parties in a timely manner.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] [Third Embodiment]

[0431] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0432] 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.

[0433] 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).

[0434] 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.

[0435] 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.

[0436] 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).

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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.

[0441] 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.

[0442] 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".

[0443] This invention is a system that evaluates the health status of elderly people by acquiring their daily conversations from voice input and analyzing that data. Elderly people can speak naturally using a dedicated terminal, and their voice is converted to text in real time.

[0444] Device: This device uses speech recognition technology to capture the everyday conversations of elderly people and converts the audio data into text data. This allows elderly people to continue conversations smoothly without feeling any inconvenience.

[0445] Server: Receives text data acquired from terminals and analyzes it using natural language processing (NLP) technology. The analysis extracts keywords indicating emotions and health status from the conversation content, and this information is then processed statistically and evaluated using evaluation algorithms to become health indicators. For example, if an elderly person mentions specific symptoms such as "I haven't been able to sleep lately" or "I have no appetite," these words are used as keywords to determine if there is a possibility of a health problem.

[0446] Based on the analysis results, the health status is assessed, and if an abnormality is detected, the server promptly generates an alert. This alert is sent to healthcare professionals, and necessary actions are taken.

[0447] The server also has a function to periodically compile information on the health status of elderly individuals and provide it to their families as a report. The report includes details of health changes and lifestyle inferred from conversations, making it easier for families to understand the elderly person's situation.

[0448] Interaction with the Agent: The device is powered by an artificial intelligence agent that provides engaging topics for seniors. Users can interact with the agent based on their interests and receive advice related to daily life and health in the process. For example, the AI ​​may ask questions about the weather or hobbies, and seniors can respond accordingly, resulting in a relaxed conversation.

[0449] By implementing this invention, health management for the elderly can become more efficient and effective, and in addition, feelings of loneliness can be reduced.

[0450] The following describes the processing flow.

[0451] Step 1:

[0452] The device starts voice input when the user begins speaking and collects voice data in real time. It then uses speech recognition technology to convert the voice into text format.

[0453] Step 2:

[0454] The device sends the converted text data to the server using a secure protocol. The data is encrypted during transmission for privacy protection.

[0455] Step 3:

[0456] The server processes the received text data using a natural language processing (NLP) engine. The analysis extracts keywords and phrases related to health status from the conversation content.

[0457] Step 4:

[0458] The server uses the extracted keywords to assess health status. It processes the data based on a health assessment algorithm to determine normal and abnormal values.

[0459] Step 5:

[0460] The server generates an alert if an anomaly is detected based on the evaluation results. The generated alert is sent to healthcare professionals via email or app.

[0461] Step 6:

[0462] The server periodically generates health status reports based on conversation data and sends them to the family. The reports include information about health status and changes in lifestyle.

[0463] Step 7:

[0464] The AI ​​agent installed in the device provides topics tailored to the user's interests and concerns, and asks appropriate questions to the elderly to facilitate continuous interaction with them.

[0465] Step 8:

[0466] Through conversations with the AI ​​agent, users can freely express their thoughts on everyday events and health-related matters, and continue the dialogue in an enjoyable way.

[0467] (Example 1)

[0468] 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."

[0469] In modern society, managing the health of the elderly is a crucial issue, but monitoring their health through natural conversations in daily life and providing timely medical intervention is difficult. Furthermore, many elderly people experience feelings of loneliness, and methods to alleviate this are needed. Considering these challenges, a system is required to safely and efficiently manage the health of the elderly and improve their quality of life.

[0470] 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.

[0471] In this invention, the server includes means for collecting user acoustic data using acoustic input and acquiring dialogue data, means for analyzing the dialogue data using natural language processing and extracting indicators of health status, and means for evaluating the health status based on the extracted indicators and generating warnings according to the evaluation results. This makes it possible to monitor the health status of elderly people in real time through natural conversations in their daily lives and to quickly notify medical professionals when necessary. Furthermore, the use of an intelligent agent in dialogue can also help reduce feelings of loneliness.

[0472] "Audio input" refers to a technology that converts voice and sound into digital data, and its role is to capture the user's voice and incorporate it into the system.

[0473] "Dialogue data" refers to information obtained from conversations with users, recorded in a structured format, and used for analyzing the content of their statements.

[0474] "Natural language processing" is a technology that enables computers to understand human language and recognize its meaning. It is a process of scrutinizing the grammar and content of text data to extract meaning.

[0475] "Health status indicators" are data points and keywords used to evaluate the user's health, which are used to determine abnormalities or situations requiring attention.

[0476] A "warning" is a notification issued by the system when it detects an abnormality in a person's health condition, and is generated to quickly communicate this information to healthcare professionals and caregivers.

[0477] An "intelligent agent" is a program that uses artificial intelligence technology to interact with the user and provide information based on their interests and concerns, contributing to the promotion of dialogue and the reduction of feelings of loneliness.

[0478] "Text data" refers to data in text format that has been converted to record speech in an analyzable form, and is used in natural language processing.

[0479] The following describes embodiments for carrying out this invention. The system aims to monitor the health status of elderly people and improve their quality of life. The system mainly includes acoustic input, analysis of conversational data, assessment of health status, generation and notification of warnings, and interaction by an intelligent agent.

[0480] Terminal:

[0481] The terminal is equipped with a high-performance microphone to acquire the voice of elderly people using acoustic input. The acquired voice is converted into text data using speech recognition technology. For this purpose, open-source speech recognition systems are used as the speech recognition software. This text data converted from the voice is sent to the server as conversation data.

[0482] server:

[0483] The server processes the received text data using natural language processing tools to extract indicators that may reflect the user's health status. This natural language processing utilizes open-source natural language processing libraries, among others. Based on these indicators, the server evaluates the user's health status and, if necessary, generates warnings and notifies others. Furthermore, by utilizing an intelligent agent, the server provides topics tailored to the interests and concerns of the elderly through everyday conversations. An open-source AI chatbot platform is used for the intelligent agent. This allows the elderly to enjoy conversations in a relaxed state, contributing to a reduction in feelings of loneliness.

[0484] User:

[0485] Elderly users can interact with an intelligent agent through the device. For example, if an elderly person says to the device, "I haven't been sleeping well lately," the content is converted into text data and sent to the server. The server analyzes the results, which are then used as an indicator of their health status, and warnings are sent to healthcare professionals as needed.

[0486] Examples of specific cases and prompt statements:

[0487] For example, if an elderly person asks the device, "What's the weather like today?", the intelligent agent might respond, "It's sunny today. It might be nice to go for a walk outside." This expands the conversation and improves the user's quality of life.

[0488] An example of a prompt for a generative AI model might be: "From this statement, select words that could potentially affect the user's health and provide advice based on them."

[0489] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0490] Step 1:

[0491] The device acquires audio data from the elderly person's surroundings via a microphone. The input is audio from the external environment, and this audio is transmitted to the speech recognition engine in real time. The speech recognition engine performs data processing, analyzing the audio data and converting it into corresponding text. The output is text data.

[0492] Step 2:

[0493] The server receives text data sent from the terminal. The input is text data in string format, and the server uses a natural language processing library to perform grammatical interpretation and semantic analysis. This process identifies keywords related to emotions and behaviors, and the output is the analyzed emotion data and a keyword list.

[0494] Step 3:

[0495] The server inputs the analyzed sentiment data and keyword list into an evaluation algorithm. This algorithm performs data calculations that quantify the health status of elderly individuals based on the keywords and generate warnings if abnormal values ​​are detected. The output is a health evaluation score and, if necessary, a warning message.

[0496] Step 4:

[0497] The server securely stores the generated health assessment scores and warning messages and notifies healthcare professionals and family members using appropriate communication methods. The input is the score and warning message generated in the previous step, and the output is the notification message delivered to healthcare professionals and family members.

[0498] Step 5:

[0499] Users engage in everyday conversations through an intelligent agent. The agent provides appropriate topics and advice based on user input. Input is the user's speech, and output is the agent's response message. This interaction engages the interest of elderly individuals and provides a relaxing environment.

[0500] (Application Example 1)

[0501] 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."

[0502] In elderly care, a key challenge is to promptly and accurately monitor the health status of the elderly and, when necessary, to quickly inform medical professionals and family members. Furthermore, providing means of communication that allow the elderly to live their daily lives with peace of mind and without feeling isolated is also crucial.

[0503] 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.

[0504] In this invention, the server includes means for collecting user voice data via voice input and acquiring conversation data, means for analyzing the conversation data using natural language processing and extracting indicators of health status, and means for uploading the evaluation results to a cloud server and providing the data as a report by visualizing it. This makes it easier to monitor the daily health of the elderly, and enables healthcare professionals and family members to detect abnormalities early and intervene. In addition, friendly dialogue using artificial intelligence enables psychological support for the elderly.

[0505] "Voice input" is a technology that converts a user's verbal instructions or everyday conversations into digital data.

[0506] "Conversation data" refers to a collection of information that is converted from voice input into text and then analyzed.

[0507] "Natural language processing" is a technology that enables computers to analyze, understand, and generate language that humans use on a daily basis.

[0508] "Health status indicators" are indicators that express a user's health status using numerical values ​​or categories.

[0509] An "alert" is a warning message issued to prompt a quick response when an abnormality is detected in the user's health condition.

[0510] A "report" is a document that summarizes the results of an assessment of the user's health status and is used to provide information to relevant parties.

[0511] Artificial intelligence is a technology that gives machines the ability to think, learn, and improve themselves like humans.

[0512] "Friendly dialogue" refers to a form of communication in which the machine responds appropriately so that users can converse naturally and with peace of mind.

[0513] A "cloud server" is a remote computing service that stores and processes data via the internet.

[0514] "Visualization" is a technique for representing data in an easily understandable format.

[0515] The system implementing this invention mainly consists of a voice input terminal, a server, and a cloud infrastructure.

[0516] The device utilizes speech recognition software to capture the elderly person's voice in real time. Specifically, it uses the Google Cloud Speech-to-Text API to convert the user's voice into text data. This allows the elderly person to have a natural conversation while recording the content in digital format.

[0517] The server receives text data acquired via cloud services and analyzes it using natural language processing technologies such as Amazon Comprehend. The server extracts relevant keywords related to emotions and health status from the conversation and uses them to derive indicators of the health status of older adults. Based on these indicators, it assesses the health status and generates alerts if problems are detected.

[0518] Furthermore, the server uploads the evaluation results to a cloud server to visualize the data and provides it to family members and healthcare professionals as regular reports. These reports allow stakeholders to understand the health status of the elderly and take prompt action as needed.

[0519] Furthermore, the device engages in natural conversations with elderly individuals through an AI-powered conversational agent. The agent aims to provide a sense of security by offering topics based on the elderly person's interests and concerns.

[0520] As a concrete example, if an elderly person speaks to the device and says, "I haven't been able to sleep lately," the AI ​​agent will provide advice on how to improve sleep. This information is then analyzed on a server, and if necessary, the system is set up to automatically notify family members.

[0521] An example of a prompt sentence would be, "Please provide a conversation example to determine how older adults are feeling these days and what health challenges they are facing."

[0522] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0523] Step 1:

[0524] The device inputs the user's voice via a microphone and converts it into digital data. The input is raw voice data, and the output is digital voice data. This data is then sent to a speech recognition API.

[0525] Step 2:

[0526] The device uses the Google Cloud Speech-to-Text API to convert digital audio data into text data. The input is digital audio data, and the output is text data. It calls the API and performs the conversion process.

[0527] Step 3:

[0528] The server receives text data on the cloud. The input is text data, and the server stores this data in a queue for analysis.

[0529] Step 4:

[0530] The server uses Amazon Comprehend to perform natural language processing on text data. The input is text data, and the output is keyword and metric data indicating emotions and health status. It analyzes the text and extracts the necessary information.

[0531] Step 5:

[0532] The server evaluates the health status of elderly individuals based on extracted indicator data and generates alerts if abnormalities are detected. The input is indicator data, and the output is health assessment data and alert data. The server executes an evaluation algorithm and performs the alert generation operation.

[0533] Step 6:

[0534] The server records evaluation results and alerts in a database and uploads them to the cloud server. Inputs are health evaluation data and alert data, and output is the recorded data. It performs write and upload operations.

[0535] Step 7:

[0536] The server periodically compiles evaluation results into a report and notifies relevant parties. The input is recorded data, and the output is report data. It performs data aggregation and report generation operations.

[0537] Step 8:

[0538] The device uses an AI agent to converse with elderly individuals, providing topics based on their interests and concerns. Input is the elderly person's responses and statements, and output is the generated conversation content. The user interacts with the agent.

[0539] 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.

[0540] This invention provides a system that evaluates the health status and emotions of elderly people from voice data obtained through their daily conversations. The system collects the user's voice input in real time and analyzes this data to understand both the user's health and emotions.

[0541] Device: To acquire audio data, the microphone activates when the user begins speaking, converting the speech into text. The device is equipped with high-precision speech recognition technology, enabling the user to continue the conversation naturally.

[0542] Server: Text data received from the terminal is analyzed by a natural language processing (NLP) engine and an emotion engine. The NLP engine identifies keywords and phrases indicating health status, and by adding the emotion engine's identification of emotional status to this, a more sophisticated health assessment can be performed. Based on this combined assessment, if an abnormality in health status or a change in emotion is detected, an appropriate alert is generated and notified to healthcare professionals or family members.

[0543] For example, if a user says, "I've been very tired lately and a little anxious," the NLP engine extracts the keywords "tired" and "anxiety," while the emotion engine identifies feelings of "anxiety" and "sadness" from the tone and content of the speech. This allows the system to provide an assessment that takes into account not only physical health but also emotional aspects.

[0544] Reporting and Interaction: The system regularly generates reports on the user's health and emotions and provides them to family members and healthcare professionals. These reports include the user's overall health status, emotional trends, and suggestions for lifestyle improvements based on these trends. The AI ​​agent encourages ongoing dialogue with the user by engaging their interests through conversation and suggesting recommended behaviors and lifestyle habits.

[0545] This embodiment of the invention enables comprehensive monitoring of the health and emotions of elderly individuals, allowing for prompt responses and helping to reduce feelings of loneliness and anxiety.

[0546] The following describes the processing flow.

[0547] Step 1:

[0548] The device uses a microphone to collect the voices that elderly people speak on a daily basis and converts them into text using a speech recognition system. This process is performed in real time, allowing elderly people to continue conversations naturally.

[0549] Step 2:

[0550] The device sends the converted text data to the server using a secure protocol. During this process, the data is encrypted, ensuring privacy is protected.

[0551] Step 3:

[0552] The server feeds the received text data into a natural language processing (NLP) engine to extract health-related keywords and phrases. This creates indicators that show specific health conditions.

[0553] Step 4:

[0554] The server then uses an emotion engine to analyze the user's emotional state from the text data. This engine identifies emotions from the content and tone of the conversation and classifies them as positive, negative, or neutral.

[0555] Step 5:

[0556] The server integrates the health assessment results from the NLP engine with the emotion analysis results from the emotion engine. This allows for a comprehensive understanding of the user's overall health and emotional state.

[0557] Step 6:

[0558] The server generates an alert if an anomaly is detected or if a persistent change in emotions is observed. The alert is promptly notified to healthcare workers and family members so that necessary actions can be taken.

[0559] Step 7:

[0560] The server generates regular health and emotional reports based on the analysis results. These reports include specific advice and trend analyses to promote a healthy lifestyle and are sent to family members and healthcare professionals.

[0561] Step 8:

[0562] The AI ​​agent installed in the device presents conversation topics to the user based on their interests and concerns. Through these conversations, users can gain a sense of security and receive emotional and healthy support.

[0563] (Example 2)

[0564] 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."

[0565] In modern society, there is a need to appropriately monitor and promptly respond to the physical and mental health of the elderly. However, conventional methods make it difficult to accurately grasp the emotional state and daily changes of the elderly, and it is difficult to provide effective support to alleviate feelings of isolation and anxiety. Therefore, there is a need for a system that comprehensively evaluates the health and emotions of the elderly through voice data and enables appropriate intervention.

[0566] 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.

[0567] In this invention, the server includes means for collecting user speech data using voice input and obtaining a language record; means for analyzing the language record via natural language processing and identifying indicators indicating health status; and means for evaluating the health status and emotional status based on the extracted indicators and generating a warning signal accordingly. This enables real-time evaluation of the health status and emotional status of elderly people and allows for quick and accurate responses.

[0568] "Voice input" refers to an input method for acquiring voice data, and includes a device for detecting sound, such as a microphone.

[0569] "Users" refers to individuals who use this system, including, in particular, those targeted for health and emotional monitoring, especially the elderly.

[0570] "Speech data" refers to voice information obtained from the user, and includes text data generated based on that voice information.

[0571] "Language recording" refers to text-formatted data obtained by analyzing audio data, and it records the content of the user's conversation.

[0572] "Natural language processing" is a technique used by computers to analyze text data and has the ability to interpret the grammar and meaning of various languages.

[0573] An "indicator" refers to standard data or information used to evaluate a user's health status.

[0574] A "warning signal" is notification data generated when an abnormality is detected in the user's health condition, and it is a signal intended to prompt relevant parties to take prompt action.

[0575] "Communication methods" refer to the infrastructure and technologies used to transmit information such as evaluation results and warnings to external parties, and include email, applications, and other digital communications.

[0576] "Emotional state" is an indicator that quantifies the user's psychological state and represents a specific emotion (such as anxiety, happiness, or sadness).

[0577] This invention provides a system for comprehensively evaluating a user's health and emotional state using voice data. The following describes a specific implementation of this system.

[0578] Acquisition of audio data

[0579] When a user begins a conversation, the device uses its built-in microphone to acquire voice data. This device runs high-precision speech recognition software, such as "Speech Recognition Engine X." The voice data is converted to text in real time.

[0580] Data Analysis

[0581] Text data generated on the terminal is sent to the server via encrypted communication. On the server, a natural language processing (NLP) engine first receives and analyzes this data. The "Natural Language Processing Engine Y" is used for analysis, extracting keywords and phrases that indicate health status.

[0582] Next, the server uses an emotion engine to identify the user's emotional state from the tone and context of the text. For example, the "Emotion Analysis Engine Z" is useful in this process. This allows the user's physical and emotional state to be evaluated.

[0583] Notifications and reports

[0584] The server immediately generates a warning signal if it detects any abnormalities in the user's health or emotional state. This information is then communicated to healthcare professionals or family members via means such as email or mobile applications.

[0585] The system also periodically generates reports based on these evaluation results and distributes them to all relevant stakeholders. These reports detail the user's health trends, emotional changes, and recommended lifestyle improvements.

[0586] Specific examples and prompt statements

[0587] For example, if a user says, "Recently, I've been experiencing pain when I breathe," the device can capture this utterance, analyze it on the server, and generate an alert prompting appropriate action. An example of a prompt to input to the generating AI model would be, "Based on conversational data of elderly people, analyze the current situation and generate suggestions for improvement."

[0588] This system allows for comprehensive, real-time management of the health and emotions of the elderly, thereby enhancing support for care and medical services.

[0589] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0590] Step 1:

[0591] The device acquires voice data through the microphone when the user begins a conversation. When the voice input reaches the device's sensor, voice recognition software converts it into text data. Specifically, a filtering process is performed to remove noise, resulting in clear text from the voice signal. The input is the user's voice data, and the output is the converted text data.

[0592] Step 2:

[0593] The terminal sends the text data converted from the voice to the server. At this stage, the text data is encrypted to enhance security. Specifically, the terminal uses the Internet Protocol to packetize the data and forward it to the server's receiving module. The input is filtered text data, and the output is secure transmission data to the server.

[0594] Step 3:

[0595] The server passes the received text data to a natural language processing engine. The engine extracts health check keywords, performs contextual analysis, and generates health status indicators. Specifically, a keyword matching algorithm is applied. The input is the text data sent to the server, and the output is the extracted health indicators.

[0596] Step 4:

[0597] The server then uses an emotion engine to analyze the emotional state of the text. Positive and negative emotions are identified during this process. For example, emotion words such as "happy" and "anxious" are referenced from the database. The input is text data containing health indicators, and the output is the analyzed emotional state.

[0598] Step 5:

[0599] If an abnormality is detected in the user's health or emotional state, the server generates an alert. The warning signal is then prepared to notify family members or healthcare professionals. Specifically, an email sending queue is processed based on a configured list. The input is the evaluation data that detected the anomaly, and the output is the generated notification alert.

[0600] Step 6:

[0601] The server generates periodic reports, recording changes in users' health and emotions. These reports include trend data and improvement suggestions. Specifically, historical data is extracted from the database and graphed using analytical software. The input is regularly updated evaluation data, and the output is the generated report.

[0602] (Application Example 2)

[0603] 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."

[0604] In the daily lives of the elderly, there is a challenge in efficiently monitoring changes in their health and emotions, detecting abnormalities early, and taking appropriate action. Furthermore, there is a need to alleviate feelings of loneliness and anxiety through continuous dialogue based on the user's interests and concerns.

[0605] 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.

[0606] In this invention, the server includes means for collecting user voice data via voice input and acquiring conversation data; means for analyzing the conversation data using natural language processing and extracting indicators of health status; and means for evaluating the health status based on the extracted indicators and information including emotional state, and generating alerts according to the evaluation results. This enables comprehensive monitoring of the health and emotions of elderly people on a daily basis and allows for prompt responses.

[0607] "Voice input" is a technology that collects and processes the voice spoken by the user in digital format.

[0608] "Conversation data" refers to text-formatted data obtained from voice input, and is used to analyze the user's intentions and state of mind.

[0609] "Natural language processing" is a technology that enables computers to understand and analyze natural language. It involves converting audio data into text data and then analyzing its meaning.

[0610] "Health status indicators" are specific keywords or phrases that indicate the user's physical and mental health, and are extracted using natural language processing.

[0611] "Emotional state" refers to a numerical or categorical representation of a user's emotions, and is the user's mood or feelings inferred from the tone and content of their conversation.

[0612] A "generative AI model" is a system that uses machine learning algorithms to learn patterns from data and provides generative content and recommendations.

[0613] "Feedback" refers to information provided to users based on analysis results and evaluations, intended to encourage user behavior.

[0614] The system of this invention evaluates a user's health status and emotions in real time through their everyday conversations. To implement this invention, a terminal for collecting voice data, a server for analyzing the voice data, and a generative AI model are mainly required.

[0615] A suitable device is one with voice input capabilities, such as a smartphone, where the microphone automatically activates when the user begins speaking. This device uses the Google Cloud Speech-to-Text API to convert the speech into text data. The converted text data is immediately sent to a server in the cloud.

[0616] The server uses a natural language processing engine, such as Amazon Comprehend, to analyze the transmitted text data and extract indicators of health status and emotional states. Microsoft Azure's Sentiment Analysis API complements this sentiment analysis. Generative AI models are further leveraged to generate proactive suggestions for the user. Based on historical data and general health information, the AI ​​models provide feedback on recommended behaviors and lifestyles to the user.

[0617] As a concrete example, suppose a user says to their smartphone, "My back has been hurting since last night, and I couldn't sleep well." In this case, the system identifies the health indicator "back pain" and the possible anxiety of "not being able to sleep," and based on this, it starts the process of sending alerts to family members or healthcare professionals. The AI ​​may also suggest simple stretches to the user.

[0618] An example of a prompt sentence to input into a generative AI model is: "Explain how to evaluate the user's health and emotions based on text data extracted from their voice input, and generate appropriate alerts."

[0619] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0620] Step 1:

[0621] The device activates the microphone when it detects user speech. It collects audio data in real time and converts it to text data using the Google Cloud Speech-to-Text API. The input for this step is the user's voice, and the output is the converted text data. Speech recognition technology is used to convert the audio signal into a stable text format.

[0622] Step 2:

[0623] The server receives text data sent from the terminal. Next, it uses Amazon Comprehend to perform natural language processing, extracting keywords from the text data that indicate health status and emotions. The input for this step is the received text data, and the output is the extracted keywords and information about emotions. The text content is then analyzed, and data processing is performed to measure important health indicators and emotional states.

[0624] Step 3:

[0625] The server uses Microsoft Azure's Sentiment Analysis API to evaluate the emotional state of the extracted keywords. This identifies changes in the user's mood and potential fluctuations in their mental state. The input for this step is the previously extracted set of keywords, and the output is the emotional evaluation associated with those keywords. The sentiment analysis engine quantifies the tone of the text.

[0626] Step 4:

[0627] The server utilizes a generative AI model to generate behavioral guidance and lifestyle suggestions based on the user's health and emotions. The input for this step is evaluated health indicators and emotional states, while the output is specific actions and advice suggested by the AI. Model learning is used to generate appropriate feedback from personal data and similar past cases.

[0628] Step 5:

[0629] The server compiles the assessment results and generated actions, and notifies family members and healthcare professionals as needed. The inputs for this step are the user's assessment results and suggested actions, and the output is notification data. An appropriate communication method is selected to send information reflecting the user's current status to third parties in a timely manner.

[0630] 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.

[0631] 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.

[0632] 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.

[0633] [Fourth Embodiment]

[0634] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0635] 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.

[0636] 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).

[0637] 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.

[0638] 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.

[0639] 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).

[0640] 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.

[0641] 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.

[0642] 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.

[0643] 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.

[0644] 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.

[0645] 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.

[0646] 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".

[0647] This invention is a system that evaluates the health status of elderly people by acquiring their daily conversations from voice input and analyzing that data. Elderly people can speak naturally using a dedicated terminal, and their voice is converted to text in real time.

[0648] Device: This device uses speech recognition technology to capture the everyday conversations of elderly people and converts the audio data into text data. This allows elderly people to continue conversations smoothly without feeling any inconvenience.

[0649] Server: Receives text data acquired from terminals and analyzes it using natural language processing (NLP) technology. The analysis extracts keywords indicating emotions and health status from the conversation content, and this information is then processed statistically and evaluated using evaluation algorithms to become health indicators. For example, if an elderly person mentions specific symptoms such as "I haven't been able to sleep lately" or "I have no appetite," these words are used as keywords to determine if there is a possibility of a health problem.

[0650] Based on the analysis results, the health status is assessed, and if an abnormality is detected, the server promptly generates an alert. This alert is sent to healthcare professionals, and necessary actions are taken.

[0651] The server also has a function to periodically compile information on the health status of elderly individuals and provide it to their families as a report. The report includes details of health changes and lifestyle inferred from conversations, making it easier for families to understand the elderly person's situation.

[0652] Interaction with the Agent: The device is powered by an artificial intelligence agent that provides engaging topics for seniors. Users can interact with the agent based on their interests and receive advice related to daily life and health in the process. For example, the AI ​​may ask questions about the weather or hobbies, and seniors can respond accordingly, resulting in a relaxed conversation.

[0653] By implementing this invention, health management for the elderly can become more efficient and effective, and in addition, feelings of loneliness can be reduced.

[0654] The following describes the processing flow.

[0655] Step 1:

[0656] The device starts voice input when the user begins speaking and collects voice data in real time. It then uses speech recognition technology to convert the voice into text format.

[0657] Step 2:

[0658] The device sends the converted text data to the server using a secure protocol. The data is encrypted during transmission for privacy protection.

[0659] Step 3:

[0660] The server processes the received text data using a natural language processing (NLP) engine. The analysis extracts keywords and phrases related to health status from the conversation content.

[0661] Step 4:

[0662] The server uses the extracted keywords to assess health status. It processes the data based on a health assessment algorithm to determine normal and abnormal values.

[0663] Step 5:

[0664] The server generates an alert if an anomaly is detected based on the evaluation results. The generated alert is sent to healthcare professionals via email or app.

[0665] Step 6:

[0666] The server periodically generates health status reports based on conversation data and sends them to the family. The reports include information about health status and changes in lifestyle.

[0667] Step 7:

[0668] The AI ​​agent installed in the device provides topics tailored to the user's interests and concerns, and asks appropriate questions to the elderly to facilitate continuous interaction with them.

[0669] Step 8:

[0670] Through conversations with the AI ​​agent, users can freely express their thoughts on everyday events and health-related matters, and continue the dialogue in an enjoyable way.

[0671] (Example 1)

[0672] 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".

[0673] In modern society, managing the health of the elderly is a crucial issue, but monitoring their health through natural conversations in daily life and providing timely medical intervention is difficult. Furthermore, many elderly people experience feelings of loneliness, and methods to alleviate this are needed. Considering these challenges, a system is required to safely and efficiently manage the health of the elderly and improve their quality of life.

[0674] 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.

[0675] In this invention, the server includes means for collecting user acoustic data using acoustic input and acquiring dialogue data, means for analyzing the dialogue data using natural language processing and extracting indicators of health status, and means for evaluating the health status based on the extracted indicators and generating warnings according to the evaluation results. This makes it possible to monitor the health status of elderly people in real time through natural conversations in their daily lives and to quickly notify medical professionals when necessary. Furthermore, the use of an intelligent agent in dialogue can also help reduce feelings of loneliness.

[0676] "Audio input" refers to a technology that converts voice and sound into digital data, and its role is to capture the user's voice and incorporate it into the system.

[0677] "Dialogue data" refers to information obtained from conversations with users, recorded in a structured format, and used for analyzing the content of their statements.

[0678] "Natural language processing" is a technology that enables computers to understand human language and recognize its meaning. It is a process of scrutinizing the grammar and content of text data to extract meaning.

[0679] "Health status indicators" are data points and keywords used to evaluate the user's health, which are used to determine abnormalities or situations requiring attention.

[0680] A "warning" is a notification issued by the system when it detects an abnormality in a person's health condition, and is generated to quickly communicate this information to healthcare professionals and caregivers.

[0681] An "intelligent agent" is a program that uses artificial intelligence technology to interact with the user and provide information based on their interests and concerns, contributing to the promotion of dialogue and the reduction of feelings of loneliness.

[0682] "Text data" refers to data in text format that has been converted to record speech in an analyzable form, and is used in natural language processing.

[0683] The following describes embodiments for carrying out this invention. The system aims to monitor the health status of elderly people and improve their quality of life. The system mainly includes acoustic input, analysis of conversational data, assessment of health status, generation and notification of warnings, and interaction by an intelligent agent.

[0684] Terminal:

[0685] The terminal is equipped with a high-performance microphone to acquire the voice of elderly people using acoustic input. The acquired voice is converted into text data using speech recognition technology. For this purpose, open-source speech recognition systems are used as the speech recognition software. This text data converted from the voice is sent to the server as conversation data.

[0686] server:

[0687] The server processes the received text data using natural language processing tools to extract indicators that may reflect the user's health status. This natural language processing utilizes open-source natural language processing libraries, among others. Based on these indicators, the server evaluates the user's health status and, if necessary, generates warnings and notifies others. Furthermore, by utilizing an intelligent agent, the server provides topics tailored to the interests and concerns of the elderly through everyday conversations. An open-source AI chatbot platform is used for the intelligent agent. This allows the elderly to enjoy conversations in a relaxed state, contributing to a reduction in feelings of loneliness.

[0688] User:

[0689] Elderly users can interact with an intelligent agent through the device. For example, if an elderly person says to the device, "I haven't been sleeping well lately," the content is converted into text data and sent to the server. The server analyzes the results, which are then used as an indicator of their health status, and warnings are sent to healthcare professionals as needed.

[0690] Examples of specific cases and prompt statements:

[0691] For example, if an elderly person asks the device, "What's the weather like today?", the intelligent agent might respond, "It's sunny today. It might be nice to go for a walk outside." This expands the conversation and improves the user's quality of life.

[0692] An example of a prompt for a generative AI model might be: "From this statement, select words that could potentially affect the user's health and provide advice based on them."

[0693] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0694] Step 1:

[0695] The device acquires audio data from the elderly person's surroundings via a microphone. The input is audio from the external environment, and this audio is transmitted to the speech recognition engine in real time. The speech recognition engine performs data processing, analyzing the audio data and converting it into corresponding text. The output is text data.

[0696] Step 2:

[0697] The server receives text data sent from the terminal. The input is text data in string format, and the server uses a natural language processing library to perform grammatical interpretation and semantic analysis. This process identifies keywords related to emotions and behaviors, and the output is the analyzed emotion data and a keyword list.

[0698] Step 3:

[0699] The server inputs the analyzed sentiment data and keyword list into an evaluation algorithm. This algorithm performs data calculations that quantify the health status of elderly individuals based on the keywords and generate warnings if abnormal values ​​are detected. The output is a health evaluation score and, if necessary, a warning message.

[0700] Step 4:

[0701] The server securely stores the generated health assessment scores and warning messages and notifies healthcare professionals and family members using appropriate communication methods. The input is the score and warning message generated in the previous step, and the output is the notification message delivered to healthcare professionals and family members.

[0702] Step 5:

[0703] Users engage in everyday conversations through an intelligent agent. The agent provides appropriate topics and advice based on user input. Input is the user's speech, and output is the agent's response message. This interaction engages the interest of elderly individuals and provides a relaxing environment.

[0704] (Application Example 1)

[0705] 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".

[0706] In elderly care, a key challenge is to promptly and accurately monitor the health status of the elderly and, when necessary, to quickly inform medical professionals and family members. Furthermore, providing means of communication that allow the elderly to live their daily lives with peace of mind and without feeling isolated is also crucial.

[0707] 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.

[0708] In this invention, the server includes means for collecting user voice data via voice input and acquiring conversation data, means for analyzing the conversation data using natural language processing and extracting indicators of health status, and means for uploading the evaluation results to a cloud server and providing the data as a report by visualizing it. This makes it easier to monitor the daily health of the elderly, and enables healthcare professionals and family members to detect abnormalities early and intervene. In addition, friendly dialogue using artificial intelligence enables psychological support for the elderly.

[0709] "Voice input" is a technology that converts a user's verbal instructions or everyday conversations into digital data.

[0710] "Conversation data" refers to a collection of information that is converted from voice input into text and then analyzed.

[0711] "Natural language processing" is a technology that enables computers to analyze, understand, and generate language that humans use on a daily basis.

[0712] "Health status indicators" are indicators that express a user's health status using numerical values ​​or categories.

[0713] An "alert" is a warning message issued to prompt a quick response when an abnormality is detected in the user's health condition.

[0714] A "report" is a document that summarizes the results of an assessment of the user's health status and is used to provide information to relevant parties.

[0715] Artificial intelligence is a technology that gives machines the ability to think, learn, and improve themselves like humans.

[0716] "Friendly dialogue" refers to a form of communication in which the machine responds appropriately so that users can converse naturally and with peace of mind.

[0717] A "cloud server" is a remote computing service that stores and processes data via the internet.

[0718] "Visualization" is a technique for representing data in an easily understandable format.

[0719] The system implementing this invention mainly consists of a voice input terminal, a server, and a cloud infrastructure.

[0720] The device utilizes speech recognition software to capture the elderly person's voice in real time. Specifically, it uses the Google Cloud Speech-to-Text API to convert the user's voice into text data. This allows the elderly person to have a natural conversation while recording the content in digital format.

[0721] The server receives text data acquired via cloud services and analyzes it using natural language processing technologies such as Amazon Comprehend. The server extracts relevant keywords related to emotions and health status from the conversation and uses them to derive indicators of the health status of older adults. Based on these indicators, it assesses the health status and generates alerts if problems are detected.

[0722] Furthermore, the server uploads the evaluation results to a cloud server to visualize the data and provides it to family members and healthcare professionals as regular reports. These reports allow stakeholders to understand the health status of the elderly and take prompt action as needed.

[0723] Furthermore, the device engages in natural conversations with elderly individuals through an AI-powered conversational agent. The agent aims to provide a sense of security by offering topics based on the elderly person's interests and concerns.

[0724] As a concrete example, if an elderly person speaks to the device and says, "I haven't been able to sleep lately," the AI ​​agent will provide advice on how to improve sleep. This information is then analyzed on a server, and if necessary, the system is set up to automatically notify family members.

[0725] An example of a prompt sentence would be, "Please provide a conversation example to determine how older adults are feeling these days and what health challenges they are facing."

[0726] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0727] Step 1:

[0728] The device inputs the user's voice via a microphone and converts it into digital data. The input is raw voice data, and the output is digital voice data. This data is then sent to a speech recognition API.

[0729] Step 2:

[0730] The device uses the Google Cloud Speech-to-Text API to convert digital audio data into text data. The input is digital audio data, and the output is text data. It calls the API and performs the conversion process.

[0731] Step 3:

[0732] The server receives text data on the cloud. The input is text data, and the server stores this data in a queue for analysis.

[0733] Step 4:

[0734] The server uses Amazon Comprehend to perform natural language processing on text data. The input is text data, and the output is keyword and metric data indicating emotions and health status. It analyzes the text and extracts the necessary information.

[0735] Step 5:

[0736] The server evaluates the health status of elderly individuals based on extracted indicator data and generates alerts if abnormalities are detected. The input is indicator data, and the output is health assessment data and alert data. The server executes an evaluation algorithm and performs the alert generation operation.

[0737] Step 6:

[0738] The server records evaluation results and alerts in a database and uploads them to the cloud server. Inputs are health evaluation data and alert data, and output is the recorded data. It performs write and upload operations.

[0739] Step 7:

[0740] The server periodically compiles evaluation results into a report and notifies relevant parties. The input is recorded data, and the output is report data. It performs data aggregation and report generation operations.

[0741] Step 8:

[0742] The device uses an AI agent to converse with elderly individuals, providing topics based on their interests and concerns. Input is the elderly person's responses and statements, and output is the generated conversation content. The user interacts with the agent.

[0743] 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.

[0744] This invention provides a system that evaluates the health status and emotions of elderly people from voice data obtained through their daily conversations. The system collects the user's voice input in real time and analyzes this data to understand both the user's health and emotions.

[0745] Device: To acquire audio data, the microphone activates when the user begins speaking, converting the speech into text. The device is equipped with high-precision speech recognition technology, enabling the user to continue the conversation naturally.

[0746] Server: Text data received from the terminal is analyzed by a natural language processing (NLP) engine and an emotion engine. The NLP engine identifies keywords and phrases indicating health status, and by adding the emotion engine's identification of emotional status to this, a more sophisticated health assessment can be performed. Based on this combined assessment, if an abnormality in health status or a change in emotion is detected, an appropriate alert is generated and notified to healthcare professionals or family members.

[0747] For example, if a user says, "I've been very tired lately and a little anxious," the NLP engine extracts the keywords "tired" and "anxiety," while the emotion engine identifies feelings of "anxiety" and "sadness" from the tone and content of the speech. This allows the system to provide an assessment that takes into account not only physical health but also emotional aspects.

[0748] Reporting and Interaction: The system regularly generates reports on the user's health and emotions and provides them to family members and healthcare professionals. These reports include the user's overall health status, emotional trends, and suggestions for lifestyle improvements based on these trends. The AI ​​agent encourages ongoing dialogue with the user by engaging their interests through conversation and suggesting recommended behaviors and lifestyle habits.

[0749] This embodiment of the invention enables comprehensive monitoring of the health and emotions of elderly individuals, allowing for prompt responses and helping to reduce feelings of loneliness and anxiety.

[0750] The following describes the processing flow.

[0751] Step 1:

[0752] The device uses a microphone to collect the voices that elderly people speak on a daily basis and converts them into text using a speech recognition system. This process is performed in real time, allowing elderly people to continue conversations naturally.

[0753] Step 2:

[0754] The device sends the converted text data to the server using a secure protocol. During this process, the data is encrypted, ensuring privacy is protected.

[0755] Step 3:

[0756] The server feeds the received text data into a natural language processing (NLP) engine to extract health-related keywords and phrases. This creates indicators that show specific health conditions.

[0757] Step 4:

[0758] The server then uses an emotion engine to analyze the user's emotional state from the text data. This engine identifies emotions from the content and tone of the conversation and classifies them as positive, negative, or neutral.

[0759] Step 5:

[0760] The server integrates the health assessment results from the NLP engine with the emotion analysis results from the emotion engine. This allows for a comprehensive understanding of the user's overall health and emotional state.

[0761] Step 6:

[0762] The server generates an alert if an anomaly is detected or if a persistent change in emotions is observed. The alert is promptly notified to healthcare workers and family members so that necessary actions can be taken.

[0763] Step 7:

[0764] The server generates regular health and emotional reports based on the analysis results. These reports include specific advice and trend analyses to promote a healthy lifestyle and are sent to family members and healthcare professionals.

[0765] Step 8:

[0766] The AI ​​agent installed in the device presents conversation topics to the user based on their interests and concerns. Through these conversations, users can gain a sense of security and receive emotional and healthy support.

[0767] (Example 2)

[0768] 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".

[0769] In modern society, there is a need to appropriately monitor and promptly respond to the physical and mental health of the elderly. However, conventional methods make it difficult to accurately grasp the emotional state and daily changes of the elderly, and it is difficult to provide effective support to alleviate feelings of isolation and anxiety. Therefore, there is a need for a system that comprehensively evaluates the health and emotions of the elderly through voice data and enables appropriate intervention.

[0770] 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.

[0771] In this invention, the server includes means for collecting user speech data using voice input and obtaining a language record; means for analyzing the language record via natural language processing and identifying indicators indicating health status; and means for evaluating the health status and emotional status based on the extracted indicators and generating a warning signal accordingly. This enables real-time evaluation of the health status and emotional status of elderly people and allows for quick and accurate responses.

[0772] "Voice input" refers to an input method for acquiring voice data, and includes a device for detecting sound, such as a microphone.

[0773] "Users" refers to individuals who use this system, including, in particular, those targeted for health and emotional monitoring, especially the elderly.

[0774] "Speech data" refers to voice information obtained from the user, and includes text data generated based on that voice information.

[0775] "Language recording" refers to text-formatted data obtained by analyzing audio data, and it records the content of the user's conversation.

[0776] "Natural language processing" is a technique used by computers to analyze text data and has the ability to interpret the grammar and meaning of various languages.

[0777] An "indicator" refers to standard data or information used to evaluate a user's health status.

[0778] A "warning signal" is notification data generated when an abnormality is detected in the user's health condition, and it is a signal intended to prompt relevant parties to take prompt action.

[0779] "Communication methods" refer to the infrastructure and technologies used to transmit information such as evaluation results and warnings to external parties, and include email, applications, and other digital communications.

[0780] "Emotional state" is an indicator that quantifies the user's psychological state and represents a specific emotion (such as anxiety, happiness, or sadness).

[0781] This invention provides a system for comprehensively evaluating a user's health and emotional state using voice data. The following describes a specific implementation of this system.

[0782] Acquisition of audio data

[0783] When a user begins a conversation, the device uses its built-in microphone to acquire voice data. This device runs high-precision speech recognition software, such as "Speech Recognition Engine X." The voice data is converted to text in real time.

[0784] Data Analysis

[0785] Text data generated on the terminal is sent to the server via encrypted communication. On the server, a natural language processing (NLP) engine first receives and analyzes this data. The "Natural Language Processing Engine Y" is used for analysis, extracting keywords and phrases that indicate health status.

[0786] Next, the server uses an emotion engine to identify the user's emotional state from the tone and context of the text. For example, the "Emotion Analysis Engine Z" is useful in this process. This allows the user's physical and emotional state to be evaluated.

[0787] Notifications and reports

[0788] The server immediately generates a warning signal if it detects any abnormalities in the user's health or emotional state. This information is then communicated to healthcare professionals or family members via means such as email or mobile applications.

[0789] The system also periodically generates reports based on these evaluation results and distributes them to all relevant stakeholders. These reports detail the user's health trends, emotional changes, and recommended lifestyle improvements.

[0790] Specific examples and prompt statements

[0791] For example, if a user says, "Recently, I've been experiencing pain when I breathe," the device can capture this utterance, analyze it on the server, and generate an alert prompting appropriate action. An example of a prompt to input to the generating AI model would be, "Based on conversational data of elderly people, analyze the current situation and generate suggestions for improvement."

[0792] This system allows for comprehensive, real-time management of the health and emotions of the elderly, thereby enhancing support for care and medical services.

[0793] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0794] Step 1:

[0795] The device acquires voice data through the microphone when the user begins a conversation. When the voice input reaches the device's sensor, voice recognition software converts it into text data. Specifically, a filtering process is performed to remove noise, resulting in clear text from the voice signal. The input is the user's voice data, and the output is the converted text data.

[0796] Step 2:

[0797] The terminal sends the text data converted from the voice to the server. At this stage, the text data is encrypted to enhance security. Specifically, the terminal uses the Internet Protocol to packetize the data and forward it to the server's receiving module. The input is filtered text data, and the output is secure transmission data to the server.

[0798] Step 3:

[0799] The server passes the received text data to a natural language processing engine. The engine extracts health check keywords, performs contextual analysis, and generates health status indicators. Specifically, a keyword matching algorithm is applied. The input is the text data sent to the server, and the output is the extracted health indicators.

[0800] Step 4:

[0801] The server then uses an emotion engine to analyze the emotional state of the text. Positive and negative emotions are identified during this process. For example, emotion words such as "happy" and "anxious" are referenced from the database. The input is text data containing health indicators, and the output is the analyzed emotional state.

[0802] Step 5:

[0803] If an abnormality is detected in the user's health or emotional state, the server generates an alert. The warning signal is then prepared to notify family members or healthcare professionals. Specifically, an email sending queue is processed based on a configured list. The input is the evaluation data that detected the anomaly, and the output is the generated notification alert.

[0804] Step 6:

[0805] The server generates periodic reports, recording changes in users' health and emotions. These reports include trend data and improvement suggestions. Specifically, historical data is extracted from the database and graphed using analytical software. The input is regularly updated evaluation data, and the output is the generated report.

[0806] (Application Example 2)

[0807] 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".

[0808] In the daily lives of the elderly, there is a challenge in efficiently monitoring changes in their health and emotions, detecting abnormalities early, and taking appropriate action. Furthermore, there is a need to alleviate feelings of loneliness and anxiety through continuous dialogue based on the user's interests and concerns.

[0809] 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.

[0810] In this invention, the server includes means for collecting user voice data via voice input and acquiring conversation data; means for analyzing the conversation data using natural language processing and extracting indicators of health status; and means for evaluating the health status based on the extracted indicators and information including emotional state, and generating alerts according to the evaluation results. This enables comprehensive monitoring of the health and emotions of elderly people on a daily basis and allows for prompt responses.

[0811] "Voice input" is a technology that collects and processes the voice spoken by the user in digital format.

[0812] "Conversation data" refers to text-formatted data obtained from voice input, and is used to analyze the user's intentions and state of mind.

[0813] "Natural language processing" is a technology that enables computers to understand and analyze natural language. It involves converting audio data into text data and then analyzing its meaning.

[0814] "Health status indicators" are specific keywords or phrases that indicate the user's physical and mental health, and are extracted using natural language processing.

[0815] "Emotional state" refers to a numerical or categorical representation of a user's emotions, and is the user's mood or feelings inferred from the tone and content of their conversation.

[0816] A "generative AI model" is a system that uses machine learning algorithms to learn patterns from data and provides generative content and recommendations.

[0817] "Feedback" refers to information provided to users based on analysis results and evaluations, intended to encourage user behavior.

[0818] The system of this invention evaluates a user's health status and emotions in real time through their everyday conversations. To implement this invention, a terminal for collecting voice data, a server for analyzing the voice data, and a generative AI model are mainly required.

[0819] A suitable device is one with voice input capabilities, such as a smartphone, where the microphone automatically activates when the user begins speaking. This device uses the Google Cloud Speech-to-Text API to convert the speech into text data. The converted text data is immediately sent to a server in the cloud.

[0820] The server uses a natural language processing engine, such as Amazon Comprehend, to analyze the transmitted text data and extract indicators of health status and emotional states. Microsoft Azure's Sentiment Analysis API complements this sentiment analysis. Generative AI models are further leveraged to generate proactive suggestions for the user. Based on historical data and general health information, the AI ​​models provide feedback on recommended behaviors and lifestyles to the user.

[0821] As a concrete example, suppose a user says to their smartphone, "My back has been hurting since last night, and I couldn't sleep well." In this case, the system identifies the health indicator "back pain" and the possible anxiety of "not being able to sleep," and based on this, it starts the process of sending alerts to family members or healthcare professionals. The AI ​​may also suggest simple stretches to the user.

[0822] An example of a prompt sentence to input into a generative AI model is: "Explain how to evaluate the user's health and emotions based on text data extracted from their voice input, and generate appropriate alerts."

[0823] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0824] Step 1:

[0825] The device activates the microphone when it detects user speech. It collects audio data in real time and converts it to text data using the Google Cloud Speech-to-Text API. The input for this step is the user's voice, and the output is the converted text data. Speech recognition technology is used to convert the audio signal into a stable text format.

[0826] Step 2:

[0827] The server receives text data sent from the terminal. Next, it uses Amazon Comprehend to perform natural language processing, extracting keywords from the text data that indicate health status and emotions. The input for this step is the received text data, and the output is the extracted keywords and information about emotions. The text content is then analyzed, and data processing is performed to measure important health indicators and emotional states.

[0828] Step 3:

[0829] The server uses Microsoft Azure's Sentiment Analysis API to evaluate the emotional state of the extracted keywords. This identifies changes in the user's mood and potential fluctuations in their mental state. The input for this step is the previously extracted set of keywords, and the output is the emotional evaluation associated with those keywords. The sentiment analysis engine quantifies the tone of the text.

[0830] Step 4:

[0831] The server utilizes a generative AI model to generate behavioral guidance and lifestyle suggestions based on the user's health and emotions. The input for this step is evaluated health indicators and emotional states, while the output is specific actions and advice suggested by the AI. Model learning is used to generate appropriate feedback from personal data and similar past cases.

[0832] Step 5:

[0833] The server compiles the assessment results and generated actions, and notifies family members and healthcare professionals as needed. The inputs for this step are the user's assessment results and suggested actions, and the output is notification data. An appropriate communication method is selected to send information reflecting the user's current status to third parties in a timely manner.

[0834] 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.

[0835] 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.

[0836] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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."

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] The following is further disclosed regarding the embodiments described above.

[0856] (Claim 1)

[0857] A means of collecting user voice data via voice input and obtaining conversation data,

[0858] A means for analyzing the aforementioned conversation data using natural language processing and extracting indicators of health status,

[0859] A means of evaluating health status based on extracted indicators and generating alerts according to the evaluation results,

[0860] A system including means for notifying a third party of the evaluation results and generated alerts using an appropriate communication method.

[0861] (Claim 2)

[0862] The system according to claim 1, comprising means for periodically compiling conversational data related to the assessment of a user's health status and providing it to a third party as a report.

[0863] (Claim 3)

[0864] The system according to claim 1, comprising means for facilitating continuous dialogue with users and providing topics that are in line with the users' interests and concerns.

[0865] "Example 1"

[0866] (Claim 1)

[0867] A means of collecting user acoustic data using acoustic input and obtaining dialogue data,

[0868] A means for analyzing the aforementioned dialogue data using natural language processing and extracting indicators of health status,

[0869] A means for evaluating health status based on extracted indicators and generating warnings according to the evaluation results,

[0870] A means for notifying others of the evaluation results and the generated warnings using appropriate communication means,

[0871] A means of using an intelligent agent to facilitate user interaction and provide topics based on the user's interests,

[0872] A means for instantly converting the aforementioned acoustic data into text data,

[0873] A system that includes this.

[0874] (Claim 2)

[0875] The system according to claim 1, comprising means for periodically compiling dialogue data related to the assessment of a user's health status and providing it to others as a report.

[0876] (Claim 3)

[0877] The system according to claim 1, comprising means for generating dynamic input sentences to support the evaluation of health status indicators using a generative artificial intelligence model.

[0878] "Application Example 1"

[0879] (Claim 1)

[0880] A means of collecting user voice data via voice input and obtaining conversation data,

[0881] A means for analyzing the aforementioned conversation data using natural language processing and extracting indicators of health status,

[0882] A means of evaluating health status based on extracted indicators and generating alerts according to the evaluation results,

[0883] A means for notifying a third party of the evaluation results and generated alerts using appropriate communication technology,

[0884] One method involves uploading the evaluation results to a cloud server and providing them as a report by visualizing the data.

[0885] A means of engaging in friendly conversation with users using artificial intelligence and providing topics that alleviate feelings of loneliness,

[0886] A system that includes this.

[0887] (Claim 2)

[0888] The system according to claim 1, comprising means for periodically compiling conversational data related to the assessment of a user's health status and providing it to a third party as a report.

[0889] (Claim 3)

[0890] The system according to claim 1, comprising means for promoting continuous dialogue with users, providing topics that match the users' interests and concerns, and sharing information related to their health status.

[0891] "Example 2 of combining an emotion engine"

[0892] (Claim 1)

[0893] A device that collects user speech data using voice input and obtains a language record,

[0894] A device that analyzes the aforementioned language record via natural language processing and identifies indicators of health status,

[0895] A device that evaluates health and emotional states based on extracted indicators and generates warning signals accordingly,

[0896] A system including a device that notifies an external party of the evaluation results and the generated warning signals using predetermined communication means.

[0897] (Claim 2)

[0898] The system according to claim 1, comprising a device for periodically compiling verbal records concerning the health and emotional state of users and providing them externally as documents.

[0899] (Claim 3)

[0900] The system according to claim 1, comprising a device that facilitates long-term dialogue with users and generates and provides topics that are tailored to the user's interests and concerns.

[0901] "Application example 2 when combining with an emotional engine"

[0902] (Claim 1)

[0903] A means of collecting user voice data via voice input and obtaining conversation data,

[0904] A means for analyzing the aforementioned conversation data using natural language processing and extracting indicators of health status,

[0905] A means for evaluating health status based on extracted indicators and information including emotional state, and for generating alerts according to the evaluation results,

[0906] Means for notifying a third party of the evaluation results and generated alerts using an appropriate communication method,

[0907] A means of monitoring changes in the user's emotions and health status in real time and displaying continuous feedback on a smart device,

[0908] A means of providing users with recommended behaviors and lifestyle habits using generative AI models,

[0909] A system that includes this.

[0910] (Claim 2)

[0911] The system according to claim 1, comprising means for periodically compiling conversational data related to the assessment of a user's health status and providing it to a third party as a report.

[0912] (Claim 3)

[0913] The system according to claim 1, comprising means for facilitating continuous dialogue with users and providing topics tailored to the user's interests and concerns using a generative AI model. [Explanation of Symbols]

[0914] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting user voice data via voice input and obtaining conversation data, A means for analyzing the aforementioned conversation data using natural language processing and extracting indicators of health status, A means of evaluating health status based on extracted indicators and generating alerts according to the evaluation results, A means for notifying a third party of the evaluation results and generated alerts using appropriate communication technology, One method involves uploading the evaluation results to a cloud server and providing them as a report by visualizing the data. A means of engaging in friendly conversation with users using artificial intelligence and providing topics that alleviate feelings of loneliness, A system that includes this.

2. The system according to claim 1, comprising means for periodically compiling conversation data related to the assessment of the user's health status and providing it to a third party as a report.

3. The system according to claim 1, comprising means for promoting continuous dialogue with users, providing topics that match the users' interests and concerns, and sharing information related to their health status.

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