system

Abstract

Provide a system. 【Solution means】 Means for converting the user's voice input into text information by a recognition device, Means for storing the converted text information in an information storage system and setting a notification for playing the information, Means for notifying the user of the time table based on the notified time, An analysis device for analyzing the user's emotions from daily conversations, A device for monitoring the trend of the analyzed emotion data and detecting abnormalities, A device for notifying when an emotion abnormality is detected, A device for creating a report based on the time table information and the emotion analysis result during a specific period and providing it to an external device via a communication device, Means for operating the above functions using a mobile terminal, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, there is a need for support in the daily life of the elderly. However, in many cases, there is a problem that it is difficult for family members living apart to grasp the living situation and mental health of the elderly. Also, due to forgetfulness and difficulty in schedule management on the part of the elderly themselves, it may interfere with their daily life. Forgetting to take medicine and overlooking important schedules also frequently occur, so there is a need to provide a system that comprehensively manages these and enables smooth communication with family members.

Means for Solving the Problems

[0005] This invention facilitates the daily schedule management of the elderly by providing a means for acquiring schedule information from a user's voice and converting it into text data using speech recognition technology. It also includes a means for storing the converted text data in a database and setting reminders, preventing important appointments from being overlooked by notifying the user according to the reminder's scheduled time. Furthermore, by combining an emotion analysis means for identifying emotions from everyday conversation with a means for monitoring trends in emotion data, it realizes a function that notifies external parties when an abnormal change in the elderly person's mental health is detected. In addition, it provides a system that makes it easier for families to understand the elderly person's living situation by creating a report based on schedule information and emotion analysis results and sending it to family members via communication means.

[0006] "User" refers to elderly people who use this system in their daily lives.

[0007] "Vocalization" refers to the act of a user verbally communicating schedules or appointments.

[0008] "Schedule information" refers to schedules, actions to be taken, and events that users communicate to the system.

[0009] "Speech recognition technology" refers to technology used to convert received speech into text data.

[0010] "Text data" refers to character information converted using speech recognition technology.

[0011] A "database" refers to a storage system for saving text data and schedule information.

[0012] A "reminder" refers to a notification function that informs users of upcoming events.

[0013] "Emotional analysis techniques" refer to technologies used to identify a user's emotional state from their everyday conversations.

[0014] "Monitoring trends" refers to the process of tracking changes in temporal emotions and detecting anomalies.

[0015] "Report" refers to a report created based on schedule information and the results of sentiment analysis.

[0016] "Communication means" refers to a medium method or technology for transmitting data and reports externally.

Brief Explanation of Drawings

[0017] [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 Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the language used in the following description will be explained.

[0020] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0021] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] 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).

[0024] 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."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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".

[0038] This invention is an AI agent system for elderly support, which provides schedule management, sentiment analysis, and communication assistance to support the user's daily life. The following describes how the system is implemented.

[0039] Schedule management

[0040] The device receives the user's spoken schedule information as voice input and converts it into text data using speech recognition technology. For example, if the user says, "I will take my medicine at 10 AM tomorrow," this is saved as text data.

[0041] The server saves text data sent from the terminal to a database. The saved data is listed as a schedule, and reminders are set based on the specified time.

[0042] The server sends a notification to the device when the scheduled time for the reminder arrives.

[0043] The device provides voice notifications of reminders, informing the user, "It's 10 AM. It's time to take your medicine," thereby helping them to follow their schedule.

[0044] sentiment analysis

[0045] The device continuously receives the user's everyday conversations as voice input. For example, if the user says, "I'm feeling a little down today," this information is converted into text data.

[0046] The server receives the text data converted by speech recognition and performs sentiment analysis using machine learning.

[0047] Based on the results of sentiment analysis, the server monitors the user's emotional trends and identifies abnormal emotional changes from data over a certain period.

[0048] Report creation and notifications

[0049] The server aggregates schedule information and sentiment analysis results for a specified period and creates a detailed report.

[0050] The server will send this report to the user's family via communication means.

[0051] Based on the reports they receive, users (family members) can check the living situation and emotional state of the elderly person and communicate directly with them as needed.

[0052] This embodiment makes it possible to create an environment in which the invention can support elderly people in leading their daily lives with peace of mind, while simultaneously allowing families to closely monitor and provide support for the elderly person's living situation and emotional fluctuations, even when they are far away.

[0053] The following describes the processing flow.

[0054] Step 1:

[0055] When a user enters their schedule by voice, the device acquires the audio. The device then uses speech recognition technology to convert this audio into text data.

[0056] Step 2:

[0057] The terminal sends the converted text data to the server. The server stores the received schedule data in its database.

[0058] Step 3:

[0059] The server sets reminders based on saved schedules. When the set time arrives, the server sends a reminder notification to the device.

[0060] Step 4:

[0061] The device provides reminder notifications to the user as voice messages. For example, it might notify the user with something like, "It's 3 PM. It's time to take your medicine."

[0062] Step 5:

[0063] The device continuously receives the user's everyday conversations. This is then converted into text data using speech recognition technology.

[0064] Step 6:

[0065] The server performs sentiment analysis based on text data sent from the terminal. It uses machine learning models to identify sentiment labels.

[0066] Step 7:

[0067] The server aggregates the results of sentiment analysis and monitors emotional trends. If an abnormal emotional change is detected, it sends an alert to the family.

[0068] Step 8:

[0069] The server generates reports based on schedule information and sentiment data collected over a certain period.

[0070] Step 9:

[0071] The server sends the generated report to the family using a communication method. The family reviews the report and understands the user's current situation.

[0072] (Example 1)

[0073] 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."

[0074] There are challenges in reducing the cognitive burden that older adults face in their daily lives and facilitating remote monitoring and support of their living situations by family members living separately. To address this challenge, it is necessary to not only manage schedules and track emotional fluctuations, providing timely reminders, but also to detect emotional abnormalities and send appropriate information to family members.

[0075] 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.

[0076] In this invention, the server includes means for acquiring schedule information from the user's voice and converting it into text information using voice processing technology, means for storing the converted text information in a storage device and setting up notifications, and means for monitoring trends in analyzed emotional information and detecting anomalies. This enables the daily schedule management and emotional monitoring of the user, allowing family members to effectively support the health and safety of the elderly even from a distance.

[0077] "User" refers to an individual who uses this system, including elderly people and others who require support in their daily lives.

[0078] "Vocalization" refers to the act of a user providing information or instructions verbally.

[0079] "Schedule information" refers to information about daily activities and events that users need to register in the system.

[0080] "Audio processing technology" refers to the technology that converts audio into digital data and makes it a format that can be processed by machines.

[0081] "Textual information" refers to data in text format converted using speech processing technology.

[0082] A "memory device" refers to a device or system used to store digital information.

[0083] "Notification" refers to the act of informing a user of specific information or a reminder, and can be done visually or audibly.

[0084] "Emotional information" refers to data about the user's emotional state, including results analyzed from everyday conversations.

[0085] "Trend" refers to changes or patterns in data that occur over time.

[0086] "Abnormal" refers to changes in emotions or behavior that deviate from the normal range.

[0087] A "memory device" refers to a device or system used to store data and information.

[0088] A "server" refers to a computer system that provides services to other computers via a network.

[0089] This invention is an AI agent system aimed at supporting the elderly, providing technology to assist users in their daily lives. It primarily includes voice-input-based schedule management, sentiment analysis, and notification functions based on the results.

[0090] The user provides their schedule, spoken in natural language, as voice input to the system's terminal. The terminal uses speech recognition technology—for example, speech processing software—to convert the speech into text. This converted data is sent to a server and securely stored in a database.

[0091] The server uses this text information to set a reminder. Based on the reminder setting, the device will notify the user by voice at the specified time. For example, if the user says, "Take my medicine at 10 AM tomorrow," this information is transcribed into text, stored on the server, and the reminder will be activated at the appropriate time.

[0092] Furthermore, the sentiment analysis function, which identifies the user's emotions in everyday conversation, is implemented using a machine learning model. This model utilizes common natural language processing libraries to analyze the user's emotional state and aggregates the results on a server.

[0093] If emotional changes are abnormal, the server immediately generates an alert and notifies the user and their registered family members. This information serves as an important tool for families to support elderly individuals remotely.

[0094] As a concrete example, the following prompt can be considered as input to a generative AI model: "Please explain how the AI ​​agent for elderly care provides notifications regarding daily schedules and sentiment analysis." This prompt clarifies how each function of the system interacts with each other and contributes to supporting the user.

[0095] This system allows users to receive proactive support while reducing their cognitive burden in daily life, and enables family members living far away to understand the user's situation in real time and take appropriate action as needed.

[0096] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0097] Step 1:

[0098] The user speaks their daily schedule aloud. The device inputs this audio via the microphone and converts it to text using speech recognition software. This process converts the audio data into text data. The output is the schedule information stored by the device in text format.

[0099] Step 2:

[0100] The terminal sends the converted text data to the server using a communication protocol. The server records the received text data in a database. Through this data processing, the schedule information is accumulated and managed in a stored state. As output, a user schedule record is formed and stored in the database.

[0101] Step 3:

[0102] The server sets reminders based on schedule information stored in the database. It calculates the notification timing based on the specified date and time. The server then creates a reminder event based on this calculation and sends instructions to the terminal as needed. The output is the event data for which the reminder was set.

[0103] Step 4:

[0104] At the designated time, the device follows instructions from the server and notifies the user with a voice reminder. Specifically, it plays a message such as "It's 10 AM. It's time to take your medicine" through its built-in speaker. The output is a voice notification provided to the user.

[0105] Step 5:

[0106] The user's everyday conversations are input as voice by the device and converted to text in real time. The server uses this data to evaluate the user's emotional state using an emotion analysis model. This data processing calculates the emotions contained in the user's statements (e.g., happy, sad). The output is the analyzed emotion data.

[0107] Step 6:

[0108] The server tracks the analyzed sentiment data over time, monitoring changes and trends in emotions. If an abnormal change is detected, the server immediately generates an alert and notifies the user and their family. The output of this process is the alert notification that is sent.

[0109] Step 7:

[0110] The server aggregates schedule information and sentiment analysis results for a specified period and generates a detailed report. The report is formatted as a PDF or Excel file and sent to family members via communication. This allows family members to receive the generated report and understand the user's situation.

[0111] (Application Example 1)

[0112] 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."

[0113] Managing daily schedules and providing emotional support are crucial for the lives of the elderly, and it is especially important for family members living far away to be aware of their situation and provide appropriate support. However, it is a significant burden for the elderly themselves to report their situation in detail every time, and it is often difficult for family members to directly monitor them. Therefore, a system is needed that naturally collects information in daily life and uses that information to accurately understand and support the elderly's situation.

[0114] 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.

[0115] In this invention, the server includes means for converting user voice input into text information using a recognition device, means for storing the converted text information in an information storage system and setting up notifications for playing back the information, and means for notifying the user of the timetable based on the notification setting time. This makes it possible to manage schedules and understand emotions in a way that is integrated into the user's daily life. Furthermore, since these functions can be operated using a mobile terminal, family members can easily check on the elderly person's situation from a distance and communicate as needed.

[0116] "Voice input" is a method of recognizing the words or voices spoken by the user as digital data and providing that information to the system.

[0117] A "recognition device" is hardware or software that collects data in various formats and converts it into an interpretable form.

[0118] "Text information" refers to information such as audio and video that is represented as character data and is in a format that can be processed and stored by machines.

[0119] An "information storage system" is a structure and technology for securely and efficiently storing data and retrieving it as needed.

[0120] "Notification settings" refer to the system preparations and configurations for delivering information to users according to specified times and conditions.

[0121] A "timetable" is a tool for managing schedules, which organizes and displays appointments and activities based on time and date.

[0122] "Emotional analysis" is a technique for inferring a user's emotional state from their statements and behavior, and then quantifying or categorizing it.

[0123] An "analysis device" is a machine or program that analyzes input data and extracts specific information or patterns.

[0124] A "mobile device" is a portable, personally usable electronic device with communication capabilities that allows for calls, messaging, and application execution.

[0125] This invention is a system for managing schedules and analyzing emotions in daily life, and it functions in conjunction with a mobile device and a server.

[0126] Mobile devices have a voice input function, allowing users to communicate schedules and emotions by speaking. This voice data is converted into text information using the Google® Cloud Speech-to-Text API. This text information is then sent to a server and stored in an information storage system.

[0127] The server stores the received text information in a database as schedule information and sets reminders. Based on these reminder settings, the mobile device notifies the user according to the timetable.

[0128] For sentiment analysis, the server uses machine learning algorithms such as scikit-learn to analyze the user's emotions from their statements. The analysis results are used to monitor the user's emotional trends, and if an anomaly is detected, the family is notified. Furthermore, reports are periodically generated from the collected schedule information and sentiment analysis results and sent to family members in remote locations via communication means.

[0129] For example, if a user voice-inputs "Take my medicine at 3 PM," that information is converted to text and added to the schedule. The device then sends a reminder at the appointed time. Furthermore, if a user says something like "I'm feeling a little anxious today," an emotion analysis algorithm analyzes that emotion, and if the anxiety persists for several days, it notifies the family. This entire process allows family members to understand the elderly person's situation and provide appropriate support, even when they are far away.

[0130] Examples of prompt statements for generative AI models include:

[0131] "My mother seems unwell lately; could you please provide any new information?"

[0132] Examples include, "Check your next appointment and set reminders for important dates."

[0133] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0134] Step 1:

[0135] The device acquires the user's speech as voice input. This voice data becomes the input. Using the Google Cloud Speech-to-Text API, this voice data is converted into text data to generate text information. The output is text information.

[0136] Step 2:

[0137] The terminal sends the text information generated in step 1 to the server. The server receives this text data and stores it in the information storage system. The input here is text information, and its storage in the database is the output.

[0138] Step 3:

[0139] The server analyzes the stored text information and interprets it as schedule information. Based on this interpreted data, it sets reminders. In this process, the input is the stored text information, and the output is the reminder setting information.

[0140] Step 4:

[0141] Based on the reminder settings, the device will notify the user at the specified time. The input is the reminder setting information, and the output is the notification to the user.

[0142] Step 5:

[0143] The server analyzes emotions based on accumulated text data. Here, a machine learning algorithm using scikit-learn analyzes the input text data and outputs the user's emotional state.

[0144] Step 6:

[0145] The server monitors the analyzed user sentiment data as a trend. It stores the results in case anomalies are detected within the data over a certain period. The input is sentiment data, and the output is information regarding the presence or absence of anomalies.

[0146] Step 7:

[0147] If an anomaly is detected, the server will notify the family using external communication methods. The server will then compile the notification content and send it. The input is the anomaly information, and the output is the notification to the family.

[0148] Step 8:

[0149] The server generates a detailed report based on schedule information and sentiment analysis results for a specified period. The inputs are schedule information and sentiment data, and the output is a report to be sent to the family.

[0150] Step 9:

[0151] Users can operate this system via their mobile devices and obtain information by entering prompt messages. Specific prompt messages include "Check my next appointment" and "Tell me about my recent emotional tendencies." The input is the prompt message, and the output is the requested information.

[0152] 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.

[0153] This invention is an AI agent system for elderly support that combines an emotion engine, comprehensively providing schedule management, emotion recognition, and communication support for users in their daily lives. The following describes a specific implementation of this system.

[0154] Schedule management and reminder notifications

[0155] The terminal accepts schedule input from the user via voice and converts it into text data using voice recognition technology. For example, information such as "I have a hospital appointment at 10 AM tomorrow" is entered into the terminal.

[0156] The server stores this text data in a database and sets up reminders based on the set time.

[0157] The server sends notification information to the device when the set time has arrived.

[0158] The device will notify the user via voice to help them remember their schedule. For example, it might notify them, "It's 10 AM. You have a hospital appointment."

[0159] Emotion recognition by an emotion engine

[0160] The device captures the user's everyday conversations in both audio and video formats. It also analyzes facial expressions and tone of voice.

[0161] The emotion engine analyzes this acquired data to identify the user's emotional state in real time. For example, if a user smiles and says in a calm voice, "It's a nice day today," the emotion engine recognizes emotions such as "joy" and "calmness."

[0162] Processing and utilizing emotional data

[0163] The server monitors emotional trends by comparing the emotional data received from the emotion engine with past data.

[0164] The server sends an alert to family members if it detects any unusual emotional changes, such as a sudden increase in the frequency of "anxiety" or "sadness."

[0165] Report creation and notifications

[0166] The server aggregates schedule completion status and sentiment analysis results to create a report.

[0167] The server will send this report to the family via communication means.

[0168] Users (family members) can understand the living conditions and mental health status of elderly individuals through the reports.

[0169] This embodiment allows the system to provide multifaceted support for the user's daily life and create an environment where the elderly and their families can live with greater peace of mind. Furthermore, the introduction of an emotion engine enhances the user's emotional support, enabling a safer and more fulfilling life.

[0170] The following describes the processing flow.

[0171] Step 1:

[0172] The user speaks their schedule into the device, saying, "Take a walk at 2 PM." The device captures this audio and converts it into text data using speech recognition technology.

[0173] Step 2:

[0174] The device sends the converted text data to the server. The server receives it and stores it in its database. It also sets a reminder for "2 PM".

[0175] Step 3:

[0176] When the set time arrives, the server sends a reminder to the device. The device then notifies the user by voice, "It's 2 PM. It's time for a walk."

[0177] Step 4:

[0178] When a user engages in everyday conversation, the device acquires audio and video data in real time. For example, it captures a scene where the user smiles and says, "It was a fun day."

[0179] Step 5:

[0180] The emotion engine analyzes acquired data to recognize the user's emotions. It determines emotions such as "joy" or "peace" from facial expressions and tone of voice.

[0181] Step 6:

[0182] The terminal sends the analysis results to the server. The server monitors the sentiment data as a trend and performs analysis over a certain period.

[0183] Step 7:

[0184] When the server detects an unusual change in emotional state, it sends an alert to the family via email or other means. The alert may include a message such as, "You've been experiencing increased anxiety lately."

[0185] Step 8:

[0186] The server aggregates key daily events and sentiment analysis results to create a report. This includes information on behavioral history and sentiment trends.

[0187] Step 9:

[0188] The generated report is sent to the family via communication means. The user (family member) can review the report and understand the living conditions of the elderly person living separately.

[0189] (Example 2)

[0190] 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".

[0191] There is a need to alleviate the complexities of schedule management and the difficulties in understanding the mental health of users, including the elderly, that they face in their daily lives. Conventional systems simply notify users of their schedules, but they cannot properly understand and manage the associated emotional fluctuations, and therefore cannot provide sufficient support to maintain users' mental health. This creates a challenge in creating an environment where users and their families can live with peace of mind.

[0192] 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.

[0193] In this invention, the server includes means for acquiring schedule data from the user's voice information and converting it into string information using acoustic processing technology; means for storing the converted string information in a storage device and setting schedule notifications; means for notifying the user of the schedule information by voice according to the set notification time; means for emotional analysis that recognizes the user's emotions from everyday conversation; means for analyzing the recognized emotional data and detecting anomalies by comparing it with past data; means for notifying an external party when an abnormal emotional change is detected; and means for creating a report based on the schedule data and emotional analysis results for a day or a predetermined period and transmitting it externally via communication means. This makes it possible to centrally manage the user's schedule management and emotional trends, and to support them in living a safer and more secure life.

[0194] A "user" refers to any individual who uses this system, including any person, such as the elderly.

[0195] "Audio information" refers to the acoustic signals of speech or conversation generated by the user, and processing is performed based on these signals.

[0196] "Schedule data" refers to information about activities and events related to specific future dates and times, provided by the user through voice information.

[0197] "Audio processing technology" refers to the technology used to convert audio information into digital string information.

[0198] "String information" refers to text data converted from audio information, in a format that can be processed by digital devices.

[0199] A "storage device" refers to hardware or software used to store scheduled data or string information for long-term or short-term storage.

[0200] "Schedule notification" refers to a record or reminder that provides users with pre-set schedule information at the appropriate time.

[0201] "Emotion" refers to the state of mind and feelings judged from the user's everyday conversation, facial expressions, voice tone, etc.

[0202] "Emotional analysis means" refers to software or a device used to recognize a user's emotions, and includes techniques for analyzing the acquired data.

[0203] "Anomaly" refers to a state where the results of an analysis based on the user's emotional data show a change that exceeds the normal range.

[0204] A "report" is a document generated based on schedule data and emotional analysis results, and is provided to understand the user's living situation.

[0205] "Communication means" refers to methods or devices for transmitting reports to external devices or individuals.

[0206] This invention is an AI agent system that utilizes voice and emotion recognition to support the lives of users, including the elderly, and provides a safe and fulfilling life through schedule management and emotional analysis. The specific configuration and operation of the system are shown below.

[0207] The user communicates their schedule to the device via voice input. The device uses voice processing software (e.g., a voice recognition API) equipped with voice recognition technology to convert the voice into text information. This resulting schedule data is sent to a server and stored in its memory. The server sets reminders based on this schedule data and sends voice notifications to the user via the device at the appropriate time. Voice synthesis software (e.g., a voice synthesis API) is used for these voice notifications.

[0208] Furthermore, the device collects user conversations in audio and video format on a daily basis and uses emotional analysis tools to recognize emotions. This analysis utilizes software that employs generative AI models (e.g., an emotional analysis API) to identify the user's emotional state. This information is sent to a server, where it is further compared and analyzed with past data to monitor emotional trends. If an anomaly is detected, the server sends a notification to the family.

[0209] This system helps users and their families live with peace of mind by integrating schedule management and emotional state monitoring. Specifically, for example, if a user says, "I have a hospital appointment at 10 AM tomorrow," the schedule is automatically set, and if there is a change in emotions, an alert is sent to the family such as, "Recently, the user has been feeling lonely more frequently."

[0210] An example of a prompt to the generating AI model is, "If an elderly user has recently been showing a sad expression frequently, please provide specific suggestions for support or words to say to improve their mood." This input will generate a response.

[0211] In this way, the system aims to improve the quality of daily life by processing complex data such as user communication, schedules, and emotional states.

[0212] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0213] Step 1:

[0214] The user communicates their schedule via voice. The device receives this voice input and uses speech recognition technology to convert the voice data into text data. Specifically, if the user voice-inputs "I have a hospital appointment at 10 AM tomorrow," the device collects the voice and inputs it into a speech recognition API. The API analyzes the voice and outputs it as text data, "I have a hospital appointment at 10 AM tomorrow."

[0215] Step 2:

[0216] The terminal sends the generated text data to the server. The server stores the received text data in its storage device and sets up a reminder. Specifically, the text data "Hospital appointment at 10am tomorrow" is stored in the database along with the date and time information. Based on the stored information, the server creates a reminder and sets it to notify at the specified time. The information stored by the server in the database is referenced when setting up reminders.

[0217] Step 3:

[0218] Based on the set notification time, the server sends a reminder notification to the device. Upon receiving this notification, the device uses speech synthesis technology to inform the user verbally. Specifically, when the server sends the notification content "It's 10 AM. You have a hospital appointment" to the device, the device uses a speech synthesis API to convert it into speech and plays it back to the user.

[0219] Step 4:

[0220] The device captures the user's everyday conversations as audio and video. This data is used for emotion analysis. The device inputs the acquired audio and video data into an emotion analysis system and uses a generative AI model to recognize the user's emotions. Specifically, it inputs the user's voice saying "It's a nice day today" and their smiling face into an emotion analysis API and outputs a recognition result such as "joy."

[0221] Step 5:

[0222] Based on the emotional recognition results, the server compares them with past data to analyze emotional trends. If the server detects an abnormal change in emotion, it generates an alert and notifies an external individual, such as a family member. Specifically, if the server finds that "the frequency of anxiety has recently increased sharply," it will send this information as a message to the family.

[0223] Step 6:

[0224] The server creates a report based on daily or predetermined period schedule data and emotional analysis results, and transmits it externally via communication means. Specifically, the server compiles the schedule performance information and emotional analysis results in a table format, outputs it as a PDF file, and sends that file to the family via email.

[0225] (Application Example 2)

[0226] 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".

[0227] To support the lives of the elderly, it is necessary not only to manage their schedules but also to understand their mental health and provide an environment where they can live with peace of mind. However, currently, there is a lack of systems to adequately understand the emotions of the elderly and to respond quickly when problems occur. As a result, it is difficult for family members and caregivers to understand the condition of the elderly and to provide a supportive environment that ensures their safety.

[0228] 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.

[0229] In this invention, the server includes means for acquiring voice data and converting it into text data using voice recognition technology, means for storing the converted text data in a data storage device and setting up notifications, and means for observing the user's state in real time using a smart device and recording emotional trends. This makes it possible to support all aspects of the elderly's lives and provide real-time information that allows families and caregivers to live with peace of mind.

[0230] "Voice data" refers to information that records the user's speech, and serves as the basis for converting it into text data using speech recognition technology.

[0231] "Speech recognition technology" is a technology that extracts information from spoken words and converts it into text data.

[0232] "Text data" refers to a form of character information converted from audio data, which is suitable for management and analysis in information processing.

[0233] A "data storage device" is a device for storing data, accumulating text data and sentiment data in the form of a database.

[0234] A "notification" is information that is sent to the user at a set time or under set conditions, and is used for managing schedules and reporting anomalies.

[0235] A "smart device" is a portable information terminal connected to the internet, equipped with sensors that allow for real-time monitoring of the user's condition.

[0236] "Emotion discrimination means" refers to methods or devices for identifying and analyzing emotions from a user's voice and facial expressions.

[0237] A "learning model" is an algorithm used to learn patterns based on data and identify emotions or user states.

[0238] The system of this invention aims to support the lives of elderly people by observing and recording the user's voice data and emotional state in real time. The server converts the voice data acquired using voice recognition technology into text data, stores it in a data storage device, and sets notifications. For example, if a user says, "I will take my medicine at 3 pm tomorrow," the server recognizes this and sets a reminder.

[0239] The server also uses sensor data from smart devices to observe the user's state. The smart devices monitor the user's daily conversations and actions, and analyze their emotions using emotion recognition tools. Machine learning models are used for this analysis, and the results are recorded in a database. If an abnormal change in emotion is detected, the family is notified.

[0240] For example, if a user says in a low voice, "I'm not feeling well today," the emotion recognition system can detect "anxiety" or "sadness," and the server can send a notification to the family as important information.

[0241] This allows family members and caregivers of elderly individuals to accurately understand their health and living conditions remotely. An example of a prompt message to be input into the AI ​​model based on this information would be: "User's emotions are unstable: They are speaking in a weak voice and saying they are not feeling well. How should we provide support?"

[0242] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0243] Step 1:

[0244] The server receives voice data spoken by the user. This voice data is collected in real time through the smart device's microphone. This input voice data is converted into text data using the Google Speech-to-Text API. The text data is formatted as the user's everyday speech and schedule information.

[0245] Step 2:

[0246] The server stores text data in a data storage device and sets reminders based on it. For example, based on time information such as "3 PM tomorrow," it records a reminder notification in the data storage device and prepares to notify the user as the time approaches. This is monitored by a Cron job or timer.

[0247] Step 3:

[0248] The device (smart device) continuously acquires the user's voice and video data and analyzes their emotions using a machine learning model as a means of emotion determination. This process analyzes the tone of voice and facial expressions in the video to estimate the user's emotional state. As output, the user's emotional state is acquired as real-time data.

[0249] Step 4:

[0250] The server records the changes in emotional data acquired in real time into a data storage device and monitors abnormal emotional changes by applying an anomaly detection algorithm. For example, a sudden surge in the values ​​for "sadness" or "anxiety" over a short period of time is considered abnormal. This judgment criterion is supported by statistical analysis and AI models.

[0251] Step 5:

[0252] If an anomaly is detected, the server will send an alert to the information terminal of a family member designated as a contact. This notification will include the specific nature of the anomaly and possible countermeasures. The notification will be sent via email or application to encourage prompt action.

[0253] Step 6:

[0254] The server generates reports based on daily text data and sentiment analysis results, and sends them to family members and caregivers via communication channels. These reports use a generative AI model to create prompts that provide detailed information about the user's living situation. Specific examples include statements such as, "User's emotions are unstable: They are speaking in a listless voice and saying they are unwell. How should we provide support?"

[0255] 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.

[0256] 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.

[0257] 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.

[0258] [Second Embodiment]

[0259] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0260] 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.

[0261] 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).

[0262] 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.

[0263] 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.

[0264] 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).

[0265] 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.

[0266] 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.

[0267] 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.

[0268] 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.

[0269] 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.

[0270] 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".

[0271] This invention is an AI agent system for elderly support, which provides schedule management, sentiment analysis, and communication assistance to support the user's daily life. The following describes how the system is implemented.

[0272] Schedule management

[0273] The device receives the user's spoken schedule information as voice input and converts it into text data using speech recognition technology. For example, if the user says, "I will take my medicine at 10 AM tomorrow," this is saved as text data.

[0274] The server stores the text data sent from the terminal in the database. The stored data is listed as a schedule, and a reminder is set based on the specified time.

[0275] When the time for which the reminder is set arrives, the server sends notification information to the terminal.

[0276] The terminal supports the execution of the schedule by audibly notifying the reminder information and informing the user that "It is 10:00 am. It is time to take medicine."

[0277] Sentiment analysis

[0278] The terminal continuously receives the user's daily conversations as voice input. For example, when the user says "I'm feeling a bit down today," this information is converted into text data.

[0279] The server receives the text data converted by voice recognition and performs sentiment analysis using machine learning.

[0280] Based on the results of the sentiment analysis, the server monitors the user's sentiment trend and identifies abnormal sentiment changes from data over a certain period.

[0281] Report creation and notification

[0282] The server aggregates the schedule information and sentiment analysis results for the specified period and creates a detailed report.

[0283] The server sends this report to the user's family via communication means.

[0284] Based on the received report, the user (family) can check the living situation and emotional state of the elderly and communicate directly if necessary.

[0285] According to this embodiment, the invention can assist the elderly to live their daily lives with confidence, and at the same time, even when the family is away, it can construct an environment that can carefully monitor the changes in the living conditions and emotions of the elderly and provide support.

[0286] The following describes the processing flow.

[0287] Step 1:

[0288] When the user inputs a schedule by voice, the terminal acquires the voice. The terminal uses voice recognition technology to convert this voice into text data.

[0289] Step 2:

[0290] The terminal transmits the converted text data to the server. The server saves the received schedule data in the database.

[0291] Step 3:

[0292] Based on the saved schedule, the server sets a reminder. When the set time arrives, the server sends a reminder notification to the terminal.

[0293] Step 4:

[0294] The terminal provides the reminder notification to the user as a voice message. For example, it notifies in the form of "It's 3 pm. It's time to take medicine".

[0295] Step 5:

[0296] The terminal continuously receives the user's daily conversations and converts them into text data using voice recognition technology.

[0297] Step 6:

[0298] The server performs sentiment analysis based on the text data sent from the terminal. It utilizes a machine learning model to identify sentiment labels.

[0299] Step 7:

[0300] The server aggregates the results of sentiment analysis and monitors the sentiment trend. If an abnormal sentiment change is detected, it sends an alert to the family.

[0301] Step 8:

[0302] The server creates a report based on the schedule information and sentiment data collected over a certain period.

[0303] Step 9:

[0304] The server sends the created report to the family using communication means. The family checks the report to understand the current situation of the user.

[0305] (Example 1)

[0306] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0307] There is an issue of reducing the cognitive burden faced by the elderly in daily life and facilitating remote monitoring and support of the living situation by family members living apart. To address this issue, it is required not only to manage schedules and track emotional fluctuations, provide reminders at appropriate times, but also to detect emotional abnormalities and send appropriate information to the family.

[0308] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0309] In this invention, the server includes means for acquiring schedule information from the user's voice and converting it into text information using voice processing technology, means for storing the converted text information in a storage device and setting up notifications, and means for monitoring trends in analyzed emotional information and detecting anomalies. This enables the daily schedule management and emotional monitoring of the user, allowing family members to effectively support the health and safety of the elderly even from a distance.

[0310] "User" refers to an individual who uses this system, including elderly people and others who require support in their daily lives.

[0311] "Vocalization" refers to the act of a user providing information or instructions verbally.

[0312] "Schedule information" refers to information about daily activities and events that users need to register in the system.

[0313] "Audio processing technology" refers to the technology that converts audio into digital data and makes it a format that can be processed by machines.

[0314] "Textual information" refers to data in text format converted using speech processing technology.

[0315] A "memory device" refers to a device or system used to store digital information.

[0316] "Notification" refers to the act of informing a user of specific information or a reminder, and can be done visually or audibly.

[0317] "Emotional information" refers to data about the user's emotional state, including results analyzed from everyday conversations.

[0318] "Trend" refers to changes or patterns in data that occur over time.

[0319] "Abnormal" refers to changes in emotions or behavior that deviate from the normal range.

[0320] A "memory device" refers to a device or system used to store data and information.

[0321] A "server" refers to a computer system that provides services to other computers via a network.

[0322] This invention is an AI agent system aimed at supporting the elderly, providing technology to assist users in their daily lives. It primarily includes voice-input-based schedule management, sentiment analysis, and notification functions based on the results.

[0323] The user provides their schedule, spoken in natural language, as voice input to the system's terminal. The terminal uses speech recognition technology—for example, speech processing software—to convert the speech into text. This converted data is sent to a server and securely stored in a database.

[0324] The server uses this text information to set a reminder. Based on the reminder setting, the device will notify the user by voice at the specified time. For example, if the user says, "Take my medicine at 10 AM tomorrow," this information is transcribed into text, stored on the server, and the reminder will be activated at the appropriate time.

[0325] Furthermore, the sentiment analysis function, which identifies the user's emotions in everyday conversation, is implemented using a machine learning model. This model utilizes common natural language processing libraries to analyze the user's emotional state and aggregates the results on a server.

[0326] If emotional changes are abnormal, the server immediately generates an alert and notifies the user and their registered family members. This information serves as an important tool for families to support elderly individuals remotely.

[0327] As a concrete example, the following prompt can be considered as input to a generative AI model: "Please explain how the AI ​​agent for elderly care provides notifications regarding daily schedules and sentiment analysis." This prompt clarifies how each function of the system interacts with each other and contributes to supporting the user.

[0328] This system allows users to receive proactive support while reducing their cognitive burden in daily life, and enables family members living far away to understand the user's situation in real time and take appropriate action as needed.

[0329] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0330] Step 1:

[0331] The user speaks their daily schedule aloud. The device inputs this audio via the microphone and converts it to text using speech recognition software. This process converts the audio data into text data. The output is the schedule information stored by the device in text format.

[0332] Step 2:

[0333] The terminal sends the converted text data to the server using a communication protocol. The server records the received text data in a database. Through this data processing, the schedule information is accumulated and managed in a stored state. As output, a user schedule record is formed and stored in the database.

[0334] Step 3:

[0335] The server sets reminders based on schedule information stored in the database. It calculates the notification timing based on the specified date and time. The server then creates a reminder event based on this calculation and sends instructions to the terminal as needed. The output is the event data for which the reminder was set.

[0336] Step 4:

[0337] At the designated time, the device follows instructions from the server and notifies the user with a voice reminder. Specifically, it plays a message such as "It's 10 AM. It's time to take your medicine" through its built-in speaker. The output is a voice notification provided to the user.

[0338] Step 5:

[0339] The user's everyday conversations are input as voice by the device and converted to text in real time. The server uses this data to evaluate the user's emotional state using an emotion analysis model. This data processing calculates the emotions contained in the user's statements (e.g., happy, sad). The output is the analyzed emotion data.

[0340] Step 6:

[0341] The server tracks the analyzed sentiment data over time, monitoring changes and trends in emotions. If an abnormal change is detected, the server immediately generates an alert and notifies the user and their family. The output of this process is the alert notification that is sent.

[0342] Step 7:

[0343] The server aggregates schedule information and sentiment analysis results for a specified period and generates a detailed report. The report is formatted as a PDF or Excel file and sent to family members via communication. This allows family members to receive the generated report and understand the user's situation.

[0344] (Application Example 1)

[0345] 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."

[0346] Managing daily schedules and providing emotional support are crucial for the lives of the elderly, and it is especially important for family members living far away to be aware of their situation and provide appropriate support. However, it is a significant burden for the elderly themselves to report their situation in detail every time, and it is often difficult for family members to directly monitor them. Therefore, a system is needed that naturally collects information in daily life and uses that information to accurately understand and support the elderly's situation.

[0347] 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.

[0348] In this invention, the server includes means for converting user voice input into text information using a recognition device, means for storing the converted text information in an information storage system and setting up notifications for playing back the information, and means for notifying the user of the timetable based on the notification setting time. This makes it possible to manage schedules and understand emotions in a way that is integrated into the user's daily life. Furthermore, since these functions can be operated using a mobile terminal, family members can easily check on the elderly person's situation from a distance and communicate as needed.

[0349] "Voice input" is a method of recognizing the words or voices spoken by the user as digital data and providing that information to the system.

[0350] A "recognition device" is hardware or software that collects data in various formats and converts it into an interpretable form.

[0351] "Text information" refers to information such as audio and video that is represented as character data and is in a format that can be processed and stored by machines.

[0352] An "information storage system" is a structure and technology for securely and efficiently storing data and retrieving it as needed.

[0353] "Notification settings" refer to the system preparations and configurations for delivering information to users according to specified times and conditions.

[0354] A "timetable" is a tool for managing schedules, which organizes and displays appointments and activities based on time and date.

[0355] "Emotional analysis" is a technique for inferring a user's emotional state from their statements and behavior, and then quantifying or categorizing it.

[0356] An "analysis device" is a machine or program that analyzes input data and extracts specific information or patterns.

[0357] A "mobile device" is a portable, personally usable electronic device with communication capabilities that allows for calls, messaging, and application execution.

[0358] This invention is a system for managing schedules and analyzing emotions in daily life, and it functions in conjunction with a mobile device and a server.

[0359] Mobile devices have a voice input function, allowing users to communicate schedules and emotions by speaking. This voice data is converted into text information using the Google Cloud Speech-to-Text API. This text information is then sent to a server and stored in an information storage system.

[0360] The server stores the received text information in a database as schedule information and sets reminders. Based on these reminder settings, the mobile device notifies the user according to the timetable.

[0361] For sentiment analysis, the server uses machine learning algorithms such as scikit-learn to analyze the user's emotions from their statements. The analysis results are used to monitor the user's emotional trends, and if an anomaly is detected, the family is notified. Furthermore, reports are periodically generated from the collected schedule information and sentiment analysis results and sent to family members in remote locations via communication means.

[0362] For example, if a user voice-inputs "Take my medicine at 3 PM," that information is converted to text and added to the schedule. The device then sends a reminder at the appointed time. Furthermore, if a user says something like "I'm feeling a little anxious today," an emotion analysis algorithm analyzes that emotion, and if the anxiety persists for several days, it notifies the family. This entire process allows family members to understand the elderly person's situation and provide appropriate support, even when they are far away.

[0363] Examples of prompt statements for generative AI models include:

[0364] "My mother seems unwell lately; could you please provide any new information?"

[0365] Examples include, "Check your next appointment and set reminders for important dates."

[0366] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0367] Step 1:

[0368] The device acquires the user's speech as voice input. This voice data becomes the input. Using the Google Cloud Speech-to-Text API, this voice data is converted into text data to generate text information. The output is text information.

[0369] Step 2:

[0370] The terminal sends the text information generated in step 1 to the server. The server receives this text data and stores it in the information storage system. The input here is text information, and its storage in the database is the output.

[0371] Step 3:

[0372] The server analyzes the stored text information and interprets it as schedule information. Based on this interpreted data, it sets reminders. In this process, the input is the stored text information, and the output is the reminder setting information.

[0373] Step 4:

[0374] Based on the reminder settings, the device will notify the user at the specified time. The input is the reminder setting information, and the output is the notification to the user.

[0375] Step 5:

[0376] The server analyzes emotions based on accumulated text data. Here, a machine learning algorithm using scikit-learn analyzes the input text data and outputs the user's emotional state.

[0377] Step 6:

[0378] The server monitors the analyzed user sentiment data as a trend. It stores the results in case anomalies are detected within the data over a certain period. The input is sentiment data, and the output is information regarding the presence or absence of anomalies.

[0379] Step 7:

[0380] If an anomaly is detected, the server will notify the family using external communication methods. The server will then compile the notification content and send it. The input is the anomaly information, and the output is the notification to the family.

[0381] Step 8:

[0382] The server generates a detailed report based on schedule information and sentiment analysis results for a specified period. The inputs are schedule information and sentiment data, and the output is a report to be sent to the family.

[0383] Step 9:

[0384] Users can operate this system via their mobile devices and obtain information by entering prompt messages. Specific prompt messages include "Check my next appointment" and "Tell me about my recent emotional tendencies." The input is the prompt message, and the output is the requested information.

[0385] 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.

[0386] This invention is an AI agent system for elderly support that combines an emotion engine, comprehensively providing schedule management, emotion recognition, and communication support for users in their daily lives. The following describes a specific implementation of this system.

[0387] Schedule management and reminder notifications

[0388] The terminal accepts schedule input from the user via voice and converts it into text data using voice recognition technology. For example, information such as "I have a hospital appointment at 10 AM tomorrow" is entered into the terminal.

[0389] The server stores this text data in a database and sets up reminders based on the set time.

[0390] The server sends notification information to the device when the set time has arrived.

[0391] The device will notify the user via voice to help them remember their schedule. For example, it might notify them, "It's 10 AM. You have a hospital appointment."

[0392] Emotion recognition by an emotion engine

[0393] The device captures the user's everyday conversations in both audio and video formats. It also analyzes facial expressions and tone of voice.

[0394] The emotion engine analyzes this acquired data to identify the user's emotional state in real time. For example, if a user smiles and says in a calm voice, "It's a nice day today," the emotion engine recognizes emotions such as "joy" and "calmness."

[0395] Processing and utilizing emotional data

[0396] The server monitors emotional trends by comparing the emotional data received from the emotion engine with past data.

[0397] The server sends an alert to family members if it detects any unusual emotional changes, such as a sudden increase in the frequency of "anxiety" or "sadness."

[0398] Report creation and notifications

[0399] The server aggregates schedule completion status and sentiment analysis results to create a report.

[0400] The server will send this report to the family via communication means.

[0401] Users (family members) can understand the living conditions and mental health status of elderly individuals through the reports.

[0402] This embodiment allows the system to provide multifaceted support for the user's daily life and create an environment where the elderly and their families can live with greater peace of mind. Furthermore, the introduction of an emotion engine enhances the user's emotional support, enabling a safer and more fulfilling life.

[0403] The following describes the processing flow.

[0404] Step 1:

[0405] The user speaks their schedule into the device, saying, "Take a walk at 2 PM." The device captures this audio and converts it into text data using speech recognition technology.

[0406] Step 2:

[0407] The device sends the converted text data to the server. The server receives it and stores it in its database. It also sets a reminder for "2 PM".

[0408] Step 3:

[0409] When the set time arrives, the server sends a reminder to the device. The device then notifies the user by voice, "It's 2 PM. It's time for a walk."

[0410] Step 4:

[0411] When a user engages in everyday conversation, the device acquires audio and video data in real time. For example, it captures a scene where the user smiles and says, "It was a fun day."

[0412] Step 5:

[0413] The emotion engine analyzes acquired data to recognize the user's emotions. It determines emotions such as "joy" or "peace" from facial expressions and tone of voice.

[0414] Step 6:

[0415] The terminal sends the analysis results to the server. The server monitors the sentiment data as a trend and performs analysis over a certain period.

[0416] Step 7:

[0417] When the server detects an unusual change in emotional state, it sends an alert to the family via email or other means. The alert may include a message such as, "You've been experiencing increased anxiety lately."

[0418] Step 8:

[0419] The server aggregates key daily events and sentiment analysis results to create a report. This includes information on behavioral history and sentiment trends.

[0420] Step 9:

[0421] The generated report is sent to the family via communication means. The user (family member) can review the report and understand the living conditions of the elderly person living separately.

[0422] (Example 2)

[0423] 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".

[0424] There is a need to alleviate the complexities of schedule management and the difficulties in understanding the mental health of users, including the elderly, that they face in their daily lives. Conventional systems simply notify users of their schedules, but they cannot properly understand and manage the associated emotional fluctuations, and therefore cannot provide sufficient support to maintain users' mental health. This creates a challenge in creating an environment where users and their families can live with peace of mind.

[0425] 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.

[0426] In this invention, the server includes means for acquiring schedule data from the user's voice information and converting it into string information using acoustic processing technology; means for storing the converted string information in a storage device and setting schedule notifications; means for notifying the user of the schedule information by voice according to the set notification time; means for emotional analysis that recognizes the user's emotions from everyday conversation; means for analyzing the recognized emotional data and detecting anomalies by comparing it with past data; means for notifying an external party when an abnormal emotional change is detected; and means for creating a report based on the schedule data and emotional analysis results for a day or a predetermined period and transmitting it externally via communication means. This makes it possible to centrally manage the user's schedule management and emotional trends, and to support them in living a safer and more secure life.

[0427] A "user" refers to any individual who uses this system, including any person, such as the elderly.

[0428] "Audio information" refers to the acoustic signals of speech or conversation generated by the user, and processing is performed based on these signals.

[0429] "Schedule data" refers to information about activities and events related to specific future dates and times, provided by the user through voice information.

[0430] "Audio processing technology" refers to the technology used to convert audio information into digital string information.

[0431] "String information" refers to text data converted from audio information, in a format that can be processed by digital devices.

[0432] A "storage device" refers to hardware or software used to store scheduled data or string information for long-term or short-term storage.

[0433] "Schedule notification" refers to a record or reminder that provides users with pre-set schedule information at the appropriate time.

[0434] "Emotion" refers to the state of mind and feelings judged from the user's everyday conversation, facial expressions, voice tone, etc.

[0435] "Emotional analysis means" refers to software or a device used to recognize a user's emotions, and includes techniques for analyzing the acquired data.

[0436] "Anomaly" refers to a state where the results of an analysis based on the user's emotional data show a change that exceeds the normal range.

[0437] A "report" is a document generated based on schedule data and emotional analysis results, and is provided to understand the user's living situation.

[0438] "Communication means" refers to methods or devices for transmitting reports to external devices or individuals.

[0439] This invention is an AI agent system that utilizes voice and emotion recognition to support the lives of users, including the elderly, and provides a safe and fulfilling life through schedule management and emotional analysis. The specific configuration and operation of the system are shown below.

[0440] The user communicates their schedule to the device via voice input. The device uses voice processing software (e.g., a voice recognition API) equipped with voice recognition technology to convert the voice into text information. This resulting schedule data is sent to a server and stored in its memory. The server sets reminders based on this schedule data and sends voice notifications to the user via the device at the appropriate time. Voice synthesis software (e.g., a voice synthesis API) is used for these voice notifications.

[0441] Furthermore, the device collects user conversations in audio and video format on a daily basis and uses emotional analysis tools to recognize emotions. This analysis utilizes software that employs generative AI models (e.g., an emotional analysis API) to identify the user's emotional state. This information is sent to a server, where it is further compared and analyzed with past data to monitor emotional trends. If an anomaly is detected, the server sends a notification to the family.

[0442] This system helps users and their families live with peace of mind by integrating schedule management and emotional state monitoring. Specifically, for example, if a user says, "I have a hospital appointment at 10 AM tomorrow," the schedule is automatically set, and if there is a change in emotions, an alert is sent to the family such as, "Recently, the user has been feeling lonely more frequently."

[0443] An example of a prompt to the generating AI model is, "If an elderly user has recently been showing a sad expression frequently, please provide specific suggestions for support or words to say to improve their mood." This input will generate a response.

[0444] In this way, the system aims to improve the quality of daily life by processing complex data such as user communication, schedules, and emotional states.

[0445] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0446] Step 1:

[0447] The user communicates their schedule via voice. The device receives this voice input and uses speech recognition technology to convert the voice data into text data. Specifically, if the user voice-inputs "I have a hospital appointment at 10 AM tomorrow," the device collects the voice and inputs it into a speech recognition API. The API analyzes the voice and outputs it as text data, "I have a hospital appointment at 10 AM tomorrow."

[0448] Step 2:

[0449] The terminal sends the generated text data to the server. The server stores the received text data in its storage device and sets up a reminder. Specifically, the text data "Hospital appointment at 10am tomorrow" is stored in the database along with the date and time information. Based on the stored information, the server creates a reminder and sets it to notify at the specified time. The information stored by the server in the database is referenced when setting up reminders.

[0450] Step 3:

[0451] Based on the set notification time, the server sends a reminder notification to the device. Upon receiving this notification, the device uses speech synthesis technology to inform the user verbally. Specifically, when the server sends the notification content "It's 10 AM. You have a hospital appointment" to the device, the device uses a speech synthesis API to convert it into speech and plays it back to the user.

[0452] Step 4:

[0453] The device captures the user's everyday conversations as audio and video. This data is used for emotion analysis. The device inputs the acquired audio and video data into an emotion analysis system and uses a generative AI model to recognize the user's emotions. Specifically, it inputs the user's voice saying "It's a nice day today" and their smiling face into an emotion analysis API and outputs a recognition result such as "joy."

[0454] Step 5:

[0455] Based on the emotional recognition results, the server compares them with past data to analyze emotional trends. If the server detects an abnormal change in emotion, it generates an alert and notifies an external individual, such as a family member. Specifically, if the server finds that "the frequency of anxiety has recently increased sharply," it will send this information as a message to the family.

[0456] Step 6:

[0457] The server creates a report based on daily or predetermined period schedule data and emotional analysis results, and transmits it externally via communication means. Specifically, the server compiles the schedule performance information and emotional analysis results in a table format, outputs it as a PDF file, and sends that file to the family via email.

[0458] (Application Example 2)

[0459] 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."

[0460] To support the lives of the elderly, it is necessary not only to manage their schedules but also to understand their mental health and provide an environment where they can live with peace of mind. However, currently, there is a lack of systems to adequately understand the emotions of the elderly and to respond quickly when problems occur. As a result, it is difficult for family members and caregivers to understand the condition of the elderly and to provide a supportive environment that ensures their safety.

[0461] 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.

[0462] In this invention, the server includes means for acquiring voice data and converting it into text data using voice recognition technology, means for storing the converted text data in a data storage device and setting up notifications, and means for observing the user's state in real time using a smart device and recording emotional trends. This makes it possible to support all aspects of the elderly's lives and provide real-time information that allows families and caregivers to live with peace of mind.

[0463] "Voice data" refers to information that records the user's speech, and serves as the basis for converting it into text data using speech recognition technology.

[0464] "Speech recognition technology" is a technology that extracts information from spoken words and converts it into text data.

[0465] "Text data" refers to a form of character information converted from audio data, which is suitable for management and analysis in information processing.

[0466] A "data storage device" is a device for storing data, accumulating text data and sentiment data in the form of a database.

[0467] A "notification" is information that is sent to the user at a set time or under set conditions, and is used for managing schedules and reporting anomalies.

[0468] A "smart device" is a portable information terminal connected to the internet, equipped with sensors that allow for real-time monitoring of the user's condition.

[0469] "Emotion discrimination means" refers to methods or devices for identifying and analyzing emotions from a user's voice and facial expressions.

[0470] A "learning model" is an algorithm used to learn patterns based on data and identify emotions or user states.

[0471] The system of this invention aims to support the lives of elderly people by observing and recording the user's voice data and emotional state in real time. The server converts the voice data acquired using voice recognition technology into text data, stores it in a data storage device, and sets notifications. For example, if a user says, "I will take my medicine at 3 pm tomorrow," the server recognizes this and sets a reminder.

[0472] The server also uses sensor data from smart devices to observe the user's state. The smart devices monitor the user's daily conversations and actions, and analyze their emotions using emotion recognition tools. Machine learning models are used for this analysis, and the results are recorded in a database. If an abnormal change in emotion is detected, the family is notified.

[0473] For example, if a user says in a low voice, "I'm not feeling well today," the emotion recognition system can detect "anxiety" or "sadness," and the server can send a notification to the family as important information.

[0474] This allows family members and caregivers of elderly individuals to accurately understand their health and living conditions remotely. An example of a prompt message to be input into the AI ​​model based on this information would be: "User's emotions are unstable: They are speaking in a weak voice and saying they are not feeling well. How should we provide support?"

[0475] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0476] Step 1:

[0477] The server receives voice data spoken by the user. This voice data is collected in real time through the smart device's microphone. This input voice data is converted into text data using the Google Speech-to-Text API. The text data is formatted as the user's everyday speech and schedule information.

[0478] Step 2:

[0479] The server stores text data in a data storage device and sets reminders based on it. For example, based on time information such as "3 PM tomorrow," it records a reminder notification in the data storage device and prepares to notify the user as the time approaches. This is monitored by a Cron job or timer.

[0480] Step 3:

[0481] The device (smart device) continuously acquires the user's voice and video data and analyzes their emotions using a machine learning model as a means of emotion determination. This process analyzes the tone of voice and facial expressions in the video to estimate the user's emotional state. As output, the user's emotional state is acquired as real-time data.

[0482] Step 4:

[0483] The server records the changes in emotional data acquired in real time into a data storage device and monitors abnormal emotional changes by applying an anomaly detection algorithm. For example, a sudden surge in the values ​​for "sadness" or "anxiety" over a short period of time is considered abnormal. This judgment criterion is supported by statistical analysis and AI models.

[0484] Step 5:

[0485] If an anomaly is detected, the server will send an alert to the information terminal of a family member designated as a contact. This notification will include the specific nature of the anomaly and possible countermeasures. The notification will be sent via email or application to encourage prompt action.

[0486] Step 6:

[0487] The server generates reports based on daily text data and sentiment analysis results, and sends them to family members and caregivers via communication channels. These reports use a generative AI model to create prompts that provide detailed information about the user's living situation. Specific examples include statements such as, "User's emotions are unstable: They are speaking in a listless voice and saying they are unwell. How should we provide support?"

[0488] 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.

[0489] 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.

[0490] 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.

[0491] [Third Embodiment]

[0492] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0493] 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.

[0494] 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).

[0495] 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.

[0496] 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.

[0497] 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).

[0498] 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.

[0499] 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.

[0500] 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.

[0501] 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.

[0502] 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.

[0503] 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".

[0504] This invention is an AI agent system for elderly support, which provides schedule management, sentiment analysis, and communication assistance to support the user's daily life. The following describes how the system is implemented.

[0505] Schedule management

[0506] The device receives the user's spoken schedule information as voice input and converts it into text data using speech recognition technology. For example, if the user says, "I will take my medicine at 10 AM tomorrow," this is saved as text data.

[0507] The server saves text data sent from the terminal to a database. The saved data is listed as a schedule, and reminders are set based on the specified time.

[0508] The server sends a notification to the device when the scheduled time for the reminder arrives.

[0509] The device provides voice notifications of reminders, informing the user, "It's 10 AM. It's time to take your medicine," thereby helping them to follow their schedule.

[0510] sentiment analysis

[0511] The device continuously receives the user's everyday conversations as voice input. For example, if the user says, "I'm feeling a little down today," this information is converted into text data.

[0512] The server receives the text data converted by speech recognition and performs sentiment analysis using machine learning.

[0513] Based on the results of sentiment analysis, the server monitors the user's emotional trends and identifies abnormal emotional changes from data over a certain period.

[0514] Report creation and notifications

[0515] The server aggregates schedule information and sentiment analysis results for a specified period and creates a detailed report.

[0516] The server will send this report to the user's family via communication means.

[0517] Based on the reports they receive, users (family members) can check the living situation and emotional state of the elderly person and communicate directly with them as needed.

[0518] This embodiment makes it possible to create an environment in which the invention can support elderly people in leading their daily lives with peace of mind, while simultaneously allowing families to closely monitor and provide support for the elderly person's living situation and emotional fluctuations, even when they are far away.

[0519] The following describes the processing flow.

[0520] Step 1:

[0521] When a user enters their schedule by voice, the device acquires the audio. The device then uses speech recognition technology to convert this audio into text data.

[0522] Step 2:

[0523] The terminal sends the converted text data to the server. The server stores the received schedule data in its database.

[0524] Step 3:

[0525] The server sets reminders based on saved schedules. When the set time arrives, the server sends a reminder notification to the device.

[0526] Step 4:

[0527] The device provides reminder notifications to the user as voice messages. For example, it might notify the user with something like, "It's 3 PM. It's time to take your medicine."

[0528] Step 5:

[0529] The device continuously receives the user's everyday conversations. This is then converted into text data using speech recognition technology.

[0530] Step 6:

[0531] The server performs sentiment analysis based on text data sent from the terminal. It uses machine learning models to identify sentiment labels.

[0532] Step 7:

[0533] The server aggregates the results of sentiment analysis and monitors emotional trends. If an abnormal emotional change is detected, it sends an alert to the family.

[0534] Step 8:

[0535] The server generates reports based on schedule information and sentiment data collected over a certain period.

[0536] Step 9:

[0537] The server sends the generated report to the family using a communication method. The family reviews the report and understands the user's current situation.

[0538] (Example 1)

[0539] 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."

[0540] There are challenges in reducing the cognitive burden that older adults face in their daily lives and facilitating remote monitoring and support of their living situations by family members living separately. To address this challenge, it is necessary to not only manage schedules and track emotional fluctuations, providing timely reminders, but also to detect emotional abnormalities and send appropriate information to family members.

[0541] 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.

[0542] In this invention, the server includes means for acquiring schedule information from the user's voice and converting it into text information using voice processing technology, means for storing the converted text information in a storage device and setting up notifications, and means for monitoring trends in analyzed emotional information and detecting anomalies. This enables the daily schedule management and emotional monitoring of the user, allowing family members to effectively support the health and safety of the elderly even from a distance.

[0543] "User" refers to an individual who uses this system, including elderly people and others who require support in their daily lives.

[0544] "Vocalization" refers to the act of a user providing information or instructions verbally.

[0545] "Schedule information" refers to information about daily activities and events that users need to register in the system.

[0546] "Audio processing technology" refers to the technology that converts audio into digital data and makes it a format that can be processed by machines.

[0547] "Textual information" refers to data in text format converted using speech processing technology.

[0548] A "memory device" refers to a device or system used to store digital information.

[0549] "Notification" refers to the act of informing a user of specific information or a reminder, and can be done visually or audibly.

[0550] "Emotional information" refers to data about the user's emotional state, including results analyzed from everyday conversations.

[0551] "Trend" refers to changes or patterns in data that occur over time.

[0552] "Abnormal" refers to changes in emotions or behavior that deviate from the normal range.

[0553] A "memory device" refers to a device or system used to store data and information.

[0554] A "server" refers to a computer system that provides services to other computers via a network.

[0555] This invention is an AI agent system aimed at supporting the elderly, providing technology to assist users in their daily lives. It primarily includes voice-input-based schedule management, sentiment analysis, and notification functions based on the results.

[0556] The user provides their schedule, spoken in natural language, as voice input to the system's terminal. The terminal uses speech recognition technology—for example, speech processing software—to convert the speech into text. This converted data is sent to a server and securely stored in a database.

[0557] The server uses this text information to set a reminder. Based on the reminder setting, the device will notify the user by voice at the specified time. For example, if the user says, "Take my medicine at 10 AM tomorrow," this information is transcribed into text, stored on the server, and the reminder will be activated at the appropriate time.

[0558] Furthermore, the sentiment analysis function, which identifies the user's emotions in everyday conversation, is implemented using a machine learning model. This model utilizes common natural language processing libraries to analyze the user's emotional state and aggregates the results on a server.

[0559] If emotional changes are abnormal, the server immediately generates an alert and notifies the user and their registered family members. This information serves as an important tool for families to support elderly individuals remotely.

[0560] As a concrete example, the following prompt can be considered as input to a generative AI model: "Please explain how the AI ​​agent for elderly care provides notifications regarding daily schedules and sentiment analysis." This prompt clarifies how each function of the system interacts with each other and contributes to supporting the user.

[0561] This system allows users to receive proactive support while reducing their cognitive burden in daily life, and enables family members living far away to understand the user's situation in real time and take appropriate action as needed.

[0562] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0563] Step 1:

[0564] The user speaks their daily schedule aloud. The device inputs this audio via the microphone and converts it to text using speech recognition software. This process converts the audio data into text data. The output is the schedule information stored by the device in text format.

[0565] Step 2:

[0566] The terminal sends the converted text data to the server using a communication protocol. The server records the received text data in a database. Through this data processing, the schedule information is accumulated and managed in a stored state. As output, a user schedule record is formed and stored in the database.

[0567] Step 3:

[0568] The server sets reminders based on schedule information stored in the database. It calculates the notification timing based on the specified date and time. The server then creates a reminder event based on this calculation and sends instructions to the terminal as needed. The output is the event data for which the reminder was set.

[0569] Step 4:

[0570] At the designated time, the device follows instructions from the server and notifies the user with a voice reminder. Specifically, it plays a message such as "It's 10 AM. It's time to take your medicine" through its built-in speaker. The output is a voice notification provided to the user.

[0571] Step 5:

[0572] The user's everyday conversations are input as voice by the device and converted to text in real time. The server uses this data to evaluate the user's emotional state using an emotion analysis model. This data processing calculates the emotions contained in the user's statements (e.g., happy, sad). The output is the analyzed emotion data.

[0573] Step 6:

[0574] The server tracks the analyzed sentiment data over time, monitoring changes and trends in emotions. If an abnormal change is detected, the server immediately generates an alert and notifies the user and their family. The output of this process is the alert notification that is sent.

[0575] Step 7:

[0576] The server aggregates schedule information and sentiment analysis results for a specified period and generates a detailed report. The report is formatted as a PDF or Excel file and sent to family members via communication. This allows family members to receive the generated report and understand the user's situation.

[0577] (Application Example 1)

[0578] 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."

[0579] Managing daily schedules and providing emotional support are crucial for the lives of the elderly, and it is especially important for family members living far away to be aware of their situation and provide appropriate support. However, it is a significant burden for the elderly themselves to report their situation in detail every time, and it is often difficult for family members to directly monitor them. Therefore, a system is needed that naturally collects information in daily life and uses that information to accurately understand and support the elderly's situation.

[0580] 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.

[0581] In this invention, the server includes means for converting user voice input into text information using a recognition device, means for storing the converted text information in an information storage system and setting up notifications for playing back the information, and means for notifying the user of the timetable based on the notification setting time. This makes it possible to manage schedules and understand emotions in a way that is integrated into the user's daily life. Furthermore, since these functions can be operated using a mobile terminal, family members can easily check on the elderly person's situation from a distance and communicate as needed.

[0582] "Voice input" is a method of recognizing the words or voices spoken by the user as digital data and providing that information to the system.

[0583] A "recognition device" is hardware or software that collects data in various formats and converts it into an interpretable form.

[0584] "Text information" refers to information such as audio and video that is represented as character data and is in a format that can be processed and stored by machines.

[0585] An "information storage system" is a structure and technology for securely and efficiently storing data and retrieving it as needed.

[0586] "Notification settings" refer to the system preparations and configurations for delivering information to users according to specified times and conditions.

[0587] A "timetable" is a tool for managing schedules, which organizes and displays appointments and activities based on time and date.

[0588] "Emotional analysis" is a technique for inferring a user's emotional state from their statements and behavior, and then quantifying or categorizing it.

[0589] An "analysis device" is a machine or program that analyzes input data and extracts specific information or patterns.

[0590] A "mobile device" is a portable, personally usable electronic device with communication capabilities that allows for calls, messaging, and application execution.

[0591] This invention is a system for managing schedules and analyzing emotions in daily life, and it functions in conjunction with a mobile device and a server.

[0592] Mobile devices have a voice input function, allowing users to communicate schedules and emotions by speaking. This voice data is converted into text information using the Google Cloud Speech-to-Text API. This text information is then sent to a server and stored in an information storage system.

[0593] The server stores the received text information in a database as schedule information and sets reminders. Based on these reminder settings, the mobile device notifies the user according to the timetable.

[0594] For sentiment analysis, the server uses machine learning algorithms such as scikit-learn to analyze the user's emotions from their statements. The analysis results are used to monitor the user's emotional trends, and if an anomaly is detected, the family is notified. Furthermore, reports are periodically generated from the collected schedule information and sentiment analysis results and sent to family members in remote locations via communication means.

[0595] For example, if a user voice-inputs "Take my medicine at 3 PM," that information is converted to text and added to the schedule. The device then sends a reminder at the appointed time. Furthermore, if a user says something like "I'm feeling a little anxious today," an emotion analysis algorithm analyzes that emotion, and if the anxiety persists for several days, it notifies the family. This entire process allows family members to understand the elderly person's situation and provide appropriate support, even when they are far away.

[0596] Examples of prompt statements for generative AI models include:

[0597] "My mother seems unwell lately; could you please provide any new information?"

[0598] Examples include, "Check your next appointment and set reminders for important dates."

[0599] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0600] Step 1:

[0601] The device acquires the user's speech as voice input. This voice data becomes the input. Using the Google Cloud Speech-to-Text API, this voice data is converted into text data to generate text information. The output is text information.

[0602] Step 2:

[0603] The terminal sends the text information generated in step 1 to the server. The server receives this text data and stores it in the information storage system. The input here is text information, and its storage in the database is the output.

[0604] Step 3:

[0605] The server analyzes the stored text information and interprets it as schedule information. Based on this interpreted data, it sets reminders. In this process, the input is the stored text information, and the output is the reminder setting information.

[0606] Step 4:

[0607] Based on the reminder settings, the device will notify the user at the specified time. The input is the reminder setting information, and the output is the notification to the user.

[0608] Step 5:

[0609] The server analyzes emotions based on accumulated text data. Here, a machine learning algorithm using scikit-learn analyzes the input text data and outputs the user's emotional state.

[0610] Step 6:

[0611] The server monitors the analyzed user sentiment data as a trend. It stores the results in case anomalies are detected within the data over a certain period. The input is sentiment data, and the output is information regarding the presence or absence of anomalies.

[0612] Step 7:

[0613] If an anomaly is detected, the server will notify the family using external communication methods. The server will then compile the notification content and send it. The input is the anomaly information, and the output is the notification to the family.

[0614] Step 8:

[0615] The server generates a detailed report based on schedule information and sentiment analysis results for a specified period. The inputs are schedule information and sentiment data, and the output is a report to be sent to the family.

[0616] Step 9:

[0617] Users can operate this system via their mobile devices and obtain information by entering prompt messages. Specific prompt messages include "Check my next appointment" and "Tell me about my recent emotional tendencies." The input is the prompt message, and the output is the requested information.

[0618] 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.

[0619] This invention is an AI agent system for elderly support that combines an emotion engine, comprehensively providing schedule management, emotion recognition, and communication support for users in their daily lives. The following describes a specific implementation of this system.

[0620] Schedule management and reminder notifications

[0621] The terminal accepts schedule input from the user via voice and converts it into text data using voice recognition technology. For example, information such as "I have a hospital appointment at 10 AM tomorrow" is entered into the terminal.

[0622] The server stores this text data in a database and sets up reminders based on the set time.

[0623] The server sends notification information to the device when the set time has arrived.

[0624] The device will notify the user via voice to help them remember their schedule. For example, it might notify them, "It's 10 AM. You have a hospital appointment."

[0625] Emotion recognition by an emotion engine

[0626] The device captures the user's everyday conversations in both audio and video formats. It also analyzes facial expressions and tone of voice.

[0627] The emotion engine analyzes this acquired data to identify the user's emotional state in real time. For example, if a user smiles and says in a calm voice, "It's a nice day today," the emotion engine recognizes emotions such as "joy" and "calmness."

[0628] Processing and utilizing emotional data

[0629] The server monitors emotional trends by comparing the emotional data received from the emotion engine with past data.

[0630] The server sends an alert to family members if it detects any unusual emotional changes, such as a sudden increase in the frequency of "anxiety" or "sadness."

[0631] Report creation and notifications

[0632] The server aggregates schedule completion status and sentiment analysis results to create a report.

[0633] The server will send this report to the family via communication means.

[0634] Users (family members) can understand the living conditions and mental health status of elderly individuals through the reports.

[0635] This embodiment allows the system to provide multifaceted support for the user's daily life and create an environment where the elderly and their families can live with greater peace of mind. Furthermore, the introduction of an emotion engine enhances the user's emotional support, enabling a safer and more fulfilling life.

[0636] The following describes the processing flow.

[0637] Step 1:

[0638] The user speaks their schedule into the device, saying, "Take a walk at 2 PM." The device captures this audio and converts it into text data using speech recognition technology.

[0639] Step 2:

[0640] The device sends the converted text data to the server. The server receives it and stores it in its database. It also sets a reminder for "2 PM".

[0641] Step 3:

[0642] When the set time arrives, the server sends a reminder to the device. The device then notifies the user by voice, "It's 2 PM. It's time for a walk."

[0643] Step 4:

[0644] When a user engages in everyday conversation, the device acquires audio and video data in real time. For example, it captures a scene where the user smiles and says, "It was a fun day."

[0645] Step 5:

[0646] The emotion engine analyzes acquired data to recognize the user's emotions. It determines emotions such as "joy" or "peace" from facial expressions and tone of voice.

[0647] Step 6:

[0648] The terminal sends the analysis results to the server. The server monitors the sentiment data as a trend and performs analysis over a certain period.

[0649] Step 7:

[0650] When the server detects an unusual change in emotional state, it sends an alert to the family via email or other means. The alert may include a message such as, "You've been experiencing increased anxiety lately."

[0651] Step 8:

[0652] The server aggregates key daily events and sentiment analysis results to create a report. This includes information on behavioral history and sentiment trends.

[0653] Step 9:

[0654] The generated report is sent to the family via communication means. The user (family member) can review the report and understand the living conditions of the elderly person living separately.

[0655] (Example 2)

[0656] 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."

[0657] There is a need to alleviate the complexities of schedule management and the difficulties in understanding the mental health of users, including the elderly, that they face in their daily lives. Conventional systems simply notify users of their schedules, but they cannot properly understand and manage the associated emotional fluctuations, and therefore cannot provide sufficient support to maintain users' mental health. This creates a challenge in creating an environment where users and their families can live with peace of mind.

[0658] 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.

[0659] In this invention, the server includes means for acquiring schedule data from the user's voice information and converting it into string information using acoustic processing technology; means for storing the converted string information in a storage device and setting schedule notifications; means for notifying the user of the schedule information by voice according to the set notification time; means for emotional analysis that recognizes the user's emotions from everyday conversation; means for analyzing the recognized emotional data and detecting anomalies by comparing it with past data; means for notifying an external party when an abnormal emotional change is detected; and means for creating a report based on the schedule data and emotional analysis results for a day or a predetermined period and transmitting it externally via communication means. This makes it possible to centrally manage the user's schedule management and emotional trends, and to support them in living a safer and more secure life.

[0660] A "user" refers to any individual who uses this system, including any person, such as the elderly.

[0661] "Audio information" refers to the acoustic signals of speech or conversation generated by the user, and processing is performed based on these signals.

[0662] "Schedule data" refers to information about activities and events related to specific future dates and times, provided by the user through voice information.

[0663] "Audio processing technology" refers to the technology used to convert audio information into digital string information.

[0664] "String information" refers to text data converted from audio information, in a format that can be processed by digital devices.

[0665] A "storage device" refers to hardware or software used to store scheduled data or string information for long-term or short-term storage.

[0666] "Schedule notification" refers to a record or reminder that provides users with pre-set schedule information at the appropriate time.

[0667] "Emotion" refers to the state of mind and feelings judged from the user's everyday conversation, facial expressions, voice tone, etc.

[0668] "Emotional analysis means" refers to software or a device used to recognize a user's emotions, and includes techniques for analyzing the acquired data.

[0669] "Anomaly" refers to a state where the results of an analysis based on the user's emotional data show a change that exceeds the normal range.

[0670] A "report" is a document generated based on schedule data and emotional analysis results, and is provided to understand the user's living situation.

[0671] "Communication means" refers to methods or devices for transmitting reports to external devices or individuals.

[0672] This invention is an AI agent system that utilizes voice and emotion recognition to support the lives of users, including the elderly, and provides a safe and fulfilling life through schedule management and emotional analysis. The specific configuration and operation of the system are shown below.

[0673] The user communicates their schedule to the device via voice input. The device uses voice processing software (e.g., a voice recognition API) equipped with voice recognition technology to convert the voice into text information. This resulting schedule data is sent to a server and stored in its memory. The server sets reminders based on this schedule data and sends voice notifications to the user via the device at the appropriate time. Voice synthesis software (e.g., a voice synthesis API) is used for these voice notifications.

[0674] Furthermore, the device collects user conversations in audio and video format on a daily basis and uses emotional analysis tools to recognize emotions. This analysis utilizes software that employs generative AI models (e.g., an emotional analysis API) to identify the user's emotional state. This information is sent to a server, where it is further compared and analyzed with past data to monitor emotional trends. If an anomaly is detected, the server sends a notification to the family.

[0675] This system helps users and their families live with peace of mind by integrating schedule management and emotional state monitoring. Specifically, for example, if a user says, "I have a hospital appointment at 10 AM tomorrow," the schedule is automatically set, and if there is a change in emotions, an alert is sent to the family such as, "Recently, the user has been feeling lonely more frequently."

[0676] An example of a prompt to the generating AI model is, "If an elderly user has recently been showing a sad expression frequently, please provide specific suggestions for support or words to say to improve their mood." This input will generate a response.

[0677] In this way, the system aims to improve the quality of daily life by processing complex data such as user communication, schedules, and emotional states.

[0678] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0679] Step 1:

[0680] The user communicates their schedule via voice. The device receives this voice input and uses speech recognition technology to convert the voice data into text data. Specifically, if the user voice-inputs "I have a hospital appointment at 10 AM tomorrow," the device collects the voice and inputs it into a speech recognition API. The API analyzes the voice and outputs it as text data, "I have a hospital appointment at 10 AM tomorrow."

[0681] Step 2:

[0682] The terminal sends the generated text data to the server. The server stores the received text data in its storage device and sets up a reminder. Specifically, the text data "Hospital appointment at 10am tomorrow" is stored in the database along with the date and time information. Based on the stored information, the server creates a reminder and sets it to notify at the specified time. The information stored by the server in the database is referenced when setting up reminders.

[0683] Step 3:

[0684] Based on the set notification time, the server sends a reminder notification to the device. Upon receiving this notification, the device uses speech synthesis technology to inform the user verbally. Specifically, when the server sends the notification content "It's 10 AM. You have a hospital appointment" to the device, the device uses a speech synthesis API to convert it into speech and plays it back to the user.

[0685] Step 4:

[0686] The device captures the user's everyday conversations as audio and video. This data is used for emotion analysis. The device inputs the acquired audio and video data into an emotion analysis system and uses a generative AI model to recognize the user's emotions. Specifically, it inputs the user's voice saying "It's a nice day today" and their smiling face into an emotion analysis API and outputs a recognition result such as "joy."

[0687] Step 5:

[0688] Based on the emotional recognition results, the server compares them with past data to analyze emotional trends. If the server detects an abnormal change in emotion, it generates an alert and notifies an external individual, such as a family member. Specifically, if the server finds that "the frequency of anxiety has recently increased sharply," it will send this information as a message to the family.

[0689] Step 6:

[0690] The server creates a report based on daily or predetermined period schedule data and emotional analysis results, and transmits it externally via communication means. Specifically, the server compiles the schedule performance information and emotional analysis results in a table format, outputs it as a PDF file, and sends that file to the family via email.

[0691] (Application Example 2)

[0692] 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."

[0693] To support the lives of the elderly, it is necessary not only to manage their schedules but also to understand their mental health and provide an environment where they can live with peace of mind. However, currently, there is a lack of systems to adequately understand the emotions of the elderly and to respond quickly when problems occur. As a result, it is difficult for family members and caregivers to understand the condition of the elderly and to provide a supportive environment that ensures their safety.

[0694] 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.

[0695] In this invention, the server includes means for acquiring voice data and converting it into text data using voice recognition technology, means for storing the converted text data in a data storage device and setting up notifications, and means for observing the user's state in real time using a smart device and recording emotional trends. This makes it possible to support all aspects of the elderly's lives and provide real-time information that allows families and caregivers to live with peace of mind.

[0696] "Voice data" refers to information that records the user's speech, and serves as the basis for converting it into text data using speech recognition technology.

[0697] "Speech recognition technology" is a technology that extracts information from spoken words and converts it into text data.

[0698] "Text data" refers to a form of character information converted from audio data, which is suitable for management and analysis in information processing.

[0699] A "data storage device" is a device for storing data, accumulating text data and sentiment data in the form of a database.

[0700] A "notification" is information that is sent to the user at a set time or under set conditions, and is used for managing schedules and reporting anomalies.

[0701] A "smart device" is a portable information terminal connected to the internet, equipped with sensors that allow for real-time monitoring of the user's condition.

[0702] "Emotion discrimination means" refers to methods or devices for identifying and analyzing emotions from a user's voice and facial expressions.

[0703] A "learning model" is an algorithm used to learn patterns based on data and identify emotions or user states.

[0704] The system of this invention aims to support the lives of elderly people by observing and recording the user's voice data and emotional state in real time. The server converts the voice data acquired using voice recognition technology into text data, stores it in a data storage device, and sets notifications. For example, if a user says, "I will take my medicine at 3 pm tomorrow," the server recognizes this and sets a reminder.

[0705] The server also uses sensor data from smart devices to observe the user's state. The smart devices monitor the user's daily conversations and actions, and analyze their emotions using emotion recognition tools. Machine learning models are used for this analysis, and the results are recorded in a database. If an abnormal change in emotion is detected, the family is notified.

[0706] For example, if a user says in a low voice, "I'm not feeling well today," the emotion recognition system can detect "anxiety" or "sadness," and the server can send a notification to the family as important information.

[0707] This allows family members and caregivers of elderly individuals to accurately understand their health and living conditions remotely. An example of a prompt message to be input into the AI ​​model based on this information would be: "User's emotions are unstable: They are speaking in a weak voice and saying they are not feeling well. How should we provide support?"

[0708] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0709] Step 1:

[0710] The server receives voice data spoken by the user. This voice data is collected in real time through the smart device's microphone. This input voice data is converted into text data using the Google Speech-to-Text API. The text data is formatted as the user's everyday speech and schedule information.

[0711] Step 2:

[0712] The server stores text data in a data storage device and sets reminders based on it. For example, based on time information such as "3 PM tomorrow," it records a reminder notification in the data storage device and prepares to notify the user as the time approaches. This is monitored by a Cron job or timer.

[0713] Step 3:

[0714] The device (smart device) continuously acquires the user's voice and video data and analyzes their emotions using a machine learning model as a means of emotion determination. This process analyzes the tone of voice and facial expressions in the video to estimate the user's emotional state. As output, the user's emotional state is acquired as real-time data.

[0715] Step 4:

[0716] The server records the changes in emotional data acquired in real time into a data storage device and monitors abnormal emotional changes by applying an anomaly detection algorithm. For example, a sudden surge in the values ​​for "sadness" or "anxiety" over a short period of time is considered abnormal. This judgment criterion is supported by statistical analysis and AI models.

[0717] Step 5:

[0718] If an anomaly is detected, the server will send an alert to the information terminal of a family member designated as a contact. This notification will include the specific nature of the anomaly and possible countermeasures. The notification will be sent via email or application to encourage prompt action.

[0719] Step 6:

[0720] The server generates reports based on daily text data and sentiment analysis results, and sends them to family members and caregivers via communication channels. These reports use a generative AI model to create prompts that provide detailed information about the user's living situation. Specific examples include statements such as, "User's emotions are unstable: They are speaking in a listless voice and saying they are unwell. How should we provide support?"

[0721] 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.

[0722] 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.

[0723] 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.

[0724] [Fourth Embodiment]

[0725] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0726] 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.

[0727] 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).

[0728] 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.

[0729] 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.

[0730] 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).

[0731] 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.

[0732] 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.

[0733] 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.

[0734] 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.

[0735] 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.

[0736] 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.

[0737] 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".

[0738] This invention is an AI agent system for elderly support, which provides schedule management, sentiment analysis, and communication assistance to support the user's daily life. The following describes how the system is implemented.

[0739] Schedule management

[0740] The device receives the user's spoken schedule information as voice input and converts it into text data using speech recognition technology. For example, if the user says, "I will take my medicine at 10 AM tomorrow," this is saved as text data.

[0741] The server saves text data sent from the terminal to a database. The saved data is listed as a schedule, and reminders are set based on the specified time.

[0742] The server sends a notification to the device when the scheduled time for the reminder arrives.

[0743] The device provides voice notifications of reminders, informing the user, "It's 10 AM. It's time to take your medicine," thereby helping them to follow their schedule.

[0744] sentiment analysis

[0745] The device continuously receives the user's everyday conversations as voice input. For example, if the user says, "I'm feeling a little down today," this information is converted into text data.

[0746] The server receives the text data converted by speech recognition and performs sentiment analysis using machine learning.

[0747] Based on the results of sentiment analysis, the server monitors the user's emotional trends and identifies abnormal emotional changes from data over a certain period.

[0748] Report creation and notifications

[0749] The server aggregates schedule information and sentiment analysis results for a specified period and creates a detailed report.

[0750] The server will send this report to the user's family via communication means.

[0751] Based on the reports they receive, users (family members) can check the living situation and emotional state of the elderly person and communicate directly with them as needed.

[0752] This embodiment makes it possible to create an environment in which the invention can support elderly people in leading their daily lives with peace of mind, while simultaneously allowing families to closely monitor and provide support for the elderly person's living situation and emotional fluctuations, even when they are far away.

[0753] The following describes the processing flow.

[0754] Step 1:

[0755] When a user enters their schedule by voice, the device acquires the audio. The device then uses speech recognition technology to convert this audio into text data.

[0756] Step 2:

[0757] The terminal sends the converted text data to the server. The server stores the received schedule data in its database.

[0758] Step 3:

[0759] The server sets reminders based on saved schedules. When the set time arrives, the server sends a reminder notification to the device.

[0760] Step 4:

[0761] The device provides reminder notifications to the user as voice messages. For example, it might notify the user with something like, "It's 3 PM. It's time to take your medicine."

[0762] Step 5:

[0763] The device continuously receives the user's everyday conversations. This is then converted into text data using speech recognition technology.

[0764] Step 6:

[0765] The server performs sentiment analysis based on text data sent from the terminal. It uses machine learning models to identify sentiment labels.

[0766] Step 7:

[0767] The server aggregates the results of sentiment analysis and monitors emotional trends. If an abnormal emotional change is detected, it sends an alert to the family.

[0768] Step 8:

[0769] The server generates reports based on schedule information and sentiment data collected over a certain period.

[0770] Step 9:

[0771] The server sends the generated report to the family using a communication method. The family reviews the report and understands the user's current situation.

[0772] (Example 1)

[0773] 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".

[0774] There are challenges in reducing the cognitive burden that older adults face in their daily lives and facilitating remote monitoring and support of their living situations by family members living separately. To address this challenge, it is necessary to not only manage schedules and track emotional fluctuations, providing timely reminders, but also to detect emotional abnormalities and send appropriate information to family members.

[0775] 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.

[0776] In this invention, the server includes means for acquiring schedule information from the user's voice and converting it into text information using voice processing technology, means for storing the converted text information in a storage device and setting up notifications, and means for monitoring trends in analyzed emotional information and detecting anomalies. This enables the daily schedule management and emotional monitoring of the user, allowing family members to effectively support the health and safety of the elderly even from a distance.

[0777] "User" refers to an individual who uses this system, including elderly people and others who require support in their daily lives.

[0778] "Vocalization" refers to the act of a user providing information or instructions verbally.

[0779] "Schedule information" refers to information about daily activities and events that users need to register in the system.

[0780] "Audio processing technology" refers to the technology that converts audio into digital data and makes it a format that can be processed by machines.

[0781] "Textual information" refers to data in text format converted using speech processing technology.

[0782] A "memory device" refers to a device or system used to store digital information.

[0783] "Notification" refers to the act of informing a user of specific information or a reminder, and can be done visually or audibly.

[0784] "Emotional information" refers to data about the user's emotional state, including results analyzed from everyday conversations.

[0785] "Trend" refers to changes or patterns in data that occur over time.

[0786] "Abnormal" refers to changes in emotions or behavior that deviate from the normal range.

[0787] A "memory device" refers to a device or system used to store data and information.

[0788] A "server" refers to a computer system that provides services to other computers via a network.

[0789] This invention is an AI agent system aimed at supporting the elderly, providing technology to assist users in their daily lives. It primarily includes voice-input-based schedule management, sentiment analysis, and notification functions based on the results.

[0790] The user provides their schedule, spoken in natural language, as voice input to the system's terminal. The terminal uses speech recognition technology—for example, speech processing software—to convert the speech into text. This converted data is sent to a server and securely stored in a database.

[0791] The server uses this text information to set a reminder. Based on the reminder setting, the device will notify the user by voice at the specified time. For example, if the user says, "Take my medicine at 10 AM tomorrow," this information is transcribed into text, stored on the server, and the reminder will be activated at the appropriate time.

[0792] Furthermore, the sentiment analysis function, which identifies the user's emotions in everyday conversation, is implemented using a machine learning model. This model utilizes common natural language processing libraries to analyze the user's emotional state and aggregates the results on a server.

[0793] If emotional changes are abnormal, the server immediately generates an alert and notifies the user and their registered family members. This information serves as an important tool for families to support elderly individuals remotely.

[0794] As a concrete example, the following prompt can be considered as input to a generative AI model: "Please explain how the AI ​​agent for elderly care provides notifications regarding daily schedules and sentiment analysis." This prompt clarifies how each function of the system interacts with each other and contributes to supporting the user.

[0795] This system allows users to receive proactive support while reducing their cognitive burden in daily life, and enables family members living far away to understand the user's situation in real time and take appropriate action as needed.

[0796] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0797] Step 1:

[0798] The user speaks their daily schedule aloud. The device inputs this audio via the microphone and converts it to text using speech recognition software. This process converts the audio data into text data. The output is the schedule information stored by the device in text format.

[0799] Step 2:

[0800] The terminal sends the converted text data to the server using a communication protocol. The server records the received text data in a database. Through this data processing, the schedule information is accumulated and managed in a stored state. As output, a user schedule record is formed and stored in the database.

[0801] Step 3:

[0802] The server sets reminders based on schedule information stored in the database. It calculates the notification timing based on the specified date and time. The server then creates a reminder event based on this calculation and sends instructions to the terminal as needed. The output is the event data for which the reminder was set.

[0803] Step 4:

[0804] At the designated time, the device follows instructions from the server and notifies the user with a voice reminder. Specifically, it plays a message such as "It's 10 AM. It's time to take your medicine" through its built-in speaker. The output is a voice notification provided to the user.

[0805] Step 5:

[0806] The user's everyday conversations are input as voice by the device and converted to text in real time. The server uses this data to evaluate the user's emotional state using an emotion analysis model. This data processing calculates the emotions contained in the user's statements (e.g., happy, sad). The output is the analyzed emotion data.

[0807] Step 6:

[0808] The server tracks the analyzed sentiment data over time, monitoring changes and trends in emotions. If an abnormal change is detected, the server immediately generates an alert and notifies the user and their family. The output of this process is the alert notification that is sent.

[0809] Step 7:

[0810] The server aggregates schedule information and sentiment analysis results for a specified period and generates a detailed report. The report is formatted as a PDF or Excel file and sent to family members via communication. This allows family members to receive the generated report and understand the user's situation.

[0811] (Application Example 1)

[0812] 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".

[0813] Managing daily schedules and providing emotional support are crucial for the lives of the elderly, and it is especially important for family members living far away to be aware of their situation and provide appropriate support. However, it is a significant burden for the elderly themselves to report their situation in detail every time, and it is often difficult for family members to directly monitor them. Therefore, a system is needed that naturally collects information in daily life and uses that information to accurately understand and support the elderly's situation.

[0814] 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.

[0815] In this invention, the server includes means for converting user voice input into text information using a recognition device, means for storing the converted text information in an information storage system and setting up notifications for playing back the information, and means for notifying the user of the timetable based on the notification setting time. This makes it possible to manage schedules and understand emotions in a way that is integrated into the user's daily life. Furthermore, since these functions can be operated using a mobile terminal, family members can easily check on the elderly person's situation from a distance and communicate as needed.

[0816] "Voice input" is a method of recognizing the words or voices spoken by the user as digital data and providing that information to the system.

[0817] A "recognition device" is hardware or software that collects data in various formats and converts it into an interpretable form.

[0818] "Text information" refers to information such as audio and video that is represented as character data and is in a format that can be processed and stored by machines.

[0819] An "information storage system" is a structure and technology for securely and efficiently storing data and retrieving it as needed.

[0820] "Notification settings" refer to the system preparations and configurations for delivering information to users according to specified times and conditions.

[0821] A "timetable" is a tool for managing schedules, which organizes and displays appointments and activities based on time and date.

[0822] "Emotional analysis" is a technique for inferring a user's emotional state from their statements and behavior, and then quantifying or categorizing it.

[0823] An "analysis device" is a machine or program that analyzes input data and extracts specific information or patterns.

[0824] A "mobile device" is a portable, personally usable electronic device with communication capabilities that allows for calls, messaging, and application execution.

[0825] This invention is a system for managing schedules and analyzing emotions in daily life, and it functions in conjunction with a mobile device and a server.

[0826] Mobile devices have a voice input function, allowing users to communicate schedules and emotions by speaking. This voice data is converted into text information using the Google Cloud Speech-to-Text API. This text information is then sent to a server and stored in an information storage system.

[0827] The server stores the received text information in a database as schedule information and sets reminders. Based on these reminder settings, the mobile device notifies the user according to the timetable.

[0828] For sentiment analysis, the server uses machine learning algorithms such as scikit-learn to analyze the user's emotions from their statements. The analysis results are used to monitor the user's emotional trends, and if an anomaly is detected, the family is notified. Furthermore, reports are periodically generated from the collected schedule information and sentiment analysis results and sent to family members in remote locations via communication means.

[0829] For example, if a user voice-inputs "Take my medicine at 3 PM," that information is converted to text and added to the schedule. The device then sends a reminder at the appointed time. Furthermore, if a user says something like "I'm feeling a little anxious today," an emotion analysis algorithm analyzes that emotion, and if the anxiety persists for several days, it notifies the family. This entire process allows family members to understand the elderly person's situation and provide appropriate support, even when they are far away.

[0830] Examples of prompt statements for generative AI models include:

[0831] "My mother seems unwell lately; could you please provide any new information?"

[0832] Examples include, "Check your next appointment and set reminders for important dates."

[0833] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0834] Step 1:

[0835] The device acquires the user's speech as voice input. This voice data becomes the input. Using the Google Cloud Speech-to-Text API, this voice data is converted into text data to generate text information. The output is text information.

[0836] Step 2:

[0837] The terminal sends the text information generated in step 1 to the server. The server receives this text data and stores it in the information storage system. The input here is text information, and its storage in the database is the output.

[0838] Step 3:

[0839] The server analyzes the stored text information and interprets it as schedule information. Based on this interpreted data, it sets reminders. In this process, the input is the stored text information, and the output is the reminder setting information.

[0840] Step 4:

[0841] Based on the reminder settings, the device will notify the user at the specified time. The input is the reminder setting information, and the output is the notification to the user.

[0842] Step 5:

[0843] The server analyzes emotions based on accumulated text data. Here, a machine learning algorithm using scikit-learn analyzes the input text data and outputs the user's emotional state.

[0844] Step 6:

[0845] The server monitors the analyzed user sentiment data as a trend. It stores the results in case anomalies are detected within the data over a certain period. The input is sentiment data, and the output is information regarding the presence or absence of anomalies.

[0846] Step 7:

[0847] If an anomaly is detected, the server will notify the family using external communication methods. The server will then compile the notification content and send it. The input is the anomaly information, and the output is the notification to the family.

[0848] Step 8:

[0849] The server generates a detailed report based on schedule information and sentiment analysis results for a specified period. The inputs are schedule information and sentiment data, and the output is a report to be sent to the family.

[0850] Step 9:

[0851] Users can operate this system via their mobile devices and obtain information by entering prompt messages. Specific prompt messages include "Check my next appointment" and "Tell me about my recent emotional tendencies." The input is the prompt message, and the output is the requested information.

[0852] 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.

[0853] This invention is an AI agent system for elderly support that combines an emotion engine, comprehensively providing schedule management, emotion recognition, and communication support for users in their daily lives. The following describes a specific implementation of this system.

[0854] Schedule management and reminder notifications

[0855] The terminal accepts schedule input from the user via voice and converts it into text data using voice recognition technology. For example, information such as "I have a hospital appointment at 10 AM tomorrow" is entered into the terminal.

[0856] The server stores this text data in a database and sets up reminders based on the set time.

[0857] The server sends notification information to the device when the set time has arrived.

[0858] The device will notify the user via voice to help them remember their schedule. For example, it might notify them, "It's 10 AM. You have a hospital appointment."

[0859] Emotion recognition by an emotion engine

[0860] The device captures the user's everyday conversations in both audio and video formats. It also analyzes facial expressions and tone of voice.

[0861] The emotion engine analyzes this acquired data to identify the user's emotional state in real time. For example, if a user smiles and says in a calm voice, "It's a nice day today," the emotion engine recognizes emotions such as "joy" and "calmness."

[0862] Processing and utilizing emotional data

[0863] The server monitors emotional trends by comparing the emotional data received from the emotion engine with past data.

[0864] The server sends an alert to family members if it detects any unusual emotional changes, such as a sudden increase in the frequency of "anxiety" or "sadness."

[0865] Report creation and notifications

[0866] The server aggregates schedule completion status and sentiment analysis results to create a report.

[0867] The server will send this report to the family via communication means.

[0868] Users (family members) can understand the living conditions and mental health status of elderly individuals through the reports.

[0869] This embodiment allows the system to provide multifaceted support for the user's daily life and create an environment where the elderly and their families can live with greater peace of mind. Furthermore, the introduction of an emotion engine enhances the user's emotional support, enabling a safer and more fulfilling life.

[0870] The following describes the processing flow.

[0871] Step 1:

[0872] The user speaks their schedule into the device, saying, "Take a walk at 2 PM." The device captures this audio and converts it into text data using speech recognition technology.

[0873] Step 2:

[0874] The device sends the converted text data to the server. The server receives it and stores it in its database. It also sets a reminder for "2 PM".

[0875] Step 3:

[0876] When the set time arrives, the server sends a reminder to the device. The device then notifies the user by voice, "It's 2 PM. It's time for a walk."

[0877] Step 4:

[0878] When a user engages in everyday conversation, the device acquires audio and video data in real time. For example, it captures a scene where the user smiles and says, "It was a fun day."

[0879] Step 5:

[0880] The emotion engine analyzes acquired data to recognize the user's emotions. It determines emotions such as "joy" or "peace" from facial expressions and tone of voice.

[0881] Step 6:

[0882] The terminal sends the analysis results to the server. The server monitors the sentiment data as a trend and performs analysis over a certain period.

[0883] Step 7:

[0884] When the server detects an unusual change in emotional state, it sends an alert to the family via email or other means. The alert may include a message such as, "You've been experiencing increased anxiety lately."

[0885] Step 8:

[0886] The server aggregates key daily events and sentiment analysis results to create a report. This includes information on behavioral history and sentiment trends.

[0887] Step 9:

[0888] The generated report is sent to the family via communication means. The user (family member) can review the report and understand the living conditions of the elderly person living separately.

[0889] (Example 2)

[0890] 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".

[0891] There is a need to alleviate the complexities of schedule management and the difficulties in understanding the mental health of users, including the elderly, that they face in their daily lives. Conventional systems simply notify users of their schedules, but they cannot properly understand and manage the associated emotional fluctuations, and therefore cannot provide sufficient support to maintain users' mental health. This creates a challenge in creating an environment where users and their families can live with peace of mind.

[0892] 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.

[0893] In this invention, the server includes means for acquiring schedule data from the user's voice information and converting it into string information using acoustic processing technology; means for storing the converted string information in a storage device and setting schedule notifications; means for notifying the user of the schedule information by voice according to the set notification time; means for emotional analysis that recognizes the user's emotions from everyday conversation; means for analyzing the recognized emotional data and detecting anomalies by comparing it with past data; means for notifying an external party when an abnormal emotional change is detected; and means for creating a report based on the schedule data and emotional analysis results for a day or a predetermined period and transmitting it externally via communication means. This makes it possible to centrally manage the user's schedule management and emotional trends, and to support them in living a safer and more secure life.

[0894] A "user" refers to any individual who uses this system, including any person, such as the elderly.

[0895] "Audio information" refers to the acoustic signals of speech or conversation generated by the user, and processing is performed based on these signals.

[0896] "Schedule data" refers to information about activities and events related to specific future dates and times, provided by the user through voice information.

[0897] "Audio processing technology" refers to the technology used to convert audio information into digital string information.

[0898] "String information" refers to text data converted from audio information, in a format that can be processed by digital devices.

[0899] A "storage device" refers to hardware or software used to store scheduled data or string information for long-term or short-term storage.

[0900] "Schedule notification" refers to a record or reminder that provides users with pre-set schedule information at the appropriate time.

[0901] "Emotion" refers to the state of mind and feelings judged from the user's everyday conversation, facial expressions, voice tone, etc.

[0902] "Emotional analysis means" refers to software or a device used to recognize a user's emotions, and includes techniques for analyzing the acquired data.

[0903] "Anomaly" refers to a state where the results of an analysis based on the user's emotional data show a change that exceeds the normal range.

[0904] A "report" is a document generated based on schedule data and emotional analysis results, and is provided to understand the user's living situation.

[0905] "Communication means" refers to methods or devices for transmitting reports to external devices or individuals.

[0906] This invention is an AI agent system that utilizes voice and emotion recognition to support the lives of users, including the elderly, and provides a safe and fulfilling life through schedule management and emotional analysis. The specific configuration and operation of the system are shown below.

[0907] The user communicates their schedule to the device via voice input. The device uses voice processing software (e.g., a voice recognition API) equipped with voice recognition technology to convert the voice into text information. This resulting schedule data is sent to a server and stored in its memory. The server sets reminders based on this schedule data and sends voice notifications to the user via the device at the appropriate time. Voice synthesis software (e.g., a voice synthesis API) is used for these voice notifications.

[0908] Furthermore, the device collects user conversations in audio and video format on a daily basis and uses emotional analysis tools to recognize emotions. This analysis utilizes software that employs generative AI models (e.g., an emotional analysis API) to identify the user's emotional state. This information is sent to a server, where it is further compared and analyzed with past data to monitor emotional trends. If an anomaly is detected, the server sends a notification to the family.

[0909] This system helps users and their families live with peace of mind by integrating schedule management and emotional state monitoring. Specifically, for example, if a user says, "I have a hospital appointment at 10 AM tomorrow," the schedule is automatically set, and if there is a change in emotions, an alert is sent to the family such as, "Recently, the user has been feeling lonely more frequently."

[0910] An example of a prompt to the generating AI model is, "If an elderly user has recently been showing a sad expression frequently, please provide specific suggestions for support or words to say to improve their mood." This input will generate a response.

[0911] In this way, the system aims to improve the quality of daily life by processing complex data such as user communication, schedules, and emotional states.

[0912] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0913] Step 1:

[0914] The user communicates their schedule via voice. The device receives this voice input and uses speech recognition technology to convert the voice data into text data. Specifically, if the user voice-inputs "I have a hospital appointment at 10 AM tomorrow," the device collects the voice and inputs it into a speech recognition API. The API analyzes the voice and outputs it as text data, "I have a hospital appointment at 10 AM tomorrow."

[0915] Step 2:

[0916] The terminal sends the generated text data to the server. The server stores the received text data in its storage device and sets up a reminder. Specifically, the text data "Hospital appointment at 10am tomorrow" is stored in the database along with the date and time information. Based on the stored information, the server creates a reminder and sets it to notify at the specified time. The information stored by the server in the database is referenced when setting up reminders.

[0917] Step 3:

[0918] Based on the set notification time, the server sends a reminder notification to the device. Upon receiving this notification, the device uses speech synthesis technology to inform the user verbally. Specifically, when the server sends the notification content "It's 10 AM. You have a hospital appointment" to the device, the device uses a speech synthesis API to convert it into speech and plays it back to the user.

[0919] Step 4:

[0920] The device captures the user's everyday conversations as audio and video. This data is used for emotion analysis. The device inputs the acquired audio and video data into an emotion analysis system and uses a generative AI model to recognize the user's emotions. Specifically, it inputs the user's voice saying "It's a nice day today" and their smiling face into an emotion analysis API and outputs a recognition result such as "joy."

[0921] Step 5:

[0922] Based on the emotional recognition results, the server compares them with past data to analyze emotional trends. If the server detects an abnormal change in emotion, it generates an alert and notifies an external individual, such as a family member. Specifically, if the server finds that "the frequency of anxiety has recently increased sharply," it will send this information as a message to the family.

[0923] Step 6:

[0924] The server creates a report based on daily or predetermined period schedule data and emotional analysis results, and transmits it externally via communication means. Specifically, the server compiles the schedule performance information and emotional analysis results in a table format, outputs it as a PDF file, and sends that file to the family via email.

[0925] (Application Example 2)

[0926] 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".

[0927] To support the lives of the elderly, it is necessary not only to manage their schedules but also to understand their mental health and provide an environment where they can live with peace of mind. However, currently, there is a lack of systems to adequately understand the emotions of the elderly and to respond quickly when problems occur. As a result, it is difficult for family members and caregivers to understand the condition of the elderly and to provide a supportive environment that ensures their safety.

[0928] 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.

[0929] In this invention, the server includes means for acquiring voice data and converting it into text data using voice recognition technology, means for storing the converted text data in a data storage device and setting up notifications, and means for observing the user's state in real time using a smart device and recording emotional trends. This makes it possible to support all aspects of the elderly's lives and provide real-time information that allows families and caregivers to live with peace of mind.

[0930] "Voice data" refers to information that records the user's speech, and serves as the basis for converting it into text data using speech recognition technology.

[0931] "Speech recognition technology" is a technology that extracts information from spoken words and converts it into text data.

[0932] "Text data" refers to a form of character information converted from audio data, which is suitable for management and analysis in information processing.

[0933] A "data storage device" is a device for storing data, accumulating text data and sentiment data in the form of a database.

[0934] A "notification" is information that is sent to the user at a set time or under set conditions, and is used for managing schedules and reporting anomalies.

[0935] A "smart device" is a portable information terminal connected to the internet, equipped with sensors that allow for real-time monitoring of the user's condition.

[0936] "Emotion discrimination means" refers to methods or devices for identifying and analyzing emotions from a user's voice and facial expressions.

[0937] A "learning model" is an algorithm used to learn patterns based on data and identify emotions or user states.

[0938] The system of this invention aims to support the lives of elderly people by observing and recording the user's voice data and emotional state in real time. The server converts the voice data acquired using voice recognition technology into text data, stores it in a data storage device, and sets notifications. For example, if a user says, "I will take my medicine at 3 pm tomorrow," the server recognizes this and sets a reminder.

[0939] The server also uses sensor data from smart devices to observe the user's state. The smart devices monitor the user's daily conversations and actions, and analyze their emotions using emotion recognition tools. Machine learning models are used for this analysis, and the results are recorded in a database. If an abnormal change in emotion is detected, the family is notified.

[0940] For example, if a user says in a low voice, "I'm not feeling well today," the emotion recognition system can detect "anxiety" or "sadness," and the server can send a notification to the family as important information.

[0941] This allows family members and caregivers of elderly individuals to accurately understand their health and living conditions remotely. An example of a prompt message to be input into the AI ​​model based on this information would be: "User's emotions are unstable: They are speaking in a weak voice and saying they are not feeling well. How should we provide support?"

[0942] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0943] Step 1:

[0944] The server receives voice data spoken by the user. This voice data is collected in real time through the smart device's microphone. This input voice data is converted into text data using the Google Speech-to-Text API. The text data is formatted as the user's everyday speech and schedule information.

[0945] Step 2:

[0946] The server stores text data in a data storage device and sets reminders based on it. For example, based on time information such as "3 PM tomorrow," it records a reminder notification in the data storage device and prepares to notify the user as the time approaches. This is monitored by a Cron job or timer.

[0947] Step 3:

[0948] The device (smart device) continuously acquires the user's voice and video data and analyzes their emotions using a machine learning model as a means of emotion determination. This process analyzes the tone of voice and facial expressions in the video to estimate the user's emotional state. As output, the user's emotional state is acquired as real-time data.

[0949] Step 4:

[0950] The server records the changes in emotional data acquired in real time into a data storage device and monitors abnormal emotional changes by applying an anomaly detection algorithm. For example, a sudden surge in the values ​​for "sadness" or "anxiety" over a short period of time is considered abnormal. This judgment criterion is supported by statistical analysis and AI models.

[0951] Step 5:

[0952] If an anomaly is detected, the server will send an alert to the information terminal of a family member designated as a contact. This notification will include the specific nature of the anomaly and possible countermeasures. The notification will be sent via email or application to encourage prompt action.

[0953] Step 6:

[0954] The server generates reports based on daily text data and sentiment analysis results, and sends them to family members and caregivers via communication channels. These reports use a generative AI model to create prompts that provide detailed information about the user's living situation. Specific examples include statements such as, "User's emotions are unstable: They are speaking in a listless voice and saying they are unwell. How should we provide support?"

[0955] 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.

[0956] 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.

[0957] 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.

[0958] 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.

[0959] 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.

[0960] 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.

[0961] 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.

[0962] 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.

[0963] 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."

[0964] 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.

[0965] 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.

[0966] 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.

[0967] 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.

[0968] 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.

[0969] 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.

[0970] 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.

[0971] 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.

[0972] 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.

[0973] 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.

[0974] 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.

[0975] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0976] The following is further disclosed regarding the embodiments described above.

[0977] (Claim 1)

[0978] A means of acquiring schedule information from the user's voice and converting it into text data using speech recognition technology,

[0979] The converted text data is saved to a database, and a means of setting reminders is provided.

[0980] A means of notifying the user of the schedule according to the set notification time of the reminder,

[0981] A sentiment analysis method for identifying user emotions from everyday conversations,

[0982] A means for monitoring trends in identified sentiment data and detecting anomalies,

[0983] A means of notifying when an abnormal emotional change is detected,

[0984] A means of creating a report based on daily or periodic schedule information and sentiment analysis results, and transmitting it externally via communication means,

[0985] A system that includes this.

[0986] (Claim 2)

[0987] The system according to claim 1, which sets schedule information and sends reminder notifications via a notification device.

[0988] (Claim 3)

[0989] The system according to claim 1, wherein the emotion analysis means uses a machine learning model.

[0990] "Example 1"

[0991] (Claim 1)

[0992] A means for acquiring schedule information from the user's voice and converting it into text information using speech processing technology,

[0993] The converted character information is stored in a storage device, and a means for setting up notifications is also provided.

[0994] A means of informing the user of the scheduled event according to the set notification time,

[0995] A method for analyzing user emotions from everyday conversations,

[0996] A means of monitoring trends in analyzed emotional information and detecting anomalies,

[0997] A means of notifying when an abnormal emotional change is detected,

[0998] A means for generating a report based on schedule information and sentiment analysis results for a certain period, and transmitting it externally via a communication means,

[0999] A system that includes this.

[1000] (Claim 2)

[1001] The system according to claim 1, wherein scheduling information is set and notified via a notification device.

[1002] (Claim 3)

[1003] The system according to claim 1, wherein the emotion analysis means uses a machine learning model.

[1004] "Application Example 1"

[1005] (Claim 1)

[1006] A means for converting user voice input into text information using a recognition device,

[1007] A means for storing the converted text information in an information storage system and for setting up notifications to play back the information,

[1008] A means of notifying the user of the timetable based on the notification setting time,

[1009] An analysis device that analyzes the emotions of users from everyday conversations,

[1010] A device that monitors trends in analyzed emotional data and detects anomalies,

[1011] A device that notifies when it detects an abnormality in emotional state,

[1012] A device that generates a report based on timetable information and emotional analysis results for a specific period and provides it to an external device via a communication device,

[1013] Means for operating the above functions using a mobile device,

[1014] A system that includes this.

[1015] (Claim 2)

[1016] The system according to claim 1, wherein timetable information is set and notified using a mobile information terminal.

[1017] (Claim 3)

[1018] The system according to claim 1, wherein the emotion analysis means uses a machine learning algorithm.

[1019] "Example 2 of combining an emotion engine"

[1020] (Claim 1)

[1021] A means for obtaining schedule data from user voice information and converting it into string information using acoustic processing technology,

[1022] A means for saving the converted string information to a storage device and setting up scheduled notifications,

[1023] A means of notifying the user of scheduled information by voice according to a set notification time,

[1024] An emotional analysis method that recognizes the user's emotions from everyday conversation,

[1025] A means for analyzing recognized emotional data and detecting anomalies by comparing it with past data,

[1026] A means of notifying an external party when an abnormal emotional change is detected,

[1027] A means of creating a report based on daily or predetermined period schedule data and emotional analysis results, and transmitting it externally via communication means,

[1028] A system that includes this.

[1029] (Claim 2)

[1030] The system according to claim 1, wherein scheduled data is set and notified via a notification device.

[1031] (Claim 3)

[1032] The system according to claim 1, wherein the emotion analysis means utilizes a generative model.

[1033] "Application example 2 when combining with an emotional engine"

[1034] (Claim 1)

[1035] A means of acquiring audio data and converting it into text data using speech recognition technology,

[1036] The converted text data is stored in a data storage device, and a means for setting up notifications is also provided.

[1037] A means of notifying the user of the scheduled event according to the set notification time,

[1038] A means of identifying a user's emotions from everyday information,

[1039] A means for monitoring the trends of identified sentiment data and detecting anomalies,

[1040] A means of notifying when an abnormal emotional change is detected,

[1041] A means of creating a report based on scheduled information and emotional assessment results for a certain period, and sending it externally,

[1042] A means of observing the user's state in real time using smart devices and recording emotional trends,

[1043] A system that includes this.

[1044] (Claim 2)

[1045] The system according to claim 1, which sets and notifies schedule information via an information device.

[1046] (Claim 3)

[1047] The system according to claim 1, wherein the emotion discrimination means uses a learning model. [Explanation of Symbols]

[1048] 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 for converting user voice input into text information using a recognition device, A means for storing the converted text information in an information storage system and for setting up notifications to play back the information, A means of notifying the user of the timetable based on the notification setting time, An analysis device that analyzes the emotions of users from everyday conversations, A device that monitors trends in analyzed emotional data and detects anomalies, A device that notifies when it detects an abnormality in emotional state, A device that generates a report based on timetable information and emotional analysis results for a specific period and provides it to an external device via a communication device, Means for operating the above functions using a mobile device, A system that includes this.

2. The system according to claim 1, wherein timetable information is set and notified using a mobile information terminal.

3. The system according to claim 1, wherein the emotion analysis means uses a machine learning algorithm.

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