system

Abstract

We provide the system. [Solution] Means for obtaining schedule information and dialogue data, A means for generating reminders based on the aforementioned schedule information, A means for analyzing emotional states based on the aforementioned dialogue data, A means for notifying the aforementioned reminder, A means for generating a situation report based on the analyzed emotional state and transmitting it externally, A means of issuing an alarm under specified conditions, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Problems such as forgetfulness faced by the elderly, difficulties in schedule management, insufficient communication with family members living separately, forgetting to take medicine, and difficulty in grasping the mental health status of the elderly. As a result, it has become difficult to maintain the independent life of the elderly and ensure the sense of security of the family.

Means for Solving the Problems

[0005] The present invention solves the above problems by providing means for acquiring schedule information and dialogue data, generating and notifying reminders based on this information, and means for analyzing emotional states and providing status reports and alarms to external parties. Specifically, it comprehensively manages the mental health and daily schedule of elderly people by analyzing emotional states using natural language processing technology and issuing alarms to family members as needed.

[0006] "Schedule information" refers to information about appointments and tasks related to the daily activities of elderly people.

[0007] "Dialogue data" refers to information obtained from voice communication between elderly individuals and systems or other people.

[0008] A "reminder" refers to instructions or warnings that inform elderly people of their schedules or activities at specific times.

[0009] "Emotional state" refers to information that indicates the psychological and emotional state of elderly individuals, and is analyzed based on dialogue data.

[0010] "Natural language processing technology" refers to the technology that computers use to understand, analyze, and generate human language.

[0011] A "situation report" refers to a document or data that compiles information about the daily life and emotional state of elderly individuals and provides it to external users.

[0012] An "alert" refers to a warning message sent to family members or administrators when an abnormality is detected in the condition of an elderly person.

[0013] "External users" refers to people other than the elderly who can receive and use the information generated by the system, especially family members and medical professionals. [Brief explanation of the drawing]

[0014] [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 a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] To implement this invention, it is necessary to provide devices suitable for the living environment of the elderly. For example, terminals such as smartphones and smart speakers can be used. These terminals will have dedicated applications or software installed to implement a system that supports the daily lives of the elderly.

[0036] First, the device collects schedule and medication information from elderly individuals. This information is entered via voice or manual input. The collected data is then sent to a server for processing. The transmitted data is stored in a secure database and used for subsequent analysis.

[0037] Next, the server analyzes the received schedule information and generates reminders at the necessary times. These reminders are sent to the elderly person via voice notifications or screen displays on their device, according to the sender's wishes. For example, as medication time approaches, it provides a specific message such as, "It's 2 PM. Please take your blood pressure medication."

[0038] The server also analyzes the audio data and uses natural language processing techniques to extract emotional states from the conversation. Emotional states are recorded as daily changes, and are monitored, especially for any sudden emotional shifts.

[0039] The server then generates daily status reports based on the analysis results. These reports include schedule progress and a summary of emotional state, and are sent to the family members who are the users. Based on this information, the family can remotely manage the health status of the elderly person.

[0040] Finally, in the event of any sudden emotional changes or persistent abnormalities being detected, the server will immediately send an alert to the user to encourage early care for the elderly.

[0041] Through these processes, the present invention aims to support the independent living of the elderly and provide peace of mind to their families who live separately. Specifically, if an elderly person suddenly exhibits negative emotions and becomes unable to carry out their activities as scheduled, this system can immediately alert the family and help provide the necessary support promptly.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The device acquires schedule and medication information from elderly individuals via voice or manual input. The acquired information is temporarily stored on the device.

[0045] Step 2:

[0046] The device sends the collected data to the server using a secure communication protocol. After transmission, the data is stored in a highly reliable database.

[0047] Step 3:

[0048] The server analyzes the received schedule information and generates reminders using the calendar service. The generated reminders are then sent back to the device at the specified time and frequency to send notifications.

[0049] Step 4:

[0050] The device will notify the elderly person of a reminder at a designated time via voice notification or display. For example, it can notify them of the time to take a specific medication.

[0051] Step 5:

[0052] The device records everyday conversation data and periodically uploads this audio data to a server.

[0053] Step 6:

[0054] The server processes the uploaded audio data using a natural language processing engine to analyze the emotional state from the dialogue. The resulting emotional data is then stored as an emotional score.

[0055] Step 7:

[0056] Based on the analyzed emotion score and schedule completion status, the server generates a status report at the end of the day. This report contains important information for understanding the lifestyle and health status of the elderly.

[0057] Step 8:

[0058] The server sends the generated status report to the user (family member) via email or a dedicated app.

[0059] Step 9:

[0060] The server continuously monitors the sentiment score and immediately sends an alert to the user if any abnormal fluctuations are detected. The alert is communicated via push notification or SMS.

[0061] Step 10:

[0062] Users review received reports and alerts, and evaluate and implement follow-up and appropriate interventions for elderly individuals.

[0063] (Example 1)

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

[0065] Elderly people face challenges in managing their schedules and understanding their emotional state in their daily lives. Furthermore, it is difficult for family members living far away to appropriately monitor the health of elderly individuals remotely. This leads to a lack of support for the independent living of the elderly and a lack of reassurance for their families.

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

[0067] In this invention, the server includes a device for acquiring schedule information and user input, a device for generating notifications based on the schedule information, and a device for analyzing emotional state based on user input. This enables efficient schedule management and emotional state monitoring of elderly individuals, allowing family members to remotely understand their health status.

[0068] "Schedule information" refers to information related to a user's appointments and tasks, including the date, time, and content of those appointments.

[0069] "User input" refers to data provided by users through voice or manual operation, which serves as the basis for system analysis and processing.

[0070] "Notifications" refer to messages and alerts sent to users for the purpose of confirming schedules or prompting them to take action.

[0071] "Emotional state" refers to data that reflects the user's psychological or emotional condition and is evaluated through methods such as linguistic analysis.

[0072] A "status report" is a document or data report generated by the system that compiles information including the user's schedule progress and emotional state.

[0073] An "alert" refers to a warning or notice issued when specified conditions are met, and is used when immediate action is required.

[0074] This invention is a system designed to support the independent living of the elderly and provide peace of mind to family members living separately. This system operates with terminals, servers, and users each playing their respective roles and working in coordination.

[0075] The devices are designed for use by the elderly and include smartphones and smart speakers. These devices have applications installed that retrieve schedules and medication information entered by the user via voice or manually. The devices also have functions to notify users of reminders, such as voice notifications and screen displays.

[0076] The server is a computing device that aggregates and analyzes data sent from elderly individuals. The collected schedule information is stored securely in a database. The server generates reminders based on the schedule information at appropriate times through an analysis module. It also uses natural language processing technology to analyze emotional states obtained from voice input. A specific artificial intelligence model is used for this analysis, which can grasp the user's daily rhythm and emotional trends. In particular, if an abnormal change in emotion is detected, a system is in place to quickly send an alert to the user's family.

[0077] The users are primarily family members who receive status reports provided by the system. The received reports include the progress of the elderly person's schedule and the results of sentiment analysis, which the users can use to remotely monitor and manage the elderly person's health.

[0078] As a concrete example, consider a scenario where an elderly person experiences emotional distress earlier than expected. In this case, the system retrieves data from their smartphone such as, "Something's different today. They seem to be thinking about something," and uses this data to send a notification to their relatives. Based on this prompt, the family can intervene early through methods such as phone calls or visits.

[0079] An example of a prompt is, "Describe a system that analyzes the emotional state of elderly individuals and notifies the user if there are any negative changes." Using this example, the system can perform the specified role.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The device collects schedule information and medication details from elderly individuals through voice or manual input. Input data includes date and time, activity details, and medication times. This data is temporarily stored on the device and formatted in preparation for transmission to the server. For example, if "I have a hospital appointment at 1 PM" is voice-input, that information is saved as text data.

[0083] Step 2:

[0084] The terminal encrypts the collected information and sends it to the server using a secure communication protocol. Schedule data and medication information, as input, become output data securely transferred to the server using an encryption algorithm. This process is crucial for protecting data privacy.

[0085] Step 3:

[0086] The server stores the received data in a database and analyzes it via the schedule management module. Based on the input schedule information, analysis is performed to generate reminders. For example, based on the reservation information for 1 PM, preparations are made to output a notification.

[0087] Step 4:

[0088] Based on the analysis results, the server creates a reminder and sends it to the device at the required time. The user is configured to receive notifications about specific activities and medication times. As output, a notification message is generated for the user and provided visually or audibly.

[0089] Step 5:

[0090] The server uses an AI model to generate voice data and perform emotion analysis. User voice recordings are used as input data, and emotional states are extracted through natural language processing techniques. An index indicating emotional changes is generated and output for anomaly detection.

[0091] Step 6:

[0092] The server comprehensively analyzes daily emotional states and schedule progress to generate a status report. Emotional analysis and schedule information are used as input, and a detailed report is generated that is sent to the family. The family, as users, can then monitor the health status based on this report.

[0093] Step 7:

[0094] If an abnormality or sudden change in emotional state is detected, the server immediately sends an alert to the user (family member). The input is emotional data indicating the abnormality, and a warning message is generated and output based on this data. This allows the family to take a quick response.

[0095] (Application Example 1)

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

[0097] In the lives of the elderly, it is essential that they maintain independent daily living while their families, who live separately, monitor their health and respond promptly as needed. However, missed schedules or overlooked emotional changes can delay appropriate care. Traditional methods have made it difficult to respond immediately to sudden emotional changes and have not been able to efficiently provide information on daily health management.

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

[0099] In this invention, the server includes means for acquiring schedule information and dialogue data, means for analyzing emotional states based on the dialogue data and detecting sudden changes in those emotions, and means for generating a detailed status report based on the analyzed emotional state and transmitting it to an external information user. This enables immediate response to sudden changes in emotions and health conditions, and allows for effective remote management of the health of the elderly.

[0100] "Schedule information" refers to data about times and dates that users use to plan their daily activities and appointments.

[0101] "Dialogue data" refers to a record of linguistic expressions generated through communication between users or with devices.

[0102] A "reminder" is an alert message that notifies a user at a specific time or under specific conditions, prompting them to take action.

[0103] "Emotional state" refers to data that indicates the user's psychological state and changes in their emotions.

[0104] "Analysis" is the process of breaking down information based on collected data and deriving understandings and conclusions.

[0105] A "notification" is a message or signal used to transmit events or information.

[0106] A "situation report" is a report that summarizes the current situation based on collected information and provides it to others.

[0107] "External users" refer to third parties or family members who do not directly use the system but receive its information.

[0108] "Anomaly" is a concept that refers to a state or change that is different from the normal state.

[0109] An "alert" is an alert signal issued to warn of danger or an emergency.

[0110] In order to implement this invention, a program that performs a specific function is required. This program is mainly composed of a server and a terminal used by the user.

[0111] The server receives and stores schedule information and dialogue data using a cloud platform (such as AWS® Lambda and DynamoDB). During this process, a speech recognition service (Google® Cloud Speech-to-Text API) is used to convert the speech data into text format. The converted data is then analyzed using natural language processing technology (AWS Comprehend) to infer emotional states.

[0112] Furthermore, the device functions as a smartphone and smart speaker, providing an interface for users to input voice commands and schedule information. Users can easily record information through voice or text input.

[0113] As a concrete example, suppose an elderly person says in the morning, "I'm going for a walk at 3 PM today." The voice is recorded on the device and sent to the server. As a result, a reminder is generated at 3 PM and displayed as a notification on the user's device.

[0114] An example of a prompt message would be: "We are designing an emotion monitoring system for the elderly. Please tell us about efficient methods for detecting emotions via voice input and reporting changes in emotions."

[0115] This system efficiently processes information based on emotions and schedules, ensuring that reminder notifications, emotion monitoring, and alarms in case of anomalies are reliably executed.

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

[0117] Step 1:

[0118] Users input schedule information and other tasks into the device via voice commands or text input. The input voice is collected by the device's microphone and converted into text data using the Google Cloud Speech-to-Text API. Input is voice data, output is text data.

[0119] Step 2:

[0120] The converted text data is sent from the terminal to the server, where it is processed by AWS Lambda. The input is text data, and the output is processed text. This process ensures that the user's schedule is properly stored in the database.

[0121] Step 3:

[0122] The server uses AWS Comprehend to analyze emotional states from conversational data. The input is text data provided by the user, and the output is the result of the emotional state analysis. In this step, emotional categories such as positive and negative are assigned based on specific keywords and contexts in the emotional state.

[0123] Step 4:

[0124] The server automatically generates a situation report, including emotional states, based on the analyzed data, and sends it to external information users. The input is the analysis of emotional states, and the output is a detailed situation report. This report contains a summary of daily behavior and emotional changes.

[0125] Step 5:

[0126] If an abnormality or emergency is detected through emotional state analysis, the server immediately sends an alert to the user and designated emergency contacts. The input is the emotional state analysis results, including any abnormalities; the output is the alert message. This enables emergency response for elderly individuals.

[0127] At each processing step, input and output are converted, and this series of steps realizes a system that supports the independent management of the user's health status.

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

[0129] To implement this invention, it is necessary to have a terminal equipped with an advanced program including an emotion engine and a server working together. This terminal is a device used daily by elderly users, such as a smartphone or smart speaker. The terminal has an app installed for managing schedules and medication, and obtains necessary information through voice input or manual input.

[0130] First, the device collects schedule and medication information from the elderly person, and uses an emotion engine to analyze the conversation data and understand their emotional state. The acquired data is securely transmitted to a server. The server analyzes this data and generates reminders at specific times.

[0131] The device notifies users of the generated reminders and provides instructions to the elderly via voice or on-screen display as schedule reminders. For example, it might prompt the elderly to take action with a message like, "It's 3 PM. It's time for a walk."

[0132] Next, the server uses an emotion engine to analyze the acquired conversation data in real time and determine the user's emotional state. This determination is saved as an emotion score and stored in a database for analyzing emotional tendencies along with the conversation history. For example, by comparing past data with the current emotional state, it can be determined whether an elderly person has been experiencing stress recently.

[0133] The server generates a status report based on the analysis results and sends it to the user's family via email or app notification daily or as needed. Through this report, the family can understand the elderly person's psychological state and daily activities.

[0134] Furthermore, if the server detects an anomaly based on emotional state or schedule information, it immediately sends an alert to the user's family. This makes it possible to respond early when an abnormality is detected in the mental state of an elderly person.

[0135] For example, if an elderly person who has consistently shown calm emotions suddenly exhibits negative emotions, the emotion engine will detect this change, the server will immediately issue an alert, and the user (family member) will be prompted to check on the situation, thereby enabling safe and smooth management of the elderly person's life.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] The device acquires schedule information and dialogue data from the user. Schedule information is acquired through manual input or voice commands. Dialogue data is collected by recording everyday conversations.

[0139] Step 2:

[0140] The terminal sends acquired schedule information and conversation data to the server via secure communication. This transmission occurs in real time, and the data is processed immediately.

[0141] Step 3:

[0142] The server analyzes the received schedule information and generates reminders based on the schedule. The generated reminders are sent to the device to notify the elderly person at the specified time.

[0143] Step 4:

[0144] When the designated time arrives, the device will notify the user of the received reminder via voice or on-screen display. For example, it might provide a message such as, "It's 6 PM. Time for dinner," encouraging the user to follow their schedule.

[0145] Step 5:

[0146] The server uses an emotion engine to analyze the received dialogue data. The audio data is converted to text, and natural language processing techniques are used to analyze the user's emotional state.

[0147] Step 6:

[0148] The server records the analysis results as an emotion score in a database and analyzes the user's emotional tendencies by comparing them with past data. This makes it possible to monitor changes in psychological state over the long term.

[0149] Step 7:

[0150] The server generates daily status reports based on analyzed emotional states and schedule progress. These reports include metrics for evaluating the user's quality of life.

[0151] Step 8:

[0152] The server sends the generated status report to the user's family at the specified time. The family receives this report and uses it to understand the health status of the elderly person.

[0153] Step 9:

[0154] When the server detects anomalies based on factors such as emotion scores and schedule non-compliance, it immediately sends an alert to the user's family. This alert includes a detailed explanation of the situation and specific follow-up recommendations.

[0155] Step 10:

[0156] The user's family can review the received alarms and reports and take appropriate action, such as visiting or calling the elderly person, as needed. This enables prompt assistance.

[0157] (Example 2)

[0158] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0159] In modern society, there is a need for systems to support the living environment of the elderly. However, existing technologies make it difficult to grasp changes in the emotional state and activities of the elderly in real time and to quickly notify relevant parties when necessary. Therefore, a system is needed that can accurately analyze the condition of the elderly and provide appropriate support.

[0160] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0161] In this invention, the server includes means for collecting activity information and interaction data using an information acquisition device, means for analyzing the interaction data to identify emotional states, and means for issuing an alarm when an abnormality is detected. This makes it possible to monitor the emotional state of elderly people in real time and promptly notify family members or caregivers of the situation as needed.

[0162] An "information acquisition device" is a device that has the function of collecting activity information and interaction data of elderly people.

[0163] "Activity information" refers to data related to events and schedules in the daily lives of elderly people.

[0164] "Interaction data" refers to information, such as voice and text, that elderly people use when communicating with others.

[0165] "Record messages" refer to the content of notifications and alerts generated based on the daily activities and schedules of elderly individuals.

[0166] "Emotional state" is an indicator that shows changes in the emotions and psychological condition of elderly people.

[0167] "Language processing technology" refers to the techniques used to analyze natural language data and understand and process its content.

[0168] A "situation report" is a report that summarizes the situation of elderly individuals based on collected activity information and emotional status.

[0169] "External recipients" refer to stakeholders who should receive notifications and reports, such as family members or caregivers of elderly individuals.

[0170] "Abnormal" refers to a situation that deviates significantly from normal activity or emotional state.

[0171] To implement this invention, a combination of a terminal and a server used daily by elderly people is required. The terminal could be a smartphone or a smart speaker, and these would be used to monitor the elderly person's daily activities and emotional state. The terminal would have software installed to accept voice input or manual input, thereby collecting schedule information and interaction data.

[0172] The server plays a central role in analyzing the collected data. Specifically, a sentiment analysis engine built on the server uses natural language processing to identify emotional states from dialogue data. This allows for real-time monitoring of the user's emotional state. Furthermore, the server analyzes activity information, generates recording messages at appropriate times, and can promptly issue alarms if anomalies are detected.

[0173] For example, if an elderly person records their daily activities using voice on a device, the device sends this data to a server, which then generates appropriate reminders based on that data. Furthermore, if negative emotions are suddenly detected, the server immediately notifies the family of this anomaly. This invention effectively supports the safety and health of the elderly by deeply analyzing communication data using a generative AI model and appropriate prompt messages. An example of a prompt message is, "Analyze the user's emotion score and generate a detailed report if any anomalies are found."

[0174] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0175] Step 1:

[0176] The device collects activity information and interaction data from elderly individuals through voice input or manual input. This activity information includes schedules and medication information. The entered data is temporarily stored on the device. After data acquisition is complete, the device sends this information to a server using encryption technology. Specifically, the device will ask the user by voice, "Please tell me your schedule for today," and save the information obtained.

[0177] Step 2:

[0178] The server receives activity information and interaction data sent from the terminal. Using this data as input, the server begins generating log messages based on the activity information. It analyzes the data using natural language processing techniques and creates reminders tailored to the day's specific schedule. For example, the server might create a reminder such as, "It's 2 PM, time to take your medicine."

[0179] Step 3:

[0180] The server inputs the received interaction data into its sentiment analysis engine to identify the user's emotional state. A generative AI model is used to identify the emotional state and calculate an emotional score based on the interaction data. This score is used to detect changes in emotion by comparing it with past data and the current state, and the server records this as output in a database.

[0181] Step 4:

[0182] The server outputs the generated record messages and sentiment scores, saving them to a designated database. Furthermore, the server retrieves current sentiment and activity information from the database and creates a status report based on this information. This status report is then sent to the user's family via email or app notification.

[0183] Step 5:

[0184] The server continuously monitors activity information and emotional status, and immediately sends an alert to the family if an anomaly is detected. Specifically, the server generates a notification such as "There is an anomaly in the user's emotional score. Please check the details" and sends it to the family. This ensures that important information about the elderly person's condition is quickly communicated to relevant parties.

[0185] (Application Example 2)

[0186] 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".

[0187] In the daily lives of the elderly, it is crucial to accurately understand their schedules and emotional changes, and to provide support and warnings at the appropriate time. However, conventional systems have struggled to integrate emotional states and schedule information, and to provide effective feedback and warnings to the elderly and their families. Therefore, there is a need for a system that accurately analyzes the emotional states of the elderly and generates appropriate reminders and reports to support their daily lives more safely and comfortably.

[0188] 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.

[0189] In this invention, the server includes means for acquiring schedule information and dialogue data, means for analyzing emotional states based on the dialogue data, and means for generating and transmitting a situation report externally based on the analyzed emotional state. This makes it possible to monitor the emotional state of elderly people while managing their daily activities and to provide timely information to family members and related parties.

[0190] "Schedule information" refers to data related to the user's daily schedule and time management.

[0191] "Dialogue data" refers to the content of voice or text communication between the user and the system.

[0192] A "reminder" is a notification or message that is pre-set to prompt a user to take a specific action.

[0193] "Emotional state" refers to the psychological state exhibited by a user, analyzed and expressed using numerical values ​​and categories.

[0194] A "Status Report" is a report summarizing a user's activity status, generated based on schedule information and emotional state.

[0195] "External users" refer to individuals other than the user, such as family members or administrators, who are authorized to receive information.

[0196] An "alert" is a notification issued when specific conditions are met, intended to draw attention to something or prompt action.

[0197] A "recommended action" is a specific action or choice suggested to the user based on their analyzed emotional state.

[0198] The system for implementing this invention consists of a terminal used daily by the elderly user and a server that performs various data processing tasks. The terminal can be a smartphone or a smart speaker. The user can input schedule information and medication information through these terminals. The input data is temporarily stored on the terminal and periodically transmitted to the server.

[0199] The server performs analysis on the received data. First, it uses an emotion engine to analyze the conversation data and determine the user's emotional state. Natural language processing techniques are used for this emotional state analysis. The emotion engine records the analysis results as an emotion score and compares it with past data to understand emotional trends. This information is used to generate a situation report for the user's family and related parties. The generated situation report is sent to external users via application notifications or email.

[0200] Furthermore, the server periodically generates reminders based on the schedule information and notifies the device. At this time, it also sends recommended actions to the device according to the user's emotional state, appropriately supporting the user's behavior.

[0201] For example, if a server determines that a user has recently been experiencing stress, it can recommend activities that promote relaxation and notify the user accordingly.

[0202] When using the generative AI model, you can enter the following prompt statements as an example.

[0203] "Design an accessible interface to sense the emotional state of elderly individuals and provide appropriate feedback."

[0204] In this way, it becomes possible to appropriately provide the necessary information and support so that elderly people can live their lives with peace of mind.

[0205] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0206] Step 1:

[0207] The terminal receives schedule information and dialogue data from the user. The entered data is temporarily stored on the terminal, and data formatting is performed automatically. The data is then sent to the server in a secure manner.

[0208] Step 2:

[0209] The server generates appropriate reminders based on the received schedule information. It analyzes the schedule data and adjusts the reminder content based on the date and time. The generated reminders are sent to the device and the user is notified.

[0210] Step 3:

[0211] The server analyzes the received dialogue data using an emotion engine. It extracts emotional states from the text data using natural language processing techniques and calculates an emotion score. The calculated emotion score is used to detect changes in emotion by comparing it to past data in the database.

[0212] Step 4:

[0213] The server generates a situation report based on the calculated emotion score. It retrieves past records from the database and performs a comparative analysis with the current emotional state. As a result, a report showing the user's emotional tendencies is created and sent to external users.

[0214] Step 5:

[0215] The server generates alerts as needed based on the situation report and emotional state. If a specific negative emotional score is continuously detected, an alert is automatically triggered and notified to external users.

[0216] Step 6:

[0217] The device notifies the user of reminders and recommended actions sent from the server. It also provides the user with specific activity suggestions based on their sentiment score, either via voice or text.

[0218] Step 7:

[0219] Users view and react to recommended activities through their devices. The device records these reactions and feeds the data back to the server for use in future analysis and suggestions.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] [Second Embodiment]

[0224] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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).

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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".

[0236] To implement this invention, it is necessary to provide devices suitable for the living environment of the elderly. For example, terminals such as smartphones and smart speakers can be used. These terminals will have dedicated applications or software installed to implement a system that supports the daily lives of the elderly.

[0237] First, the device collects schedule and medication information from elderly individuals. This information is entered via voice or manual input. The collected data is then sent to a server for processing. The transmitted data is stored in a secure database and used for subsequent analysis.

[0238] Next, the server analyzes the received schedule information and generates reminders at the necessary times. These reminders are sent to the elderly person via voice notifications or screen displays on their device, according to the sender's wishes. For example, as medication time approaches, it provides a specific message such as, "It's 2 PM. Please take your blood pressure medication."

[0239] The server also analyzes the audio data and uses natural language processing techniques to extract emotional states from the conversation. Emotional states are recorded as daily changes, and are monitored, especially for any sudden emotional shifts.

[0240] The server then generates daily status reports based on the analysis results. These reports include schedule progress and a summary of emotional state, and are sent to the family members who are the users. Based on this information, the family can remotely manage the health status of the elderly person.

[0241] Finally, in the event of any sudden emotional changes or persistent abnormalities being detected, the server will immediately send an alert to the user to encourage early care for the elderly.

[0242] Through these processes, the present invention aims to support the independent living of the elderly and provide peace of mind to their families who live separately. Specifically, if an elderly person suddenly exhibits negative emotions and becomes unable to carry out their activities as scheduled, this system can immediately alert the family and help provide the necessary support promptly.

[0243] The following describes the processing flow.

[0244] Step 1:

[0245] The device acquires schedule and medication information from elderly individuals via voice or manual input. The acquired information is temporarily stored on the device.

[0246] Step 2:

[0247] The device sends the collected data to the server using a secure communication protocol. After transmission, the data is stored in a highly reliable database.

[0248] Step 3:

[0249] The server analyzes the received schedule information and generates reminders using the calendar service. The generated reminders are then sent back to the device at predetermined times and frequencies.

[0250] Step 4:

[0251] The device will notify the elderly person of a reminder at a designated time via voice notification or display. For example, it can notify them of the time to take a specific medication.

[0252] Step 5:

[0253] The device records everyday conversation data and periodically uploads this audio data to a server.

[0254] Step 6:

[0255] The server processes the uploaded audio data using a natural language processing engine to analyze the emotional state from the dialogue. The resulting emotional data is then stored as an emotional score.

[0256] Step 7:

[0257] Based on the analyzed emotion score and schedule completion status, the server generates a status report at the end of the day. This report contains important information for understanding the lifestyle and health status of the elderly.

[0258] Step 8:

[0259] The server sends the generated status report to the user (family member) via email or a dedicated app.

[0260] Step 9:

[0261] The server continuously monitors the sentiment score and immediately sends an alert to the user if any abnormal fluctuations are detected. The alert is communicated via push notification or SMS.

[0262] Step 10:

[0263] Users review received reports and alerts, and evaluate and implement follow-up and appropriate interventions for elderly individuals.

[0264] (Example 1)

[0265] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0266] Elderly people face challenges in managing their schedules and understanding their emotional state in their daily lives. Furthermore, it is difficult for family members living far away to appropriately monitor the health of elderly individuals remotely. This leads to a lack of support for the independent living of the elderly and a lack of reassurance for their families.

[0267] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0268] In this invention, the server includes a device for acquiring schedule information and user input, a device for generating notifications based on the schedule information, and a device for analyzing emotional state based on user input. This enables efficient schedule management and emotional state monitoring of elderly individuals, allowing family members to remotely understand their health status.

[0269] "Schedule information" refers to information related to a user's appointments and tasks, including the date, time, and content of those appointments.

[0270] "User input" refers to data provided by users through voice or manual operation, which serves as the basis for system analysis and processing.

[0271] "Notifications" refer to messages and alerts sent to users for the purpose of confirming schedules or prompting them to take action.

[0272] "Emotional state" refers to data that reflects the user's psychological or emotional condition and is evaluated through methods such as linguistic analysis.

[0273] A "status report" is a document or data report generated by the system that compiles information including the user's schedule progress and emotional state.

[0274] An "alert" refers to a warning or notice issued when specified conditions are met, and is used when immediate action is required.

[0275] This invention is a system designed to support the independent living of the elderly and provide peace of mind to family members living separately. This system operates with terminals, servers, and users each playing their respective roles and working in coordination.

[0276] The devices are designed for use by the elderly and include smartphones and smart speakers. These devices have applications installed that retrieve schedules and medication information entered by the user via voice or manually. The devices also have functions to notify users of reminders, such as voice notifications and screen displays.

[0277] The server is a computing device that aggregates and analyzes data sent from elderly individuals. The collected schedule information is stored securely in a database. The server generates reminders based on the schedule information at appropriate times through an analysis module. It also uses natural language processing technology to analyze emotional states obtained from voice input. A specific artificial intelligence model is used for this analysis, which can grasp the user's daily rhythm and emotional trends. In particular, if an abnormal change in emotion is detected, a system is in place to quickly send an alert to the user's family.

[0278] The users are primarily family members who receive status reports provided by the system. The received reports include the progress of the elderly person's schedule and the results of sentiment analysis, which the users can use to remotely monitor and manage the elderly person's health.

[0279] As a concrete example, consider a scenario where an elderly person experiences emotional distress earlier than expected. In this case, the system retrieves data from their smartphone such as, "Something's different today. They seem to be thinking about something," and uses this data to send a notification to their relatives. Based on this prompt, the family can intervene early through methods such as phone calls or visits.

[0280] An example of a prompt is, "Describe a system that analyzes the emotional state of elderly individuals and notifies the user if there are any negative changes." Using this example, the system can perform the specified role.

[0281] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0282] Step 1:

[0283] The terminal collects schedule information and medication details from the elderly through voice input or manual input. The input data includes date and time, activity content, and medication time. These data are temporarily stored on the terminal and formatted in preparation for transmission to the server. For example, if "Reservation at the hospital at 1 PM" is voice-input, the content is stored as text data.

[0284] Step 2:

[0285] The terminal encrypts the collected information and transmits it to the server using a secure communication protocol. The schedule data and medication information as input become output data that is securely transferred to the server using an encryption algorithm. This process is important for protecting data privacy.

[0286] Step 3:

[0287] The server stores the received data in a database and analyzes the data through a schedule management module. Based on the input schedule information, analysis is performed to generate reminders. For example, based on the reservation information at 1 PM, preparations are made to output a notification.

[0288] Step 4:

[0289] Based on the analysis results, the server creates reminders and transmits them to the terminal at the required time. The user is set to receive notifications informing them of specific activities and medication times. As output, notification messages for the user are generated and provided visually or aurally.

[0290] Step 5:

[0291] The server performs sentiment analysis on the voice data using a generative AI model. As input data, the user's voice recordings are used, and the emotional state is extracted through natural language processing technology. An indicator showing the change in emotion is generated and output for anomaly detection.

[0292] Step 6:

[0293] The server comprehensively analyzes daily emotional states and schedule progress to generate a status report. Emotional analysis and schedule information are used as input, and a detailed report is generated that is sent to the family. The family, as users, can then monitor the health status based on this report.

[0294] Step 7:

[0295] If an abnormality or sudden change in emotional state is detected, the server immediately sends an alert to the user (family member). The input is emotional data indicating the abnormality, and a warning message is generated and output based on this data. This allows the family to take a quick response.

[0296] (Application Example 1)

[0297] 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."

[0298] In the lives of the elderly, it is essential that they maintain independent daily living while their families, who live separately, monitor their health and respond promptly as needed. However, missed schedules or overlooked emotional changes can delay appropriate care. Traditional methods have made it difficult to respond immediately to sudden emotional changes and have not been able to efficiently provide information on daily health management.

[0299] 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.

[0300] In this invention, the server includes means for acquiring schedule information and dialogue data, means for analyzing the emotional state based on the dialogue data and detecting a sudden change in the emotion, and means for generating a detailed situation report based on the analyzed emotional state and transmitting it to an external information user. Thereby, it becomes possible to immediately respond to sudden changes in emotions and health conditions and effectively manage the health of the elderly remotely.

[0301] "Schedule information" is data regarding time and date used by a user to plan daily activities and schedules.

[0302] "Dialogue data" is a record of language expressions generated by communication between users or between a device and a user.

[0303] "Reminder" is an alert message that notifies a user at a specific time or under specific conditions and prompts an action.

[0304] "Emotional state" is data indicating the psychological state and emotional changes of a user.

[0305] "Analysis" is a process of decomposing information based on collected data and deriving understandings and conclusions.

[0306] "Notification" is a message or signal for transmitting an event or information.

[0307] "Situation report" is a report for summarizing the current situation based on collected information and providing it to others.

[0308] "External user" refers to a third party or family member who does not directly use the system but receives its information.

[0309] "Abnormality" is a concept referring to a state different from normal or an unexpected change.

[0310] An "alert" is an alert signal issued to warn of danger or an emergency.

[0311] In order to implement this invention, a program that performs a specific function is required. This program is mainly composed of a server and a terminal used by the user.

[0312] The server uses a cloud platform (such as AWS Lambda and DynamoDB) to receive and store schedule information and dialogue data. During this process, a speech recognition service (Google Cloud Speech-to-Text API) is used to convert the audio data into text format. The converted data is then analyzed using natural language processing technology (AWS Comprehend) to infer emotional states.

[0313] Furthermore, the device functions as a smartphone and smart speaker, providing an interface for users to input voice commands and schedule information. Users can easily record information through voice or text input.

[0314] As a concrete example, suppose an elderly person says in the morning, "I'm going for a walk at 3 PM today." The voice is recorded on the device and sent to the server. As a result, a reminder is generated at 3 PM and displayed as a notification on the user's device.

[0315] An example of a prompt message would be: "We are designing an emotion monitoring system for the elderly. Please tell us about efficient methods for detecting emotions via voice input and reporting changes in emotions."

[0316] This system efficiently processes information based on emotions and schedules, ensuring that reminder notifications, emotion monitoring, and alarms in case of anomalies are reliably executed.

[0317] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0318] Step 1:

[0319] Users input schedule information and other tasks into the device via voice commands or text input. The input voice is collected by the device's microphone and converted into text data using the Google Cloud Speech-to-Text API. Input is voice data, output is text data.

[0320] Step 2:

[0321] The converted text data is sent from the terminal to the server, where it is processed by AWS Lambda. The input is text data, and the output is processed text. This process ensures that the user's schedule is properly stored in the database.

[0322] Step 3:

[0323] The server uses AWS Comprehend to analyze emotional states from conversational data. The input is text data provided by the user, and the output is the result of the emotional state analysis. In this step, emotional categories such as positive and negative are assigned based on specific keywords and contexts in the emotional state.

[0324] Step 4:

[0325] The server automatically generates a situation report, including emotional states, based on the analyzed data, and sends it to external information users. The input is the analysis of emotional states, and the output is a detailed situation report. This report contains a summary of daily behavior and emotional changes.

[0326] Step 5:

[0327] If an abnormality or emergency is detected through emotional state analysis, the server immediately sends an alert to the user and designated emergency contacts. The input is the emotional state analysis results, including any abnormalities; the output is the alert message. This enables emergency response for elderly individuals.

[0328] At each processing step, input and output are converted, and this series of steps realizes a system that supports the independent management of the user's health status.

[0329] 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.

[0330] To implement this invention, it is necessary to have a terminal equipped with an advanced program including an emotion engine and a server working together. This terminal is a device used daily by elderly users, such as a smartphone or smart speaker. The terminal has an app installed for managing schedules and medication, and obtains necessary information through voice input or manual input.

[0331] First, the device collects schedule and medication information from the elderly person, and uses an emotion engine to analyze the conversation data and understand their emotional state. The acquired data is securely transmitted to a server. The server analyzes this data and generates reminders at specific times.

[0332] The device notifies users of the generated reminders and provides instructions to the elderly via voice or on-screen display as schedule reminders. For example, it might prompt the elderly to take action with a message like, "It's 3 PM. It's time for a walk."

[0333] Next, the server uses an emotion engine to analyze the acquired conversation data in real time and determine the user's emotional state. This determination is saved as an emotion score and stored in a database for analyzing emotional tendencies along with the conversation history. For example, by comparing past data with the current emotional state, it can be determined whether an elderly person has been experiencing stress recently.

[0334] The server generates a status report based on the analysis results and sends it to the user's family via email or app notification daily or as needed. Through this report, the family can understand the elderly person's psychological state and daily activities.

[0335] Furthermore, if the server detects an anomaly based on emotional state or schedule information, it immediately sends an alert to the user's family. This makes it possible to respond early when an abnormality is detected in the mental state of an elderly person.

[0336] For example, if an elderly person who has consistently shown calm emotions suddenly exhibits negative emotions, the emotion engine will detect this change, the server will immediately issue an alert, and the user (family member) will be prompted to check on the situation, thereby enabling safe and smooth management of the elderly person's life.

[0337] The following describes the processing flow.

[0338] Step 1:

[0339] The device acquires schedule information and dialogue data from the user. Schedule information is acquired through manual input or voice commands. Dialogue data is collected by recording everyday conversations.

[0340] Step 2:

[0341] The terminal sends acquired schedule information and conversation data to the server via secure communication. This transmission occurs in real time, and the data is processed immediately.

[0342] Step 3:

[0343] The server analyzes the received schedule information and generates reminders based on the schedule. The generated reminders are sent to the device to notify the elderly person at the specified time.

[0344] Step 4:

[0345] When the designated time arrives, the device will notify the user of the received reminder via voice or on-screen display. For example, it might provide a message such as, "It's 6 PM. Time for dinner," encouraging the user to follow their schedule.

[0346] Step 5:

[0347] The server uses an emotion engine to analyze the received dialogue data. The audio data is converted to text, and natural language processing techniques are used to analyze the user's emotional state.

[0348] Step 6:

[0349] The server records the analysis results as an emotion score in a database and analyzes the user's emotional tendencies by comparing them with past data. This makes it possible to monitor changes in psychological state over the long term.

[0350] Step 7:

[0351] The server generates daily status reports based on the analyzed emotional state and schedule progress. These reports include metrics for evaluating the user's quality of life.

[0352] Step 8:

[0353] The server sends the generated status report to the user's family at the specified time. The family receives this report and uses it to understand the health status of the elderly person.

[0354] Step 9:

[0355] When the server detects anomalies based on factors such as emotion scores and schedule non-compliance, it immediately sends an alert to the user's family. This alert includes a detailed explanation of the situation and specific follow-up recommendations.

[0356] Step 10:

[0357] The user's family can review the received alarms and reports and take appropriate action, such as visiting or calling the elderly person, as needed. This enables prompt assistance.

[0358] (Example 2)

[0359] 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".

[0360] In modern society, there is a need for systems to support the living environment of the elderly. However, existing technologies make it difficult to grasp changes in the emotional state and activities of the elderly in real time and to quickly notify relevant parties when necessary. Therefore, a system is needed that can accurately analyze the condition of the elderly and provide appropriate support.

[0361] 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.

[0362] In this invention, the server includes means for collecting activity information and interaction data using an information acquisition device, means for analyzing the interaction data to identify emotional states, and means for issuing an alarm when an abnormality is detected. This makes it possible to monitor the emotional state of elderly people in real time and to promptly notify family members or caregivers of the situation as needed.

[0363] An "information acquisition device" is a device that has the function of collecting activity information and interaction data of elderly people.

[0364] "Activity information" refers to data related to events and schedules in the daily lives of elderly people.

[0365] "Interaction data" refers to information, such as voice and text, that elderly people use when communicating with others.

[0366] "Record messages" refer to the content of notifications and alerts generated based on the daily activities and schedules of elderly individuals.

[0367] "Emotional state" is an indicator that shows changes in the emotions and psychological condition of elderly people.

[0368] "Language processing technology" refers to the techniques used to analyze natural language data and understand and process its content.

[0369] A "situation report" is a report that summarizes the situation of elderly individuals based on collected activity information and emotional status.

[0370] "External recipients" refer to stakeholders who should receive notifications and reports, such as family members or caregivers of elderly individuals.

[0371] "Abnormal" refers to a situation that deviates significantly from normal activity or emotional state.

[0372] To implement this invention, a combination of a terminal and a server used daily by elderly people is required. The terminal could be a smartphone or a smart speaker, and these would be used to monitor the elderly person's daily activities and emotional state. The terminal would have software installed to accept voice input or manual input, thereby collecting schedule information and interaction data.

[0373] The server plays a central role in analyzing the collected data. Specifically, a sentiment analysis engine built on the server uses natural language processing to identify emotional states from dialogue data. This allows for real-time monitoring of the user's emotional state. Furthermore, the server analyzes activity information, generates recording messages at appropriate times, and can promptly issue alarms if anomalies are detected.

[0374] For example, if an elderly person records their daily activities using voice on a device, the device sends this data to a server, which then generates appropriate reminders based on that data. Furthermore, if negative emotions are suddenly detected, the server immediately notifies the family of this anomaly. This invention effectively supports the safety and health of the elderly by deeply analyzing communication data using a generative AI model and appropriate prompt messages. An example of a prompt message is, "Analyze the user's emotion score and generate a detailed report if any anomalies are found."

[0375] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0376] Step 1:

[0377] The device collects activity information and interaction data from elderly individuals through voice input or manual input. This activity information includes schedules and medication information. The entered data is temporarily stored on the device. After data acquisition is complete, the device sends this information to a server using encryption technology. Specifically, the device will ask the user by voice, "Please tell me your schedule for today," and save the information obtained.

[0378] Step 2:

[0379] The server receives activity information and interaction data sent from the terminal. Using this data as input, the server begins generating log messages based on the activity information. It analyzes the data using natural language processing techniques and creates reminders tailored to the day's specific schedule. For example, the server might create a reminder such as, "It's 2 PM, time to take your medicine."

[0380] Step 3:

[0381] The server inputs the received interaction data into its sentiment analysis engine to identify the user's emotional state. A generative AI model is used to identify the emotional state and calculate an emotional score based on the interaction data. This score is used to detect changes in emotion by comparing it with past data and the current state, and the server records this as output in a database.

[0382] Step 4:

[0383] The server outputs the generated record messages and sentiment scores, saving them to a designated database. Furthermore, the server retrieves current sentiment and activity information from the database and creates a status report based on this information. This status report is then sent to the user's family via email or app notification.

[0384] Step 5:

[0385] The server continuously monitors activity information and emotional status, and immediately sends an alert to the family if an anomaly is detected. Specifically, the server generates a notification such as "There is an anomaly in the user's emotional score. Please check the details" and sends it to the family. This ensures that important information about the elderly person's condition is quickly communicated to relevant parties.

[0386] (Application Example 2)

[0387] 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."

[0388] In the daily lives of the elderly, it is crucial to accurately understand their schedules and emotional changes, and to provide support and warnings at the appropriate time. However, conventional systems have struggled to integrate emotional states and schedule information, and to provide effective feedback and warnings to the elderly and their families. Therefore, there is a need for a system that accurately analyzes the emotional states of the elderly and generates appropriate reminders and reports to support their daily lives more safely and comfortably.

[0389] 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.

[0390] In this invention, the server includes means for acquiring schedule information and dialogue data, means for analyzing emotional states based on the dialogue data, and means for generating and transmitting a situation report externally based on the analyzed emotional state. This makes it possible to monitor the emotional state of elderly people while managing their daily activities and to provide timely information to family members and related parties.

[0391] "Schedule information" refers to data related to the user's daily schedule and time management.

[0392] "Dialogue data" refers to the content of voice or text communication between the user and the system.

[0393] A "reminder" is a notification or message that is pre-set to prompt a user to take a specific action.

[0394] "Emotional state" refers to the psychological state exhibited by a user, analyzed and expressed using numerical values ​​and categories.

[0395] A "Status Report" is a report summarizing a user's activity status, generated based on schedule information and emotional state.

[0396] "External users" refer to individuals other than the user, such as family members or administrators, who are authorized to receive information.

[0397] An "alert" is a notification issued when specific conditions are met, intended to draw attention to something or prompt action.

[0398] A "recommended action" is a specific action or choice suggested to the user based on their analyzed emotional state.

[0399] The system for implementing this invention consists of a terminal used daily by the elderly user and a server that performs various data processing tasks. The terminal can be a smartphone or a smart speaker. The user can input schedule information and medication information through these terminals. The input data is temporarily stored on the terminal and periodically transmitted to the server.

[0400] The server performs analysis on the received data. First, it uses an emotion engine to analyze the conversation data and determine the user's emotional state. Natural language processing techniques are used for this emotional state analysis. The emotion engine records the analysis results as an emotion score and compares it with past data to understand emotional trends. This information is used to generate a situation report for the user's family and related parties. The generated situation report is sent to external users via application notifications or email.

[0401] Furthermore, the server periodically generates reminders based on the schedule information and notifies the device. At this time, it also sends recommended actions to the device according to the user's emotional state, appropriately supporting the user's behavior.

[0402] For example, if a server determines that a user has recently been experiencing stress, it can recommend activities that promote relaxation and notify the user accordingly.

[0403] When using the generative AI model, you can enter the following prompt statements as an example.

[0404] "Design an accessible interface to sense the emotional state of elderly individuals and provide appropriate feedback."

[0405] In this way, it becomes possible to appropriately provide the necessary information and support so that elderly people can live their lives with peace of mind.

[0406] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0407] Step 1:

[0408] The terminal receives schedule information and dialogue data from the user. The entered data is temporarily stored on the terminal, and data formatting is performed automatically. The data is then sent to the server in a secure manner.

[0409] Step 2:

[0410] The server generates appropriate reminders based on the received schedule information. It analyzes the schedule data and adjusts the reminder content based on the date and time. The generated reminders are sent to the device and the user is notified.

[0411] Step 3:

[0412] The server analyzes the received dialogue data using an emotion engine. It extracts emotional states from the text data using natural language processing techniques and calculates an emotion score. The calculated emotion score is used to detect changes in emotion by comparing it to past data in the database.

[0413] Step 4:

[0414] The server generates a situation report based on the calculated emotion score. It retrieves past records from the database and performs a comparative analysis with the current emotional state. As a result, a report showing the user's emotional tendencies is created and sent to external users.

[0415] Step 5:

[0416] The server generates alerts as needed based on the situation report and emotional state. If a specific negative emotional score is continuously detected, an alert is automatically triggered and notified to external users.

[0417] Step 6:

[0418] The device notifies the user of reminders and recommended actions sent from the server. It also provides the user with specific activity suggestions based on their sentiment score, either via voice or text.

[0419] Step 7:

[0420] Users view and react to recommended activities through their devices. The device records these reactions and feeds the data back to the server for use in future analysis and suggestions.

[0421] 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.

[0422] 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.

[0423] 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.

[0424] [Third Embodiment]

[0425] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0426] 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.

[0427] 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).

[0428] 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.

[0429] 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.

[0430] 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).

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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.

[0435] 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.

[0436] 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".

[0437] To implement this invention, it is necessary to provide devices suitable for the living environment of the elderly. For example, terminals such as smartphones and smart speakers can be used. These terminals will have dedicated applications or software installed to implement a system that supports the daily lives of the elderly.

[0438] First, the device collects schedule and medication information from elderly individuals. This information is entered via voice or manual input. The collected data is then sent to a server for processing. The transmitted data is stored in a secure database and used for subsequent analysis.

[0439] Next, the server analyzes the received schedule information and generates reminders at the necessary times. These reminders are sent to the elderly person via voice notifications or screen displays on their device, according to the sender's wishes. For example, as medication time approaches, it provides a specific message such as, "It's 2 PM. Please take your blood pressure medication."

[0440] The server also analyzes the audio data and uses natural language processing techniques to extract emotional states from the conversation. Emotional states are recorded as daily changes, and are monitored, especially for any sudden emotional shifts.

[0441] The server then generates daily status reports based on the analysis results. These reports include schedule progress and a summary of emotional state, and are sent to the family members who are the users. Based on this information, the family can remotely manage the health status of the elderly person.

[0442] Finally, in the event of any sudden emotional changes or persistent abnormalities being detected, the server will immediately send an alert to the user to encourage early care for the elderly.

[0443] Through these processes, the present invention aims to support the independent living of the elderly and provide peace of mind to their families who live separately. Specifically, if an elderly person suddenly exhibits negative emotions and becomes unable to carry out their activities as scheduled, this system can immediately alert the family and help provide the necessary support promptly.

[0444] The following describes the processing flow.

[0445] Step 1:

[0446] The device acquires schedule and medication information from elderly individuals via voice or manual input. The acquired information is temporarily stored on the device.

[0447] Step 2:

[0448] The device sends the collected data to the server using a secure communication protocol. After transmission, the data is stored in a highly reliable database.

[0449] Step 3:

[0450] The server analyzes the received schedule information and generates reminders using the calendar service. The generated reminders are then sent back to the device at predetermined times and frequencies.

[0451] Step 4:

[0452] The device will notify the elderly person of a reminder at a designated time via voice notification or display. For example, it can notify them of the time to take a specific medication.

[0453] Step 5:

[0454] The device records everyday conversation data and periodically uploads this audio data to a server.

[0455] Step 6:

[0456] The server processes the uploaded audio data using a natural language processing engine to analyze the emotional state from the dialogue. The resulting emotional data is then stored as an emotional score.

[0457] Step 7:

[0458] Based on the analyzed emotion score and schedule completion status, the server generates a status report at the end of the day. This report contains important information for understanding the lifestyle and health status of the elderly.

[0459] Step 8:

[0460] The server sends the generated status report to the user (family member) via email or a dedicated app.

[0461] Step 9:

[0462] The server continuously monitors the sentiment score and immediately sends an alert to the user if any abnormal fluctuations are detected. The alert is communicated via push notification or SMS.

[0463] Step 10:

[0464] Users review received reports and alerts, and evaluate and implement follow-up and appropriate interventions for elderly individuals.

[0465] (Example 1)

[0466] 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."

[0467] Elderly people face challenges in managing their schedules and understanding their emotional state in their daily lives. Furthermore, it is difficult for family members living far away to appropriately monitor the health of elderly individuals remotely. This leads to a lack of support for the independent living of the elderly and a lack of reassurance for their families.

[0468] 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.

[0469] In this invention, the server includes a device for acquiring schedule information and user input, a device for generating notifications based on the schedule information, and a device for analyzing emotional state based on user input. This enables efficient schedule management and emotional state monitoring of elderly individuals, allowing family members to remotely understand their health status.

[0470] "Schedule information" refers to information related to a user's appointments and tasks, including the date, time, and content of those appointments.

[0471] "User input" refers to data provided by users through voice or manual operation, which serves as the basis for system analysis and processing.

[0472] "Notifications" refer to messages and alerts sent to users for the purpose of confirming schedules or prompting them to take action.

[0473] "Emotional state" refers to data that reflects the user's psychological or emotional condition and is evaluated through methods such as linguistic analysis.

[0474] A "status report" is a document or data report generated by the system that compiles information including the user's schedule progress and emotional state.

[0475] An "alert" refers to a warning or notice issued when specified conditions are met, and is used when immediate action is required.

[0476] This invention is a system designed to support the independent living of the elderly and provide peace of mind to family members living separately. This system operates with terminals, servers, and users each playing their respective roles and working in coordination.

[0477] The devices are designed for use by the elderly and include smartphones and smart speakers. These devices have applications installed that retrieve schedules and medication information entered by the user via voice or manually. The devices also have functions to notify users of reminders, such as voice notifications and screen displays.

[0478] The server is a computing device that aggregates and analyzes data sent from elderly individuals. The collected schedule information is stored securely in a database. The server generates reminders based on the schedule information at appropriate times through an analysis module. It also uses natural language processing technology to analyze emotional states obtained from voice input. A specific artificial intelligence model is used for this analysis, which can grasp the user's daily rhythm and emotional trends. In particular, if an abnormal change in emotion is detected, a system is in place to quickly send an alert to the user's family.

[0479] The users are primarily family members who receive status reports provided by the system. The received reports include the progress of the elderly person's schedule and the results of sentiment analysis, which the users can use to remotely monitor and manage the elderly person's health.

[0480] As a concrete example, consider a scenario where an elderly person experiences emotional distress earlier than expected. In this case, the system retrieves data from their smartphone such as, "Something's different today. They seem to be thinking about something," and uses this data to send a notification to their relatives. Based on this prompt, the family can intervene early through methods such as phone calls or visits.

[0481] An example of a prompt is, "Describe a system that analyzes the emotional state of elderly individuals and notifies the user if there are any negative changes." Using this example, the system can perform the specified role.

[0482] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0483] Step 1:

[0484] The device collects schedule information and medication details from elderly individuals through voice or manual input. Input data includes date and time, activity details, and medication times. This data is temporarily stored on the device and formatted in preparation for transmission to the server. For example, if "I have a hospital appointment at 1 PM" is voice-input, that information is saved as text data.

[0485] Step 2:

[0486] The terminal encrypts the collected information and sends it to the server using a secure communication protocol. Schedule data and medication information, as input, become output data securely transferred to the server using an encryption algorithm. This process is crucial for protecting data privacy.

[0487] Step 3:

[0488] The server stores the received data in a database and analyzes it via the schedule management module. Based on the input schedule information, analysis is performed to generate reminders. For example, based on the reservation information for 1 PM, preparations are made to output a notification.

[0489] Step 4:

[0490] Based on the analysis results, the server creates a reminder and sends it to the device at the required time. The user is configured to receive notifications about specific activities and medication times. As output, a notification message is generated for the user and provided visually or audibly.

[0491] Step 5:

[0492] The server uses an AI model to generate voice data and perform emotion analysis. User voice recordings are used as input data, and emotional states are extracted through natural language processing techniques. An index indicating emotional changes is generated and output for anomaly detection.

[0493] Step 6:

[0494] The server comprehensively analyzes daily emotional states and schedule progress to generate a status report. Emotional analysis and schedule information are used as input, and a detailed report is generated that is sent to the family. The family, as users, can then monitor the health status based on this report.

[0495] Step 7:

[0496] If an abnormality or sudden change in emotional state is detected, the server immediately sends an alert to the user (family member). The input is emotional data indicating the abnormality, and a warning message is generated and output based on this data. This allows the family to take a quick response.

[0497] (Application Example 1)

[0498] 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."

[0499] In the lives of the elderly, it is essential that they maintain independent daily living while their families, who live separately, monitor their health and respond promptly as needed. However, missed schedules or overlooked emotional changes can delay appropriate care. Traditional methods have made it difficult to respond immediately to sudden emotional changes and have not been able to efficiently provide information on daily health management.

[0500] 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.

[0501] In this invention, the server includes means for acquiring schedule information and dialogue data, means for analyzing emotional states based on the dialogue data and detecting sudden changes in those emotions, and means for generating a detailed status report based on the analyzed emotional state and transmitting it to an external information user. This enables immediate response to sudden changes in emotions and health conditions, and allows for effective remote management of the health of the elderly.

[0502] "Schedule information" refers to data about times and dates that users use to plan their daily activities and appointments.

[0503] "Dialogue data" refers to a record of linguistic expressions generated through communication between users or with devices.

[0504] A "reminder" is an alert message that notifies a user at a specific time or under specific conditions, prompting them to take action.

[0505] "Emotional state" refers to data that indicates the user's psychological state and changes in their emotions.

[0506] "Analysis" is the process of breaking down information based on collected data and deriving understandings and conclusions.

[0507] A "notification" is a message or signal used to transmit events or information.

[0508] A "situation report" is a report that summarizes the current situation based on collected information and provides it to others.

[0509] "External users" refer to third parties or family members who do not directly use the system but receive its information.

[0510] "Anomaly" is a concept that refers to a state or change that is different from the normal state.

[0511] An "alert" is an alert signal issued to warn of danger or an emergency.

[0512] In order to implement this invention, a program that performs a specific function is required. This program is mainly composed of a server and a terminal used by the user.

[0513] The server uses a cloud platform (such as AWS Lambda and DynamoDB) to receive and store schedule information and dialogue data. During this process, a speech recognition service (Google Cloud Speech-to-Text API) is used to convert the audio data into text format. The converted data is then analyzed using natural language processing technology (AWS Comprehend) to infer emotional states.

[0514] Furthermore, the device functions as a smartphone and smart speaker, providing an interface for users to input voice commands and schedule information. Users can easily record information through voice or text input.

[0515] As a concrete example, suppose an elderly person says in the morning, "I'm going for a walk at 3 PM today." The voice is recorded on the device and sent to the server. As a result, a reminder is generated at 3 PM and displayed as a notification on the user's device.

[0516] An example of a prompt message would be: "We are designing an emotion monitoring system for the elderly. Please tell us about efficient methods for detecting emotions via voice input and reporting changes in emotions."

[0517] This system efficiently processes information based on emotions and schedules, ensuring that reminder notifications, emotion monitoring, and alarms in case of anomalies are reliably executed.

[0518] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0519] Step 1:

[0520] Users input schedule information and other tasks into the device via voice commands or text input. The input voice is collected by the device's microphone and converted into text data using the Google Cloud Speech-to-Text API. Input is voice data, output is text data.

[0521] Step 2:

[0522] The converted text data is sent from the terminal to the server, where it is processed by AWS Lambda. The input is text data, and the output is processed text. This process ensures that the user's schedule is properly stored in the database.

[0523] Step 3:

[0524] The server uses AWS Comprehend to analyze emotional states from conversational data. The input is text data provided by the user, and the output is the result of the emotional state analysis. In this step, emotional categories such as positive and negative are assigned based on specific keywords and contexts in the emotional state.

[0525] Step 4:

[0526] The server automatically generates a situation report, including emotional states, based on the analyzed data, and sends it to external information users. The input is the analysis of emotional states, and the output is a detailed situation report. This report contains a summary of daily behavior and emotional changes.

[0527] Step 5:

[0528] If an abnormality or emergency is detected through emotional state analysis, the server immediately sends an alert to the user and designated emergency contacts. The input is the emotional state analysis results, including any abnormalities; the output is the alert message. This enables emergency response for elderly individuals.

[0529] At each processing step, input and output are converted, and this series of steps realizes a system that supports the independent management of the user's health status.

[0530] 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.

[0531] To implement this invention, it is necessary to have a terminal equipped with an advanced program including an emotion engine and a server working together. This terminal is a device used daily by elderly users, such as a smartphone or smart speaker. The terminal has an app installed for managing schedules and medication, and obtains necessary information through voice input or manual input.

[0532] First, the device collects schedule and medication information from the elderly person, and uses an emotion engine to analyze the conversation data and understand their emotional state. The acquired data is securely transmitted to a server. The server analyzes this data and generates reminders at specific times.

[0533] The device notifies users of the generated reminders and provides instructions to the elderly via voice or on-screen display as schedule reminders. For example, it might prompt the elderly to take action with a message like, "It's 3 PM. It's time for a walk."

[0534] Next, the server uses an emotion engine to analyze the acquired conversation data in real time and determine the user's emotional state. This determination is saved as an emotion score and stored in a database for analyzing emotional tendencies along with the conversation history. For example, by comparing past data with the current emotional state, it can be determined whether an elderly person has been experiencing stress recently.

[0535] The server generates a status report based on the analysis results and sends it to the user's family via email or app notification daily or as needed. Through this report, the family can understand the elderly person's psychological state and daily activities.

[0536] Furthermore, if the server detects an anomaly based on emotional state or schedule information, it immediately sends an alert to the user's family. This makes it possible to respond early when an abnormality is detected in the mental state of an elderly person.

[0537] For example, if an elderly person who has consistently shown calm emotions suddenly exhibits negative emotions, the emotion engine will detect this change, the server will immediately issue an alert, and the user (family member) will be prompted to check on the situation, thereby enabling safe and smooth management of the elderly person's life.

[0538] The following describes the processing flow.

[0539] Step 1:

[0540] The device acquires schedule information and dialogue data from the user. Schedule information is acquired through manual input or voice commands. Dialogue data is collected by recording everyday conversations.

[0541] Step 2:

[0542] The terminal sends acquired schedule information and conversation data to the server via secure communication. This transmission occurs in real time, and the data is processed immediately.

[0543] Step 3:

[0544] The server analyzes the received schedule information and generates reminders based on the schedule. The generated reminders are sent to the device to notify the elderly person at the specified time.

[0545] Step 4:

[0546] When the designated time arrives, the device will notify the user of the received reminder via voice or on-screen display. For example, it might provide a message such as, "It's 6 PM. Time for dinner," encouraging the user to follow their schedule.

[0547] Step 5:

[0548] The server uses an emotion engine to analyze the received dialogue data. The audio data is converted to text, and natural language processing techniques are used to analyze the user's emotional state.

[0549] Step 6:

[0550] The server records the analysis results as an emotion score in a database and analyzes the user's emotional tendencies by comparing them with past data. This makes it possible to monitor changes in psychological state over the long term.

[0551] Step 7:

[0552] The server generates daily status reports based on the analyzed emotional state and schedule progress. These reports include metrics for evaluating the user's quality of life.

[0553] Step 8:

[0554] The server sends the generated status report to the user's family at the specified time. The family receives this report and uses it to understand the health status of the elderly person.

[0555] Step 9:

[0556] When the server detects anomalies based on factors such as emotion scores and schedule non-compliance, it immediately sends an alert to the user's family. This alert includes a detailed explanation of the situation and specific follow-up recommendations.

[0557] Step 10:

[0558] The user's family can review the received alarms and reports and take appropriate action, such as visiting or calling the elderly person, as needed. This enables prompt assistance.

[0559] (Example 2)

[0560] 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."

[0561] In modern society, there is a need for systems to support the living environment of the elderly. However, existing technologies make it difficult to grasp changes in the emotional state and activities of the elderly in real time and to quickly notify relevant parties when necessary. Therefore, a system is needed that can accurately analyze the condition of the elderly and provide appropriate support.

[0562] 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.

[0563] In this invention, the server includes means for collecting activity information and interaction data using an information acquisition device, means for analyzing the interaction data to identify emotional states, and means for issuing an alarm when an abnormality is detected. This makes it possible to monitor the emotional state of elderly people in real time and to promptly notify family members or caregivers of the situation as needed.

[0564] An "information acquisition device" is a device that has the function of collecting activity information and interaction data of elderly people.

[0565] "Activity information" refers to data related to events and schedules in the daily lives of elderly people.

[0566] "Interaction data" refers to information, such as voice and text, that elderly people use when communicating with others.

[0567] "Record messages" refer to the content of notifications and alerts generated based on the daily activities and schedules of elderly individuals.

[0568] "Emotional state" is an indicator that shows changes in the emotions and psychological condition of elderly people.

[0569] "Language processing technology" refers to the techniques used to analyze natural language data and understand and process its content.

[0570] A "situation report" is a report that summarizes the situation of elderly individuals based on collected activity information and emotional status.

[0571] "External recipients" refer to stakeholders who should receive notifications and reports, such as family members or caregivers of elderly individuals.

[0572] "Abnormal" refers to a situation that deviates significantly from normal activity or emotional state.

[0573] To implement this invention, a combination of a terminal and a server used daily by elderly people is required. The terminal could be a smartphone or a smart speaker, and these would be used to monitor the elderly person's daily activities and emotional state. The terminal would have software installed to accept voice input or manual input, thereby collecting schedule information and interaction data.

[0574] The server plays a central role in analyzing the collected data. Specifically, a sentiment analysis engine built on the server uses natural language processing to identify emotional states from dialogue data. This allows for real-time monitoring of the user's emotional state. Furthermore, the server analyzes activity information, generates recording messages at appropriate times, and can promptly issue alarms if anomalies are detected.

[0575] For example, if an elderly person records their daily activities using voice on a device, the device sends this data to a server, which then generates appropriate reminders based on that data. Furthermore, if negative emotions are suddenly detected, the server immediately notifies the family of this anomaly. This invention effectively supports the safety and health of the elderly by deeply analyzing communication data using a generative AI model and appropriate prompt messages. An example of a prompt message is, "Analyze the user's emotion score and generate a detailed report if any anomalies are found."

[0576] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0577] Step 1:

[0578] The device collects activity information and interaction data from elderly individuals through voice input or manual input. This activity information includes schedules and medication information. The entered data is temporarily stored on the device. After data acquisition is complete, the device sends this information to a server using encryption technology. Specifically, the device will ask the user by voice, "Please tell me your schedule for today," and save the information obtained.

[0579] Step 2:

[0580] The server receives activity information and interaction data sent from the terminal. Using this data as input, the server begins generating log messages based on the activity information. It analyzes the data using natural language processing techniques and creates reminders tailored to the day's specific schedule. For example, the server might create a reminder such as, "It's 2 PM, time to take your medicine."

[0581] Step 3:

[0582] The server inputs the received interaction data into its sentiment analysis engine to identify the user's emotional state. A generative AI model is used to identify the emotional state and calculate an emotional score based on the interaction data. This score is used to detect changes in emotion by comparing it with past data and the current state, and the server records this as output in a database.

[0583] Step 4:

[0584] The server outputs the generated record messages and sentiment scores, saving them to a designated database. Furthermore, the server retrieves current sentiment and activity information from the database and creates a status report based on this information. This status report is then sent to the user's family via email or app notification.

[0585] Step 5:

[0586] The server continuously monitors activity information and emotional status, and immediately sends an alert to the family if an anomaly is detected. Specifically, the server generates a notification such as "There is an anomaly in the user's emotional score. Please check the details" and sends it to the family. This ensures that important information about the elderly person's condition is quickly communicated to relevant parties.

[0587] (Application Example 2)

[0588] 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."

[0589] In the daily lives of the elderly, it is crucial to accurately understand their schedules and emotional changes, and to provide support and warnings at the appropriate time. However, conventional systems have struggled to integrate emotional states and schedule information, and to provide effective feedback and warnings to the elderly and their families. Therefore, there is a need for a system that accurately analyzes the emotional states of the elderly and generates appropriate reminders and reports to support their daily lives more safely and comfortably.

[0590] 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.

[0591] In this invention, the server includes means for acquiring schedule information and dialogue data, means for analyzing emotional states based on the dialogue data, and means for generating and transmitting a situation report externally based on the analyzed emotional state. This makes it possible to monitor the emotional state of elderly people while managing their daily activities and to provide timely information to family members and related parties.

[0592] "Schedule information" refers to data related to the user's daily schedule and time management.

[0593] "Dialogue data" refers to the content of voice or text communication between the user and the system.

[0594] A "reminder" is a notification or message that is pre-set to prompt a user to take a specific action.

[0595] "Emotional state" refers to the psychological state exhibited by a user, analyzed and expressed using numerical values ​​and categories.

[0596] A "Status Report" is a report summarizing a user's activity status, generated based on schedule information and emotional state.

[0597] "External users" refer to individuals other than the user, such as family members or administrators, who are authorized to receive information.

[0598] An "alert" is a notification issued when specific conditions are met, intended to draw attention to something or prompt action.

[0599] A "recommended action" is a specific action or choice suggested to the user based on their analyzed emotional state.

[0600] The system for implementing this invention consists of a terminal used daily by the elderly user and a server that performs various data processing tasks. The terminal can be a smartphone or a smart speaker. The user can input schedule information and medication information through these terminals. The input data is temporarily stored on the terminal and periodically transmitted to the server.

[0601] The server performs analysis on the received data. First, it uses an emotion engine to analyze the conversation data and determine the user's emotional state. Natural language processing techniques are used for this emotional state analysis. The emotion engine records the analysis results as an emotion score and compares it with past data to understand emotional trends. This information is used to generate a situation report for the user's family and related parties. The generated situation report is sent to external users via application notifications or email.

[0602] Furthermore, the server periodically generates reminders based on the schedule information and notifies the device. At this time, it also sends recommended actions to the device according to the user's emotional state, appropriately supporting the user's behavior.

[0603] For example, if a server determines that a user has recently been experiencing stress, it can recommend activities that promote relaxation and notify the user accordingly.

[0604] When using the generative AI model, you can enter the following prompt statements as an example.

[0605] "Design an accessible interface to sense the emotional state of elderly individuals and provide appropriate feedback."

[0606] In this way, it becomes possible to appropriately provide the necessary information and support so that elderly people can live their lives with peace of mind.

[0607] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0608] Step 1:

[0609] The terminal receives schedule information and dialogue data from the user. The entered data is temporarily stored on the terminal, and data formatting is performed automatically. The data is then sent to the server in a secure manner.

[0610] Step 2:

[0611] The server generates appropriate reminders based on the received schedule information. It analyzes the schedule data and adjusts the reminder content based on the date and time. The generated reminders are sent to the device and the user is notified.

[0612] Step 3:

[0613] The server analyzes the received dialogue data using an emotion engine. It extracts emotional states from the text data using natural language processing techniques and calculates an emotion score. The calculated emotion score is used to detect changes in emotion by comparing it to past data in the database.

[0614] Step 4:

[0615] The server generates a situation report based on the calculated emotion score. It retrieves past records from the database and performs a comparative analysis with the current emotional state. As a result, a report showing the user's emotional tendencies is created and sent to external users.

[0616] Step 5:

[0617] The server generates alerts as needed based on the situation report and emotional state. If a specific negative emotional score is continuously detected, an alert is automatically triggered and notified to external users.

[0618] Step 6:

[0619] The device notifies the user of reminders and recommended actions sent from the server. It also provides the user with specific activity suggestions based on their sentiment score, either via voice or text.

[0620] Step 7:

[0621] Users view and react to recommended activities through their devices. The device records these reactions and feeds the data back to the server for use in future analysis and suggestions.

[0622] 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.

[0623] 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.

[0624] 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.

[0625] [Fourth Embodiment]

[0626] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0627] 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.

[0628] 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).

[0629] 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.

[0630] 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.

[0631] 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).

[0632] 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.

[0633] 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.

[0634] 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.

[0635] 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.

[0636] 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.

[0637] 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.

[0638] 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".

[0639] To implement this invention, it is necessary to provide devices suitable for the living environment of the elderly. For example, terminals such as smartphones and smart speakers can be used. These terminals will have dedicated applications or software installed to implement a system that supports the daily lives of the elderly.

[0640] First, the device collects schedule and medication information from elderly individuals. This information is entered via voice or manual input. The collected data is then sent to a server for processing. The transmitted data is stored in a secure database and used for subsequent analysis.

[0641] Next, the server analyzes the received schedule information and generates reminders at the necessary times. These reminders are sent to the elderly person via voice notifications or screen displays on their device, according to the sender's wishes. For example, as medication time approaches, it provides a specific message such as, "It's 2 PM. Please take your blood pressure medication."

[0642] The server also analyzes the audio data and uses natural language processing techniques to extract emotional states from the conversation. Emotional states are recorded as daily changes, and are monitored, especially for any sudden emotional shifts.

[0643] The server then generates daily status reports based on the analysis results. These reports include schedule progress and a summary of emotional state, and are sent to the family members who are the users. Based on this information, the family can remotely manage the health status of the elderly person.

[0644] Finally, in the event of any sudden emotional changes or persistent abnormalities being detected, the server will immediately send an alert to the user to encourage early care for the elderly.

[0645] Through these processes, the present invention aims to support the independent living of the elderly and provide peace of mind to their families who live separately. Specifically, if an elderly person suddenly exhibits negative emotions and becomes unable to carry out their activities as scheduled, this system can immediately alert the family and help provide the necessary support promptly.

[0646] The following describes the processing flow.

[0647] Step 1:

[0648] The device acquires schedule and medication information from elderly individuals via voice or manual input. The acquired information is temporarily stored on the device.

[0649] Step 2:

[0650] The device sends the collected data to the server using a secure communication protocol. After transmission, the data is stored in a highly reliable database.

[0651] Step 3:

[0652] The server analyzes the received schedule information and generates reminders using the calendar service. The generated reminders are then sent back to the device at predetermined times and frequencies.

[0653] Step 4:

[0654] The device will notify the elderly person of a reminder at a designated time via voice notification or display. For example, it can notify them of the time to take a specific medication.

[0655] Step 5:

[0656] The device records everyday conversation data and periodically uploads this audio data to a server.

[0657] Step 6:

[0658] The server processes the uploaded audio data using a natural language processing engine to analyze the emotional state from the dialogue. The resulting emotional data is then stored as an emotional score.

[0659] Step 7:

[0660] Based on the analyzed emotion score and schedule completion status, the server generates a status report at the end of the day. This report contains important information for understanding the lifestyle and health status of the elderly.

[0661] Step 8:

[0662] The server sends the generated status report to the user (family member) via email or a dedicated app.

[0663] Step 9:

[0664] The server continuously monitors the sentiment score and immediately sends an alert to the user if any abnormal fluctuations are detected. The alert is communicated via push notification or SMS.

[0665] Step 10:

[0666] Users review received reports and alerts, and evaluate and implement follow-up and appropriate interventions for elderly individuals.

[0667] (Example 1)

[0668] 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".

[0669] Elderly people face challenges in managing their schedules and understanding their emotional state in their daily lives. Furthermore, it is difficult for family members living far away to appropriately monitor the health of elderly individuals remotely. This leads to a lack of support for the independent living of the elderly and a lack of reassurance for their families.

[0670] 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.

[0671] In this invention, the server includes a device for acquiring schedule information and user input, a device for generating notifications based on the schedule information, and a device for analyzing emotional state based on user input. This enables efficient schedule management and emotional state monitoring of elderly individuals, allowing family members to remotely understand their health status.

[0672] "Schedule information" refers to information related to a user's appointments and tasks, including the date, time, and content of those appointments.

[0673] "User input" refers to data provided by users through voice or manual operation, which serves as the basis for system analysis and processing.

[0674] "Notifications" refer to messages and alerts sent to users for the purpose of confirming schedules or prompting them to take action.

[0675] "Emotional state" refers to data that reflects the user's psychological or emotional condition and is evaluated through methods such as linguistic analysis.

[0676] A "status report" is a document or data report generated by the system that compiles information including the user's schedule progress and emotional state.

[0677] An "alert" refers to a warning or notice issued when specified conditions are met, and is used when immediate action is required.

[0678] This invention is a system designed to support the independent living of the elderly and provide peace of mind to family members living separately. This system operates with terminals, servers, and users each playing their respective roles and working in coordination.

[0679] The devices are designed for use by the elderly and include smartphones and smart speakers. These devices have applications installed that retrieve schedules and medication information entered by the user via voice or manually. The devices also have functions to notify users of reminders, such as voice notifications and screen displays.

[0680] The server is a computing device that aggregates and analyzes data sent from elderly individuals. The collected schedule information is stored securely in a database. The server generates reminders based on the schedule information at appropriate times through an analysis module. It also uses natural language processing technology to analyze emotional states obtained from voice input. A specific artificial intelligence model is used for this analysis, which can grasp the user's daily rhythm and emotional trends. In particular, if an abnormal change in emotion is detected, a system is in place to quickly send an alert to the user's family.

[0681] The users are primarily family members who receive status reports provided by the system. The received reports include the progress of the elderly person's schedule and the results of sentiment analysis, which the users can use to remotely monitor and manage the elderly person's health.

[0682] As a concrete example, consider a scenario where an elderly person experiences emotional distress earlier than expected. In this case, the system retrieves data from their smartphone such as, "Something's different today. They seem to be thinking about something," and uses this data to send a notification to their relatives. Based on this prompt, the family can intervene early through methods such as phone calls or visits.

[0683] An example of a prompt is, "Describe a system that analyzes the emotional state of elderly individuals and notifies the user if there are any negative changes." Using this example, the system can perform the specified role.

[0684] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0685] Step 1:

[0686] The device collects schedule information and medication details from elderly individuals through voice or manual input. Input data includes date and time, activity details, and medication times. This data is temporarily stored on the device and formatted in preparation for transmission to the server. For example, if "I have a hospital appointment at 1 PM" is voice-input, that information is saved as text data.

[0687] Step 2:

[0688] The terminal encrypts the collected information and sends it to the server using a secure communication protocol. Schedule data and medication information, as input, become output data securely transferred to the server using an encryption algorithm. This process is crucial for protecting data privacy.

[0689] Step 3:

[0690] The server stores the received data in a database and analyzes it via the schedule management module. Based on the input schedule information, analysis is performed to generate reminders. For example, based on the reservation information for 1 PM, preparations are made to output a notification.

[0691] Step 4:

[0692] Based on the analysis results, the server creates a reminder and sends it to the device at the required time. The user is configured to receive notifications about specific activities and medication times. As output, a notification message is generated for the user and provided visually or audibly.

[0693] Step 5:

[0694] The server uses an AI model to generate voice data and perform emotion analysis. User voice recordings are used as input data, and emotional states are extracted through natural language processing techniques. An index indicating emotional changes is generated and output for anomaly detection.

[0695] Step 6:

[0696] The server comprehensively analyzes daily emotional states and schedule progress to generate a status report. Emotional analysis and schedule information are used as input, and a detailed report is generated that is sent to the family. The family, as users, can then monitor the health status based on this report.

[0697] Step 7:

[0698] If an abnormality or sudden change in emotional state is detected, the server immediately sends an alert to the user (family member). The input is emotional data indicating the abnormality, and a warning message is generated and output based on this data. This allows the family to take a quick response.

[0699] (Application Example 1)

[0700] 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".

[0701] In the lives of the elderly, it is essential that they maintain independent daily living while their families, who live separately, monitor their health and respond promptly as needed. However, missed schedules or overlooked emotional changes can delay appropriate care. Traditional methods have made it difficult to respond immediately to sudden emotional changes and have not been able to efficiently provide information on daily health management.

[0702] 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.

[0703] In this invention, the server includes means for acquiring schedule information and dialogue data, means for analyzing emotional states based on the dialogue data and detecting sudden changes in those emotions, and means for generating a detailed status report based on the analyzed emotional state and transmitting it to an external information user. This enables immediate response to sudden changes in emotions and health conditions, and allows for effective remote management of the health of the elderly.

[0704] "Schedule information" refers to data about times and dates that users use to plan their daily activities and appointments.

[0705] "Dialogue data" refers to a record of linguistic expressions generated through communication between users or with devices.

[0706] A "reminder" is an alert message that notifies a user at a specific time or under specific conditions, prompting them to take action.

[0707] "Emotional state" refers to data that indicates the user's psychological state and changes in their emotions.

[0708] "Analysis" is the process of breaking down information based on collected data and deriving understandings and conclusions.

[0709] A "notification" is a message or signal used to transmit events or information.

[0710] A "situation report" is a report that summarizes the current situation based on collected information and provides it to others.

[0711] "External users" refer to third parties or family members who do not directly use the system but receive its information.

[0712] "Anomaly" is a concept that refers to a state or change that is different from the normal state.

[0713] An "alert" is an alert signal issued to warn of danger or an emergency.

[0714] In order to implement this invention, a program that performs a specific function is required. This program is mainly composed of a server and a terminal used by the user.

[0715] The server uses a cloud platform (such as AWS Lambda and DynamoDB) to receive and store schedule information and dialogue data. During this process, a speech recognition service (Google Cloud Speech-to-Text API) is used to convert the audio data into text format. The converted data is then analyzed using natural language processing technology (AWS Comprehend) to infer emotional states.

[0716] Furthermore, the device functions as a smartphone and smart speaker, providing an interface for users to input voice commands and schedule information. Users can easily record information through voice or text input.

[0717] As a concrete example, suppose an elderly person says in the morning, "I'm going for a walk at 3 PM today." The voice is recorded on the device and sent to the server. As a result, a reminder is generated at 3 PM and displayed as a notification on the user's device.

[0718] An example of a prompt message would be: "We are designing an emotion monitoring system for the elderly. Please tell us about efficient methods for detecting emotions via voice input and reporting changes in emotions."

[0719] This system efficiently processes information based on emotions and schedules, ensuring that reminder notifications, emotion monitoring, and alarms in case of anomalies are reliably executed.

[0720] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0721] Step 1:

[0722] Users input schedule information and other tasks into the device via voice commands or text input. The input voice is collected by the device's microphone and converted into text data using the Google Cloud Speech-to-Text API. Input is voice data, output is text data.

[0723] Step 2:

[0724] The converted text data is sent from the terminal to the server, where it is processed by AWS Lambda. The input is text data, and the output is processed text. This process ensures that the user's schedule is properly stored in the database.

[0725] Step 3:

[0726] The server uses AWS Comprehend to analyze emotional states from conversational data. The input is text data provided by the user, and the output is the result of the emotional state analysis. In this step, emotional categories such as positive and negative are assigned based on specific keywords and contexts in the emotional state.

[0727] Step 4:

[0728] The server automatically generates a situation report, including emotional states, based on the analyzed data, and sends it to external information users. The input is the analysis of emotional states, and the output is a detailed situation report. This report contains a summary of daily behavior and emotional changes.

[0729] Step 5:

[0730] If an abnormality or emergency is detected through emotional state analysis, the server immediately sends an alert to the user and designated emergency contacts. The input is the emotional state analysis results, including any abnormalities; the output is the alert message. This enables emergency response for elderly individuals.

[0731] At each processing step, input and output are converted, and this series of steps realizes a system that supports the independent management of the user's health status.

[0732] 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.

[0733] To implement this invention, it is necessary to have a terminal equipped with an advanced program including an emotion engine and a server working together. This terminal is a device used daily by elderly users, such as a smartphone or smart speaker. The terminal has an app installed for managing schedules and medication, and obtains necessary information through voice input or manual input.

[0734] First, the device collects schedule and medication information from the elderly person, and uses an emotion engine to analyze the conversation data and understand their emotional state. The acquired data is securely transmitted to a server. The server analyzes this data and generates reminders at specific times.

[0735] The device notifies users of the generated reminders and provides instructions to the elderly via voice or on-screen display as schedule reminders. For example, it might prompt the elderly to take action with a message like, "It's 3 PM. It's time for a walk."

[0736] Next, the server uses an emotion engine to analyze the acquired conversation data in real time and determine the user's emotional state. This determination is saved as an emotion score and stored in a database for analyzing emotional tendencies along with the conversation history. For example, by comparing past data with the current emotional state, it can be determined whether an elderly person has been experiencing stress recently.

[0737] The server generates a status report based on the analysis results and sends it to the user's family via email or app notification daily or as needed. Through this report, the family can understand the elderly person's psychological state and daily activities.

[0738] Furthermore, if the server detects an anomaly based on emotional state or schedule information, it immediately sends an alert to the user's family. This makes it possible to respond early when an abnormality is detected in the mental state of an elderly person.

[0739] For example, if an elderly person who has consistently shown calm emotions suddenly exhibits negative emotions, the emotion engine will detect this change, the server will immediately issue an alert, and the user (family member) will be prompted to check on the situation, thereby enabling safe and smooth management of the elderly person's life.

[0740] The following describes the processing flow.

[0741] Step 1:

[0742] The device acquires schedule information and dialogue data from the user. Schedule information is acquired through manual input or voice commands. Dialogue data is collected by recording everyday conversations.

[0743] Step 2:

[0744] The terminal sends acquired schedule information and conversation data to the server via secure communication. This transmission occurs in real time, and the data is processed immediately.

[0745] Step 3:

[0746] The server analyzes the received schedule information and generates reminders based on the schedule. The generated reminders are sent to the device to notify the elderly person at the specified time.

[0747] Step 4:

[0748] When the designated time arrives, the device will notify the user of the received reminder via voice or on-screen display. For example, it might provide a message such as, "It's 6 PM. Time for dinner," encouraging the user to follow their schedule.

[0749] Step 5:

[0750] The server uses an emotion engine to analyze the received dialogue data. The audio data is converted to text, and natural language processing techniques are used to analyze the user's emotional state.

[0751] Step 6:

[0752] The server records the analysis results as an emotion score in a database and analyzes the user's emotional tendencies by comparing them with past data. This makes it possible to monitor changes in psychological state over the long term.

[0753] Step 7:

[0754] The server generates daily status reports based on the analyzed emotional state and schedule progress. These reports include metrics for evaluating the user's quality of life.

[0755] Step 8:

[0756] The server sends the generated status report to the user's family at the specified time. The family receives this report and uses it to understand the health status of the elderly person.

[0757] Step 9:

[0758] When the server detects anomalies based on factors such as emotion scores and schedule non-compliance, it immediately sends an alert to the user's family. This alert includes a detailed explanation of the situation and specific follow-up recommendations.

[0759] Step 10:

[0760] The user's family can review the received alarms and reports and take appropriate action, such as visiting or calling the elderly person, as needed. This enables prompt assistance.

[0761] (Example 2)

[0762] 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".

[0763] In modern society, there is a need for systems to support the living environment of the elderly. However, existing technologies make it difficult to grasp changes in the emotional state and activities of the elderly in real time and to quickly notify relevant parties when necessary. Therefore, a system is needed that can accurately analyze the condition of the elderly and provide appropriate support.

[0764] 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.

[0765] In this invention, the server includes means for collecting activity information and interaction data using an information acquisition device, means for analyzing the interaction data to identify emotional states, and means for issuing an alarm when an abnormality is detected. This makes it possible to monitor the emotional state of elderly people in real time and to promptly notify family members or caregivers of the situation as needed.

[0766] An "information acquisition device" is a device that has the function of collecting activity information and interaction data of elderly people.

[0767] "Activity information" refers to data related to events and schedules in the daily lives of elderly people.

[0768] "Interaction data" refers to information, such as voice and text, that elderly people use when communicating with others.

[0769] "Record messages" refer to the content of notifications and alerts generated based on the daily activities and schedules of elderly individuals.

[0770] "Emotional state" is an indicator that shows changes in the emotions and psychological condition of elderly people.

[0771] "Language processing technology" refers to the techniques used to analyze natural language data and understand and process its content.

[0772] A "situation report" is a report that summarizes the situation of elderly individuals based on collected activity information and emotional status.

[0773] "External recipients" refer to stakeholders who should receive notifications and reports, such as family members or caregivers of elderly individuals.

[0774] "Abnormal" refers to a situation that deviates significantly from normal activity or emotional state.

[0775] To implement this invention, a combination of a terminal and a server used daily by elderly people is required. The terminal could be a smartphone or a smart speaker, and these would be used to monitor the elderly person's daily activities and emotional state. The terminal would have software installed to accept voice input or manual input, thereby collecting schedule information and interaction data.

[0776] The server plays a central role in analyzing the collected data. Specifically, a sentiment analysis engine built on the server uses natural language processing to identify emotional states from dialogue data. This allows for real-time monitoring of the user's emotional state. Furthermore, the server analyzes activity information, generates recording messages at appropriate times, and can promptly issue alarms if anomalies are detected.

[0777] For example, if an elderly person records their daily activities using voice on a device, the device sends this data to a server, which then generates appropriate reminders based on that data. Furthermore, if negative emotions are suddenly detected, the server immediately notifies the family of this anomaly. This invention effectively supports the safety and health of the elderly by deeply analyzing communication data using a generative AI model and appropriate prompt messages. An example of a prompt message is, "Analyze the user's emotion score and generate a detailed report if any anomalies are found."

[0778] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0779] Step 1:

[0780] The device collects activity information and interaction data from elderly individuals through voice input or manual input. This activity information includes schedules and medication information. The entered data is temporarily stored on the device. After data acquisition is complete, the device sends this information to a server using encryption technology. Specifically, the device will ask the user by voice, "Please tell me your schedule for today," and save the information obtained.

[0781] Step 2:

[0782] The server receives activity information and interaction data sent from the terminal. Using this data as input, the server begins generating log messages based on the activity information. It analyzes the data using natural language processing techniques and creates reminders tailored to the day's specific schedule. For example, the server might create a reminder such as, "It's 2 PM, time to take your medicine."

[0783] Step 3:

[0784] The server inputs the received interaction data into its sentiment analysis engine to identify the user's emotional state. A generative AI model is used to identify the emotional state and calculate an emotional score based on the interaction data. This score is used to detect changes in emotion by comparing it with past data and the current state, and the server records this as output in a database.

[0785] Step 4:

[0786] The server outputs the generated record messages and sentiment scores, saving them to a designated database. Furthermore, the server retrieves current sentiment and activity information from the database and creates a status report based on this information. This status report is then sent to the user's family via email or app notification.

[0787] Step 5:

[0788] The server continuously monitors activity information and emotional status, and immediately sends an alert to the family if an anomaly is detected. Specifically, the server generates a notification such as "There is an anomaly in the user's emotional score. Please check the details" and sends it to the family. This ensures that important information about the elderly person's condition is quickly communicated to relevant parties.

[0789] (Application Example 2)

[0790] 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".

[0791] In the daily lives of the elderly, it is crucial to accurately understand their schedules and emotional changes, and to provide support and warnings at the appropriate time. However, conventional systems have struggled to integrate emotional states and schedule information, and to provide effective feedback and warnings to the elderly and their families. Therefore, there is a need for a system that accurately analyzes the emotional states of the elderly and generates appropriate reminders and reports to support their daily lives more safely and comfortably.

[0792] 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.

[0793] In this invention, the server includes means for acquiring schedule information and dialogue data, means for analyzing emotional states based on the dialogue data, and means for generating and transmitting a situation report externally based on the analyzed emotional state. This makes it possible to monitor the emotional state of elderly people while managing their daily activities and to provide timely information to family members and related parties.

[0794] "Schedule information" refers to data related to the user's daily schedule and time management.

[0795] "Dialogue data" refers to the content of voice or text communication between the user and the system.

[0796] A "reminder" is a notification or message that is pre-set to prompt a user to take a specific action.

[0797] "Emotional state" refers to the psychological state exhibited by a user, analyzed and expressed using numerical values ​​and categories.

[0798] A "Status Report" is a report summarizing a user's activity status, generated based on schedule information and emotional state.

[0799] "External users" refer to individuals other than the user, such as family members or administrators, who are authorized to receive information.

[0800] An "alert" is a notification issued when specific conditions are met, intended to draw attention to something or prompt action.

[0801] A "recommended action" is a specific action or choice suggested to the user based on their analyzed emotional state.

[0802] The system for implementing this invention consists of a terminal used daily by the elderly user and a server that performs various data processing tasks. The terminal can be a smartphone or a smart speaker. The user can input schedule information and medication information through these terminals. The input data is temporarily stored on the terminal and periodically transmitted to the server.

[0803] The server performs analysis on the received data. First, it uses an emotion engine to analyze the conversation data and determine the user's emotional state. Natural language processing techniques are used for this emotional state analysis. The emotion engine records the analysis results as an emotion score and compares it with past data to understand emotional trends. This information is used to generate a situation report for the user's family and related parties. The generated situation report is sent to external users via application notifications or email.

[0804] Furthermore, the server periodically generates reminders based on the schedule information and notifies the device. At this time, it also sends recommended actions to the device according to the user's emotional state, appropriately supporting the user's behavior.

[0805] For example, if a server determines that a user has recently been experiencing stress, it can recommend activities that promote relaxation and notify the user accordingly.

[0806] When using the generative AI model, you can enter the following prompt statements as an example.

[0807] "Design an accessible interface to sense the emotional state of elderly individuals and provide appropriate feedback."

[0808] In this way, it becomes possible to appropriately provide the necessary information and support so that elderly people can live their lives with peace of mind.

[0809] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0810] Step 1:

[0811] The terminal receives schedule information and dialogue data from the user. The entered data is temporarily stored on the terminal, and data formatting is performed automatically. The data is then sent to the server in a secure manner.

[0812] Step 2:

[0813] The server generates appropriate reminders based on the received schedule information. It analyzes the schedule data and adjusts the reminder content based on the date and time. The generated reminders are sent to the device and the user is notified.

[0814] Step 3:

[0815] The server analyzes the received dialogue data using an emotion engine. It extracts emotional states from the text data using natural language processing techniques and calculates an emotion score. The calculated emotion score is used to detect changes in emotion by comparing it to past data in the database.

[0816] Step 4:

[0817] The server generates a situation report based on the calculated emotion score. It retrieves past records from the database and performs a comparative analysis with the current emotional state. As a result, a report showing the user's emotional tendencies is created and sent to external users.

[0818] Step 5:

[0819] The server generates alerts as needed based on the situation report and emotional state. If a specific negative emotional score is continuously detected, an alert is automatically triggered and notified to external users.

[0820] Step 6:

[0821] The device notifies the user of reminders and recommended actions sent from the server. It also provides the user with specific activity suggestions based on their sentiment score, either via voice or text.

[0822] Step 7:

[0823] Users view and react to recommended activities through their devices. The device records these reactions and feeds the data back to the server for use in future analysis and suggestions.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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."

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0845] The following is further disclosed regarding the embodiments described above.

[0846] (Claim 1)

[0847] Means for obtaining schedule information and dialogue data,

[0848] A means for generating reminders based on the aforementioned schedule information,

[0849] A means for analyzing emotional states based on the aforementioned dialogue data,

[0850] A means for notifying the aforementioned reminder,

[0851] A means for generating a situation report based on the analyzed emotional state and transmitting it externally,

[0852] A means of issuing an alarm under specified conditions,

[0853] A system that includes this.

[0854] (Claim 2)

[0855] The system according to claim 1, wherein the analysis of the emotional state is performed using natural language processing technology.

[0856] (Claim 3)

[0857] The system according to claim 1, wherein the status report is generated based on at least one of the schedule and emotional state and transmitted to an external user.

[0858] "Example 1"

[0859] (Claim 1)

[0860] A device for acquiring schedule information and user input,

[0861] A device that generates a notification based on the aforementioned schedule information,

[0862] A device that analyzes emotional states based on user input,

[0863] A device that transmits the aforementioned notification using a display device,

[0864] A device that generates a situation report based on the analyzed emotional state and transmits it externally,

[0865] A device that sends an alarm when the conditions are met,

[0866] A system that includes this.

[0867] (Claim 2)

[0868] The system according to claim 1, wherein the analysis of the emotional state is performed using natural language processing technology.

[0869] (Claim 3)

[0870] The system according to claim 1, wherein the status report is prepared based on at least one of the schedule and emotional state and transmitted to an external recipient.

[0871] "Application Example 1"

[0872] (Claim 1)

[0873] Means for obtaining schedule information and dialogue data,

[0874] A means for generating reminders based on the aforementioned schedule information,

[0875] A means for analyzing the emotional state based on the aforementioned dialogue data and detecting a sudden change in the aforementioned emotion,

[0876] A means for notifying the aforementioned reminder,

[0877] A means for generating a detailed situation report based on the analyzed emotional state and transmitting it to an external information user,

[0878] A means to immediately issue an alarm when an abnormality is detected,

[0879] A system that includes this.

[0880] (Claim 2)

[0881] The system according to claim 1, wherein the analysis of the emotional state is performed using natural language processing technology, and the change in the emotion is quantitatively evaluated.

[0882] (Claim 3)

[0883] The system according to claim 1, wherein the status report is generated based on at least one of the schedule and emotional state and transmitted daily to an external information user.

[0884] "Example 2 of combining an emotion engine"

[0885] (Claim 1)

[0886] A means for collecting activity information and communication data using an information acquisition device,

[0887] Means for generating a recording message based on the aforementioned activity information,

[0888] A means for analyzing the aforementioned interaction data to identify the emotional state,

[0889] A means for notifying the aforementioned recording message,

[0890] A means for generating a situation report based on the identified emotional state and transmitting it to an external target,

[0891] A means of issuing an alarm when an anomaly is detected,

[0892] A system that includes this.

[0893] (Claim 2)

[0894] The system according to claim 1, wherein the identification of the aforementioned emotional state is performed using language processing technology.

[0895] (Claim 3)

[0896] The system according to claim 1, wherein the status report is generated based on at least one of the activity information and the emotional state and transmitted to an external user.

[0897] "Application example 2 when combining with an emotional engine"

[0898] (Claim 1)

[0899] Means for obtaining schedule information and dialogue data,

[0900] A means for generating reminders based on the aforementioned schedule information,

[0901] A means for analyzing emotional states based on the aforementioned dialogue data,

[0902] A means for notifying the aforementioned reminder,

[0903] A means for generating a situation report based on the analyzed emotional state and transmitting it externally,

[0904] A means of issuing an alarm under specified conditions,

[0905] A means for displaying the aforementioned status report in a format accessible to external users,

[0906] A means of generating recommended actions based on emotional state and notifying individuals,

[0907] A system that includes this.

[0908] (Claim 2)

[0909] The system according to claim 1, wherein the analysis of the emotional state is performed using natural language processing technology, and the recommended action is dynamically adjusted based on the generated emotional state.

[0910] (Claim 3)

[0911] The system according to claim 1, wherein the status report is generated based on at least one of the schedule and emotional state and transmitted in a manner that allows external users to refer to the emotional history. [Explanation of Symbols]

[0912] 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. Means for obtaining schedule information and dialogue data, A means for generating reminders based on the aforementioned schedule information, A means for analyzing emotional states based on the aforementioned dialogue data, A means for notifying the aforementioned reminder, A means for generating a situation report based on the analyzed emotional state and transmitting it externally, A means of issuing an alarm under specified conditions, A system that includes this.

2. The system according to claim 1, wherein the analysis of the emotional state is performed using natural language processing technology.

3. The system according to claim 1, wherein the status report is generated based on at least one of the schedule and emotional state and transmitted to an external user.

Images