system

Abstract

The system according to this embodiment aims to support the daily lives of elderly people and enable them to continue living independently. [Solution] The system according to the embodiment comprises a learning unit, a support unit, and an emergency response unit. The learning unit learns the daily behavior patterns and routines of the elderly person and predicts and suggests the next necessary action. The support unit provides the necessary support based on the behavior patterns learned by the learning unit. The emergency response unit is equipped with a "Help Button" to be pressed when in trouble and notifies emergency contacts.

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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult for the elderly to receive appropriate support when they encounter difficulties in daily life.

[0005] The system according to the embodiment aims to support the daily life of the elderly and enable them to continue an independent life.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a learning unit, a support unit, and an emergency response unit. The learning unit learns the daily behavior patterns and routines of the elderly person and predicts and suggests the next necessary action. The support unit provides the necessary support based on the behavior patterns learned by the learning unit. The emergency response unit is equipped with a "help button" to be pressed when in trouble and notifies emergency contacts. [Effects of the Invention]

[0007] The system according to this embodiment supports the daily lives of elderly people and enables them to continue living independently. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​assistant system according to an embodiment of the present invention is a system that learns and predicts the daily routines of elderly people and supports their actions even when they are in trouble or unable to verbalize their needs. This system provides multi-sensory support that is tailored to the physical and cognitive limitations of elderly people, is designed to be intuitively usable, assists in independent living, and fulfills the desire of many elderly people to "continue to live independently at home for as long as possible." For example, the AI ​​assistant system learns the daily behavior patterns and routines of elderly people and predicts and suggests the next necessary action. For example, if an elderly person has a habit of eating lunch at 12 o'clock every day, the system will automatically set suggestions and reminders. Next, the AI ​​assistant system provides a hybrid interface of voice and touch operation. Even if vocabulary is unavailable, it can be used with simple voice commands and touch operations, and the interface is designed to be easy for elderly people to use. Furthermore, the AI ​​assistant system uses a large screen and high contrast display that is easy for people with impaired vision to see, and displays information using simple icons and images. For people with hearing impairments, it provides notifications via text display and vibration, and the voice guidance is designed to be easy to hear with adjustable volume and clear pronunciation. Furthermore, the AI ​​assistant system includes an emergency response function, featuring a "Help Button" on the device for immediate notification to emergency contacts and communication of the situation. The AI ​​assistant system also automatically monitors the daily lives of elderly individuals and provides necessary support. For example, it automatically notifies caregivers if the elderly person fails to get up in the morning or eat. It features simple language and repetition functions, with an easy-to-understand dialogue design, providing reassurance through regular checks and follow-ups. When specific instructions or confirmations are needed, the device vibrates slightly to provide notification, offering information through means other than sight and hearing. Anticipated use scenarios include medication reminders, meal and hydration reminders, emergency notifications, and suggestions for exercise and relaxation. For instance, it learns daily medication routines and automatically reminds users. It also learns regular meal and hydration timings and notifies users with messages like, "It's almost lunchtime."This allows the AI ​​assistant system to function as a comprehensive support system that enables elderly people to live their daily lives with peace of mind, even when they encounter difficulties or are unable to express themselves verbally. In this way, the AI ​​assistant system supports the independence of the elderly and allows them to live their daily lives with confidence.

[0029] The AI ​​assistant system according to this embodiment comprises a learning unit, a support unit, and an emergency response unit. The learning unit learns the daily behavior patterns and routines of the elderly person and predicts and suggests the next necessary action. For example, if the elderly person has a habit of eating lunch at 12:00 every day, the learning unit learns this behavior and predicts and suggests the next necessary action. The learning unit can also use AI to analyze the elderly person's behavior patterns and predict the next necessary action. For example, the learning unit takes the elderly person's behavior data as input and outputs the next necessary action. The support unit provides necessary support based on the behavior patterns learned by the learning unit. The support unit provides, for example, a hybrid interface of voice and touch operation. The support unit can also use a large screen and high contrast display to display information using simple icons and images. Furthermore, the support unit can provide text display and vibration notifications for the hearing impaired, and voice guidance can be provided with adjustable volume and clear pronunciation. For example, the support unit provides a hybrid interface of voice and touch operation, realizing an interface that is easy for the elderly to use. The support unit uses a large, high-contrast display to provide a display that is easy for elderly people with impaired vision to see. The support unit provides text and vibration notifications for people with hearing impairments, and voice guidance is provided with adjustable volume and clear pronunciation. The emergency response unit is equipped with a "Help Button" to press when in trouble and notifies emergency contacts. For example, the emergency response unit can be equipped with a "Help Button" to press when in trouble, immediately notify emergency contacts and inform them of the situation. The emergency response unit can also use AI to monitor the daily life of elderly people and provide necessary support. For example, the emergency response unit uses AI to input data on the elderly person's behavior, detect abnormalities, and notify caregivers or family members. In this way, the AI ​​assistant system according to the embodiment can support the daily life of elderly people and respond quickly in emergencies.

[0030] The learning unit learns the daily behavioral patterns and routines of elderly individuals and predicts and suggests the next necessary actions. Specifically, the learning unit meticulously records the elderly individual's daily rhythm. For example, if an individual has a habit of eating lunch at 12:00 every day, the unit learns this behavior and predicts and suggests the next necessary action. This includes analyzing behavioral patterns using AI. The AI ​​takes the elderly individual's behavioral data as input and outputs the next necessary action. For example, if the AI ​​learns that an elderly individual has a habit of going for a walk after breakfast, it will suggest a walk after breakfast. The AI ​​can also consider the elderly individual's health condition and past behavioral history and suggest hydration and rest at appropriate times. Furthermore, the learning unit can provide customized suggestions according to the elderly individual's preferences and specific needs. For example, if an individual has a habit of performing a specific activity on a specific day of the week, the learning unit will notify them in advance and encourage preparation. In this way, the learning unit can make the elderly individual's life smoother and reduce daily stress.

[0031] The support unit provides necessary support based on behavioral patterns learned by the learning unit. Specifically, it provides a hybrid interface of voice and touch operation, creating an interface that is easy for seniors to use. Voice operation uses natural language processing technology to recognize the voice commands of seniors and provide appropriate responses. For example, in response to a voice command such as "Tell me the weather," it provides current weather information by voice. Touch operation uses simple icons and images that can be operated intuitively, and a large screen and high contrast display is used to provide a display that is easy for seniors with impaired vision to see. Furthermore, the support unit provides notifications via text display and vibration for those with hearing impairments, and voice guidance is provided with adjustable volume and clear pronunciation. For example, if there is an important notification, the device vibrates along with a text display to alert the senior. Voice guidance also adjusts the volume according to ambient noise and individual hearing ability, and provides information with clear pronunciation. In this way, the support unit can meet the diverse needs of seniors and support their daily lives more comfortably.

[0032] The emergency response unit is equipped with a "Help Button" to be pressed when in need, and it notifies emergency contacts. Specifically, when the "Help Button" on the device is pressed, an immediate notification is sent to pre-registered emergency contacts. The notification includes information such as the elderly person's current location and situation, enabling a quick response. The emergency response unit can also use AI to monitor the elderly person's daily life and provide necessary support. For example, the AI ​​receives data on the elderly person's behavior as input, detects anomalies, and notifies caregivers and family members. Specifically, it detects movements that deviate from normal behavior patterns or periods of inactivity, and alerts them to the possibility of an emergency. Furthermore, the emergency response unit can use voice recognition technology to respond to the elderly person's calls and automatically call for assistance in emergencies. For example, if the elderly person shouts "Help!", the device recognizes the voice and immediately sends a notification to emergency contacts. This allows the emergency response unit to ensure the elderly person's safety and support a quick and appropriate response.

[0033] The support unit can provide a hybrid interface combining voice and touch operation. For example, the support unit provides a hybrid interface combining voice and touch operation, creating an interface that is easy for the elderly to use. The support unit implements voice operation using voice recognition technology and touch operation using a touchscreen. For example, the support unit recognizes voice commands and executes corresponding operations. The support unit can also perform operations by touching icons and buttons on the screen using the touchscreen. This allows the support unit to provide an interface that is easy for the elderly to use. Voice operation is implemented, for example, using voice recognition technology. Voice recognition technology analyzes voice commands and executes corresponding operations. Touch operation is implemented, for example, using a touchscreen. The touchscreen allows operations to be performed by touching icons and buttons on the screen. The hybrid interface is an interface that combines voice and touch operation and is designed to be easy for the elderly to use. This allows the support unit to provide an interface that is easy for the elderly to use.

[0034] The support unit can display information using a large screen and high contrast display, employing simple icons and images. For example, the support unit uses a large screen and high contrast display to provide a display that is easy for elderly people with impaired vision to see. The support unit displays information using simple icons and images, enabling elderly people to understand it intuitively. For example, the support unit uses a large screen display to show text and icons in a larger size. The support unit can also use a high contrast display to improve the visibility of text and icons. Furthermore, the support unit displays information using simple icons and images, enabling elderly people to understand it intuitively. For example, the support unit uses simple icons to convey information visually. The support unit can also use images to supplement information, providing a display that is easy to understand visually. This allows the support unit to provide a display that is easy for elderly people with impaired vision to see. A large screen display, for example, uses a display with a large screen size or pixel count. A high contrast display, for example, uses a display with a high contrast ratio. Simple icons and images are achieved, for example, by simplifying the size, color, and shape of the icons. This allows the support unit to provide a display that is easy for elderly people with impaired vision to read.

[0035] The support unit can provide notifications via text display and vibration for people with hearing impairments, and voice guidance with adjustable volume and clear pronunciation. For example, the support unit can provide notifications via text display and vibration for people with hearing impairments, making it easy for elderly people with hearing limitations to use. The support unit provides voice guidance with adjustable volume and clear pronunciation to make it easy for elderly people to hear. For example, the support unit can provide notifications using text display. The support unit can also provide notifications using vibration. Furthermore, the support unit provides voice guidance with adjustable volume and clear pronunciation to make it easy for elderly people to hear. For example, the support unit has a volume adjustment function, allowing users to adjust the volume. The support unit can also use speech synthesis technology to achieve clear pronunciation. This allows the support unit to provide notification methods that are easy for elderly people with hearing limitations to use. Text display can be achieved, for example, by adjusting the font size and display position. Vibration notifications can be achieved, for example, by adjusting the vibration intensity and pattern. Adjustable volume can be achieved, for example, by setting a volume range or adjustment steps. Clear pronunciation can be achieved, for example, by using speech synthesis technology or noise cancellation technology. This allows the support unit to provide a notification method that is easy for elderly people with hearing impairments to use.

[0036] The support unit can provide an easy-to-understand dialogue design by incorporating simple language and a repetition confirmation function. For example, the support unit can provide a dialogue design that is easy for the elderly to understand by incorporating simple language and a repetition confirmation function. The support unit uses simple language in its dialogues to make them easy for the elderly to understand. For example, the support unit can adjust the difficulty level of the words used to achieve simple language. The support unit can also adjust the length of sentences to conduct dialogues in shorter sentences. Furthermore, the support unit incorporates a repetition confirmation function to make it easy for the elderly to understand. For example, the support unit has a function to repeatedly confirm important information to ensure that the elderly understand the information. The support unit can also adjust the frequency and method of confirmation. This allows the support unit to provide a dialogue design that is easy for the elderly to understand. Simple language is achieved, for example, by adjusting the difficulty level of the words used and the length of sentences. The repetition confirmation function is achieved, for example, by adjusting the frequency and method of confirmation. An easy-to-understand dialogue design is designed, for example, based on the dialogue flow and usability test results. This allows the support unit to provide a dialogue design that is easy for the elderly to understand.

[0037] The support unit can notify users by gently vibrating the device when specific instructions or confirmations are needed. For example, the support unit can provide information through means other than sight and hearing, such as by gently vibrating the device when specific instructions or confirmations are needed. The support unit ensures that elderly users receive information reliably by gently vibrating the device. For example, the support unit can notify users of specific instructions by gently vibrating the device. The support unit can also notify users of confirmations by gently vibrating the device. Furthermore, the support unit can adjust the intensity and pattern of the vibrations to ensure that elderly users receive information reliably. For example, the support unit has a function to adjust the vibration intensity, which the user can adjust. The support unit also has a function to adjust the vibration pattern, which the user can adjust. This allows the support unit to provide information through means other than sight and hearing. Gentle vibrations are achieved, for example, by adjusting the vibration intensity and pattern. Specific instructions and confirmations are achieved, for example, by setting the type of instruction and the method of confirmation. This allows the support unit to provide information through means other than sight and hearing.

[0038] The learning unit can learn the routine of taking medication at a fixed time each day and provide reminders. For example, the learning unit can learn the routine of taking medication at a fixed time each day, ensuring that elderly individuals do not forget when to take their medication. The learning unit uses AI to analyze the elderly individual's behavioral patterns, predict medication times, and provide reminders. For example, the learning unit uses AI to take the elderly individual's behavioral data as input and outputs the medication time. Furthermore, the learning unit can adjust the notification method and frequency to provide reminders. For example, the learning unit can provide reminders using voice or text notifications. The learning unit can also adjust the notification frequency to ensure that elderly individuals remember when to take their medication. This allows the learning unit to ensure that elderly individuals do not forget when to take their medication. A fixed time each day can be achieved, for example, by adjusting the time range and setting method. Reminders can be achieved, for example, by adjusting the notification method and frequency. This allows the learning unit to ensure that elderly individuals do not forget when to take their medication.

[0039] The learning unit can learn the timing of regular meals and hydration and provide reminders. For example, the learning unit can learn the timing of regular meals and hydration so that elderly individuals do not forget. The learning unit uses AI to analyze the behavioral patterns of elderly individuals, predict the timing of meals and hydration, and provide reminders. For example, the learning unit uses the elderly individual's behavioral data as input and outputs the timing of meals and hydration. Furthermore, the learning unit can adjust the notification method and frequency to provide reminders. For example, the learning unit can provide reminders using voice or text notifications. The learning unit can also adjust the frequency of notifications to ensure that elderly individuals remember the timing of meals and hydration. This allows the learning unit to ensure that elderly individuals do not forget the timing of meals and hydration. The timing of regular meals and hydration can be achieved, for example, by adjusting meal times and hydration frequency. Reminders can be achieved, for example, by adjusting the notification method and frequency. This allows the learning department to ensure that elderly people don't forget when to eat and drink.

[0040] The emergency response unit can detect anomalies from daily behavioral patterns and notify caregivers and family members. For example, the emergency response unit can detect anomalies from daily behavioral patterns, enabling early detection of abnormalities in elderly individuals and prompt response. The emergency response unit uses AI to analyze the elderly person's behavioral patterns, detect anomalies, and notify caregivers and family members. For example, the emergency response unit uses AI to input the elderly person's behavioral data and output anomalies. Furthermore, the emergency response unit can adjust the notification method and content. For example, the emergency response unit uses voice or text notifications to notify of anomalies. The emergency response unit can also adjust the notification content to ensure caregivers and family members quickly understand the anomaly. This allows the emergency response unit to detect abnormalities in elderly individuals early and respond promptly. Anomaly detection is achieved, for example, by analyzing changes in behavioral patterns or the type of anomaly. Notification is achieved, for example, by adjusting the notification method and content. This allows the emergency response unit to detect abnormalities in elderly individuals early and respond promptly.

[0041] The learning unit can optimize its learning algorithm by referencing the user's past behavior history during training. For example, the learning unit can optimize the learning algorithm by referencing the user's past behavior history during training, enabling optimal learning based on past behavior history. The learning unit uses AI to analyze the user's past behavior history and optimize the learning algorithm. For example, the learning unit uses the user's past behavior data as input and outputs the optimal learning algorithm. The learning unit can also set adjustment methods and evaluation criteria for the learning algorithm in order to provide optimal learning based on past behavior history. For example, the learning unit adjusts the learning algorithm based on actions the user has frequently performed in the past. The learning unit can also predict actions to be taken during specific time periods from the user's past behavior history and optimize the learning algorithm. Furthermore, the learning unit can analyze the user's past behavior history and learn the most efficient behavior patterns. This enables the learning unit to perform optimal learning based on past behavior history. Past behavior history can be accessed, for example, by setting the data type and data storage method. Optimization of the learning algorithm can be achieved, for example, by setting the optimization method and evaluation criteria. This allows the learning unit to perform optimal learning based on past behavioral history.

[0042] The learning unit can customize learning content based on the user's living environment and health condition during the learning process. For example, the learning unit can customize learning content based on the user's living environment and health condition during the learning process, enabling learning tailored to the user's living environment and health condition. The learning unit uses AI to analyze the user's living environment and health condition and customize the learning content. For example, the learning unit takes the user's living environment data and health condition data as input and outputs customized learning content. The learning unit can also set customization methods and evaluation criteria to provide learning content based on the user's living environment and health condition. For example, if the user has high blood pressure, the learning unit will learn behavioral patterns that take health into consideration. The learning unit can also learn behavioral patterns that reduce feelings of loneliness if the user lives alone. Furthermore, if the user has a specific allergy, the learning unit can learn behavioral patterns that take that allergy into consideration. This enables the learning unit to provide learning tailored to the user's living environment and health condition. The living environment can be realized, for example, by setting the type of residence and lifestyle habits. The health condition can be realized, for example, by setting the medical history and current health indicators. Customizing learning content can be achieved, for example, by setting customization methods and evaluation criteria. This allows the learning unit to tailor learning to the user's living environment and health condition.

[0043] The learning unit can learn behavioral patterns by considering the user's geographical location information during the learning process. For example, the learning unit can learn behavioral patterns by considering the user's geographical location information during the learning process, enabling it to learn behavioral patterns based on the user's geographical location information. The learning unit uses AI to analyze the user's geographical location information and learn behavioral patterns. For example, the learning unit's AI takes the user's geographical location information data as input and outputs behavioral patterns. The learning unit can also set learning algorithms and evaluation criteria to provide behavioral patterns based on geographical location information. For example, if the user is at home, the learning unit learns behavioral patterns at home. The learning unit can also learn behavioral patterns when the user is out, if they are away from home. Furthermore, if the user is traveling, the learning unit can learn behavioral patterns at their travel destination. This enables the learning unit to learn behavioral patterns based on the user's geographical location information. Geographical location information can be obtained, for example, by setting GPS data or location information accuracy. Learning behavioral patterns can be achieved, for example, by setting learning algorithms and data collection methods. This enables the learning unit to learn behavioral patterns based on the user's geographical location information.

[0044] The learning unit can analyze a user's social media activity during training and learn related behavioral patterns. For example, the learning unit can analyze a user's social media activity during training, learn related behavioral patterns, and learn behavioral patterns based on social media activity. The learning unit uses AI to analyze a user's social media activity and learn behavioral patterns. For example, the learning unit's AI takes the user's social media data as input and outputs behavioral patterns. The learning unit can also set learning algorithms and evaluation criteria to provide behavioral patterns based on social media activity. For example, the learning unit learns behavioral patterns based on the content a user frequently posts on social media. The learning unit can also analyze a user's social media friendships and learn related behavioral patterns. Furthermore, the learning unit can learn behavioral patterns based on the user's social media activity time. This enables the learning unit to learn behavioral patterns based on social media activity. Social media activity can be realized, for example, by setting the type of data and how the data is stored. Learning behavioral patterns can be realized, for example, by setting learning algorithms and data collection methods. This will enable the learning department to learn behavioral patterns based on social media activity.

[0045] The support unit can select the optimal support method by referring to the user's past support history during support. For example, the support unit can select the optimal support method by referring to the user's past support history during support and provide the optimal support method based on that history. The support unit uses AI to analyze the user's past support history and select the optimal support method. For example, the support unit's AI takes the user's past support data as input and outputs the optimal support method. Furthermore, the support unit can set evaluation criteria and decision algorithms for support methods in order to provide the optimal support method based on past support history. For example, the support unit selects the optimal support method based on the support content the user has received in the past. The support unit can also select the support content to be provided at a specific time period from the user's past support history. In addition, the support unit can analyze the user's past support history and select the most effective support method. As a result, the support unit can provide the optimal support method based on past support history. Past support history can be realized, for example, by setting the data type and data storage method. The optimal support method can be realized, for example, by setting evaluation criteria and decision algorithms for support methods. This allows the support department to provide the most suitable support method based on past support history.

[0046] The support unit can customize support content based on the user's current health status during support sessions. For example, the support unit can customize support content based on the user's current health status during support sessions, providing optimal support tailored to the user's health condition. The support unit uses AI to analyze the user's health status and customize support content. For example, the support unit's AI takes the user's health status data as input and outputs customized support content. The support unit can also set customization methods and evaluation criteria to provide support content based on health status. For example, if the user has high blood pressure, the support unit will provide health-conscious support content. The support unit can also provide support content to alleviate feelings of loneliness if the user lives alone. Furthermore, if the support unit has a specific allergy, it can provide support content that takes that allergy into consideration. This allows the support unit to provide optimal support content tailored to the user's health status. Current health status can be determined, for example, by setting medical history and current health indicators. Customization of support content can be achieved, for example, by setting customization methods and evaluation criteria. This allows the support unit to provide optimal support content tailored to the user's health status.

[0047] The support unit can select the optimal support method by considering the user's geographical location information during support. For example, the support unit can select the optimal support method by considering the user's geographical location information during support and provide the optimal support method based on the user's geographical location information. The support unit uses AI to analyze the user's geographical location information and select the optimal support method. For example, the support unit's AI takes the user's geographical location information data as input and outputs the optimal support method. Furthermore, the support unit can set evaluation criteria and decision algorithms for support methods in order to provide the optimal support method based on geographical location information. For example, if the user is at home, the support unit will provide support at home. Also, if the user is out, the support unit can provide support at their destination. Moreover, if the user is traveling, the support unit can provide support at their travel destination. In this way, the support unit can provide the optimal support method based on the user's geographical location information. Geographical location information can be realized, for example, by setting GPS data or location information accuracy. The optimal support method can be realized, for example, by setting evaluation criteria and decision algorithms for support methods. This allows the support department to provide the optimal support method based on the user's geographical location information.

[0048] The support department can analyze a user's social media activity during support and provide relevant support. For example, the support department can analyze a user's social media activity during support, provide relevant support, and provide optimal support based on that activity. The support department uses AI to analyze a user's social media activity and provide support. For example, the support department uses AI to take the user's social media data as input and output support. The support department can also set evaluation criteria and delivery methods for support to provide optimal support based on social media activity. For example, the support department can provide support based on the content that the user frequently posts on social media. The support department can also analyze a user's social media friendships and provide relevant support. Furthermore, the support department can also provide support based on the user's social media activity time. This allows the support department to provide optimal support based on social media activity. Social media activity can be realized, for example, by setting the type of data and how the data is stored. The provision of support can be realized, for example, by setting the delivery method and the type of content provided. This allows the support department to provide optimal support based on social media activity.

[0049] The emergency response unit can select the optimal response method by referring to the user's past emergency response history during an emergency. For example, the emergency response unit can select the optimal response method by referring to the user's past emergency response history during an emergency and provide the optimal response method based on that history. The emergency response unit uses AI to analyze the user's past emergency response history and select the optimal response method. For example, the emergency response unit's AI takes the user's past emergency response data as input and outputs the optimal response method. Furthermore, the emergency response unit can set evaluation criteria and decision algorithms for response methods in order to provide the optimal response method based on past emergency response history. For example, the emergency response unit selects the optimal response method based on the content of emergency responses the user has received in the past. The emergency response unit can also select the content of emergency responses to provide at a specific time period from the user's past emergency response history. In addition, the emergency response unit can analyze the user's past emergency response history and select the most effective response method. As a result, the emergency response unit can provide the optimal response method based on past emergency response history. Past emergency response history can be realized, for example, by setting the data type and data storage method. The optimal response method can be achieved, for example, by setting evaluation criteria and decision algorithms for response methods. This allows the emergency response unit to provide the optimal response method based on past emergency response history.

[0050] The emergency response unit can customize its response based on the user's current health condition during an emergency. For example, the emergency response unit can customize its response based on the user's current health condition during an emergency, providing the optimal emergency response tailored to the user's health. The emergency response unit uses AI to analyze the user's health condition and customize the response. For example, the emergency response unit takes the user's health condition data as input and outputs a customized response. The emergency response unit can also set customization methods and evaluation criteria to provide emergency response based on the user's health condition. For example, if the user has high blood pressure, the emergency response unit will provide a health-conscious emergency response. The emergency response unit can also provide an emergency response that alleviates feelings of loneliness if the user lives alone. Furthermore, if the emergency response unit has a specific allergy, it can provide an emergency response that takes that allergy into consideration. In this way, the emergency response unit can provide the optimal emergency response tailored to the user's health condition. The current health condition can be determined, for example, by setting medical history and current health indicators. The customization of the response can be achieved, for example, by setting customization methods and evaluation criteria. This allows the emergency response unit to provide the most appropriate emergency response tailored to the user's health condition.

[0051] The emergency response unit can select the optimal response method in the event of an emergency, taking into account the user's geographical location information. For example, the emergency response unit can select the optimal response method in the event of an emergency, taking into account the user's geographical location information, and provide the optimal emergency response method based on the user's geographical location information. The emergency response unit uses AI to analyze the user's geographical location information and select the optimal response method. For example, the emergency response unit's AI takes the user's geographical location information data as input and outputs the optimal response method. Furthermore, the emergency response unit can set evaluation criteria and decision algorithms for response methods in order to provide the optimal emergency response method based on geographical location information. For example, if the user is at home, the emergency response unit will provide emergency response information for the user's home. If the user is out, the emergency response unit can also provide emergency response information for the user's location. In addition, if the user is traveling, the emergency response unit can also provide emergency response information for the user's travel destination. In this way, the emergency response unit can provide the optimal emergency response method based on the user's geographical location information. Geographical location information can be realized, for example, by setting GPS data or location information accuracy. The optimal response method can be achieved, for example, by setting evaluation criteria and decision algorithms for the response method. This allows the emergency response unit to provide the optimal emergency response method based on the user's geographical location information.

[0052] The emergency response unit can analyze a user's social media activity during an emergency and provide relevant response content. For example, the emergency response unit can analyze a user's social media activity during an emergency, provide relevant response content, and provide the optimal emergency response content based on social media activity. The emergency response unit uses AI to analyze a user's social media activity and provide response content. For example, the emergency response unit's AI takes the user's social media data as input and outputs response content. Furthermore, the emergency response unit can set evaluation criteria and delivery methods for response content in order to provide the optimal emergency response content based on social media activity. For example, the emergency response unit can provide emergency response content based on the content that the user frequently posts on social media. The emergency response unit can also analyze a user's social media friendships and provide relevant emergency response content. In addition, the emergency response unit can also provide emergency response content based on the user's social media activity time. This allows the emergency response unit to provide the optimal emergency response content based on social media activity. Social media activity can be realized, for example, by setting the type of data and how the data is stored. The provision of response content can be realized, for example, by setting the delivery method and the type of content provided. This allows the emergency response department to provide optimal emergency response plans based on social media activity.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The AI ​​assistant system can also include a health management unit. This unit monitors the user's health status by collecting and analyzing their health data. For example, it can periodically measure vital signs such as heart rate, blood pressure, and body temperature, and notify the user or caregiver if an abnormality is detected. The health management unit can also manage the user's diet and exercise records and provide advice to support healthy lifestyle habits. Furthermore, based on the user's health status, the health management unit can recommend appropriate medical consultations. This allows the AI ​​assistant system to comprehensively support the user's health management.

[0055] The AI ​​assistant system can also be equipped with a communication support unit. This unit provides assistance to help users communicate smoothly with others. For example, it can use speech recognition technology to convert user speech into text and send it as a text message. It can also read aloud messages received by the user. Furthermore, it can suggest emojis and stickers to help users express their emotions. In this way, the AI ​​assistant system can support users in communicating smoothly with others.

[0056] The AI ​​assistant system can also be equipped with an entertainment section. This section provides entertainment content tailored to the user's hobbies and interests. For example, it can recommend and play music and movies the user likes. It can also provide news and articles of interest to the user. Furthermore, it can offer games and quizzes for the user to enjoy. In this way, the AI ​​assistant system can provide enjoyment and a sense of fulfillment to the user's life.

[0057] AI assistant systems can also be equipped with a learning support function. This function provides support for users to acquire new knowledge and skills. For example, it can recommend online courses and learning materials in areas of interest to the user. It can also manage the user's learning progress and provide reviews and tests at appropriate times. Furthermore, it can provide customized learning plans tailored to the user's learning style. In this way, AI assistant systems can support users in efficiently acquiring new knowledge and skills.

[0058] The AI ​​assistant system can also be equipped with an exercise support unit. This unit provides appropriate exercise programs to help users maintain their health. For example, it can create and support exercise menus tailored to the user's fitness level and health condition. It can also record the user's exercise history and manage their progress. Furthermore, it can provide encouraging messages and feedback that fosters a sense of accomplishment to motivate users to continue exercising. In this way, the AI ​​assistant system can support users in maintaining their health.

[0059] The AI ​​assistant system can also be equipped with an environmental adaptation unit. This unit provides optimal settings tailored to the user's living environment. For example, it can monitor the temperature and humidity of the user's living environment and make adjustments to maintain a comfortable environment. It can also adjust the user's lighting and sound environment to provide a relaxing space. Furthermore, it can automatically adjust environmental settings according to the user's daily rhythm. In this way, the AI ​​assistant system can optimize the user's living environment.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The learning unit learns the daily behavioral patterns and routines of elderly individuals and predicts and suggests the next necessary actions. For example, if an individual has a habit of eating lunch at 12:00 every day, the unit learns this behavior and predicts and suggests the next necessary action. The learning unit can also use AI to analyze the behavioral patterns of elderly individuals and predict the next necessary actions. Step 2: The support unit provides necessary support based on the behavioral patterns learned by the learning unit. For example, it provides a hybrid interface of voice and touch operation, and displays information using simple icons and images on a large, high-contrast display. Furthermore, it provides text and vibration notifications for people with hearing impairments, and voice guidance is provided with adjustable volume and clear pronunciation. Step 3: The emergency response unit is equipped with a "Help Button" to press when in trouble and notifies emergency contacts. For example, by pressing the "Help Button" on the device, emergency contacts can be immediately notified and the situation can be communicated. It can also use AI to monitor the daily lives of elderly people, detect abnormalities and notify caregivers and family members.

[0062] (Example of form 2) The AI ​​assistant system according to an embodiment of the present invention is a system that learns and predicts the daily routines of elderly people and supports their actions even when they are in trouble or unable to verbalize their needs. This system provides multi-sensory support that is tailored to the physical and cognitive limitations of elderly people, is designed to be intuitively usable, assists in independent living, and fulfills the desire of many elderly people to "continue to live independently at home for as long as possible." For example, the AI ​​assistant system learns the daily behavior patterns and routines of elderly people and predicts and suggests the next necessary action. For example, if an elderly person has a habit of eating lunch at 12 o'clock every day, the system will automatically set suggestions and reminders. Next, the AI ​​assistant system provides a hybrid interface of voice and touch operation. Even if vocabulary is unavailable, it can be used with simple voice commands and touch operations, and the interface is designed to be easy for elderly people to use. Furthermore, the AI ​​assistant system uses a large screen and high contrast display that is easy for people with impaired vision to see, and displays information using simple icons and images. For people with hearing impairments, it provides notifications via text display and vibration, and the voice guidance is designed to be easy to hear with adjustable volume and clear pronunciation. Furthermore, the AI ​​assistant system includes an emergency response function, featuring a "Help Button" on the device for immediate notification to emergency contacts and communication of the situation. The AI ​​assistant system also automatically monitors the daily lives of elderly individuals and provides necessary support. For example, it automatically notifies caregivers if the elderly person fails to get up in the morning or eat. It features simple language and repetition functions, with an easy-to-understand dialogue design, providing reassurance through regular checks and follow-ups. When specific instructions or confirmations are needed, the device vibrates slightly to provide notification, offering information through means other than sight and hearing. Anticipated use scenarios include medication reminders, meal and hydration reminders, emergency notifications, and suggestions for exercise and relaxation. For instance, it learns daily medication routines and automatically reminds users. It also learns regular meal and hydration timings and notifies users with messages like, "It's almost lunchtime."This allows the AI ​​assistant system to function as a comprehensive support system that enables elderly people to live their daily lives with peace of mind, even when they encounter difficulties or are unable to express themselves verbally. In this way, the AI ​​assistant system supports the independence of the elderly and allows them to live their daily lives with confidence.

[0063] The AI ​​assistant system according to this embodiment comprises a learning unit, a support unit, and an emergency response unit. The learning unit learns the daily behavior patterns and routines of the elderly person and predicts and suggests the next necessary action. For example, if the elderly person has a habit of eating lunch at 12:00 every day, the learning unit learns this behavior and predicts and suggests the next necessary action. The learning unit can also use AI to analyze the elderly person's behavior patterns and predict the next necessary action. For example, the learning unit takes the elderly person's behavior data as input and outputs the next necessary action. The support unit provides necessary support based on the behavior patterns learned by the learning unit. The support unit provides, for example, a hybrid interface of voice and touch operation. The support unit can also use a large screen and high contrast display to display information using simple icons and images. Furthermore, the support unit can provide text display and vibration notifications for the hearing impaired, and voice guidance can be provided with adjustable volume and clear pronunciation. For example, the support unit provides a hybrid interface of voice and touch operation, realizing an interface that is easy for the elderly to use. The support unit uses a large, high-contrast display to provide a display that is easy for elderly people with impaired vision to see. The support unit provides text and vibration notifications for people with hearing impairments, and voice guidance is provided with adjustable volume and clear pronunciation. The emergency response unit is equipped with a "Help Button" to press when in trouble and notifies emergency contacts. For example, the emergency response unit can be equipped with a "Help Button" to press when in trouble, immediately notify emergency contacts and inform them of the situation. The emergency response unit can also use AI to monitor the daily life of elderly people and provide necessary support. For example, the emergency response unit uses AI to input data on the elderly person's behavior, detect abnormalities, and notify caregivers or family members. In this way, the AI ​​assistant system according to the embodiment can support the daily life of elderly people and respond quickly in emergencies.

[0064] The learning unit learns the daily behavioral patterns and routines of elderly individuals and predicts and suggests the next necessary actions. Specifically, the learning unit meticulously records the elderly individual's daily rhythm. For example, if an individual has a habit of eating lunch at 12:00 every day, the unit learns this behavior and predicts and suggests the next necessary action. This includes analyzing behavioral patterns using AI. The AI ​​takes the elderly individual's behavioral data as input and outputs the next necessary action. For example, if the AI ​​learns that an elderly individual has a habit of going for a walk after breakfast, it will suggest a walk after breakfast. The AI ​​can also consider the elderly individual's health condition and past behavioral history and suggest hydration and rest at appropriate times. Furthermore, the learning unit can provide customized suggestions according to the elderly individual's preferences and specific needs. For example, if an individual has a habit of performing a specific activity on a specific day of the week, the learning unit will notify them in advance and encourage preparation. In this way, the learning unit can make the elderly individual's life smoother and reduce daily stress.

[0065] The support unit provides necessary support based on behavioral patterns learned by the learning unit. Specifically, it provides a hybrid interface of voice and touch operation, creating an interface that is easy for seniors to use. Voice operation uses natural language processing technology to recognize the voice commands of seniors and provide appropriate responses. For example, in response to a voice command such as "Tell me the weather," it provides current weather information by voice. Touch operation uses simple icons and images that can be operated intuitively, and a large screen and high contrast display is used to provide a display that is easy for seniors with impaired vision to see. Furthermore, the support unit provides notifications via text display and vibration for those with hearing impairments, and voice guidance is provided with adjustable volume and clear pronunciation. For example, if there is an important notification, the device vibrates along with a text display to alert the senior. Voice guidance also adjusts the volume according to ambient noise and individual hearing ability, and provides information with clear pronunciation. In this way, the support unit can meet the diverse needs of seniors and support their daily lives more comfortably.

[0066] The emergency response unit is equipped with a "Help Button" to be pressed when in need, and it notifies emergency contacts. Specifically, when the "Help Button" on the device is pressed, an immediate notification is sent to pre-registered emergency contacts. The notification includes information such as the elderly person's current location and situation, enabling a quick response. The emergency response unit can also use AI to monitor the elderly person's daily life and provide necessary support. For example, the AI ​​receives data on the elderly person's behavior as input, detects anomalies, and notifies caregivers and family members. Specifically, it detects movements that deviate from normal behavior patterns or periods of inactivity, and alerts them to the possibility of an emergency. Furthermore, the emergency response unit can use voice recognition technology to respond to the elderly person's calls and automatically call for assistance in emergencies. For example, if the elderly person shouts "Help!", the device recognizes the voice and immediately sends a notification to emergency contacts. This allows the emergency response unit to ensure the elderly person's safety and support a quick and appropriate response.

[0067] The support unit can provide a hybrid interface combining voice and touch operation. For example, the support unit provides a hybrid interface combining voice and touch operation, creating an interface that is easy for the elderly to use. The support unit implements voice operation using voice recognition technology and touch operation using a touchscreen. For example, the support unit recognizes voice commands and executes corresponding operations. The support unit can also perform operations by touching icons and buttons on the screen using the touchscreen. This allows the support unit to provide an interface that is easy for the elderly to use. Voice operation is implemented, for example, using voice recognition technology. Voice recognition technology analyzes voice commands and executes corresponding operations. Touch operation is implemented, for example, using a touchscreen. The touchscreen allows operations to be performed by touching icons and buttons on the screen. The hybrid interface is an interface that combines voice and touch operation and is designed to be easy for the elderly to use. This allows the support unit to provide an interface that is easy for the elderly to use.

[0068] The support unit can display information using a large screen and high contrast display, employing simple icons and images. For example, the support unit uses a large screen and high contrast display to provide a display that is easy for elderly people with impaired vision to see. The support unit displays information using simple icons and images, enabling elderly people to understand it intuitively. For example, the support unit uses a large screen display to show text and icons in a larger size. The support unit can also use a high contrast display to improve the visibility of text and icons. Furthermore, the support unit displays information using simple icons and images, enabling elderly people to understand it intuitively. For example, the support unit uses simple icons to convey information visually. The support unit can also use images to supplement information, providing a display that is easy to understand visually. This allows the support unit to provide a display that is easy for elderly people with impaired vision to see. A large screen display, for example, uses a display with a large screen size or pixel count. A high contrast display, for example, uses a display with a high contrast ratio. Simple icons and images are achieved, for example, by simplifying the size, color, and shape of the icons. This allows the support unit to provide a display that is easy for elderly people with impaired vision to read.

[0069] The support unit can provide notifications via text display and vibration for people with hearing impairments, and voice guidance with adjustable volume and clear pronunciation. For example, the support unit can provide notifications via text display and vibration for people with hearing impairments, making it easy for elderly people with hearing limitations to use. The support unit provides voice guidance with adjustable volume and clear pronunciation to make it easy for elderly people to hear. For example, the support unit can provide notifications using text display. The support unit can also provide notifications using vibration. Furthermore, the support unit provides voice guidance with adjustable volume and clear pronunciation to make it easy for elderly people to hear. For example, the support unit has a volume adjustment function, allowing users to adjust the volume. The support unit can also use speech synthesis technology to achieve clear pronunciation. This allows the support unit to provide notification methods that are easy for elderly people with hearing limitations to use. Text display can be achieved, for example, by adjusting the font size and display position. Vibration notifications can be achieved, for example, by adjusting the vibration intensity and pattern. Adjustable volume can be achieved, for example, by setting a volume range or adjustment steps. Clear pronunciation can be achieved, for example, by using speech synthesis technology or noise cancellation technology. This allows the support unit to provide a notification method that is easy for elderly people with hearing impairments to use.

[0070] The support unit can provide an easy-to-understand dialogue design by incorporating simple language and a repetition confirmation function. For example, the support unit can provide a dialogue design that is easy for the elderly to understand by incorporating simple language and a repetition confirmation function. The support unit uses simple language in its dialogues to make them easy for the elderly to understand. For example, the support unit can adjust the difficulty level of the words used to achieve simple language. The support unit can also adjust the length of sentences to conduct dialogues in shorter sentences. Furthermore, the support unit incorporates a repetition confirmation function to make it easy for the elderly to understand. For example, the support unit has a function to repeatedly confirm important information to ensure that the elderly understand the information. The support unit can also adjust the frequency and method of confirmation. This allows the support unit to provide a dialogue design that is easy for the elderly to understand. Simple language is achieved, for example, by adjusting the difficulty level of the words used and the length of sentences. The repetition confirmation function is achieved, for example, by adjusting the frequency and method of confirmation. An easy-to-understand dialogue design is designed, for example, based on the dialogue flow and usability test results. This allows the support unit to provide a dialogue design that is easy for the elderly to understand.

[0071] The support unit can notify users by gently vibrating the device when specific instructions or confirmations are needed. For example, the support unit can provide information through means other than sight and hearing, such as by gently vibrating the device when specific instructions or confirmations are needed. The support unit ensures that elderly users receive information reliably by gently vibrating the device. For example, the support unit can notify users of specific instructions by gently vibrating the device. The support unit can also notify users of confirmations by gently vibrating the device. Furthermore, the support unit can adjust the intensity and pattern of the vibrations to ensure that elderly users receive information reliably. For example, the support unit has a function to adjust the vibration intensity, which the user can adjust. The support unit also has a function to adjust the vibration pattern, which the user can adjust. This allows the support unit to provide information through means other than sight and hearing. Gentle vibrations are achieved, for example, by adjusting the vibration intensity and pattern. Specific instructions and confirmations are achieved, for example, by setting the type of instruction and the method of confirmation. This allows the support unit to provide information through means other than sight and hearing.

[0072] The learning unit can learn the routine of taking medication at a fixed time each day and provide reminders. For example, the learning unit can learn the routine of taking medication at a fixed time each day, ensuring that elderly individuals do not forget when to take their medication. The learning unit uses AI to analyze the elderly individual's behavioral patterns, predict medication times, and provide reminders. For example, the learning unit uses AI to take the elderly individual's behavioral data as input and outputs the medication time. Furthermore, the learning unit can adjust the notification method and frequency to provide reminders. For example, the learning unit can provide reminders using voice or text notifications. The learning unit can also adjust the notification frequency to ensure that elderly individuals remember when to take their medication. This allows the learning unit to ensure that elderly individuals do not forget when to take their medication. A fixed time each day can be achieved, for example, by adjusting the time range and setting method. Reminders can be achieved, for example, by adjusting the notification method and frequency. This allows the learning unit to ensure that elderly individuals do not forget when to take their medication.

[0073] The learning unit can learn the timing of regular meals and hydration and provide reminders. For example, the learning unit can learn the timing of regular meals and hydration so that elderly individuals do not forget. The learning unit uses AI to analyze the behavioral patterns of elderly individuals, predict the timing of meals and hydration, and provide reminders. For example, the learning unit uses the elderly individual's behavioral data as input and outputs the timing of meals and hydration. Furthermore, the learning unit can adjust the notification method and frequency to provide reminders. For example, the learning unit can provide reminders using voice or text notifications. The learning unit can also adjust the frequency of notifications to ensure that elderly individuals remember the timing of meals and hydration. This allows the learning unit to ensure that elderly individuals do not forget the timing of meals and hydration. The timing of regular meals and hydration can be achieved, for example, by adjusting meal times and hydration frequency. Reminders can be achieved, for example, by adjusting the notification method and frequency. This allows the learning department to ensure that elderly people don't forget when to eat and drink.

[0074] The emergency response unit can detect anomalies from daily behavioral patterns and notify caregivers and family members. For example, the emergency response unit can detect anomalies from daily behavioral patterns, enabling early detection of abnormalities in elderly individuals and prompt response. The emergency response unit uses AI to analyze the elderly person's behavioral patterns, detect anomalies, and notify caregivers and family members. For example, the emergency response unit uses AI to input the elderly person's behavioral data and output anomalies. Furthermore, the emergency response unit can adjust the notification method and content. For example, the emergency response unit uses voice or text notifications to notify of anomalies. The emergency response unit can also adjust the notification content to ensure caregivers and family members quickly understand the anomaly. This allows the emergency response unit to detect abnormalities in elderly individuals early and respond promptly. Anomaly detection is achieved, for example, by analyzing changes in behavioral patterns or the type of anomaly. Notification is achieved, for example, by adjusting the notification method and content. This allows the emergency response unit to detect abnormalities in elderly individuals early and respond promptly.

[0075] The learning unit can estimate the user's emotions and determine the priority of behavioral patterns to learn based on the estimated emotions. For example, the learning unit can estimate the user's emotions, determine the priority of behavioral patterns to learn based on the estimated emotions, and learn behavioral patterns that correspond to the user's emotions. The learning unit is implemented using emotion estimation functionality with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is stressed, the learning unit will prioritize learning behavioral patterns that promote relaxation. Also, if the user is tired, the learning unit can prioritize learning behavioral patterns that encourage rest. Furthermore, if the user is energetic, the learning unit can prioritize learning active behavioral patterns. This enables the learning unit to learn behavioral patterns that correspond to the user's emotions. Emotion estimation is implemented, for example, using emotion recognition technology or data collection methods. Prioritization of behavioral patterns is implemented, for example, using priority evaluation criteria or decision algorithms. This enables the learning unit to learn behavioral patterns that respond to the user's emotions.

[0076] The learning unit can optimize its learning algorithm by referencing the user's past behavior history during training. For example, the learning unit can optimize the learning algorithm by referencing the user's past behavior history during training, enabling optimal learning based on past behavior history. The learning unit uses AI to analyze the user's past behavior history and optimize the learning algorithm. For example, the learning unit uses the user's past behavior data as input and outputs the optimal learning algorithm. The learning unit can also set adjustment methods and evaluation criteria for the learning algorithm in order to provide optimal learning based on past behavior history. For example, the learning unit adjusts the learning algorithm based on actions the user has frequently performed in the past. The learning unit can also predict actions to be taken during specific time periods from the user's past behavior history and optimize the learning algorithm. Furthermore, the learning unit can analyze the user's past behavior history and learn the most efficient behavior patterns. This enables the learning unit to perform optimal learning based on past behavior history. Past behavior history can be accessed, for example, by setting the data type and data storage method. Optimization of the learning algorithm can be achieved, for example, by setting the optimization method and evaluation criteria. This allows the learning unit to perform optimal learning based on past behavioral history.

[0077] The learning unit can customize learning content based on the user's living environment and health condition during the learning process. For example, the learning unit can customize learning content based on the user's living environment and health condition during the learning process, enabling learning tailored to the user's living environment and health condition. The learning unit uses AI to analyze the user's living environment and health condition and customize the learning content. For example, the learning unit takes the user's living environment data and health condition data as input and outputs customized learning content. The learning unit can also set customization methods and evaluation criteria to provide learning content based on the user's living environment and health condition. For example, if the user has high blood pressure, the learning unit will learn behavioral patterns that take health into consideration. The learning unit can also learn behavioral patterns that reduce feelings of loneliness if the user lives alone. Furthermore, if the user has a specific allergy, the learning unit can learn behavioral patterns that take that allergy into consideration. This enables the learning unit to provide learning tailored to the user's living environment and health condition. The living environment can be realized, for example, by setting the type of residence and lifestyle habits. The health condition can be realized, for example, by setting the medical history and current health indicators. Customizing learning content can be achieved, for example, by setting customization methods and evaluation criteria. This allows the learning unit to tailor learning to the user's living environment and health condition.

[0078] The learning unit can estimate the user's emotions and adjust the learning timing based on the estimated emotions. For example, the learning unit can estimate the user's emotions, adjust the learning timing based on the estimated emotions, and provide the optimal learning timing according to the user's emotions. The learning unit is implemented using emotion estimation functionality with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is relaxed, the learning unit can set the learning timing to a relaxed time. Also, if the user is busy, the learning unit can set the learning timing to a less busy time. Furthermore, if the user is tired, the learning unit can set the learning timing to after rest. In this way, the learning unit can provide the optimal learning timing according to the user's emotions. Emotion estimation is implemented, for example, using emotion recognition technology or data collection methods. The learning timing is implemented, for example, using timing evaluation criteria or decision algorithms. In this way, the learning unit can provide the optimal learning timing according to the user's emotions.

[0079] The learning unit can learn behavioral patterns by considering the user's geographical location information during the learning process. For example, the learning unit can learn behavioral patterns by considering the user's geographical location information during the learning process, enabling it to learn behavioral patterns based on the user's geographical location information. The learning unit uses AI to analyze the user's geographical location information and learn behavioral patterns. For example, the learning unit's AI takes the user's geographical location information data as input and outputs behavioral patterns. The learning unit can also set learning algorithms and evaluation criteria to provide behavioral patterns based on geographical location information. For example, if the user is at home, the learning unit learns behavioral patterns at home. The learning unit can also learn behavioral patterns when the user is out, if they are away from home. Furthermore, if the user is traveling, the learning unit can learn behavioral patterns at their travel destination. This enables the learning unit to learn behavioral patterns based on the user's geographical location information. Geographical location information can be obtained, for example, by setting GPS data or location information accuracy. Learning behavioral patterns can be achieved, for example, by setting learning algorithms and data collection methods. This enables the learning unit to learn behavioral patterns based on the user's geographical location information.

[0080] The learning unit can analyze a user's social media activity during training and learn related behavioral patterns. For example, the learning unit can analyze a user's social media activity during training, learn related behavioral patterns, and learn behavioral patterns based on social media activity. The learning unit uses AI to analyze a user's social media activity and learn behavioral patterns. For example, the learning unit's AI takes the user's social media data as input and outputs behavioral patterns. The learning unit can also set learning algorithms and evaluation criteria to provide behavioral patterns based on social media activity. For example, the learning unit learns behavioral patterns based on the content a user frequently posts on social media. The learning unit can also analyze a user's social media friendships and learn related behavioral patterns. Furthermore, the learning unit can learn behavioral patterns based on the user's social media activity time. This enables the learning unit to learn behavioral patterns based on social media activity. Social media activity can be realized, for example, by setting the type of data and how the data is stored. Learning behavioral patterns can be realized, for example, by setting learning algorithms and data collection methods. This will enable the learning department to learn behavioral patterns based on social media activity.

[0081] The support unit can estimate the user's emotions and adjust the support content based on the estimated emotions. For example, the support unit can estimate the user's emotions, adjust the support content based on the estimated emotions, and provide optimal support content according to the user's emotions. The support unit is implemented using emotion estimation functionality with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the support unit is feeling stressed, it can provide support content that helps the user relax. Also, if the support unit is tired, it can provide support content that encourages rest. Furthermore, if the support unit is feeling energetic, it can provide active support content. In this way, the support unit can provide optimal support content according to the user's emotions. Emotion estimation is implemented, for example, using emotion recognition technology or data collection methods. Adjusting the support content is implemented, for example, by setting adjustment methods and evaluation criteria. In this way, the support unit can provide optimal support content according to the user's emotions.

[0082] The support unit can select the optimal support method by referring to the user's past support history during support. For example, the support unit can select the optimal support method by referring to the user's past support history during support and provide the optimal support method based on that history. The support unit uses AI to analyze the user's past support history and select the optimal support method. For example, the support unit's AI takes the user's past support data as input and outputs the optimal support method. Furthermore, the support unit can set evaluation criteria and decision algorithms for support methods in order to provide the optimal support method based on past support history. For example, the support unit selects the optimal support method based on the support content the user has received in the past. The support unit can also select the support content to be provided at a specific time period from the user's past support history. In addition, the support unit can analyze the user's past support history and select the most effective support method. As a result, the support unit can provide the optimal support method based on past support history. Past support history can be realized, for example, by setting the data type and data storage method. The optimal support method can be realized, for example, by setting evaluation criteria and decision algorithms for support methods. This allows the support department to provide the most suitable support method based on past support history.

[0083] The support unit can customize support content based on the user's current health status during support sessions. For example, the support unit can customize support content based on the user's current health status during support sessions, providing optimal support tailored to the user's health condition. The support unit uses AI to analyze the user's health status and customize support content. For example, the support unit's AI takes the user's health status data as input and outputs customized support content. The support unit can also set customization methods and evaluation criteria to provide support content based on health status. For example, if the user has high blood pressure, the support unit will provide health-conscious support content. The support unit can also provide support content to alleviate feelings of loneliness if the user lives alone. Furthermore, if the support unit has a specific allergy, it can provide support content that takes that allergy into consideration. This allows the support unit to provide optimal support content tailored to the user's health status. Current health status can be determined, for example, by setting medical history and current health indicators. Customization of support content can be achieved, for example, by setting customization methods and evaluation criteria. This allows the support unit to provide optimal support content tailored to the user's health status.

[0084] The support unit can estimate the user's emotions and determine support priorities based on those emotions. For example, the support unit can estimate the user's emotions, determine support priorities based on those emotions, and provide the optimal support priorities according to the user's emotions. The support unit is implemented using emotion estimation functionality with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is stressed, the support unit will prioritize support that promotes relaxation. The support unit can also prioritize support that encourages rest if the user is tired. Furthermore, if the user is energetic, the support unit can prioritize active support. This allows the support unit to provide the optimal support priorities according to the user's emotions. Emotion estimation is implemented, for example, using emotion recognition technology or data collection methods. Support prioritization is implemented, for example, using priority evaluation criteria or decision algorithms. This allows the support unit to provide the optimal support priorities according to the user's emotions.

[0085] The support unit can select the optimal support method by considering the user's geographical location information during support. For example, the support unit can select the optimal support method by considering the user's geographical location information during support and provide the optimal support method based on the user's geographical location information. The support unit uses AI to analyze the user's geographical location information and select the optimal support method. For example, the support unit's AI takes the user's geographical location information data as input and outputs the optimal support method. Furthermore, the support unit can set evaluation criteria and decision algorithms for support methods in order to provide the optimal support method based on geographical location information. For example, if the user is at home, the support unit will provide support at home. Also, if the user is out, the support unit can provide support at their destination. Moreover, if the user is traveling, the support unit can provide support at their travel destination. In this way, the support unit can provide the optimal support method based on the user's geographical location information. Geographical location information can be realized, for example, by setting GPS data or location information accuracy. The optimal support method can be realized, for example, by setting evaluation criteria and decision algorithms for support methods. This allows the support department to provide the optimal support method based on the user's geographical location information.

[0086] The support department can analyze a user's social media activity during support and provide relevant support. For example, the support department can analyze a user's social media activity during support, provide relevant support, and provide optimal support based on that activity. The support department uses AI to analyze a user's social media activity and provide support. For example, the support department uses AI to take the user's social media data as input and output support. The support department can also set evaluation criteria and delivery methods for support to provide optimal support based on social media activity. For example, the support department can provide support based on the content that the user frequently posts on social media. The support department can also analyze a user's social media friendships and provide relevant support. Furthermore, the support department can also provide support based on the user's social media activity time. This allows the support department to provide optimal support based on social media activity. Social media activity can be realized, for example, by setting the type of data and how the data is stored. The provision of support can be realized, for example, by setting the delivery method and the type of content provided. This allows the support department to provide optimal support based on social media activity.

[0087] The emergency response unit can estimate the user's emotions and adjust its emergency response method based on the estimated emotions. For example, the emergency response unit can estimate the user's emotions, adjust its emergency response method based on the estimated emotions, and provide the optimal emergency response method according to the user's emotions. The emergency response unit is implemented using emotion estimation functionality with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the emergency response unit is in a panic state, it will take emergency action to calm the user. The emergency response unit can also take rapid emergency action if the user is calm. Furthermore, if the emergency response unit is confused, it can provide simple and clear instructions. This allows the emergency response unit to provide the optimal emergency response method according to the user's emotions. Emotion estimation is implemented, for example, using emotion recognition technology or data collection methods. Adjusting the emergency response method is implemented, for example, by setting adjustment methods and evaluation criteria. This allows the emergency response unit to provide the optimal emergency response method according to the user's emotions.

[0088] The emergency response unit can select the optimal response method by referring to the user's past emergency response history during an emergency. For example, the emergency response unit can select the optimal response method by referring to the user's past emergency response history during an emergency and provide the optimal response method based on that history. The emergency response unit uses AI to analyze the user's past emergency response history and select the optimal response method. For example, the emergency response unit's AI takes the user's past emergency response data as input and outputs the optimal response method. Furthermore, the emergency response unit can set evaluation criteria and decision algorithms for response methods in order to provide the optimal response method based on past emergency response history. For example, the emergency response unit selects the optimal response method based on the content of emergency responses the user has received in the past. The emergency response unit can also select the content of emergency responses to provide at a specific time period from the user's past emergency response history. In addition, the emergency response unit can analyze the user's past emergency response history and select the most effective response method. As a result, the emergency response unit can provide the optimal response method based on past emergency response history. Past emergency response history can be realized, for example, by setting the data type and data storage method. The optimal response method can be achieved, for example, by setting evaluation criteria and decision algorithms for response methods. This allows the emergency response unit to provide the optimal response method based on past emergency response history.

[0089] The emergency response unit can customize its response based on the user's current health condition during an emergency. For example, the emergency response unit can customize its response based on the user's current health condition during an emergency, providing the optimal emergency response tailored to the user's health. The emergency response unit uses AI to analyze the user's health condition and customize the response. For example, the emergency response unit takes the user's health condition data as input and outputs a customized response. The emergency response unit can also set customization methods and evaluation criteria to provide emergency response based on the user's health condition. For example, if the user has high blood pressure, the emergency response unit will provide a health-conscious emergency response. The emergency response unit can also provide an emergency response that alleviates feelings of loneliness if the user lives alone. Furthermore, if the emergency response unit has a specific allergy, it can provide an emergency response that takes that allergy into consideration. In this way, the emergency response unit can provide the optimal emergency response tailored to the user's health condition. The current health condition can be determined, for example, by setting medical history and current health indicators. The customization of the response can be achieved, for example, by setting customization methods and evaluation criteria. This allows the emergency response unit to provide the most appropriate emergency response tailored to the user's health condition.

[0090] The emergency response unit can estimate the user's emotions and determine the priority of emergency responses based on those emotions. For example, the emergency response unit can estimate the user's emotions, determine the priority of emergency responses based on those emotions, and provide the optimal priority of emergency responses according to the user's emotions. The emergency response unit is implemented using emotion estimation functionality, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is in a panic state, the emergency response unit will provide the highest priority emergency response. The emergency response unit can also provide a rapid emergency response if the user is calm. Furthermore, if the user is confused, the emergency response unit can provide simple and clear instructions. This allows the emergency response unit to provide the optimal priority of emergency responses according to the user's emotions. Emotion estimation is implemented, for example, using emotion recognition technology or data collection methods. Emergency response prioritization is implemented, for example, using priority evaluation criteria or decision algorithms. This allows the emergency response unit to provide the optimal priority of emergency responses according to the user's emotions.

[0091] The emergency response unit can select the optimal response method in the event of an emergency, taking into account the user's geographical location information. For example, the emergency response unit can select the optimal response method in the event of an emergency, taking into account the user's geographical location information, and provide the optimal emergency response method based on the user's geographical location information. The emergency response unit uses AI to analyze the user's geographical location information and select the optimal response method. For example, the emergency response unit's AI takes the user's geographical location information data as input and outputs the optimal response method. Furthermore, the emergency response unit can set evaluation criteria and decision algorithms for response methods in order to provide the optimal emergency response method based on geographical location information. For example, if the user is at home, the emergency response unit will provide emergency response information for the user's home. If the user is out, the emergency response unit can also provide emergency response information for the user's location. In addition, if the user is traveling, the emergency response unit can also provide emergency response information for the user's travel destination. In this way, the emergency response unit can provide the optimal emergency response method based on the user's geographical location information. Geographical location information can be realized, for example, by setting GPS data or location information accuracy. The optimal response method can be achieved, for example, by setting evaluation criteria and decision algorithms for the response method. This allows the emergency response unit to provide the optimal emergency response method based on the user's geographical location information.

[0092] The emergency response unit can analyze a user's social media activity during an emergency and provide relevant response content. For example, the emergency response unit can analyze a user's social media activity during an emergency, provide relevant response content, and provide the optimal emergency response content based on social media activity. The emergency response unit uses AI to analyze a user's social media activity and provide response content. For example, the emergency response unit's AI takes the user's social media data as input and outputs response content. Furthermore, the emergency response unit can set evaluation criteria and delivery methods for response content in order to provide the optimal emergency response content based on social media activity. For example, the emergency response unit can provide emergency response content based on the content that the user frequently posts on social media. The emergency response unit can also analyze a user's social media friendships and provide relevant emergency response content. In addition, the emergency response unit can also provide emergency response content based on the user's social media activity time. This allows the emergency response unit to provide the optimal emergency response content based on social media activity. Social media activity can be realized, for example, by setting the type of data and how the data is stored. The provision of response content can be realized, for example, by setting the delivery method and the type of content provided. This allows the emergency response department to provide optimal emergency response plans based on social media activity.

[0093] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0094] The AI ​​assistant system can also include a health management unit. This unit monitors the user's health status by collecting and analyzing their health data. For example, it can periodically measure vital signs such as heart rate, blood pressure, and body temperature, and notify the user or caregiver if an abnormality is detected. The health management unit can also manage the user's diet and exercise records and provide advice to support healthy lifestyle habits. Furthermore, based on the user's health status, the health management unit can recommend appropriate medical consultations. This allows the AI ​​assistant system to comprehensively support the user's health management.

[0095] The AI ​​assistant system can also be equipped with a communication support unit. This unit provides assistance to help users communicate smoothly with others. For example, it can use speech recognition technology to convert user speech into text and send it as a text message. It can also read aloud messages received by the user. Furthermore, it can suggest emojis and stickers to help users express their emotions. In this way, the AI ​​assistant system can support users in communicating smoothly with others.

[0096] AI assistant systems can also be equipped with a reminder function. This function manages the user's schedule and tasks, providing reminders at appropriate times. For example, it can record user-set appointments and tasks and notify the user when the scheduled time approaches. Furthermore, it can learn the user's behavioral patterns and automatically remind them of recurring tasks and routines. Additionally, it can adjust the content and timing of reminders based on the user's emotional state. This allows the AI ​​assistant system to help users remember and complete important appointments and tasks.

[0097] The AI ​​assistant system can also be equipped with an entertainment section. This section provides entertainment content tailored to the user's hobbies and interests. For example, it can recommend and play music and movies the user likes. It can also provide news and articles of interest to the user. Furthermore, it can offer games and quizzes for the user to enjoy. In this way, the AI ​​assistant system can provide enjoyment and a sense of fulfillment to the user's life.

[0098] AI assistant systems can also be equipped with an emotion analysis unit. This unit estimates the user's emotions from their speech and actions and responds appropriately. For example, it can analyze the user's tone of voice and facial expressions and provide relaxing content if the user is feeling stressed. It can also display encouraging messages if the user is sad, and empathetic messages if the user is happy. This allows the AI ​​assistant system to provide support that is sensitive to the user's emotions.

[0099] AI assistant systems can also be equipped with a learning support function. This function provides support for users to acquire new knowledge and skills. For example, it can recommend online courses and learning materials in areas of interest to the user. It can also manage the user's learning progress and provide reviews and tests at appropriate times. Furthermore, it can provide customized learning plans tailored to the user's learning style. In this way, AI assistant systems can support users in efficiently acquiring new knowledge and skills.

[0100] The AI ​​assistant system can also be equipped with an exercise support unit. This unit provides appropriate exercise programs to help users maintain their health. For example, it can create and support exercise menus tailored to the user's fitness level and health condition. It can also record the user's exercise history and manage their progress. Furthermore, it can provide encouraging messages and feedback that fosters a sense of accomplishment to motivate users to continue exercising. In this way, the AI ​​assistant system can support users in maintaining their health.

[0101] The AI ​​assistant system can also be equipped with an emotion diary function. This function provides a tool for users to record and reflect on their daily emotions. For example, the emotion diary allows users to record their emotions for the day in text or voice. It can also analyze the recorded emotional data to visualize the user's emotional trends and changes. Furthermore, the emotion diary can support users in reviewing past emotional records to deepen their self-understanding. In this way, the AI ​​assistant system can support the user's emotional management.

[0102] The AI ​​assistant system can also be equipped with an environmental adaptation unit. This unit provides optimal settings tailored to the user's living environment. For example, it can monitor the temperature and humidity of the user's living environment and make adjustments to maintain a comfortable environment. It can also adjust the user's lighting and sound environment to provide a relaxing space. Furthermore, it can automatically adjust environmental settings according to the user's daily rhythm. In this way, the AI ​​assistant system can optimize the user's living environment.

[0103] AI assistant systems can also be equipped with an emotional feedback unit. This unit provides appropriate feedback based on the user's emotions. For example, if the user is feeling stressed, it can offer relaxing music or meditation guidance. It can also display empathetic or congratulatory messages if the user is happy. Furthermore, if the user is sad, it can offer encouraging messages or support resources. This allows AI assistant systems to provide feedback that is sensitive to the user's emotions.

[0104] The following briefly describes the processing flow for example form 2.

[0105] Step 1: The learning unit learns the daily behavioral patterns and routines of elderly individuals and predicts and suggests the next necessary actions. For example, if an individual has a habit of eating lunch at 12:00 every day, the unit learns this behavior and predicts and suggests the next necessary action. The learning unit can also use AI to analyze the behavioral patterns of elderly individuals and predict the next necessary actions. Step 2: The support unit provides necessary support based on the behavioral patterns learned by the learning unit. For example, it provides a hybrid interface of voice and touch operation, and displays information using simple icons and images on a large, high-contrast display. Furthermore, it provides text and vibration notifications for people with hearing impairments, and voice guidance is provided with adjustable volume and clear pronunciation. Step 3: The emergency response unit is equipped with a "Help Button" to press when in trouble and notifies emergency contacts. For example, by pressing the "Help Button" on the device, emergency contacts can be immediately notified and the situation can be communicated. It can also use AI to monitor the daily lives of elderly people, detect abnormalities and notify caregivers and family members.

[0106] 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.

[0107] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0108] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0109] Each of the multiple elements described above, including the learning unit, support unit, and emergency response unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The support unit is implemented by the control unit 46A of the smart device 14 and provides a hybrid interface of voice and touch operation. The emergency response unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12 and includes a "help button" to press when in trouble, which notifies emergency contacts. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0110] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0111] 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.

[0112] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0113] 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.

[0114] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0115] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0116] 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.

[0117] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0118] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0119] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0120] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] Each of the multiple elements described above, including the learning unit, support unit, and emergency response unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The support unit is implemented by the control unit 46A of the smart glasses 214 and provides a hybrid interface of voice and touch operation. The emergency response unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12 and includes a "help button" to press when in trouble, which notifies emergency contacts. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0126] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0127] 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.

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0129] 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.

[0130] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0131] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0132] 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.

[0133] 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.

[0134] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0135] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0136] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0138] 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.

[0139] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0140] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the learning unit, support unit, and emergency response unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The support unit is implemented by the control unit 46A of the headset terminal 314 and provides a hybrid interface of voice and touch operation. The emergency response unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12 and includes a "Help Button" to press when in trouble, which notifies emergency contacts. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0142] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0145] 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.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0147] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] 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.

[0149] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0150] 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.

[0151] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] 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.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the learning unit, support unit, and emergency response unit, is implemented by at least one of the robot 414 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The support unit is implemented by the control unit 46A of the robot 414 and provides a hybrid interface of voice and touch operation. The emergency response unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12 and includes a "help button" to press when in trouble, which notifies emergency contacts. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] 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.

[0160] Figure 9 shows the 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.

[0161] 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.

[0162] 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.

[0163] 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, and motorcycles, 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 based, for example, 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.

[0164] 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."

[0165] 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.

[0166] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0175] 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 other things 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.

[0176] 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.

[0177] (Note 1) The learning unit learns the daily behavioral patterns and routines of the elderly and predicts and suggests the next necessary actions. A support unit provides necessary support based on the behavioral patterns learned by the learning unit, It is equipped with a "Help button" to press when in trouble, and an emergency response unit that notifies emergency contacts. A system characterized by the following features. (Note 2) The aforementioned support unit is It provides a hybrid interface combining voice and touch controls. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned support unit is It uses a large, high-contrast display to show information using simple icons and images. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned support unit is For those with hearing impairments, we provide text and vibration notifications, and voice guidance is provided with adjustable volume and clear pronunciation. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned support unit is It provides an easy-to-understand dialogue design with simple language and a repetition function. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned support unit is The device will vibrate slightly to notify you when specific instructions or confirmations are needed. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned learning unit, It helps you learn a daily routine of taking medication at a set time and provides reminders. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned learning unit, Learns and reminds you about regular meal and hydration schedules. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned emergency response unit, It detects abnormalities from daily behavioral patterns and notifies caregivers and family members. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned learning unit, It estimates the user's emotions and prioritizes learning behavioral patterns based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to the user's past behavior history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned learning unit, During learning, the learning content is customized based on the user's living environment and health condition. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned learning unit, It estimates the user's emotions and adjusts the timing of learning based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned learning unit, During training, the system learns behavioral patterns while taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned learning unit, During training, the system analyzes users' social media activity and learns related behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned support unit is The system estimates the user's emotions and adjusts the support provided based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned support unit is During support, the system will refer to the user's past support history to select the most appropriate support method. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned support unit is During support, customize the support content based on the user's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned support unit is The system estimates the user's emotions and determines support priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned support unit is During support, the optimal support method will be selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned support unit is During support, we analyze the user's social media activity and provide relevant support. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned emergency response unit, It estimates the user's emotions and adjusts emergency response methods based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned emergency response unit, During an emergency, the system will refer to the user's past emergency response history to select the most appropriate response method. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned emergency response unit, During emergency response, the response will be customized based on the user's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned emergency response unit, It estimates the user's emotions and determines the priority of emergency responses based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned emergency response unit, During emergency response, the system selects the optimal response method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned emergency response unit, During emergency situations, we analyze users' social media activity and provide relevant responses. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The learning unit learns the daily behavioral patterns and routines of the elderly and predicts and suggests the next necessary actions. A support unit provides necessary support based on the behavioral patterns learned by the learning unit, It is equipped with a "Help" button to press when in trouble, and an emergency response unit that notifies emergency contacts. A system characterized by the following features.

2. The aforementioned support unit is It provides a hybrid interface combining voice and touch controls. The system according to feature 1.

3. The aforementioned support unit is It uses a large, high-contrast display to show information using simple icons and images. The system according to feature 1.

4. The aforementioned support unit is For those with hearing impairments, we provide text and vibration notifications, and voice guidance is provided with adjustable volume and clear pronunciation. The system according to feature 1.

5. The aforementioned support unit is It provides an easy-to-understand dialogue design with simple language and a repetition function. The system according to feature 1.

6. The aforementioned support unit is The device will vibrate slightly to notify you when specific instructions or confirmations are needed. The system according to feature 1.

7. The aforementioned learning unit, It helps you learn a daily routine of taking medication at a set time and provides reminders. The system according to feature 1.

8. The aforementioned learning unit, Learns and reminds you about regular meal and hydration schedules. The system according to feature 1.

9. The aforementioned emergency response unit, It detects abnormalities from daily behavioral patterns and notifies caregivers and family members. The system according to feature 1.

10. The aforementioned learning unit, It estimates the user's emotions and prioritizes learning behavioral patterns based on those estimated emotions. The system according to feature 1.

Images