System

Abstract

To provide a system for performing in an integrated manner, mental care, medicine-taking management, risk detection in walking of an elderly person, for reducing a burden applied to a care giver.SOLUTION: A system in an embodiment comprises: a conversation part; a management part; and a detection part. The conversion part performs mental care through a conversation with an elderly person. The management part outputs alert of medicine taking management on the basis of the information obtained by the conversation part. The detection part detects risk in walking on the basis of the information managed by the management part, and notifies the elderly person of the detected risk.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to provide centralized mental care for elderly people, medication management, and danger detection while walking, which resulted in a heavy burden on caregivers.

[0005] The system according to the embodiment aims to reduce the burden on caregivers by providing mental care for elderly people, managing their medication, and detecting dangers while walking in an integrated manner. [Means for solving the problem]

[0006] The system according to the embodiment includes a conversation unit, a management unit, and a detection unit. The conversation unit provides mental care through conversation with the elderly. The management unit issues an alert for medication management based on information obtained by the conversation unit and reports it to a caregiver. The detection unit detects dangers while walking based on information managed by the management unit and notifies the elderly. Effect of the Invention

[0007] The system according to the embodiment can centrally provide mental care for elderly people, manage their medication, and detect dangers while walking, thereby reducing the burden on caregivers. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

[0017] 1, a 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. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific process program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The care partner system according to the embodiment of the present invention is a system that aims to improve the satisfaction of each elderly person while reducing the burden on caregivers who care for the elderly. This care partner system provides mental care for elderly people who suffer from loneliness through conversation with AI, and even provides medication management alerts, reports to caregivers, detects dangers while walking and notifies the elderly, and supports tools for communication with family members who live far away. For example, the care partner system allows elderly people to enjoy daily conversations with AI, which can reduce the sense of loneliness. AI provides topics based on the elderly person's hobbies and interests, and eases the elderly's feelings through conversation. In addition, the care partner system has an alert function for medication management, where AI manages the elderly person's medication schedule and issues an alert to encourage them to take their medication at the appropriate time. In addition, it also has a reporting function to caregivers, and notifies the caregiver if the elderly person forgets to take their medication or if there is an abnormality. In addition, the care partner system has a danger detection function when walking, where AI monitors the elderly person's walking and notifies the elderly person if there is a risk of falling. For example, AI analyzes the elderly person's walking pattern and immediately issues a warning if their balance is lost. In addition, the Care Partner system also provides tools to support communication with family members who live far away. AI supports video calls and messaging between the elderly and their families, helping them maintain connections with their families. For example, AI can read messages from family members to the elderly and help set up video calls. In this way, the Care Partner system using AI can reduce the burden on caregivers and improve the satisfaction of the elderly by providing a wide range of functions, including mental care for the elderly, medication management, danger detection while walking, and communication support with family members. This allows the Care Partner system to consistently provide mental care for the elderly, medication management, danger detection while walking, and communication support with family members.

[0029] The care partner system according to the embodiment includes a conversation unit, a management unit, and a detection unit. The conversation unit provides mental care through conversation with the elderly. The conversation unit, for example, uses AI to have daily conversation with the elderly. Conversations with the elderly include, for example, daily conversations, conversations about health, and conversations about hobbies, but are not limited to such examples. For example, the conversation unit provides topics based on the hobbies and interests of the elderly using AI, and eases the feelings of the elderly through dialogue. The conversation unit can also estimate the emotions of the elderly using AI and select a conversation topic based on the estimated emotions of the elderly. For example, the AI ​​analyzes the voice and facial expressions of the elderly to estimate the emotions. The management unit issues an alert for medication management based on the information obtained by the conversation unit and reports to the caregiver. The management unit, for example, uses AI to manage the elderly's medication schedule and issues an alert to encourage the elderly to take medication at an appropriate time. The medication schedule includes, for example, setting a time period and managing the amount of medication, but is not limited to such examples. The management unit can also use AI to notify the caregiver when the elderly forgets to take medication or when there is an abnormality. For example, the AI ​​analyzes the elderly person's medication history and detects abnormalities. The detection unit detects dangers during walking based on the information managed by the management unit and notifies the elderly person. The detection unit, for example, uses AI to monitor the elderly person's walking and notifies the elderly person when there is a risk of falling. The monitoring of walking includes, for example, but is not limited to, step counting, walking speed measurement, posture detection, and the like. The detection unit can also analyze the elderly person's walking pattern using AI and issue a warning when balance is lost. For example, the AI ​​analyzes the elderly person's walking data and evaluates the risk of falling. As a result, the care partner system according to the embodiment can consistently provide mental care for the elderly person, medication management, and danger detection during walking.

[0030] The conversation part provides mental care through conversation with the elderly. The conversation part, for example, uses AI to have daily conversations with the elderly. Specifically, the AI ​​uses natural language processing technology to understand what the elderly say and generate appropriate responses. Daily conversations include topics such as the weather, news, and family topics, and respond flexibly according to the interests and concerns of the elderly. In health-related conversations, the AI ​​asks questions about the elderly's health and lifestyle habits, and provides advice as necessary. In hobbies-related conversations, topics related to the elderly's hobbies and interests are provided based on the elderly's past comments and profile information. For example, for an elderly person whose hobby is gardening, topics about how to grow seasonal flowers and plants are provided. In addition, the AI ​​can analyze the elderly's voice and facial expressions to estimate emotions. Voice analysis detects changes in tone, speed, and volume of the voice, and facial expression analysis estimates emotions by analyzing the movement of facial muscles. This makes it possible to select appropriate conversation topics according to the elderly's emotions, such as when they are stressed or relaxed. For example, if the elderly person looks sad, the AI ​​provides fun topics to ease their mood. This enables the conversation section to effectively provide mental care for the elderly and reduce feelings of loneliness and stress.

[0031] The management unit issues a medication management alert based on the information obtained by the conversation unit and reports it to the caregiver. For example, the management unit uses AI to manage the elderly's medication schedule and issues an alert to encourage the elderly to take the medication at the appropriate time. Specifically, the AI ​​registers the elderly's medication schedule in a database and issues an alert at the set time. The alert is issued through voice notification, smartphone notification, or even a dedicated device. The medication schedule includes the type of medication, dosage, and time of medication, and is set based on the elderly's health condition and the doctor's instructions. The AI ​​can analyze the medication history and detect forgotten or abnormal medication. For example, if the elderly forgets to take the medication, the AI ​​issues another alert and notifies the caregiver. It also detects abnormalities when the amount of medication is excessive or insufficient, or when the timing of medication is off, and reports them to the caregiver. This allows the management unit to efficiently manage the elderly's medication and prevent medication errors. In addition, the management unit can collect data on the elderly's health condition and provide it to doctors and caregivers. This will enable doctors and caregivers to grasp the health status of elderly people in real time and take appropriate measures.

[0032] The detection unit detects dangers during walking based on the information managed by the management unit and notifies the elderly. For example, the detection unit uses AI to monitor the elderly's walking and notifies the elderly if there is a risk of falling. Specifically, the AI ​​analyzes the elderly's walking data in real time and detects changes in walking patterns and posture. Walking monitoring includes step counting, walking speed measurement, posture detection, etc. In step counting, the AI ​​counts the elderly's steps and grasps the amount of daily activity. In measuring walking speed, the AI ​​analyzes the walking speed and rhythm and detects abnormal changes. In posture detection, the AI ​​analyzes the elderly's body tilt and changes in balance to evaluate the risk of falling. For example, if the elderly suddenly loses balance, the AI ​​immediately issues a warning and alerts the elderly. The detection unit can also accumulate the elderly's walking data and analyze long-term changes in walking patterns. This makes it possible to detect the risk of falling early and plan preventive measures. In addition, the detection unit can provide the elderly's walking data to caregivers and doctors to use as a reference for rehabilitation and treatment. This enables the detection unit to ensure the safety of elderly people and minimize the risk of falls.

[0033] The care partner system includes a tool unit that supports communication with family members. The tool unit, for example, uses AI to support video calls and message exchanges between the elderly and family members. For example, the tool unit uses AI to read messages from family members to the elderly and assist in setting up video calls. Specific methods for supporting communication include, but are not limited to, video calls, messaging, and voice calls. In this way, the care partner system can maintain a connection with family members by supporting communication between the elderly and family members.

[0034] The conversation unit can use AI to have daily conversations with the elderly. The conversation unit, for example, uses AI to have daily conversations with the elderly. Daily conversations include, but are not limited to, for example, talking about the weather, talking about food, and talking about hobbies. For example, the conversation unit uses AI to provide topics based on the hobbies and interests of the elderly, and eases the elderly's feelings through dialogue. In this way, by using AI, mental care can be provided through daily conversations with the elderly.

[0035] The management unit can use AI to manage the elderly person's medication schedule and issue an alert to encourage medication. The management unit, for example, uses AI to manage the elderly person's medication schedule and issue an alert to encourage medication at the appropriate time. The medication schedule includes, for example, but is not limited to, setting time periods and managing the amount of medication. For example, the management unit uses AI to analyze the elderly person's medication history and determine the appropriate timing for medication. As a result, by using AI, medication management for the elderly can be performed efficiently.

[0036] The management unit can use AI to notify the caregiver when the elderly person forgets to take their medicine or when there is an abnormality. The management unit, for example, uses AI to notify the caregiver when the elderly person forgets to take their medicine or when there is an abnormality. Abnormalities include, for example, forgetting to take medicine, overdosing, poor health, etc., but are not limited to such examples. For example, the management unit uses AI to analyze the elderly person's medication history and detect abnormalities. As a result, by using AI, it is possible to quickly notify the caregiver when medication is forgotten or an abnormality occurs.

[0037] The detection unit can monitor the elderly person's walking using AI and notify the elderly person when there is a risk of falling. The detection unit can, for example, monitor the elderly person's walking using AI and notify the elderly person when there is a risk of falling. The monitoring of walking includes, for example, but is not limited to, measuring the number of steps, measuring walking speed, detecting posture, and the like. For example, the detection unit uses AI to analyze the elderly person's walking data and evaluate the risk of falling. As a result, by using AI, it is possible to detect and notify the elderly person of the risk of falling early.

[0038] The detection unit can analyze the walking pattern of the elderly person using AI and issue a warning when the balance is lost. The detection unit can, for example, analyze the walking pattern of the elderly person using AI and issue a warning when the balance is lost. The walking pattern includes, for example, stride length, walking speed, walking rhythm, etc., but is not limited to such examples. For example, the detection unit uses AI to analyze the walking data of the elderly person and evaluate the risk of falling. As a result, by using AI, the walking pattern of the elderly person can be analyzed and a warning can be issued when the balance is lost.

[0039] The conversation unit can analyze the elderly person's past conversation history and select a method of proceeding with the conversation. The conversation unit can, for example, use AI to analyze the elderly person's past conversation history and select an optimal method of proceeding with the conversation. The method of proceeding with the conversation can include, for example, but is not limited to, the selection of a topic, the way of asking questions, and the tempo of the conversation. For example, the conversation unit can retake a topic that the elderly person has shown interest in in the past and proceed with the conversation. The conversation unit can also avoid topics that the elderly person has avoided in the past and focus on positive topics. Furthermore, the conversation unit can provide new related topics based on topics that the elderly person has frequently talked about in the past. This allows more effective mental care to be provided by selecting an optimal method of proceeding with the conversation based on the past conversation history.

[0040] The conversation unit can provide personalized topics based on the elderly person's hobbies and interests during conversation. The conversation unit, for example, uses AI to provide personalized topics based on the elderly person's hobbies and interests during conversation. Personalized topics include, but are not limited to, hobbies, interests, past experiences, and the like. For example, if the elderly person is interested in gardening, the conversation unit can provide topics about seasonal flowers and plants. In addition, if the elderly person is interested in music, the conversation unit can also talk about favorite artists and new music. Furthermore, if the elderly person is interested in traveling, the conversation unit can also talk about past travel experiences and places the elderly person wants to go. This improves the quality of the conversation by providing topics based on the elderly person's hobbies and interests.

[0041] The conversation unit can provide topics related to the area during the conversation, taking into account the geographical location information of the elderly person. The conversation unit, for example, uses AI to provide topics related to the area, taking into account the geographical location information of the elderly person during the conversation. The geographical location information includes, for example, GPS data, address information, characteristics of the area, and the like, but is not limited to such examples. The conversation unit, for example, talks about events and news in the area where the elderly person lives. The conversation unit can also talk about memories and history of the area where the elderly person lived in the past. Furthermore, the conversation unit can also talk about tourist spots and culture in the area where the elderly person wants to travel. This improves the quality of the conversation by providing topics based on the geographical location information of the elderly person.

[0042] The conversation unit can analyze the social media activity of the elderly person during the conversation and provide related topics. The conversation unit can, for example, use AI to analyze the social media activity of the elderly person during the conversation and provide related topics. Social media activities include, for example, but are not limited to, the content of posts, the number of likes, the number of followers, and the like. The conversation unit can, for example, talk about photos and posts shared by the elderly person on social media. The conversation unit can also provide topics about accounts and groups that the elderly person follows. Furthermore, the conversation unit can talk about online events and communities in which the elderly person participates. This improves the quality of the conversation by providing topics based on the elderly person's social media activity.

[0043] The management unit can analyze the elderly person's past medication history and propose a medication schedule. The management unit can, for example, use AI to analyze the elderly person's past medication history and propose an optimal medication schedule. The medication schedule includes, for example, past medication history, doctor's instructions, changes in physical condition, and the like, but is not limited to such examples. For example, the management unit can propose a schedule that avoids time periods when the elderly person has forgotten to take medication in the past. The management unit can also propose a schedule based on time periods when the elderly person has taken medication in the past. Furthermore, the management unit can also analyze the elderly person's past medication patterns and propose an optimal schedule. As a result, the accuracy of medication management is improved by proposing an optimal medication schedule based on past medication history.

[0044] The management unit can adjust the frequency of alerts based on the health condition of the elderly person during medication management. The management unit adjusts the frequency of alerts based on the health condition of the elderly person during medication management, for example, using AI. The frequency of alerts includes, for example, health condition, importance of medication, emotional state, etc., but is not limited to such examples. For example, the management unit increases the frequency of alerts when the elderly person's health condition is deteriorating. In addition, the management unit can also issue alerts at a normal frequency when the elderly person's health condition is stable. Furthermore, the management unit can also reduce the frequency of alerts when the elderly person's health condition is improving. This enables more appropriate medication management by adjusting the frequency of alerts according to the elderly person's health condition.

[0045] The management unit can provide relevant drug information in consideration of the geographic location information of the elderly person during medication management. The management unit, for example, uses AI to provide relevant drug information in consideration of the geographic location information of the elderly person during medication management. Drug information includes, for example, drug efficacy, side effects, and usage, but is not limited to such examples. The management unit, for example, provides information on pharmacies in the area where the elderly person lives. In addition, if the elderly person is traveling, the management unit can also provide information on nearby pharmacies. Furthermore, if the elderly person is in a specific area, the management unit can also provide information on medicines available in that area. This improves the accuracy of medication management by providing drug information based on the geographic location information of the elderly person.

[0046] The management unit can analyze the social media activity of the elderly person during medication management and provide related health information. The management unit can, for example, use AI to analyze the social media activity of the elderly person during medication management and provide related health information. The health information includes, for example, health management advice, disease prevention methods, and health food information, but is not limited to such examples. The management unit can, for example, issue an alert based on health information shared by the elderly person on social media. The management unit can also provide information on health-related accounts followed by the elderly person. Furthermore, the management unit can provide information on online health communities in which the elderly person participates. This improves the accuracy of medication management by providing health information based on the elderly person's social media activity.

[0047] The detection unit can analyze the elderly person's past walking history and apply a danger detection algorithm. The detection unit can, for example, use AI to analyze the elderly person's past walking history and apply an optimal danger detection algorithm. The danger detection algorithm includes, for example, a walking pattern analysis method, a fall risk evaluation criterion, and the like, but is not limited to such examples. The detection unit can, for example, adjust the danger detection algorithm based on the place and time period in which the elderly person has fallen in the past. The detection unit can also analyze the elderly person's past walking pattern and identify situations with a high fall risk. Furthermore, the detection unit can also apply an optimal danger detection algorithm based on the elderly person's past walking history. As a result, the risk of falling can be effectively detected by applying an optimal danger detection algorithm based on the past walking history.

[0048] The detection unit can adjust the frequency of notifications based on the health condition of the elderly person when danger is detected while walking. The detection unit adjusts the frequency of notifications based on the health condition of the elderly person when danger is detected while walking, for example, using AI. The frequency of notifications includes, for example, health condition, degree of danger, emotional state, etc., but is not limited to such examples. For example, the detection unit increases the frequency of notifications when the elderly person's health condition is deteriorating. In addition, the detection unit can also notify at a normal frequency when the elderly person's health condition is stable. Furthermore, the detection unit can reduce the frequency of notifications when the elderly person's health condition is improving. This allows more appropriate danger notifications to be made by adjusting the frequency of notifications according to the elderly person's health condition.

[0049] The detection unit can provide relevant safety information in consideration of the geographical location information of the elderly person when detecting danger while walking. The detection unit, for example, uses AI to provide relevant safety information in consideration of the geographical location information of the elderly person when detecting danger while walking. The safety information includes, for example, local safety information, evacuation routes, emergency contact information, etc., but is not limited to such examples. The detection unit provides, for example, safety information of the area where the elderly person lives. In addition, the detection unit can also provide information on nearby safe places when the elderly person is traveling. Furthermore, the detection unit can also provide safety information of a specific area when the elderly person is in that area. This makes it possible to avoid danger by providing safety information based on the geographical location information of the elderly person.

[0050] The detection unit can analyze the social media activity of the elderly person when danger is detected while walking, and provide related safety information. The detection unit can, for example, use AI to analyze the social media activity of the elderly person when danger is detected while walking, and provide related safety information. The safety information includes, for example, local safety information, evacuation routes, emergency contacts, and the like, but is not limited to such examples. The detection unit can, for example, issue a notification based on safety information shared by the elderly person on social media. The detection unit can also provide information on safety-related accounts followed by the elderly person. Furthermore, the detection unit can provide information on online safety communities in which the elderly person participates. This makes it possible to avoid danger by providing safety information based on the elderly person's social media activity.

[0051] The tool unit can analyze the elderly person's past communication history and suggest how to use the tool. The tool unit can, for example, use AI to analyze the elderly person's past communication history and suggest the optimal way to use the tool. The way to use the tool includes, for example, past usage history, purpose of use, frequency of use, etc., but is not limited to such examples. The tool unit, for example, preferentially suggests tools that the elderly person has frequently used in the past. The tool unit can also analyze the elderly person's past communication patterns and suggest the optimal tool. Furthermore, the tool unit can also suggest tools that avoid tools that the elderly person has avoided in the past. As a result, the quality of communication is improved by suggesting the optimal way to use the tool based on the past communication history.

[0052] The tool unit can adjust the frequency of use based on the health condition of the elderly person when using the communication tool. The tool unit, for example, uses AI to adjust the frequency of use based on the health condition of the elderly person when using the communication tool. The frequency of use includes, for example, health condition, purpose of use, emotional state, and the like, but is not limited to such examples. For example, the tool unit reduces the frequency of use when the elderly person's health condition is deteriorating. In addition, the tool unit can also be used at a normal frequency when the elderly person's health condition is stable. Furthermore, the tool unit can increase the frequency of use when the elderly person's health condition is improving. This enables more appropriate communication by adjusting the frequency of use according to the elderly person's health condition.

[0053] The tool unit can provide relevant family information in consideration of the geographic location information of the elderly person when using a communication tool. The tool unit, for example, uses AI to provide relevant family information in consideration of the geographic location information of the elderly person when using a communication tool. The family information includes, for example, family contact information, family health status, family interests, and the like, but is not limited to such examples. The tool unit provides, for example, information on family members in the area where the elderly person lives. In addition, the tool unit can also provide information on family members living nearby when the elderly person is traveling. Furthermore, the tool unit can also provide information on family members living in a specific area when the elderly person is in that area. This improves the quality of communication by providing family information based on the geographic location information of the elderly person.

[0054] The tool unit can analyze the social media activity of the elderly person when using the communication tool and provide related family information. The tool unit can, for example, use AI to analyze the social media activity of the elderly person when using the communication tool and provide related family information. The family information includes, for example, family contact information, family health status, family interests, and the like, but is not limited to such examples. The tool unit can provide tools based on, for example, family information shared by the elderly person on social media. The tool unit can also provide information on family accounts that the elderly person follows. Furthermore, the tool unit can provide information on family-related online communities in which the elderly person participates. This improves the quality of communication by providing family information based on the elderly person's social media activity.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The care partner system further includes a reminder unit. The reminder unit can manage important schedules and tasks in the elderly person's daily life and provide reminders at appropriate times. For example, the reminder unit can manage the elderly person's medical appointments and social activity schedules and notify them in advance. The reminder unit can also remind the elderly person of daily exercise and meal times that they should do. Furthermore, the reminder unit can notify the elderly person not to forget appointments with family and friends. This makes it easier for the elderly person to manage their daily life and improves their quality of life.

[0057] The care partner system further includes an entertainment unit. The entertainment unit can provide appropriate entertainment content based on the hobbies and interests of the elderly. For example, the entertainment unit recommends and plays music and movies that the elderly like. The entertainment unit can also provide games and puzzles that the elderly can enjoy. Furthermore, the entertainment unit can introduce online events and community activities that the elderly can participate in. This can provide fun to the lives of the elderly and improve their mental satisfaction.

[0058] The care partner system further includes a health monitoring unit. The health monitoring unit monitors the health condition of the elderly person in real time, and can take appropriate measures if an abnormality is detected. For example, the health monitoring unit measures the elderly person's heart rate and blood pressure, and notifies the elderly person if an abnormality is detected. The health monitoring unit can also analyze the elderly person's sleep patterns and provide advice for improving sleep quality. Furthermore, the health monitoring unit can manage the elderly person's weight and dietary records, and support healthy lifestyle habits. This allows the elderly person's health to be managed efficiently, and health risks to be reduced.

[0059] The care partner system further includes a learning support unit. The learning support unit can support the elderly to acquire new knowledge and skills. For example, the learning support unit can provide online courses and teaching materials in areas that interest the elderly. The learning support unit can also introduce workshops and seminars that the elderly can participate in. The learning support unit can also provide quizzes and tests for the elderly to review what they have learned. This can satisfy the elderly's intellectual curiosity and promote self-growth.

[0060] The care partner system further includes a relaxation section. The relaxation section can reduce the stress of the elderly and provide an environment in which the elderly can relax. For example, the relaxation section plays music or sounds of nature that can relax the elderly. The relaxation section can also guide simple meditation or breathing techniques that the elderly can perform. Furthermore, the relaxation section can introduce aromatherapy or massage methods that can relax the elderly. This can reduce the stress of the elderly and maintain their physical and mental health.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The conversation unit provides mental care through conversation with the elderly. For example, AI is used to have daily conversations with the elderly, including daily conversations, conversations about health, and conversations about hobbies. The conversation unit provides topics based on the elderly's hobbies and interests, and eases the elderly's feelings through dialogue. AI can also be used to estimate the elderly's emotions and select conversation topics based on the estimated emotions. For example, AI can analyze the elderly's voice and facial expressions to estimate emotions. Step 2: The management unit issues medication management alerts based on the information obtained by the conversation unit and reports to the caregiver. For example, AI can be used to manage the elderly person's medication schedule and issue alerts to encourage them to take medication at appropriate times. The medication schedule includes setting time periods and managing the amount of medication taken. The management unit can also use AI to notify the caregiver if the elderly person forgets to take their medication or if there is an abnormality. For example, AI can analyze the elderly person's medication history and detect abnormalities. Step 3: The detection unit detects dangers while walking based on the information managed by the management unit and notifies the elderly person. For example, AI can be used to monitor the elderly person's walking and notify the elderly person if there is a risk of falling. Walking monitoring includes counting steps, measuring walking speed, detecting posture, etc. The detection unit can also use AI to analyze the elderly person's walking pattern and issue a warning if balance is lost. For example, AI can analyze the elderly person's walking data and evaluate the risk of falling.

[0063] (Example 2) The care partner system according to the embodiment of the present invention is a system that aims to improve the satisfaction of each elderly person while reducing the burden on caregivers who care for the elderly. This care partner system provides mental care for elderly people who suffer from loneliness through conversation with AI, and even provides medication management alerts, reports to caregivers, detects dangers while walking and notifies the elderly, and supports tools for communication with family members who live far away. For example, the care partner system allows elderly people to enjoy daily conversations with AI, which can reduce the sense of loneliness. AI provides topics based on the elderly person's hobbies and interests, and eases the elderly's feelings through conversation. In addition, the care partner system has an alert function for medication management, where AI manages the elderly person's medication schedule and issues an alert to encourage them to take their medication at the appropriate time. In addition, it also has a reporting function to caregivers, and notifies the caregiver if the elderly person forgets to take their medication or if there is an abnormality. In addition, the care partner system has a danger detection function when walking, where AI monitors the elderly person's walking and notifies the elderly person if there is a risk of falling. For example, AI analyzes the elderly person's walking pattern and immediately issues a warning if their balance is lost. In addition, the Care Partner system also provides tools to support communication with family members who live far away. AI supports video calls and messaging between the elderly and their families, helping them maintain connections with their families. For example, AI can read messages from family members to the elderly and help set up video calls. In this way, the Care Partner system using AI can reduce the burden on caregivers and improve the satisfaction of the elderly by providing a wide range of functions, including mental care for the elderly, medication management, danger detection while walking, and communication support with family members. This allows the Care Partner system to consistently provide mental care for the elderly, medication management, danger detection while walking, and communication support with family members.

[0064] The care partner system according to the embodiment includes a conversation unit, a management unit, and a detection unit. The conversation unit provides mental care through conversation with the elderly. The conversation unit, for example, uses AI to have daily conversation with the elderly. Conversations with the elderly include, for example, daily conversations, conversations about health, and conversations about hobbies, but are not limited to such examples. For example, the conversation unit provides topics based on the hobbies and interests of the elderly using AI, and eases the feelings of the elderly through dialogue. The conversation unit can also estimate the emotions of the elderly using AI and select a conversation topic based on the estimated emotions of the elderly. For example, the AI ​​analyzes the voice and facial expressions of the elderly to estimate the emotions. The management unit issues an alert for medication management based on the information obtained by the conversation unit and reports to the caregiver. The management unit, for example, uses AI to manage the elderly's medication schedule and issues an alert to encourage the elderly to take medication at an appropriate time. The medication schedule includes, for example, setting a time period and managing the amount of medication, but is not limited to such examples. The management unit can also use AI to notify the caregiver when the elderly forgets to take medication or when there is an abnormality. For example, the AI ​​analyzes the elderly person's medication history and detects abnormalities. The detection unit detects dangers during walking based on the information managed by the management unit and notifies the elderly person. The detection unit, for example, uses AI to monitor the elderly person's walking and notifies the elderly person when there is a risk of falling. The monitoring of walking includes, for example, but is not limited to, step counting, walking speed measurement, posture detection, and the like. The detection unit can also analyze the elderly person's walking pattern using AI and issue a warning when balance is lost. For example, the AI ​​analyzes the elderly person's walking data and evaluates the risk of falling. As a result, the care partner system according to the embodiment can consistently provide mental care for the elderly person, medication management, and danger detection during walking.

[0065] The conversation part provides mental care through conversation with the elderly. The conversation part, for example, uses AI to have daily conversations with the elderly. Specifically, the AI ​​uses natural language processing technology to understand what the elderly say and generate appropriate responses. Daily conversations include topics such as the weather, news, and family topics, and respond flexibly according to the interests and concerns of the elderly. In health-related conversations, the AI ​​asks questions about the elderly's health and lifestyle habits, and provides advice as necessary. In hobbies-related conversations, topics related to the elderly's hobbies and interests are provided based on the elderly's past comments and profile information. For example, for an elderly person whose hobby is gardening, topics about how to grow seasonal flowers and plants are provided. In addition, the AI ​​can analyze the elderly's voice and facial expressions to estimate emotions. Voice analysis detects changes in tone, speed, and volume of the voice, and facial expression analysis estimates emotions by analyzing the movement of facial muscles. This makes it possible to select appropriate conversation topics according to the elderly's emotions, such as when they are stressed or relaxed. For example, if the elderly person looks sad, the AI ​​provides fun topics to ease their mood. This enables the conversation section to effectively provide mental care for the elderly and reduce feelings of loneliness and stress.

[0066] The management unit issues a medication management alert based on the information obtained by the conversation unit and reports it to the caregiver. For example, the management unit uses AI to manage the elderly's medication schedule and issues an alert to encourage the elderly to take the medication at the appropriate time. Specifically, the AI ​​registers the elderly's medication schedule in a database and issues an alert at the set time. The alert is issued through voice notification, smartphone notification, or even a dedicated device. The medication schedule includes the type of medication, dosage, and time of medication, and is set based on the elderly's health condition and the doctor's instructions. The AI ​​can analyze the medication history and detect forgotten or abnormal medication. For example, if the elderly forgets to take the medication, the AI ​​issues another alert and notifies the caregiver. It also detects abnormalities when the amount of medication is excessive or insufficient, or when the timing of medication is off, and reports them to the caregiver. This allows the management unit to efficiently manage the elderly's medication and prevent medication errors. In addition, the management unit can collect data on the elderly's health condition and provide it to doctors and caregivers. This will enable doctors and caregivers to grasp the health status of elderly people in real time and take appropriate measures.

[0067] The detection unit detects dangers during walking based on the information managed by the management unit and notifies the elderly. For example, the detection unit uses AI to monitor the elderly's walking and notifies the elderly if there is a risk of falling. Specifically, the AI ​​analyzes the elderly's walking data in real time and detects changes in walking patterns and posture. Walking monitoring includes step counting, walking speed measurement, posture detection, etc. In step counting, the AI ​​counts the elderly's steps and grasps the amount of daily activity. In measuring walking speed, the AI ​​analyzes the walking speed and rhythm and detects abnormal changes. In posture detection, the AI ​​analyzes the elderly's body tilt and changes in balance to evaluate the risk of falling. For example, if the elderly suddenly loses balance, the AI ​​immediately issues a warning and alerts the elderly. The detection unit can also accumulate the elderly's walking data and analyze long-term changes in walking patterns. This makes it possible to detect the risk of falling early and plan preventive measures. In addition, the detection unit can provide the elderly's walking data to caregivers and doctors to use as a reference for rehabilitation and treatment. This enables the detection unit to ensure the safety of elderly people and minimize the risk of falls.

[0068] The care partner system includes a tool unit that supports communication with family members. The tool unit, for example, uses AI to support video calls and message exchanges between the elderly and family members. For example, the tool unit uses AI to read messages from family members to the elderly and assist in setting up video calls. Specific methods for supporting communication include, but are not limited to, video calls, messaging, and voice calls. In this way, the care partner system can maintain a connection with family members by supporting communication between the elderly and family members.

[0069] The conversation unit can use AI to have daily conversations with the elderly. The conversation unit, for example, uses AI to have daily conversations with the elderly. Daily conversations include, but are not limited to, for example, talking about the weather, talking about food, and talking about hobbies. For example, the conversation unit uses AI to provide topics based on the hobbies and interests of the elderly, and eases the elderly's feelings through dialogue. In this way, by using AI, mental care can be provided through daily conversations with the elderly.

[0070] The management unit can use AI to manage the elderly person's medication schedule and issue an alert to encourage medication. The management unit, for example, uses AI to manage the elderly person's medication schedule and issue an alert to encourage medication at the appropriate time. The medication schedule includes, for example, but is not limited to, setting time periods and managing the amount of medication. For example, the management unit uses AI to analyze the elderly person's medication history and determine the appropriate timing for medication. As a result, by using AI, medication management for the elderly can be performed efficiently.

[0071] The management unit can use AI to notify the caregiver when the elderly person forgets to take their medicine or when there is an abnormality. The management unit, for example, uses AI to notify the caregiver when the elderly person forgets to take their medicine or when there is an abnormality. Abnormalities include, for example, forgetting to take medicine, overdosing, poor health, etc., but are not limited to such examples. For example, the management unit uses AI to analyze the elderly person's medication history and detect abnormalities. As a result, by using AI, it is possible to quickly notify the caregiver when medication is forgotten or an abnormality occurs.

[0072] The detection unit can monitor the elderly person's walking using AI and notify the elderly person when there is a risk of falling. The detection unit can, for example, monitor the elderly person's walking using AI and notify the elderly person when there is a risk of falling. The monitoring of walking includes, for example, but is not limited to, measuring the number of steps, measuring walking speed, detecting posture, and the like. For example, the detection unit uses AI to analyze the elderly person's walking data and evaluate the risk of falling. As a result, by using AI, it is possible to detect and notify the elderly person of the risk of falling early.

[0073] The detection unit can analyze the walking pattern of the elderly person using AI and issue a warning when the balance is lost. The detection unit can, for example, analyze the walking pattern of the elderly person using AI and issue a warning when the balance is lost. The walking pattern includes, for example, stride length, walking speed, walking rhythm, etc., but is not limited to such examples. For example, the detection unit uses AI to analyze the walking data of the elderly person and evaluate the risk of falling. As a result, by using AI, the walking pattern of the elderly person can be analyzed and a warning can be issued when the balance is lost.

[0074] The conversation unit can estimate the elderly person's emotions and select a conversation topic based on the elderly person's estimated emotions. The conversation unit can estimate the elderly person's emotions using, for example, AI, and select a conversation topic based on the elderly person's estimated emotions. The estimation of emotions includes, for example, voice analysis, facial expression analysis, text analysis, and the like, but is not limited to such examples. For example, when the elderly person is sad, the conversation unit can provide a topic related to past happy memories or hobbies. In addition, when the elderly person is excited, the conversation unit can also talk about topics that can relax or relaxation methods. Furthermore, when the elderly person feels lonely, the conversation unit can provide a topic related to family and friends to make them feel connected. This allows more appropriate mental care to be provided by selecting a conversation topic according to the elderly person's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0075] The conversation unit can analyze the elderly person's past conversation history and select a method of proceeding with the conversation. The conversation unit can, for example, use AI to analyze the elderly person's past conversation history and select an optimal method of proceeding with the conversation. The method of proceeding with the conversation can include, for example, but is not limited to, the selection of a topic, the way of asking questions, and the tempo of the conversation. For example, the conversation unit can retake a topic that the elderly person has shown interest in in the past and proceed with the conversation. The conversation unit can also avoid topics that the elderly person has avoided in the past and focus on positive topics. Furthermore, the conversation unit can provide new related topics based on topics that the elderly person has frequently talked about in the past. This allows more effective mental care to be provided by selecting an optimal method of proceeding with the conversation based on the past conversation history.

[0076] The conversation unit can provide personalized topics based on the elderly person's hobbies and interests during conversation. The conversation unit, for example, uses AI to provide personalized topics based on the elderly person's hobbies and interests during conversation. Personalized topics include, but are not limited to, hobbies, interests, past experiences, and the like. For example, if the elderly person is interested in gardening, the conversation unit can provide topics about seasonal flowers and plants. In addition, if the elderly person is interested in music, the conversation unit can also talk about favorite artists and new music. Furthermore, if the elderly person is interested in traveling, the conversation unit can also talk about past travel experiences and places the elderly person wants to go. This improves the quality of the conversation by providing topics based on the elderly person's hobbies and interests.

[0077] The conversation unit can estimate the elderly person's emotions and adjust the tempo of the conversation based on the elderly person's estimated emotions. The conversation unit can estimate the elderly person's emotions using, for example, AI, and adjust the tempo of the conversation based on the elderly person's estimated emotions. The tempo of the conversation includes, for example, the speaking speed, the pause, the timing of the response, and the like, but is not limited to such examples. For example, when the elderly person is relaxed, the conversation unit can proceed with the conversation at a slow tempo. In addition, when the elderly person is excited, the conversation unit can also proceed with the conversation at a faster tempo. Furthermore, when the elderly person is tired, the conversation unit can also proceed with the conversation at a slower tempo. This makes it possible to provide more appropriate mental care by adjusting the tempo of the conversation according to the elderly person's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0078] The conversation unit can provide topics related to the area during the conversation, taking into account the geographical location information of the elderly person. The conversation unit, for example, uses AI to provide topics related to the area, taking into account the geographical location information of the elderly person during the conversation. The geographical location information includes, for example, GPS data, address information, characteristics of the area, and the like, but is not limited to such examples. The conversation unit, for example, talks about events and news in the area where the elderly person lives. The conversation unit can also talk about memories and history of the area where the elderly person lived in the past. Furthermore, the conversation unit can also talk about tourist spots and culture in the area where the elderly person wants to travel. This improves the quality of the conversation by providing topics based on the geographical location information of the elderly person.

[0079] The conversation unit can analyze the social media activity of the elderly person during the conversation and provide related topics. The conversation unit can, for example, use AI to analyze the social media activity of the elderly person during the conversation and provide related topics. Social media activities include, for example, but are not limited to, the content of posts, the number of likes, the number of followers, and the like. The conversation unit can, for example, talk about photos and posts shared by the elderly person on social media. The conversation unit can also provide topics about accounts and groups that the elderly person follows. Furthermore, the conversation unit can talk about online events and communities in which the elderly person participates. This improves the quality of the conversation by providing topics based on the elderly person's social media activity.

[0080] The management unit can estimate the elderly person's emotions and adjust the timing of the medication alert based on the elderly person's estimated emotions. The management unit, for example, uses AI to estimate the elderly person's emotions and adjusts the timing of the medication alert based on the elderly person's estimated emotions. The timing of the medication alert includes, for example, the time of taking the medication, changes in physical condition, and emotional state, but is not limited to such examples. For example, when the elderly person is stressed, the management unit issues a medication alert at a relaxed time. In addition, when the elderly person is relaxed, the management unit can also issue a medication alert at a normal time. Furthermore, when the elderly person is excited, the management unit can also issue a medication alert at a calm time. This enables more appropriate medication management by adjusting the timing of the medication alert according to the elderly person's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0081] The management unit can analyze the elderly person's past medication history and propose a medication schedule. The management unit can, for example, use AI to analyze the elderly person's past medication history and propose an optimal medication schedule. The medication schedule includes, for example, past medication history, doctor's instructions, changes in physical condition, and the like, but is not limited to such examples. For example, the management unit can propose a schedule that avoids time periods when the elderly person has forgotten to take medication in the past. The management unit can also propose a schedule based on time periods when the elderly person has taken medication in the past. Furthermore, the management unit can also analyze the elderly person's past medication patterns and propose an optimal schedule. As a result, the accuracy of medication management is improved by proposing an optimal medication schedule based on past medication history.

[0082] The management unit can adjust the frequency of alerts based on the health condition of the elderly person during medication management. The management unit adjusts the frequency of alerts based on the health condition of the elderly person during medication management, for example, using AI. The frequency of alerts includes, for example, health condition, importance of medication, emotional state, etc., but is not limited to such examples. For example, the management unit increases the frequency of alerts when the elderly person's health condition is deteriorating. In addition, the management unit can also issue alerts at a normal frequency when the elderly person's health condition is stable. Furthermore, the management unit can also reduce the frequency of alerts when the elderly person's health condition is improving. This enables more appropriate medication management by adjusting the frequency of alerts according to the elderly person's health condition.

[0083] The management unit can estimate the elderly person's emotions and determine the priority of the medication alert based on the elderly person's estimated emotions. The management unit can estimate the elderly person's emotions using AI, for example, and determine the priority of the medication alert based on the elderly person's estimated emotions. The priority of the medication alert includes, for example, the importance of taking the medication, changes in physical condition, and emotional state, but is not limited to such examples. For example, when the elderly person is stressed, the management unit can give priority to an important medication alert. In addition, when the elderly person is relaxed, the management unit can also give priority to an important medication alert when the elderly person is excited. This enables more appropriate medication management by determining the priority of the medication alert according to the elderly person's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0084] The management unit can provide relevant drug information in consideration of the geographic location information of the elderly person during medication management. The management unit, for example, uses AI to provide relevant drug information in consideration of the geographic location information of the elderly person during medication management. Drug information includes, for example, drug efficacy, side effects, and usage, but is not limited to such examples. The management unit, for example, provides information on pharmacies in the area where the elderly person lives. In addition, if the elderly person is traveling, the management unit can also provide information on nearby pharmacies. Furthermore, if the elderly person is in a specific area, the management unit can also provide information on medicines available in that area. This improves the accuracy of medication management by providing drug information based on the geographic location information of the elderly person.

[0085] The management unit can analyze the social media activity of the elderly person during medication management and provide related health information. The management unit can, for example, use AI to analyze the social media activity of the elderly person during medication management and provide related health information. The health information includes, for example, health management advice, disease prevention methods, and health food information, but is not limited to such examples. The management unit can, for example, issue an alert based on health information shared by the elderly person on social media. The management unit can also provide information on health-related accounts followed by the elderly person. Furthermore, the management unit can provide information on online health communities in which the elderly person participates. This improves the accuracy of medication management by providing health information based on the elderly person's social media activity.

[0086] The detection unit can estimate the elderly person's emotion and adjust the danger notification method based on the elderly person's estimated emotion. The detection unit can estimate the elderly person's emotion using, for example, AI, and adjust the danger notification method based on the elderly person's estimated emotion. The danger notification method includes, for example, the timing of notification, the content of notification, and the means of notification, but is not limited to such examples. For example, when the elderly person is nervous, the detection unit can notify the elderly person of the danger in a calm voice. Also, when the elderly person is relaxed, the detection unit can notify the elderly person of the danger in a normal manner. Furthermore, when the elderly person is excited, the detection unit can notify the elderly person of the danger in a quick and concise manner. This allows for more appropriate danger notification by adjusting the danger notification method according to the elderly person's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0087] The detection unit can analyze the elderly person's past walking history and apply a danger detection algorithm. The detection unit can, for example, use AI to analyze the elderly person's past walking history and apply an optimal danger detection algorithm. The danger detection algorithm includes, for example, a walking pattern analysis method, a fall risk evaluation criterion, and the like, but is not limited to such examples. The detection unit can, for example, adjust the danger detection algorithm based on the place and time period in which the elderly person has fallen in the past. The detection unit can also analyze the elderly person's past walking pattern and identify situations with a high fall risk. Furthermore, the detection unit can also apply an optimal danger detection algorithm based on the elderly person's past walking history. As a result, the risk of falling can be effectively detected by applying an optimal danger detection algorithm based on the past walking history.

[0088] The detection unit can adjust the frequency of notifications based on the health condition of the elderly person when danger is detected while walking. The detection unit adjusts the frequency of notifications based on the health condition of the elderly person when danger is detected while walking, for example, using AI. The frequency of notifications includes, for example, health condition, degree of danger, emotional state, etc., but is not limited to such examples. For example, the detection unit increases the frequency of notifications when the elderly person's health condition is deteriorating. In addition, the detection unit can also notify at a normal frequency when the elderly person's health condition is stable. Furthermore, the detection unit can reduce the frequency of notifications when the elderly person's health condition is improving. This allows more appropriate danger notifications to be made by adjusting the frequency of notifications according to the elderly person's health condition.

[0089] The detection unit can estimate the emotion of the elderly person and determine the priority of the danger notification based on the estimated emotion of the elderly person. The detection unit can estimate the emotion of the elderly person using, for example, AI and determine the priority of the danger notification based on the estimated emotion of the elderly person. The priority of the danger notification includes, for example, the degree of danger, changes in physical condition, and emotional state, but is not limited to such examples. For example, when the elderly person is stressed, the detection unit can give a priority to an important danger notification. In addition, when the elderly person is relaxed, the detection unit can also give a priority to an important danger notification. In addition, when the elderly person is excited, the detection unit can also give a priority to an important danger notification. In this way, by determining the priority of the danger notification according to the emotion of the elderly person, more appropriate danger notification is possible. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0090] The detection unit can provide relevant safety information in consideration of the geographical location information of the elderly person when detecting danger while walking. The detection unit, for example, uses AI to provide relevant safety information in consideration of the geographical location information of the elderly person when detecting danger while walking. The safety information includes, for example, local safety information, evacuation routes, emergency contact information, etc., but is not limited to such examples. The detection unit provides, for example, safety information of the area where the elderly person lives. In addition, the detection unit can also provide information on nearby safe places when the elderly person is traveling. Furthermore, the detection unit can also provide safety information of a specific area when the elderly person is in that area. This makes it possible to avoid danger by providing safety information based on the geographical location information of the elderly person.

[0091] The detection unit can analyze the social media activity of the elderly person when danger is detected while walking, and provide related safety information. The detection unit can, for example, use AI to analyze the social media activity of the elderly person when danger is detected while walking, and provide related safety information. The safety information includes, for example, local safety information, evacuation routes, emergency contacts, and the like, but is not limited to such examples. The detection unit can, for example, issue a notification based on safety information shared by the elderly person on social media. The detection unit can also provide information on safety-related accounts followed by the elderly person. Furthermore, the detection unit can provide information on online safety communities in which the elderly person participates. This makes it possible to avoid danger by providing safety information based on the elderly person's social media activity.

[0092] The tool unit can estimate the elderly person's emotions and adjust the usage of the communication tool based on the elderly person's estimated emotions. The tool unit can estimate the elderly person's emotions using, for example, AI, and adjust the usage of the communication tool based on the elderly person's estimated emotions. The usage of the communication tool includes, for example, the frequency of use, the usage time, and the purpose of use, but is not limited to such examples. For example, when the elderly person is nervous, the tool unit provides a simple and easy-to-use interface. In addition, when the elderly person is relaxed, the tool unit can also provide detailed options. Furthermore, when the elderly person is excited, the tool unit can also provide a visually stimulating interface. This enables more appropriate communication by adjusting the usage of the communication tool according to the elderly person's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0093] The tool unit can analyze the elderly person's past communication history and suggest how to use the tool. The tool unit can, for example, use AI to analyze the elderly person's past communication history and suggest the optimal way to use the tool. The way to use the tool includes, for example, past usage history, purpose of use, frequency of use, etc., but is not limited to such examples. The tool unit, for example, preferentially suggests tools that the elderly person has frequently used in the past. The tool unit can also analyze the elderly person's past communication patterns and suggest the optimal tool. Furthermore, the tool unit can also suggest tools that avoid tools that the elderly person has avoided in the past. As a result, the quality of communication is improved by suggesting the optimal way to use the tool based on the past communication history.

[0094] The tool unit can adjust the frequency of use based on the health condition of the elderly person when using the communication tool. The tool unit, for example, uses AI to adjust the frequency of use based on the health condition of the elderly person when using the communication tool. The frequency of use includes, for example, health condition, purpose of use, emotional state, and the like, but is not limited to such examples. For example, the tool unit reduces the frequency of use when the elderly person's health condition is deteriorating. In addition, the tool unit can also be used at a normal frequency when the elderly person's health condition is stable. Furthermore, the tool unit can increase the frequency of use when the elderly person's health condition is improving. This enables more appropriate communication by adjusting the frequency of use according to the elderly person's health condition.

[0095] The tool unit can estimate the elderly person's emotions and determine the priority of communication tools based on the elderly person's estimated emotions. The tool unit can estimate the elderly person's emotions using, for example, AI and determine the priority of communication tools based on the elderly person's estimated emotions. The priority of communication tools includes, for example, the purpose of use, the frequency of use, and the emotional state, but is not limited to such examples. For example, when the elderly person is stressed, the tool unit can prioritize and provide a simple and easy-to-use tool. In addition, when the elderly person is relaxed, the tool unit can also prioritize a tool that provides detailed options. Furthermore, when the elderly person is excited, the tool unit can also prioritize and provide a visually stimulating tool. This enables more appropriate communication by determining the priority of communication tools according to the elderly person's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0096] The tool unit can provide relevant family information in consideration of the geographic location information of the elderly person when using a communication tool. The tool unit, for example, uses AI to provide relevant family information in consideration of the geographic location information of the elderly person when using a communication tool. The family information includes, for example, family contact information, family health status, family interests, and the like, but is not limited to such examples. The tool unit provides, for example, information on family members in the area where the elderly person lives. In addition, the tool unit can also provide information on family members living nearby when the elderly person is traveling. Furthermore, the tool unit can also provide information on family members living in a specific area when the elderly person is in that area. This improves the quality of communication by providing family information based on the geographic location information of the elderly person.

[0097] The tool unit can analyze the social media activity of the elderly person when using the communication tool and provide related family information. The tool unit can, for example, use AI to analyze the social media activity of the elderly person when using the communication tool and provide related family information. The family information includes, for example, family contact information, family health status, family interests, and the like, but is not limited to such examples. The tool unit can provide tools based on, for example, family information shared by the elderly person on social media. The tool unit can also provide information on family accounts that the elderly person follows. Furthermore, the tool unit can provide information on family-related online communities in which the elderly person participates. This improves the quality of communication by providing family information based on the elderly person's social media activity.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The care partner system further includes a reminder unit. The reminder unit can manage important schedules and tasks in the elderly person's daily life and provide reminders at appropriate times. For example, the reminder unit can manage the elderly person's medical appointments and social activity schedules and notify them in advance. The reminder unit can also remind the elderly person of daily exercise and meal times that they should do. Furthermore, the reminder unit can notify the elderly person not to forget appointments with family and friends. This makes it easier for the elderly person to manage their daily life and improves their quality of life.

[0100] The care partner system further includes an entertainment unit. The entertainment unit can provide appropriate entertainment content based on the hobbies and interests of the elderly. For example, the entertainment unit recommends and plays music and movies that the elderly like. The entertainment unit can also provide games and puzzles that the elderly can enjoy. Furthermore, the entertainment unit can introduce online events and community activities that the elderly can participate in. This can provide fun to the lives of the elderly and improve their mental satisfaction.

[0101] The care partner system further includes a health monitoring unit. The health monitoring unit monitors the health condition of the elderly person in real time, and can take appropriate measures if an abnormality is detected. For example, the health monitoring unit measures the elderly person's heart rate and blood pressure, and notifies the elderly person if an abnormality is detected. The health monitoring unit can also analyze the elderly person's sleep patterns and provide advice for improving sleep quality. Furthermore, the health monitoring unit can manage the elderly person's weight and dietary records, and support healthy lifestyle habits. This allows the elderly person's health to be managed efficiently, and health risks to be reduced.

[0102] The care partner system further includes a learning support unit. The learning support unit can support the elderly to acquire new knowledge and skills. For example, the learning support unit can provide online courses and teaching materials in areas that interest the elderly. The learning support unit can also introduce workshops and seminars that the elderly can participate in. The learning support unit can also provide quizzes and tests for the elderly to review what they have learned. This can satisfy the elderly's intellectual curiosity and promote self-growth.

[0103] The care partner system further includes a relaxation section. The relaxation section can reduce the stress of the elderly and provide an environment in which the elderly can relax. For example, the relaxation section plays music or sounds of nature that can relax the elderly. The relaxation section can also guide simple meditation or breathing techniques that the elderly can perform. Furthermore, the relaxation section can introduce aromatherapy or massage methods that can relax the elderly. This can reduce the stress of the elderly and maintain their physical and mental health.

[0104] The care partner system further includes an emotion diary unit. The emotion diary unit can support the elderly to record their daily emotions and deepen their self-understanding. For example, the emotion diary unit provides an interface for the elderly to record their emotions and events of the day. The emotion diary unit can also analyze the recorded emotion data and visualize emotion patterns and trends. Furthermore, the emotion diary unit can provide advice for the elderly to organize their emotions and increase positive emotions. This can improve the elderly's self-management of their emotions and maintain their mental health.

[0105] The care partner system further includes an emotion sharing unit. The emotion sharing unit can support the elderly to share their emotions with others and gain sympathy. For example, the emotion sharing unit provides a message function for the elderly to convey emotions to family and friends. The emotion sharing unit can also introduce online communities in which the elderly can participate and interact with people who have common interests and experiences. Furthermore, the emotion sharing unit can support art and music activities for the elderly to express emotions. This promotes the sharing of emotions among the elderly and strengthens social connections.

[0106] The care partner system further includes an emotion feedback unit. The emotion feedback unit can provide feedback on the emotions of the elderly person and support the elderly person in deepening their understanding of emotions. For example, the emotion feedback unit can analyze emotion data recorded by the elderly person and provide feedback on changes and trends in emotions. The emotion feedback unit can also analyze the cause and background of why the elderly person felt a particular emotion and provide information for deepening their understanding. Furthermore, the emotion feedback unit can provide specific advice for the elderly person to increase their positive emotions. This can improve the elderly person's self-understanding of emotions and maintain their mental health.

[0107] The care partner system further includes an emotion prediction unit. The emotion prediction unit can predict future emotions based on the elderly person's past emotion data and provide support for appropriate responses. For example, the emotion prediction unit analyzes the elderly person's past emotion data and predicts emotions for specific situations or events. The emotion prediction unit can also suggest relaxation methods before the elderly person feels stress based on the predicted emotions. Furthermore, the emotion prediction unit can also suggest activities or events to elicit positive emotions. This makes it possible to predict and respond to the elderly person's emotions, thereby maintaining mental health.

[0108] The care partner system further includes an emotion training unit. The emotion training unit can support the elderly to learn emotion control and increase positive emotions. For example, the emotion training unit can provide a training program for the elderly to learn relaxation and stress management techniques. The emotion training unit can also suggest activities for the elderly to develop gratitude. Furthermore, the emotion training unit can provide mental training for the elderly to cultivate positive thinking. This can improve the elderly's emotion control and maintain mental health.

[0109] The process flow of the second embodiment will be briefly described below.

[0110] Step 1: The conversation unit provides mental care through conversation with the elderly. For example, AI is used to have daily conversations with the elderly, including daily conversations, conversations about health, and conversations about hobbies. The conversation unit provides topics based on the elderly's hobbies and interests, and eases the elderly's feelings through dialogue. AI can also be used to estimate the elderly's emotions and select conversation topics based on the estimated emotions. For example, AI can analyze the elderly's voice and facial expressions to estimate emotions. Step 2: The management unit issues medication management alerts based on the information obtained by the conversation unit and reports to the caregiver. For example, AI can be used to manage the elderly person's medication schedule and issue alerts to encourage them to take medication at appropriate times. The medication schedule includes setting time periods and managing the amount of medication taken. The management unit can also use AI to notify the caregiver if the elderly person forgets to take their medication or if there is an abnormality. For example, AI can analyze the elderly person's medication history and detect abnormalities. Step 3: The detection unit detects dangers while walking based on the information managed by the management unit and notifies the elderly person. For example, AI can be used to monitor the elderly person's walking and notify the elderly person if there is a risk of falling. Walking monitoring includes counting steps, measuring walking speed, detecting posture, etc. The detection unit can also use AI to analyze the elderly person's walking pattern and issue a warning if balance is lost. For example, AI can analyze the elderly person's walking data and evaluate the risk of falling.

[0111] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the 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 voice data.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0113] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements including the conversation unit, management unit, detection unit, and tool unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the conversation unit is realized by the control unit 46A of the smart device 14, and has daily conversations with the elderly. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and issues an alert for medication management and reports it to the caregiver. The detection unit is realized, for example, by the control unit 46A of the smart device 14, and monitors the elderly's walking and notifies the elderly if there is a risk of falling. The tool unit is realized, for example, by the control unit 46A of the smart device 14, and supports video calls and message exchanges between the elderly and their families. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0122] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0125] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0127] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0128] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0129] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements including the conversation unit, management unit, detection unit, and tool unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the conversation unit is realized by the control unit 46A of the smart glasses 214, and has daily conversations with the elderly. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and issues an alert for medication management and reports it to the caregiver. The detection unit is realized, for example, by the control unit 46A of the smart glasses 214, and monitors the elderly's walking and notifies the elderly if there is a risk of falling. The tool unit is realized, for example, by the control unit 46A of the smart glasses 214, and supports video calls and message exchanges between the elderly and their families. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0133] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0138] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0141] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0145] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements including the conversation unit, management unit, detection unit, and tool unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the conversation unit is realized by the control unit 46A of the headset type terminal 314, and has daily conversations with the elderly. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and issues an alert for medication management and reports it to the caregiver. The detection unit is realized, for example, by the control unit 46A of the headset type terminal 314, and monitors the elderly's walking and notifies the elderly if there is a risk of falling. The tool unit is realized, for example, by the control unit 46A of the headset type terminal 314, and supports video calls and message exchanges between the elderly and their families. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 7, a 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.

[0149] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0155] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0156] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0157] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0158] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0159] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0160] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0161] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0162] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0163] Each of the multiple elements including the conversation unit, management unit, detection unit, and tool unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the conversation unit is realized by the control unit 46A of the robot 414, and has daily conversations with the elderly. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and issues an alert for medication management and reports it to the caregiver. The detection unit is realized, for example, by the control unit 46A of the robot 414, and monitors the elderly's walking and notifies the elderly if there is a risk of falling. The tool unit is realized, for example, by the control unit 46A of the robot 414, and supports video calls and message exchanges between the elderly and their families. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0164] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0165] FIG. 9 is a diagram showing an emotion map 400 on 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 emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0167] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0168] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0169] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0172] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0173] In addition, 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 upon request from the data processing device 12.

[0174] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0175] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0176] The hardware resource that executes the specific process may be one of these various processors, or may be 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). Also, the hardware resource that executes the specific process may be a single processor.

[0177] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0178] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0180] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0182] (Appendix 1) The system comprises a conversation unit that provides mental care through conversation with the elderly, a management unit that issues medication management alerts based on information obtained by the conversation unit and reports them to a caregiver, and a detection unit that detects dangers while walking based on the information managed by the management unit and notifies the elderly. (Appendix 2) A system equipped with a tool section that assists in communication with family members. (Appendix 3) The conversation unit is a system that uses AI to conduct everyday conversations with elderly people. (Appendix 4) The management unit is a system that uses AI to manage the medication schedules of elderly people and issue alerts to encourage them to take their medication. (Appendix 5) The management unit is a system that uses AI to notify caregivers if an elderly person forgets to take their medication or if there is an abnormality. (Appendix 6) The detection unit is a system that uses AI to monitor the walking of elderly people and notify them if there is a risk of them falling. (Appendix 7) The detection unit is a system that uses AI to analyze the walking patterns of elderly people and issue a warning if they lose their balance. (Appendix 8) The conversation unit is a system that estimates the emotions of the elderly person and selects a conversation topic based on the estimated emotions of the elderly person. (Appendix 9) The conversation unit is a system that analyzes the elderly person's past conversation history and selects how to proceed with the conversation. (Appendix 10) The conversation unit is a system that provides personalized topics based on the elderly person's hobbies and interests during conversation. (Appendix 11) The conversation unit is a system that estimates the elderly person's emotions and adjusts the tempo of the conversation based on the elderly person's estimated emotions. (Appendix 12) The conversation unit is a system that provides local topics during conversation by taking into account the elderly person's geographical location information. (Appendix 13) The conversation unit is a system that analyzes the elderly person's social media activity during the conversation and provides related topics. (Appendix 14) The management unit is a system that estimates the elderly person's emotions and adjusts the timing of medication alerts based on the elderly person's estimated emotions. (Appendix 15) The management unit is a system that analyzes the elderly person's past medication history and proposes a medication schedule. (Appendix 16) The management unit is a system that adjusts the frequency of alerts based on the health condition of an elderly person during medication management. (Appendix 17) The management unit is a system that estimates the emotions of an elderly person and determines the priority of medication alerts based on the estimated emotions of the elderly person. (Appendix 18) The management unit is a system that provides relevant pharmaceutical information taking into account the geographical location information of elderly people when managing medication. (Appendix 19) The management unit is a system that analyzes the social media activity of elderly people and provides relevant health information during medication management. (Appendix 20) The detection unit is a system that estimates the emotions of an elderly person and adjusts the method of danger notification based on the estimated emotions of the elderly person. (Appendix 21) The detection unit is a system that analyzes the elderly person's past walking history and applies a danger detection algorithm. (Appendix 22) The detection unit is a system that adjusts the frequency of notifications based on the elderly person's health condition when it detects danger while walking. (Appendix 23) The detection unit is a system that estimates the emotions of an elderly person and determines the priority of danger notifications based on the estimated emotions of the elderly person. (Appendix 24) The detection unit is a system that provides relevant safety information taking into account the geographical location information of an elderly person when a danger is detected while walking. (Appendix 25) The detection unit is a system that analyzes the social media activity of elderly people when it detects a danger while walking and provides relevant safety information. (Appendix 26) The tool unit is a system that estimates the emotions of the elderly person and adjusts the way in which communication tools are used based on the estimated emotions of the elderly person. (Appendix 27) The tool unit is a system that analyzes the elderly person's past communication history and suggests how to use the tool. (Appendix 28) The tool unit is a system that adjusts the frequency of use of a communication tool based on the elderly person's health condition when using the communication tool. (Appendix 29) The tool unit is a system that estimates the emotions of the elderly person and determines the priority order of communication tools based on the estimated emotions of the elderly person. (Appendix 30) The tool unit is a system that provides related family information taking into account the geographical location information of an elderly person when using a communication tool. (Appendix 31) The tool unit is a system that analyzes the social media activity of elderly people when they use communication tools and provides related family information. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The conversation department provides mental care through conversation with the elderly. a management unit that issues an alert for medication management based on the information obtained by the conversation unit and reports the alert to a caregiver; A detection unit that detects danger during walking based on the information managed by the management unit and notifies the elderly person of the danger. A system characterized by:

2. Equipped with tools to support communication with family members 2. The system of claim 1 .

3. The conversation unit includes: Using AI to conduct everyday conversations with the elderly 2. The system of claim 1 .

4. The management unit Using AI to manage medication schedules for the elderly and send out alerts to encourage them to take their medication 2. The system of claim 1 .

5. The management unit Using AI to notify caregivers if elderly people forget to take their medication or if there is an abnormality 2. The system of claim 1 .

6. The detection unit is Using AI to monitor the walking of elderly people and notify them if they are at risk of falling 2. The system of claim 1 .

7. The detection unit is AI is used to analyze the walking patterns of elderly people and issue a warning if they lose their balance.

2. The system of claim 1 .

8. The conversation unit includes: Estimating the emotions of elderly people and selecting conversation topics based on the estimated emotions of elderly people 2. The system of claim 1 .

9. The conversation unit includes: Analyzing the elderly person's past conversation history and determining how to proceed with the conversation 2. The system of claim 1 .

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