system

Abstract

The system according to the embodiment aims to keep track of the health status of elderly people living alone and to respond quickly in emergencies. [Solution] A system according to an embodiment includes a conversation unit, an analysis unit, a monitoring unit, and a reporting unit. The conversation unit converses with the elderly person. The analysis unit analyzes the content of the conversation conducted by the conversation unit. The monitoring unit monitors the elderly person's health condition. The reporting unit makes an emergency call if an abnormality is detected by the monitoring unit.

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 an instruction sentence related to a description of 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem of making it difficult to constantly monitor the health status of elderly people living alone and to respond quickly in emergencies.

[0005] The system according to the embodiment aims to keep track of the health status of elderly people living alone and to respond quickly in emergencies. [Means for solving the problem]

[0006] The system according to the embodiment includes a conversation unit, an analysis unit, a monitoring unit, and a notification unit. The conversation unit converses with the elderly person. The analysis unit analyzes the content of the conversation conducted by the conversation unit. The monitoring unit monitors the elderly person's health condition. The notification unit makes an emergency call when an abnormality is detected by the monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment can keep track of the health status of elderly people living alone and respond quickly in emergencies. [Brief explanation 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. [Figure 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 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. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 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 INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single 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 coded 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, 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 symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "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, RAM 30, and 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 (for example, a pen or a finger) to receive user input by 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., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 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 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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) In an embodiment of the present invention, an AI-equipped robot lives with an elderly person living alone, preventing dementia and ensuring their safety. In this system, the robot converses with the elderly person daily, stimulating their brain to prevent cognitive decline. The robot also constantly monitors the elderly person's health and issues an emergency call if an abnormality is detected. For example, if the elderly person falls or experiences a sudden change in their physical condition, the robot automatically issues an emergency call to prompt a prompt response. The robot uses voice recognition technology to converse with the elderly person, analyzes the conversation, and provides an appropriate response. Heart rate, body temperature, and motion sensors are used to monitor the elderly person's health. If an abnormality is detected, an emergency call system is activated, transmitting the elderly person's location and details of their health condition. For example, the robot monitors the elderly person's heart rate and issues an emergency call if an abnormal heart rate is detected. The robot also monitors the elderly person's body temperature and issues an emergency call if a sudden change in body temperature is detected. Furthermore, the robot monitors the elderly person's movements and issues an emergency call if a fall or abnormal movement is detected. This ensures the elderly person's safety and enables prompt response. The robot also estimates the elderly person's emotions through conversation and responds appropriately based on those emotions. For example, if the elderly person is sad, the robot will speak to them in a gentle tone to comfort them. If the elderly person is excited, the robot will speak to them in a calm tone to relax them. The robot also adjusts the frequency of conversations to match the elderly person's lifestyle, making conversations more effective. For example, if the elderly person is a morning person, the robot will increase the frequency of conversations in the morning, and if the elderly person is a night owl, the robot will increase the frequency of conversations in the evening. This enables effective conversations that are tailored to the elderly person's lifestyle. The robot also personalizes conversations based on the elderly person's past conversation history, making the conversation more friendly. For example, it may revisit topics about hobbies that the elderly person has previously discussed, or provide topics about news or events that the elderly person has previously shown interest in. This facilitates communication with the elderly and prevents cognitive decline. The robot also estimates the elderly person's emotions and adjusts the tone and content of conversations based on their emotions.For example, if an elderly person is sad, the robot responds in a way that reflects their feelings, and if they are excited, it responds calmly. This enables appropriate communication with the elderly. The robot can also offer new topics based on the elderly's hobbies and interests to pique their interest. For example, if an elderly person is interested in gardening, it can offer topics about seasonal flowers and plants. If an elderly person is interested in cooking, it can offer topics about new recipes and cooking tips. This piques the elderly's interest and prevents cognitive decline. Furthermore, the robot can refer to past conversations with the elderly's family and friends to advance the conversation and make it more intimate. For example, it can revisit family stories that the elderly have shared in the past, or continue the conversation based on the elderly's memories with friends. This facilitates communication with the elderly and prevents cognitive decline. This enables the AI-equipped robot system to converse with the elderly, monitor their health, and make emergency calls in the event of an abnormality.

[0029] The AI-equipped robot system according to the embodiment includes a conversation unit, an analysis unit, a monitoring unit, and a reporting unit. The conversation unit converses with the elderly person. The conversation unit includes, for example, a voice recognition unit that converses with the elderly person using voice recognition technology. The voice recognition unit recognizes voice using, for example, an acoustic model based on deep learning. The voice recognition unit can also recognize the elderly person's dialect or accent. For example, the voice recognition unit can recognize dialects such as Kansai dialect and Tohoku dialect and conduct natural conversations. The voice recognition unit can also analyze the elderly person's tone and speed of voice to grasp changes in emotion. For example, if the voice tone is low or the voice speed is slow, the emotion can be estimated. The analysis unit analyzes the content of the conversation conducted by the conversation unit. The analysis unit includes, for example, a response generation unit that analyzes the content of the conversation using natural language processing technology and generates an appropriate response. The response generation unit can generate, for example, a response based on context or emotion. For example, if the elderly person is sad, the response can be made in a gentle tone, and if the elderly person is excited, the response can be made in a calm tone. The response generation unit can also generate a response by referring to the elderly person's past conversation history. For example, the system can generate responses about hobbies that the elderly person has talked about in the past, or about news or events that the elderly person has been interested in in the past. The monitoring unit monitors the elderly person's health condition. The monitoring unit, for example, includes a heart rate sensor and can accurately monitor the elderly person's heart rate. The heart rate sensor measures the heart rate using, for example, an optical sensor or an electrical sensor. The monitoring unit also includes a body temperature sensor and can accurately monitor the elderly person's body temperature. The body temperature sensor measures the body temperature using, for example, an infrared sensor or a contact sensor. The monitoring unit also includes a motion sensor and can accurately monitor the elderly person's movements. The motion sensor detects movements using, for example, an acceleration sensor or a gyro sensor. The reporting unit makes an emergency report when the monitoring unit detects an abnormality. The reporting unit, for example, includes a location information transmitting unit that transmits location information and can accurately transmit the elderly person's location information. The location information transmitting unit acquires the location information using, for example, GPS or Wi-Fi location information.The notification unit also includes a health condition transmission unit that transmits detailed health status information, enabling accurate transmission of the details of the elderly person's health status. The health condition transmission unit transmits data such as heart rate, body temperature, and blood pressure. This enables the AI-equipped robot system according to the embodiment to converse with the elderly person, monitor their health status, and issue emergency notifications in the event of an abnormality.

[0030] The conversation unit may include a speech recognition unit that uses speech recognition technology to converse with the elderly person. The speech recognition unit recognizes speech using, for example, an acoustic model based on deep learning. For example, the speech recognition unit can analyze the elderly person's speech in real time and convert it into text data. The speech recognition unit can also recognize the elderly person's dialect and accent. For example, dialects such as Kansai dialect and Tohoku dialect can be recognized to enable natural conversation. Furthermore, the speech recognition unit can analyze the tone and speed of the elderly person's voice to grasp changes in emotion. For example, if the voice tone is low or the voice speed is slow, the emotion can be estimated. This makes it possible to have a natural conversation with the elderly person using speech recognition technology. Some or all of the above-described processing in the speech recognition unit may be performed using, for example, AI, or may be performed without AI. For example, the speech recognition unit may input the elderly person's speech data into a generation AI and have the generation AI convert the speech data into text data.

[0031] The conversation unit may include a response generation unit that analyzes the conversation content and generates an appropriate response. The response generation unit may analyze the conversation content using, for example, natural language processing technology and generate an appropriate response. For example, the response generation unit may generate a response based on context or emotion. For example, if the elderly person is sad, the response may be generated in a gentle tone, and if the elderly person is excited, the response may be generated in a calm tone. The response generation unit may also generate a response by referring to the elderly person's past conversation history. For example, the response generation unit may generate a response about hobbies that the elderly person has previously talked about, or a response about news or events that the elderly person has previously shown interest in. This allows for analysis of the conversation content and generation of an appropriate response, thereby facilitating communication with the elderly. Some or all of the above-described processing in the response generation unit may be performed using, for example, AI, or may be performed without AI. For example, the response generation unit may input the conversation content into a generation AI and have the generation AI generate an appropriate response.

[0032] The monitoring unit may include a heart rate sensor. The heart rate sensor measures the heart rate using, for example, an optical sensor or an electrical sensor. For example, the heart rate sensor may be worn on the wrist or chest of the elderly person to monitor the heart rate in real time. The heart rate sensor may also analyze heart rate fluctuations and detect abnormal heart rates. For example, it may issue an alert if the heart rate suddenly increases or decreases. Furthermore, the heart rate sensor may compare the heart rate during exercise with the heart rate at rest to determine the person's health condition. For example, if the heart rate after exercise exceeds the normal range, it may detect an abnormality. Thus, the heart rate sensor may accurately monitor the elderly person's heart rate. Some or all of the above-described processing in the heart rate sensor may be performed using, for example, AI, or may be performed without AI. For example, the heart rate sensor may input heart rate data to the generation AI and cause the generation AI to detect abnormal heart rates.

[0033] The monitoring unit may include a body temperature sensor. The body temperature sensor measures body temperature using, for example, an infrared sensor or a contact sensor. For example, the body temperature sensor may be attached to the elderly person's forehead or ear to monitor their body temperature in real time. The body temperature sensor may also analyze body temperature fluctuations and detect abnormal body temperature. For example, it may issue an alert if the body temperature suddenly rises or falls. Furthermore, the body temperature sensor may monitor the body temperature during bathing or going out to understand the health condition. For example, it may monitor body temperature fluctuations during bathing or going out in real time. Thus, the body temperature sensor may be used to accurately monitor the body temperature of the elderly person. Some or all of the above-described processing in the body temperature sensor may be performed using, for example, AI, or may be performed without AI. For example, the body temperature sensor may input body temperature data into the generation AI and cause the generation AI to detect abnormal body temperature.

[0034] The monitoring unit may include a motion sensor. The motion sensor detects motion using, for example, an acceleration sensor or a gyro sensor. For example, the motion sensor may be attached to the elderly person's waist or feet and monitor their motion in real time. The motion sensor may also analyze walking patterns to identify the risk of falling. For example, it may monitor changes in walking speed and stride length and issue an alert if the risk of falling is high. Furthermore, the motion sensor may detect falls by the elderly person and prompt a prompt response. For example, if a fall is detected, an emergency call may be made to notify family members or medical institutions. Thus, the motion sensor can accurately monitor the elderly person's motion. Some or all of the above-described processing in the motion sensor may be performed using, for example, AI, or may be performed without AI. For example, the motion sensor may input motion data into a generation AI and cause the generation AI to analyze the risk of falling.

[0035] The reporting unit may include a location information transmitting unit that transmits location information. The location information transmitting unit acquires location information using, for example, GPS or Wi-Fi location information. For example, the location information transmitting unit may acquire the elderly person's location information in real time and transmit it to family members or medical institutions. The location information transmitting unit may also detect abnormal movement patterns and issue an alert. For example, an alert may be issued if the elderly person deviates from their normal movement pattern or exhibits abnormal behavior. Furthermore, the location information transmitting unit may monitor the elderly person's location information within their home and detect abnormal behavior early. For example, an alert may be issued if the elderly person remains motionless within their home for a long period of time or exhibits abnormal behavior. Thus, by using the location information transmitting unit, the elderly person's location information can be accurately transmitted. Some or all of the above-described processing in the location information transmitting unit may be performed using, for example, AI, or may be performed without AI. For example, the location information transmitting unit may input location information data to a generation AI and cause the generation AI to detect abnormal movement patterns.

[0036] The notification unit may include a health condition transmission unit that transmits details of the elderly person's health condition. The health condition transmission unit transmits data such as heart rate, body temperature, and blood pressure. For example, the health condition transmission unit may periodically transmit the elderly person's health data to family members or medical institutions. The health condition transmission unit may also detect abnormal health conditions and issue alerts. For example, an alert may be issued if the heart rate suddenly increases or the body temperature suddenly drops. Furthermore, the health condition transmission unit may store the health data in the cloud to facilitate data management. For example, heart rate data and body temperature data may be stored in the cloud and accessed as needed. This allows the health condition transmission unit to accurately transmit details of the elderly person's health condition. Some or all of the above-described processing in the health condition transmission unit may be performed using, for example, AI, or may be performed without AI. For example, the health condition transmission unit may input health data to a generation AI and cause the generation AI to detect abnormal health conditions.

[0037] The conversation unit can adjust the frequency of conversations to match the elderly person's lifestyle rhythm. The conversation unit, for example, analyzes the elderly person's lifestyle rhythm and adjusts the frequency of conversations. For example, the conversation unit can monitor the elderly person's lifestyle rhythm, such as wake-up time, bedtime, and meal times, and adjust the frequency of conversations. For example, if the elderly person is a morning person, the frequency of conversations can be increased in the morning, and if the elderly person is a night owl, the frequency of conversations can be increased in the evening. Furthermore, if the elderly person has a habit of taking afternoon naps, the frequency of conversations before and after the nap can be adjusted. This allows for more effective conversations by adjusting the frequency of conversations to match the elderly person's lifestyle rhythm. Some or all of the above-mentioned processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can input lifestyle rhythm data into the generation AI and cause the generation AI to adjust the frequency of conversations.

[0038] The conversation unit can personalize the content of the conversation based on the elderly person's past conversation history. The conversation unit, for example, refers to the elderly person's past conversation history to personalize the content of the conversation. For example, the conversation unit can revisit topics about hobbies and interests that the elderly person has talked about in the past. For example, the conversation unit can revisit topics about gardening that the elderly person has talked about in the past, or provide topics about news or events that the elderly person has been interested in in the past. The conversation unit can also revisit topics about family and friends that the elderly person has talked about in the past. This makes the conversation more friendly by personalizing the conversation based on the elderly person's past conversation history. Some or all of the above-mentioned processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can input past conversation history data into a generation AI and have the generation AI personalize the conversation.

[0039] The conversation unit can provide new topics based on the elderly person's hobbies and interests. The conversation unit, for example, analyzes the elderly person's hobbies and interests and provides new topics. For example, if the elderly person is interested in gardening, the conversation unit can provide topics about seasonal flowers and plants. If the elderly person is interested in cooking, the conversation unit can provide topics about new recipes and cooking tips. Furthermore, if the elderly person is interested in traveling, the conversation unit can provide topics about recommended travel spots and experiences. By providing new topics based on the elderly person's hobbies and interests, more interesting conversations are possible. Some or all of the above-mentioned processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can input hobby and interest data into a generation AI and have the generation AI provide new topics.

[0040] The conversation unit can refer to the content of past conversations between the elderly person and family and friends to progress the conversation. For example, the conversation unit can refer to the content of past conversations between the elderly person and family and friends to progress the conversation. For example, the conversation unit can revisit family anecdotes that the elderly person has talked about in the past, or progress the conversation based on the content of the elderly person's conversations with friends. The conversation unit can also progress the conversation based on the content of the elderly person's conversations about recent events with family and friends. In this way, by referring to the content of past conversations between the elderly person and family and friends, a more intimate conversation can be made. Some or all of the above-described processing in the conversation unit may be performed, for example, using AI, or may be performed without using AI. For example, the conversation unit can input past conversation content data into a generation AI and have the generation AI execute the conversation progression.

[0041] The analysis unit can extract keywords related to the health condition from the conversation content. The analysis unit, for example, uses natural language processing technology to analyze the conversation content and extract keywords related to the health condition. For example, the analysis unit can extract the keyword "tired" from the conversation to understand the elderly person's fatigue state. The analysis unit can also extract the keyword "pain" from the conversation to understand the elderly person's pain state. Furthermore, the analysis unit can extract the keyword "can't sleep" from the conversation to understand the elderly person's sleep state. In this way, by extracting keywords related to the health condition from the conversation content, the elderly person's health condition can be more accurately understood. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the conversation data to a generation AI and cause the generation AI to extract keywords related to the health condition.

[0042] The analysis unit can analyze the flow of conversation in real time and generate an appropriate response. The analysis unit analyzes the flow of conversation in real time using, for example, natural language processing technology. For example, when a question is asked during a conversation, the analysis unit can generate an appropriate response in real time. Furthermore, when an emotional change occurs during a conversation, the analysis unit can generate a response in real time that corresponds to that change. Furthermore, when a new topic arises during a conversation, the analysis unit can generate a response in real time that corresponds to that topic. This enables more natural conversation by analyzing the flow of conversation in real time. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input conversation data into a generation AI and cause the generation AI to generate an appropriate response.

[0043] The analysis unit can analyze the elderly person's lifestyle patterns and detect abnormalities. The analysis unit analyzes the elderly person's lifestyle patterns using, for example, daily behavioral data and sensor data. For example, the analysis unit can analyze the elderly person's lifestyle patterns, such as wake-up time, bedtime, meal times, and activity times, and detect abnormalities. For example, an abnormality can be detected if the elderly person does not wake up at their usual wake-up time or does not eat at their usual meal times. An abnormality can also be detected if the elderly person does not engage in activities at their usual activity times. By analyzing the elderly person's lifestyle patterns, abnormalities can be detected early. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input lifestyle pattern data to a generation AI and cause the generation AI to detect abnormalities.

[0044] The analysis unit can complement the analysis results by referring to the elderly person's past health data. The analysis unit can refer to the elderly person's past health data using, for example, data from an electronic medical record or a health management app. For example, the analysis unit can analyze the current heart rate by referring to past heart rate data. It can also analyze the current body temperature by referring to past body temperature data. It can also analyze the current movement by referring to past movement data. As a result, by referring to the elderly person's past health data, more accurate analysis results can be obtained. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past health data into the generation AI and have the generation AI complement the analysis results.

[0045] The monitoring unit can monitor the sleep patterns of the elderly person. The monitoring unit monitors the sleep patterns of the elderly person, for example, by analyzing sleep stages and recording sleep duration. For example, the monitoring unit can monitor whether the elderly person is sleeping at a normal sleep time. It can also monitor how often the elderly person wakes up during the night. It can also monitor the quality of the elderly person's sleep. In this way, by monitoring the elderly person's sleep patterns, the quality of sleep can be understood. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input sleep data to a generation AI and cause the generation AI to monitor the sleep patterns.

[0046] The monitoring unit can monitor the dietary content of the elderly person. The monitoring unit monitors the dietary content of the elderly person, for example, by using photo analysis of meals and nutrient records. For example, the monitoring unit can monitor whether the elderly person is eating a balanced diet. It can also monitor the frequency at which the elderly person eats meals. It can also monitor the amount of food the elderly person eats. In this way, by monitoring the dietary content of the elderly person, it is possible to understand the nutritional status. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the monitoring unit can input dietary data into the generation AI and cause the generation AI to monitor the dietary content.

[0047] The monitoring unit can monitor the amount of exercise of the elderly person. The monitoring unit monitors the amount of exercise of the elderly person using, for example, a pedometer or an acceleration sensor. For example, the monitoring unit can monitor how much the elderly person walks daily. It can also monitor how often the elderly person exercises. It can also monitor the amount of time the elderly person exercises. In this way, by monitoring the amount of exercise of the elderly person, it is possible to understand their exercise habits. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input exercise data into the generation AI and cause the generation AI to monitor the amount of exercise.

[0048] The monitoring unit can monitor the elderly person's medication status. The monitoring unit monitors the elderly person's medication status using, for example, a medication record app or a medicine box with a sensor. For example, the monitoring unit can monitor whether the elderly person takes their medication at the correct time. It can also monitor whether the elderly person takes the correct amount of medication. It can also monitor whether the elderly person remembers to take their medication. In this way, monitoring the elderly person's medication status enables appropriate medication management. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input medication data into the generation AI and cause the generation AI to monitor the medication status.

[0049] The notification unit can automatically notify the elderly person's family and medical institutions. The notification unit automatically notifies the elderly person's family and medical institutions, for example, using an automatic emergency notification system. For example, the notification unit can automatically notify the family if the elderly person falls. Also, it can automatically notify a medical institution if the elderly person's physical condition suddenly changes. Furthermore, it can automatically notify the family if the elderly person does not move for a long period of time. This enables a rapid response by automatically notifying the elderly person's family and medical institutions. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input emergency data into the generation AI and have the generation AI execute an automatic notification.

[0050] The notification unit can customize the content of the notification based on the elderly person's past health data. The notification unit, for example, refers to the elderly person's past health data using data from an electronic medical record or a health management app, and customizes the content of the notification. For example, the notification unit can customize the content of the notification based on past heart rate data. The notification unit can also customize the content of the notification based on past body temperature data. Furthermore, the notification unit can customize the content of the notification based on past movement data. As a result, by customizing the content of the notification based on the elderly person's past health data, more appropriate information can be provided. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input past health data into a generation AI and have the generation AI customize the content of the notification.

[0051] The reporting unit can periodically report the elderly person's health condition. The reporting unit periodically reports health data such as heart rate, body temperature, and amount of exercise to family members and medical institutions. For example, the reporting unit can periodically report the elderly person's heart rate to family members. It can also periodically report the elderly person's body temperature to medical institutions. It can also periodically report the elderly person's amount of exercise to family members. By periodically reporting the elderly person's health condition, family members and medical institutions can understand the elderly person's health condition. Some or all of the above-mentioned processing in the reporting unit may be performed, for example, using AI, or may be performed without using AI. For example, the reporting unit can input health data into a generation AI and have the generation AI execute periodic reports.

[0052] The speech recognition unit can recognize the dialect and accent of elderly people. The speech recognition unit recognizes the dialect and accent of elderly people, for example, by extracting features from speech data or using a dialect dictionary. For example, the speech recognition unit can recognize dialects such as Kansai dialect, Tohoku dialect, and Okinawa dialect. The speech recognition unit can also analyze the accent of elderly people and conduct natural conversations. This enables more natural conversations by recognizing the dialect and accent of elderly people. Some or all of the above-described processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input dialect data to a generation AI and have the generation AI recognize the dialect and accent.

[0053] The voice recognition unit can analyze the tone and speed of the elderly person's voice. The voice recognition unit analyzes the tone and speed of the elderly person's voice, for example, by extracting acoustic features or using a voice analysis algorithm. For example, the voice recognition unit can estimate emotions when the elderly person's voice has a low tone or a slow speed. The voice recognition unit can also analyze changes in the tone and speed of the elderly person's voice to grasp changes in emotions. In this way, changes in emotions can be grasped by analyzing the tone and speed of the elderly person's voice. Some or all of the above-mentioned processing in the voice recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice recognition unit can input voice data to a generation AI and have the generation AI analyze the tone and speed of the voice.

[0054] The voice recognition unit can filter the elderly person's environmental sounds. The voice recognition unit filters the elderly person's environmental sounds using, for example, noise canceling technology or an acoustic filtering algorithm. For example, the voice recognition unit can filter the television sounds when the elderly person is watching television. Also, the voice recognition unit can filter the ambient noise when the elderly person is out. Furthermore, the voice recognition unit can filter the housework sounds when the elderly person is doing housework. In this way, filtering the elderly person's environmental sounds enables more accurate voice recognition. Some or all of the above-described processing in the voice recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice recognition unit can input environmental sound data to a generation AI and have the generation AI perform filtering of the environmental sounds.

[0055] The voice recognition unit can monitor changes in the voice of the elderly person. The voice recognition unit monitors changes in the voice of the elderly person, for example, by analyzing changes in acoustic features or voice data. For example, the voice recognition unit can monitor changes when the elderly person's voice becomes hoarse or trembling. It can also monitor changes when the elderly person's voice suddenly becomes louder. In this way, by monitoring changes in the elderly person's voice, it is possible to grasp changes in their health condition. Some or all of the above-mentioned processing in the voice recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice recognition unit can input voice data to a generation AI and have the generation AI monitor changes in the voice.

[0056] The reply generation unit can generate a reply by referring to the elderly person's past conversation history. The reply generation unit generates a reply by referring to, for example, the elderly person's past conversation history. For example, the reply generation unit can generate a reply about hobbies that the elderly person has talked about in the past. It can also generate a reply about news or events that the elderly person has been interested in in the past. It can also generate a reply about family and friends that the elderly person has talked about in the past. In this way, by referring to the elderly person's past conversation history, it is possible to provide a more friendly reply. Some or all of the above-mentioned processing in the reply generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reply generation unit can input past conversation history data into a generation AI and have the generation AI generate a reply.

[0057] The reply generation unit can generate appropriate replies to questions about the elderly person's health condition. The reply generation unit generates appropriate replies to questions about the elderly person's health condition, for example. For example, the reply generation unit can generate appropriate replies when the elderly person asks about their physical condition. It can also generate appropriate replies when the elderly person asks about taking medicine. It can also generate appropriate replies when the elderly person asks about exercise. As a result, generating appropriate replies to questions about the elderly person's health condition makes health management easier. Some or all of the above-mentioned processing in the reply generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reply generation unit can input health condition data into the generation AI and cause the generation AI to generate appropriate replies.

[0058] The reply generation unit can generate a reply based on the hobbies and interests of the elderly. The reply generation unit, for example, analyzes the hobbies and interests of the elderly and generates a reply. For example, if the elderly is interested in gardening, the reply generation unit can generate a reply related to gardening. Also, if the elderly is interested in cooking, the reply generation unit can generate a reply related to cooking. Furthermore, if the elderly is interested in traveling, the reply generation unit can generate a reply related to traveling. By generating a reply based on the elderly's hobbies and interests, more interesting conversations can be made. Some or all of the above-mentioned processing in the reply generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reply generation unit can input hobby and interest data into a generation AI and have the generation AI generate a reply.

[0059] The reply generation unit can generate a reply by referring to the content of past conversations between the elderly person and family and friends. The reply generation unit generates a reply by referring to, for example, the content of past conversations between the elderly person and family and friends. For example, the reply generation unit can generate a reply about a family episode that the elderly person has talked about in the past. The reply generation unit can also generate a reply based on the content of conversations the elderly person has had with friends about memories. Furthermore, the reply generation unit can generate a reply based on the content of conversations the elderly person has had with family and friends about recent events. In this way, by referring to the content of past conversations between the elderly person and family and friends, a more friendly reply can be generated. Some or all of the above-mentioned processing in the reply generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reply generation unit can input past conversation content data into a generation AI and have the generation AI generate a reply.

[0060] The heart rate sensor can compare the elderly person's heart rate during exercise and at rest. The heart rate sensor can compare the elderly person's heart rate during exercise and at rest using, for example, an exercise stress test or heart rate variability analysis. For example, the heart rate sensor can monitor the elderly person's heart rate after exercise. It can also monitor the elderly person's heart rate while at rest. Furthermore, the heart rate sensor can compare the changes in the elderly person's heart rate during exercise and at rest. This allows for a more accurate understanding of the elderly person's health condition by comparing the elderly person's heart rate during exercise and at rest. Some or all of the above-described processing in the heart rate sensor may be performed, for example, using AI or without AI. For example, the heart rate sensor can input heart rate data to a generation AI and have the generation AI compare the heart rate during exercise and at rest.

[0061] The heart rate sensor can issue an alert if it detects an abnormal heart rate. The heart rate sensor detects an abnormal heart rate, for example, by using a heart rate threshold setting or an abnormality detection algorithm. For example, the heart rate sensor can issue an alert if an elderly person's heart rate suddenly increases or decreases. It can also issue an alert if the elderly person's heart rate exceeds the normal range. This allows for a prompt response by issuing an alert when an abnormal heart rate is detected. Some or all of the above-described processing in the heart rate sensor may be performed using, for example, AI, or may be performed without using AI. For example, the heart rate sensor can input heart rate data to a generation AI and have the generation AI detect an abnormal heart rate and issue an alert.

[0062] The heart rate sensor can monitor the heart rate of an elderly person while they are sleeping. The heart rate sensor monitors the heart rate of an elderly person while they are sleeping, for example, by analyzing sleep stages or heart rate variability. For example, the heart rate sensor can monitor whether the elderly person is sleeping at a normal sleep time. It can also monitor how often the elderly person wakes up during the night. It can also monitor the quality of the elderly person's sleep. Thus, by monitoring the elderly person's heart rate while they are sleeping, the quality of their sleep can be understood. Some or all of the above-mentioned processing in the heart rate sensor may be performed, for example, using AI or without AI. For example, the heart rate sensor can input sleep data to a generation AI and cause the generation AI to monitor the heart rate while they are sleeping.

[0063] The heart rate sensor can monitor the stress level of an elderly person. The heart rate sensor monitors the stress level of an elderly person using, for example, heart rate variability analysis or a stress assessment algorithm. For example, the heart rate sensor can monitor changes in the heart rate when the elderly person is feeling stressed. It can also monitor changes in the heart rate when the elderly person is relaxed. Furthermore, the heart rate sensor can analyze the stress level of an elderly person from the heart rate data. This makes it possible to manage stress by monitoring the stress level of an elderly person. Some or all of the above-mentioned processing in the heart rate sensor may be performed using, for example, AI, or may be performed without using AI. For example, the heart rate sensor can input heart rate data to a generation AI and cause the generation AI to monitor the stress level.

[0064] The body temperature sensor can monitor the fluctuations in the elderly person's body temperature in real time. The body temperature sensor monitors the fluctuations in the elderly person's body temperature in real time using, for example, time series analysis or body temperature fluctuation pattern analysis. For example, the body temperature sensor can monitor if the elderly person's body temperature suddenly rises or falls. It can also monitor if the elderly person's body temperature exceeds the normal range. By monitoring the fluctuations in the elderly person's body temperature in real time, it is possible to more accurately grasp their health condition. Some or all of the above-mentioned processing in the body temperature sensor may be performed using, for example, AI, or may be performed without using AI. For example, the body temperature sensor can input body temperature data into a generation AI and have the generation AI monitor the fluctuations in body temperature.

[0065] The body temperature sensor can issue an alert if it detects an abnormal body temperature. The body temperature sensor detects abnormal body temperature using, for example, a body temperature threshold setting or an abnormality detection algorithm. For example, the body temperature sensor can issue an alert if an elderly person's body temperature suddenly rises or falls. It can also issue an alert if the elderly person's body temperature exceeds the normal range. This allows for a rapid response by issuing an alert when an abnormal body temperature is detected. Some or all of the above-mentioned processing in the body temperature sensor may be performed using, for example, AI, or may be performed without using AI. For example, the body temperature sensor can input body temperature data into a generation AI, which can then detect an abnormal body temperature and issue an alert.

[0066] The body temperature sensor can monitor the body temperature of an elderly person while bathing. The body temperature sensor monitors the body temperature of an elderly person while bathing, for example, by analyzing body temperature fluctuations during bathing or by using a body temperature sensor installation method. For example, the body temperature sensor can monitor the body temperature of an elderly person while bathing. It can also monitor the body temperature of an elderly person after bathing. It can also monitor changes in body temperature while bathing. Thus, by monitoring the body temperature of an elderly person while bathing, it is possible to more accurately grasp their health condition. Some or all of the above-mentioned processing in the body temperature sensor may be performed, for example, using AI, or may be performed without using AI. For example, the body temperature sensor can input body temperature data into a generation AI and have the generation AI monitor the body temperature while bathing.

[0067] The body temperature sensor can monitor the body temperature of an elderly person when they go out. The body temperature sensor monitors the body temperature of an elderly person when they go out, for example, by analyzing body temperature fluctuations while they are out or by using a body temperature sensor installation method. For example, the body temperature sensor can monitor the body temperature of an elderly person while they are out. It can also monitor the body temperature of an elderly person after they go out. It can also monitor changes in the body temperature of an elderly person when they are out. Thus, by monitoring the body temperature of an elderly person when they are out, it is possible to more accurately grasp their health condition. Some or all of the above-mentioned processing in the body temperature sensor may be performed, for example, using AI, or may be performed without using AI. For example, the body temperature sensor can input body temperature data into a generation AI and have the generation AI monitor the body temperature when they are out.

[0068] The motion sensor can monitor the walking pattern of an elderly person. The motion sensor analyzes walking patterns such as walking speed, stride length, and walking rhythm to monitor the walking pattern of the elderly person. For example, the motion sensor can monitor whether the elderly person is walking in a normal walking pattern. It can also monitor whether the elderly person is at risk of falling while walking. Furthermore, it can monitor changes in the elderly person's walking speed and stride length. In this way, by monitoring the elderly person's walking pattern, the risk of falling can be identified. Some or all of the above-mentioned processing in the motion sensor may be performed using, for example, AI, or may be performed without using AI. For example, the motion sensor can input walking data to a generation AI and cause the generation AI to monitor the walking pattern.

[0069] The motion sensor can issue an alert when it detects a fall by an elderly person. The motion sensor detects a fall by an elderly person using, for example, an acceleration sensor or a fall detection algorithm. For example, the motion sensor can issue an alert when the elderly person falls. It can also issue an alert when the elderly person is at high risk of falling. It can also issue an alert when the elderly person shows signs of falling. This enables a prompt response by issuing an alert when it detects a fall by an elderly person. Some or all of the above-described processing in the motion sensor may be performed, for example, using AI or without AI. For example, the motion sensor can input fall data to a generation AI and cause the generation AI to detect a fall and issue an alert.

[0070] The motion sensor can monitor the amount of exercise of an elderly person. The motion sensor monitors the amount of exercise of an elderly person using, for example, a pedometer or an acceleration sensor. For example, the motion sensor can monitor how much an elderly person walks daily. It can also monitor how often the elderly person exercises. It can also monitor the amount of time the elderly person exercises. In this way, by monitoring the amount of exercise of an elderly person, it is possible to understand their exercise habits. Some or all of the above-mentioned processing in the motion sensor may be performed using, for example, AI, or may be performed without using AI. For example, the motion sensor can input exercise data to a generation AI and have the generation AI monitor the amount of exercise.

[0071] The motion sensor can monitor the movements of the elderly when they wake up and when they go to bed. The motion sensor can monitor the movements of the elderly when they wake up and when they go to bed, for example, using a motion sensor or motion pattern analysis. For example, the motion sensor can monitor whether the elderly wakes up at their usual wake-up time. It can also monitor whether the elderly goes to bed at their usual bedtime. It can also monitor changes in the movements of the elderly when they wake up and when they go to bed. In this way, by monitoring the movements of the elderly when they wake up and when they go to bed, it is possible to understand their daily rhythm. Some or all of the above-mentioned processing in the motion sensor may be performed, for example, using AI or may be performed without using AI. For example, the motion sensor can input movement data to a generation AI and have the generation AI monitor the movements of the elderly when they wake up and when they go to bed.

[0072] The location information transmission unit can analyze the elderly person's movement pattern. The location information transmission unit analyzes movement patterns such as movement speed, movement route, and movement frequency. For example, the location information transmission unit can analyze whether the elderly person is moving in a normal movement pattern. It can also analyze whether the elderly person is exhibiting an abnormal pattern while moving. Furthermore, it can analyze changes in the elderly person's movement speed and movement distance. By analyzing the elderly person's movement pattern, abnormal behavior can be detected early. Some or all of the above-mentioned processing in the location information transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information transmission unit can input movement data to a generation AI and have the generation AI analyze the movement pattern.

[0073] The location information transmission unit can issue an alert when an abnormal movement pattern is detected. The location information transmission unit detects an abnormal movement pattern using, for example, an algorithm that detects abnormalities in the movement route or the movement speed. For example, the location information transmission unit can issue an alert when an elderly person deviates from their normal movement pattern. The location information transmission unit can also issue an alert when the elderly person exhibits abnormal behavior while moving. Furthermore, the location information transmission unit can also issue an alert when the elderly person is at risk of falling while moving. This enables a prompt response by issuing an alert when an abnormal movement pattern is detected. Some or all of the above-described processing in the location information transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information transmission unit can input movement data to a generation AI and cause the generation AI to detect an abnormal movement pattern and issue an alert.

[0074] The location information transmission unit can transmit location information of the elderly person when they go out in real time. The location information transmission unit transmits location information of the elderly person when they go out, for example, using real-time transmission or periodic transmission. For example, the location information transmission unit can transmit location information in real time when the elderly person is out. It can also transmit location information in real time when the elderly person is moving. It can also transmit location information in real time when the elderly person is moving within their home. This enables a quick response by transmitting location information of the elderly person when they go out in real time. Some or all of the above-mentioned processing in the location information transmission unit may be performed, for example, using AI or without using AI. For example, the location information transmission unit can input location information data to a generation AI and cause the generation AI to perform real-time transmission.

[0075] The location information transmission unit can monitor the location information of the elderly person within their home. The location information transmission unit monitors the location information of the elderly person within their home, for example, using an indoor location information system or a sensor installation method. For example, the location information transmission unit can monitor the location information when the elderly person is moving within their home. It can also monitor the location information when the elderly person does not move within their home for a long period of time. It can also monitor the location information when the elderly person exhibits abnormal behavior within their home. Thus, by monitoring the location information of the elderly person within their home, abnormal behavior can be detected early. Some or all of the above-described processing in the location information transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information transmission unit can input location information data to a generation AI and cause the generation AI to monitor the location information within their home.

[0076] The health condition transmission unit can periodically transmit the elderly person's health data. The health condition transmission unit periodically transmits health data such as heart rate, body temperature, and amount of exercise to family members or medical institutions. For example, the health condition transmission unit can periodically transmit the elderly person's heart rate to family members. It can also periodically transmit the elderly person's body temperature to medical institutions. It can also periodically transmit the elderly person's amount of exercise to family members. In this way, by periodically transmitting the elderly person's health data, family members and medical institutions can understand the elderly person's health condition. Some or all of the above-mentioned processing in the health condition transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the health condition transmission unit can input health data to a generation AI and have the generation AI perform periodic transmission.

[0077] The health condition transmission unit can issue an alert when an abnormal health condition is detected. The health condition transmission unit detects an abnormal health condition, for example, using an algorithm that detects abnormal heart rate or body temperature. For example, the health condition transmission unit can issue an alert when the elderly person's heart rate suddenly rises or falls. It can also issue an alert when the elderly person's body temperature exceeds a normal range. It can also issue an alert when the elderly person's exercise volume exceeds a normal range. This enables a prompt response by issuing an alert when an abnormal health condition is detected. Some or all of the above-mentioned processing in the health condition transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the health condition transmission unit can input health data to a generation AI and cause the generation AI to detect an abnormal health condition and issue an alert.

[0078] The health condition transmission unit can transmit the elderly person's health data to family members and medical institutions. The health condition transmission unit transmits the elderly person's health data to family members and medical institutions, for example, by email or cloud transmission. For example, the health condition transmission unit can transmit the elderly person's heart rate data to family members. It can also transmit the elderly person's body temperature data to medical institutions. It can also transmit the elderly person's exercise amount data to family members. In this way, transmitting the elderly person's health data to family members and medical institutions makes it easier to understand the elderly person's health condition. Some or all of the above-mentioned processing in the health condition transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the health condition transmission unit can input the health data to a generation AI and have the generation AI transmit the data.

[0079] The health condition transmission unit can store the elderly person's health data in the cloud. The health condition transmission unit stores the elderly person's health data in the cloud using, for example, data encryption and access control. For example, the health condition transmission unit can store the elderly person's heart rate data in the cloud. Also, the elderly person's body temperature data can be stored in the cloud. Furthermore, the elderly person's exercise amount data can be stored in the cloud. Storing the elderly person's health data in the cloud thus facilitates data management. Some or all of the above-described processing in the health condition transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the health condition transmission unit can input the health data into a generation AI and have the generation AI store the data in the cloud.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The AI-equipped robot system can further include an environmental monitoring unit. The environmental monitoring unit can monitor the elderly person's living environment and provide information to maintain a comfortable environment. For example, a temperature sensor can be used to monitor the indoor temperature and adjust it to an appropriate temperature. A humidity sensor can be used to monitor the indoor humidity and adjust it to an appropriate humidity. Furthermore, an air quality sensor can be used to monitor the indoor air quality and prompt ventilation as necessary. This can maintain a comfortable living environment for the elderly person.

[0082] The AI-equipped robot system can further include a dietary management unit. The dietary management unit can manage the elderly person's dietary content and provide information to provide nutritionally balanced meals. For example, it can analyze photos of meals to calculate nutrients and suggest balanced meals. It can also record food intake and determine whether it is excessive or insufficient. It can also manage meal times and encourage regular eating habits. This makes it possible to manage the elderly person's diet to maintain their health.

[0083] The AI-equipped robot system can further be equipped with an exercise support unit. The exercise support unit can support the elderly in exercising and provide exercise programs to maintain health. For example, it can propose an exercise program based on the elderly's physical strength and health condition and support the implementation of the program. It can also record exercise progress and provide feedback on the degree of achievement. Furthermore, it can monitor to ensure safety during exercise and issue an alert if an abnormality is detected. This makes it possible to provide exercise support to maintain the health of the elderly.

[0084] The AI-equipped robot system can further be equipped with a reminder unit. The reminder unit can provide reminder functions to support the elderly in their daily lives. For example, it can remind them when to take their medicine and encourage them to take it at the appropriate time. It can also remind them of regular health checkups and medical appointments. It can also remind them of daily plans and events and support schedule management. This makes it possible to support the elderly in smoothly carrying out their daily lives.

[0085] The AI-equipped robot system can further include an entertainment provider. The entertainment provider can provide entertainment functions to bring enjoyment to the elderly's lives. For example, it can recommend music, movies, and TV programs and support their viewing. It can also provide intellectual activities such as games and puzzles to stimulate the brain while having fun. It can also suggest activities based on hobbies and interests and encourage participation. This can bring enjoyment to the elderly's lives and maintain their mental health.

[0086] The processing flow of the first embodiment will be briefly explained below.

[0087] Step 1: The conversation unit engages in a conversation with the elderly person. The conversation unit includes a speech recognition unit that engages in a conversation with the elderly person using speech recognition technology. The speech recognition unit recognizes speech using an acoustic model that uses deep learning, and is able to recognize the elderly person's dialect and accent. Furthermore, the speech recognition unit can analyze the tone and speed of the elderly person's voice and grasp changes in emotion. Step 2: The analysis unit analyzes the content of the conversation carried out by the conversation unit. The analysis unit includes a response generation unit that analyzes the content of the conversation using natural language processing technology and generates an appropriate response. The response generation unit generates responses based on context or emotion, and can also generate responses by referring to the elderly person's past conversation history. Step 3: The monitoring unit monitors the health condition of the elderly person. The monitoring unit is equipped with a heart rate sensor, a body temperature sensor, and a movement sensor, and can accurately monitor the heart rate, body temperature, and movement of the elderly person. Step 4: The notification unit issues an emergency call if the monitoring unit detects an abnormality. The notification unit includes a location information transmission unit that transmits location information and a health condition transmission unit that transmits details of the elderly person's health condition, and can accurately transmit the elderly person's location information and details of their health condition.

[0088] (Example 2) In an embodiment of the present invention, an AI-equipped robot lives with an elderly person living alone, preventing dementia and ensuring their safety. In this system, the robot converses with the elderly person daily, stimulating their brain to prevent cognitive decline. The robot also constantly monitors the elderly person's health and issues an emergency call if an abnormality is detected. For example, if the elderly person falls or experiences a sudden change in their physical condition, the robot automatically issues an emergency call to prompt a prompt response. The robot uses voice recognition technology to converse with the elderly person, analyzes the conversation, and provides an appropriate response. Heart rate, body temperature, and motion sensors are used to monitor the elderly person's health. If an abnormality is detected, an emergency call system is activated, transmitting the elderly person's location and details of their health condition. For example, the robot monitors the elderly person's heart rate and issues an emergency call if an abnormal heart rate is detected. The robot also monitors the elderly person's body temperature and issues an emergency call if a sudden change in body temperature is detected. Furthermore, the robot monitors the elderly person's movements and issues an emergency call if a fall or abnormal movement is detected. This ensures the elderly person's safety and enables prompt response. The robot also estimates the elderly person's emotions through conversation and responds appropriately based on those emotions. For example, if the elderly person is sad, the robot will speak to them in a gentle tone to comfort them. If the elderly person is excited, the robot will speak to them in a calm tone to relax them. The robot also adjusts the frequency of conversations to match the elderly person's lifestyle, making conversations more effective. For example, if the elderly person is a morning person, the robot will increase the frequency of conversations in the morning, and if the elderly person is a night owl, the robot will increase the frequency of conversations in the evening. This enables effective conversations that are tailored to the elderly person's lifestyle. The robot also personalizes conversations based on the elderly person's past conversation history, making the conversation more friendly. For example, it may revisit topics about hobbies that the elderly person has previously discussed, or provide topics about news or events that the elderly person has previously shown interest in. This facilitates communication with the elderly and prevents cognitive decline. The robot also estimates the elderly person's emotions and adjusts the tone and content of conversations based on their emotions.For example, if an elderly person is sad, the robot responds in a way that reflects their feelings, and if they are excited, it responds calmly. This enables appropriate communication with the elderly. The robot can also offer new topics based on the elderly's hobbies and interests to pique their interest. For example, if an elderly person is interested in gardening, it can offer topics about seasonal flowers and plants. If an elderly person is interested in cooking, it can offer topics about new recipes and cooking tips. This piques the elderly's interest and prevents cognitive decline. Furthermore, the robot can refer to past conversations with the elderly's family and friends to advance the conversation and make it more intimate. For example, it can revisit family stories that the elderly have shared in the past, or continue the conversation based on the elderly's memories with friends. This facilitates communication with the elderly and prevents cognitive decline. This enables the AI-equipped robot system to converse with the elderly, monitor their health, and make emergency calls in the event of an abnormality.

[0089] The AI-equipped robot system according to the embodiment includes a conversation unit, an analysis unit, a monitoring unit, and a reporting unit. The conversation unit converses with the elderly person. The conversation unit includes, for example, a voice recognition unit that converses with the elderly person using voice recognition technology. The voice recognition unit recognizes voice using, for example, an acoustic model based on deep learning. The voice recognition unit can also recognize the elderly person's dialect or accent. For example, the voice recognition unit can recognize dialects such as Kansai dialect and Tohoku dialect and conduct natural conversations. The voice recognition unit can also analyze the elderly person's tone and speed of voice to grasp changes in emotion. For example, if the voice tone is low or the voice speed is slow, the emotion can be estimated. The analysis unit analyzes the content of the conversation conducted by the conversation unit. The analysis unit includes, for example, a response generation unit that analyzes the content of the conversation using natural language processing technology and generates an appropriate response. The response generation unit can generate, for example, a response based on context or emotion. For example, if the elderly person is sad, the response can be made in a gentle tone, and if the elderly person is excited, the response can be made in a calm tone. The response generation unit can also generate a response by referring to the elderly person's past conversation history. For example, the system can generate responses about hobbies that the elderly person has talked about in the past, or about news or events that the elderly person has been interested in in the past. The monitoring unit monitors the elderly person's health condition. The monitoring unit, for example, includes a heart rate sensor and can accurately monitor the elderly person's heart rate. The heart rate sensor measures the heart rate using, for example, an optical sensor or an electrical sensor. The monitoring unit also includes a body temperature sensor and can accurately monitor the elderly person's body temperature. The body temperature sensor measures the body temperature using, for example, an infrared sensor or a contact sensor. The monitoring unit also includes a motion sensor and can accurately monitor the elderly person's movements. The motion sensor detects movements using, for example, an acceleration sensor or a gyro sensor. The reporting unit makes an emergency report when the monitoring unit detects an abnormality. The reporting unit, for example, includes a location information transmitting unit that transmits location information and can accurately transmit the elderly person's location information. The location information transmitting unit acquires the location information using, for example, GPS or Wi-Fi location information.The notification unit also includes a health condition transmission unit that transmits detailed health status information, enabling accurate transmission of the details of the elderly person's health status. The health condition transmission unit transmits data such as heart rate, body temperature, and blood pressure. This enables the AI-equipped robot system according to the embodiment to converse with the elderly person, monitor their health status, and issue emergency notifications in the event of an abnormality.

[0090] The conversation unit may include a speech recognition unit that uses speech recognition technology to converse with the elderly person. The speech recognition unit recognizes speech using, for example, an acoustic model based on deep learning. For example, the speech recognition unit can analyze the elderly person's speech in real time and convert it into text data. The speech recognition unit can also recognize the elderly person's dialect and accent. For example, dialects such as Kansai dialect and Tohoku dialect can be recognized to enable natural conversation. Furthermore, the speech recognition unit can analyze the tone and speed of the elderly person's voice to grasp changes in emotion. For example, if the voice tone is low or the voice speed is slow, the emotion can be estimated. This makes it possible to have a natural conversation with the elderly person using speech recognition technology. Some or all of the above-described processing in the speech recognition unit may be performed using, for example, AI, or may be performed without AI. For example, the speech recognition unit may input the elderly person's speech data into a generation AI and have the generation AI convert the speech data into text data.

[0091] The conversation unit may include a response generation unit that analyzes the conversation content and generates an appropriate response. The response generation unit may analyze the conversation content using, for example, natural language processing technology and generate an appropriate response. For example, the response generation unit may generate a response based on context or emotion. For example, if the elderly person is sad, the response may be generated in a gentle tone, and if the elderly person is excited, the response may be generated in a calm tone. The response generation unit may also generate a response by referring to the elderly person's past conversation history. For example, the response generation unit may generate a response about hobbies that the elderly person has previously talked about, or a response about news or events that the elderly person has previously shown interest in. This allows for analysis of the conversation content and generation of an appropriate response, thereby facilitating communication with the elderly. Some or all of the above-described processing in the response generation unit may be performed using, for example, AI, or may be performed without AI. For example, the response generation unit may input the conversation content into a generation AI and have the generation AI generate an appropriate response.

[0092] The monitoring unit may include a heart rate sensor. The heart rate sensor measures the heart rate using, for example, an optical sensor or an electrical sensor. For example, the heart rate sensor may be worn on the wrist or chest of the elderly person to monitor the heart rate in real time. The heart rate sensor may also analyze heart rate fluctuations and detect abnormal heart rates. For example, it may issue an alert if the heart rate suddenly increases or decreases. Furthermore, the heart rate sensor may compare the heart rate during exercise with the heart rate at rest to determine the person's health condition. For example, if the heart rate after exercise exceeds the normal range, it may detect an abnormality. Thus, the heart rate sensor may accurately monitor the elderly person's heart rate. Some or all of the above-described processing in the heart rate sensor may be performed using, for example, AI, or may be performed without AI. For example, the heart rate sensor may input heart rate data to the generation AI and cause the generation AI to detect abnormal heart rates.

[0093] The monitoring unit may include a body temperature sensor. The body temperature sensor measures body temperature using, for example, an infrared sensor or a contact sensor. For example, the body temperature sensor may be attached to the elderly person's forehead or ear to monitor their body temperature in real time. The body temperature sensor may also analyze body temperature fluctuations and detect abnormal body temperature. For example, it may issue an alert if the body temperature suddenly rises or falls. Furthermore, the body temperature sensor may monitor the body temperature during bathing or going out to understand the health condition. For example, it may monitor body temperature fluctuations during bathing or going out in real time. Thus, the body temperature sensor may be used to accurately monitor the body temperature of the elderly person. Some or all of the above-described processing in the body temperature sensor may be performed using, for example, AI, or may be performed without AI. For example, the body temperature sensor may input body temperature data into the generation AI and cause the generation AI to detect abnormal body temperature.

[0094] The monitoring unit may include a motion sensor. The motion sensor detects motion using, for example, an acceleration sensor or a gyro sensor. For example, the motion sensor may be attached to the elderly person's waist or feet and monitor their motion in real time. The motion sensor may also analyze walking patterns to identify the risk of falling. For example, it may monitor changes in walking speed and stride length and issue an alert if the risk of falling is high. Furthermore, the motion sensor may detect falls by the elderly person and prompt a prompt response. For example, if a fall is detected, an emergency call may be made to notify family members or medical institutions. Thus, the motion sensor can accurately monitor the elderly person's motion. Some or all of the above-described processing in the motion sensor may be performed using, for example, AI, or may be performed without AI. For example, the motion sensor may input motion data into a generation AI and cause the generation AI to analyze the risk of falling.

[0095] The reporting unit may include a location information transmitting unit that transmits location information. The location information transmitting unit acquires location information using, for example, GPS or Wi-Fi location information. For example, the location information transmitting unit may acquire the elderly person's location information in real time and transmit it to family members or medical institutions. The location information transmitting unit may also detect abnormal movement patterns and issue an alert. For example, an alert may be issued if the elderly person deviates from their normal movement pattern or exhibits abnormal behavior. Furthermore, the location information transmitting unit may monitor the elderly person's location information within their home and detect abnormal behavior early. For example, an alert may be issued if the elderly person remains motionless within their home for a long period of time or exhibits abnormal behavior. Thus, by using the location information transmitting unit, the elderly person's location information can be accurately transmitted. Some or all of the above-described processing in the location information transmitting unit may be performed using, for example, AI, or may be performed without AI. For example, the location information transmitting unit may input location information data to a generation AI and cause the generation AI to detect abnormal movement patterns.

[0096] The notification unit may include a health condition transmission unit that transmits details of the elderly person's health condition. The health condition transmission unit transmits data such as heart rate, body temperature, and blood pressure. For example, the health condition transmission unit may periodically transmit the elderly person's health data to family members or medical institutions. The health condition transmission unit may also detect abnormal health conditions and issue alerts. For example, an alert may be issued if the heart rate suddenly increases or the body temperature suddenly drops. Furthermore, the health condition transmission unit may store the health data in the cloud to facilitate data management. For example, heart rate data and body temperature data may be stored in the cloud and accessed as needed. This allows the health condition transmission unit to accurately transmit details of the elderly person's health condition. Some or all of the above-described processing in the health condition transmission unit may be performed using, for example, AI, or may be performed without AI. For example, the health condition transmission unit may input health data to a generation AI and cause the generation AI to detect abnormal health conditions.

[0097] The conversation unit can estimate the elderly person's emotions and select a conversation topic based on the estimated elderly person's emotions. The conversation unit can estimate the elderly person's emotions using, for example, voice analysis or facial expression analysis. For example, the conversation unit can analyze the elderly person's tone of voice, speed, and changes in facial expressions to estimate emotions. The conversation unit can also select a conversation topic based on the estimated emotions. For example, if the elderly person is sad, the conversation unit can select a topic to talk about happy memories from the past, and if the elderly person is excited, the conversation unit can select a topic that will help them relax. Furthermore, if the elderly person is feeling lonely, the conversation unit can select a topic about family and friends. This enables more appropriate conversation by selecting a conversation topic based on the elderly person's emotions. Some or all of the above-mentioned processing in the conversation unit may be performed using, for example, AI, or may be performed without AI. For example, the conversation unit can input emotion data into a generation AI and have the generation AI select a conversation topic.

[0098] The conversation unit can adjust the frequency of conversations to match the elderly person's lifestyle rhythm. The conversation unit, for example, analyzes the elderly person's lifestyle rhythm and adjusts the frequency of conversations. For example, the conversation unit can monitor the elderly person's lifestyle rhythm, such as wake-up time, bedtime, and meal times, and adjust the frequency of conversations. For example, if the elderly person is a morning person, the frequency of conversations can be increased in the morning, and if the elderly person is a night owl, the frequency of conversations can be increased in the evening. Furthermore, if the elderly person has a habit of taking afternoon naps, the frequency of conversations before and after the nap can be adjusted. This allows for more effective conversations by adjusting the frequency of conversations to match the elderly person's lifestyle rhythm. Some or all of the above-mentioned processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can input lifestyle rhythm data into the generation AI and cause the generation AI to adjust the frequency of conversations.

[0099] The conversation unit can personalize the content of the conversation based on the elderly person's past conversation history. The conversation unit, for example, refers to the elderly person's past conversation history to personalize the content of the conversation. For example, the conversation unit can revisit topics about hobbies and interests that the elderly person has talked about in the past. For example, the conversation unit can revisit topics about gardening that the elderly person has talked about in the past, or provide topics about news or events that the elderly person has been interested in in the past. The conversation unit can also revisit topics about family and friends that the elderly person has talked about in the past. This makes the conversation more friendly by personalizing the conversation based on the elderly person's past conversation history. Some or all of the above-mentioned processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can input past conversation history data into a generation AI and have the generation AI personalize the conversation.

[0100] The conversation unit can estimate the elderly person's emotions and adjust the tone of the conversation based on the estimated emotions. The conversation unit estimates the elderly person's emotions using, for example, voice analysis or facial expression analysis. For example, the conversation unit can analyze the tone and speed of the elderly person's voice and changes in facial expressions to estimate the emotions. The conversation unit can also adjust the tone of the conversation based on the estimated emotions. For example, if the elderly person is sad, the conversation unit can speak to them in a gentle tone, and if the elderly person is excited, the conversation unit can speak to them in a calm tone. Furthermore, if the elderly person feels lonely, the conversation unit can speak to them in a friendly tone. This allows for more appropriate conversation by adjusting the tone of the conversation based on the elderly person's emotions. Some or all of the above-mentioned processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can input emotion data to a generation AI and have the generation AI adjust the tone of the conversation.

[0101] The conversation unit can provide new topics based on the elderly person's hobbies and interests. The conversation unit, for example, analyzes the elderly person's hobbies and interests and provides new topics. For example, if the elderly person is interested in gardening, the conversation unit can provide topics about seasonal flowers and plants. If the elderly person is interested in cooking, the conversation unit can provide topics about new recipes and cooking tips. Furthermore, if the elderly person is interested in traveling, the conversation unit can provide topics about recommended travel spots and experiences. By providing new topics based on the elderly person's hobbies and interests, more interesting conversations are possible. Some or all of the above-mentioned processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can input hobby and interest data into a generation AI and have the generation AI provide new topics.

[0102] The conversation unit can refer to the content of past conversations between the elderly person and family and friends to progress the conversation. For example, the conversation unit can refer to the content of past conversations between the elderly person and family and friends to progress the conversation. For example, the conversation unit can revisit family anecdotes that the elderly person has talked about in the past, or progress the conversation based on the content of the elderly person's conversations with friends. The conversation unit can also progress the conversation based on the content of the elderly person's conversations about recent events with family and friends. In this way, by referring to the content of past conversations between the elderly person and family and friends, a more intimate conversation can be made. Some or all of the above-described processing in the conversation unit may be performed, for example, using AI, or may be performed without using AI. For example, the conversation unit can input past conversation content data into a generation AI and have the generation AI execute the conversation progression.

[0103] The analysis unit can estimate the elderly person's emotions and feed back the analysis results based on the estimated elderly person's emotions. The analysis unit estimates the elderly person's emotions using, for example, voice analysis or facial expression analysis. For example, the analysis unit can analyze the tone and speed of the elderly person's voice and changes in facial expressions to estimate the emotions. The analysis unit can also feed back the analysis results based on the estimated emotions. For example, if the elderly person is sad, the analysis unit can feed back advice to calm the emotions, and if the elderly person is excited, the analysis unit can feed back methods for relaxing. Furthermore, if the elderly person feels lonely, the analysis unit can feed back suggestions for building social connections. This allows for more appropriate advice to be provided by feeding back the analysis results based on the elderly person's emotions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input emotion data to a generation AI and have the generation AI execute feedback of the analysis results.

[0104] The analysis unit can extract keywords related to the health condition from the conversation content. The analysis unit, for example, uses natural language processing technology to analyze the conversation content and extract keywords related to the health condition. For example, the analysis unit can extract the keyword "tired" from the conversation to understand the elderly person's fatigue state. The analysis unit can also extract the keyword "pain" from the conversation to understand the elderly person's pain state. Furthermore, the analysis unit can extract the keyword "can't sleep" from the conversation to understand the elderly person's sleep state. In this way, by extracting keywords related to the health condition from the conversation content, the elderly person's health condition can be more accurately understood. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the conversation data to a generation AI and cause the generation AI to extract keywords related to the health condition.

[0105] The analysis unit can analyze the flow of conversation in real time and generate an appropriate response. The analysis unit analyzes the flow of conversation in real time using, for example, natural language processing technology. For example, when a question is asked during a conversation, the analysis unit can generate an appropriate response in real time. Furthermore, when an emotional change occurs during a conversation, the analysis unit can generate a response in real time that corresponds to that change. Furthermore, when a new topic arises during a conversation, the analysis unit can generate a response in real time that corresponds to that topic. This enables more natural conversation by analyzing the flow of conversation in real time. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input conversation data into a generation AI and cause the generation AI to generate an appropriate response.

[0106] The analysis unit can estimate the elderly person's emotions and adjust the accuracy of the analysis based on the estimated elderly person's emotions. The analysis unit estimates the elderly person's emotions using, for example, voice analysis or facial expression analysis. For example, the analysis unit can analyze the tone and speed of the elderly person's voice and changes in facial expressions to estimate the emotions. The analysis unit can also adjust the accuracy of the analysis based on the estimated emotions. For example, if the elderly person is sad, an analysis that is sensitive to emotions can be performed, and if the elderly person is excited, a calm analysis can be performed. Furthermore, if the elderly person is feeling lonely, an analysis that is sensitive to emotions can be performed. This allows for more accurate analysis by adjusting the accuracy of the analysis based on the elderly person's emotions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input emotion data into a generation AI and have the generation AI adjust the accuracy of the analysis.

[0107] The analysis unit can analyze the elderly person's lifestyle patterns and detect abnormalities. The analysis unit analyzes the elderly person's lifestyle patterns using, for example, daily behavioral data and sensor data. For example, the analysis unit can analyze the elderly person's lifestyle patterns, such as wake-up time, bedtime, meal times, and activity times, and detect abnormalities. For example, an abnormality can be detected if the elderly person does not wake up at their usual wake-up time or does not eat at their usual meal times. An abnormality can also be detected if the elderly person does not engage in activities at their usual activity times. By analyzing the elderly person's lifestyle patterns, abnormalities can be detected early. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input lifestyle pattern data to a generation AI and cause the generation AI to detect abnormalities.

[0108] The analysis unit can complement the analysis results by referring to the elderly person's past health data. The analysis unit can refer to the elderly person's past health data using, for example, data from an electronic medical record or a health management app. For example, the analysis unit can analyze the current heart rate by referring to past heart rate data. It can also analyze the current body temperature by referring to past body temperature data. It can also analyze the current movement by referring to past movement data. As a result, by referring to the elderly person's past health data, more accurate analysis results can be obtained. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past health data into the generation AI and have the generation AI complement the analysis results.

[0109] The monitoring unit can estimate the elderly person's emotions and adjust the monitoring frequency based on the estimated elderly person's emotions. The monitoring unit estimates the elderly person's emotions using, for example, voice analysis or facial expression analysis. For example, the monitoring unit can analyze the elderly person's tone of voice, speed, and changes in facial expressions to estimate emotions. The monitoring unit can also adjust the monitoring frequency based on the estimated emotions. For example, if the elderly person is sad, the monitoring frequency can be increased, and if the elderly person is excited, the monitoring frequency can be decreased. Furthermore, if the elderly person feels lonely, the monitoring frequency can be increased. This enables more appropriate monitoring by adjusting the monitoring frequency based on the elderly person's emotions. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input emotion data to a generation AI and cause the generation AI to adjust the monitoring frequency.

[0110] The monitoring unit can monitor the sleep patterns of the elderly person. The monitoring unit monitors the sleep patterns of the elderly person, for example, by analyzing sleep stages and recording sleep duration. For example, the monitoring unit can monitor whether the elderly person is sleeping at a normal sleep time. It can also monitor how often the elderly person wakes up during the night. It can also monitor the quality of the elderly person's sleep. In this way, by monitoring the elderly person's sleep patterns, the quality of sleep can be understood. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input sleep data to a generation AI and cause the generation AI to monitor the sleep patterns.

[0111] The monitoring unit can monitor the dietary content of the elderly person. The monitoring unit monitors the dietary content of the elderly person, for example, by using photo analysis of meals and nutrient records. For example, the monitoring unit can monitor whether the elderly person is eating a balanced diet. It can also monitor the frequency at which the elderly person eats meals. It can also monitor the amount of food the elderly person eats. In this way, by monitoring the dietary content of the elderly person, it is possible to understand the nutritional status. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the monitoring unit can input dietary data into the generation AI and cause the generation AI to monitor the dietary content.

[0112] The monitoring unit can estimate the elderly person's emotions and select monitoring items based on the estimated elderly person's emotions. The monitoring unit can estimate the elderly person's emotions using, for example, voice analysis or facial expression analysis. For example, the monitoring unit can analyze the elderly person's tone and speed of voice and changes in facial expressions to estimate emotions. The monitoring unit can also select monitoring items based on the estimated emotions. For example, if the elderly person is sad, the monitoring unit can focus on monitoring their heart rate and body temperature, and if the elderly person is excited, the monitoring unit can focus on monitoring their movements and activity level. Furthermore, if the elderly person feels lonely, the monitoring unit can focus on monitoring the frequency and content of conversations. This enables more appropriate monitoring by selecting monitoring items based on the elderly person's emotions. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input emotion data into a generation AI and have the generation AI select monitoring items.

[0113] The monitoring unit can monitor the amount of exercise of the elderly person. The monitoring unit monitors the amount of exercise of the elderly person using, for example, a pedometer or an acceleration sensor. For example, the monitoring unit can monitor how much the elderly person walks daily. It can also monitor how often the elderly person exercises. It can also monitor the amount of time the elderly person exercises. In this way, by monitoring the amount of exercise of the elderly person, it is possible to understand their exercise habits. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input exercise data into the generation AI and cause the generation AI to monitor the amount of exercise.

[0114] The monitoring unit can monitor the elderly person's medication status. The monitoring unit monitors the elderly person's medication status using, for example, a medication record app or a medicine box with a sensor. For example, the monitoring unit can monitor whether the elderly person takes their medication at the correct time. It can also monitor whether the elderly person takes the correct amount of medication. It can also monitor whether the elderly person remembers to take their medication. In this way, monitoring the elderly person's medication status enables appropriate medication management. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input medication data into the generation AI and cause the generation AI to monitor the medication status.

[0115] The notification unit can estimate the elderly person's emotions and adjust the urgency of the notification based on the estimated elderly person's emotions. The notification unit estimates the elderly person's emotions using, for example, voice analysis or facial expression analysis. For example, the notification unit can analyze the tone and speed of the elderly person's voice and changes in facial expressions to estimate the emotions. The notification unit can also adjust the urgency of the notification based on the estimated emotions. For example, if the elderly person is sad, the notification can be made with a higher urgency, and if the elderly person is excited, the notification can be made with a lower urgency. Furthermore, if the elderly person feels lonely, the notification can be made with a higher urgency. This enables a more appropriate emergency response by adjusting the urgency of the notification based on the elderly person's emotions. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input emotion data into a generation AI and have the generation AI adjust the urgency of the notification.

[0116] The notification unit can automatically notify the elderly person's family and medical institutions. The notification unit automatically notifies the elderly person's family and medical institutions, for example, using an automatic emergency notification system. For example, the notification unit can automatically notify the family if the elderly person falls. Also, it can automatically notify a medical institution if the elderly person's physical condition suddenly changes. Furthermore, it can automatically notify the family if the elderly person does not move for a long period of time. This enables a rapid response by automatically notifying the elderly person's family and medical institutions. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input emergency data into the generation AI and have the generation AI execute an automatic notification.

[0117] The notification unit can customize the content of the notification based on the elderly person's past health data. The notification unit, for example, refers to the elderly person's past health data using data from an electronic medical record or a health management app, and customizes the content of the notification. For example, the notification unit can customize the content of the notification based on past heart rate data. The notification unit can also customize the content of the notification based on past body temperature data. Furthermore, the notification unit can customize the content of the notification based on past movement data. As a result, by customizing the content of the notification based on the elderly person's past health data, more appropriate information can be provided. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input past health data into a generation AI and have the generation AI customize the content of the notification.

[0118] The reporting unit can estimate the elderly person's emotions and select a reporting method based on the estimated elderly person's emotions. The reporting unit estimates the elderly person's emotions using, for example, voice analysis or facial expression analysis. For example, the reporting unit can analyze the elderly person's tone and speed of voice and changes in facial expressions to estimate emotions. The reporting unit can also select a reporting method based on the estimated emotions. For example, if the elderly person is sad, the reporting can be made by phone, and if the elderly person is excited, the reporting can be made by email. Furthermore, if the elderly person feels lonely, the reporting can be made by video call. This enables more appropriate reporting by selecting a reporting method based on the elderly person's emotions. Some or all of the above-mentioned processing in the reporting unit may be performed using, for example, AI, or may be performed without using AI. For example, the reporting unit can input emotion data into a generation AI and have the generation AI select a reporting method.

[0119] The reporting unit can periodically report the elderly person's health condition. The reporting unit periodically reports health data such as heart rate, body temperature, and amount of exercise to family members and medical institutions. For example, the reporting unit can periodically report the elderly person's heart rate to family members. It can also periodically report the elderly person's body temperature to medical institutions. It can also periodically report the elderly person's amount of exercise to family members. By periodically reporting the elderly person's health condition, family members and medical institutions can understand the elderly person's health condition. Some or all of the above-mentioned processing in the reporting unit may be performed, for example, using AI, or may be performed without using AI. For example, the reporting unit can input health data into a generation AI and have the generation AI execute periodic reports.

[0120] The voice recognition unit can estimate the elderly person's emotions and adjust the accuracy of voice recognition based on the estimated elderly person's emotions. The voice recognition unit estimates the elderly person's emotions using, for example, voice analysis. For example, the voice recognition unit can analyze the tone and speed of the elderly person's voice to estimate the emotions. The voice recognition unit can also adjust the accuracy of voice recognition based on the estimated emotions. For example, if the elderly person is sad, emotion-sensitive voice recognition can be performed, and if the elderly person is excited, calm voice recognition can be performed. Furthermore, if the elderly person is feeling lonely, emotion-sensitive voice recognition can be performed. This enables more accurate voice recognition by adjusting the accuracy of voice recognition based on the elderly person's emotions. Some or all of the above-described processing in the voice recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice recognition unit can input emotion data to a generation AI and cause the generation AI to adjust the accuracy of voice recognition.

[0121] The speech recognition unit can recognize the dialect and accent of elderly people. The speech recognition unit recognizes the dialect and accent of elderly people, for example, by extracting features from speech data or using a dialect dictionary. For example, the speech recognition unit can recognize dialects such as Kansai dialect, Tohoku dialect, and Okinawa dialect. The speech recognition unit can also analyze the accent of elderly people and conduct natural conversations. This enables more natural conversations by recognizing the dialect and accent of elderly people. Some or all of the above-described processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input dialect data to a generation AI and have the generation AI recognize the dialect and accent.

[0122] The voice recognition unit can analyze the tone and speed of the elderly person's voice. The voice recognition unit analyzes the tone and speed of the elderly person's voice, for example, by extracting acoustic features or using a voice analysis algorithm. For example, the voice recognition unit can estimate emotions when the elderly person's voice has a low tone or a slow speed. The voice recognition unit can also analyze changes in the tone and speed of the elderly person's voice to grasp changes in emotions. In this way, changes in emotions can be grasped by analyzing the tone and speed of the elderly person's voice. Some or all of the above-mentioned processing in the voice recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice recognition unit can input voice data to a generation AI and have the generation AI analyze the tone and speed of the voice.

[0123] The voice recognition unit can estimate the emotion of the elderly person and provide voice recognition feedback based on the estimated emotion of the elderly person. The voice recognition unit estimates the emotion of the elderly person using, for example, voice analysis. For example, the voice recognition unit can analyze the tone and speed of the elderly person's voice to estimate the emotion. The voice recognition unit can also provide voice recognition feedback based on the estimated emotion. For example, if the elderly person is sad, gentle feedback can be provided, and if the elderly person is excited, calm feedback can be provided. Furthermore, if the elderly person feels lonely, friendly feedback can be provided. This enables more appropriate voice recognition feedback by providing voice recognition feedback based on the emotion of the elderly person. Some or all of the above-described processing in the voice recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice recognition unit can input emotion data to a generation AI and cause the generation AI to perform voice recognition feedback.

[0124] The voice recognition unit can filter the elderly person's environmental sounds. The voice recognition unit filters the elderly person's environmental sounds using, for example, noise canceling technology or an acoustic filtering algorithm. For example, the voice recognition unit can filter the television sounds when the elderly person is watching television. Also, the voice recognition unit can filter the ambient noise when the elderly person is out. Furthermore, the voice recognition unit can filter the housework sounds when the elderly person is doing housework. In this way, filtering the elderly person's environmental sounds enables more accurate voice recognition. Some or all of the above-described processing in the voice recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice recognition unit can input environmental sound data to a generation AI and have the generation AI perform filtering of the environmental sounds.

[0125] The voice recognition unit can monitor changes in the voice of the elderly person. The voice recognition unit monitors changes in the voice of the elderly person, for example, by analyzing changes in acoustic features or voice data. For example, the voice recognition unit can monitor changes when the elderly person's voice becomes hoarse or trembling. It can also monitor changes when the elderly person's voice suddenly becomes louder. In this way, by monitoring changes in the elderly person's voice, it is possible to grasp changes in their health condition. Some or all of the above-mentioned processing in the voice recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice recognition unit can input voice data to a generation AI and have the generation AI monitor changes in the voice.

[0126] The reply generation unit can estimate the elderly person's emotions and adjust the tone of the reply based on the estimated elderly person's emotions. The reply generation unit estimates the elderly person's emotions using, for example, voice analysis or facial expression analysis. For example, the reply generation unit can analyze the tone and speed of the elderly person's voice and changes in facial expressions to estimate the emotions. The reply generation unit can also adjust the tone of the reply based on the estimated emotions. For example, if the elderly person is sad, the reply can be made in a gentle tone, and if the elderly person is excited, the reply can be made in a calm tone. Furthermore, if the elderly person is feeling lonely, the reply can be made in a friendly tone. Thus, by adjusting the tone of the reply based on the elderly person's emotions, a more appropriate reply can be made. Some or all of the above-described processing in the reply generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reply generation unit can input emotion data into the generation AI and have the generation AI adjust the tone of the reply.

[0127] The reply generation unit can generate a reply by referring to the elderly person's past conversation history. The reply generation unit generates a reply by referring to, for example, the elderly person's past conversation history. For example, the reply generation unit can generate a reply about hobbies that the elderly person has talked about in the past. It can also generate a reply about news or events that the elderly person has been interested in in the past. It can also generate a reply about family and friends that the elderly person has talked about in the past. In this way, by referring to the elderly person's past conversation history, it is possible to provide a more friendly reply. Some or all of the above-mentioned processing in the reply generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reply generation unit can input past conversation history data into a generation AI and have the generation AI generate a reply.

[0128] The reply generation unit can generate appropriate replies to questions about the elderly person's health condition. The reply generation unit generates appropriate replies to questions about the elderly person's health condition, for example. For example, the reply generation unit can generate appropriate replies when the elderly person asks about their physical condition. It can also generate appropriate replies when the elderly person asks about taking medicine. It can also generate appropriate replies when the elderly person asks about exercise. As a result, generating appropriate replies to questions about the elderly person's health condition makes health management easier. Some or all of the above-mentioned processing in the reply generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reply generation unit can input health condition data into the generation AI and cause the generation AI to generate appropriate replies.

[0129] The reply generation unit can estimate the elderly person's emotions and adjust the content of the reply based on the estimated elderly person's emotions. The reply generation unit estimates the elderly person's emotions using, for example, voice analysis or facial expression analysis. For example, the reply generation unit can analyze the elderly person's tone of voice, speed, and changes in facial expression to estimate the emotions. The reply generation unit can also adjust the content of the reply based on the estimated emotions. For example, if the elderly person is sad, a reply that is considerate of the elderly person's emotions can be provided, and if the elderly person is excited, a calm reply can be provided. Furthermore, if the elderly person is feeling lonely, a friendly reply can be provided. This allows for a more appropriate reply by adjusting the content of the reply based on the elderly person's emotions. Some or all of the above-described processing in the reply generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reply generation unit can input emotion data into a generation AI and have the generation AI adjust the content of the reply.

[0130] The reply generation unit can generate a reply based on the hobbies and interests of the elderly. The reply generation unit, for example, analyzes the hobbies and interests of the elderly and generates a reply. For example, if the elderly is interested in gardening, the reply generation unit can generate a reply related to gardening. Also, if the elderly is interested in cooking, the reply generation unit can generate a reply related to cooking. Furthermore, if the elderly is interested in traveling, the reply generation unit can generate a reply related to traveling. By generating a reply based on the elderly's hobbies and interests, more interesting conversations can be made. Some or all of the above-mentioned processing in the reply generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reply generation unit can input hobby and interest data into a generation AI and have the generation AI generate a reply.

[0131] The reply generation unit can generate a reply by referring to the content of past conversations between the elderly person and family and friends. The reply generation unit generates a reply by referring to, for example, the content of past conversations between the elderly person and family and friends. For example, the reply generation unit can generate a reply about a family episode that the elderly person has talked about in the past. The reply generation unit can also generate a reply based on the content of conversations the elderly person has had with friends about memories. Furthermore, the reply generation unit can generate a reply based on the content of conversations the elderly person has had with family and friends about recent events. In this way, by referring to the content of past conversations between the elderly person and family and friends, a more friendly reply can be generated. Some or all of the above-mentioned processing in the reply generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reply generation unit can input past conversation content data into a generation AI and have the generation AI generate a reply.

[0132] The heart rate sensor can estimate the elderly person's emotions and adjust the heart rate monitoring frequency based on the estimated emotions. The heart rate sensor estimates the elderly person's emotions using, for example, voice analysis or facial expression analysis. For example, the heart rate sensor can analyze the tone and rate of the elderly person's voice and changes in facial expressions to estimate emotions. The heart rate sensor can also adjust the heart rate monitoring frequency based on the estimated emotions. For example, if the elderly person is sad, the heart rate monitoring frequency can be increased, and if the elderly person is excited, the heart rate monitoring frequency can be decreased. Furthermore, if the elderly person feels lonely, the heart rate monitoring frequency can be increased. This allows for more appropriate monitoring by adjusting the heart rate monitoring frequency based on the elderly person's emotions. Some or all of the above-described processing in the heart rate sensor may be performed using, for example, AI, or may be performed without AI. For example, the heart rate sensor can input emotion data to a generation AI and cause the generation AI to adjust the heart rate monitoring frequency.

[0133] The heart rate sensor can compare the elderly person's heart rate during exercise and at rest. The heart rate sensor can compare the elderly person's heart rate during exercise and at rest using, for example, an exercise stress test or heart rate variability analysis. For example, the heart rate sensor can monitor the elderly person's heart rate after exercise. It can also monitor the elderly person's heart rate while at rest. Furthermore, the heart rate sensor can compare the changes in the elderly person's heart rate during exercise and at rest. This allows for a more accurate understanding of the elderly person's health condition by comparing the elderly person's heart rate during exercise and at rest. Some or all of the above-described processing in the heart rate sensor may be performed, for example, using AI or without AI. For example, the heart rate sensor can input heart rate data to a generation AI and have the generation AI compare the heart rate during exercise and at rest.

[0134] The heart rate sensor can issue an alert if it detects an abnormal heart rate. The heart rate sensor detects an abnormal heart rate, for example, by using a heart rate threshold setting or an abnormality detection algorithm. For example, the heart rate sensor can issue an alert if an elderly person's heart rate suddenly increases or decreases. It can also issue an alert if the elderly person's heart rate exceeds the normal range. This allows for a prompt response by issuing an alert when an abnormal heart rate is detected. Some or all of the above-described processing in the heart rate sensor may be performed using, for example, AI, or may be performed without using AI. For example, the heart rate sensor can input heart rate data to a generation AI and have the generation AI detect an abnormal heart rate and issue an alert.

[0135] The heart rate sensor can estimate the elderly person's emotions and analyze heart rate data based on the estimated emotions. The heart rate sensor estimates the elderly person's emotions using, for example, voice analysis or facial expression analysis. For example, the heart rate sensor can estimate emotions by analyzing the tone and speed of the elderly person's voice and changes in facial expressions. The heart rate sensor can also analyze heart rate data based on the estimated emotions. For example, if the elderly person is sad, the heart rate data can be analyzed in a way that is empathetic to the elderly person's emotions, and if the elderly person is excited, the heart rate data can be analyzed calmly. Furthermore, if the elderly person is feeling lonely, the heart rate data can be analyzed in a way that is empathetic to the elderly person's emotions. This allows for a more accurate understanding of the elderly person's health condition by analyzing heart rate data based on the elderly person's emotions. Some or all of the above-described processing in the heart rate sensor may be performed using, for example, AI, or may be performed without AI. For example, the heart rate sensor can input emotion data into a generation AI and have the generation AI analyze the heart rate data.

[0136] The heart rate sensor can monitor the heart rate of an elderly person while they are sleeping. The heart rate sensor monitors the heart rate of an elderly person while they are sleeping, for example, by analyzing sleep stages or heart rate variability. For example, the heart rate sensor can monitor whether the elderly person is sleeping at a normal sleep time. It can also monitor how often the elderly person wakes up during the night. It can also monitor the quality of the elderly person's sleep. Thus, by monitoring the elderly person's heart rate while they are sleeping, the quality of their sleep can be understood. Some or all of the above-mentioned processing in the heart rate sensor may be performed, for example, using AI or without AI. For example, the heart rate sensor can input sleep data to a generation AI and cause the generation AI to monitor the heart rate while they are sleeping.

[0137] The heart rate sensor can monitor the stress level of an elderly person. The heart rate sensor monitors the stress level of an elderly person using, for example, heart rate variability analysis or a stress assessment algorithm. For example, the heart rate sensor can monitor changes in the heart rate when the elderly person is feeling stressed. It can also monitor changes in the heart rate when the elderly person is relaxed. Furthermore, the heart rate sensor can analyze the stress level of an elderly person from the heart rate data. This makes it possible to manage stress by monitoring the stress level of an elderly person. Some or all of the above-mentioned processing in the heart rate sensor may be performed using, for example, AI, or may be performed without using AI. For example, the heart rate sensor can input heart rate data to a generation AI and cause the generation AI to monitor the stress level.

[0138] The body temperature sensor can estimate the elderly person's emotions and adjust the temperature monitoring frequency based on the estimated emotions. The body temperature sensor estimates the elderly person's emotions using, for example, voice analysis or facial expression analysis. For example, the body temperature sensor can analyze the tone and speed of the elderly person's voice and changes in facial expressions to estimate emotions. The body temperature sensor can also adjust the temperature monitoring frequency based on the estimated emotions. For example, if the elderly person is sad, the body temperature monitoring frequency can be increased, and if the elderly person is excited, the body temperature monitoring frequency can be decreased. Furthermore, if the elderly person feels lonely, the body temperature monitoring frequency can be increased. This allows for more appropriate monitoring by adjusting the body temperature monitoring frequency based on the elderly person's emotions. Some or all of the above-described processing in the body temperature sensor may be performed using, for example, AI, or may be performed without AI. For example, the body temperature sensor can input emotion data into a generation AI and have the generation AI adjust the body temperature monitoring frequency.

[0139] The body temperature sensor can monitor the fluctuations in the elderly person's body temperature in real time. The body temperature sensor monitors the fluctuations in the elderly person's body temperature in real time using, for example, time series analysis or body temperature fluctuation pattern analysis. For example, the body temperature sensor can monitor if the elderly person's body temperature suddenly rises or falls. It can also monitor if the elderly person's body temperature exceeds the normal range. By monitoring the fluctuations in the elderly person's body temperature in real time, it is possible to more accurately grasp their health condition. Some or all of the above-mentioned processing in the body temperature sensor may be performed using, for example, AI, or may be performed without using AI. For example, the body temperature sensor can input body temperature data into a generation AI and have the generation AI monitor the fluctuations in body temperature.

[0140] The body temperature sensor can issue an alert if it detects an abnormal body temperature. The body temperature sensor detects abnormal body temperature using, for example, a body temperature threshold setting or an abnormality detection algorithm. For example, the body temperature sensor can issue an alert if an elderly person's body temperature suddenly rises or falls. It can also issue an alert if the elderly person's body temperature exceeds the normal range. This allows for a rapid response by issuing an alert when an abnormal body temperature is detected. Some or all of the above-mentioned processing in the body temperature sensor may be performed using, for example, AI, or may be performed without using AI. For example, the body temperature sensor can input body temperature data into a generation AI, which can then detect an abnormal body temperature and issue an alert.

[0141] The body temperature sensor can estimate the elderly person's emotions and analyze body temperature data based on the estimated emotions. The body temperature sensor estimates the elderly person's emotions using, for example, voice analysis or facial expression analysis. For example, the body temperature sensor can estimate emotions by analyzing the tone and speed of the elderly person's voice and changes in facial expressions. The body temperature sensor can also analyze body temperature data based on the estimated emotions. For example, if the elderly person is sad, the body temperature data can be analyzed in a way that is empathetic to the elderly person's emotions, and if the elderly person is excited, the body temperature data can be analyzed calmly. Furthermore, if the elderly person is feeling lonely, the body temperature data can be analyzed in a way that is empathetic to the elderly person's emotions. This allows for a more accurate understanding of the elderly person's health condition by analyzing the body temperature data based on their emotions. Some or all of the above-described processing in the body temperature sensor may be performed using, for example, AI, or may be performed without AI. For example, the body temperature sensor can input emotion data into a generation AI and have the generation AI analyze the body temperature data.

[0142] The body temperature sensor can monitor the body temperature of an elderly person while bathing. The body temperature sensor monitors the body temperature of an elderly person while bathing, for example, by analyzing body temperature fluctuations during bathing or by using a body temperature sensor installation method. For example, the body temperature sensor can monitor the body temperature of an elderly person while bathing. It can also monitor the body temperature of an elderly person after bathing. It can also monitor changes in body temperature while bathing. Thus, by monitoring the body temperature of an elderly person while bathing, it is possible to more accurately grasp their health condition. Some or all of the above-mentioned processing in the body temperature sensor may be performed, for example, using AI, or may be performed without using AI. For example, the body temperature sensor can input body temperature data into a generation AI and have the generation AI monitor the body temperature while bathing.

[0143] The body temperature sensor can monitor the body temperature of an elderly person when they go out. The body temperature sensor monitors the body temperature of an elderly person when they go out, for example, by analyzing body temperature fluctuations while they are out or by using a body temperature sensor installation method. For example, the body temperature sensor can monitor the body temperature of an elderly person while they are out. It can also monitor the body temperature of an elderly person after they go out. It can also monitor changes in the body temperature of an elderly person when they are out. Thus, by monitoring the body temperature of an elderly person when they are out, it is possible to more accurately grasp their health condition. Some or all of the above-mentioned processing in the body temperature sensor may be performed, for example, using AI, or may be performed without using AI. For example, the body temperature sensor can input body temperature data into a generation AI and have the generation AI monitor the body temperature when they are out.

[0144] The motion sensor can estimate the elderly person's emotions and adjust the frequency of motion monitoring based on the estimated emotions. The motion sensor estimates the elderly person's emotions using, for example, voice analysis or facial expression analysis. For example, the motion sensor can analyze the tone and speed of the elderly person's voice and changes in facial expressions to estimate emotions. The motion sensor can also adjust the frequency of motion monitoring based on the estimated emotions. For example, if the elderly person is sad, the frequency of motion monitoring can be increased, and if the elderly person is excited, the frequency of motion monitoring can be decreased. Furthermore, if the elderly person feels lonely, the frequency of motion monitoring can be increased. This allows for more appropriate monitoring by adjusting the frequency of motion monitoring based on the elderly person's emotions. Some or all of the above-described processing in the motion sensor may be performed using, for example, AI, or may be performed without AI. For example, the motion sensor can input emotion data to a generation AI and cause the generation AI to adjust the frequency of motion monitoring.

[0145] The motion sensor can monitor the walking pattern of an elderly person. The motion sensor analyzes walking patterns such as walking speed, stride length, and walking rhythm to monitor the walking pattern of the elderly person. For example, the motion sensor can monitor whether the elderly person is walking in a normal walking pattern. It can also monitor whether the elderly person is at risk of falling while walking. Furthermore, it can monitor changes in the elderly person's walking speed and stride length. In this way, by monitoring the elderly person's walking pattern, the risk of falling can be identified. Some or all of the above-mentioned processing in the motion sensor may be performed using, for example, AI, or may be performed without using AI. For example, the motion sensor can input walking data to a generation AI and cause the generation AI to monitor the walking pattern.

[0146] The motion sensor can issue an alert when it detects a fall by an elderly person. The motion sensor detects a fall by an elderly person using, for example, an acceleration sensor or a fall detection algorithm. For example, the motion sensor can issue an alert when the elderly person falls. It can also issue an alert when the elderly person is at high risk of falling. It can also issue an alert when the elderly person shows signs of falling. This enables a prompt response by issuing an alert when it detects a fall by an elderly person. Some or all of the above-described processing in the motion sensor may be performed, for example, using AI or without AI. For example, the motion sensor can input fall data to a generation AI and cause the generation AI to detect a fall and issue an alert.

[0147] The motion sensor can estimate the elderly person's emotions and analyze the motion data based on the estimated emotions. The motion sensor estimates the elderly person's emotions using, for example, voice analysis or facial expression analysis. For example, the motion sensor can estimate emotions by analyzing the tone and speed of the elderly person's voice and changes in facial expressions. The motion sensor can also analyze the motion data based on the estimated emotions. For example, if the elderly person is sad, the motion data can be analyzed in a way that is empathetic to the elderly person's emotions, and if the elderly person is excited, the motion data can be analyzed calmly. Furthermore, if the elderly person feels lonely, the motion data can be analyzed in a way that is empathetic to the elderly person's emotions. This allows for a more accurate understanding of the elderly person's health condition by analyzing the motion data based on the elderly person's emotions. Some or all of the above-described processing in the motion sensor may be performed using, for example, AI, or may be performed without AI. For example, the motion sensor can input emotion data into a generation AI and have the generation AI analyze the motion data.

[0148] The motion sensor can monitor the amount of exercise of an elderly person. The motion sensor monitors the amount of exercise of an elderly person using, for example, a pedometer or an acceleration sensor. For example, the motion sensor can monitor how much an elderly person walks daily. It can also monitor how often the elderly person exercises. It can also monitor the amount of time the elderly person exercises. In this way, by monitoring the amount of exercise of an elderly person, it is possible to understand their exercise habits. Some or all of the above-mentioned processing in the motion sensor may be performed using, for example, AI, or may be performed without using AI. For example, the motion sensor can input exercise data to a generation AI and have the generation AI monitor the amount of exercise.

[0149] The motion sensor can monitor the movements of the elderly when they wake up and when they go to bed. The motion sensor can monitor the movements of the elderly when they wake up and when they go to bed, for example, using a motion sensor or motion pattern analysis. For example, the motion sensor can monitor whether the elderly wakes up at their usual wake-up time. It can also monitor whether the elderly goes to bed at their usual bedtime. It can also monitor changes in the movements of the elderly when they wake up and when they go to bed. In this way, by monitoring the movements of the elderly when they wake up and when they go to bed, it is possible to understand their daily rhythm. Some or all of the above-mentioned processing in the motion sensor may be performed, for example, using AI or may be performed without using AI. For example, the motion sensor can input movement data to a generation AI and have the generation AI monitor the movements of the elderly when they wake up and when they go to bed.

[0150] The location information transmission unit can estimate the elderly person's emotions and adjust the frequency of transmitting location information based on the estimated elderly person's emotions. The location information transmission unit can estimate the elderly person's emotions using, for example, voice analysis or facial expression analysis. For example, the location information transmission unit can analyze the elderly person's tone of voice, speed, and changes in facial expressions to estimate emotions. The location information transmission unit can also adjust the frequency of transmitting location information based on the estimated emotions. For example, if the elderly person is sad, the frequency of transmitting location information can be increased, and if the elderly person is excited, the frequency of transmitting location information can be decreased. Furthermore, if the elderly person feels lonely, the frequency of transmitting location information can be increased. This enables more appropriate monitoring by adjusting the frequency of transmitting location information based on the elderly person's emotions. Some or all of the above-described processing in the location information transmission unit can be performed using, for example, AI, or without AI. For example, the location information transmission unit can input emotion data to a generation AI and cause the generation AI to adjust the frequency of transmitting location information.

[0151] The location information transmission unit can analyze the elderly person's movement pattern. The location information transmission unit analyzes movement patterns such as movement speed, movement route, and movement frequency. For example, the location information transmission unit can analyze whether the elderly person is moving in a normal movement pattern. It can also analyze whether the elderly person is exhibiting an abnormal pattern while moving. Furthermore, it can analyze changes in the elderly person's movement speed and movement distance. By analyzing the elderly person's movement pattern, abnormal behavior can be detected early. Some or all of the above-mentioned processing in the location information transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information transmission unit can input movement data to a generation AI and have the generation AI analyze the movement pattern.

[0152] The location information transmission unit can issue an alert when an abnormal movement pattern is detected. The location information transmission unit detects an abnormal movement pattern using, for example, an algorithm that detects abnormalities in the movement route or the movement speed. For example, the location information transmission unit can issue an alert when an elderly person deviates from their normal movement pattern. The location information transmission unit can also issue an alert when the elderly person exhibits abnormal behavior while moving. Furthermore, the location information transmission unit can also issue an alert when the elderly person is at risk of falling while moving. This enables a prompt response by issuing an alert when an abnormal movement pattern is detected. Some or all of the above-described processing in the location information transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information transmission unit can input movement data to a generation AI and cause the generation AI to detect an abnormal movement pattern and issue an alert.

[0153] The location information transmission unit can estimate the elderly person's emotions and select a method for transmitting the location information based on the estimated elderly person's emotions. The location information transmission unit can estimate the elderly person's emotions using, for example, voice analysis or facial expression analysis. For example, the location information transmission unit can analyze the elderly person's tone of voice, speed, and changes in facial expressions to estimate the emotions. The location information transmission unit can also select a method for transmitting the location information based on the estimated emotions. For example, if the elderly person is sad, the location information can be transmitted by telephone, and if the elderly person is excited, the location information can be transmitted by email. Furthermore, if the elderly person is feeling lonely, the location information can be transmitted by video call. This enables more appropriate information provision by selecting a method for transmitting the location information based on the elderly person's emotions. Some or all of the above-described processing in the location information transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information transmission unit can input emotion data to a generation AI and have the generation AI select a method for transmitting the location information.

[0154] The location information transmission unit can transmit location information of the elderly person when they go out in real time. The location information transmission unit transmits location information of the elderly person when they go out, for example, using real-time transmission or periodic transmission. For example, the location information transmission unit can transmit location information in real time when the elderly person is out. It can also transmit location information in real time when the elderly person is moving. It can also transmit location information in real time when the elderly person is moving within their home. This enables a quick response by transmitting location information of the elderly person when they go out in real time. Some or all of the above-mentioned processing in the location information transmission unit may be performed, for example, using AI or without using AI. For example, the location information transmission unit can input location information data to a generation AI and cause the generation AI to perform real-time transmission.

[0155] The location information transmission unit can monitor the location information of the elderly person within their home. The location information transmission unit monitors the location information of the elderly person within their home, for example, using an indoor location information system or a sensor installation method. For example, the location information transmission unit can monitor the location information when the elderly person is moving within their home. It can also monitor the location information when the elderly person does not move within their home for a long period of time. It can also monitor the location information when the elderly person exhibits abnormal behavior within their home. Thus, by monitoring the location information of the elderly person within their home, abnormal behavior can be detected early. Some or all of the above-described processing in the location information transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information transmission unit can input location information data to a generation AI and cause the generation AI to monitor the location information within their home.

[0156] The health status transmission unit can estimate the elderly person's emotions and adjust the frequency of health status transmissions based on the estimated elderly person's emotions. The health status transmission unit can estimate the elderly person's emotions using, for example, voice analysis or facial expression analysis. For example, the health status transmission unit can analyze the elderly person's tone of voice, speed, and changes in facial expressions to estimate the emotions. The health status transmission unit can also adjust the frequency of health status transmissions based on the estimated emotions. For example, if the elderly person is sad, the frequency of health status transmissions can be increased, and if the elderly person is excited, the frequency of health status transmissions can be decreased. Furthermore, if the elderly person feels lonely, the frequency of health status transmissions can be increased. This allows for more appropriate information provision by adjusting the frequency of health status transmissions based on the elderly person's emotions. Some or all of the above-described processing in the health status transmission unit can be performed using, for example, AI, or without AI. For example, the health status transmission unit can input emotion data to a generation AI and have the generation AI adjust the frequency of health status transmissions.

[0157] The health condition transmission unit can periodically transmit the elderly person's health data. The health condition transmission unit periodically transmits health data such as heart rate, body temperature, and amount of exercise to family members or medical institutions. For example, the health condition transmission unit can periodically transmit the elderly person's heart rate to family members. It can also periodically transmit the elderly person's body temperature to medical institutions. It can also periodically transmit the elderly person's amount of exercise to family members. In this way, by periodically transmitting the elderly person's health data, family members and medical institutions can understand the elderly person's health condition. Some or all of the above-mentioned processing in the health condition transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the health condition transmission unit can input health data to a generation AI and have the generation AI perform periodic transmission.

[0158] The health condition transmission unit can issue an alert when an abnormal health condition is detected. The health condition transmission unit detects an abnormal health condition, for example, using an algorithm that detects abnormal heart rate or body temperature. For example, the health condition transmission unit can issue an alert when the elderly person's heart rate suddenly rises or falls. It can also issue an alert when the elderly person's body temperature exceeds a normal range. It can also issue an alert when the elderly person's exercise volume exceeds a normal range. This enables a prompt response by issuing an alert when an abnormal health condition is detected. Some or all of the above-mentioned processing in the health condition transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the health condition transmission unit can input health data to a generation AI and cause the generation AI to detect an abnormal health condition and issue an alert.

[0159] The health condition transmission unit can estimate the elderly person's emotions and select a health condition transmission method based on the estimated elderly person's emotions. The health condition transmission unit can estimate the elderly person's emotions using, for example, voice analysis or facial expression analysis. For example, the health condition transmission unit can analyze the elderly person's tone of voice, speed, and changes in facial expressions to estimate emotions. The health condition transmission unit can also select a health condition transmission method based on the estimated emotions. For example, if the elderly person is sad, the health condition can be transmitted by phone, and if the elderly person is excited, the health condition can be transmitted by email. Furthermore, if the elderly person feels lonely, the health condition can be transmitted by video call. This enables more appropriate information provision by selecting a health condition transmission method based on the elderly person's emotions. Some or all of the above-described processing in the health condition transmission unit may be performed using, for example, AI, or may be performed without AI. For example, the health condition transmission unit can input emotion data to a generation AI and have the generation AI select a health condition transmission method.

[0160] The health condition transmission unit can transmit the elderly person's health data to family members and medical institutions. The health condition transmission unit transmits the elderly person's health data to family members and medical institutions, for example, by email or cloud transmission. For example, the health condition transmission unit can transmit the elderly person's heart rate data to family members. It can also transmit the elderly person's body temperature data to medical institutions. It can also transmit the elderly person's exercise amount data to family members. In this way, transmitting the elderly person's health data to family members and medical institutions makes it easier to understand the elderly person's health condition. Some or all of the above-mentioned processing in the health condition transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the health condition transmission unit can input the health data to a generation AI and have the generation AI transmit the data.

[0161] The health condition transmission unit can store the elderly person's health data in the cloud. The health condition transmission unit stores the elderly person's health data in the cloud using, for example, data encryption and access control. For example, the health condition transmission unit can store the elderly person's heart rate data in the cloud. Also, the elderly person's body temperature data can be stored in the cloud. Furthermore, the elderly person's exercise amount data can be stored in the cloud. Storing the elderly person's health data in the cloud thus facilitates data management. Some or all of the above-described processing in the health condition transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the health condition transmission unit can input the health data into a generation AI and have the generation AI store the data in the cloud. === Hard Collateral 1-1 === Each of the multiple elements including the conversation unit, analysis unit, monitoring unit, and notification 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 a voice recognition unit of the smart device 14 and converses with the elderly. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of the conversation and generates an appropriate response. The monitoring unit monitors the health condition of the elderly using a sensor of the smart device 14, for example. The notification unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes an emergency call when an abnormality is detected. === Hard Collateral 1-2 === Each of the multiple elements including the conversation unit, analysis unit, monitoring unit, and notification 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 a voice recognition unit of the smart glasses 214 and converses with the elderly. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of the conversation and generates an appropriate response. The monitoring unit monitors the elderly's health condition using a sensor of the smart glasses 214, for example. The notification unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes an emergency call when an abnormality is detected. === Hard Collateral 1-3 === Each of the multiple elements including the conversation unit, analysis unit, monitoring unit, and notification 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 a voice recognition unit of the headset type terminal 314 and converses with the elderly. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of the conversation and generates an appropriate response. The monitoring unit monitors the health condition of the elderly using a sensor of the headset type terminal 314, for example. The notification unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes an emergency call when an abnormality is detected. === Hard Collateral 1-4 === Each of the multiple elements including the conversation unit, analysis unit, monitoring unit, and reporting 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 a voice recognition unit of the robot 414 and converses with the elderly. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of the conversation and generates an appropriate response. The monitoring unit monitors the health condition of the elderly using a sensor of the robot 414, for example. The reporting unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes an emergency call when an abnormality is detected.

[0162] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0163] The AI-equipped robot system can further include an environmental monitoring unit. The environmental monitoring unit can monitor the elderly person's living environment and provide information to maintain a comfortable environment. For example, a temperature sensor can be used to monitor the indoor temperature and adjust it to an appropriate temperature. A humidity sensor can be used to monitor the indoor humidity and adjust it to an appropriate humidity. Furthermore, an air quality sensor can be used to monitor the indoor air quality and prompt ventilation as necessary. This can maintain a comfortable living environment for the elderly person.

[0164] The AI-equipped robot system can further include a dietary management unit. The dietary management unit can manage the elderly person's dietary content and provide information to provide nutritionally balanced meals. For example, it can analyze photos of meals to calculate nutrients and suggest balanced meals. It can also record food intake and determine whether it is excessive or insufficient. It can also manage meal times and encourage regular eating habits. This makes it possible to manage the elderly person's diet to maintain their health.

[0165] The AI-equipped robot system can further be equipped with an exercise support unit. The exercise support unit can support the elderly in exercising and provide exercise programs to maintain health. For example, it can propose an exercise program based on the elderly's physical strength and health condition and support the implementation of the program. It can also record exercise progress and provide feedback on the degree of achievement. Furthermore, it can monitor to ensure safety during exercise and issue an alert if an abnormality is detected. This makes it possible to provide exercise support to maintain the health of the elderly.

[0166] The AI-equipped robot system can further be equipped with a reminder unit. The reminder unit can provide reminder functions to support the elderly in their daily lives. For example, it can remind them when to take their medicine and encourage them to take it at the appropriate time. It can also remind them of regular health checkups and medical appointments. It can also remind them of daily plans and events and support schedule management. This makes it possible to support the elderly in smoothly carrying out their daily lives.

[0167] The AI-equipped robot system can further include an entertainment provider. The entertainment provider can provide entertainment functions to bring enjoyment to the elderly's lives. For example, it can recommend music, movies, and TV programs and support their viewing. It can also provide intellectual activities such as games and puzzles to stimulate the brain while having fun. It can also suggest activities based on hobbies and interests and encourage participation. This can bring enjoyment to the elderly's lives and maintain their mental health.

[0168] The AI-equipped robot system can estimate the emotions of the elderly and select music based on the estimated emotions. For example, if the elderly is sad, it can select relaxing music, and if the elderly is excited, it can select calming music. Also, if the elderly is feeling lonely, it can select uplifting music. This makes it possible to provide psychological support by providing appropriate music based on the elderly's emotions.

[0169] The AI-equipped robot system can estimate the emotions of the elderly and adjust the brightness of the lighting based on the estimated emotions. For example, if the elderly is sad, it can select warm lighting colors, and if the elderly is excited, it can select calm lighting colors. Also, if the elderly is feeling lonely, it can select bright lighting. This allows for psychological support by providing appropriate lighting based on the elderly's emotions.

[0170] The AI-equipped robot system can estimate the emotions of the elderly and provide scents based on the estimated emotions. For example, if the elderly is sad, a relaxing scent can be provided, and if the elderly is excited, a calming scent can be provided. Also, if the elderly is feeling lonely, an uplifting scent can be provided. This makes it possible to provide psychological support by providing appropriate scents based on the elderly's emotions.

[0171] The AI-equipped robot system can estimate the emotions of the elderly and adjust the content of the conversation based on the estimated emotions. For example, if the elderly is sad, the system can engage in emotionally sympathetic conversation, and if the elderly is excited, the system can engage in calm conversation. Also, if the elderly is feeling lonely, the system can engage in friendly conversation. This makes it possible to provide psychological support by providing appropriate conversation based on the elderly's emotions.

[0172] The AI-equipped robot system can estimate the emotions of the elderly and suggest relaxation activities based on the estimated emotions. For example, if the elderly is sad, it can suggest relaxation activities such as meditation or deep breathing, and if the elderly is excited, it can suggest relaxation activities such as yoga or stretching. Also, if the elderly is feeling lonely, it can suggest relaxation activities such as interacting with pets or gardening. This makes it possible to provide psychological support by providing appropriate relaxation activities based on the elderly's emotions.

[0173] The processing flow of the second embodiment will be briefly explained below.

[0174] Step 1: The conversation unit engages in a conversation with the elderly person. The conversation unit includes a speech recognition unit that engages in a conversation with the elderly person using speech recognition technology. The speech recognition unit recognizes speech using an acoustic model that uses deep learning, and is able to recognize the elderly person's dialect and accent. Furthermore, the speech recognition unit can analyze the tone and speed of the elderly person's voice and grasp changes in emotion. Step 2: The analysis unit analyzes the content of the conversation carried out by the conversation unit. The analysis unit includes a response generation unit that analyzes the content of the conversation using natural language processing technology and generates an appropriate response. The response generation unit generates responses based on context or emotion, and can also generate responses by referring to the elderly person's past conversation history. Step 3: The monitoring unit monitors the health condition of the elderly person. The monitoring unit is equipped with a heart rate sensor, a body temperature sensor, and a movement sensor, and can accurately monitor the heart rate, body temperature, and movement of the elderly person. Step 4: The notification unit issues an emergency call if the monitoring unit detects an abnormality. The notification unit includes a location information transmission unit that transmits location information and a health condition transmission unit that transmits details of the elderly person's health condition, and can accurately transmit the elderly person's location information and details of their health condition.

[0175] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0176] 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 engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0177] Furthermore, the processing by the data processing system 10 described above 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 necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0178] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0179] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0180] 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.

[0181] 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, RAM 30, and 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.

[0182] 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.

[0183] 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 audio according to instructions from the processor 46.

[0184] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0185] 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 carried out in a secure state.

[0186] Fig. 4 shows an example of the 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.

[0187] 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.

[0188] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0189] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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. The smart glasses 214 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0190] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0191] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0192] 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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0193] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, 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, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0194] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0195] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0196] 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.

[0197] 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, RAM 30, and 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.

[0198] 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.

[0199] 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 audio according to instructions from the processor 46.

[0200] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0201] 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 carried out in a secure state.

[0202] 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.

[0203] 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.

[0204] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0205] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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.

[0206] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0207] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0208] 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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0209] 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 also 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. Furthermore, 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, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0210] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0211] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0212] 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.

[0213] 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, RAM 30, and 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.

[0214] 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.

[0215] 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 audio according to instructions from the processor 46.

[0216] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0217] 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 carried out in a secure state.

[0218] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0219] 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.

[0220] 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.

[0221] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0222] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0223] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0224] 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 voice indicating a user input regarding 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.

[0225] 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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0226] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, 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.

[0227] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0228] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0229] FIG. 9 illustrates 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. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0230] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically 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.

[0231] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0232] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0233] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0234] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of 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, "relieved," "calm," and "reassuring," have similar emotion values.

[0235] 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.

[0236] 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 USB (Universal Serial Bus) 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.

[0237] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0238] 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.

[0239] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0240] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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.

[0241] As an example of a system configured with a single 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 system that uses a processor that realizes the functions of an 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.

[0242] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0243] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0244] The above-described description and illustrations are a detailed explanation 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, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects 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 replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0245] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0246] [Explanation of symbols]

[0247] 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. A conversation section where you talk to the elderly person, an analysis unit that analyzes the content of the conversation carried out by the conversation unit; a monitoring unit for monitoring the health status of the elderly; a reporting unit that issues an emergency call when an abnormality is detected by the monitoring unit; Equipped with A system characterized by:

2. The conversation unit is It is equipped with a voice recognition unit that uses voice recognition technology to converse with the elderly.

2. The system of claim 1.

3. The conversation unit is Equipped with a response generator that analyzes the conversation content and provides appropriate responses 2. The system of claim 1.

4. The monitoring unit Equipped with a heart rate sensor 2. The system of claim 1.

5. The monitoring unit Equipped with a body temperature sensor 2. The system of claim 1.

6. The monitoring unit Equipped with a motion sensor 2. The system of claim 1.

7. The reporting unit A location information transmitting unit that transmits location information is provided.

2. The system of claim 1.

8. The reporting unit A health condition sending unit is provided to send details of the health condition.

2. The system of claim 1.

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