System

Abstract

An object of a system according to an embodiment is to watch the life of an elderly person, detect an abnormality at an early stage, and take appropriate measures.SOLUTION: A system includes a conversation unit, a camera unit, an analysis unit, and a notification unit. The conversation unit has a conversation with the elderly person. The camera unit detects daily abnormalities through a camera. The analysis unit analyzes information acquired by the conversation unit and the camera unit and picks up a dangerous matter. The notification unit notifies the relative of the dangerous matter picked up by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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] With conventional technology, there were issues with systems not being adequately developed to monitor the lives of the elderly, making it difficult to detect abnormalities early and take appropriate action.

[0005] The system according to the embodiment aims to monitor the lives of elderly people, detect abnormalities early, and take appropriate measures. [Means for solving the problem]

[0006] The system according to the embodiment includes a conversation unit, a camera unit, an analysis unit, and a notification unit. The conversation unit converses with the elderly. The camera unit detects daily abnormalities through the camera. The analysis unit analyzes information acquired by the conversation unit and the camera unit and identifies dangerous items. The notification unit notifies relatives of the dangerous items identified by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can monitor the lives of elderly people, detect abnormalities early, and take appropriate measures. [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 touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The monitoring system according to an embodiment of the present invention is a system that monitors the lives of elderly people using a robot (camera) equipped with a generative AI. This system can converse with elderly people and detect daily abnormalities through the camera. This allows the monitoring system to monitor the lives of elderly people efficiently and safely.

[0029] A monitoring system according to an embodiment includes a conversation unit, a camera unit, an analysis unit, and a notification unit. The conversation unit converses with the elderly person. For example, the conversation unit grasps the elderly person's living situation by asking questions such as, "What kind of food do you plan to cook today?" The conversation unit also analyzes the content of the conversation using a generation AI to check for any abnormalities. For example, the generation AI detects changes in the elderly person's health condition and emotions based on the content of the conversation. The camera unit detects daily abnormalities through the camera. For example, the camera unit monitors the state of seasonings before and after cooking and the use of medicine. The camera unit also analyzes camera footage using the generation AI to check for any abnormalities. For example, the generation AI detects excessive use of seasonings or excessive use of medicine based on the camera footage. The analysis unit analyzes the information acquired by the conversation unit and the camera unit to identify risk factors. For example, the analysis unit uses the generation AI to analyze the content of the conversation and camera footage to detect risk factors. The analysis unit also uses the generation AI to classify risk factors and set priorities. The notification unit notifies relatives of any dangerous situations picked up by the analysis unit. For example, the notification unit uses generation AI to create a report and send it to relatives. The notification unit also has a function to automatically make a phone call if a dangerous situation occurs. For example, the notification unit uses generation AI to generate the content of a phone call and notify relatives. This allows the monitoring system to efficiently and safely monitor the lives of elderly people. For example, the monitoring system can detect excessive use of seasonings or excessive use of medicine early on and promptly notify relatives. The monitoring system can also be used in nursing homes, where it can monitor the lives of multiple elderly people at once and notify staff if an abnormality occurs.

[0030] The camera unit can monitor the use of seasonings or medicines and detect abnormalities. For example, the camera unit analyzes camera footage to monitor not only the use of seasonings and medicines, but also the content and amount of meals. For example, the nutritional balance of meals is evaluated, and the generation AI provides advice. The camera unit also analyzes the content and amount of meals eaten by elderly people from camera footage to evaluate nutritional balance. For example, if the content of the meal is unbalanced, the generation AI issues a warning. The camera unit also monitors the use of seasonings and medicines, and evaluates nutritional balance by analyzing the content and amount of meals. For example, if the amount of food eaten is small, the generation AI provides advice. This makes it possible to monitor the use of seasonings and medicines and detect abnormalities early on.

[0031] The analysis unit can analyze the movement patterns of the elderly from camera footage and detect falls or abnormal movements. The analysis unit, for example, analyzes camera footage and monitors the movement patterns of the elderly in real time. For example, if a fall or abnormal movement is detected, the generation AI issues a warning. The analysis unit also analyzes the movement patterns of the elderly over a long period of time and detects abnormal movements as trends. For example, if an abnormality is found compared with past data, the generation AI issues a warning. The analysis unit also uses camera footage to analyze the movement patterns of the elderly and detect falls or abnormal movements in real time. For example, if it detects movement that poses a high risk of falling, the generation AI issues a warning. This makes it possible to detect falls or abnormal movements of the elderly in real time.

[0032] The notification unit can send a report to the relative that details changes in the elderly person's health condition and lifestyle habits. The notification unit, for example, details changes in the elderly person's health condition and lifestyle habits in the report, allowing the relative to take appropriate measures. For example, it specifically describes the contents of meals, medication use, amount of exercise, etc. The notification unit also reflects changes in the elderly person's health condition and lifestyle habits in the report, allowing the relative to take appropriate measures. For example, it details weight gain or loss and changes in sleep hours. The notification unit also details changes in the elderly person's health condition and lifestyle habits in the report, allowing the relative to take appropriate measures. For example, it specifically describes fluctuations in blood pressure and blood sugar levels, etc. This allows the relative to understand changes in the elderly person's health condition and lifestyle habits and take appropriate measures.

[0033] The notification unit may have a function to automatically make a phone call if a dangerous situation occurs. For example, the notification unit may include detailed information about changes in the elderly person's health condition and lifestyle habits in the notification content, allowing relatives to take appropriate action. For example, it may specifically describe the contents of meals, medication use, and amount of exercise. The notification unit may also reflect changes in the elderly person's health condition and lifestyle habits in the notification content, allowing relatives to take appropriate action. For example, it may specifically describe weight gain or loss and changes in sleep hours. The notification unit may also include detailed information about changes in the elderly person's health condition and lifestyle habits in the notification content, allowing relatives to take appropriate action. For example, it may specifically describe fluctuations in blood pressure and blood sugar levels. This allows for a rapid response if a dangerous situation occurs.

[0034] The system can be used in nursing homes. For example, the system can monitor the lives of multiple elderly people at once and enhance the function of notifying staff if an abnormality occurs. For example, if a fall or abnormal movement is detected, the generation AI will alert staff. The system can also monitor the lives of multiple elderly people at once and enhance the function of notifying staff if an abnormality occurs. For example, if an abnormal amount of medication is used, the generation AI will alert staff. The system can also monitor the lives of multiple elderly people at once and enhance the function of notifying staff if an abnormality occurs. For example, if the content or amount of food is abnormal, the generation AI will alert staff. This makes it possible to manage the lives of elderly people in nursing homes from a centralized perspective.

[0035] When sending a report to relatives, the system can visualize the report contents in graphs and charts, allowing the relatives to intuitively understand. For example, the system visualizes the report contents in graphs and charts, allowing the relatives to intuitively understand. For example, it displays changes in health status in a line graph. The system also visualizes changes in the elderly person's lifestyle habits in graphs and charts, allowing the relatives to intuitively understand. For example, it displays dietary details and exercise volume in a pie chart. The system also visualizes the report contents in graphs and charts, allowing the relatives to intuitively understand. For example, it displays medication usage in a bar graph. This allows the relatives to intuitively understand the elderly person's health status and changes in lifestyle habits.

[0036] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0037] The monitoring system can further include an environmental sensor unit. The environmental sensor unit can monitor the temperature, humidity, illuminance, etc. in the room and provide advice on maintaining a comfortable environment. For example, if the room temperature is too high, a notification can be sent urging the use of an air conditioner. Also, if the humidity is low, a notification can be sent recommending the use of a humidifier. Furthermore, if the illuminance is low, it can also suggest adjusting the lighting. This can support the elderly in living in a comfortable environment.

[0038] The monitoring system can further include an exercise recommendation unit. The exercise recommendation unit can monitor the amount of exercise an elderly person does and recommend appropriate exercise. For example, if the elderly person has been sitting for a long time, it can send a notification suggesting light stretching or a walk. If the elderly person continues to lack exercise, it can also provide videos of simple exercises and workouts. Furthermore, the system can also be equipped with a function to record exercise results and report them to relatives or staff. This can help maintain the health of the elderly.

[0039] The monitoring system can further include a dietary management unit. The dietary management unit can record the dietary content of the elderly person and evaluate the nutritional balance. For example, it can evaluate the nutritional balance by taking a photo of the meal and analyzing the type and amount of ingredients. It can also notify the risk of nutritional deficiency or overconsumption based on the dietary content. It can also have a function to propose a meal plan based on the advice of a nutritionist. This can support healthy eating habits for the elderly.

[0040] The monitoring system can also be equipped with a medication management unit. The medication management unit can monitor the elderly person's medication status and encourage them to take their medication at the appropriate time. For example, it can notify them when it is almost time to take their medication. It can also send reminders if they forget to take their medication. It can also record the type and amount of medication and notify them of the risk of overdosing or forgetting to take their medication. This can help elderly people take their medication appropriately.

[0041] The monitoring system can further include an outing management unit. The outing management unit can monitor the elderly's outings and support safe outings. For example, when the elderly go out, it can record their location information using GPS and notify relatives or staff. It can also quickly notify them if it detects any abnormalities while they are out. It can also have a function to record their activities while they are out and provide advice on maintaining their health. This can support the elderly in going out safely.

[0042] The processing flow of the first embodiment will be briefly explained below.

[0043] Step 1: The conversation unit converses with the elderly person. For example, the conversation unit grasps the elderly person's living situation by asking questions such as, "What kind of food do you plan to cook today?" The conversation unit also uses a generation AI to analyze the content of the conversation and check for any abnormalities. For example, the generation AI detects changes in the elderly person's health condition or emotions based on the content of the conversation. Step 2: The camera unit detects daily abnormalities through the camera. For example, the camera unit monitors the state of seasonings before and after cooking, as well as the use of medicine. The camera unit also uses the generative AI to analyze the camera footage and check for abnormalities. For example, the generative AI can detect excessive use of seasonings or medicine based on the camera footage. Step 3: The analysis unit analyzes the information acquired by the conversation unit and camera unit and identifies dangerous items. For example, the analysis unit uses the generation AI to analyze the content of the conversation and camera footage and detect dangerous items. The analysis unit also uses the generation AI to classify the dangerous items and set priorities. Step 4: The notification unit notifies relatives of the dangerous situations picked up by the analysis unit. For example, the notification unit uses the generation AI to create a report and send it to the relatives. The notification unit also has a function to automatically make a phone call if a dangerous situation occurs. For example, the notification unit uses the generation AI to generate the contents of a phone call and notify the relatives.

[0044] (Example 2) The monitoring system according to an embodiment of the present invention is a system that monitors the lives of elderly people using a robot (camera) equipped with a generative AI. This system can converse with elderly people and detect daily abnormalities through the camera. This allows the monitoring system to monitor the lives of elderly people efficiently and safely.

[0045] A monitoring system according to an embodiment includes a conversation unit, a camera unit, an analysis unit, and a notification unit. The conversation unit converses with the elderly person. For example, the conversation unit grasps the elderly person's living situation by asking questions such as, "What kind of food do you plan to cook today?" The conversation unit also analyzes the content of the conversation using a generation AI to check for any abnormalities. For example, the generation AI detects changes in the elderly person's health condition and emotions based on the content of the conversation. The camera unit detects daily abnormalities through the camera. For example, the camera unit monitors the state of seasonings before and after cooking and the use of medicine. The camera unit also analyzes camera footage using the generation AI to check for any abnormalities. For example, the generation AI detects excessive use of seasonings or excessive use of medicine based on the camera footage. The analysis unit analyzes the information acquired by the conversation unit and the camera unit to identify risk factors. For example, the analysis unit uses the generation AI to analyze the content of the conversation and camera footage to detect risk factors. The analysis unit also uses the generation AI to classify risk factors and set priorities. The notification unit notifies relatives of any dangerous situations picked up by the analysis unit. For example, the notification unit uses generation AI to create a report and send it to relatives. The notification unit also has a function to automatically make a phone call if a dangerous situation occurs. For example, the notification unit uses generation AI to generate the content of a phone call and notify relatives. This allows the monitoring system to efficiently and safely monitor the lives of elderly people. For example, the monitoring system can detect excessive use of seasonings or excessive use of medicine early on and promptly notify relatives. The monitoring system can also be used in nursing homes, where it can monitor the lives of multiple elderly people at once and notify staff if an abnormality occurs.

[0046] The conversation unit analyzes the tone and speaking rate of an elderly person's voice to detect changes in their health condition and emotions. For example, the conversation unit analyzes the tone and speaking rate of an elderly person's voice in real time to detect changes in their health condition and emotions. For example, if their voice becomes hoarse or their speaking rate slows down, the generation AI will detect this as an abnormality. The conversation unit also monitors the elderly person's tone and speaking rate over a long period of time to analyze changes in their health condition and emotions as trends. For example, if an abnormality is found compared with past data, the generation AI will issue a warning. The conversation unit also analyzes the elderly person's tone and speaking rate to detect changes in emotions in real time. For example, if their voice gets higher or their speaking rate gets faster, the generation AI will detect stress or excitement. This makes it possible to grasp changes in the elderly person's health condition and emotions in real time.

[0047] The camera unit can monitor the use of seasonings or medicines and detect abnormalities. For example, the camera unit analyzes camera footage to monitor not only the use of seasonings and medicines, but also the content and amount of meals. For example, the nutritional balance of meals is evaluated, and the generation AI provides advice. The camera unit also analyzes the content and amount of meals eaten by elderly people from camera footage to evaluate nutritional balance. For example, if the content of the meal is unbalanced, the generation AI issues a warning. The camera unit also monitors the use of seasonings and medicines, and evaluates nutritional balance by analyzing the content and amount of meals. For example, if the amount of food eaten is small, the generation AI provides advice. This makes it possible to monitor the use of seasonings and medicines and detect abnormalities early on.

[0048] The analysis unit can analyze the movement patterns of the elderly from camera footage and detect falls or abnormal movements. The analysis unit, for example, analyzes camera footage and monitors the movement patterns of the elderly in real time. For example, if a fall or abnormal movement is detected, the generation AI issues a warning. The analysis unit also analyzes the movement patterns of the elderly over a long period of time and detects abnormal movements as trends. For example, if an abnormality is found compared with past data, the generation AI issues a warning. The analysis unit also uses camera footage to analyze the movement patterns of the elderly and detect falls or abnormal movements in real time. For example, if it detects movement that poses a high risk of falling, the generation AI issues a warning. This makes it possible to detect falls or abnormal movements of the elderly in real time.

[0049] The notification unit can send a report to the relative that details changes in the elderly person's health condition and lifestyle habits. The notification unit, for example, details changes in the elderly person's health condition and lifestyle habits in the report, allowing the relative to take appropriate measures. For example, it specifically describes the contents of meals, medication use, amount of exercise, etc. The notification unit also reflects changes in the elderly person's health condition and lifestyle habits in the report, allowing the relative to take appropriate measures. For example, it details weight gain or loss and changes in sleep hours. The notification unit also details changes in the elderly person's health condition and lifestyle habits in the report, allowing the relative to take appropriate measures. For example, it specifically describes fluctuations in blood pressure and blood sugar levels, etc. This allows the relative to understand changes in the elderly person's health condition and lifestyle habits and take appropriate measures.

[0050] The notification unit may have a function to automatically make a phone call if a dangerous situation occurs. For example, the notification unit may include detailed information about changes in the elderly person's health condition and lifestyle habits in the notification content, allowing relatives to take appropriate action. For example, it may specifically describe the contents of meals, medication use, and amount of exercise. The notification unit may also reflect changes in the elderly person's health condition and lifestyle habits in the notification content, allowing relatives to take appropriate action. For example, it may specifically describe weight gain or loss and changes in sleep hours. The notification unit may also include detailed information about changes in the elderly person's health condition and lifestyle habits in the notification content, allowing relatives to take appropriate action. For example, it may specifically describe fluctuations in blood pressure and blood sugar levels. This allows for a rapid response if a dangerous situation occurs.

[0051] The system can be used in nursing homes. For example, the system can monitor the lives of multiple elderly people at once and enhance the function of notifying staff if an abnormality occurs. For example, if a fall or abnormal movement is detected, the generation AI will alert staff. The system can also monitor the lives of multiple elderly people at once and enhance the function of notifying staff if an abnormality occurs. For example, if an abnormal amount of medication is used, the generation AI will alert staff. The system can also monitor the lives of multiple elderly people at once and enhance the function of notifying staff if an abnormality occurs. For example, if the content or amount of food is abnormal, the generation AI will alert staff. This makes it possible to manage the lives of elderly people in nursing homes from a centralized perspective.

[0052] The system can use the emotion estimation function to analyze the emotional state of the elderly in real time and generate dialogue to reduce feelings of loneliness and stress. For example, the system analyzes the elderly's facial expressions and tone of voice and uses the emotion estimation function to detect emotions in real time. For example, if the elderly person has a sad expression or a low tone of voice, the generation AI will offer words of comfort. The system can also monitor the elderly's emotional state in real time and generate dialogue to reduce feelings of loneliness and stress. For example, if the elderly person is feeling depressed, the generation AI will offer a pleasant topic of conversation. The system can also detect changes in the elderly person's emotions using the emotion estimation function and generate dialogue accordingly. For example, if the elderly person is feeling excited, the generation AI will offer advice on how to relax. This can reduce feelings of loneliness and stress in the elderly.

[0053] When sending a report to relatives, the system can visualize the report contents in graphs and charts, allowing the relatives to intuitively understand. For example, the system visualizes the report contents in graphs and charts, allowing the relatives to intuitively understand. For example, it displays changes in health status in a line graph. The system also visualizes changes in the elderly person's lifestyle habits in graphs and charts, allowing the relatives to intuitively understand. For example, it displays dietary details and exercise volume in a pie chart. The system also visualizes the report contents in graphs and charts, allowing the relatives to intuitively understand. For example, it displays medication usage in a bar graph. This allows the relatives to intuitively understand the elderly person's health status and changes in lifestyle habits.

[0054] The system can use the emotion estimation function to monitor the emotional state of the elderly in real time and provide advice to staff. For example, the system uses the emotion estimation function to monitor the emotional state of the elderly in real time and provide advice to staff. For example, if the elderly is feeling depressed, the system advises the staff to offer words of encouragement. The system also monitors the emotional state of the elderly in real time and provides advice to staff. For example, if the elderly is feeling excited, the system suggests ways for the staff to relax. The system also uses the emotion estimation function to monitor the emotional state of the elderly in real time and provide advice to staff. For example, if the elderly is feeling unstable, the system recommends that the staff seek professional consultation. This allows the emotional state of the elderly to be monitored in real time and allows staff to take appropriate action.

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

[0056] The monitoring system can further include an environmental sensor unit. The environmental sensor unit can monitor the temperature, humidity, illuminance, etc. in the room and provide advice on maintaining a comfortable environment. For example, if the room temperature is too high, a notification can be sent urging the use of an air conditioner. Also, if the humidity is low, a notification can be sent recommending the use of a humidifier. Furthermore, if the illuminance is low, it can also suggest adjusting the lighting. This can support the elderly in living in a comfortable environment.

[0057] The monitoring system can further include an exercise recommendation unit. The exercise recommendation unit can monitor the amount of exercise an elderly person does and recommend appropriate exercise. For example, if the elderly person has been sitting for a long time, it can send a notification suggesting light stretching or a walk. If the elderly person continues to lack exercise, it can also provide videos of simple exercises and workouts. Furthermore, the system can also be equipped with a function to record exercise results and report them to relatives or staff. This can help maintain the health of the elderly.

[0058] The monitoring system can further include a dietary management unit. The dietary management unit can record the dietary content of the elderly person and evaluate the nutritional balance. For example, it can evaluate the nutritional balance by taking a photo of the meal and analyzing the type and amount of ingredients. It can also notify the risk of nutritional deficiency or overconsumption based on the dietary content. It can also have a function to propose a meal plan based on the advice of a nutritionist. This can support healthy eating habits for the elderly.

[0059] The monitoring system can also be equipped with a medication management unit. The medication management unit can monitor the elderly person's medication status and encourage them to take their medication at the appropriate time. For example, it can notify them when it is almost time to take their medication. It can also send reminders if they forget to take their medication. It can also record the type and amount of medication and notify them of the risk of overdosing or forgetting to take their medication. This can help elderly people take their medication appropriately.

[0060] The monitoring system can further include an outing management unit. The outing management unit can monitor the elderly's outings and support safe outings. For example, when the elderly go out, it can record their location information using GPS and notify relatives or staff. It can also quickly notify them if it detects any abnormalities while they are out. It can also have a function to record their activities while they are out and provide advice on maintaining their health. This can support the elderly in going out safely.

[0061] The monitoring system can also use an emotion estimation function to analyze the elderly person's emotional state in real time and generate dialogue to reduce feelings of loneliness and stress. For example, it can analyze the elderly person's facial expressions and tone of voice and use the emotion estimation function to detect their emotions in real time. For example, if the elderly person looks sad or has a low tone of voice, the generation AI will offer words of comfort. The system can also monitor the elderly person's emotional state in real time and generate dialogue to reduce feelings of loneliness and stress. For example, if the elderly person is feeling depressed, the generation AI will offer a pleasant topic of conversation. The system can also use the emotion estimation function to detect changes in the elderly person's emotions and generate dialogue accordingly. For example, if the elderly person is feeling excited, the generation AI will offer advice on how to relax. This can reduce feelings of loneliness and stress in the elderly.

[0062] The monitoring system can further use an emotion estimation function to monitor the emotional state of the elderly person in real time and provide advice to staff. For example, the emotion estimation function can be used to monitor the emotional state of the elderly person in real time and provide advice to staff. For example, if the elderly person is feeling depressed, the system can advise the staff to offer words of encouragement. The system can also monitor the emotional state of the elderly person in real time and provide advice to staff. For example, if the elderly person is feeling excited, the system can suggest ways for the staff to relax. The system can also use the emotion estimation function to monitor the emotional state of the elderly person in real time and provide advice to staff. For example, if the elderly person is feeling unstable, the system can recommend that the staff seek professional consultation. This allows the emotional state of the elderly person to be monitored in real time and the staff to take appropriate action.

[0063] The monitoring system can further use an emotion estimation function to analyze the emotional state of the elderly person and reflect emotional changes in reports to relatives. For example, the emotion estimation function can be used to analyze the emotional state of the elderly person and reflect emotional changes in reports to relatives. For example, if the elderly person has been feeling depressed for a long period of time, the system can warn the relative. The system can also analyze the emotional state of the elderly person and reflect emotional changes in reports to relatives. For example, if the elderly person is feeling excited, the system can suggest ways for the relative to relax. The system can also use the emotion estimation function to analyze the emotional state of the elderly person and reflect emotional changes in reports to relatives. For example, if the elderly person is emotionally unstable, the system can recommend that the relative seek professional advice. This allows the relative to understand the elderly person's emotional state and take appropriate action.

[0064] The monitoring system can further use an emotion estimation function to analyze the emotional state of the elderly person and provide music and videos that correspond to the emotion. For example, the emotion estimation function can be used to analyze the emotional state of the elderly person and provide music and videos that correspond to the emotion. For example, if the elderly person is feeling depressed, relaxing music can be played. The system can also analyze the emotional state of the elderly person and provide music and videos that correspond to the emotion. For example, if the elderly person is feeling excited, calming videos can be played. The system can also use the emotion estimation function to analyze the emotional state of the elderly person and provide music and videos that correspond to the emotion. For example, if the elderly person is feeling unstable, relaxing music and videos can be provided. This can stabilize the emotional state of the elderly person.

[0065] The monitoring system can further use an emotion estimation function to analyze the emotional state of the elderly person and suggest exercise or relaxation activities according to the emotion. For example, the emotion estimation function can be used to analyze the emotional state of the elderly person and suggest exercise or relaxation activities according to the emotion. For example, if the elderly person is feeling depressed, light exercise or stretching can be suggested. The system can also analyze the emotional state of the elderly person and suggest exercise or relaxation activities according to the emotion. For example, if the elderly person is feeling excited, deep breathing or meditation can be suggested to help them relax. The system can also use the emotion estimation function to analyze the emotional state of the elderly person and suggest exercise or relaxation activities according to the emotion. For example, if the elderly person is feeling emotionally unstable, relaxing yoga or massage can be suggested. This can stabilize the elderly person's emotional state.

[0066] The processing flow of the second embodiment will be briefly explained below.

[0067] Step 1: The conversation unit converses with the elderly person. For example, the conversation unit grasps the elderly person's living situation by asking questions such as, "What kind of food do you plan to cook today?" The conversation unit also uses a generation AI to analyze the content of the conversation and check for any abnormalities. For example, the generation AI detects changes in the elderly person's health condition or emotions based on the content of the conversation. Step 2: The camera unit detects daily abnormalities through the camera. For example, the camera unit monitors the state of seasonings before and after cooking, as well as the use of medicine. The camera unit also uses the generative AI to analyze the camera footage and check for abnormalities. For example, the generative AI can detect excessive use of seasonings or medicine based on the camera footage. Step 3: The analysis unit analyzes the information acquired by the conversation unit and camera unit and identifies dangerous items. For example, the analysis unit uses the generation AI to analyze the content of the conversation and camera footage and detect dangerous items. The analysis unit also uses the generation AI to classify the dangerous items and set priorities. Step 4: The notification unit notifies relatives of the dangerous situations picked up by the analysis unit. For example, the notification unit uses the generation AI to create a report and send it to the relatives. The notification unit also has a function to automatically make a phone call if a dangerous situation occurs. For example, the notification unit uses the generation AI to generate the contents of a phone call and notify the relatives.

[0068] 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.

[0069] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0070] 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.

[0071] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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).

[0077] 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.

[0078] 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.

[0079] 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.

[0080] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0081] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0082] 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.

[0083] 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.

[0084] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] 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.

[0086] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] 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.

[0101] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] 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.

[0114] 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.

[0115] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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 area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0122] 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."

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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, to avoid confusion and 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.

[0134] 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. [Explanation of symbols]

[0135] 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 system that uses a robot equipped with generative AI to monitor the lives of the elderly. A conversation section that talks with elderly people, The camera unit detects abnormalities on a daily basis, an analysis unit that analyzes information acquired by the conversation unit and the camera unit and identifies dangerous items; a notification unit that notifies relatives of the risk items picked up by the analysis unit. A system characterized by:

2. The conversation unit is Analyzing the tone of voice and speaking rate of the elderly person to detect changes in the elderly person's health condition and emotions 2. The system of claim 1.

3. The camera unit Monitor the use of seasonings or medicines and detect abnormalities 2. The system of claim 1.

4. The notification unit Sending the relative a report detailing the elderly person's health status and lifestyle changes 2. The system of claim 1.

5. The system comprises: Analyzing the emotional state of the elderly person in real time and generating dialogue to reduce feelings of loneliness and stress 2. The system of claim 1.

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