System

Abstract

An object of a system according to an embodiment is to reduce a burden on a nursing care staff through conversation with and watching over a dementia patient.SOLUTION: A system according to an embodiment includes a conversation partner unit, a watching unit, and a burden reducing unit. The conversation partner unit becomes a conversation partner of the dementia patient by using the generated AI. The watching unit monitors the behavior of the dementia patient by the conversation partner unit, and notifies the nursing care staff when there is an abnormality. The burden reduction unit reduces a burden on the nursing care staff by the conversation partner unit and the watching 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] Conventional technology places a heavy burden on care staff when talking to and monitoring dementia patients, leaving room for improvement.

[0005] The system according to the embodiment aims to reduce the burden on care staff by talking to and watching over dementia patients. [Means for solving the problem]

[0006] The system according to the embodiment includes a conversation partner unit, a monitoring unit, and a burden reduction unit. The conversation partner unit serves as a conversation partner for the dementia patient using a generation AI. The monitoring unit monitors the behavior of the dementia patient using the conversation partner unit and notifies the care staff if an abnormality is detected. The burden reduction unit reduces the burden on the care staff using the conversation partner unit and monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the burden on care staff by talking to and watching over dementia patients. [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 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) The nursing care system according to the embodiment of the present invention uses generative AI to act as a conversation partner for elderly people and at the same time act as a watchdog. This reduces the difficulty of interacting with dementia patients and the burden on care staff.

[0029] A care system according to an embodiment includes a conversation partner unit, a monitoring unit, and a burden reduction unit. The conversation partner unit uses a generation AI to serve as a conversation partner for a dementia patient. For example, if the dementia patient says, "No one is coming to visit today," the generation AI responds by saying, "The weather is nice today. Would you like to go for a walk?" The generation AI also understands the patient's comments and generates an appropriate response. The monitoring unit monitors the dementia patient's behavior using the conversation partner unit and notifies the care staff if an abnormality is detected. For example, if the patient tries to leave the room, the monitoring unit asks, "Where are you going?" and notifies the care staff as needed. The burden reduction unit reduces the burden on the care staff using the conversation partner unit and monitoring unit. For example, by having the generation AI take charge of conversations with dementia patients, the care staff can focus on other tasks. Furthermore, because the generation AI constantly monitors the patient's condition, the staff does not need to constantly monitor the patient. As a result, the care system according to an embodiment can reduce the burden on care staff and ensure the patient's safety through conversations with dementia patients.

[0030] The conversation partner section can learn the past conversation history of dementia patients and provide personalized conversations tailored to each individual patient. For example, the conversation partner section uses a generation AI to store the patient's past conversation history in a database and learn the content and topics of the conversation. For example, it develops a conversation based on information about the patient's favorite topics and hobbies. The conversation partner section also analyzes the patient's past conversation history and learns specific phrases and vocabulary. This allows the generation AI to provide more friendly conversations to the patient. The conversation partner section also uses the generation AI to select topics related to the patient's memories and experiences based on the patient's past conversation history and advance the conversation. For example, it may revisit a travel memory that the patient previously talked about. This allows conversations tailored to each individual patient to contribute to their mental stability.

[0031] The conversation partner unit supports multiple languages, making it possible to converse with foreign elderly people. For example, the generation AI uses multilingual natural language processing technology to converse in multiple languages. For example, it supports the patient's native language, such as English, Spanish, or Chinese. The generation AI also has a language switching function to facilitate smooth conversations with foreign elderly people. For example, if a patient speaks to it in a different language, it automatically switches to the corresponding language. The generation AI also has a multilingual conversation database to facilitate smooth conversations with foreign elderly people. For example, it provides conversation content that takes into account different cultural backgrounds. This makes it possible to converse with foreign elderly people, thereby broadening the scope of care.

[0032] The conversation partner unit can engage in conversations using music and poetry to pique the interest of dementia patients. For example, the conversation partner unit's generation AI learns the patient's favorite music and poetry and incorporates it into the conversation. For example, the conversation partner unit plays a song the patient likes and talks about that song. The conversation partner unit can also pique the patient's interest through conversations using music and poetry. For example, the conversation partner unit can recite a poem the patient used to like and talk about memories related to that poem. The conversation partner unit can also use music and poetry to engage in conversations that draw out the patient's emotions. For example, when a patient is feeling down, the conversation partner unit can play an upbeat song to cheer them up. In this way, the conversation partner unit can pique the patient's interest through conversations using music and poetry.

[0033] The monitoring unit learns the movement patterns of dementia patients and can detect abnormal behavior early. For example, the monitoring unit's generation AI stores the patient's daily movement patterns in a database and develops an algorithm to detect abnormal behavior. For example, it issues an alert if it detects any movement that is different from normal. The monitoring unit also monitors the patient's movement patterns in real time and builds a system to detect abnormal behavior early. For example, it notifies the system if it detects any movement that poses a high risk of falling. The monitoring unit's generation AI also learns the patient's movement patterns and predicts abnormal behavior. For example, it notifies care staff if it detects any movement that is different from normal. This makes it possible to ensure the safety of patients by detecting abnormal behavior early.

[0034] The monitoring unit can monitor the vital signs of dementia patients and notify them if any abnormalities are detected. For example, the monitoring unit will build a system in which the generating AI monitors the patient's vital signs in real time and notifies care staff if any abnormalities are detected. For example, an alert will be issued if the heart rate suddenly rises. The monitoring unit will also develop an algorithm that regularly records the patient's vital signs and detects abnormal values. For example, an alert will be issued if the body temperature is abnormally high. The monitoring unit will also build a system in which the generating AI analyzes the patient's vital signs and responds immediately if any abnormalities are detected. For example, an alert will be issued if there is a sudden change in blood pressure. In this way, the patient's health can be maintained by monitoring vital signs and notifying them if any abnormalities are detected.

[0035] The monitoring unit manages the meals and medication of dementia patients and can remind them at the appropriate times. For example, the monitoring unit will build a system in which the generation AI manages the patient's meal and medication schedule and reminds them at the appropriate times. For example, it will notify them when it is time to eat. The monitoring unit will also record the patient's meal and medication history and calculate the next timing to remind them. For example, it will issue an alert when it is nearing time to take medication. The monitoring unit will also manage the patient's meals and medication with the generation AI and provide a reminder function. For example, it will notify them when a meal is ready. In this way, the patient's health can be maintained by managing meals and medication and reminding them at the appropriate times.

[0036] The monitoring unit can monitor the room environment of a dementia patient and maintain a comfortable environment. For example, the monitoring unit uses a generating AI to monitor the temperature and humidity in the patient's room in real time and build a system to maintain a comfortable environment. For example, it adjusts the air conditioner if the temperature is too high. The monitoring unit also collects environmental data from the patient's room and develops algorithms to maintain a comfortable environment. For example, it activates a humidifier if the humidity is too low. The monitoring unit also uses a generating AI to monitor the lighting in the patient's room and maintain appropriate brightness. For example, it dims the lights at night and brightens them during the day. This allows the room environment to be monitored and a comfortable environment to be maintained, improving the patient's quality of life.

[0037] The burden reduction unit can manage the schedules of nursing staff and propose efficient work allocation. For example, the burden reduction unit builds a system in which a generation AI manages the schedules of nursing staff and proposes efficient work allocation. For example, it optimizes staff break times. The burden reduction unit also analyzes the work content of nursing staff and develops an algorithm that proposes efficient schedules. For example, it sets priorities for work and carries out work efficiently. The burden reduction unit also manages the schedules of nursing staff in real time using a generation AI and proposes efficient work allocation. For example, it makes it possible to respond to sudden changes in work. In this way, the burden on nursing staff can be reduced by managing schedules and proposing efficient work allocation.

[0038] The burden reduction unit can monitor the stress levels of nursing staff and suggest appropriate break times. For example, the burden reduction unit will build a system in which a generation AI monitors the stress levels of nursing staff in real time and suggests appropriate break times. For example, it will detect stress by analyzing heart rate and electrodermal activity. The burden reduction unit will also develop an algorithm that regularly records the stress levels of nursing staff and suggests breaks when stress increases. For example, it will notify the staff when stress exceeds a certain threshold. The burden reduction unit will also analyze the stress levels of nursing staff using a generation AI and suggest appropriate break times. For example, it will encourage breaks when working for long periods of time. In this way, the health of nursing staff can be maintained by monitoring stress levels and suggesting appropriate break times.

[0039] The burden reduction unit can support the education and training of nursing staff and promote skill improvement. For example, the burden reduction unit builds a system in which the generative AI supports the education and training of nursing staff. For example, it provides online courses and simulations. The burden reduction unit also develops an algorithm that analyzes the skill level of nursing staff and proposes optimal training plans for each individual. For example, it provides supplemental training to staff who lack specific skills. The burden reduction unit also monitors the training progress of nursing staff using the generative AI and promotes skill improvement. For example, it conducts regular evaluations and adjusts the training content. This supports education and training, promotes skill improvement, and can improve the capabilities of nursing staff.

[0040] The Burden Reduction Unit can support communication among nursing staff and strengthen teamwork. For example, the Burden Reduction Unit builds a system in which the Generative AI supports communication between nursing staff. For example, it uses a chatbot to facilitate information sharing. The Burden Reduction Unit also develops algorithms that analyze communication data among nursing staff and make suggestions to strengthen teamwork. For example, it suggests areas for improvement based on the frequency and content of communication. The Burden Reduction Unit also monitors communication among nursing staff using the Generative AI and provides support to strengthen teamwork. For example, it suggests scheduling regular meetings. This supports communication, strengthens teamwork, and improves the work efficiency of nursing staff.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The conversation partner can suggest activities based on the hobbies and interests of patients with dementia. For example, if a patient is interested in gardening, the generating AI can provide topics related to gardening and suggest working in the garden. If the patient likes cooking, the generating AI can introduce simple recipes and suggest cooking together. Furthermore, if the patient likes drawing, the generating AI can suggest a painting theme and set up time to draw together. This can improve the quality of life of patients by suggesting activities based on their hobbies and interests.

[0043] The conversation partner section can support dementia patients' communication with their families. For example, the generation AI can set up a video call with the family and support the conversation. The generation AI can also read out messages from the family and support the patient when they respond. The generation AI can also elicit memories of family members and liven up the conversation. By supporting communication with family members, this can reduce the patient's sense of loneliness and promote mental stability.

[0044] The conversation partner can provide advice based on the health condition of a dementia patient. For example, the generating AI can record the patient's diet and suggest nutritionally balanced meals. The generating AI can also monitor the patient's exercise habits and suggest appropriate exercises. Furthermore, the generating AI can analyze the patient's sleep patterns and provide advice to promote good quality sleep. This allows the patient's health to be maintained by providing advice based on their health condition.

[0045] The monitoring unit can learn the daily rhythms of dementia patients and send reminders at appropriate times. For example, the generation AI can record the patient's wake-up and bedtime times and encourage them to wake up and go to bed at appropriate times. The generation AI can also record the patient's meal times and encourage them to eat at appropriate times. Furthermore, the generation AI can record the patient's medication times and encourage them to take their medication at appropriate times. In this way, by learning the patient's daily rhythms and sending reminders at appropriate times, the patient's health can be maintained.

[0046] The burden reduction unit can propose work allocation based on the skills and experience of nursing staff. For example, the generation AI analyzes the skills and experience of staff and assigns the most appropriate tasks. The generation AI also takes into account the staff's areas of expertise when allocating tasks. Furthermore, the generation AI can set priorities for tasks based on the staff's experience. This makes it possible to reduce the burden on nursing staff by proposing work allocation based on their skills and experience.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The conversation partner uses a generation AI to become a conversation partner for the dementia patient. For example, if the dementia patient says, "No one is coming to see me today," the generation AI will respond by saying, "The weather is nice today. Would you like to go for a walk?" The generation AI also understands what the patient says and generates an appropriate response. Step 2: The monitoring unit monitors the behavior of the dementia patient using the conversation partner unit and notifies the care staff if there is anything unusual. For example, if the patient tries to leave the room, it will ask, "Where are you going?" and notify the care staff as necessary. Step 3: The burden reduction unit reduces the burden on care staff through the conversation partner unit and monitoring unit. For example, the generation AI can handle conversations with dementia patients, allowing care staff to focus on other tasks. In addition, the generation AI constantly monitors the patient's condition, eliminating the need for staff to constantly monitor them.

[0049] (Example 2) The nursing care system according to the embodiment of the present invention uses generative AI to act as a conversation partner for elderly people and at the same time act as a watchdog. This reduces the difficulty of interacting with dementia patients and the burden on care staff.

[0050] A care system according to an embodiment includes a conversation partner unit, a monitoring unit, and a burden reduction unit. The conversation partner unit uses a generation AI to serve as a conversation partner for a dementia patient. For example, if the dementia patient says, "No one is coming to visit today," the generation AI responds by saying, "The weather is nice today. Would you like to go for a walk?" The generation AI also understands the patient's comments and generates an appropriate response. The monitoring unit monitors the dementia patient's behavior using the conversation partner unit and notifies the care staff if an abnormality is detected. For example, if the patient tries to leave the room, the monitoring unit asks, "Where are you going?" and notifies the care staff as needed. The burden reduction unit reduces the burden on the care staff using the conversation partner unit and monitoring unit. For example, by having the generation AI take charge of conversations with dementia patients, the care staff can focus on other tasks. Furthermore, because the generation AI constantly monitors the patient's condition, the staff does not need to constantly monitor the patient. As a result, the care system according to an embodiment can reduce the burden on care staff and ensure the patient's safety through conversations with dementia patients.

[0051] The conversation partner section can learn the past conversation history of dementia patients and provide personalized conversations tailored to each individual patient. For example, the conversation partner section uses a generation AI to store the patient's past conversation history in a database and learn the content and topics of the conversation. For example, it develops a conversation based on information about the patient's favorite topics and hobbies. The conversation partner section also analyzes the patient's past conversation history and learns specific phrases and vocabulary. This allows the generation AI to provide more friendly conversations to the patient. The conversation partner section also uses the generation AI to select topics related to the patient's memories and experiences based on the patient's past conversation history and advance the conversation. For example, it may revisit a travel memory that the patient previously talked about. This allows conversations tailored to each individual patient to contribute to their mental stability.

[0052] The conversation partner unit can analyze the facial expressions and tone of voice of a dementia patient and generate appropriate responses based on their emotions. For example, the generation AI in the conversation partner unit uses a camera and microphone to analyze the patient's facial expressions and tone of voice in real time and estimate their emotions. For example, if the patient looks sad, it will offer words of comfort. The conversation partner unit also analyzes the patient's tone of voice to detect changes in emotions. For example, if the patient's voice is trembling, the generation AI will respond with kind, reassuring words. The conversation partner unit also uses facial expression recognition technology to capture subtle changes in the patient's facial expressions and generate responses based on their emotions. For example, if the patient is smiling, the generation AI will offer an enjoyable topic of conversation. This allows the patient's mental stability to be achieved by responding according to their emotions.

[0053] The conversation partner unit can use the emotion estimation function to estimate the emotions of a dementia patient in real time and provide conversation content that corresponds to the emotions. The conversation partner unit, for example, uses the emotion estimation function to analyze the patient's emotions in real time and adjust the conversation content based on the results. For example, if the patient is feeling anxious, it provides topics that will give the patient a sense of security. The conversation partner unit also collects emotional data on the patient and changes the tone and content of the conversation according to changes in emotions. For example, if the patient is excited, it selects gentle topics to calm the patient. The conversation partner unit also uses the emotion estimation function to monitor the patient's emotional state and responds according to the emotion at an appropriate time. For example, if the patient is happy, it provides a response that shows empathy. In this way, by providing conversation content that corresponds to the patient's emotions, it is possible to achieve mental stability for the patient.

[0054] The conversation partner unit supports multiple languages, making it possible to converse with foreign elderly people. For example, the generation AI uses multilingual natural language processing technology to converse in multiple languages. For example, it supports the patient's native language, such as English, Spanish, or Chinese. The generation AI also has a language switching function to facilitate smooth conversations with foreign elderly people. For example, if a patient speaks to it in a different language, it automatically switches to the corresponding language. The generation AI also has a multilingual conversation database to facilitate smooth conversations with foreign elderly people. For example, it provides conversation content that takes into account different cultural backgrounds. This makes it possible to converse with foreign elderly people, thereby broadening the scope of care.

[0055] The conversation partner unit can engage in conversations using music and poetry to pique the interest of dementia patients. For example, the conversation partner unit's generation AI learns the patient's favorite music and poetry and incorporates it into the conversation. For example, the conversation partner unit plays a song the patient likes and talks about that song. The conversation partner unit can also pique the patient's interest through conversations using music and poetry. For example, the conversation partner unit can recite a poem the patient used to like and talk about memories related to that poem. The conversation partner unit can also use music and poetry to engage in conversations that draw out the patient's emotions. For example, when a patient is feeling down, the conversation partner unit can play an upbeat song to cheer them up. In this way, the conversation partner unit can pique the patient's interest through conversations using music and poetry.

[0056] The conversation partner unit can use the emotion estimation function to suggest music and videos that correspond to the emotions of the dementia patient and use them in the conversation. The conversation partner unit, for example, uses the emotion estimation function to analyze the patient's emotional state and suggest appropriate music and videos based on the results. For example, when the patient wants to relax, it plays calming music. The conversation partner unit also uses music and videos that correspond to the patient's emotions in the conversation. For example, when the patient is excited, it shows natural videos to calm the patient. The conversation partner unit also uses the emotion estimation function to select music and videos that match the patient's emotions and use them in the conversation. For example, it shows videos that the patient finds nostalgic to elicit reminiscences. In this way, by suggesting music and videos that correspond to the patient's emotions and using them in the conversation, it is possible to achieve mental stability for the patient.

[0057] The monitoring unit learns the movement patterns of dementia patients and can detect abnormal behavior early. For example, the monitoring unit's generation AI stores the patient's daily movement patterns in a database and develops an algorithm to detect abnormal behavior. For example, it issues an alert if it detects any movement that is different from normal. The monitoring unit also monitors the patient's movement patterns in real time and builds a system to detect abnormal behavior early. For example, it notifies the system if it detects any movement that poses a high risk of falling. The monitoring unit's generation AI also learns the patient's movement patterns and predicts abnormal behavior. For example, it notifies care staff if it detects any movement that is different from normal. This makes it possible to ensure the safety of patients by detecting abnormal behavior early.

[0058] The monitoring unit can monitor the vital signs of dementia patients and notify them if any abnormalities are detected. For example, the monitoring unit will build a system in which the generating AI monitors the patient's vital signs in real time and notifies care staff if any abnormalities are detected. For example, an alert will be issued if the heart rate suddenly rises. The monitoring unit will also develop an algorithm that regularly records the patient's vital signs and detects abnormal values. For example, an alert will be issued if the body temperature is abnormally high. The monitoring unit will also build a system in which the generating AI analyzes the patient's vital signs and responds immediately if any abnormalities are detected. For example, an alert will be issued if there is a sudden change in blood pressure. In this way, the patient's health can be maintained by monitoring vital signs and notifying them if any abnormalities are detected.

[0059] The monitoring unit can use the emotion estimation function to monitor changes in the emotions of dementia patients and notify them if any abnormal emotional changes are detected. For example, the monitoring unit uses the emotion estimation function to monitor changes in the patient's emotions in real time and build a system that notifies care staff if any abnormal emotional changes are detected. For example, an alert is issued if the patient suddenly feels anxious. The monitoring unit also collects patient emotional data and develops algorithms to detect abnormal emotional changes. For example, a notification is issued if an abnormal emotional state persists. The monitoring unit also uses the emotion estimation function to monitor changes in the patient's emotions and build a system that responds immediately if any abnormalities are detected. For example, an alert is issued if the patient suddenly feels anger. In this way, the patient's mental health can be maintained by monitoring changes in emotions and notifying them if any abnormal emotional changes are detected.

[0060] The monitoring unit manages the meals and medication of dementia patients and can remind them at the appropriate times. For example, the monitoring unit will build a system in which the generation AI manages the patient's meal and medication schedule and reminds them at the appropriate times. For example, it will notify them when it is time to eat. The monitoring unit will also record the patient's meal and medication history and calculate the next timing to remind them. For example, it will issue an alert when it is nearing time to take medication. The monitoring unit will also manage the patient's meals and medication with the generation AI and provide a reminder function. For example, it will notify them when a meal is ready. In this way, the patient's health can be maintained by managing meals and medication and reminding them at the appropriate times.

[0061] The monitoring unit can monitor the room environment of a dementia patient and maintain a comfortable environment. For example, the monitoring unit uses a generating AI to monitor the temperature and humidity in the patient's room in real time and build a system to maintain a comfortable environment. For example, it adjusts the air conditioner if the temperature is too high. The monitoring unit also collects environmental data from the patient's room and develops algorithms to maintain a comfortable environment. For example, it activates a humidifier if the humidity is too low. The monitoring unit also uses a generating AI to monitor the lighting in the patient's room and maintain appropriate brightness. For example, it dims the lights at night and brightens them during the day. This allows the room environment to be monitored and a comfortable environment to be maintained, improving the patient's quality of life.

[0062] The monitoring unit can use the emotion estimation function to suggest relaxation methods according to the emotions of a dementia patient. For example, the monitoring unit uses the emotion estimation function to analyze the patient's emotional state and suggest relaxation methods based on the results. For example, if the patient is feeling anxious, it may suggest deep breathing. The monitoring unit also collects emotional data on the patient and develops an algorithm to suggest relaxation methods. For example, if the patient is feeling stressed, it may suggest meditation. The monitoring unit also uses the emotion estimation function to build a system that suggests relaxation methods according to the patient's emotions. For example, if the patient is tense, it may play relaxing music. In this way, by suggesting relaxation methods according to the patient's emotions, it is possible to maintain the patient's mental health.

[0063] The burden reduction unit can manage the schedules of nursing staff and propose efficient work allocation. For example, the burden reduction unit builds a system in which a generation AI manages the schedules of nursing staff and proposes efficient work allocation. For example, it optimizes staff break times. The burden reduction unit also analyzes the work content of nursing staff and develops an algorithm that proposes efficient schedules. For example, it sets priorities for work and carries out work efficiently. The burden reduction unit also manages the schedules of nursing staff in real time using a generation AI and proposes efficient work allocation. For example, it makes it possible to respond to sudden changes in work. In this way, the burden on nursing staff can be reduced by managing schedules and proposing efficient work allocation.

[0064] The burden reduction unit can monitor the stress levels of nursing staff and suggest appropriate break times. For example, the burden reduction unit will build a system in which a generation AI monitors the stress levels of nursing staff in real time and suggests appropriate break times. For example, it will detect stress by analyzing heart rate and electrodermal activity. The burden reduction unit will also develop an algorithm that regularly records the stress levels of nursing staff and suggests breaks when stress increases. For example, it will notify the staff when stress exceeds a certain threshold. The burden reduction unit will also analyze the stress levels of nursing staff using a generation AI and suggest appropriate break times. For example, it will encourage breaks when working for long periods of time. In this way, the health of nursing staff can be maintained by monitoring stress levels and suggesting appropriate break times.

[0065] The burden reduction unit can use the emotion estimation function to analyze the emotions of the care staff and provide support according to those emotions. For example, the burden reduction unit uses the emotion estimation function to analyze the emotions of the care staff in real time and build a system that provides support based on the results. For example, if the staff is tired, it sends an encouraging message. The burden reduction unit also collects emotional data of the care staff and develops an algorithm that provides support according to their emotions. For example, if the staff is feeling stressed, it suggests relaxation methods. The burden reduction unit also uses the emotion estimation function to monitor the emotional state of the care staff and provide appropriate support. For example, if the staff is feeling depressed, it provides positive feedback. In this way, the mental health of the care staff can be maintained by analyzing their emotions and providing support according to their emotions.

[0066] The burden reduction unit can support the education and training of nursing staff and promote skill improvement. For example, the burden reduction unit builds a system in which the generative AI supports the education and training of nursing staff. For example, it provides online courses and simulations. The burden reduction unit also develops an algorithm that analyzes the skill level of nursing staff and proposes optimal training plans for each individual. For example, it provides supplemental training to staff who lack specific skills. The burden reduction unit also monitors the training progress of nursing staff using the generative AI and promotes skill improvement. For example, it conducts regular evaluations and adjusts the training content. This supports education and training, promotes skill improvement, and can improve the capabilities of nursing staff.

[0067] The Burden Reduction Unit can support communication among nursing staff and strengthen teamwork. For example, the Burden Reduction Unit builds a system in which the Generative AI supports communication between nursing staff. For example, it uses a chatbot to facilitate information sharing. The Burden Reduction Unit also develops algorithms that analyze communication data among nursing staff and make suggestions to strengthen teamwork. For example, it suggests areas for improvement based on the frequency and content of communication. The Burden Reduction Unit also monitors communication among nursing staff using the Generative AI and provides support to strengthen teamwork. For example, it suggests scheduling regular meetings. This supports communication, strengthens teamwork, and improves the work efficiency of nursing staff.

[0068] The burden reduction unit can use the emotion estimation function to suggest relaxation methods according to the emotions of the care staff. For example, the burden reduction unit uses the emotion estimation function to analyze the emotional state of the care staff and build a system that suggests relaxation methods based on the results. For example, if the staff is feeling stressed, it plays music to help them relax. The burden reduction unit also collects emotional data on the care staff and develops an algorithm that suggests relaxation methods. For example, if the staff is tired, it suggests aromatherapy. The burden reduction unit also uses the emotion estimation function to suggest relaxation methods according to the emotions of the care staff. For example, if the staff is tense, it suggests meditation to help them relax. In this way, by suggesting relaxation methods according to their emotions, the mental health of the care staff can be maintained.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The conversation partner can suggest activities based on the hobbies and interests of patients with dementia. For example, if a patient is interested in gardening, the generating AI can provide topics related to gardening and suggest working in the garden. If the patient likes cooking, the generating AI can introduce simple recipes and suggest cooking together. Furthermore, if the patient likes drawing, the generating AI can suggest a painting theme and set up time to draw together. This can improve the quality of life of patients by suggesting activities based on their hobbies and interests.

[0071] The conversation partner section can support dementia patients' communication with their families. For example, the generation AI can set up a video call with the family and support the conversation. The generation AI can also read out messages from the family and support the patient when they respond. The generation AI can also elicit memories of family members and liven up the conversation. By supporting communication with family members, this can reduce the patient's sense of loneliness and promote mental stability.

[0072] The conversation partner can provide advice based on the health condition of a dementia patient. For example, the generating AI can record the patient's diet and suggest nutritionally balanced meals. The generating AI can also monitor the patient's exercise habits and suggest appropriate exercises. Furthermore, the generating AI can analyze the patient's sleep patterns and provide advice to promote good quality sleep. This allows the patient's health to be maintained by providing advice based on their health condition.

[0073] The conversation partner unit can estimate the emotions of a dementia patient and suggest relaxation methods according to their emotions. For example, if the patient is feeling anxious, the generation AI can suggest deep breathing or meditation. If the patient is feeling stressed, the generation AI can play relaxing music. Furthermore, if the patient is tense, the generation AI can suggest aromatherapy to help them relax. In this way, by suggesting relaxation methods according to their emotions, the mental health of patients can be maintained.

[0074] The conversation partner unit can estimate the emotions of a dementia patient and suggest activities that correspond to their emotions. For example, if the patient is sad, the generation AI can suggest fun games or puzzles. If the patient is excited, the generation AI can suggest reading or painting to calm them down. Furthermore, if the patient is feeling lonely, the generation AI can suggest online social events. This allows the patient's mental health to be maintained by suggesting activities that correspond to their emotions.

[0075] The monitoring unit can learn the daily rhythms of dementia patients and send reminders at appropriate times. For example, the generation AI can record the patient's wake-up and bedtime times and encourage them to wake up and go to bed at appropriate times. The generation AI can also record the patient's meal times and encourage them to eat at appropriate times. Furthermore, the generation AI can record the patient's medication times and encourage them to take their medication at appropriate times. In this way, by learning the patient's daily rhythms and sending reminders at appropriate times, the patient's health can be maintained.

[0076] The monitoring unit can estimate the emotions of dementia patients and adjust the environment accordingly. For example, if the patient is feeling anxious, the generating AI will adjust the lighting in the room to create a relaxing environment. If the patient is feeling stressed, the generating AI will adjust the temperature and humidity in the room to provide a comfortable environment. Furthermore, if the patient is tense, the generating AI can adjust the music in the room to create a relaxing environment. In this way, by adjusting the environment according to emotions, it is possible to maintain the mental health of patients.

[0077] The monitoring unit can estimate the emotions of dementia patients and suggest safety measures according to their emotions. For example, if the patient feels anxious, the generating AI will guide them to a safe place. If the patient is agitated, the generating AI will suggest a place for them to calm down. Furthermore, if the patient feels lonely, the generating AI can suggest interaction with other people. This makes it possible to ensure the safety of patients by suggesting safety measures according to their emotions.

[0078] The burden reduction unit can estimate the emotions of care staff and suggest work allocation according to their emotions. For example, if a staff member is tired, the generation AI will suggest a break. Also, if a staff member is feeling stressed, the generation AI will suggest work to relax them. Furthermore, if a staff member is tense, the generation AI can suggest an activity to relax them. In this way, the burden on care staff can be reduced by suggesting work allocation according to their emotions.

[0079] The burden reduction unit can propose work allocation based on the skills and experience of nursing staff. For example, the generation AI analyzes the skills and experience of staff and assigns the most appropriate tasks. The generation AI also takes into account the staff's areas of expertise when allocating tasks. Furthermore, the generation AI can set priorities for tasks based on the staff's experience. This makes it possible to reduce the burden on nursing staff by proposing work allocation based on their skills and experience.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The conversation partner uses a generation AI to become a conversation partner for the dementia patient. For example, if the dementia patient says, "No one is coming to see me today," the generation AI will respond by saying, "The weather is nice today. Would you like to go for a walk?" The generation AI also understands what the patient says and generates an appropriate response. Step 2: The monitoring unit monitors the behavior of the dementia patient using the conversation partner unit and notifies the care staff if there is anything unusual. For example, if the patient tries to leave the room, it will ask, "Where are you going?" and notify the care staff as necessary. Step 3: The burden reduction unit reduces the burden on care staff through the conversation partner unit and monitoring unit. For example, the generation AI can handle conversations with dementia patients, allowing care staff to focus on other tasks. In addition, the generation AI constantly monitors the patient's condition, eliminating the need for staff to constantly monitor them.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] In the headset type terminal 314, 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 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 specific processing unit 290 using these models.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0118] 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.

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

[0120] 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).

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

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] In the robot 414, 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 robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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]

[0149] 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 partner part that uses generation AI to become a conversation partner for dementia patients, a monitoring unit that monitors the behavior of the dementia patient using the conversation partner unit and notifies care staff if any abnormality is detected; a burden reduction unit that reduces the burden on care staff by the conversation partner unit and the monitoring unit. A system characterized by:

2. The conversation partner unit is The system learns the past conversation history of the dementia patient and provides personalized conversations tailored to each individual patient.

2. The system of claim 1.

3. The conversation partner unit is Analyzing the facial expressions and tone of voice of the dementia patient and generating appropriate responses according to their emotions 2. The system of claim 1.

4. The conversation partner unit is The system estimates the emotions of the dementia patient in real time and provides conversation content that corresponds to the emotions.

2. The system of claim 1.

5. The conversation partner unit is Supports multiple languages ​​and enables conversations with elderly foreigners 2. The system of claim 1.

6. The conversation partner unit is Conversations using music and poetry to stimulate the interest of the dementia patient 2. The system of claim 1.

7. The conversation partner unit is Suggest music and images that correspond to the emotions of the dementia patient and use them in conversation 2. The system of claim 1.

8. The monitoring unit Learning the movement patterns of dementia patients and detecting abnormal behavior early 2. The system of claim 1.

Images