System

Abstract

An object of a system according to an embodiment is to generate an efficient work shift in response to a shortage of nursing care workers.SOLUTION: A system includes an emotion recognition unit, a life pattern collection unit, a vital data collection unit, and a generation unit. The emotion recognition unit converts the emotion of the cared person into data. The life pattern collection unit collects a life pattern of the cared person. The vital data collection unit collects vital data of a cared person. The generation unit generates a work shift based on the data collected from the emotion recognition unit, the life pattern collection unit, and the vital data collection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it is difficult to generate efficient work shifts to deal with a shortage of care workers, and there is room for improvement.

[0005] The system according to the embodiment aims to address the shortage of caregivers and generate efficient work shifts. [Means for solving the problem]

[0006] The system according to the embodiment includes an emotion recognition unit, a lifestyle pattern collection unit, a vital data collection unit, and a generation unit. The emotion recognition unit digitizes the emotions of the care recipient. The lifestyle pattern collection unit collects the lifestyle patterns of the care recipient. The vital data collection unit collects vital data of the care recipient. The generation unit generates work shifts based on the data collected from the emotion recognition unit, the lifestyle pattern collection unit, and the vital data collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can address the shortage of care staff and generate efficient work shifts. [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 AI ​​system according to the embodiment of the present invention generates optimal work shifts based on the emotions, lifestyle patterns, and vital data of the care recipient, thereby enabling the AI ​​system to efficiently and effectively address the shortage of caregivers.

[0029] The AI ​​system according to the embodiment includes an emotion recognition unit, a lifestyle pattern collection unit, a vital data collection unit, and a generation unit. The emotion recognition unit digitizes the emotions of the care recipient. For example, the emotion recognition unit analyzes the facial expressions of the care recipient using facial expression recognition technology and digitizes the emotions. The emotion recognition unit can also analyze the tone of the care recipient's voice using voice analysis technology and digitize the emotions. The emotion recognition unit can also analyze the physiological responses of the care recipient using biometric technology and digitize the emotions. The lifestyle pattern collection unit collects the lifestyle patterns of the care recipient. For example, the lifestyle pattern collection unit collects daily activity histories and stores them in a database. The lifestyle pattern collection unit can also measure activity levels and store them in the database. The lifestyle pattern collection unit can also analyze sleep patterns and store them in the database. The vital data collection unit collects vital data of the care recipient. For example, the vital data collection unit measures heart rate and stores them in a database. The vital data collection unit can also measure blood pressure and store them in the database. The vital data collection unit can also measure body temperature and store the data in a database. The generation unit generates work shifts based on data collected from the emotion recognition unit, lifestyle pattern collection unit, and vital data collection unit. For example, the generation unit integrates emotion data, lifestyle pattern data, and vital data to generate optimal work shifts. The generation unit can also use generation AI to generate work shifts based on data about the care recipient. The generation unit can also use generation AI to generate optimal shifts based on the skills and experience of caregivers. As a result, the AI ​​system according to the embodiment can generate optimal work shifts based on the emotions, lifestyle patterns, and vital data of the care recipient. For example, the AI ​​system adjusts shifts according to changes in the emotions of the care recipient, thereby reducing the burden on caregivers. The AI ​​system also adjusts shifts according to the lifestyle patterns of the care recipient to provide efficient care. The AI ​​system also adjusts shifts based on the vital data of the care recipient to maintain their health.

[0030] The emotion recognition unit accumulates emotional data of the care recipient and analyzes long-term patterns of emotional fluctuations to predict future emotional fluctuations. For example, the emotion recognition unit uses emotion recognition AI to accumulate emotional data of the care recipient over a long period of time and analyze emotional fluctuation patterns based on that data. For example, it predicts that emotions are more likely to fluctuate during certain seasons based on data from the past year. The emotion recognition unit also analyzes the emotional data of the care recipient and identifies patterns of emotional fluctuations in response to specific events or situations. For example, it finds a tendency for emotions to stabilize when family members visit. The emotion recognition unit also develops an algorithm to predict future emotional fluctuations based on the emotional data of the care recipient. For example, it predicts that emotions will become unstable during certain time periods and takes countermeasures in advance. This makes it possible to understand the long-term patterns of emotional fluctuations of the care recipient and predict future emotional fluctuations.

[0031] The emotion recognition unit can propose the optimal care method for emotional fluctuations based on the emotional data of the care recipient. For example, the emotion recognition unit uses emotion recognition AI to analyze the emotional data of the care recipient and propose the optimal care method for emotional fluctuations. For example, if the care recipient is feeling anxious, it may propose relaxing music. The emotion recognition unit also uses emotion recognition AI to propose specific care actions for emotional fluctuations based on the emotional data of the care recipient. For example, if the care recipient is feeling angry, it may encourage deep breathing. The emotion recognition unit also uses emotion recognition AI to analyze the emotional data of the care recipient and propose the optimal care schedule for emotional fluctuations. For example, it may propose rehabilitation at a time when emotions are stable. This makes it possible to propose the optimal care method for emotional fluctuations of the care recipient.

[0032] The emotion recognition unit can integrate the emotional data of the care recipient with other health data to assess their overall health condition. For example, the emotion recognition AI in the emotion recognition unit integrates the emotional data of the care recipient with their dietary content to assess their overall health condition. For example, it analyzes the impact of a particular meal on emotions. The emotion recognition unit also integrates the emotional data of the care recipient with their exercise amount to assess their overall health condition. For example, it analyzes emotional fluctuations after exercise and suggests an optimal exercise plan. The emotion recognition AI in the emotion recognition unit also integrates the emotional data of the care recipient with their sleep patterns to assess their overall health condition. For example, it analyzes the impact of lack of sleep on emotions. This allows the emotion data of the care recipient to be integrated with other health data to assess their overall health condition.

[0033] The lifestyle pattern collection unit can collect the lifestyle patterns of the care recipient in real time and update the data instantly. For example, the lifestyle pattern collection unit uses conventional AI to collect the lifestyle patterns of the care recipient (e.g., meal times and bedtimes) in real time and instantly update the database. For example, it records daily meal times. The lifestyle pattern collection unit also uses conventional AI to collect vital data of the care recipient (e.g., heart rate and blood pressure) in real time and instantly update the database. For example, it records heart rate fluctuations. The lifestyle pattern collection unit also uses conventional AI to integrate the lifestyle patterns and vital data of the care recipient and build a system that updates the data in real time. For example, it analyzes the relationship between meal times and heart rate. This allows the lifestyle patterns of the care recipient to be understood in real time and enables immediate response.

[0034] The lifestyle pattern collection unit can detect abnormal patterns based on the lifestyle patterns and vital data of the care recipient and issue early warnings. For example, the lifestyle pattern collection unit uses conventional AI to analyze the lifestyle patterns and vital data of the care recipient and build a system to detect abnormal patterns. For example, it issues a warning if the care recipient does not eat at their usual mealtimes. The lifestyle pattern collection unit also uses conventional AI to detect abnormal patterns based on the care recipient's vital data and issue early warnings. For example, it issues an alert if the heart rate suddenly rises. The lifestyle pattern collection unit also uses conventional AI to integrate the lifestyle patterns and vital data of the care recipient and develop an algorithm to detect abnormal patterns. For example, it issues a warning if the care recipient wakes up at their usual bedtime. This makes it possible to detect abnormal patterns early and issue a warning.

[0035] The lifestyle pattern collection unit can suggest an optimal lifestyle rhythm based on the lifestyle patterns and vital data of the care recipient. For example, the lifestyle pattern collection unit uses conventional AI to analyze the lifestyle patterns and vital data of the care recipient and build a system that suggests an optimal lifestyle rhythm. For example, it makes suggestions to adjust meal times and bedtimes. The lifestyle pattern collection unit also uses conventional AI to suggest an optimal lifestyle rhythm based on the vital data of the care recipient. For example, it suggests exercise time based on heart rate and blood pressure data. The lifestyle pattern collection unit also develops an algorithm that uses conventional AI to integrate the lifestyle patterns and vital data of the care recipient and suggest an optimal lifestyle rhythm. For example, it suggests a balance between meal times and exercise time. This makes it possible to suggest an optimal lifestyle rhythm for the care recipient.

[0036] The lifestyle pattern collection unit can provide dietary and exercise advice to the care recipient based on the care recipient's lifestyle patterns and vital data. For example, the lifestyle pattern collection unit uses conventional AI to analyze the care recipient's lifestyle patterns and vital data and build a system that provides dietary and exercise advice. For example, it suggests the timing and content of meals. The lifestyle pattern collection unit also uses conventional AI to provide exercise advice based on the care recipient's vital data. For example, it suggests the appropriate amount of exercise based on heart rate and blood pressure data. The lifestyle pattern collection unit also uses conventional AI to develop an algorithm that integrates the care recipient's lifestyle patterns and vital data and provides dietary and exercise advice. For example, it suggests a balance between dietary content and exercise amount. This makes it possible to provide dietary and exercise advice to the care recipient.

[0037] The lifestyle pattern collection unit can notify the care recipient's family and friends of their health condition and encourage them to provide support based on their lifestyle patterns and vital data. For example, the lifestyle pattern collection unit uses conventional AI to analyze the care recipient's lifestyle patterns and vital data and build a system that notifies family and friends of their health condition. For example, it notifies family members of abnormal vital data. The lifestyle pattern collection unit also uses conventional AI to suggest specific support actions to family and friends based on the care recipient's vital data. For example, it suggests dietary support. The lifestyle pattern collection unit also uses conventional AI to integrate the care recipient's lifestyle patterns and vital data and develop an algorithm that notifies family and friends of their health condition. For example, it notifies family members of lack of exercise. This makes it possible to notify family and friends of the care recipient's health condition and encourage support.

[0038] The lifestyle pattern collection unit can optimize caregiver work shifts based on the lifestyle patterns and vital data of the care recipient. For example, the lifestyle pattern collection unit uses conventional AI to analyze the lifestyle patterns and vital data of the care recipient and build a system that optimizes caregiver work shifts. For example, shifts are adjusted according to fluctuations in vital data. The lifestyle pattern collection unit also uses conventional AI to optimize caregiver work shifts based on the care recipient's vital data. For example, staff are assigned to specific time periods based on heart rate and blood pressure data. The lifestyle pattern collection unit also uses conventional AI to integrate the lifestyle patterns and vital data of the care recipient and develop an algorithm that optimizes caregiver work shifts. For example, shifts are adjusted to match meal times and bedtimes. This makes it possible to optimize caregiver work shifts.

[0039] The generation unit can generate optimal nursing care shifts in real time based on the emotional data, lifestyle pattern data, and vital data of the care recipient. For example, the generation unit constructs a system in which a generation AI analyzes the emotional data, lifestyle pattern data, and vital data of the care recipient and generates optimal nursing care shifts in real time. For example, shifts are adjusted according to emotional fluctuations. The generation unit also generates optimal nursing care shifts in real time based on the data of the care recipient using the generation AI. For example, staff allocation is adjusted according to fluctuations in vital data. The generation unit also develops an algorithm in which the generation AI integrates the emotional data, lifestyle pattern data, and vital data of the care recipient and generates optimal nursing care shifts in real time. For example, shifts are adjusted to match meal times and bedtimes. This makes it possible to generate optimal nursing care shifts in real time.

[0040] The generation unit can predict future care needs based on data on the care recipient and create long-term shift plans. For example, the generation unit uses a generation AI to analyze data on the care recipient and build a system to predict future care needs. For example, the generation unit creates long-term shift plans based on emotional data and vital sign data. The generation unit also uses a generation AI to predict future care needs based on data on the care recipient and create long-term shift plans. For example, the generation unit adjusts shifts according to changes in lifestyle patterns. The generation unit also develops an algorithm in which the generation AI integrates the emotional data, lifestyle pattern data, and vital sign data of the care recipient to predict future care needs. For example, the generation unit creates a plan to assign staff to specific time periods. This makes it possible to predict future care needs and create long-term shift plans.

[0041] The generation unit can generate optimal shifts based on the care recipient's data in accordance with the skills and experience of the care staff. For example, the generation unit builds a system in which a generation AI analyzes the care recipient's data and generates optimal shifts based on the care staff's skills and experience. For example, it assigns staff with specific skills to the required time periods. The generation unit also generates optimal shifts based on the care recipient's data in accordance with the care staff's skills and experience using a generation AI. For example, it assigns experienced staff to unstable time periods. The generation unit also develops an algorithm in which the generation AI integrates the care recipient's emotional data, lifestyle pattern data, and vital sign data to generate optimal shifts based on the care staff's skills and experience. For example, it assigns staff with specific skills to the required time periods. This makes it possible to generate optimal shifts based on the care staff's skills and experience.

[0042] The generation unit can optimize the training program for care staff based on the data of the care recipient. For example, the generation unit constructs a system in which the generation AI analyzes the data of the care recipient and optimizes the training program for care staff. For example, it identifies the necessary skills based on emotional data and vital data. The generation unit also optimizes the training program for care staff based on the data of the care recipient. For example, it adjusts the training content according to changes in lifestyle patterns. The generation unit also develops an algorithm in which the generation AI integrates the emotional data, lifestyle pattern data, and vital data of the care recipient and optimizes the training program for care staff. For example, it suggests training to strengthen specific skills. This makes it possible to optimize the training program for care staff.

[0043] The generation unit can propose the optimal allocation of resources in the nursing facility based on the data of the care recipient. For example, the generation unit builds a system in which the generation AI analyzes the data of the care recipient and proposes the optimal allocation of resources in the nursing facility. For example, it identifies necessary equipment based on emotional data and vital data. The generation unit also proposes the optimal allocation of resources in the nursing facility based on the data of the care recipient. For example, it adjusts the allocation of equipment according to changes in lifestyle patterns. The generation unit also develops an algorithm in which the generation AI integrates the emotional data, lifestyle pattern data, and vital data of the care recipient and proposes the optimal allocation of resources in the nursing facility. For example, it arranges medications that are needed at specific times of the day. This makes it possible to propose the optimal allocation of resources in the nursing facility.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The lifestyle pattern collection unit can collect the lifestyle patterns of the care recipient in real time and update the data instantly. For example, the lifestyle pattern collection unit uses conventional AI to collect the lifestyle patterns of the care recipient (e.g., meal times and bedtimes) in real time and instantly update the database. For example, it records daily meal times. The lifestyle pattern collection unit also uses conventional AI to collect vital data of the care recipient (e.g., heart rate and blood pressure) in real time and instantly update the database. For example, it records heart rate fluctuations. The lifestyle pattern collection unit also uses conventional AI to integrate the lifestyle patterns and vital data of the care recipient and build a system that updates the data in real time. For example, it analyzes the relationship between meal times and heart rate. This allows the lifestyle patterns of the care recipient to be understood in real time and enables immediate response.

[0046] The lifestyle pattern collection unit can detect abnormal patterns based on the lifestyle patterns and vital data of the care recipient and issue early warnings. For example, the lifestyle pattern collection unit uses conventional AI to analyze the lifestyle patterns and vital data of the care recipient and build a system to detect abnormal patterns. For example, it issues a warning if the care recipient does not eat at their usual mealtimes. The lifestyle pattern collection unit also uses conventional AI to detect abnormal patterns based on the care recipient's vital data and issue early warnings. For example, it issues an alert if the heart rate suddenly rises. The lifestyle pattern collection unit also uses conventional AI to integrate the lifestyle patterns and vital data of the care recipient and develop an algorithm to detect abnormal patterns. For example, it issues a warning if the care recipient wakes up at their usual bedtime. This makes it possible to detect abnormal patterns early and issue a warning.

[0047] The generation unit can generate optimal shifts based on the care recipient's data in accordance with the skills and experience of the care staff. For example, the generation unit builds a system in which a generation AI analyzes the care recipient's data and generates optimal shifts based on the care staff's skills and experience. For example, it assigns staff with specific skills to the required time periods. The generation unit also generates optimal shifts based on the care recipient's data in accordance with the care staff's skills and experience using a generation AI. For example, it assigns experienced staff to unstable time periods. The generation unit also develops an algorithm in which the generation AI integrates the care recipient's emotional data, lifestyle pattern data, and vital sign data to generate optimal shifts based on the care staff's skills and experience. For example, it assigns staff with specific skills to the required time periods. This makes it possible to generate optimal shifts based on the care staff's skills and experience.

[0048] The lifestyle pattern collection unit can suggest an optimal lifestyle rhythm based on the lifestyle patterns and vital data of the care recipient. For example, the lifestyle pattern collection unit uses conventional AI to analyze the lifestyle patterns and vital data of the care recipient and build a system that suggests an optimal lifestyle rhythm. For example, it makes suggestions to adjust meal times and bedtimes. The lifestyle pattern collection unit also uses conventional AI to suggest an optimal lifestyle rhythm based on the vital data of the care recipient. For example, it suggests exercise time based on heart rate and blood pressure data. The lifestyle pattern collection unit also develops an algorithm that uses conventional AI to integrate the lifestyle patterns and vital data of the care recipient and suggest an optimal lifestyle rhythm. For example, it suggests a balance between meal times and exercise time. This makes it possible to suggest an optimal lifestyle rhythm for the care recipient.

[0049] The generation unit can predict future care needs based on data on the care recipient and create long-term shift plans. For example, the generation unit uses a generation AI to analyze data on the care recipient and build a system to predict future care needs. For example, the generation unit creates long-term shift plans based on emotional data and vital sign data. The generation unit also uses a generation AI to predict future care needs based on data on the care recipient and create long-term shift plans. For example, the generation unit adjusts shifts according to changes in lifestyle patterns. The generation unit also develops an algorithm in which the generation AI integrates the emotional data, lifestyle pattern data, and vital sign data of the care recipient to predict future care needs. For example, the generation unit creates a plan to assign staff to specific time periods. This makes it possible to predict future care needs and create long-term shift plans.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The emotion recognition unit digitizes the emotions of the care recipient. For example, the emotion recognition unit uses facial expression recognition technology to analyze the facial expressions of the care recipient and digitize the emotions. It can also use voice analysis technology to analyze the tone of the care recipient's voice and digitize the emotions. It can also use biometrics technology to analyze the physiological reactions of the care recipient and digitize the emotions. Step 2: The lifestyle pattern collection unit collects the lifestyle patterns of the care recipient, such as daily behavior history, activity level, and sleep pattern, and stores them in a database. Step 3: The vital data collection unit collects vital data of the care recipient, such as heart rate, blood pressure, and body temperature, and stores the data in a database. Step 4: The generation unit generates work shifts based on the data collected from the emotion recognition unit, lifestyle pattern collection unit, and vital data collection unit. For example, it combines emotion data, lifestyle pattern data, and vital data to generate optimal work shifts. It is also possible to use the generation AI to generate optimal shifts based on the data of the care recipient and the skills and experience of the caregivers.

[0052] (Example 2) The AI ​​system according to the embodiment of the present invention generates optimal work shifts based on the emotions, lifestyle patterns, and vital data of the care recipient, thereby enabling the AI ​​system to efficiently and effectively address the shortage of caregivers.

[0053] The AI ​​system according to the embodiment includes an emotion recognition unit, a lifestyle pattern collection unit, a vital data collection unit, and a generation unit. The emotion recognition unit digitizes the emotions of the care recipient. For example, the emotion recognition unit analyzes the facial expressions of the care recipient using facial expression recognition technology and digitizes the emotions. The emotion recognition unit can also analyze the tone of the care recipient's voice using voice analysis technology and digitize the emotions. The emotion recognition unit can also analyze the physiological responses of the care recipient using biometric technology and digitize the emotions. The lifestyle pattern collection unit collects the lifestyle patterns of the care recipient. For example, the lifestyle pattern collection unit collects daily activity histories and stores them in a database. The lifestyle pattern collection unit can also measure activity levels and store them in the database. The lifestyle pattern collection unit can also analyze sleep patterns and store them in the database. The vital data collection unit collects vital data of the care recipient. For example, the vital data collection unit measures heart rate and stores them in a database. The vital data collection unit can also measure blood pressure and store them in the database. The vital data collection unit can also measure body temperature and store the data in a database. The generation unit generates work shifts based on data collected from the emotion recognition unit, lifestyle pattern collection unit, and vital data collection unit. For example, the generation unit integrates emotion data, lifestyle pattern data, and vital data to generate optimal work shifts. The generation unit can also use generation AI to generate work shifts based on data about the care recipient. The generation unit can also use generation AI to generate optimal shifts based on the skills and experience of caregivers. As a result, the AI ​​system according to the embodiment can generate optimal work shifts based on the emotions, lifestyle patterns, and vital data of the care recipient. For example, the AI ​​system adjusts shifts according to changes in the emotions of the care recipient, thereby reducing the burden on caregivers. The AI ​​system also adjusts shifts according to the lifestyle patterns of the care recipient to provide efficient care. The AI ​​system also adjusts shifts based on the vital data of the care recipient to maintain their health.

[0054] The emotion recognition unit monitors the emotions of the care recipient in real time and can instantly update data in response to emotional fluctuations. For example, the emotion recognition AI in the emotion recognition unit analyzes the care recipient's facial expressions and tone of voice in real time and instantly reflects emotional fluctuations in the database. For example, the moment the care recipient smiles, the data is updated as "joy." The emotion recognition unit also monitors the care recipient's behavioral patterns in real time and instantly records emotional fluctuations. For example, if the care recipient suddenly moves, the data is updated as "surprise." The emotion recognition unit also analyzes the care recipient's physiological reactions (for example, fluctuations in heart rate) in real time and instantly reflects emotional fluctuations in the database. For example, if the heart rate suddenly rises, the data is updated as "tension." This allows the care recipient's emotional fluctuations to be grasped in real time and immediate response can be made.

[0055] The emotion recognition unit accumulates emotional data of the care recipient and analyzes long-term patterns of emotional fluctuations to predict future emotional fluctuations. For example, the emotion recognition unit uses emotion recognition AI to accumulate emotional data of the care recipient over a long period of time and analyze emotional fluctuation patterns based on that data. For example, it predicts that emotions are more likely to fluctuate during certain seasons based on data from the past year. The emotion recognition unit also analyzes the emotional data of the care recipient and identifies patterns of emotional fluctuations in response to specific events or situations. For example, it finds a tendency for emotions to stabilize when family members visit. The emotion recognition unit also develops an algorithm to predict future emotional fluctuations based on the emotional data of the care recipient. For example, it predicts that emotions will become unstable during certain time periods and takes countermeasures in advance. This makes it possible to understand the long-term patterns of emotional fluctuations of the care recipient and predict future emotional fluctuations.

[0056] The emotion recognition unit can propose the optimal care method for emotional fluctuations based on the emotional data of the care recipient. For example, the emotion recognition unit uses emotion recognition AI to analyze the emotional data of the care recipient and propose the optimal care method for emotional fluctuations. For example, if the care recipient is feeling anxious, it may propose relaxing music. The emotion recognition unit also uses emotion recognition AI to propose specific care actions for emotional fluctuations based on the emotional data of the care recipient. For example, if the care recipient is feeling angry, it may encourage deep breathing. The emotion recognition unit also uses emotion recognition AI to analyze the emotional data of the care recipient and propose the optimal care schedule for emotional fluctuations. For example, it may propose rehabilitation at a time when emotions are stable. This makes it possible to propose the optimal care method for emotional fluctuations of the care recipient.

[0057] The emotion recognition unit can integrate the emotional data of the care recipient with other health data to assess their overall health condition. For example, the emotion recognition AI in the emotion recognition unit integrates the emotional data of the care recipient with their dietary content to assess their overall health condition. For example, it analyzes the impact of a particular meal on emotions. The emotion recognition unit also integrates the emotional data of the care recipient with their exercise amount to assess their overall health condition. For example, it analyzes emotional fluctuations after exercise and suggests an optimal exercise plan. The emotion recognition AI in the emotion recognition unit also integrates the emotional data of the care recipient with their sleep patterns to assess their overall health condition. For example, it analyzes the impact of lack of sleep on emotions. This allows the emotion data of the care recipient to be integrated with other health data to assess their overall health condition.

[0058] The emotion recognition unit can notify the care recipient's family and friends of emotional fluctuations based on the care recipient's emotional data and encourage them to provide support. For example, the emotion recognition unit uses emotion recognition AI to analyze the care recipient's emotional data and build a system that notifies family and friends of emotional fluctuations. For example, it notifies family members if the care recipient is feeling anxious. The emotion recognition unit also uses emotion recognition AI to suggest specific support actions to family and friends based on the care recipient's emotional data. For example, it suggests encouraging them over the phone. The emotion recognition unit also uses emotion recognition AI to analyze the care recipient's emotional data and develop a notification system that encourages family and friends to provide support for emotional fluctuations. For example, it suggests visiting during times when the care recipient is emotionally unstable. This makes it possible to notify family and friends of the care recipient's emotional fluctuations and encourage their support.

[0059] The emotion recognition unit can also monitor the emotional state of care staff based on the emotional data of the care recipient, thereby supporting stress management for staff. For example, the emotion recognition unit will build a system in which emotion recognition AI monitors the emotional state of care staff based on the emotional data of the care recipient. For example, it will analyze the staff's facial expressions and tone of voice to evaluate their stress level. The emotion recognition unit will also suggest specific actions to support stress management for care staff based on the emotional data of the care recipient. For example, it will suggest relaxing break times. The emotion recognition unit will also develop a system in which emotion recognition AI analyzes the emotional data of the care recipient and monitors the emotional state of care staff in real time. For example, it will issue an alert if stress increases. This will enable monitoring of the emotional state of care staff and support stress management.

[0060] The lifestyle pattern collection unit can collect the lifestyle patterns of the care recipient in real time and update the data instantly. For example, the lifestyle pattern collection unit uses conventional AI to collect the lifestyle patterns of the care recipient (e.g., meal times and bedtimes) in real time and instantly update the database. For example, it records daily meal times. The lifestyle pattern collection unit also uses conventional AI to collect vital data of the care recipient (e.g., heart rate and blood pressure) in real time and instantly update the database. For example, it records heart rate fluctuations. The lifestyle pattern collection unit also uses conventional AI to integrate the lifestyle patterns and vital data of the care recipient and build a system that updates the data in real time. For example, it analyzes the relationship between meal times and heart rate. This allows the lifestyle patterns of the care recipient to be understood in real time and enables immediate response.

[0061] The lifestyle pattern collection unit can detect abnormal patterns based on the lifestyle patterns and vital data of the care recipient and issue early warnings. For example, the lifestyle pattern collection unit uses conventional AI to analyze the lifestyle patterns and vital data of the care recipient and build a system to detect abnormal patterns. For example, it issues a warning if the care recipient does not eat at their usual mealtimes. The lifestyle pattern collection unit also uses conventional AI to detect abnormal patterns based on the care recipient's vital data and issue early warnings. For example, it issues an alert if the heart rate suddenly rises. The lifestyle pattern collection unit also uses conventional AI to integrate the lifestyle patterns and vital data of the care recipient and develop an algorithm to detect abnormal patterns. For example, it issues a warning if the care recipient wakes up at their usual bedtime. This makes it possible to detect abnormal patterns early and issue a warning.

[0062] The lifestyle pattern collection unit can suggest an optimal lifestyle rhythm based on the lifestyle patterns and vital data of the care recipient. For example, the lifestyle pattern collection unit uses conventional AI to analyze the lifestyle patterns and vital data of the care recipient and build a system that suggests an optimal lifestyle rhythm. For example, it makes suggestions to adjust meal times and bedtimes. The lifestyle pattern collection unit also uses conventional AI to suggest an optimal lifestyle rhythm based on the vital data of the care recipient. For example, it suggests exercise time based on heart rate and blood pressure data. The lifestyle pattern collection unit also develops an algorithm that uses conventional AI to integrate the lifestyle patterns and vital data of the care recipient and suggest an optimal lifestyle rhythm. For example, it suggests a balance between meal times and exercise time. This makes it possible to suggest an optimal lifestyle rhythm for the care recipient.

[0063] The lifestyle pattern collection unit can provide dietary and exercise advice to the care recipient based on the care recipient's lifestyle patterns and vital data. For example, the lifestyle pattern collection unit uses conventional AI to analyze the care recipient's lifestyle patterns and vital data and build a system that provides dietary and exercise advice. For example, it suggests the timing and content of meals. The lifestyle pattern collection unit also uses conventional AI to provide exercise advice based on the care recipient's vital data. For example, it suggests the appropriate amount of exercise based on heart rate and blood pressure data. The lifestyle pattern collection unit also uses conventional AI to develop an algorithm that integrates the care recipient's lifestyle patterns and vital data and provides dietary and exercise advice. For example, it suggests a balance between dietary content and exercise amount. This makes it possible to provide dietary and exercise advice to the care recipient.

[0064] The lifestyle pattern collection unit can notify the care recipient's family and friends of their health condition and encourage them to provide support based on their lifestyle patterns and vital data. For example, the lifestyle pattern collection unit uses conventional AI to analyze the care recipient's lifestyle patterns and vital data and build a system that notifies family and friends of their health condition. For example, it notifies family members of abnormal vital data. The lifestyle pattern collection unit also uses conventional AI to suggest specific support actions to family and friends based on the care recipient's vital data. For example, it suggests dietary support. The lifestyle pattern collection unit also uses conventional AI to integrate the care recipient's lifestyle patterns and vital data and develop an algorithm that notifies family and friends of their health condition. For example, it notifies family members of lack of exercise. This makes it possible to notify family and friends of the care recipient's health condition and encourage support.

[0065] The lifestyle pattern collection unit can optimize caregiver work shifts based on the lifestyle patterns and vital data of the care recipient. For example, the lifestyle pattern collection unit uses conventional AI to analyze the lifestyle patterns and vital data of the care recipient and build a system that optimizes caregiver work shifts. For example, shifts are adjusted according to fluctuations in vital data. The lifestyle pattern collection unit also uses conventional AI to optimize caregiver work shifts based on the care recipient's vital data. For example, staff are assigned to specific time periods based on heart rate and blood pressure data. The lifestyle pattern collection unit also uses conventional AI to integrate the lifestyle patterns and vital data of the care recipient and develop an algorithm that optimizes caregiver work shifts. For example, shifts are adjusted to match meal times and bedtimes. This makes it possible to optimize caregiver work shifts.

[0066] The generation unit can generate optimal nursing care shifts in real time based on the emotional data, lifestyle pattern data, and vital data of the care recipient. For example, the generation unit constructs a system in which a generation AI analyzes the emotional data, lifestyle pattern data, and vital data of the care recipient and generates optimal nursing care shifts in real time. For example, shifts are adjusted according to emotional fluctuations. The generation unit also generates optimal nursing care shifts in real time based on the data of the care recipient using the generation AI. For example, staff allocation is adjusted according to fluctuations in vital data. The generation unit also develops an algorithm in which the generation AI integrates the emotional data, lifestyle pattern data, and vital data of the care recipient and generates optimal nursing care shifts in real time. For example, shifts are adjusted to match meal times and bedtimes. This makes it possible to generate optimal nursing care shifts in real time.

[0067] The generation unit can predict future care needs based on data on the care recipient and create long-term shift plans. For example, the generation unit uses a generation AI to analyze data on the care recipient and build a system to predict future care needs. For example, the generation unit creates long-term shift plans based on emotional data and vital sign data. The generation unit also uses a generation AI to predict future care needs based on data on the care recipient and create long-term shift plans. For example, the generation unit adjusts shifts according to changes in lifestyle patterns. The generation unit also develops an algorithm in which the generation AI integrates the emotional data, lifestyle pattern data, and vital sign data of the care recipient to predict future care needs. For example, the generation unit creates a plan to assign staff to specific time periods. This makes it possible to predict future care needs and create long-term shift plans.

[0068] The generation unit can generate optimal shifts based on the care recipient's data in accordance with the skills and experience of the care staff. For example, the generation unit builds a system in which a generation AI analyzes the care recipient's data and generates optimal shifts based on the care staff's skills and experience. For example, it assigns staff with specific skills to the required time periods. The generation unit also generates optimal shifts based on the care recipient's data in accordance with the care staff's skills and experience using a generation AI. For example, it assigns experienced staff to unstable time periods. The generation unit also develops an algorithm in which the generation AI integrates the care recipient's emotional data, lifestyle pattern data, and vital sign data to generate optimal shifts based on the care staff's skills and experience. For example, it assigns staff with specific skills to the required time periods. This makes it possible to generate optimal shifts based on the care staff's skills and experience.

[0069] The generation unit can optimize the training program for care staff based on the data of the care recipient. For example, the generation unit constructs a system in which the generation AI analyzes the data of the care recipient and optimizes the training program for care staff. For example, it identifies the necessary skills based on emotional data and vital data. The generation unit also optimizes the training program for care staff based on the data of the care recipient. For example, it adjusts the training content according to changes in lifestyle patterns. The generation unit also develops an algorithm in which the generation AI integrates the emotional data, lifestyle pattern data, and vital data of the care recipient and optimizes the training program for care staff. For example, it suggests training to strengthen specific skills. This makes it possible to optimize the training program for care staff.

[0070] The generation unit can propose the optimal allocation of resources in the nursing facility based on the data of the care recipient. For example, the generation unit builds a system in which the generation AI analyzes the data of the care recipient and proposes the optimal allocation of resources in the nursing facility. For example, it identifies necessary equipment based on emotional data and vital data. The generation unit also proposes the optimal allocation of resources in the nursing facility based on the data of the care recipient. For example, it adjusts the allocation of equipment according to changes in lifestyle patterns. The generation unit also develops an algorithm in which the generation AI integrates the emotional data, lifestyle pattern data, and vital data of the care recipient and proposes the optimal allocation of resources in the nursing facility. For example, it arranges medications that are needed at specific times of the day. This makes it possible to propose the optimal allocation of resources in the nursing facility.

[0071] The generation unit generates shifts that take into account the emotional state of care staff based on data about the care recipient, thereby supporting stress management for staff. For example, the generation unit builds a system in which a generation AI analyzes data about the care recipient and generates shifts that take into account the emotional state of care staff. For example, the generation unit evaluates the stress level of staff based on emotional data. The generation unit also generates shifts that take into account the emotional state of care staff based on data about the care recipient. For example, the generation AI suggests breaks during times when stress is high. The generation unit also develops an algorithm in which the generation AI integrates the emotional data, lifestyle pattern data, and vital sign data of the care recipient, and generates shifts that take into account the emotional state of care staff. For example, the generation AI suggests breaks during times when stress is high. This allows for the generation of shifts that take into account the emotional state of care staff to support stress management.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The emotion recognition unit can propose the optimal care method for emotional fluctuations based on the emotional data of the care recipient. For example, the emotion recognition unit uses emotion recognition AI to analyze the emotional data of the care recipient and propose the optimal care method for emotional fluctuations. For example, if the care recipient is feeling anxious, it may propose relaxing music. The emotion recognition unit also uses emotion recognition AI to propose specific care actions for emotional fluctuations based on the emotional data of the care recipient. For example, if the care recipient is feeling angry, it may encourage deep breathing. The emotion recognition unit also uses emotion recognition AI to analyze the emotional data of the care recipient and propose the optimal care schedule for emotional fluctuations. For example, it may propose rehabilitation at a time when emotions are stable. This makes it possible to propose the optimal care method for emotional fluctuations of the care recipient.

[0074] The lifestyle pattern collection unit can collect the lifestyle patterns of the care recipient in real time and update the data instantly. For example, the lifestyle pattern collection unit uses conventional AI to collect the lifestyle patterns of the care recipient (e.g., meal times and bedtimes) in real time and instantly update the database. For example, it records daily meal times. The lifestyle pattern collection unit also uses conventional AI to collect vital data of the care recipient (e.g., heart rate and blood pressure) in real time and instantly update the database. For example, it records heart rate fluctuations. The lifestyle pattern collection unit also uses conventional AI to integrate the lifestyle patterns and vital data of the care recipient and build a system that updates the data in real time. For example, it analyzes the relationship between meal times and heart rate. This allows the lifestyle patterns of the care recipient to be understood in real time and enables immediate response.

[0075] The emotion recognition unit accumulates emotional data of the care recipient and analyzes long-term patterns of emotional fluctuations to predict future emotional fluctuations. For example, the emotion recognition unit uses emotion recognition AI to accumulate emotional data of the care recipient over a long period of time and analyze emotional fluctuation patterns based on that data. For example, it predicts that emotions are more likely to fluctuate during certain seasons based on data from the past year. The emotion recognition unit also analyzes the emotional data of the care recipient and identifies patterns of emotional fluctuations in response to specific events or situations. For example, it finds a tendency for emotions to stabilize when family members visit. The emotion recognition unit also develops an algorithm to predict future emotional fluctuations based on the emotional data of the care recipient. For example, it predicts that emotions will become unstable during certain time periods and takes countermeasures in advance. This makes it possible to understand the long-term patterns of emotional fluctuations of the care recipient and predict future emotional fluctuations.

[0076] The lifestyle pattern collection unit can detect abnormal patterns based on the lifestyle patterns and vital data of the care recipient and issue early warnings. For example, the lifestyle pattern collection unit uses conventional AI to analyze the lifestyle patterns and vital data of the care recipient and build a system to detect abnormal patterns. For example, it issues a warning if the care recipient does not eat at their usual mealtimes. The lifestyle pattern collection unit also uses conventional AI to detect abnormal patterns based on the care recipient's vital data and issue early warnings. For example, it issues an alert if the heart rate suddenly rises. The lifestyle pattern collection unit also uses conventional AI to integrate the lifestyle patterns and vital data of the care recipient and develop an algorithm to detect abnormal patterns. For example, it issues a warning if the care recipient wakes up at their usual bedtime. This makes it possible to detect abnormal patterns early and issue a warning.

[0077] The emotion recognition unit can also monitor the emotional state of care staff based on the emotional data of the care recipient, thereby supporting stress management for staff. For example, the emotion recognition unit will build a system in which emotion recognition AI monitors the emotional state of care staff based on the emotional data of the care recipient. For example, it will analyze the staff's facial expressions and tone of voice to evaluate their stress level. The emotion recognition unit will also suggest specific actions to support stress management for care staff based on the emotional data of the care recipient. For example, it will suggest relaxing break times. The emotion recognition unit will also develop a system in which emotion recognition AI analyzes the emotional data of the care recipient and monitors the emotional state of care staff in real time. For example, it will issue an alert if stress increases. This will enable monitoring of the emotional state of care staff and support stress management.

[0078] The generation unit can generate optimal shifts based on the care recipient's data in accordance with the skills and experience of the care staff. For example, the generation unit builds a system in which a generation AI analyzes the care recipient's data and generates optimal shifts based on the care staff's skills and experience. For example, it assigns staff with specific skills to the required time periods. The generation unit also generates optimal shifts based on the care recipient's data in accordance with the care staff's skills and experience using a generation AI. For example, it assigns experienced staff to unstable time periods. The generation unit also develops an algorithm in which the generation AI integrates the care recipient's emotional data, lifestyle pattern data, and vital sign data to generate optimal shifts based on the care staff's skills and experience. For example, it assigns staff with specific skills to the required time periods. This makes it possible to generate optimal shifts based on the care staff's skills and experience.

[0079] The emotion recognition unit can notify the care recipient's family and friends of emotional fluctuations based on the care recipient's emotional data and encourage them to provide support. For example, the emotion recognition unit uses emotion recognition AI to analyze the care recipient's emotional data and build a system that notifies family and friends of emotional fluctuations. For example, it notifies family members if the care recipient is feeling anxious. The emotion recognition unit also uses emotion recognition AI to suggest specific support actions to family and friends based on the care recipient's emotional data. For example, it suggests encouraging them over the phone. The emotion recognition unit also uses emotion recognition AI to analyze the care recipient's emotional data and develop a notification system that encourages family and friends to provide support for emotional fluctuations. For example, it suggests visiting during times when the care recipient is emotionally unstable. This makes it possible to notify family and friends of the care recipient's emotional fluctuations and encourage their support.

[0080] The lifestyle pattern collection unit can suggest an optimal lifestyle rhythm based on the lifestyle patterns and vital data of the care recipient. For example, the lifestyle pattern collection unit uses conventional AI to analyze the lifestyle patterns and vital data of the care recipient and build a system that suggests an optimal lifestyle rhythm. For example, it makes suggestions to adjust meal times and bedtimes. The lifestyle pattern collection unit also uses conventional AI to suggest an optimal lifestyle rhythm based on the vital data of the care recipient. For example, it suggests exercise time based on heart rate and blood pressure data. The lifestyle pattern collection unit also develops an algorithm that uses conventional AI to integrate the lifestyle patterns and vital data of the care recipient and suggest an optimal lifestyle rhythm. For example, it suggests a balance between meal times and exercise time. This makes it possible to suggest an optimal lifestyle rhythm for the care recipient.

[0081] The generation unit can predict future care needs based on data on the care recipient and create long-term shift plans. For example, the generation unit uses a generation AI to analyze data on the care recipient and build a system to predict future care needs. For example, the generation unit creates long-term shift plans based on emotional data and vital sign data. The generation unit also uses a generation AI to predict future care needs based on data on the care recipient and create long-term shift plans. For example, the generation unit adjusts shifts according to changes in lifestyle patterns. The generation unit also develops an algorithm in which the generation AI integrates the emotional data, lifestyle pattern data, and vital sign data of the care recipient to predict future care needs. For example, the generation unit creates a plan to assign staff to specific time periods. This makes it possible to predict future care needs and create long-term shift plans.

[0082] The emotion recognition unit can notify the care recipient's family and friends of emotional fluctuations based on the care recipient's emotional data and encourage them to provide support. For example, the emotion recognition unit uses emotion recognition AI to analyze the care recipient's emotional data and build a system that notifies family and friends of emotional fluctuations. For example, it notifies family members if the care recipient is feeling anxious. The emotion recognition unit also uses emotion recognition AI to suggest specific support actions to family and friends based on the care recipient's emotional data. For example, it suggests encouraging them over the phone. The emotion recognition unit also uses emotion recognition AI to analyze the care recipient's emotional data and develop a notification system that encourages family and friends to provide support for emotional fluctuations. For example, it suggests visiting during times when the care recipient is emotionally unstable. This makes it possible to notify family and friends of the care recipient's emotional fluctuations and encourage their support.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The emotion recognition unit digitizes the emotions of the care recipient. For example, the emotion recognition unit uses facial expression recognition technology to analyze the facial expressions of the care recipient and digitize the emotions. It can also use voice analysis technology to analyze the tone of the care recipient's voice and digitize the emotions. It can also use biometrics technology to analyze the physiological reactions of the care recipient and digitize the emotions. Step 2: The lifestyle pattern collection unit collects the lifestyle patterns of the care recipient, such as daily behavior history, activity level, and sleep pattern, and stores them in a database. Step 3: The vital data collection unit collects vital data of the care recipient, such as heart rate, blood pressure, and body temperature, and stores the data in a database. Step 4: The generation unit generates work shifts based on the data collected from the emotion recognition unit, lifestyle pattern collection unit, and vital data collection unit. For example, it combines emotion data, lifestyle pattern data, and vital data to generate optimal work shifts. It is also possible to use the generation AI to generate optimal shifts based on the data of the care recipient and the skills and experience of the caregivers.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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 AI 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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 AI 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] 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.

[0131] 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.

[0132] 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 AI 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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]

[0152] 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. an emotion recognition unit that converts the emotions of the care recipient into data; a life pattern collection unit that collects life patterns of a care recipient; a vital data collection unit that collects vital data of the care recipient; a generation unit that generates a work shift based on the data collected from the emotion recognition unit, the life pattern collection unit, and the vital data collection unit. A system characterized by:

2. The emotion recognition unit Monitor the emotions of the care recipient in real time and update the data immediately according to changes in emotions.

2. The system of claim 1.

3. The emotion recognition unit By accumulating emotional data of the care recipient and analyzing long-term patterns of emotional fluctuations, future emotional fluctuations can be predicted.

2. The system of claim 1.

4. The emotion recognition unit Based on the emotional data of the care recipient, we propose the optimal care method for responding to emotional fluctuations.

2. The system of claim 1.

5. The emotion recognition unit Integrating the emotional data of the care recipient with other health data to assess their overall health status.

2. The system of claim 1.

6. The emotion recognition unit Based on the emotional data of the care recipient, the care recipient's family and friends are notified of emotional fluctuations and are encouraged to provide support.

2. The system of claim 1.

7. The emotion recognition unit Based on the emotional data of the care recipient, the emotional state of the caregiver is also monitored to support stress management for the caregiver.

2. The system of claim 1.

8. The lifestyle pattern collection unit Collecting the life patterns of the care recipient in real time and updating the data immediately 2. The system of claim 1.

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