system

Abstract

The system according to this embodiment aims to evaluate the health status and behavioral patterns of the subject and to formulate an efficient care plan. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit monitors the subject's movements in real time using a camera and collects the data. The analysis unit analyzes the data collected by the collection unit and evaluates the health status and behavioral patterns. The provision unit formulates a care plan based on the evaluation results obtained by the analysis unit and provides it to the care staff.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to appropriately grasp the health state of the subject.

[0005] The system according to the embodiment aims to evaluate the health state and behavior pattern of the subject and formulate an efficient care plan.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit monitors the subject's movements in real time using a camera and collects the data. The analysis unit analyzes the data collected by the data collection unit and evaluates the health status and behavioral patterns. The data provision unit formulates a care plan based on the evaluation results obtained by the analysis unit and provides it to the care staff. [Effects of the Invention]

[0007] The system according to this embodiment can evaluate the health status and behavioral patterns of the subject and formulate an efficient care plan. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The care support system according to an embodiment of the present invention is a system that supports care by analyzing the movements of a person using a camera installed in a room, and analyzing their health status and behavioral patterns. The care support system uses a camera to monitor the movements of the person in real time and collects the data. Next, an AI analyzes the collected data and evaluates the health status and behavioral patterns. Furthermore, it formulates a care plan based on the evaluation results and provides it to the care staff. This system reduces the burden on care staff and enables efficient care planning and optimization of health management. It also contributes to the early detection of dementia and enables the provision of effective care. For example, the care support system uses a camera to monitor the movements of the person in real time. For example, the camera captures the person walking around the room or eating. This data is transmitted to the AI ​​and analyzed. Next, the AI ​​analyzes the collected data. The AI ​​analyzes the movements of the person in detail and evaluates their health status and behavioral patterns. For example, it analyzes the person's walking speed, changes in posture, and food intake to evaluate their health status. Furthermore, by analyzing behavioral patterns, it can also be used for the early detection of dementia. Furthermore, it formulates a care plan based on the evaluation results. Based on the evaluation results, the AI ​​creates an optimal care plan for each individual. For example, it suggests exercise programs and meal plans according to the individual's health condition. This care plan is provided to and implemented by care staff. This allows the care support system to reduce the burden on care staff. By having the AI ​​evaluate health conditions and behavioral patterns, care staff can more easily understand the individual's condition. Furthermore, by developing efficient care plans, the quality of care can be improved. In addition, it contributes to the early detection of dementia. By analyzing behavioral patterns, the AI ​​can detect signs of dementia early and provide appropriate care. This can improve the individual's QOL (quality of life). The care support system is intended for use in nursing homes and home healthcare settings. In an aging society, the shortage of care personnel and the increase in dementia patients are becoming problems, so the demand for AI-powered care support systems is growing. This system is expected to improve care services based on scientific data.This allows the care support system to gain a detailed understanding of the health status and behavioral patterns of the individuals it serves, enabling efficient care planning and optimized health management.

[0029] The care support system according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit monitors the movements of a subject in real time using a camera and collects the data. The data collection unit can, for example, use a camera installed in the room to monitor the movements of the subject in real time. The data collection unit can, for example, capture the subject walking around the room or eating meals using the camera. The data collection unit can, for example, collect the data captured by the camera in real time and transmit it to an AI. The analysis unit analyzes the data collected by the data collection unit and evaluates the health status and behavioral patterns. The analysis unit can, for example, use an AI to analyze the collected data in detail. The analysis unit can, for example, analyze the subject's walking speed, changes in posture, and food intake to evaluate their health status. The analysis unit can, for example, use behavioral patterns to help in the early detection of dementia. The data provision unit formulates a care plan based on the evaluation results obtained by the analysis unit and provides it to the care staff. The data provision unit can, for example, use an AI to create an optimal care plan for the subject based on the evaluation results. The service provider can, for example, propose exercise programs and meal plans according to the health condition of the individual. The service provider can, for example, provide the created care plan to the care staff and enable its implementation. As a result, the care support system according to this embodiment can monitor the individual's movements in real time, evaluate their health condition and behavioral patterns, and provide a care plan, thereby enabling efficient care planning and optimization of health management.

[0030] The data collection unit uses cameras to monitor the subject's movements in real time and collects the data. Specifically, it uses high-resolution cameras installed in the room to capture the subject's movements in detail. The cameras are equipped with wide-angle lenses and can cover the entire room, so the subject's movements can be monitored no matter where they are in the room. The data collection unit can capture all kinds of daily activities, such as the subject walking around the room, eating, or watching television. Furthermore, the cameras are equipped with infrared sensors, allowing for accurate monitoring of the subject's movements even at night or in dark places. The data collection unit can process the video data captured by the cameras in real time and transmit it to AI. This allows the data collection unit to continuously monitor the subject's movements and immediately notify if an abnormality occurs. For example, if the subject falls or remains motionless for a long period of time, the data collection unit can detect the abnormality and send an alert to the care staff. This allows the data collection unit to ensure the subject's safety and enable a quick response. In addition, the data collection unit stores the collected data on a cloud server, making it accessible to the analysis unit and the data provision unit. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the data collected by the data collection unit to evaluate health status and behavioral patterns. Specifically, it can use AI to analyze the collected data in detail. The AI ​​uses image recognition technology to analyze the subject's movements and evaluate changes in walking speed, posture, and food intake. For example, the AI ​​can detect changes in health status if the subject's walking speed becomes slower than usual or their posture becomes unnatural. By analyzing food intake, it can evaluate nutritional status and encourage the intake of necessary nutrients. Furthermore, by analyzing behavioral patterns, the analysis unit can help in the early detection of dementia. For example, if the subject repeatedly walks around the same place or experiences increased forgetfulness, it can detect signs of dementia and allow for early intervention. The analysis unit can also utilize past data and statistical information to evaluate long-term changes in health status and predict future risks. This allows the analysis unit to continuously monitor the subject's health status and provide information for developing appropriate care plans. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term health management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The service provider develops care plans based on evaluation results obtained by the analysis department and provides them to care staff. Specifically, using AI, it can create optimal care plans for each individual based on the evaluation results. The AI ​​can propose individually optimized exercise programs and meal plans, taking into account the individual's health condition and behavioral patterns. For example, if an individual's walking speed has slowed, it can propose an exercise program including muscle training and balance training, and if their food intake has decreased, it can propose a highly nutritious meal plan. The service provider provides the created care plan to the care staff, who can then implement it. The care staff can provide care to the individual based on the provided care plan. For example, when implementing an exercise program, care staff can support the individual and instruct them to perform the exercises appropriately. Similarly, when implementing a meal plan, care staff can prepare the individual's meals and support them in obtaining the necessary nutrients. Furthermore, the service provider monitors the implementation status of the care plan and can revise the plan as needed. For example, if the individual's health condition improves, the exercise program and meal plan can be reviewed to provide more effective care. This allows the service provider to offer flexible care plans tailored to the health condition of the individuals receiving care, thereby enabling efficient and effective care.

[0033] The analysis unit can analyze the subject's walking speed, changes in posture, food intake, etc., and evaluate their health status. For example, the analysis unit can analyze the subject's walking speed. For example, the analysis unit can analyze changes in the subject's posture. For example, the analysis unit can analyze the subject's food intake. In this way, the analysis unit can evaluate the subject's health status in detail by analyzing changes in walking speed, changes in posture, food intake, etc. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the subject's walking speed data into a generating AI and have the generating AI perform the walking speed analysis.

[0034] The analysis unit can analyze behavioral patterns and contribute to the early detection of dementia. For example, the analysis unit can analyze the subject's daily living activities. For example, the analysis unit can analyze the subject's exercise habits. For example, the analysis unit can analyze the subject's behavioral patterns in detail. In this way, the analysis unit can help in the early detection of dementia by analyzing behavioral patterns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the subject's behavioral pattern data into a generating AI and have the generating AI perform the analysis of the behavioral patterns.

[0035] The service provider can propose exercise programs and meal plans based on the evaluation results. For example, the service provider can propose an optimal exercise program for the subject based on the evaluation results. For example, the service provider can propose an optimal meal plan for the subject based on the evaluation results. For example, the service provider can propose a care plan tailored to the subject's health condition based on the evaluation results. In this way, the service provider can provide an optimal care plan for the subject by proposing exercise programs and meal plans based on the evaluation results. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the evaluation results into a generating AI and have the generating AI execute the proposal of exercise programs and meal plans.

[0036] The service provider can provide evaluation results to care staff and implement care plans. For example, the service provider can provide evaluation results to care staff. For example, the service provider can provide care plans to care staff and implement them. For example, the service provider can provide care staff with specific care plans based on evaluation results. This enables the service provider to efficiently implement care plans by providing evaluation results to care staff. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input evaluation results into a generating AI and have the generating AI execute the implementation of the care plan.

[0037] The data collection unit can analyze the subject's past behavioral data and select the optimal data collection method. For example, the data collection unit can collect data from the subject's past behavioral data during the most stable time period. For example, the data collection unit can focus on collecting specific behaviors based on the subject's past behavioral data. For example, the data collection unit can analyze the subject's past behavioral data and optimize the data collection frequency. This allows the data collection unit to select the optimal data collection method by analyzing the subject's past behavioral data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the subject's past behavioral data into a generating AI and have the generating AI select the optimal data collection method.

[0038] The data collection unit can filter motion data based on the subject's current health status and lifestyle. For example, if the subject is unwell, the data collection unit can limit the amount of data collected. For example, if the subject is busy, the data collection unit can reduce the amount of data collected. For example, if the subject is relaxed, the data collection unit can collect more detailed data. This allows the data collection unit to collect more appropriate data by filtering the data based on the subject's current health status and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the subject's health status data into a generating AI and have the generating AI perform data filtering.

[0039] The data collection unit can prioritize the collection of highly relevant data by considering the subject's geographical location information when collecting motion data. For example, if the subject is at home, the data collection unit can prioritize the collection of motion data related to daily life. For example, if the subject is out, the data collection unit can prioritize the collection of motion data related to movement. For example, if the subject is at a specific facility, the data collection unit can prioritize the collection of motion data related to activities at that facility. In this way, the data collection unit can prioritize the collection of more relevant data by considering the subject's geographical location information when collecting data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the subject's geographical location information data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0040] The data collection unit can analyze the subject's social media activity and collect relevant data when collecting behavioral data. For example, if the subject is very active on social media, the data collection unit can collect behavioral data related to that activity. For example, if the subject is inactive on social media, the data collection unit can collect behavioral data related to other activities. For example, the data collection unit can collect behavioral data related to specific interests or concerns from the subject's social media activity. In this way, the data collection unit can collect relevant data by analyzing the subject's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the subject's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0041] The analysis unit can adjust the accuracy of the evaluation based on the level of detail of the subject's movement data during analysis. For example, if detailed movement data is collected, the analysis unit can perform a highly accurate health assessment. For example, if simplified movement data is collected, the analysis unit can perform a general health assessment. For example, the analysis unit can adjust the accuracy of the evaluation in stages according to the level of detail of the movement data. This allows the analysis unit to perform a more accurate health assessment by adjusting the accuracy of the evaluation based on the level of detail of the subject's movement data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the subject's movement data into a generating AI and have the generating AI perform the adjustment of the evaluation accuracy.

[0042] The analysis unit can apply different analysis algorithms depending on the category of the subject during analysis. For example, in the case of elderly individuals, the analysis unit can apply an analysis algorithm that corresponds to specific health risks. For example, in the case of young people, the analysis unit can apply an analysis algorithm that corresponds to their activity level. For example, in the case of subjects with specific diseases, the analysis unit can apply an analysis algorithm that corresponds to those diseases. In this way, the analysis unit can perform a more appropriate health assessment by applying different analysis algorithms depending on the category of the subject. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input subject category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0043] The analysis unit can determine the evaluation priority based on when the subject's motion data was collected during the analysis. For example, the analysis unit can prioritize the evaluation of recently collected motion data. For example, the analysis unit can prioritize the evaluation of motion data collected during a specific time period. For example, the analysis unit can evaluate the latest data while referring to past motion data. In this way, the analysis unit can prioritize the evaluation of more important data by determining the evaluation priority based on when the subject's motion data was collected. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the subject's motion data into a generating AI and have the generating AI perform the determination of the evaluation priority.

[0044] The analysis unit can improve the accuracy of its evaluation by referring to relevant literature on the subject during the analysis. For example, the analysis unit can refer to the latest research papers related to the subject's health status. For example, the analysis unit can refer to specialized books on the subject's diseases. For example, the analysis unit can refer to guidelines related to the subject's health assessment. In this way, the analysis unit can improve the accuracy of its evaluation by referring to relevant literature on the subject. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the improvement of the evaluation accuracy.

[0045] The service provider can adjust the level of detail in the care plan based on the importance of the evaluation results when making a proposal. For example, the service provider can propose a detailed care plan based on important evaluation results. For example, the service provider can propose a simplified care plan based on general evaluation results. For example, the service provider can adjust the level of detail in the care plan in stages according to the importance of the evaluation results. This allows the service provider to provide a more appropriate care plan by adjusting the level of detail in the care plan based on the importance of the evaluation results. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input evaluation result data into a generating AI and have the generating AI perform the adjustment of the level of detail in the care plan.

[0046] The service provider can apply different care plan algorithms depending on the category of the target individual when making a proposal. For example, in the case of an elderly person, the service provider can apply a care plan algorithm that corresponds to a specific health risk. For example, in the case of a young person, the service provider can apply a care plan algorithm that corresponds to the activity level. For example, in the case of a target individual with a specific disease, the service provider can apply a care plan algorithm that corresponds to that disease. In this way, the service provider can provide a more appropriate care plan by applying different care plan algorithms depending on the category of the target individual. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the target individual's category data into a generating AI and have the generating AI execute the application of the care plan algorithm.

[0047] The service provider can determine the priority of care plans based on the timing of evaluation results collection when making a proposal. For example, the service provider can determine the priority of care plans based on recent evaluation results. For example, the service provider can determine the priority of care plans based on evaluation results collected during a specific time period. For example, the service provider can determine the priority of care plans by giving more weight to the latest evaluation results while referring to past evaluation results. This allows the service provider to provide more appropriate care plans by determining the priority of care plans based on the timing of evaluation results collection. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input evaluation result data into a generating AI and have the generating AI perform the determination of care plan priorities.

[0048] The service provider can adjust the care plan by referring to relevant market data for the target individual when making a proposal. For example, the service provider can adjust the care plan by referring to market data related to the target individual's health status. For example, the service provider can adjust the care plan by referring to market data related to the target individual's illness. For example, the service provider can adjust the care plan by referring to market data related to the target individual's health assessment. This allows the service provider to provide a more appropriate care plan by referring to relevant market data for the target individual. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input relevant market data into a generating AI and have the generating AI perform the adjustment of the care plan.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The data collection unit can analyze the subject's past health data and select the optimal data collection method. For example, it can collect data at specific time periods based on the subject's past health data. Based on the subject's past health data, it can focus on collecting specific health indicators. It can analyze the subject's past health data and optimize the collection frequency. In this way, the data collection unit can select the optimal data collection method by analyzing the subject's past health data.

[0051] The analysis unit can analyze the lifestyle data of subjects and reflect it in the assessment of their health status. For example, it can analyze the subjects' sleep patterns and reflect them in the health assessment. It can analyze the subjects' eating habits and reflect them in the health assessment. It can analyze the subjects' exercise habits and reflect them in the health assessment. As a result, the analysis unit can perform a more accurate health assessment by reflecting the subjects' lifestyle data in the health assessment.

[0052] The service provider can determine the priority of care plans based on the health condition of the person receiving care. For example, if the person's health condition is deteriorating, urgent care plans can be prioritized. If the person's health condition is stable, long-term care plans can be prioritized. The priority of care plans can be adjusted in stages according to the person's health condition. This allows the service provider to provide appropriate care plans based on the person's health condition.

[0053] The data collection unit can adjust its data collection methods to take into account the geographical location of the subjects. For example, if a subject is at home, it can collect data related to their daily life. If a subject is out, it can collect data related to their movement. If a subject is at a specific facility, it can collect data related to their activities at that facility. This allows the data collection unit to collect more relevant data by taking into account the geographical location of the subjects.

[0054] The analysis unit can integrate the subject's health data with other data sources to perform a comprehensive health assessment. For example, it can integrate the subject's medical records and reflect them in the health assessment. It can integrate data from the subject's fitness tracker and reflect it in the health assessment. It can integrate the subject's dietary records and reflect them in the health assessment. As a result, the analysis unit can perform a more accurate health assessment by integrating multiple data sources.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The data collection unit monitors the subject's movements in real time using cameras and collects the data. For example, a camera installed in the room can capture the subject walking around the room or eating, collect the data in real time, and transmit it to the AI. Step 2: The analysis unit analyzes the data collected by the collection unit to evaluate health status and behavioral patterns. For example, it uses AI to analyze the collected data in detail, analyzing changes in the subject's walking speed and posture, food intake, etc., to evaluate their health status. Furthermore, by analyzing behavioral patterns, it can be useful in the early detection of dementia. Step 3: The service provider develops a care plan based on the evaluation results obtained by the analysis unit and provides it to the care staff. For example, using AI, they create an optimal care plan for the individual based on the evaluation results and propose exercise programs and meal plans. The created care plan is then provided to the care staff and implemented.

[0057] (Example of form 2) The care support system according to an embodiment of the present invention is a system that supports care by analyzing the movements of a person using a camera installed in a room, and analyzing their health status and behavioral patterns. The care support system uses a camera to monitor the movements of the person in real time and collects the data. Next, an AI analyzes the collected data and evaluates the health status and behavioral patterns. Furthermore, it formulates a care plan based on the evaluation results and provides it to the care staff. This system reduces the burden on care staff and enables efficient care planning and optimization of health management. It also contributes to the early detection of dementia and enables the provision of effective care. For example, the care support system uses a camera to monitor the movements of the person in real time. For example, the camera captures the person walking around the room or eating. This data is transmitted to the AI ​​and analyzed. Next, the AI ​​analyzes the collected data. The AI ​​analyzes the movements of the person in detail and evaluates their health status and behavioral patterns. For example, it analyzes the person's walking speed, changes in posture, and food intake to evaluate their health status. Furthermore, by analyzing behavioral patterns, it can also be used for the early detection of dementia. Furthermore, it formulates a care plan based on the evaluation results. Based on the evaluation results, the AI ​​creates an optimal care plan for each individual. For example, it suggests exercise programs and meal plans according to the individual's health condition. This care plan is provided to and implemented by care staff. This allows the care support system to reduce the burden on care staff. By having the AI ​​evaluate health conditions and behavioral patterns, care staff can more easily understand the individual's condition. Furthermore, by developing efficient care plans, the quality of care can be improved. In addition, it contributes to the early detection of dementia. By analyzing behavioral patterns, the AI ​​can detect signs of dementia early and provide appropriate care. This can improve the individual's QOL (quality of life). The care support system is intended for use in nursing homes and home healthcare settings. In an aging society, the shortage of care personnel and the increase in dementia patients are becoming problems, so the demand for AI-powered care support systems is growing. This system is expected to improve care services based on scientific data.This allows the care support system to gain a detailed understanding of the health status and behavioral patterns of the individuals it serves, enabling efficient care planning and optimized health management.

[0058] The care support system according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit monitors the movements of a subject in real time using a camera and collects the data. The data collection unit can, for example, use a camera installed in the room to monitor the movements of the subject in real time. The data collection unit can, for example, capture the subject walking around the room or eating meals using the camera. The data collection unit can, for example, collect the data captured by the camera in real time and transmit it to an AI. The analysis unit analyzes the data collected by the data collection unit and evaluates the health status and behavioral patterns. The analysis unit can, for example, use an AI to analyze the collected data in detail. The analysis unit can, for example, analyze the subject's walking speed, changes in posture, and food intake to evaluate their health status. The analysis unit can, for example, use behavioral patterns to help in the early detection of dementia. The data provision unit formulates a care plan based on the evaluation results obtained by the analysis unit and provides it to the care staff. The data provision unit can, for example, use an AI to create an optimal care plan for the subject based on the evaluation results. The service provider can, for example, propose exercise programs and meal plans according to the health condition of the individual. The service provider can, for example, provide the created care plan to the care staff and enable its implementation. As a result, the care support system according to this embodiment can monitor the individual's movements in real time, evaluate their health condition and behavioral patterns, and provide a care plan, thereby enabling efficient care planning and optimization of health management.

[0059] The data collection unit uses cameras to monitor the subject's movements in real time and collects the data. Specifically, it uses high-resolution cameras installed in the room to capture the subject's movements in detail. The cameras are equipped with wide-angle lenses and can cover the entire room, so the subject's movements can be monitored no matter where they are in the room. The data collection unit can capture all kinds of daily activities, such as the subject walking around the room, eating, or watching television. Furthermore, the cameras are equipped with infrared sensors, allowing for accurate monitoring of the subject's movements even at night or in dark places. The data collection unit can process the video data captured by the cameras in real time and transmit it to AI. This allows the data collection unit to continuously monitor the subject's movements and immediately notify if an abnormality occurs. For example, if the subject falls or remains motionless for a long period of time, the data collection unit can detect the abnormality and send an alert to the care staff. This allows the data collection unit to ensure the subject's safety and enable a quick response. In addition, the data collection unit stores the collected data on a cloud server, making it accessible to the analysis unit and the data provision unit. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0060] The analysis unit analyzes the data collected by the data collection unit to evaluate health status and behavioral patterns. Specifically, it can use AI to analyze the collected data in detail. The AI ​​uses image recognition technology to analyze the subject's movements and evaluate changes in walking speed, posture, and food intake. For example, the AI ​​can detect changes in health status if the subject's walking speed becomes slower than usual or their posture becomes unnatural. By analyzing food intake, it can evaluate nutritional status and encourage the intake of necessary nutrients. Furthermore, by analyzing behavioral patterns, the analysis unit can help in the early detection of dementia. For example, if the subject repeatedly walks around the same place or experiences increased forgetfulness, it can detect signs of dementia and allow for early intervention. The analysis unit can also utilize past data and statistical information to evaluate long-term changes in health status and predict future risks. This allows the analysis unit to continuously monitor the subject's health status and provide information for developing appropriate care plans. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term health management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0061] The service provider develops care plans based on evaluation results obtained by the analysis department and provides them to care staff. Specifically, using AI, it can create optimal care plans for each individual based on the evaluation results. The AI ​​can propose individually optimized exercise programs and meal plans, taking into account the individual's health condition and behavioral patterns. For example, if an individual's walking speed has slowed, it can propose an exercise program including muscle training and balance training, and if their food intake has decreased, it can propose a highly nutritious meal plan. The service provider provides the created care plan to the care staff, who can then implement it. The care staff can provide care to the individual based on the provided care plan. For example, when implementing an exercise program, care staff can support the individual and instruct them to perform the exercises appropriately. Similarly, when implementing a meal plan, care staff can prepare the individual's meals and support them in obtaining the necessary nutrients. Furthermore, the service provider monitors the implementation status of the care plan and can revise the plan as needed. For example, if the individual's health condition improves, the exercise program and meal plan can be reviewed to provide more effective care. This allows the service provider to offer flexible care plans tailored to the health condition of the individuals receiving care, thereby enabling efficient and effective care.

[0062] The analysis unit can analyze the subject's walking speed, changes in posture, food intake, etc., and evaluate their health status. For example, the analysis unit can analyze the subject's walking speed. For example, the analysis unit can analyze changes in the subject's posture. For example, the analysis unit can analyze the subject's food intake. In this way, the analysis unit can evaluate the subject's health status in detail by analyzing changes in walking speed, changes in posture, food intake, etc. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the subject's walking speed data into a generating AI and have the generating AI perform the walking speed analysis.

[0063] The analysis unit can analyze behavioral patterns and contribute to the early detection of dementia. For example, the analysis unit can analyze the subject's daily living activities. For example, the analysis unit can analyze the subject's exercise habits. For example, the analysis unit can analyze the subject's behavioral patterns in detail. In this way, the analysis unit can help in the early detection of dementia by analyzing behavioral patterns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the subject's behavioral pattern data into a generating AI and have the generating AI perform the analysis of the behavioral patterns.

[0064] The service provider can propose exercise programs and meal plans based on the evaluation results. For example, the service provider can propose an optimal exercise program for the subject based on the evaluation results. For example, the service provider can propose an optimal meal plan for the subject based on the evaluation results. For example, the service provider can propose a care plan tailored to the subject's health condition based on the evaluation results. In this way, the service provider can provide an optimal care plan for the subject by proposing exercise programs and meal plans based on the evaluation results. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the evaluation results into a generating AI and have the generating AI execute the proposal of exercise programs and meal plans.

[0065] The service provider can provide evaluation results to care staff and implement care plans. For example, the service provider can provide evaluation results to care staff. For example, the service provider can provide care plans to care staff and implement them. For example, the service provider can provide care staff with specific care plans based on evaluation results. This enables the service provider to efficiently implement care plans by providing evaluation results to care staff. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input evaluation results into a generating AI and have the generating AI execute the implementation of the care plan.

[0066] The data collection unit can estimate the subject's emotions and adjust the timing of motion data collection based on the estimated emotions. For example, if the subject is stressed, the data collection unit can collect motion data during periods of relaxation. For example, if the subject is tired, the data collection unit can collect motion data after rest. For example, if the subject is excited, the data collection unit can collect motion data after calming down. This allows the data collection unit to collect more appropriate data by adjusting the timing of motion data collection based on the subject's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the subject's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0067] The data collection unit can analyze the subject's past behavioral data and select the optimal data collection method. For example, the data collection unit can collect data from the subject's past behavioral data during the most stable time period. For example, the data collection unit can focus on collecting specific behaviors based on the subject's past behavioral data. For example, the data collection unit can analyze the subject's past behavioral data and optimize the data collection frequency. This allows the data collection unit to select the optimal data collection method by analyzing the subject's past behavioral data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the subject's past behavioral data into a generating AI and have the generating AI select the optimal data collection method.

[0068] The data collection unit can filter motion data based on the subject's current health status and lifestyle. For example, if the subject is unwell, the data collection unit can limit the amount of data collected. For example, if the subject is busy, the data collection unit can reduce the amount of data collected. For example, if the subject is relaxed, the data collection unit can collect more detailed data. This allows the data collection unit to collect more appropriate data by filtering the data based on the subject's current health status and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the subject's health status data into a generating AI and have the generating AI perform data filtering.

[0069] The data collection unit can estimate the subject's emotions and determine the priority of behavioral data to collect based on the estimated emotions. For example, if the subject is stressed, the data collection unit can prioritize collecting behavioral data related to relaxation. For example, if the subject is tired, the data collection unit can prioritize collecting behavioral data related to rest. For example, if the subject is excited, the data collection unit can prioritize collecting behavioral data related to calmness. In this way, the data collection unit can prioritize collecting more important data by determining the priority of behavioral data to collect based on the subject's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the subject's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0070] The data collection unit can prioritize the collection of highly relevant data by considering the subject's geographical location information when collecting motion data. For example, if the subject is at home, the data collection unit can prioritize the collection of motion data related to daily life. For example, if the subject is out, the data collection unit can prioritize the collection of motion data related to movement. For example, if the subject is at a specific facility, the data collection unit can prioritize the collection of motion data related to activities at that facility. In this way, the data collection unit can prioritize the collection of more relevant data by considering the subject's geographical location information when collecting data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the subject's geographical location information data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0071] The data collection unit can analyze the subject's social media activity and collect relevant data when collecting behavioral data. For example, if the subject is very active on social media, the data collection unit can collect behavioral data related to that activity. For example, if the subject is inactive on social media, the data collection unit can collect behavioral data related to other activities. For example, the data collection unit can collect behavioral data related to specific interests or concerns from the subject's social media activity. In this way, the data collection unit can collect relevant data by analyzing the subject's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the subject's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0072] The analysis unit can estimate the subject's emotions and adjust the health assessment method based on the estimated emotions. For example, if the subject is stressed, the analysis unit can perform a health assessment that emphasizes the stress level. For example, if the subject is relaxed, the analysis unit can perform an assessment that emphasizes the overall health state. For example, if the subject is tired, the analysis unit can perform a health assessment that emphasizes the degree of fatigue. This allows the analysis unit to perform a more appropriate health assessment by adjusting the health assessment method based on the subject's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the subject's emotion data into the generative AI and have the generative AI perform the adjustment of the health assessment method.

[0073] The analysis unit can adjust the accuracy of the evaluation based on the level of detail of the subject's movement data during analysis. For example, if detailed movement data is collected, the analysis unit can perform a highly accurate health assessment. For example, if simplified movement data is collected, the analysis unit can perform a general health assessment. For example, the analysis unit can adjust the accuracy of the evaluation in stages according to the level of detail of the movement data. This allows the analysis unit to perform a more accurate health assessment by adjusting the accuracy of the evaluation based on the level of detail of the subject's movement data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the subject's movement data into a generating AI and have the generating AI perform the adjustment of the evaluation accuracy.

[0074] The analysis unit can apply different analysis algorithms depending on the category of the subject during analysis. For example, in the case of elderly individuals, the analysis unit can apply an analysis algorithm that corresponds to specific health risks. For example, in the case of young people, the analysis unit can apply an analysis algorithm that corresponds to their activity level. For example, in the case of subjects with specific diseases, the analysis unit can apply an analysis algorithm that corresponds to those diseases. In this way, the analysis unit can perform a more appropriate health assessment by applying different analysis algorithms depending on the category of the subject. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input subject category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0075] The analysis unit can estimate the subject's emotions and adjust the display method of the evaluation results based on the estimated emotions. For example, if the subject is stressed, the analysis unit can provide a simple and highly visible display method. For example, if the subject is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the subject is tired, the analysis unit can provide a display method that focuses on the essentials. This allows the analysis unit to provide more appropriate information by adjusting the display method of the evaluation results based on the subject's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the subject's emotion data into the generative AI and have the generative AI adjust the display method of the evaluation results.

[0076] The analysis unit can determine the evaluation priority based on when the subject's motion data was collected during the analysis. For example, the analysis unit can prioritize the evaluation of recently collected motion data. For example, the analysis unit can prioritize the evaluation of motion data collected during a specific time period. For example, the analysis unit can evaluate the latest data while referring to past motion data. In this way, the analysis unit can prioritize the evaluation of more important data by determining the evaluation priority based on when the subject's motion data was collected. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the subject's motion data into a generating AI and have the generating AI perform the determination of the evaluation priority.

[0077] The analysis unit can improve the accuracy of its evaluation by referring to relevant literature on the subject during the analysis. For example, the analysis unit can refer to the latest research papers related to the subject's health status. For example, the analysis unit can refer to specialized books on the subject's diseases. For example, the analysis unit can refer to guidelines related to the subject's health assessment. In this way, the analysis unit can improve the accuracy of its evaluation by referring to relevant literature on the subject. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the improvement of the evaluation accuracy.

[0078] The service provider can estimate the emotions of the subject and adjust the method of proposing care plans based on the estimated emotions. For example, if the subject is feeling stressed, the service provider can propose a care plan that promotes relaxation. For example, if the subject is relaxed, the service provider can propose an active care plan. For example, if the subject is tired, the service provider can propose a care plan that emphasizes rest. In this way, the service provider can provide a more appropriate care plan by adjusting the method of proposing care plans based on the emotions of the subject. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the subject's emotion data into a generative AI and have the generative AI adjust the method of proposing care plans.

[0079] The service provider can adjust the level of detail in the care plan based on the importance of the evaluation results when making a proposal. For example, the service provider can propose a detailed care plan based on important evaluation results. For example, the service provider can propose a simplified care plan based on general evaluation results. For example, the service provider can adjust the level of detail in the care plan in stages according to the importance of the evaluation results. This allows the service provider to provide a more appropriate care plan by adjusting the level of detail in the care plan based on the importance of the evaluation results. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input evaluation result data into a generating AI and have the generating AI perform the adjustment of the level of detail in the care plan.

[0080] The service provider can apply different care plan algorithms depending on the category of the target individual when making a proposal. For example, in the case of an elderly person, the service provider can apply a care plan algorithm that corresponds to a specific health risk. For example, in the case of a young person, the service provider can apply a care plan algorithm that corresponds to the activity level. For example, in the case of a target individual with a specific disease, the service provider can apply a care plan algorithm that corresponds to that disease. In this way, the service provider can provide a more appropriate care plan by applying different care plan algorithms depending on the category of the target individual. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the target individual's category data into a generating AI and have the generating AI execute the application of the care plan algorithm.

[0081] The service provider can estimate the emotions of the subject and determine the priority of care plans based on the estimated emotions. For example, if the subject is stressed, the service provider can prioritize a care plan that promotes relaxation. For example, if the subject is relaxed, the service provider can prioritize an active care plan. For example, if the subject is tired, the service provider can prioritize a care plan that emphasizes rest. In this way, the service provider can provide a more appropriate care plan by determining the priority of care plans based on the emotions of the subject. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the subject's emotion data into a generative AI and have the generative AI determine the priority of care plans.

[0082] The service provider can determine the priority of care plans based on the timing of evaluation results collection when making a proposal. For example, the service provider can determine the priority of care plans based on recent evaluation results. For example, the service provider can determine the priority of care plans based on evaluation results collected during a specific time period. For example, the service provider can determine the priority of care plans by giving more weight to the latest evaluation results while referring to past evaluation results. This allows the service provider to provide more appropriate care plans by determining the priority of care plans based on the timing of evaluation results collection. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input evaluation result data into a generating AI and have the generating AI perform the determination of care plan priorities.

[0083] The service provider can adjust the care plan by referring to relevant market data for the target individual when making a proposal. For example, the service provider can adjust the care plan by referring to market data related to the target individual's health status. For example, the service provider can adjust the care plan by referring to market data related to the target individual's illness. For example, the service provider can adjust the care plan by referring to market data related to the target individual's health assessment. This allows the service provider to provide a more appropriate care plan by referring to relevant market data for the target individual. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input relevant market data into a generating AI and have the generating AI perform the adjustment of the care plan.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The service provider can estimate the emotions of the person receiving care and adjust the notification method to care staff based on the estimated emotions. For example, if the person is stressed, the notification can be concise and include advice to reduce stress. If the person is relaxed, a detailed notification can be provided, offering information to help them maintain their relaxation. If the person is tired, the notification can be subdued and encourage rest. In this way, the service provider can optimize the response of care staff by providing appropriate notification methods tailored to the emotions of the person receiving care.

[0086] The analysis unit can estimate the subject's emotions and dynamically change the health assessment criteria based on those estimated emotions. For example, if the subject is stressed, the assessment criteria can be changed to emphasize stress-related health indicators. If the subject is relaxed, the assessment criteria can be changed to emphasize overall health. If the subject is tired, the assessment criteria can be changed to emphasize fatigue levels. This allows the analysis unit to perform flexible health assessments that respond to the subject's emotions.

[0087] The service provider can estimate the emotions of the person receiving care and adjust the timing of the care plan based on those estimates. For example, if the person is feeling stressed, the care plan can be implemented during a time when they can relax. If the person is relaxed, an active care plan can be implemented. If the person is tired, a care plan prioritizing rest can be implemented. This allows the service provider to implement the care plan at the optimal time according to the person's emotions.

[0088] The data collection unit can estimate the subject's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the subject is stressed, the frequency of data collection can be reduced to alleviate stress. If the subject is relaxed, the frequency of data collection can be increased to collect more detailed data. If the subject is tired, the frequency of data collection can be adjusted to prioritize rest. This allows the data collection unit to collect data appropriately according to the subject's emotions.

[0089] The analysis unit can estimate the subject's emotions and adjust the feedback method of the analysis results based on the estimated emotions. For example, if the subject is stressed, the feedback can be concise and include advice to reduce stress. If the subject is relaxed, detailed feedback can be provided, offering information to maintain relaxation. If the subject is tired, the feedback can be restrained and encourage rest. In this way, the analysis unit can provide appropriate feedback that is tailored to the subject's emotions.

[0090] The data collection unit can analyze the subject's past health data and select the optimal data collection method. For example, it can collect data at specific time periods based on the subject's past health data. Based on the subject's past health data, it can focus on collecting specific health indicators. It can analyze the subject's past health data and optimize the collection frequency. In this way, the data collection unit can select the optimal data collection method by analyzing the subject's past health data.

[0091] The analysis unit can analyze the lifestyle data of subjects and reflect it in the assessment of their health status. For example, it can analyze the subjects' sleep patterns and reflect them in the health assessment. It can analyze the subjects' eating habits and reflect them in the health assessment. It can analyze the subjects' exercise habits and reflect them in the health assessment. As a result, the analysis unit can perform a more accurate health assessment by reflecting the subjects' lifestyle data in the health assessment.

[0092] The service provider can determine the priority of care plans based on the health condition of the person receiving care. For example, if the person's health condition is deteriorating, urgent care plans can be prioritized. If the person's health condition is stable, long-term care plans can be prioritized. The priority of care plans can be adjusted in stages according to the person's health condition. This allows the service provider to provide appropriate care plans based on the person's health condition.

[0093] The data collection unit can adjust its data collection methods to take into account the geographical location of the subjects. For example, if a subject is at home, it can collect data related to their daily life. If a subject is out, it can collect data related to their movement. If a subject is at a specific facility, it can collect data related to their activities at that facility. This allows the data collection unit to collect more relevant data by taking into account the geographical location of the subjects.

[0094] The analysis unit can integrate the subject's health data with other data sources to perform a comprehensive health assessment. For example, it can integrate the subject's medical records and reflect them in the health assessment. It can integrate data from the subject's fitness tracker and reflect it in the health assessment. It can integrate the subject's dietary records and reflect them in the health assessment. As a result, the analysis unit can perform a more accurate health assessment by integrating multiple data sources.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The data collection unit monitors the subject's movements in real time using cameras and collects the data. For example, a camera installed in the room can capture the subject walking around the room or eating, collect the data in real time, and transmit it to the AI. Step 2: The analysis unit analyzes the data collected by the collection unit to evaluate health status and behavioral patterns. For example, it uses AI to analyze the collected data in detail, analyzing changes in the subject's walking speed and posture, food intake, etc., to evaluate their health status. Furthermore, by analyzing behavioral patterns, it can be useful in the early detection of dementia. Step 3: The service provider develops a care plan based on the evaluation results obtained by the analysis unit and provides it to the care staff. For example, using AI, they create an optimal care plan for the individual based on the evaluation results and propose exercise programs and meal plans. The created care plan is then provided to the care staff and implemented.

[0097] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0098] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0099] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0100] Each of the multiple elements described above, including the data collection unit, analysis unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit monitors the subject's movements in real time using the camera 42 of the smart device 14 and collects the data. The analysis unit is implemented in detail using the specific processing unit 290 of the data processing unit 12, and analyzes the collected data in detail using AI to evaluate the health status and behavioral patterns. The provision unit is implemented in detail using the specific processing unit 290 of the data processing unit 12, and formulates a care plan based on the evaluation results and provides it to the care staff. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0103] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0104] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0105] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0107] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0108] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0109] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0110] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0111] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0112] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0113] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0115] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the data collection unit, analysis unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit monitors the subject's movements in real time using the camera 42 of the smart glasses 214 and collects the data. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, which uses AI to analyze the collected data in detail and evaluate the health status and behavioral patterns. The provision unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, which formulates a care plan based on the evaluation results and provides it to the care staff. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the data collection unit, analysis unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit monitors the subject's movements in real time using the camera 42 of the headset terminal 314 and collects the data. The analysis unit is implemented in detail using the specific processing unit 290 of the data processing unit 12, and analyzes the collected data in detail using AI to evaluate the health status and behavioral patterns. The provision unit is implemented in detail using the specific processing unit 290 of the data processing unit 12, and formulates a care plan based on the evaluation results and provides it to the care staff. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] As shown in Figure 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.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0141] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the data collection unit, analysis unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit monitors the subject's movements in real time using the camera 42 of the robot 414 and collects the data. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which uses AI to analyze the collected data in detail and evaluate the health status and behavioral patterns. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which formulates a care plan based on the evaluation results and provides it to the care staff. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0150] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0152] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0153] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0154] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0157] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0158] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0159] 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.

[0160] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0161] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0162] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0163] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0165] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0166] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0167] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0168] (Note 1) A data collection unit that uses a camera to monitor the subject's movements in real time and collects the data, An analysis unit analyzes the data collected by the aforementioned collection unit and evaluates health status and behavioral patterns, The system comprises: a provision unit that formulates a care plan based on the evaluation results obtained by the analysis unit and provides it to the care staff; A system characterized by the following features. (Note 2) The aforementioned analysis unit, The study analyzes the subjects' walking speed, posture changes, and food intake to assess their health status. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyzing behavioral patterns contributes to the early detection of dementia. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Based on the evaluation results, we propose exercise programs and meal plans. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide evaluation results to care staff and implement care plans. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the emotions of the subjects and adjusts the timing of behavioral data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the subject's past behavioral data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting motion data, filtering is performed based on the subject's current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates the emotions of the subjects and determines the priority of behavioral data to collect based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting motion data, the system prioritizes collecting highly relevant data by considering the geographical location information of the subjects. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting behavioral data, analyze the social media activity of the subjects and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the emotions of the subjects and adjusts the health assessment method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the accuracy of the evaluation is adjusted based on the level of detail in the subject's movement data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the subjects. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the emotions of the subjects and adjusts the display method of the evaluation results based on the estimated emotions of the subjects. The system described in Appendix 1, characterized by the features described herein. (Note 16) The analysis unit determines the evaluation priority based on the timing of the collection of the subject's motion data during the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit improves the accuracy of the evaluation based on relevant literature for the subject during the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned provisioning unit estimates the emotions of the target person and adjusts the method of proposing a care plan based on the estimated emotions of the target person. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned service provider adjusts the level of detail in the care plan based on the importance of the evaluation results when making a proposal. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned provisioning unit applies different care plan algorithms depending on the category of the target person when making a proposal. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned provisioning unit estimates the emotions of the subject and determines the priority of the care plan based on the estimated emotions of the subject. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned provisioning unit determines the priority of care plans based on the timing of collection of evaluation results when making a proposal. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned service provider adjusts the care plan based on relevant market data for the target individual at the time of proposal. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that uses a camera to monitor the subject's movements in real time and collects the data, An analysis unit analyzes the data collected by the aforementioned collection unit and evaluates health status and behavioral patterns, The system comprises: a provision unit that formulates a care plan based on the evaluation results obtained by the analysis unit and provides it to the care staff; A system characterized by the following features.

2. The aforementioned analysis unit, The study analyzes the subjects' walking speed, posture changes, and food intake to assess their health status. The system according to feature 1.

3. The aforementioned analysis unit, Analyzing behavioral patterns contributes to the early detection of dementia. The system according to feature 1.

4. The aforementioned supply unit is, Based on the evaluation results, we propose exercise programs and meal plans. The system according to feature 1.

5. The aforementioned supply unit is, Provide evaluation results to care staff and implement care plans. The system according to feature 1.

6. The aforementioned collection unit is The system estimates the emotions of the subjects and adjusts the timing of behavioral data collection based on the estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the subject's past behavioral data and select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting motion data, filtering is performed based on the subject's current health status and lifestyle. The system according to feature 1.

9. The aforementioned collection unit is The system estimates the emotions of the subjects and determines the priority of behavioral data to collect based on the estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is When collecting motion data, the system prioritizes collecting highly relevant data by considering the geographical location information of the subjects. The system according to feature 1.

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