system

Abstract

The system according to this embodiment aims to monitor the daily activities of Parkinson's disease patients in real time and provide appropriate interventions. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data for monitoring the patient's daily activities. The analysis unit analyzes the data collected by the collection unit in real time. The generation unit generates a customized intervention plan based on the analysis results obtained by the analysis unit. The provision unit provides the intervention plan generated by the generation unit to the patient and the care team.

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, including 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 prior art, there is a problem that it is difficult to monitor the daily activities of Parkinson's disease patients in real time and provide appropriate interventions.

[0005] The system according to the embodiment aims to monitor the daily activities of Parkinson's disease patients in real time and provide appropriate interventions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a data generation unit, and a data provision unit. The data collection unit collects data for monitoring the patient's daily activities. The analysis unit analyzes the data collected by the data collection unit in real time. The data generation unit generates a customized intervention plan based on the analysis results obtained by the analysis unit. The data provision unit provides the intervention plan generated by the data generation unit to the patient and the care team. [Effects of the Invention]

[0007] The system according to this embodiment can monitor the daily activities of Parkinson's disease patients in real time and provide appropriate interventions. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied 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 reception 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 reception 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) An AI agent system according to an embodiment of the present invention is a system that monitors the daily activities of Parkinson's disease patients and tracks changes in their movement patterns and behavior. This AI agent system collects data using IoT devices to monitor the patient's daily activities. Next, the AI ​​analyzes the collected data in real time and tracks changes in movement patterns and behavior. The AI ​​uses deep learning to analyze the patient's behavior patterns and generates a customized intervention plan tailored to the symptoms. The generated intervention plan is fed back to the patient and the care team to prevent the worsening of symptoms and enable appropriate intervention. This mechanism is expected to delay the progression of symptoms, reduce the burden on caregivers, and improve the patient's quality of life. For example, the AI ​​agent system collects data using IoT devices to monitor the patient's daily activities. For example, it collects movement data and behavioral data using wearable devices worn by the patient or sensors installed in the home. This data is transmitted in real time to a cloud-based data analysis platform. Next, the AI ​​analyzes the collected data in real time. The AI ​​uses deep learning to analyze changes in the patient's movement patterns and behavior. For example, it analyzes the patient's walking patterns and the frequency of hand tremors to detect abnormal changes. This allows for the early detection of symptom exacerbation. Furthermore, the AI ​​generates a customized intervention plan based on the analysis results. For example, if a patient's symptoms worsen, it suggests adjustments to exercise therapy or medication. This intervention plan is fed back to the patient and care team, ensuring appropriate action is taken. This is expected to delay symptom progression, reduce the burden on caregivers, and improve the patient's quality of life. For example, delaying symptom progression improves the patient's quality of life and reduces the burden on caregivers. In addition, real-time monitoring and feedback enable appropriate intervention and can prevent symptom exacerbation. Thus, the AI ​​agent system can monitor the patient's daily activities, track changes in exercise patterns and behavior, and enable appropriate intervention.

[0029] The AI ​​agent system according to this embodiment comprises a data collection unit, an analysis unit, a data generation unit, and a data provision unit. The data collection unit collects data for monitoring the patient's daily activities. The data collection unit collects data using, for example, wearable devices worn by the patient or sensors installed in the home. The data collection unit can collect exercise data and behavioral data from the patient using, for example, wearable devices such as smartwatches and fitness trackers. The data collection unit can also collect behavioral data from the patient using motion sensors and temperature sensors installed in the home. For example, the data collection unit collects exercise data such as heart rate and steps using a smartwatch worn by the patient. The data collection unit can also collect the patient's movement patterns using, for example, motion sensors installed in the home. The data collection unit can also collect data on the patient's living environment using, for example, a temperature sensor. The analysis unit analyzes the data collected by the data collection unit in real time. The analysis unit analyzes changes in the patient's exercise patterns and behavior using deep learning. The analysis unit can analyze changes in a patient's movement patterns and behavior with high accuracy using deep learning algorithms such as CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network). For example, the analysis unit can analyze a patient's gait pattern and detect abnormal changes. For example, the analysis unit can analyze the frequency of a patient's hand tremors and detect symptom exacerbation early. For example, the analysis unit can analyze a patient's movement data and detect abnormal behavioral patterns. The generation unit generates a customized intervention plan based on the analysis results obtained by the analysis unit. For example, the generation unit proposes adjustments to exercise therapy and drug therapy tailored to the patient's symptoms. For example, the generation unit can propose exercise therapy such as rehabilitation exercises and stretching based on the patient's movement data. For example, the generation unit can adjust the medication schedule and type of medication according to the patient's symptoms. For example, the generation unit can generate a customized intervention plan based on the patient's lifestyle and medical history. The provision unit provides the intervention plan generated by the generation unit to the patient and the care team.The service provider provides feedback to the patient and care team, for example, through notifications, reports, and alerts, regarding the generated intervention plan. The service provider can, for example, notify the patient of the generated intervention plan via a device such as a smartphone or tablet. The service provider can also, for example, provide the generated intervention plan to the care team as a report. The service provider can also, for example, notify the patient and care team of the generated intervention plan as an alert. This allows the AI ​​agent system according to the embodiment to monitor the patient's daily activities, track changes in exercise patterns and behavior, and enable appropriate interventions.

[0030] The data collection unit collects data to monitor the patient's daily activities. This data collection unit uses, for example, wearable devices worn by the patient or sensors installed in the home. Specifically, wearable devices such as smartwatches and fitness trackers can be used to collect the patient's exercise and behavioral data. These devices record detailed data such as heart rate, steps, calories burned, and sleep patterns in real time and transmit it to the data collection unit. The data collection unit can also collect behavioral data using motion sensors and temperature sensors installed in the home. For example, motion sensors detect the patient's movement patterns and activity levels, while temperature sensors monitor temperature changes in the patient's living environment. This allows the data collection unit to centrally collect diverse data about the patient's living environment and daily activities and monitor it in real time. Furthermore, the data collection unit can transmit this data to a cloud server and collaborate with other systems and departments. For example, collected data is stored on a cloud server for access by the analysis and generation units. The data collection unit can also adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. 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 in real time. The analysis unit uses deep learning to analyze changes in patients' movement patterns and behavior. Specifically, it can analyze changes in patients' movement patterns and behavior with high accuracy using deep learning algorithms such as CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network). For example, it can use CNN to analyze heart rate data obtained from a smartwatch and detect abnormal heart rate patterns. It can also use RNN to analyze patients' walking patterns and detect abnormal changes. Furthermore, the analysis unit can analyze the frequency of hand tremors in patients and detect symptom worsening at an early stage. As a result, the analysis unit can quickly and accurately analyze the collected data and grasp changes in patients' health status and behavior in real time. In addition, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past exercise data, it can predict fluctuations in risk during specific time periods or activities and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect patterns that are different from the norm or abnormal data and issue warnings early. This allows the analysis department to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and security of the entire system.

[0032] The generation unit generates a customized intervention plan based on the analysis results obtained by the analysis unit. For example, the generation unit proposes adjustments to exercise therapy and drug therapy tailored to the patient's symptoms. Specifically, it can propose exercise therapy such as rehabilitation exercises and stretching based on the patient's exercise data. For example, if an abnormality is observed in the patient's gait pattern, the generation unit proposes a rehabilitation program such as gait training and balance training. It can also adjust the medication schedule and type of medication according to the patient's symptoms. For example, if the frequency of a patient's hand tremors increases, the generation unit will propose to the physician a review of the drug therapy. Furthermore, the generation unit can also generate a customized intervention plan based on the patient's lifestyle and medical history. For example, it can provide advice to promote healthy lifestyle habits, taking into account the patient's dietary and exercise habits. In this way, the generation unit can generate an optimal intervention plan that meets the individual needs of the patient and support the patient's health management. Furthermore, the generation unit can continuously review the generated intervention plan and adjust it according to the latest data and circumstances. In this way, the generation unit can always provide the optimal intervention plan and support the improvement of the patient's health status.

[0033] The service provider provides the intervention plan generated by the generation unit to the patient and the care team. Specifically, it provides feedback to the patient and the care team on the generated intervention plan through methods such as notifications, reports, and alerts. For example, the generated intervention plan can be notified to the patient via a device such as a smartphone or tablet. The patient can receive instructions for exercise therapy and medication therapy through a smartphone app and incorporate them into their daily life. The generated intervention plan can also be provided to the care team as a report. The care team can understand the patient's health status and the progress of the intervention plan and adjust the intervention plan as needed. Furthermore, the service provider can also notify the patient and the care team of the generated intervention plan as an alert. For example, if a sudden change in the patient's health status is observed, the service provider can issue an alert to encourage a quick response. This allows the service provider to quickly provide appropriate information to the patient and the care team and support the patient's health management. In addition, the service provider can collect feedback from the patient and the care team and continuously improve the accuracy and effectiveness of the intervention plan. This allows the service provider to support the improvement of the patient's health status and improve the reliability and effectiveness of the overall system.

[0034] The data collection unit can collect data using wearable devices worn by the patient or sensors installed in the home. For example, the data collection unit can collect exercise data and behavioral data using wearable devices such as smartwatches and fitness trackers worn by the patient. For example, the data collection unit can collect exercise data such as heart rate and steps using a smartwatch. For example, the data collection unit can also collect data such as exercise volume and calorie consumption using a fitness tracker. For example, the data collection unit can collect behavioral data using motion sensors and temperature sensors installed in the home. For example, the data collection unit can collect the patient's movement patterns using a motion sensor. For example, the data collection unit can collect data on the patient's living environment using a temperature sensor. This allows the data collection unit to efficiently collect data for monitoring the patient's daily activities. 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 data acquired from a smartwatch worn by the patient into a generating AI, which can then analyze and collect the data.

[0035] The analysis unit can analyze changes in a patient's movement patterns and behavior using deep learning. The analysis unit uses deep learning algorithms such as CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network) to analyze changes in a patient's movement patterns and behavior with high accuracy. For example, the analysis unit can analyze a patient's walking pattern and detect abnormal changes. For example, the analysis unit can analyze the frequency of a patient's hand tremors to detect early worsening of symptoms. For example, the analysis unit can analyze a patient's movement data and detect abnormal behavioral patterns. Thus, by using deep learning, the analysis unit can analyze changes in a patient's movement patterns and behavior with high accuracy. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input patient movement data into a generating AI, which can analyze the data to detect changes in movement patterns and behavior.

[0036] The generation unit can generate customized intervention plans tailored to the patient's symptoms. For example, the generation unit can suggest adjustments to exercise therapy and drug therapy according to the patient's symptoms. For example, the generation unit can suggest exercise therapy such as rehabilitation exercises and stretching based on the patient's exercise data. For example, the generation unit can also adjust the medication schedule and type of medication according to the patient's symptoms. For example, the generation unit can generate customized intervention plans based on the patient's lifestyle and medical history. This allows the generation unit to provide appropriate intervention plans tailored to the patient's symptoms. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input patient symptom data into a generation AI, which can then analyze the data and generate a customized intervention plan.

[0037] The service provider can provide feedback on the generated intervention plan to the patient and care team. For example, the service provider can provide feedback on the generated intervention plan to the patient and care team through methods such as notifications, reports, and alerts. For example, the service provider can notify the patient of the generated intervention plan via a device such as a smartphone or tablet. For example, the service provider can provide the generated intervention plan to the care team as a report. For example, the service provider can notify the patient and care team of the generated intervention plan as an alert. This allows the service provider to provide the patient and care team with the generated intervention plan, enabling appropriate responses. 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 generated intervention plan into a generating AI, which can analyze the data and generate feedback.

[0038] The generation unit can propose adjustments to exercise therapy and drug therapy. For example, based on the patient's exercise data, the generation unit can propose exercise therapy such as rehabilitation exercises and stretching. The generation unit can also adjust the medication schedule and type of medication according to the patient's symptoms. The generation unit can also generate a customized intervention plan based on the patient's lifestyle and medical history. This allows the generation unit to propose adjustments to exercise therapy and drug therapy according to the patient's symptoms. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input patient symptom data into a generation AI, which can analyze the data and propose adjustments to exercise therapy and drug therapy.

[0039] The data collection unit can analyze the patient's past activity history and select the optimal data collection method. For example, the data collection unit can optimize the timing of data collection based on activities the patient has frequently performed in the past. For example, the data collection unit can collect data at specific time periods based on the patient's past activity history. For example, the data collection unit can analyze the patient's past activity patterns and select the most efficient data collection method. This allows the data collection unit to select the optimal data collection method based on the patient's past activity history. 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 patient's past activity data into a generating AI, which can then analyze the data and select the optimal data collection method.

[0040] The data collection unit can filter data based on the patient's current health status and living environment during data collection. For example, if the patient's health status deteriorates, the data collection unit can temporarily stop data collection. For example, if the patient's living environment changes, the data collection unit can select a data collection method adapted to the new environment. The data collection unit can also adjust the types of data to be collected based on the patient's health status and living environment. This allows the data collection unit to adjust the data to be collected according to the patient's health status and living environment. 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 patient health status data into a generating AI, which can then analyze and filter the data.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the patient's geographical location information during data collection. For example, if the patient is at home, the data collection unit can prioritize the collection of activity data within the home. For example, if the patient is out, the data collection unit can prioritize the collection of exercise data taken while out. For example, if the patient is at a specific facility, the data collection unit can prioritize the collection of activity data taken at that facility. In this way, the data collection unit can prioritize the collection of highly relevant data based on the patient's geographical location information. 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 patient's geographical location information into a generating AI, which can then analyze the data and prioritize the collection of highly relevant data.

[0042] The data collection unit can analyze the patient's social media activity and collect relevant data during data collection. For example, if the patient is active on social media, the data collection unit can collect activity data. For example, the data collection unit can analyze the content of the patient's social media posts and collect relevant data. For example, the data collection unit can analyze the patient's social media friendships and collect relevant data. In this way, the data collection unit can collect relevant data based on the patient'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 patient's social media data into a generating AI, which can then analyze the data and collect relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance, and a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. This allows the analysis unit to adjust the level of detail of the analysis according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI, which can then analyze the data and adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an exercise analysis algorithm to exercise data. For example, the analysis unit can apply a behavioral analysis algorithm to behavioral data. For example, the analysis unit can apply a health analysis algorithm to health data. This allows the analysis unit to apply the most suitable analysis algorithm depending on the data category. 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 data category into a generating AI, which can then analyze the data and apply the most suitable analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may analyze the most recent data while referring to past data. The analysis unit may also adjust the priority of analysis according to the data collection timing. In this way, the analysis unit can adjust the priority of analysis according to the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI, and the generating AI can analyze the data and determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit can also adjust the order of analysis according to the relevance of the data. In this way, the analysis unit can adjust the order of analysis according to the relevance of the 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 relevance of the data into a generating AI, and the generating AI can analyze the data and adjust the order of analysis.

[0047] The generation unit can adjust the level of detail of an intervention plan based on the severity of the patient's symptoms when generating the plan. For example, the generation unit can provide a detailed intervention plan for symptoms of high severity, and a simplified intervention plan for symptoms of low severity. The generation unit can also determine the priority of the intervention plan according to the severity of the symptoms. This allows the generation unit to adjust the level of detail of the intervention plan according to the severity of the patient's symptoms. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the severity of the patient's symptoms into a generation AI, which can then analyze the data and adjust the level of detail of the intervention plan.

[0048] The generation unit can apply different plan generation algorithms depending on the patient's symptom category when generating intervention plans. For example, the generation unit can apply an exercise therapy algorithm for motor symptoms. For example, the generation unit can apply a behavioral therapy algorithm for behavioral symptoms. For example, the generation unit can apply a health therapy algorithm for health symptoms. This allows the generation unit to generate an optimal intervention plan according to the patient's symptom category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the patient's symptom category into a generation AI, which can analyze the data and apply the optimal plan generation algorithm.

[0049] The generation unit can determine the priority of intervention plans based on the timing of the patient's symptom onset when generating intervention plans. For example, the generation unit can prioritize providing intervention plans for the most recent symptoms. For example, the generation unit can provide intervention plans for the most recent symptoms while referring to past symptoms. The generation unit can also adjust the priority of intervention plans according to the timing of symptom onset. This allows the generation unit to adjust the priority of intervention plans according to the timing of the patient's symptom onset. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the timing of the patient's symptom onset into a generation AI, which can analyze the data to determine the priority of intervention plans.

[0050] The generation unit can adjust the order of intervention plans based on the relevance of the patient's symptoms when generating intervention plans. For example, the generation unit can prioritize providing intervention plans for highly relevant symptoms. For example, it can postpone interventions for less relevant symptoms. The generation unit can also adjust the order of intervention plans according to the relevance of the symptoms. In this way, the generation unit can adjust the order of intervention plans according to the relevance of the patient's symptoms. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of the patient's symptoms into a generation AI, and the generation AI can analyze the data and adjust the order of intervention plans.

[0051] The service provider can select the optimal service delivery method by referring to the patient's past feedback history when providing feedback. For example, the service provider may prioritize providing feedback methods that the patient has preferred in the past. For example, the service provider may select the optimal service delivery method from the patient's past feedback history. For example, the service provider may analyze the patient's past feedback history and select the most effective service delivery method. In this way, the service provider can select the optimal feedback method based on the patient's past feedback history. 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 may input the patient's past feedback history into a generating AI, which can then analyze the data and select the optimal service delivery method.

[0052] The service provider can adjust the content of the feedback based on the patient's current health condition and living environment when providing feedback. For example, if the patient's health condition is deteriorating, the service provider can provide simple and visually easy-to-understand feedback. For example, if the patient's living environment changes, the service provider can provide feedback adapted to the new environment. The service provider can also adjust the content of the feedback based on the patient's health condition and living environment. This allows the service provider to adjust the content of the feedback according to the patient's health condition and living environment. 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 patient health data into a generating AI, and the generating AI can analyze the data and adjust the content of the feedback.

[0053] The service provider can select the optimal method of providing feedback by considering the patient's device information. For example, if the patient is using a smartphone, the service provider can provide feedback tailored to the screen size. For example, if the patient is using a tablet, the service provider can provide feedback optimized for a larger screen. For example, if the patient is using a smartwatch, the service provider can provide concise and highly visible feedback. This allows the service provider to select the optimal feedback method based on the patient's device information. 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 patient's device information into a generating AI, which can then analyze the data and select the optimal method of providing feedback.

[0054] The service provider can provide multilingual feedback according to the patient's language settings when providing feedback. For example, the service provider can automatically set the language of the feedback based on the language settings of the patient's device. For example, the service provider can provide a language switching function if the patient uses multiple languages. For example, the service provider can also provide feedback in a specific language if the patient selects that language. In this way, the service provider can provide multilingual feedback according to the patient's language settings. 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 patient's language settings into a generating AI, and the generating AI can analyze the data to provide multilingual feedback.

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

[0056] The data collection unit can analyze the patient's past activity history and select the optimal data collection method. For example, it can optimize the timing of data collection based on activities the patient frequently performed in the past. It can also collect data at specific time periods based on the patient's past activity history. Furthermore, it can analyze the patient's past activity patterns and select the most efficient data collection method. In this way, the data collection unit can select the optimal data collection method based on the patient's past activity history.

[0057] The data collection unit can filter data based on the patient's current health status and living environment during data collection. For example, if the patient's health status deteriorates, data collection can be temporarily suspended. Furthermore, if the patient's living environment changes, a data collection method adapted to the new environment can be selected. In addition, the type of data collected can be adjusted based on the patient's health status and living environment. This allows the data collection unit to adjust the data collected according to the patient's health status and living environment.

[0058] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. Furthermore, it can determine the priority of the analysis according to the importance of the data. In this way, the analysis unit can adjust the level of detail of the analysis according to the importance of the data.

[0059] The generation unit can adjust the level of detail in the intervention plan based on the importance of the patient's symptoms when generating the intervention plan. For example, it can provide a detailed intervention plan for high-importance symptoms and a simplified intervention plan for low-importance symptoms. Furthermore, it can also determine the priority of the intervention plan according to the importance of the symptoms. In this way, the generation unit can adjust the level of detail in the intervention plan according to the importance of the patient's symptoms.

[0060] The service provider can select the optimal feedback method by referring to the patient's past feedback history when providing feedback. For example, it can prioritize providing feedback methods that the patient has preferred in the past. It can also select the optimal feedback method based on the patient's past feedback history. Furthermore, it can analyze the patient's past feedback history to select the most effective feedback method. In this way, the service provider can select the optimal feedback method based on the patient's past feedback history.

[0061] The service provider can select the optimal feedback delivery method by considering the patient's device information. For example, if the patient is using a smartphone, feedback can be provided that is optimized for the screen size. If the patient is using a tablet, feedback optimized for a larger screen can be provided. Furthermore, if the patient is using a smartwatch, concise and highly visible feedback can be provided. This allows the service provider to select the optimal feedback method based on the patient's device information.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects data to monitor the patient's daily activities. The data collection unit collects data using, for example, wearable devices worn by the patient or sensors installed in the home. Specifically, it collects the patient's exercise and behavioral data using wearable devices such as smartwatches and fitness trackers. It can also collect the patient's behavioral data and data on their living environment using motion sensors and temperature sensors installed in the home. Step 2: The analysis unit analyzes the data collected by the collection unit in real time. The analysis unit uses deep learning to analyze changes in the patient's movement patterns and behavior. Specifically, it uses deep learning algorithms such as CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network) to analyze changes in the patient's movement patterns and behavior with high accuracy. This makes it possible to detect changes in the patient's walking patterns, the frequency of hand tremors, and abnormal behavioral patterns. Step 3: The generation unit generates a customized intervention plan based on the analysis results obtained by the analysis unit. The generation unit proposes adjustments to exercise therapy and drug therapy tailored to the patient's symptoms. Specifically, it proposes exercise therapy such as rehabilitation exercises and stretching based on the patient's exercise data, and adjusts the medication schedule and type of medication according to the patient's symptoms. It can also generate a customized intervention plan based on the patient's lifestyle and medical history. Step 4: The delivery unit provides the intervention plan generated by the generation unit to the patient and care team. The delivery unit provides feedback to the patient and care team on the generated intervention plan through methods such as notifications, reports, and alerts. Specifically, the generated intervention plan is notified to the patient via devices such as smartphones and tablets, and provided to the care team as a report. It can also be used to notify the patient and care team of the generated intervention plan as an alert.

[0064] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that monitors the daily activities of Parkinson's disease patients and tracks changes in their movement patterns and behavior. This AI agent system collects data using IoT devices to monitor the patient's daily activities. Next, the AI ​​analyzes the collected data in real time and tracks changes in movement patterns and behavior. The AI ​​uses deep learning to analyze the patient's behavior patterns and generates a customized intervention plan tailored to the symptoms. The generated intervention plan is fed back to the patient and the care team to prevent the worsening of symptoms and enable appropriate intervention. This mechanism is expected to delay the progression of symptoms, reduce the burden on caregivers, and improve the patient's quality of life. For example, the AI ​​agent system collects data using IoT devices to monitor the patient's daily activities. For example, it collects movement data and behavioral data using wearable devices worn by the patient or sensors installed in the home. This data is transmitted in real time to a cloud-based data analysis platform. Next, the AI ​​analyzes the collected data in real time. The AI ​​uses deep learning to analyze changes in the patient's movement patterns and behavior. For example, it analyzes the patient's walking patterns and the frequency of hand tremors to detect abnormal changes. This allows for the early detection of symptom exacerbation. Furthermore, the AI ​​generates a customized intervention plan based on the analysis results. For example, if a patient's symptoms worsen, it suggests adjustments to exercise therapy or medication. This intervention plan is fed back to the patient and care team, ensuring appropriate action is taken. This is expected to delay symptom progression, reduce the burden on caregivers, and improve the patient's quality of life. For example, delaying symptom progression improves the patient's quality of life and reduces the burden on caregivers. In addition, real-time monitoring and feedback enable appropriate intervention and can prevent symptom exacerbation. Thus, the AI ​​agent system can monitor the patient's daily activities, track changes in exercise patterns and behavior, and enable appropriate intervention.

[0065] The AI ​​agent system according to this embodiment comprises a data collection unit, an analysis unit, a data generation unit, and a data provision unit. The data collection unit collects data for monitoring the patient's daily activities. The data collection unit collects data using, for example, wearable devices worn by the patient or sensors installed in the home. The data collection unit can collect exercise data and behavioral data from the patient using, for example, wearable devices such as smartwatches and fitness trackers. The data collection unit can also collect behavioral data from the patient using motion sensors and temperature sensors installed in the home. For example, the data collection unit collects exercise data such as heart rate and steps using a smartwatch worn by the patient. The data collection unit can also collect the patient's movement patterns using, for example, motion sensors installed in the home. The data collection unit can also collect data on the patient's living environment using, for example, a temperature sensor. The analysis unit analyzes the data collected by the data collection unit in real time. The analysis unit analyzes changes in the patient's exercise patterns and behavior using deep learning. The analysis unit can analyze changes in a patient's movement patterns and behavior with high accuracy using deep learning algorithms such as CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network). For example, the analysis unit can analyze a patient's gait pattern and detect abnormal changes. For example, the analysis unit can analyze the frequency of a patient's hand tremors and detect symptom exacerbation early. For example, the analysis unit can analyze a patient's movement data and detect abnormal behavioral patterns. The generation unit generates a customized intervention plan based on the analysis results obtained by the analysis unit. For example, the generation unit proposes adjustments to exercise therapy and drug therapy tailored to the patient's symptoms. For example, the generation unit can propose exercise therapy such as rehabilitation exercises and stretching based on the patient's movement data. For example, the generation unit can adjust the medication schedule and type of medication according to the patient's symptoms. For example, the generation unit can generate a customized intervention plan based on the patient's lifestyle and medical history. The provision unit provides the intervention plan generated by the generation unit to the patient and the care team.The service provider provides feedback to the patient and care team, for example, through notifications, reports, and alerts, regarding the generated intervention plan. The service provider can, for example, notify the patient of the generated intervention plan via a device such as a smartphone or tablet. The service provider can also, for example, provide the generated intervention plan to the care team as a report. The service provider can also, for example, notify the patient and care team of the generated intervention plan as an alert. This allows the AI ​​agent system according to the embodiment to monitor the patient's daily activities, track changes in exercise patterns and behavior, and enable appropriate interventions.

[0066] The data collection unit collects data to monitor the patient's daily activities. This data collection unit uses, for example, wearable devices worn by the patient or sensors installed in the home. Specifically, wearable devices such as smartwatches and fitness trackers can be used to collect the patient's exercise and behavioral data. These devices record detailed data such as heart rate, steps, calories burned, and sleep patterns in real time and transmit it to the data collection unit. The data collection unit can also collect behavioral data using motion sensors and temperature sensors installed in the home. For example, motion sensors detect the patient's movement patterns and activity levels, while temperature sensors monitor temperature changes in the patient's living environment. This allows the data collection unit to centrally collect diverse data about the patient's living environment and daily activities and monitor it in real time. Furthermore, the data collection unit can transmit this data to a cloud server and collaborate with other systems and departments. For example, collected data is stored on a cloud server for access by the analysis and generation units. The data collection unit can also adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0067] The analysis unit analyzes the data collected by the data collection unit in real time. The analysis unit uses deep learning to analyze changes in patients' movement patterns and behavior. Specifically, it can analyze changes in patients' movement patterns and behavior with high accuracy using deep learning algorithms such as CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network). For example, it can use CNN to analyze heart rate data obtained from a smartwatch and detect abnormal heart rate patterns. It can also use RNN to analyze patients' walking patterns and detect abnormal changes. Furthermore, the analysis unit can analyze the frequency of hand tremors in patients and detect symptom worsening at an early stage. As a result, the analysis unit can quickly and accurately analyze the collected data and grasp changes in patients' health status and behavior in real time. In addition, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past exercise data, it can predict fluctuations in risk during specific time periods or activities and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect patterns that are different from the norm or abnormal data and issue warnings early. This allows the analysis department to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and security of the entire system.

[0068] The generation unit generates a customized intervention plan based on the analysis results obtained by the analysis unit. For example, the generation unit proposes adjustments to exercise therapy and drug therapy tailored to the patient's symptoms. Specifically, it can propose exercise therapy such as rehabilitation exercises and stretching based on the patient's exercise data. For example, if an abnormality is observed in the patient's gait pattern, the generation unit proposes a rehabilitation program such as gait training and balance training. It can also adjust the medication schedule and type of medication according to the patient's symptoms. For example, if the frequency of a patient's hand tremors increases, the generation unit will propose to the physician a review of the drug therapy. Furthermore, the generation unit can also generate a customized intervention plan based on the patient's lifestyle and medical history. For example, it can provide advice to promote healthy lifestyle habits, taking into account the patient's dietary and exercise habits. In this way, the generation unit can generate an optimal intervention plan that meets the individual needs of the patient and support the patient's health management. Furthermore, the generation unit can continuously review the generated intervention plan and adjust it according to the latest data and circumstances. In this way, the generation unit can always provide the optimal intervention plan and support the improvement of the patient's health status.

[0069] The service provider provides the intervention plan generated by the generation unit to the patient and the care team. Specifically, it provides feedback to the patient and the care team on the generated intervention plan through methods such as notifications, reports, and alerts. For example, the generated intervention plan can be notified to the patient via a device such as a smartphone or tablet. The patient can receive instructions for exercise therapy and medication therapy through a smartphone app and incorporate them into their daily life. The generated intervention plan can also be provided to the care team as a report. The care team can understand the patient's health status and the progress of the intervention plan and adjust the intervention plan as needed. Furthermore, the service provider can also notify the patient and the care team of the generated intervention plan as an alert. For example, if a sudden change in the patient's health status is observed, the service provider can issue an alert to encourage a quick response. This allows the service provider to quickly provide appropriate information to the patient and the care team and support the patient's health management. In addition, the service provider can collect feedback from the patient and the care team and continuously improve the accuracy and effectiveness of the intervention plan. This allows the service provider to support the improvement of the patient's health status and improve the reliability and effectiveness of the overall system.

[0070] The data collection unit can collect data using wearable devices worn by the patient or sensors installed in the home. For example, the data collection unit can collect exercise data and behavioral data using wearable devices such as smartwatches and fitness trackers worn by the patient. For example, the data collection unit can collect exercise data such as heart rate and steps using a smartwatch. For example, the data collection unit can also collect data such as exercise volume and calorie consumption using a fitness tracker. For example, the data collection unit can collect behavioral data using motion sensors and temperature sensors installed in the home. For example, the data collection unit can collect the patient's movement patterns using a motion sensor. For example, the data collection unit can collect data on the patient's living environment using a temperature sensor. This allows the data collection unit to efficiently collect data for monitoring the patient's daily activities. 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 data acquired from a smartwatch worn by the patient into a generating AI, which can then analyze and collect the data.

[0071] The analysis unit can analyze changes in a patient's movement patterns and behavior using deep learning. The analysis unit uses deep learning algorithms such as CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network) to analyze changes in a patient's movement patterns and behavior with high accuracy. For example, the analysis unit can analyze a patient's walking pattern and detect abnormal changes. For example, the analysis unit can analyze the frequency of a patient's hand tremors to detect early worsening of symptoms. For example, the analysis unit can analyze a patient's movement data and detect abnormal behavioral patterns. Thus, by using deep learning, the analysis unit can analyze changes in a patient's movement patterns and behavior with high accuracy. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input patient movement data into a generating AI, which can analyze the data to detect changes in movement patterns and behavior.

[0072] The generation unit can generate customized intervention plans tailored to the patient's symptoms. For example, the generation unit can suggest adjustments to exercise therapy and drug therapy according to the patient's symptoms. For example, the generation unit can suggest exercise therapy such as rehabilitation exercises and stretching based on the patient's exercise data. For example, the generation unit can also adjust the medication schedule and type of medication according to the patient's symptoms. For example, the generation unit can generate customized intervention plans based on the patient's lifestyle and medical history. This allows the generation unit to provide appropriate intervention plans tailored to the patient's symptoms. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input patient symptom data into a generation AI, which can then analyze the data and generate a customized intervention plan.

[0073] The service provider can provide feedback on the generated intervention plan to the patient and care team. For example, the service provider can provide feedback on the generated intervention plan to the patient and care team through methods such as notifications, reports, and alerts. For example, the service provider can notify the patient of the generated intervention plan via a device such as a smartphone or tablet. For example, the service provider can provide the generated intervention plan to the care team as a report. For example, the service provider can notify the patient and care team of the generated intervention plan as an alert. This allows the service provider to provide the patient and care team with the generated intervention plan, enabling appropriate responses. 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 generated intervention plan into a generating AI, which can analyze the data and generate feedback.

[0074] The generation unit can propose adjustments to exercise therapy and drug therapy. For example, based on the patient's exercise data, the generation unit can propose exercise therapy such as rehabilitation exercises and stretching. The generation unit can also adjust the medication schedule and type of medication according to the patient's symptoms. The generation unit can also generate a customized intervention plan based on the patient's lifestyle and medical history. This allows the generation unit to propose adjustments to exercise therapy and drug therapy according to the patient's symptoms. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input patient symptom data into a generation AI, which can analyze the data and propose adjustments to exercise therapy and drug therapy.

[0075] The data collection unit can estimate the patient's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the patient is stressed, the data collection unit can reduce the frequency of data collection and collect data when the patient is relaxed. For example, if the patient is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. For example, if the patient is tired, the data collection unit can temporarily stop data collection and resume it after rest. This allows the data collection unit to collect more appropriate data by adjusting the timing of data collection according to the patient'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 patient's emotion data into a generative AI, which can analyze the data and adjust the timing of data collection.

[0076] The data collection unit can analyze the patient's past activity history and select the optimal data collection method. For example, the data collection unit can optimize the timing of data collection based on activities the patient has frequently performed in the past. For example, the data collection unit can collect data at specific time periods based on the patient's past activity history. For example, the data collection unit can analyze the patient's past activity patterns and select the most efficient data collection method. This allows the data collection unit to select the optimal data collection method based on the patient's past activity history. 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 patient's past activity data into a generating AI, which can then analyze the data and select the optimal data collection method.

[0077] The data collection unit can filter data based on the patient's current health status and living environment during data collection. For example, if the patient's health status deteriorates, the data collection unit can temporarily stop data collection. For example, if the patient's living environment changes, the data collection unit can select a data collection method adapted to the new environment. The data collection unit can also adjust the types of data to be collected based on the patient's health status and living environment. This allows the data collection unit to adjust the data to be collected according to the patient's health status and living environment. 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 patient health status data into a generating AI, which can then analyze and filter the data.

[0078] The data collection unit can estimate the patient's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the patient is stressed, the data collection unit may prioritize collecting stress-related data. For example, if the patient is relaxed, the data collection unit may prioritize collecting exercise data. For example, if the patient is tired, the data collection unit may prioritize collecting rest-related data. In this way, the data collection unit can prioritize collecting more important data by determining the priority of data to collect according to the patient'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 not using AI. For example, the data collection unit can input the patient's emotion data into a generative AI, which can analyze the data and determine the priority of data to collect.

[0079] The data collection unit can prioritize the collection of highly relevant data by considering the patient's geographical location information during data collection. For example, if the patient is at home, the data collection unit can prioritize the collection of activity data within the home. For example, if the patient is out, the data collection unit can prioritize the collection of exercise data taken while out. For example, if the patient is at a specific facility, the data collection unit can prioritize the collection of activity data taken at that facility. In this way, the data collection unit can prioritize the collection of highly relevant data based on the patient's geographical location information. 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 patient's geographical location information into a generating AI, which can then analyze the data and prioritize the collection of highly relevant data.

[0080] The data collection unit can analyze the patient's social media activity and collect relevant data during data collection. For example, if the patient is active on social media, the data collection unit can collect activity data. For example, the data collection unit can analyze the content of the patient's social media posts and collect relevant data. For example, the data collection unit can analyze the patient's social media friendships and collect relevant data. In this way, the data collection unit can collect relevant data based on the patient'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 patient's social media data into a generating AI, which can then analyze the data and collect relevant data.

[0081] The analysis unit can estimate the patient's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the patient is stressed, the analysis unit can provide a simple and visually easy-to-understand analysis result. For example, if the patient is relaxed, the analysis unit can provide a detailed analysis result. For example, if the patient is tired, the analysis unit can provide a concise analysis result that gets straight to the point. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the analysis according to the patient'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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input patient emotion data into a generative AI, and the generative AI can analyze the data and adjust the presentation of the analysis.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance, and a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. This allows the analysis unit to adjust the level of detail of the analysis according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI, which can then analyze the data and adjust the level of detail of the analysis.

[0083] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an exercise analysis algorithm to exercise data. For example, the analysis unit can apply a behavioral analysis algorithm to behavioral data. For example, the analysis unit can apply a health analysis algorithm to health data. This allows the analysis unit to apply the most suitable analysis algorithm depending on the data category. 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 data category into a generating AI, which can then analyze the data and apply the most suitable analysis algorithm.

[0084] The analysis unit can estimate the patient's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the patient is stressed, the analysis unit can provide a short, concise analysis. For example, if the patient is relaxed, the analysis unit can provide a detailed analysis. For example, if the patient is tired, the analysis unit can also provide a brief analysis. This allows the analysis unit to provide more appropriate analysis results by adjusting the length of the analysis according to the patient'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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input patient emotion data into a generative AI, which can analyze the data and adjust the length of the analysis.

[0085] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may analyze the most recent data while referring to past data. The analysis unit may also adjust the priority of analysis according to the data collection timing. In this way, the analysis unit can adjust the priority of analysis according to the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI, and the generating AI can analyze the data and determine the priority of analysis.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit can also adjust the order of analysis according to the relevance of the data. In this way, the analysis unit can adjust the order of analysis according to the relevance of the 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 relevance of the data into a generating AI, and the generating AI can analyze the data and adjust the order of analysis.

[0087] The generation unit can estimate the patient's emotions and adjust the way the intervention plan is presented based on the estimated emotions. For example, if the patient is stressed, the generation unit can provide a simple and visually easy-to-understand intervention plan. For example, if the patient is relaxed, the generation unit can provide a detailed intervention plan. For example, if the patient is tired, the generation unit can provide a concise intervention plan that gets straight to the point. In this way, the generation unit can provide a more appropriate intervention plan by adjusting the way the intervention plan is presented according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input patient emotion data into the generation AI, which can analyze the data and adjust the way the intervention plan is presented.

[0088] The generation unit can adjust the level of detail of an intervention plan based on the severity of the patient's symptoms when generating the plan. For example, the generation unit can provide a detailed intervention plan for symptoms of high severity, and a simplified intervention plan for symptoms of low severity. The generation unit can also determine the priority of the intervention plan according to the severity of the symptoms. This allows the generation unit to adjust the level of detail of the intervention plan according to the severity of the patient's symptoms. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the severity of the patient's symptoms into a generation AI, which can then analyze the data and adjust the level of detail of the intervention plan.

[0089] The generation unit can apply different plan generation algorithms depending on the patient's symptom category when generating intervention plans. For example, the generation unit can apply an exercise therapy algorithm for motor symptoms. For example, the generation unit can apply a behavioral therapy algorithm for behavioral symptoms. For example, the generation unit can apply a health therapy algorithm for health symptoms. This allows the generation unit to generate an optimal intervention plan according to the patient's symptom category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the patient's symptom category into a generation AI, which can analyze the data and apply the optimal plan generation algorithm.

[0090] The generation unit can estimate the patient's emotions and adjust the length of the intervention plan based on the estimated emotions. For example, if the patient is stressed, the generation unit can provide a short, concise intervention plan. For example, if the patient is relaxed, the generation unit can provide a detailed intervention plan. For example, if the patient is tired, the generation unit can provide a brief intervention plan. In this way, the generation unit can provide a more appropriate intervention plan by adjusting the length of the intervention plan according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input patient emotion data into a generation AI, which can analyze the data and adjust the length of the intervention plan.

[0091] The generation unit can determine the priority of intervention plans based on the timing of the patient's symptom onset when generating intervention plans. For example, the generation unit can prioritize providing intervention plans for the most recent symptoms. For example, the generation unit can provide intervention plans for the most recent symptoms while referring to past symptoms. The generation unit can also adjust the priority of intervention plans according to the timing of symptom onset. This allows the generation unit to adjust the priority of intervention plans according to the timing of the patient's symptom onset. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the timing of the patient's symptom onset into a generation AI, which can analyze the data to determine the priority of intervention plans.

[0092] The generation unit can adjust the order of intervention plans based on the relevance of the patient's symptoms when generating intervention plans. For example, the generation unit can prioritize providing intervention plans for highly relevant symptoms. For example, it can postpone interventions for less relevant symptoms. The generation unit can also adjust the order of intervention plans according to the relevance of the symptoms. In this way, the generation unit can adjust the order of intervention plans according to the relevance of the patient's symptoms. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of the patient's symptoms into a generation AI, and the generation AI can analyze the data and adjust the order of intervention plans.

[0093] The service provider can estimate the patient's emotions and adjust the way feedback is presented based on the estimated emotions. For example, if the patient is stressed, the service provider can provide simple, visually easy-to-understand feedback. For example, if the patient is relaxed, the service provider can provide detailed feedback. For example, if the patient is tired, the service provider can provide concise, to-the-point feedback. This allows the service provider to provide more appropriate feedback by adjusting the way feedback is presented according to the patient'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 service provider may be performed using AI, for example, or not using AI. For example, the service provider can input patient emotion data into a generative AI, which can analyze the data and adjust the way feedback is presented.

[0094] The service provider can select the optimal service delivery method by referring to the patient's past feedback history when providing feedback. For example, the service provider may prioritize providing feedback methods that the patient has preferred in the past. For example, the service provider may select the optimal service delivery method from the patient's past feedback history. For example, the service provider may analyze the patient's past feedback history and select the most effective service delivery method. In this way, the service provider can select the optimal feedback method based on the patient's past feedback history. 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 may input the patient's past feedback history into a generating AI, which can then analyze the data and select the optimal service delivery method.

[0095] The service provider can adjust the content of the feedback based on the patient's current health condition and living environment when providing feedback. For example, if the patient's health condition is deteriorating, the service provider can provide simple and visually easy-to-understand feedback. For example, if the patient's living environment changes, the service provider can provide feedback adapted to the new environment. The service provider can also adjust the content of the feedback based on the patient's health condition and living environment. This allows the service provider to adjust the content of the feedback according to the patient's health condition and living environment. 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 patient health data into a generating AI, and the generating AI can analyze the data and adjust the content of the feedback.

[0096] The service provider can estimate the patient's emotions and prioritize feedback based on the estimated emotions. For example, if the patient is stressed, the service provider can prioritize stress-related feedback. For example, if the patient is relaxed, the service provider can prioritize exercise-related feedback. For example, if the patient is tired, the service provider can prioritize rest-related feedback. This allows the service provider to prioritize more important feedback by prioritizing it according to the patient'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 processing described above in the service provider may be performed using AI or not using AI. For example, the service provider can input patient emotion data into a generative AI, which can analyze the data to determine the priority of feedback.

[0097] The service provider can select the optimal method of providing feedback by considering the patient's device information. For example, if the patient is using a smartphone, the service provider can provide feedback tailored to the screen size. For example, if the patient is using a tablet, the service provider can provide feedback optimized for a larger screen. For example, if the patient is using a smartwatch, the service provider can provide concise and highly visible feedback. This allows the service provider to select the optimal feedback method based on the patient's device information. 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 patient's device information into a generating AI, which can then analyze the data and select the optimal method of providing feedback.

[0098] The service provider can provide multilingual feedback according to the patient's language settings when providing feedback. For example, the service provider can automatically set the language of the feedback based on the language settings of the patient's device. For example, the service provider can provide a language switching function if the patient uses multiple languages. For example, the service provider can also provide feedback in a specific language if the patient selects that language. In this way, the service provider can provide multilingual feedback according to the patient's language settings. 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 patient's language settings into a generating AI, and the generating AI can analyze the data to provide multilingual feedback.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The data collection unit can estimate the patient's emotions and adjust the timing of data collection based on those estimates. For example, if the patient is stressed, the frequency of data collection can be reduced, and data can be collected when the patient is relaxed. Conversely, if the patient is relaxed, the frequency of data collection can be increased to collect more detailed data. Furthermore, if the patient is tired, data collection can be temporarily stopped and resumed after rest. In this way, the data collection unit can adjust the timing of data collection according to the patient's emotions, enabling more appropriate data collection.

[0101] The data collection unit can analyze the patient's past activity history and select the optimal data collection method. For example, it can optimize the timing of data collection based on activities the patient frequently performed in the past. It can also collect data at specific time periods based on the patient's past activity history. Furthermore, it can analyze the patient's past activity patterns and select the most efficient data collection method. In this way, the data collection unit can select the optimal data collection method based on the patient's past activity history.

[0102] The analysis unit can estimate the patient's emotions and adjust the presentation of the analysis based on those estimated emotions. For example, if the patient is stressed, it can provide a simple and visually easy-to-understand analysis. If the patient is relaxed, it can provide a detailed analysis. Furthermore, if the patient is tired, it can provide a concise analysis that gets straight to the point. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the analysis according to the patient's emotions.

[0103] The generation unit can estimate the patient's emotions and adjust the way the intervention plan is presented based on those emotions. For example, if the patient is stressed, it can provide a simple and visually easy-to-understand intervention plan. If the patient is relaxed, it can provide a detailed intervention plan. Furthermore, if the patient is tired, it can provide a concise intervention plan that gets straight to the point. In this way, the generation unit can provide a more appropriate intervention plan by adjusting the way the intervention plan is presented according to the patient's emotions.

[0104] The service provider can estimate the patient's emotions and adjust the way feedback is presented based on those estimates. For example, if the patient is stressed, it can provide simple, visually easy-to-understand feedback. If the patient is relaxed, it can provide detailed feedback. Furthermore, if the patient is tired, it can provide concise, to-the-point feedback. In this way, the service provider can provide more appropriate feedback by adjusting the way feedback is presented according to the patient's emotions.

[0105] The data collection unit can filter data based on the patient's current health status and living environment during data collection. For example, if the patient's health status deteriorates, data collection can be temporarily suspended. Furthermore, if the patient's living environment changes, a data collection method adapted to the new environment can be selected. In addition, the type of data collected can be adjusted based on the patient's health status and living environment. This allows the data collection unit to adjust the data collected according to the patient's health status and living environment.

[0106] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. Furthermore, it can determine the priority of the analysis according to the importance of the data. In this way, the analysis unit can adjust the level of detail of the analysis according to the importance of the data.

[0107] The generation unit can adjust the level of detail in the intervention plan based on the importance of the patient's symptoms when generating the intervention plan. For example, it can provide a detailed intervention plan for high-importance symptoms and a simplified intervention plan for low-importance symptoms. Furthermore, it can also determine the priority of the intervention plan according to the importance of the symptoms. In this way, the generation unit can adjust the level of detail in the intervention plan according to the importance of the patient's symptoms.

[0108] The service provider can select the optimal feedback method by referring to the patient's past feedback history when providing feedback. For example, it can prioritize providing feedback methods that the patient has preferred in the past. It can also select the optimal feedback method based on the patient's past feedback history. Furthermore, it can analyze the patient's past feedback history to select the most effective feedback method. In this way, the service provider can select the optimal feedback method based on the patient's past feedback history.

[0109] The service provider can select the optimal feedback delivery method by considering the patient's device information. For example, if the patient is using a smartphone, feedback can be provided that is optimized for the screen size. If the patient is using a tablet, feedback optimized for a larger screen can be provided. Furthermore, if the patient is using a smartwatch, concise and highly visible feedback can be provided. This allows the service provider to select the optimal feedback method based on the patient's device information.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The data collection unit collects data to monitor the patient's daily activities. The data collection unit collects data using, for example, wearable devices worn by the patient or sensors installed in the home. Specifically, it collects the patient's exercise and behavioral data using wearable devices such as smartwatches and fitness trackers. It can also collect the patient's behavioral data and data on their living environment using motion sensors and temperature sensors installed in the home. Step 2: The analysis unit analyzes the data collected by the collection unit in real time. The analysis unit uses deep learning to analyze changes in the patient's movement patterns and behavior. Specifically, it uses deep learning algorithms such as CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network) to analyze changes in the patient's movement patterns and behavior with high accuracy. This makes it possible to detect changes in the patient's walking patterns, the frequency of hand tremors, and abnormal behavioral patterns. Step 3: The generation unit generates a customized intervention plan based on the analysis results obtained by the analysis unit. The generation unit proposes adjustments to exercise therapy and drug therapy tailored to the patient's symptoms. Specifically, it proposes exercise therapy such as rehabilitation exercises and stretching based on the patient's exercise data, and adjusts the medication schedule and type of medication according to the patient's symptoms. It can also generate a customized intervention plan based on the patient's lifestyle and medical history. Step 4: The delivery unit provides the intervention plan generated by the generation unit to the patient and care team. The delivery unit provides feedback to the patient and care team on the generated intervention plan through methods such as notifications, reports, and alerts. Specifically, the generated intervention plan is notified to the patient via devices such as smartphones and tablets, and provided to the care team as a report. It can also be used to notify the patient and care team of the generated intervention plan as an alert.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects data using the smart device 14's wearable device or sensors installed in the home. The analysis unit is implemented in real time by the specific processing unit 290 of the data processing unit 12. The generation unit is implemented in real time by the specific processing unit 290 of the data processing unit 12. Based on the analysis results, a customized intervention plan is generated. The provision unit is implemented in real time by the control unit 46A of the smart device 14. The generated intervention plan is provided to the patient and the care team. 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.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the collection unit, analysis unit, generation 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 collection unit collects data using the wearable device of the smart glasses 214 or sensors installed in the home. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data in real time. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and generates a customized intervention plan based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, and provides the generated intervention plan to the patient and the care team. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the collection unit collects data using the wearable device of the headset terminal 314 or sensors installed in the home. The analysis unit is implemented in real time by the specific processing unit 290 of the data processing unit 12. The generation unit is implemented in real time by the specific processing unit 290 of the data processing unit 12. Based on the analysis results, a customized intervention plan is generated. The provision unit is implemented in real time by the control unit 46A of the headset terminal 314. The generated intervention plan is provided to the patient and the care team. 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.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects data using the robot 414's wearable device or sensors installed in the home. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data in real time. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates a customized intervention plan based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the robot 414, and provides the generated intervention plan to the patient and the care team. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A data collection unit that collects data to monitor patients' daily activities, An analysis unit analyzes the data collected by the aforementioned collection unit in real time, A generation unit generates a customized intervention plan based on the analysis results obtained by the analysis unit, The system includes a provisioning unit that provides the intervention plan generated by the generation unit to the patient and the care team. A system characterized by the following features. (Note 2) The aforementioned collection unit is Data is collected using wearable devices worn by patients and sensors installed in their homes. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is We use deep learning to analyze changes in patients' movement patterns and behavior. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate a customized intervention plan tailored to the patient's symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide feedback to the patient and care team on the generated intervention plan. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is We propose adjustments to exercise therapy and medication. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the patient's emotions and adjust the timing of data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the patient's past activity history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the patient's current health status and living environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the patient's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, analyze patients' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is The system estimates the patient's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is The system estimates the patient's emotions and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is Estimate the patient's emotions and adjust how the intervention plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating an intervention plan, adjust the level of detail based on the severity of the patient's symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating intervention plans, different plan generation algorithms are applied depending on the patient's symptom category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is Estimate the patient's emotions and adjust the length of the intervention plan based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating an intervention plan, prioritize the plan based on when the patient's symptoms started. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating an intervention plan, adjust the order of the plan based on the relevance of the patient's symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, The system estimates the patient's emotions and adjusts the way feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing feedback, the optimal method of delivery is selected by referring to the patient's past feedback history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing feedback, adjust the content of the feedback based on the patient's current health status and living environment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, The system estimates the patient's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing feedback, the optimal method of delivery will be selected, taking into account the patient's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing feedback, multilingual feedback is provided according to the patient's language settings. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0184] 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 collects data to monitor patients' daily activities, An analysis unit analyzes the data collected by the aforementioned collection unit in real time, A generation unit generates a customized intervention plan based on the analysis results obtained by the analysis unit, The system includes a provisioning unit that provides the intervention plan generated by the generation unit to the patient and the care team. A system characterized by the following features.

2. The aforementioned collection unit is Data is collected using wearable devices worn by patients and sensors installed in their homes. The system according to feature 1.

3. The aforementioned analysis unit is We use deep learning to analyze changes in patients' movement patterns and behavior. The system according to feature 1.

4. The generating unit is Generate a customized intervention plan tailored to the patient's symptoms. The system according to feature 1.

5. The aforementioned supply unit is, Provide feedback to the patient and care team on the generated intervention plan. The system according to feature 1.

6. The generating unit is We propose adjustments to exercise therapy and medication. The system according to feature 1.

7. The aforementioned collection unit is We estimate the patient's emotions and adjust the timing of data collection based on the estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the patient's past activity history and select the optimal data collection method. The system according to feature 1.

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