system

Abstract

The system according to this embodiment aims to efficiently improve and evaluate the skills of care workers. [Solution] The system according to the embodiment comprises a generation unit, a feedback unit, an evaluation unit, a stress management unit, and a community support unit. The generation unit generates a customized training plan in real time based on each caregiver's skill level, learning history, and evaluation data. The feedback unit provides real-time feedback based on data collected during care activities. The evaluation unit comprehensively analyzes the caregiver's evaluation data and generates a detailed report in which the evaluation results are reflected in compensation and career paths. The stress management unit evaluates the stress level based on data obtained from the caregiver's wearable device and proposes appropriate stress relief and relaxation methods. The community support unit automatically generates forum posts and QA sessions to promote knowledge sharing among caregivers.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the improvement and evaluation of the skills of caregivers have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently improve and evaluate the skills of caregivers.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a generation unit, a feedback unit, an evaluation unit, a stress management unit, and a community support unit. The generation unit generates customized training plans in real time based on each caregiver's skill level, learning history, and evaluation data. The feedback unit provides real-time feedback based on data collected during care activities. The evaluation unit comprehensively analyzes the caregiver's evaluation data and generates a detailed report in which the evaluation results are reflected in compensation and career paths. The stress management unit evaluates stress levels based on data obtained from the caregiver's wearable device and proposes appropriate stress relief and relaxation methods. The community support unit automatically generates forum posts and Q&A sessions to promote knowledge sharing among caregivers. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently improve and evaluate the skills of caregivers. [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 tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memories (SSDs (Solid State Drives)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the tagged 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 applicable to the communication I / F include wireless communication standards including 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] [[ID=③]] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires 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 according to an embodiment of the present invention is a system that provides personalized training and real-time evaluation for individual caregivers, and a mechanism in which the evaluation results are directly reflected in their compensation and careers. This AI agent aims to improve the skills and stress management of caregivers and to improve the overall service level of the facility. The AI ​​agent generates a customized training plan in real time based on each caregiver's skill level, learning history, and evaluation data. For example, the generating AI generates a customized training plan in real time based on each caregiver's skill level, learning history, and evaluation data. Next, based on data collected during care activities, the generating AI provides real-time feedback and immediately generates accurate advice and improvement suggestions for situations faced by caregivers. For example, the generating AI provides real-time feedback based on data collected during care activities. Furthermore, the generating AI comprehensively analyzes the caregiver's evaluation data and generates a detailed report in which the evaluation results are reflected in their compensation and career paths. For example, the generating AI comprehensively analyzes the caregiver's evaluation data and generates a detailed report in which the evaluation results are reflected in their compensation and career paths. Furthermore, based on data obtained from caregivers' wearable devices, the generating AI assesses stress levels and suggests appropriate stress relief and relaxation methods. For example, the generating AI assesses stress levels based on data obtained from caregivers' wearable devices and suggests appropriate stress relief and relaxation methods. Finally, the generating AI automatically generates forum posts and QA sessions to promote knowledge sharing among caregivers, thereby activating information exchange within the community. For example, the generating AI automatically generates forum posts and QA sessions to promote knowledge sharing among caregivers, thereby activating information exchange within the community. This mechanism improves the skills and stress management of caregivers, thereby improving the overall service level of the facility. In this way, the AI ​​agent can improve the skills and stress management of caregivers, thereby improving the overall service level of the facility.

[0029] The AI ​​agent according to this embodiment comprises a generation unit, a feedback unit, an evaluation unit, a stress management unit, and a community support unit. The generation unit generates a customized training plan in real time based on each caregiver's skill level, learning history, and evaluation data. The generation unit, for example, uses a generation AI to generate a customized training plan in real time based on each caregiver's skill level, learning history, and evaluation data. The generation unit, for example, uses a generation AI to generate a customized training plan in real time based on each caregiver's skill level, learning history, and evaluation data. The generation unit, for example, uses a generation AI to generate a customized training plan in real time based on each caregiver's skill level, learning history, and evaluation data. The feedback unit provides real-time feedback based on data collected during care activities. The feedback unit, for example, uses a generation AI to provide real-time feedback based on data collected during care activities. The feedback unit provides real-time feedback based on data collected during care activities The evaluation department comprehensively analyzes caregivers' evaluation data and generates detailed reports that reflect the evaluation results in compensation and career paths. The evaluation department, for example, uses a generation AI to comprehensively analyze caregivers' evaluation data and generates detailed reports that reflect the evaluation results in compensation and career paths. The evaluation department, for example, uses a generation AI to comprehensively analyze caregivers' evaluation data and generates detailed reports that reflect the evaluation results in compensation and career paths. The evaluation department, for example, uses a generation AI to comprehensively analyze caregivers' evaluation data and generates detailed reports that reflect the evaluation results in compensation and career paths. The stress management department evaluates stress levels based on data obtained from caregivers' wearable devices and proposes appropriate stress relief and relaxation methods. The stress management department, for example, uses a generation AI to evaluate stress levels based on data obtained from caregivers' wearable devices and proposes appropriate stress relief and relaxation methods.The stress management department, for example, uses a generation AI to evaluate stress levels based on data obtained from caregivers' wearable devices and proposes appropriate stress relief and relaxation methods. The community support department automatically generates forum posts and QA sessions to promote knowledge sharing among caregivers. The community support department, for example, uses a generation AI to automatically generate forum posts and QA sessions to promote knowledge sharing among caregivers. The community support department, for example, uses a generation AI to automatically generate forum posts and QA sessions to promote knowledge sharing among caregivers. The community support department, for example, uses a generation AI to automatically generate forum posts and QA sessions to promote knowledge sharing among caregivers. As a result, the AI ​​agent according to the embodiment can improve the skills and stress management of caregivers and improve the overall service level of the facility.

[0030] The generation unit generates customized training plans in real time based on each caregiver's skill level, learning history, and evaluation data. Specifically, the generating AI analyzes each caregiver's past training history and actual work performance data to propose the most suitable training content for each individual caregiver. For example, the generating AI analyzes the content and results of training a caregiver has received in the past and creates a new training plan that matches their current skill level. In addition, the generating AI identifies areas where specific skills or knowledge are lacking based on the caregiver's evaluation data and proposes training that focuses on those areas. Furthermore, the generating AI adjusts the training plan to match the caregiver's learning style and pace, supporting effective learning. For example, it provides training content that makes extensive use of videos and diagrams for caregivers who prefer visual learning, and proposes a plan that includes simulations and on-the-job training for caregivers who prefer practical learning. In this way, the generation unit can provide training plans that meet the individual needs of each caregiver and effectively support skill improvement.

[0031] The feedback unit provides real-time feedback based on data collected during care activities. Specifically, the generating AI analyzes data from caregivers' care activities in real time and provides appropriate feedback. For example, it analyzes motion data when caregivers assist patients with movement and points out areas for improvement in posture and movement. The generating AI also monitors patients' vital signs and behavioral data and immediately notifies caregivers if any abnormalities are detected. Furthermore, the generating AI also provides feedback on caregivers' communication and interpersonal skills. For example, it analyzes voice data from caregivers' conversations with patients and points out areas for improvement in appropriate language and tone. In this way, the feedback unit helps caregivers improve their care activities in real time and provide higher quality care. In addition, the feedback unit records the feedback received by caregivers so that they can review it later. This allows caregivers to track their own growth and continuously improve their skills.

[0032] The evaluation department comprehensively analyzes caregiver evaluation data and generates detailed reports that reflect the evaluation results in compensation and career paths. Specifically, the generating AI integrates caregiver work performance data, training results, and feedback data to perform a comprehensive evaluation. For example, the generating AI evaluates a caregiver's overall performance based on the quality of care activities performed, patient feedback, and evaluations from colleagues and supervisors. The generating AI also makes suggestions regarding the caregiver's compensation and career path based on the evaluation results. For example, it may suggest opportunities for salary increases and promotions to caregivers who demonstrate excellent performance, and suggest additional training and support to caregivers who lack specific skills. Furthermore, the generating AI generates detailed reports of the evaluation results and provides them to caregivers and managers. This allows the evaluation department to fairly and transparently evaluate caregivers' work performance and reflect it in their compensation and career paths.

[0033] The Stress Management Department evaluates stress levels based on data obtained from caregivers' wearable devices and proposes appropriate stress relief and relaxation methods. Specifically, the Generating AI analyzes data such as the caregiver's heart rate, sleep patterns, and activity level to assess stress levels. For example, if there are fluctuations in heart rate or a decline in sleep quality, the Generating AI may determine that the caregiver is experiencing high stress and proposes appropriate stress relief methods. Suggested stress relief methods include deep breathing, meditation, and light exercise. The Generating AI also proposes relaxation methods tailored to the individual needs and preferences of the caregiver. For example, it suggests relaxing music for a caregiver who enjoys listening to music, and nature walks for a caregiver who enjoys spending time in nature. Furthermore, the Generating AI monitors the results of the caregiver practicing the suggested stress relief methods and evaluates their effectiveness. In this way, the Stress Management Department supports caregivers in effectively managing stress and maintaining a healthy state.

[0034] The Community Support Department automatically generates forum posts and Q&A sessions to promote knowledge sharing among caregivers. Specifically, the generating AI proposes solutions to common challenges and questions faced by caregivers and automatically generates them as forum posts and Q&A sessions. For example, if a caregiver posts a question about a specific care technique, the generating AI searches the database for relevant information and proposes the best solution. The generating AI also proposes discussion themes and topics to promote communication among caregivers and posts them to the forum. Furthermore, the generating AI analyzes the questions and answers posted by caregivers and continuously updates the knowledge base. This allows the Community Support Department to promote knowledge sharing among caregivers and improve the overall knowledge level of the facility. In addition, the generating AI can improve the forum content based on caregiver feedback and provide more useful information. This allows the Community Support Department to provide a platform for caregivers to learn from each other and improve their skills.

[0035] The generation unit can analyze a caregiver's past training history and select the optimal training method. For example, the generation unit can prioritize providing training methods that were effective for the caregiver in the past. The generation unit can also avoid training methods that the caregiver struggled with in the past. For example, the generation unit can select methods that are effective for skill improvement based on the caregiver's past training history. This allows the generation unit to provide the optimal training method based on past training history. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the caregiver's past training history data into a generation AI and have the generation AI select the optimal training method.

[0036] The generation unit can customize training plans based on the caregiver's current workload and schedule when generating them. For example, if the caregiver is busy, the generation unit can provide a short and effective training plan. If the caregiver has ample time in their schedule, the generation unit can also provide a detailed training plan. The generation unit can also provide relevant training content depending on the caregiver's job duties. This allows for the provision of training plans tailored to the workload and schedule. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input caregiver workload data into the generation AI and have the generation AI customize the training plan.

[0037] The generation unit can prioritize providing highly relevant training content by considering the caregiver's geographical location information when generating training plans. For example, if the caregiver is in an urban area, the generation unit will provide training content specific to urban areas. For example, if the caregiver is in a rural area, the generation unit can also provide training content specific to rural areas. For example, the generation unit can also provide optimal training content according to the caregiver's workplace. This allows for the provision of training content based on geographical location information. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the caregiver's geographical location information data into the generation AI and have the generation AI select highly relevant training content.

[0038] The generation unit can analyze the caregiver's social media activity when generating a training plan and provide relevant training content. For example, the generation unit can provide training content based on topics the caregiver has shown interest in on social media. For example, the generation unit can suggest training content that the caregiver might be interested in based on their social media activity. For example, the generation unit can provide training content that the caregiver's social media followers and friends are interested in. This allows for the provision of training content based on social media activity. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the caregiver's social media activity data into a generation AI and have the generation AI select relevant training content.

[0039] The feedback unit can adjust the level of detail of the feedback provided according to the caregiver's skill level. For example, if the caregiver has a high skill level, the feedback unit will provide detailed feedback. For example, if the caregiver has a low skill level, the feedback unit can also provide basic feedback. For example, the feedback unit can select an appropriate level of detail for the feedback according to the caregiver's skill level. This allows for the provision of feedback with a level of detail appropriate to the skill level. Some or all of the above processing in the feedback unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the feedback unit can input the caregiver's skill level data into a generative AI and have the generative AI perform the adjustment of the level of detail for the feedback.

[0040] The feedback unit can apply different feedback algorithms depending on the caregiver's work content when providing feedback. For example, the feedback unit can select an appropriate feedback algorithm depending on the caregiver's work content. The feedback unit can also customize the content of the feedback based on the caregiver's work content. For example, the feedback unit can apply different feedback algorithms depending on the caregiver's work content. This allows the feedback unit to provide feedback algorithms tailored to the work content. Some or all of the above processing in the feedback unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the feedback unit can input caregiver work content data into a generation AI and have the generation AI execute the application of the feedback algorithm.

[0041] The feedback unit can determine the priority of feedback based on the caregiver's work history when providing feedback. For example, the feedback unit can prioritize providing important feedback from the caregiver's work history. The feedback unit can also determine the priority of feedback based on the caregiver's work history. For example, the feedback unit can analyze the caregiver's work history and select the optimal priority of feedback. This allows for the provision of feedback priorities based on work history. Some or all of the above processing in the feedback unit may be performed using a generative AI, or not. For example, the feedback unit can input the caregiver's work history data into a generative AI and have the generative AI perform the task of determining the priority of feedback.

[0042] The feedback unit can adjust the order of feedback by referring to the caregiver's relevant work data when providing feedback. For example, the feedback unit adjusts the order of feedback based on the caregiver's relevant work data. The feedback unit can also determine the optimal order of feedback by referring to the caregiver's relevant work data. The feedback unit can also optimize the order of feedback by analyzing the caregiver's relevant work data. This allows the feedback unit to provide an order of feedback based on relevant work data. Some or all of the above processing in the feedback unit may be performed using a generation AI, or not. For example, the feedback unit can input the caregiver's relevant work data into a generation AI and have the generation AI perform the adjustment of the order of feedback.

[0043] The evaluation unit can improve the accuracy of its evaluations by considering the relationships between caregivers during the evaluation process. For example, the evaluation unit can improve the accuracy of its evaluations by considering the relationships between caregivers. The evaluation unit can also improve the accuracy of its evaluations by analyzing the relationships between caregivers. For example, the evaluation unit can improve the accuracy of its evaluations based on the relationships between caregivers. This allows for the provision of evaluation accuracy based on relationships. Some or all of the above-described processes in the evaluation unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the evaluation unit can input data on the relationships between caregivers into a generative AI and have the generative AI perform the evaluation accuracy improvement.

[0044] The evaluation unit can perform evaluations while considering the attribute information of caregivers. For example, the evaluation unit can perform evaluations based on the attribute information of caregivers. The evaluation unit can also improve the accuracy of evaluations by considering the attribute information of caregivers. For example, the evaluation unit can analyze the attribute information of caregivers and perform evaluations. This allows for the provision of evaluations based on attribute information. Some or all of the above processing in the evaluation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the evaluation unit can input caregiver attribute information data into a generation AI and have the generation AI perform the evaluation.

[0045] The evaluation unit can perform evaluations while considering the geographical distribution of caregivers. For example, the evaluation unit can perform evaluations based on the geographical distribution of caregivers. The evaluation unit can also improve the accuracy of evaluations by considering the geographical distribution of caregivers. For example, the evaluation unit can analyze the geographical distribution of caregivers and perform evaluations. This allows for the provision of evaluations based on geographical distribution. Some or all of the above-described processes in the evaluation unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the evaluation unit can input geographical distribution data of caregivers into a generative AI and have the generative AI perform the evaluation.

[0046] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature on caregivers during the evaluation process. The evaluation unit can improve the accuracy of its evaluation based on relevant literature on caregivers, for example. The evaluation unit can also improve the accuracy of its evaluation by referring to relevant literature on caregivers, for example. The evaluation unit can also analyze relevant literature on caregivers and perform an evaluation, for example. This allows for the provision of evaluation accuracy based on relevant literature. Some or all of the above processing in the evaluation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the evaluation unit can input data on relevant literature on caregivers into a generative AI and have the generative AI perform the evaluation.

[0047] The stress management department can select the optimal management method by referring to the caregiver's past stress history during stress management. For example, the stress management department can select the optimal management method based on the caregiver's past stress history. For example, the stress management department can also suggest stress relief methods by referring to the caregiver's past stress history. For example, the stress management department can analyze the caregiver's past stress history and select the optimal stress management method. This allows for the provision of an optimal management method based on past stress history. Some or all of the above processes in the stress management department may be performed using a generation AI, or they may not be performed using a generation AI. For example, the stress management department can input the caregiver's past stress history data into a generation AI and have the generation AI select the optimal management method.

[0048] The stress management unit can customize stress management methods based on the caregiver's current living situation during stress management. For example, the stress management unit can provide optimal stress management methods according to the caregiver's living situation. For example, the stress management unit can also suggest stress relief methods considering the caregiver's living situation. For example, the stress management unit can customize stress management methods based on the caregiver's living situation. This allows for the provision of management methods based on the current living situation. Some or all of the above processing in the stress management unit may be performed using a generation AI, or not. For example, the stress management unit can input caregiver living situation data into a generation AI and have the generation AI perform the customization of the management methods.

[0049] The stress management department can select the optimal management method when managing stress, taking into account the geographical location information of caregivers. For example, the stress management department can select the optimal stress management method based on the geographical location information of caregivers. For example, the stress management department can also propose stress relief methods, taking into account the geographical location information of caregivers. For example, the stress management department can analyze the geographical location information of caregivers and select the optimal stress management method. This allows for the provision of optimal management methods based on geographical location information. Some or all of the above processing in the stress management department may be performed using a generation AI, or it may be performed without a generation AI. For example, the stress management department can input the geographical location information data of caregivers into a generation AI and have the generation AI select the optimal management method.

[0050] The stress management department can analyze the social media activities of caregivers and propose stress management measures during stress management. For example, the stress management department can propose optimal stress management measures based on the caregivers' social media activities. The stress management department can also propose stress relief methods by analyzing the caregivers' social media activities. The stress management department can also propose stress management measures by referring to the caregivers' social media activities. This allows for the provision of management measures based on social media activities. Some or all of the above processing in the stress management department may be performed using a generative AI, or it may be performed without a generative AI. For example, the stress management department can input the caregivers' social media activity data into a generative AI and have the generative AI execute the proposal of management measures.

[0051] The Community Support Department can provide optimal support by referring to the caregiver's past community activity history when providing community support. For example, the Community Support Department can provide optimal support based on the caregiver's past community activity history. The Community Support Department can also propose support by referring to the caregiver's past community activity history. The Community Support Department can also analyze the caregiver's past community activity history to provide optimal support. This allows for the provision of optimal support based on past community activity history. Some or all of the above processes in the Community Support Department may be performed using a generative AI, or not. For example, the Community Support Department can input the caregiver's past community activity history data into a generative AI and have the generative AI select the optimal support.

[0052] The Community Support Department can customize the support provided based on the caregiver's current work situation. For example, the Community Support Department can provide the most suitable support according to the caregiver's work situation. The Community Support Department can also propose support considering the caregiver's work situation. The Community Support Department can also customize the support based on the caregiver's work situation. This allows for the provision of support based on the current work situation. Some or all of the above processes in the Community Support Department may be performed using a generative AI, or not. For example, the Community Support Department can input caregiver work situation data into a generative AI and have the generative AI perform the customization of the support.

[0053] The Community Support Department can provide optimal support content by considering the geographical location information of caregivers when providing community support. For example, the Community Support Department can provide optimal support content based on the geographical location information of caregivers. For example, the Community Support Department can also propose support content considering the geographical location information of caregivers. For example, the Community Support Department can analyze the geographical location information of caregivers and provide optimal support content. This enables the provision of optimal support content based on geographical location information. Some or all of the above processing in the Community Support Department may be performed using a generative AI, or it may be performed without a generative AI. For example, the Community Support Department can input the geographical location information data of caregivers into a generative AI and have the generative AI select the optimal support content.

[0054] The Community Support Department can analyze the social media activity of caregivers and propose support content when providing community support. For example, the Community Support Department can propose the most suitable support content based on the caregiver's social media activity. The Community Support Department can also propose support content by analyzing the caregiver's social media activity. The Community Support Department can also propose support content by referring to the caregiver's social media activity. This allows for the provision of support content based on social media activity. Some or all of the above processing in the Community Support Department may be performed using a generative AI, or not. For example, the Community Support Department can input the caregiver's social media activity data into a generative AI and have the generative AI propose support content.

[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 generation unit can also monitor the health status of caregivers and provide training plans tailored to their health condition. For example, if a caregiver is unwell, it can provide lighter training. If the caregiver is healthy, it can provide regular training. Furthermore, if a caregiver has a specific health problem, it can provide training tailored to that problem. This allows for the provision of training plans that are appropriate for the caregiver's health condition.

[0057] The generation unit can also customize training plans to take into account the caregiver's hobbies and interests. For example, if a caregiver is interested in music, the training program can incorporate music. If a caregiver is interested in sports, the training program can incorporate sports. Furthermore, if a caregiver is interested in art, the training program can incorporate art. This allows for the provision of training plans tailored to the caregiver's hobbies and interests.

[0058] The generation unit can also customize training plans according to the caregiver's learning style. For example, if the caregiver is a visual learner, the training content can be provided with a lot of visual content. If the caregiver is an auditory learner, the training content can be provided with a lot of audio content. Furthermore, if the caregiver is an experiential learner, practical training content can be provided. This allows for the provision of training plans tailored to the caregiver's learning style.

[0059] The generation unit can also customize training plans according to the caregiver's career goals. For example, if a caregiver aims for a management position, it can provide training content to improve their leadership skills. If a caregiver wants to improve their professional skills, it can provide specialized training content. Furthermore, if a caregiver wants to challenge themselves in a new field, it can provide training content related to that field. This allows for the provision of training plans tailored to the caregiver's career goals.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The generation unit generates customized training plans in real time based on each caregiver's skill level, learning history, and evaluation data. For example, using a generation AI, it generates customized training plans in real time based on each caregiver's skill level, learning history, and evaluation data. Step 2: The feedback unit provides real-time feedback based on data collected during care activities. For example, it uses generative AI to provide real-time feedback based on data collected during care activities. Step 3: The evaluation department comprehensively analyzes the caregiver's evaluation data and generates a detailed report that reflects the evaluation results in compensation and career paths. For example, it uses a generation AI to comprehensively analyze the caregiver's evaluation data and generate a detailed report that reflects the evaluation results in compensation and career paths. Step 4: The stress management department evaluates stress levels based on data obtained from caregivers' wearable devices and proposes appropriate stress relief and relaxation methods. For example, it uses generative AI to evaluate stress levels based on data obtained from caregivers' wearable devices and proposes appropriate stress relief and relaxation methods. Step 5: The Community Support Department automatically generates forum posts and Q&A sessions to promote knowledge sharing among caregivers. For example, it uses a generation AI to automatically generate forum posts and Q&A sessions to promote knowledge sharing among caregivers.

[0062] (Example of form 2) An AI agent according to an embodiment of the present invention is a system that provides personalized training and real-time evaluation for individual caregivers, and a mechanism in which the evaluation results are directly reflected in their compensation and careers. This AI agent aims to improve the skills and stress management of caregivers and to improve the overall service level of the facility. The AI ​​agent generates a customized training plan in real time based on each caregiver's skill level, learning history, and evaluation data. For example, the generating AI generates a customized training plan in real time based on each caregiver's skill level, learning history, and evaluation data. Next, based on data collected during care activities, the generating AI provides real-time feedback and immediately generates accurate advice and improvement suggestions for situations faced by caregivers. For example, the generating AI provides real-time feedback based on data collected during care activities. Furthermore, the generating AI comprehensively analyzes the caregiver's evaluation data and generates a detailed report in which the evaluation results are reflected in their compensation and career paths. For example, the generating AI comprehensively analyzes the caregiver's evaluation data and generates a detailed report in which the evaluation results are reflected in their compensation and career paths. Furthermore, based on data obtained from caregivers' wearable devices, the generating AI assesses stress levels and suggests appropriate stress relief and relaxation methods. For example, the generating AI assesses stress levels based on data obtained from caregivers' wearable devices and suggests appropriate stress relief and relaxation methods. Finally, the generating AI automatically generates forum posts and QA sessions to promote knowledge sharing among caregivers, thereby activating information exchange within the community. For example, the generating AI automatically generates forum posts and QA sessions to promote knowledge sharing among caregivers, thereby activating information exchange within the community. This mechanism improves the skills and stress management of caregivers, thereby improving the overall service level of the facility. In this way, the AI ​​agent can improve the skills and stress management of caregivers, thereby improving the overall service level of the facility.

[0063] The AI ​​agent according to this embodiment comprises a generation unit, a feedback unit, an evaluation unit, a stress management unit, and a community support unit. The generation unit generates a customized training plan in real time based on each caregiver's skill level, learning history, and evaluation data. The generation unit, for example, uses a generation AI to generate a customized training plan in real time based on each caregiver's skill level, learning history, and evaluation data. The generation unit, for example, uses a generation AI to generate a customized training plan in real time based on each caregiver's skill level, learning history, and evaluation data. The generation unit, for example, uses a generation AI to generate a customized training plan in real time based on each caregiver's skill level, learning history, and evaluation data. The feedback unit provides real-time feedback based on data collected during care activities. The feedback unit, for example, uses a generation AI to provide real-time feedback based on data collected during care activities. The feedback unit provides real-time feedback based on data collected during care activities The evaluation department comprehensively analyzes caregivers' evaluation data and generates detailed reports that reflect the evaluation results in compensation and career paths. The evaluation department, for example, uses a generation AI to comprehensively analyze caregivers' evaluation data and generates detailed reports that reflect the evaluation results in compensation and career paths. The evaluation department, for example, uses a generation AI to comprehensively analyze caregivers' evaluation data and generates detailed reports that reflect the evaluation results in compensation and career paths. The evaluation department, for example, uses a generation AI to comprehensively analyze caregivers' evaluation data and generates detailed reports that reflect the evaluation results in compensation and career paths. The stress management department evaluates stress levels based on data obtained from caregivers' wearable devices and proposes appropriate stress relief and relaxation methods. The stress management department, for example, uses a generation AI to evaluate stress levels based on data obtained from caregivers' wearable devices and proposes appropriate stress relief and relaxation methods.The stress management department, for example, uses a generation AI to evaluate stress levels based on data obtained from caregivers' wearable devices and proposes appropriate stress relief and relaxation methods. The community support department automatically generates forum posts and QA sessions to promote knowledge sharing among caregivers. The community support department, for example, uses a generation AI to automatically generate forum posts and QA sessions to promote knowledge sharing among caregivers. The community support department, for example, uses a generation AI to automatically generate forum posts and QA sessions to promote knowledge sharing among caregivers. The community support department, for example, uses a generation AI to automatically generate forum posts and QA sessions to promote knowledge sharing among caregivers. As a result, the AI ​​agent according to the embodiment can improve the skills and stress management of caregivers and improve the overall service level of the facility.

[0064] The generation unit generates customized training plans in real time based on each caregiver's skill level, learning history, and evaluation data. Specifically, the generating AI analyzes each caregiver's past training history and actual work performance data to propose the most suitable training content for each individual caregiver. For example, the generating AI analyzes the content and results of training a caregiver has received in the past and creates a new training plan that matches their current skill level. In addition, the generating AI identifies areas where specific skills or knowledge are lacking based on the caregiver's evaluation data and proposes training that focuses on those areas. Furthermore, the generating AI adjusts the training plan to match the caregiver's learning style and pace, supporting effective learning. For example, it provides training content that makes extensive use of videos and diagrams for caregivers who prefer visual learning, and proposes a plan that includes simulations and on-the-job training for caregivers who prefer practical learning. In this way, the generation unit can provide training plans that meet the individual needs of each caregiver and effectively support skill improvement.

[0065] The feedback unit provides real-time feedback based on data collected during care activities. Specifically, the generating AI analyzes data from caregivers' care activities in real time and provides appropriate feedback. For example, it analyzes motion data when caregivers assist patients with movement and points out areas for improvement in posture and movement. The generating AI also monitors patients' vital signs and behavioral data and immediately notifies caregivers if any abnormalities are detected. Furthermore, the generating AI also provides feedback on caregivers' communication and interpersonal skills. For example, it analyzes voice data from caregivers' conversations with patients and points out areas for improvement in appropriate language and tone. In this way, the feedback unit helps caregivers improve their care activities in real time and provide higher quality care. In addition, the feedback unit records the feedback received by caregivers so that they can review it later. This allows caregivers to track their own growth and continuously improve their skills.

[0066] The evaluation department comprehensively analyzes caregiver evaluation data and generates detailed reports that reflect the evaluation results in compensation and career paths. Specifically, the generating AI integrates caregiver work performance data, training results, and feedback data to perform a comprehensive evaluation. For example, the generating AI evaluates a caregiver's overall performance based on the quality of care activities performed, patient feedback, and evaluations from colleagues and supervisors. The generating AI also makes suggestions regarding the caregiver's compensation and career path based on the evaluation results. For example, it may suggest opportunities for salary increases and promotions to caregivers who demonstrate excellent performance, and suggest additional training and support to caregivers who lack specific skills. Furthermore, the generating AI generates detailed reports of the evaluation results and provides them to caregivers and managers. This allows the evaluation department to fairly and transparently evaluate caregivers' work performance and reflect it in their compensation and career paths.

[0067] The Stress Management Department evaluates stress levels based on data obtained from caregivers' wearable devices and proposes appropriate stress relief and relaxation methods. Specifically, the Generating AI analyzes data such as the caregiver's heart rate, sleep patterns, and activity level to assess stress levels. For example, if there are fluctuations in heart rate or a decline in sleep quality, the Generating AI may determine that the caregiver is experiencing high stress and proposes appropriate stress relief methods. Suggested stress relief methods include deep breathing, meditation, and light exercise. The Generating AI also proposes relaxation methods tailored to the individual needs and preferences of the caregiver. For example, it suggests relaxing music for a caregiver who enjoys listening to music, and nature walks for a caregiver who enjoys spending time in nature. Furthermore, the Generating AI monitors the results of the caregiver practicing the suggested stress relief methods and evaluates their effectiveness. In this way, the Stress Management Department supports caregivers in effectively managing stress and maintaining a healthy state.

[0068] The Community Support Department automatically generates forum posts and Q&A sessions to promote knowledge sharing among caregivers. Specifically, the generating AI proposes solutions to common challenges and questions faced by caregivers and automatically generates them as forum posts and Q&A sessions. For example, if a caregiver posts a question about a specific care technique, the generating AI searches the database for relevant information and proposes the best solution. The generating AI also proposes discussion themes and topics to promote communication among caregivers and posts them to the forum. Furthermore, the generating AI analyzes the questions and answers posted by caregivers and continuously updates the knowledge base. This allows the Community Support Department to promote knowledge sharing among caregivers and improve the overall knowledge level of the facility. In addition, the generating AI can improve the forum content based on caregiver feedback and provide more useful information. This allows the Community Support Department to provide a platform for caregivers to learn from each other and improve their skills.

[0069] The generation unit can estimate the caregiver's emotions and adjust the training plan based on the estimated emotions. For example, if the caregiver is stressed, the generation unit will prioritize providing relaxing training content. For example, if the caregiver is highly motivated, the generation unit can also provide challenging training content. For example, if the caregiver is tired, the generation unit can also provide lighter training content. This allows for the provision of training plans tailored to the caregiver's emotions. Emotion estimation is performed, for example, using a generation AI. The generation AI takes, for example, the caregiver's facial expression data as input and outputs emotions. Some or all of the above-described processes in the generation unit may be performed using a generation AI or not.

[0070] The generation unit can analyze a caregiver's past training history and select the optimal training method. For example, the generation unit can prioritize providing training methods that were effective for the caregiver in the past. The generation unit can also avoid training methods that the caregiver struggled with in the past. For example, the generation unit can select methods that are effective for skill improvement based on the caregiver's past training history. This allows the generation unit to provide the optimal training method based on past training history. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the caregiver's past training history data into a generation AI and have the generation AI select the optimal training method.

[0071] The generation unit can customize training plans based on the caregiver's current workload and schedule when generating them. For example, if the caregiver is busy, the generation unit can provide a short and effective training plan. If the caregiver has ample time in their schedule, the generation unit can also provide a detailed training plan. The generation unit can also provide relevant training content depending on the caregiver's job duties. This allows for the provision of training plans tailored to the workload and schedule. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input caregiver workload data into the generation AI and have the generation AI customize the training plan.

[0072] The generation unit can estimate the caregiver's emotions and determine the priority of the training plan based on the estimated emotions. For example, if the caregiver is stressed, the generation unit will prioritize relaxing training. For example, if the caregiver is highly motivated, the generation unit may also prioritize challenging training. For example, if the caregiver is tired, the generation unit may also prioritize lighter training. This allows for the provision of training plan priorities that correspond to the caregiver's emotions. Emotion estimation is performed, for example, using a generation AI. For example, the generation AI takes the caregiver's facial expression data as input and outputs emotions. Some or all of the above processing in the generation unit may be performed using a generation AI or not.

[0073] The generation unit can prioritize providing highly relevant training content by considering the caregiver's geographical location information when generating training plans. For example, if the caregiver is in an urban area, the generation unit will provide training content specific to urban areas. For example, if the caregiver is in a rural area, the generation unit can also provide training content specific to rural areas. For example, the generation unit can also provide optimal training content according to the caregiver's workplace. This allows for the provision of training content based on geographical location information. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the caregiver's geographical location information data into the generation AI and have the generation AI select highly relevant training content.

[0074] The generation unit can analyze the caregiver's social media activity when generating a training plan and provide relevant training content. For example, the generation unit can provide training content based on topics the caregiver has shown interest in on social media. For example, the generation unit can suggest training content that the caregiver might be interested in based on their social media activity. For example, the generation unit can provide training content that the caregiver's social media followers and friends are interested in. This allows for the provision of training content based on social media activity. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the caregiver's social media activity data into a generation AI and have the generation AI select relevant training content.

[0075] The feedback unit can estimate the caregiver's emotions and adjust the way it expresses the feedback based on the estimated emotions. For example, if the caregiver is stressed, the feedback unit will provide feedback in gentle words. For example, if the caregiver is highly motivated, the feedback unit may provide feedback that includes many words of encouragement. For example, if the caregiver is tired, the feedback unit may provide concise and easy-to-understand feedback. This allows for the expression of feedback to be tailored to the caregiver's emotions. Emotion estimation is performed, for example, using generative AI. For example, the generative AI takes the caregiver's facial expression data as input and outputs emotions. Some or all of the above-described processes in the feedback unit may be performed using generative AI or not.

[0076] The feedback unit can adjust the level of detail of the feedback provided according to the caregiver's skill level. For example, if the caregiver has a high skill level, the feedback unit will provide detailed feedback. For example, if the caregiver has a low skill level, the feedback unit can also provide basic feedback. For example, the feedback unit can select an appropriate level of detail for the feedback according to the caregiver's skill level. This allows for the provision of feedback with a level of detail appropriate to the skill level. Some or all of the above processing in the feedback unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the feedback unit can input the caregiver's skill level data into a generative AI and have the generative AI perform the adjustment of the level of detail for the feedback.

[0077] The feedback unit can apply different feedback algorithms depending on the caregiver's work content when providing feedback. For example, the feedback unit can select an appropriate feedback algorithm depending on the caregiver's work content. The feedback unit can also customize the content of the feedback based on the caregiver's work content. For example, the feedback unit can apply different feedback algorithms depending on the caregiver's work content. This allows the feedback unit to provide feedback algorithms tailored to the work content. Some or all of the above processing in the feedback unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the feedback unit can input caregiver work content data into a generation AI and have the generation AI execute the application of the feedback algorithm.

[0078] The feedback unit can estimate the caregiver's emotions and adjust the length of the feedback based on the estimated emotions. For example, if the caregiver is stressed, the feedback unit can provide short, concise feedback. For example, if the caregiver is highly motivated, the feedback unit can also provide detailed feedback. For example, if the caregiver is tired, the feedback unit can also provide brief feedback. This allows for feedback lengths to be tailored to the caregiver's emotions. Emotion estimation is performed, for example, using generative AI. For example, the generative AI takes the caregiver's facial expression data as input and outputs emotions. Some or all of the above-described processes in the feedback unit may be performed using generative AI or not.

[0079] The feedback unit can determine the priority of feedback based on the caregiver's work history when providing feedback. For example, the feedback unit can prioritize providing important feedback from the caregiver's work history. The feedback unit can also determine the priority of feedback based on the caregiver's work history. For example, the feedback unit can analyze the caregiver's work history and select the optimal priority of feedback. This allows for the provision of feedback priorities based on work history. Some or all of the above processing in the feedback unit may be performed using a generative AI, or not. For example, the feedback unit can input the caregiver's work history data into a generative AI and have the generative AI perform the task of determining the priority of feedback.

[0080] The feedback unit can adjust the order of feedback by referring to the caregiver's relevant work data when providing feedback. For example, the feedback unit adjusts the order of feedback based on the caregiver's relevant work data. The feedback unit can also determine the optimal order of feedback by referring to the caregiver's relevant work data. The feedback unit can also optimize the order of feedback by analyzing the caregiver's relevant work data. This allows the feedback unit to provide an order of feedback based on relevant work data. Some or all of the above processing in the feedback unit may be performed using a generation AI, or not. For example, the feedback unit can input the caregiver's relevant work data into a generation AI and have the generation AI perform the adjustment of the order of feedback.

[0081] The evaluation unit can estimate the caregiver's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if the caregiver is stressed, the evaluation unit may relax the evaluation criteria. For example, if the caregiver is highly motivated, the evaluation unit may tighten the evaluation criteria. For example, if the caregiver is tired, the evaluation unit may adjust the evaluation criteria. This allows for the provision of evaluation criteria that are appropriate to the caregiver's emotions. Emotion estimation is performed, for example, using a generative AI. The generative AI takes, for example, the caregiver's facial expression data as input and outputs emotions. Some or all of the above processing in the evaluation unit may be performed using a generative AI, or it may be performed without using a generative AI.

[0082] The evaluation unit can improve the accuracy of its evaluations by considering the relationships between caregivers during the evaluation process. For example, the evaluation unit can improve the accuracy of its evaluations by considering the relationships between caregivers. The evaluation unit can also improve the accuracy of its evaluations by analyzing the relationships between caregivers. For example, the evaluation unit can improve the accuracy of its evaluations based on the relationships between caregivers. This allows for the provision of evaluation accuracy based on relationships. Some or all of the above-described processes in the evaluation unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the evaluation unit can input data on the relationships between caregivers into a generative AI and have the generative AI perform the evaluation accuracy improvement.

[0083] The evaluation unit can perform evaluations while considering the attribute information of caregivers. For example, the evaluation unit can perform evaluations based on the attribute information of caregivers. The evaluation unit can also improve the accuracy of evaluations by considering the attribute information of caregivers. For example, the evaluation unit can analyze the attribute information of caregivers and perform evaluations. This allows for the provision of evaluations based on attribute information. Some or all of the above processing in the evaluation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the evaluation unit can input caregiver attribute information data into a generation AI and have the generation AI perform the evaluation.

[0084] The evaluation unit can estimate the caregiver's emotions and adjust the display order of the evaluation results based on the estimated emotions of the caregiver. For example, if the caregiver is feeling stressed, the evaluation unit will prioritize displaying important evaluation results. For example, if the caregiver is highly motivated, the evaluation unit may also display detailed evaluation results. For example, if the caregiver is tired, the evaluation unit may also display concise evaluation results. This provides an order for displaying evaluation results that corresponds to the caregiver's emotions. Emotion estimation is performed, for example, using a generative AI. The generative AI takes, for example, the caregiver's facial expression data as input and outputs emotions. Some or all of the above processing in the evaluation unit may be performed using a generative AI, or it may be performed without using a generative AI.

[0085] The evaluation unit can perform evaluations while considering the geographical distribution of caregivers. For example, the evaluation unit can perform evaluations based on the geographical distribution of caregivers. The evaluation unit can also improve the accuracy of evaluations by considering the geographical distribution of caregivers. For example, the evaluation unit can analyze the geographical distribution of caregivers and perform evaluations. This allows for the provision of evaluations based on geographical distribution. Some or all of the above-described processes in the evaluation unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the evaluation unit can input geographical distribution data of caregivers into a generative AI and have the generative AI perform the evaluation.

[0086] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature on caregivers during the evaluation process. The evaluation unit can improve the accuracy of its evaluation based on relevant literature on caregivers, for example. The evaluation unit can also improve the accuracy of its evaluation by referring to relevant literature on caregivers, for example. The evaluation unit can also analyze relevant literature on caregivers and perform an evaluation, for example. This allows for the provision of evaluation accuracy based on relevant literature. Some or all of the above processing in the evaluation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the evaluation unit can input data on relevant literature on caregivers into a generative AI and have the generative AI perform the evaluation.

[0087] The stress management department can estimate the emotions of caregivers and adjust stress management methods based on the estimated emotions. For example, if a caregiver is feeling stressed, the stress management department can suggest relaxation methods. For example, if a caregiver is highly motivated, the stress management department can also adjust stress management methods. For example, if a caregiver is tired, the stress management department can simplify stress management methods. This allows for the provision of stress management methods that are tailored to the emotions of caregivers. Emotion estimation is performed, for example, using a generative AI. The generative AI takes, for example, the caregiver's facial expression data as input and outputs emotions. Some or all of the above-described processes in the stress management department may be performed using a generative AI or not.

[0088] The stress management department can select the optimal management method by referring to the caregiver's past stress history during stress management. For example, the stress management department can select the optimal management method based on the caregiver's past stress history. For example, the stress management department can also suggest stress relief methods by referring to the caregiver's past stress history. For example, the stress management department can analyze the caregiver's past stress history and select the optimal stress management method. This allows for the provision of an optimal management method based on past stress history. Some or all of the above processes in the stress management department may be performed using a generation AI, or they may not be performed using a generation AI. For example, the stress management department can input the caregiver's past stress history data into a generation AI and have the generation AI select the optimal management method.

[0089] The stress management unit can customize stress management methods based on the caregiver's current living situation during stress management. For example, the stress management unit can provide optimal stress management methods according to the caregiver's living situation. For example, the stress management unit can also suggest stress relief methods considering the caregiver's living situation. For example, the stress management unit can customize stress management methods based on the caregiver's living situation. This allows for the provision of management methods based on the current living situation. Some or all of the above processing in the stress management unit may be performed using a generation AI, or not. For example, the stress management unit can input caregiver living situation data into a generation AI and have the generation AI perform the customization of the management methods.

[0090] The stress management unit can estimate the emotions of caregivers and determine stress management priorities based on the estimated emotions. For example, if a caregiver is feeling stressed, the stress management unit will prioritize stress management. For example, if a caregiver is highly motivated, the stress management unit can also adjust the stress management priorities. For example, if a caregiver is tired, the stress management unit can also determine the stress management priorities. This allows for the provision of stress management priorities that correspond to the emotions of caregivers. Emotion estimation is performed, for example, using a generative AI. The generative AI takes, for example, the caregiver's facial expression data as input and outputs emotions. Some or all of the above processing in the stress management unit may be performed using a generative AI, or it may be performed without using a generative AI.

[0091] The stress management department can select the optimal management method when managing stress, taking into account the geographical location information of caregivers. For example, the stress management department can select the optimal stress management method based on the geographical location information of caregivers. For example, the stress management department can also propose stress relief methods, taking into account the geographical location information of caregivers. For example, the stress management department can analyze the geographical location information of caregivers and select the optimal stress management method. This allows for the provision of optimal management methods based on geographical location information. Some or all of the above processing in the stress management department may be performed using a generation AI, or it may be performed without a generation AI. For example, the stress management department can input the geographical location information data of caregivers into a generation AI and have the generation AI select the optimal management method.

[0092] The stress management department can analyze the social media activities of caregivers and propose stress management measures during stress management. For example, the stress management department can propose optimal stress management measures based on the caregivers' social media activities. The stress management department can also propose stress relief methods by analyzing the caregivers' social media activities. The stress management department can also propose stress management measures by referring to the caregivers' social media activities. This allows for the provision of management measures based on social media activities. Some or all of the above processing in the stress management department may be performed using a generative AI, or it may be performed without a generative AI. For example, the stress management department can input the caregivers' social media activity data into a generative AI and have the generative AI execute the proposal of management measures.

[0093] The Community Support Department can estimate the emotions of caregivers and adjust the content of community support based on the estimated emotions of the caregivers. For example, if a caregiver is feeling stressed, the Community Support Department can provide community support related to relaxation. For example, if a caregiver is highly motivated, the Community Support Department can also provide community support related to skill improvement. For example, if a caregiver is tired, the Community Support Department can also provide community support related to refreshing oneself. This allows for the provision of community support tailored to the emotions of the caregivers. Emotion estimation is performed, for example, using generative AI. For example, the generative AI takes the caregiver's facial expression data as input and outputs emotions. Some or all of the above-described processes in the Community Support Department may be performed using generative AI or not.

[0094] The Community Support Department can provide optimal support by referring to the caregiver's past community activity history when providing community support. For example, the Community Support Department can provide optimal support based on the caregiver's past community activity history. The Community Support Department can also propose support by referring to the caregiver's past community activity history. The Community Support Department can also analyze the caregiver's past community activity history to provide optimal support. This allows for the provision of optimal support based on past community activity history. Some or all of the above processes in the Community Support Department may be performed using a generative AI, or not. For example, the Community Support Department can input the caregiver's past community activity history data into a generative AI and have the generative AI select the optimal support.

[0095] The Community Support Department can customize the support provided based on the caregiver's current work situation. For example, the Community Support Department can provide the most suitable support according to the caregiver's work situation. The Community Support Department can also propose support considering the caregiver's work situation. The Community Support Department can also customize the support based on the caregiver's work situation. This allows for the provision of support based on the current work situation. Some or all of the above processes in the Community Support Department may be performed using a generative AI, or not. For example, the Community Support Department can input caregiver work situation data into a generative AI and have the generative AI perform the customization of the support.

[0096] The Community Support Department can estimate the emotions of caregivers and determine the priority of community support based on the estimated emotions. For example, if a caregiver is stressed, the Community Support Department will prioritize support related to relaxation. For example, if a caregiver is highly motivated, the Community Support Department may also prioritize support related to skill improvement. For example, if a caregiver is tired, the Community Support Department may also prioritize support related to refreshment. This allows for the provision of community support priorities that correspond to the emotions of caregivers. Emotion estimation is performed, for example, using generative AI. For example, the generative AI takes the caregiver's facial expression data as input and outputs emotions. Some or all of the above processing in the Community Support Department may be performed using generative AI or not.

[0097] The Community Support Department can provide optimal support content by considering the geographical location information of caregivers when providing community support. For example, the Community Support Department can provide optimal support content based on the geographical location information of caregivers. For example, the Community Support Department can also propose support content considering the geographical location information of caregivers. For example, the Community Support Department can analyze the geographical location information of caregivers and provide optimal support content. This enables the provision of optimal support content based on geographical location information. Some or all of the above processing in the Community Support Department may be performed using a generative AI, or it may be performed without a generative AI. For example, the Community Support Department can input the geographical location information data of caregivers into a generative AI and have the generative AI select the optimal support content.

[0098] The Community Support Department can analyze the social media activity of caregivers and propose support content when providing community support. For example, the Community Support Department can propose the most suitable support content based on the caregiver's social media activity. The Community Support Department can also propose support content by analyzing the caregiver's social media activity. The Community Support Department can also propose support content by referring to the caregiver's social media activity. This allows for the provision of support content based on social media activity. Some or all of the above processing in the Community Support Department may be performed using a generative AI, or not. For example, the Community Support Department can input the caregiver's social media activity data into a generative AI and have the generative AI propose support content.

[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 generation unit can also monitor the health status of caregivers and provide training plans tailored to their health condition. For example, if a caregiver is unwell, it can provide lighter training. If the caregiver is healthy, it can provide regular training. Furthermore, if a caregiver has a specific health problem, it can provide training tailored to that problem. This allows for the provision of training plans that are appropriate for the caregiver's health condition.

[0101] The generation unit can also customize training plans to take into account the caregiver's hobbies and interests. For example, if a caregiver is interested in music, the training program can incorporate music. If a caregiver is interested in sports, the training program can incorporate sports. Furthermore, if a caregiver is interested in art, the training program can incorporate art. This allows for the provision of training plans tailored to the caregiver's hobbies and interests.

[0102] The generation unit can also customize training plans according to the caregiver's learning style. For example, if the caregiver is a visual learner, the training content can be provided with a lot of visual content. If the caregiver is an auditory learner, the training content can be provided with a lot of audio content. Furthermore, if the caregiver is an experiential learner, practical training content can be provided. This allows for the provision of training plans tailored to the caregiver's learning style.

[0103] The generation unit can also customize training plans according to the caregiver's career goals. For example, if a caregiver aims for a management position, it can provide training content to improve their leadership skills. If a caregiver wants to improve their professional skills, it can provide specialized training content. Furthermore, if a caregiver wants to challenge themselves in a new field, it can provide training content related to that field. This allows for the provision of training plans tailored to the caregiver's career goals.

[0104] The generation unit can also estimate the caregiver's emotions and adjust the difficulty level of the training plan based on those emotions. For example, if the caregiver is stressed, it can provide training of a lower difficulty level. If the caregiver is highly motivated, it can provide training of a higher difficulty level. Furthermore, if the caregiver is tired, it can provide training of a moderate difficulty level. This allows for the provision of training plan difficulty levels that are appropriate to the caregiver's emotions.

[0105] The generation unit can also estimate the caregiver's emotions and adjust the feedback frequency in the training plan based on the estimated emotions. For example, if the caregiver is stressed, the feedback frequency can be reduced. If the caregiver is highly motivated, the feedback frequency can be increased. Furthermore, if the caregiver is tired, the feedback frequency can be kept at a moderate level. This allows for feedback frequency tailored to the caregiver's emotions.

[0106] The generation unit can also estimate the caregiver's emotions and adjust the training plan based on those emotions. For example, if the caregiver is stressed, it can provide relaxing training. If the caregiver is highly motivated, it can provide challenging training. Furthermore, if the caregiver is tired, it can provide lighter training. This allows for training plans tailored to the caregiver's emotions.

[0107] The generation unit can also estimate the caregiver's emotions and adjust the pace of the training plan based on those emotions. For example, if the caregiver is stressed, the pace can be slowed down. If the caregiver is highly motivated, the pace can be increased. Furthermore, if the caregiver is tired, the pace can be kept at a moderate level. This allows for a training plan pace that is tailored to the caregiver's emotions.

[0108] The generation unit can estimate the caregiver's emotions and adjust the training plan's feedback content based on those emotions. For example, if the caregiver is stressed, it can provide feedback in gentle words. If the caregiver is highly motivated, it can provide feedback that includes many words of encouragement. Furthermore, if the caregiver is tired, it can provide concise and easy-to-understand feedback. This allows for the provision of feedback tailored to the caregiver's emotions.

[0109] The generation unit can also estimate the caregiver's emotions and adjust the evaluation criteria for the training plan based on those estimated emotions. For example, if the caregiver is stressed, the evaluation criteria can be relaxed. If the caregiver is highly motivated, the evaluation criteria can be made stricter. Furthermore, if the caregiver is tired, the evaluation criteria can be adjusted. This allows for the provision of evaluation criteria that are tailored to the caregiver's emotions.

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

[0111] Step 1: The generation unit generates customized training plans in real time based on each caregiver's skill level, learning history, and evaluation data. For example, using a generation AI, it generates customized training plans in real time based on each caregiver's skill level, learning history, and evaluation data. Step 2: The feedback unit provides real-time feedback based on data collected during care activities. For example, it uses generative AI to provide real-time feedback based on data collected during care activities. Step 3: The evaluation department comprehensively analyzes the caregiver's evaluation data and generates a detailed report that reflects the evaluation results in compensation and career paths. For example, it uses a generation AI to comprehensively analyze the caregiver's evaluation data and generate a detailed report that reflects the evaluation results in compensation and career paths. Step 4: The stress management department evaluates stress levels based on data obtained from caregivers' wearable devices and proposes appropriate stress relief and relaxation methods. For example, it uses generative AI to evaluate stress levels based on data obtained from caregivers' wearable devices and proposes appropriate stress relief and relaxation methods. Step 5: The Community Support Department automatically generates forum posts and Q&A sessions to promote knowledge sharing among caregivers. For example, it uses a generation AI to automatically generate forum posts and Q&A sessions to promote knowledge sharing among caregivers.

[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 generation unit, feedback unit, evaluation unit, stress management unit, and community support unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the smart device 14 and generates a customized training plan in real time based on each caregiver's skill level, learning history, and evaluation data. The feedback unit is implemented by, for example, the control unit 46A of the smart device 14 and provides real-time feedback based on data collected during care activities. The evaluation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and comprehensively analyzes the caregiver's evaluation data and generates a detailed report in which the evaluation results are reflected in compensation and career paths. The stress management unit is implemented by, for example, the control unit 46A of the smart device 14 and evaluates the stress level based on data obtained from the caregiver's wearable device and suggests appropriate stress relief and relaxation methods. The community support section is implemented, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates forum posts and QA sessions to promote knowledge sharing among caregivers. The correspondence between each section 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 generation unit, feedback unit, evaluation unit, stress management unit, and community support unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the smart glasses 214 and generates a customized training plan in real time based on each caregiver's skill level, learning history, and evaluation data. The feedback unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides real-time feedback based on data collected during care activities. The evaluation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and comprehensively analyzes the caregiver's evaluation data and generates a detailed report in which the evaluation results are reflected in compensation and career paths. The stress management unit is implemented, for example, by the control unit 46A of the smart glasses 214 and evaluates the stress level based on data obtained from the caregiver's wearable device and suggests appropriate stress relief and relaxation methods. The community support section is implemented, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates forum posts and QA sessions to promote knowledge sharing among caregivers. The correspondence between each section and the device or control unit is not limited to the example described above and can be modified in various ways.

[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 generation unit, feedback unit, evaluation unit, stress management unit, and community support unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the headset terminal 314 and generates a customized training plan in real time based on each caregiver's skill level, learning history, and evaluation data. The feedback unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides real-time feedback based on data collected during care activities. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and comprehensively analyzes the caregiver's evaluation data and generates a detailed report in which the evaluation results are reflected in compensation and career paths. The stress management unit is implemented by, for example, the control unit 46A of the headset terminal 314 and evaluates the stress level based on data obtained from the caregiver's wearable device and suggests appropriate stress relief and relaxation methods. The community support section is implemented, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates forum posts and QA sessions to promote knowledge sharing among caregivers. The correspondence between each section 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 generation unit, feedback unit, evaluation unit, stress management unit, and community support unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the robot 414 and generates a customized training plan in real time based on each caregiver's skill level, learning history, and evaluation data. The feedback unit is implemented by the control unit 46A of the robot 414 and provides real-time feedback based on data collected during care activities. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and comprehensively analyzes the caregiver's evaluation data and generates a detailed report in which the evaluation results are reflected in compensation and career paths. The stress management unit is implemented by the control unit 46A of the robot 414 and evaluates the stress level based on data obtained from the caregiver's wearable device and suggests appropriate stress relief and relaxation methods. The community support section is implemented, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates forum posts and QA sessions to promote knowledge sharing among caregivers. The correspondence between each section and the device or control unit is not limited to the example described above and can be modified in various ways.

[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 generation unit that generates customized training plans in real time based on each caregiver's skill level, learning history, and evaluation data, The Feedback Department provides real-time feedback based on data collected during care activities, The evaluation department comprehensively analyzes the evaluation data of caregivers and generates detailed reports that reflect the evaluation results in compensation and career paths. The Stress Management Department evaluates stress levels based on data obtained from caregivers' wearable devices and proposes appropriate stress relief and relaxation methods. It includes a community support section that automatically generates forum posts and Q&A sessions to promote knowledge sharing among caregivers. A system characterized by the following features. (Note 2) The generating unit is The system estimates the caregiver's emotions and adjusts the training plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Analyze the caregiver's past training history to select the optimal training method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is When generating a training plan, customize the plan based on the caregiver's current workload and schedule. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is The system estimates the emotions of caregivers and prioritizes training plans based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is When generating training plans, the system prioritizes providing highly relevant training content by considering the caregiver's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is When generating training plans, we analyze the social media activity of caregivers and provide relevant training content. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned feedback unit is The system estimates the caregiver'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 9) The aforementioned feedback unit is When providing feedback, adjust the level of detail in the feedback according to the caregiver's skill level. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned feedback unit is When providing feedback, different feedback algorithms are applied depending on the caregiver's job duties. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned feedback unit is The system estimates the caregiver's emotions and adjusts the length of the feedback based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned feedback unit is When providing feedback, we prioritize the feedback based on the caregiver's work history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned feedback unit is When providing feedback, the order of feedback is adjusted by referring to the caregiver's relevant work data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The evaluation unit, The system estimates the emotions of caregivers and adjusts evaluation criteria based on the estimated emotions of the caregivers. The system described in Appendix 1, characterized by the features described herein. (Note 15) The evaluation unit, When conducting evaluations, consider the interpersonal relationships among caregivers to improve the accuracy of the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 16) The evaluation unit, During the evaluation, the caregiver's attribute information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The evaluation unit, The system estimates the caregiver's emotions and adjusts the display order of the evaluation results based on the estimated emotions of the caregiver. The system described in Appendix 1, characterized by the features described herein. (Note 18) The evaluation unit, During the evaluation, the geographical distribution of caregivers should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit, During evaluation, refer to relevant literature for caregivers to improve the accuracy of the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned stress management unit, The system estimates the emotions of caregivers and adjusts stress management methods based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned stress management unit, When managing stress, the caregiver's past stress history is referenced to select the most appropriate management method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned stress management unit, When managing stress, customize the management methods based on the caregiver's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned stress management unit, The system estimates the emotions of caregivers and determines stress management priorities based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned stress management unit, When managing stress, the optimal management method should be selected by considering the geographical location of the caregiver. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned stress management unit, When managing stress, we analyze the social media activity of caregivers and propose management strategies. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned Community Support Department The system estimates the emotions of caregivers and adjusts the content of community support based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned Community Support Department When providing community support, we refer to the caregiver's past community activity history to provide the most appropriate support. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned Community Support Department When providing community support, customize the support content based on the caregiver's current workload. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned Community Support Department The system estimates the emotions of caregivers and determines the priority of community support based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned Community Support Department When providing community support, we take into account the geographical location of caregivers to provide the most appropriate support. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned Community Support Department When providing community support, we analyze the social media activity of caregivers and propose support tailored to their needs. 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 generation unit that generates customized training plans in real time based on each caregiver's skill level, learning history, and evaluation data, The Feedback Department provides real-time feedback based on data collected during care activities, The evaluation department comprehensively analyzes the evaluation data of caregivers and generates detailed reports that reflect the evaluation results in compensation and career paths. The Stress Management Department evaluates stress levels based on data obtained from caregivers' wearable devices and proposes appropriate stress relief and relaxation methods. It includes a community support section that automatically generates forum posts and Q&A sessions to promote knowledge sharing among caregivers. A system characterized by the following features.

2. The generating unit is The system estimates the caregiver's emotions and adjusts the training plan based on those estimated emotions. The system according to feature 1.

3. The generating unit is Analyze the caregiver's past training history to select the optimal training method. The system according to feature 1.

4. The generating unit is When generating a training plan, customize the plan based on the caregiver's current workload and schedule. The system according to feature 1.

5. The generating unit is The system estimates the emotions of caregivers and prioritizes training plans based on these estimated emotions. The system according to feature 1.

6. The generating unit is When generating training plans, the system prioritizes providing highly relevant training content by considering the caregiver's geographical location. The system according to feature 1.

7. The generating unit is When generating training plans, we analyze the social media activity of caregivers and provide relevant training content. The system according to feature 1.

8. The aforementioned feedback unit is The system estimates the caregiver's emotions and adjusts the way feedback is expressed based on those estimated emotions. The system according to feature 1.

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