system

Abstract

The system according to this embodiment aims to remove barriers in the information gathering stage for care services and enable users to access appropriate services. [Solution] The system according to the embodiment comprises a collection unit, a matching unit, an information provision unit, and a procedure support unit. The collection unit collects basic user information. The matching unit performs service matching based on the information collected by the collection unit. The information provision unit provides personalized information based on the services proposed by the matching unit. The procedure support unit provides procedure support based on the information provided by the information provision unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that many people gave up using it at the information collection stage of care services.

[0005] The system according to the embodiment aims to remove barriers at the information collection stage of care services and enable users to access appropriate services.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a matching unit, an information provision unit, and a procedure support unit. The collection unit collects basic user information. The matching unit performs service matching based on the information collected by the collection unit. The information provision unit provides personalized information based on the services proposed by the matching unit. The procedure support unit provides procedural support based on the information provided by the information provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can remove barriers in the information gathering stage for care services, enabling users to access appropriate services. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example of form 1) The care service support system according to an embodiment of the present invention is a system that proposes an innovative solution to the social problem of a high percentage of people giving up on using care services, by utilizing generative AI and a hybrid system. This care service support system provides integrated basic service matching by a conventional system, personalized information provision and psychological support by generative AI, and procedural support by a hybrid system. For example, the care service support system takes the user's basic information as input and proposes appropriate care services. For example, by inputting information such as the user's age, health condition, and living environment, it proposes the optimal care service. Next, the care service support system uses generative AI to make personalized suggestions according to the user's family structure and living environment. For example, if the user lives alone, it proposes nearby care services, and if the family lives far away, it proposes remote support. In addition, the care service support system generates explanations that take into account the resistance to care services, so that users can use the service with peace of mind. Furthermore, the care service support system assists the user with the procedures for using care services. For example, it provides guidance on how to prepare and submit necessary documents and manages the progress of the procedures. As a result, users can use care services smoothly without feeling the complexity of the procedures. This will enable the long-term care service support system to create a society where people who need care can reliably access services, reducing the burden on families and ensuring they receive appropriate care services. This will remove barriers to using long-term care services, allowing users to access services smoothly.

[0029] The care service support system according to the embodiment comprises a collection unit, a matching unit, an information provision unit, and a procedure support unit. The collection unit collects basic user information. For example, the collection unit collects basic information such as the user's name, address, age, and gender. The collection unit can also collect information such as the user's health status and living environment. For example, the collection unit collects information such as the user's medical history, current health status, type of residence, and family structure. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can provide an interface for inputting the user's basic information, and the AI ​​can analyze and collect the input information. The matching unit performs service matching based on the information collected by the collection unit. For example, the matching unit proposes the optimal care service using a matching algorithm based on the user's needs. For example, the matching unit proposes an appropriate care service based on the user's health status and living environment. Some or all of the above-described processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can perform service matching using an AI model that proposes the most suitable care service, taking the information collected by the collection unit as input. The information provision unit provides personalized information based on the services proposed by the matching unit. For example, the information provision unit makes personalized suggestions according to the user's family structure and living environment. For example, if the user lives alone, the information provision unit will suggest nearby care services, and if the family lives far away, it will suggest remote support. The information provision unit also generates explanations that take into account any resistance to care services, so that users can use the services with peace of mind. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without AI. For example, the information provision unit can provide information using an AI model that generates personalized suggestions based on the user's family structure and living environment. The procedure support unit provides procedure support based on the information provided by the information provision unit.The procedural support department handles tasks such as preparing and submitting necessary documents, and managing the progress of procedures. Some or all of the above-mentioned processes in the procedural support department may be performed using AI, for example, or not. For example, the procedural support department can provide procedural support using an AI model that assists users in the procedures for using long-term care services. As a result, the long-term care service support system according to the embodiment can remove barriers to using long-term care services by collecting basic user information, performing service matching, providing personalized information, and providing procedural support.

[0030] The data collection unit collects basic user information. For example, it collects basic information such as the user's name, address, age, and gender. Specifically, when the user accesses the system, the data collection unit prompts them to input this information through a dedicated interface. The interface is designed with ease of use in mind, making it easy for the elderly and their families to operate. For example, it supports voice input and touch panel operation, accommodating users with visual or hearing impairments. The data collection unit can also collect information such as the user's health status and living environment. For example, it collects information such as the user's medical history, current health status, type of residence, and family structure. This information can be collected not only through user input but also in conjunction with data provided by medical institutions and care facilities. For example, with the user's consent, data can be acquired from electronic medical record systems and health management apps and integrated into the data collection unit. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can provide an interface for inputting the user's basic information, and AI can analyze and collect the inputted information. The AI ​​checks the accuracy of the input information and automatically generates questions to supplement any missing information. The AI ​​also analyzes user input and identifies patterns related to health status and living environment, providing guidelines for collecting more detailed information. This allows the data collection unit to efficiently and accurately collect basic user information, health status, and living environment information. Furthermore, the data collection unit securely manages the collected information and implements measures to protect privacy. For example, collected data is encrypted and can only be viewed by those with access rights. The data retention period and purpose of use are also clearly defined and managed appropriately with the user's consent. This allows the data collection unit to gain user trust and reliably collect necessary information.

[0031] The matching unit performs service matching based on the information collected by the collection unit. The matching unit proposes the most suitable care services using, for example, a matching algorithm based on the user's needs. Specifically, the matching unit proposes appropriate care services based on the user's health condition and living environment. For example, if the user has a specific medical history, it proposes specialized care services that correspond to that medical history. Also, if the user lives alone, it proposes services that allow for regular home visits and emergency response. Some or all of the above processing in the matching unit may be performed using, for example, AI, or not using AI. For example, the matching unit can perform service matching using an AI model that takes the information collected by the collection unit as input and proposes the most suitable care services. The AI ​​model learns an algorithm to identify the service best suited to the user's needs based on past data and statistical information. For example, the AI ​​analyzes data on the user's health condition and living environment and proposes the most suitable service by comparing it with data from other users with similar needs. The AI ​​can also collect user feedback and continuously improve the accuracy of its suggestions. As a result, the matching unit can quickly and accurately propose the most suitable care services that meet the user's needs. Furthermore, the matching system can flexibly respond to changes in user needs. For example, if a user's health deteriorates, the AI ​​will re-match them based on new data and propose appropriate services. Similarly, if a user's living environment changes, the AI ​​will reflect these changes and re-propose services. This allows the matching system to provide optimal care services tailored to the user's needs, thereby improving the user's quality of life.

[0032] The Information Provision Department provides personalized information based on the services proposed by the Matching Department. Specifically, the Information Provision Department makes personalized suggestions tailored to the user's family structure and living environment. For example, if the user lives alone, the Information Provision Department will suggest nearby care services, and if family members live far away, it will suggest remote support. The Information Provision Department proposes services that match the user's lifestyle and preferences, ensuring that users can use the services with peace of mind. The Information Provision Department also generates explanations that take into account any resistance to care services, ensuring that users can use the services with confidence. For example, if a user is using a care service for the first time, the Information Provision Department will provide a detailed explanation of the service's content and how to use it. Furthermore, if a user feels anxious about a particular service, the Information Provision Department will provide information to alleviate that anxiety. Some or all of the above processing in the Information Provision Department may be performed using AI, for example, or without AI. For example, the Information Provision Department can provide information using an AI model that generates personalized suggestions based on the user's family structure and living environment. The AI ​​model analyzes user data and learns algorithms to generate optimal information. For example, AI analyzes data on a user's family structure and living environment, and compares it with data from other users with similar needs to generate optimal information. Furthermore, AI can collect user feedback and continuously improve the accuracy of the information it provides. This allows the information provider to quickly and accurately deliver the most relevant information to the user's needs. Moreover, the information provider can flexibly respond to changes in user needs. For instance, if a user's health changes, the AI ​​regenerates information based on the new data and makes appropriate suggestions. Similarly, if a user's living environment changes, the AI ​​regenerates information to reflect those changes. This enables the information provider to deliver optimal information tailored to the user's needs and improve their quality of life.

[0033] The Procedure Support Department provides procedural support based on information provided by the Information Provision Department. Specifically, the Procedure Support Department handles tasks such as preparing necessary documents, providing guidance on submission methods, and managing the progress of procedures. For example, the Procedure Support Department automatically generates and provides to users the documents necessary for them to use care services. The Procedure Support Department also provides detailed guidance on how to submit documents, supporting users so that they can proceed with the procedures smoothly. Some or all of the above-mentioned processes in the Procedure Support Department may be performed using AI, for example, or not using AI. For example, the Procedure Support Department can provide procedural support using an AI model that assists users in the procedures for using care services. The AI ​​model analyzes user data and learns algorithms for managing necessary documents and the progress of procedures. For example, the AI ​​automatically generates necessary documents based on user data and provides detailed guidance on submission deadlines and methods. The AI ​​can also monitor the progress of procedures in real time and send reminders to users at appropriate times. This allows the Procedure Support Department to support users so that they can proceed with the procedures smoothly and prevent delays and errors. Furthermore, the procedural support department can collect user feedback and continuously improve the accuracy and effectiveness of its procedural support. For example, based on user feedback, it can review the content of procedural guidance and the method of generating documents, improving the system to make it more user-friendly. In addition, the procedural support department can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using a combination of email, SMS, and voice calls. As a result, the procedural support department can provide users with prompt and reliable procedural support, removing barriers to accessing care services.

[0034] The data collection unit can collect information such as the user's age, health status, and living environment. For example, the data collection unit can collect the user's age as a specific numerical value. The data collection unit can also collect the user's health status as a medical history or current health status. For example, the data collection unit can collect the user's medical history and current health status in detail. Furthermore, the data collection unit can collect the user's living environment as the type of residence and family structure. For example, the data collection unit can collect the type of residence and family structure of the user in detail. As a result, by collecting information such as the user's age, health status, and living environment, the data collection unit can enable appropriate service matching. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can provide an interface for inputting information such as the user's age, health status, and living environment, and the AI ​​can analyze and collect the input information.

[0035] The matching unit can propose the most suitable care services based on the collected information. For example, the matching unit proposes the most suitable care services based on the user's health condition and living environment. The matching unit can also propose the most suitable care services using a matching algorithm based on the user's needs. For example, the matching unit proposes appropriate care services based on the user's health condition and living environment. In this way, the matching unit can provide services that are suitable for the user by proposing the most suitable care services based on the collected information. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can perform service matching using an AI model that takes information collected by the collection unit as input and proposes the most suitable care services.

[0036] The information provision department can provide personalized suggestions tailored to the user's family structure and living environment. For example, if the user lives alone, the information provision department can suggest nearby care services. If the user's family lives far away, the information provision department can also suggest remote support. For example, the information provision department provides personalized suggestions tailored to the user's family structure and living environment. In this way, the information provision department can provide information that is suitable for the user by providing personalized suggestions tailored to the user's family structure and living environment. Some or all of the above processing in the information provision department may be performed using AI, for example, or not using AI. For example, the information provision department can provide information using an AI model that generates personalized suggestions based on the user's family structure and living environment.

[0037] The information provision unit can generate explanations that take into account users' resistance to care services. For example, the information provision unit generates explanations that take into account the anxieties and concerns that users may have about care services. The information provision unit can also generate explanations that take into account users so that they can use the services with peace of mind. For example, the information provision unit generates explanations that take into account users' resistance to care services. In this way, the information provision unit can generate explanations that take into account users' resistance to care services, so that users can use the services with peace of mind. Some or all of the above processing in the information provision unit may be performed using AI, for example, or not using AI. For example, the information provision unit can generate explanations using an AI model that generates explanations that take into account the anxieties and concerns that users may have about care services.

[0038] The procedural support department can prepare and guide users on how to submit necessary documents and manage the progress of procedures. For example, the procedural support department can assist users in preparing the documents necessary to use care services. The procedural support department can also guide users on how to submit documents and manage the progress of procedures. For example, the procedural support department prepares and guides users on how to submit necessary documents and manages the progress of procedures. In this way, the procedural support department can help users use care services smoothly by preparing and guiding users on how to submit necessary documents and managing the progress of procedures. Some or all of the above processes in the procedural support department may be performed using AI, for example, or not using AI. For example, the procedural support department can provide procedural support using an AI model that assists users in the procedures for using care services.

[0039] The data collection unit can analyze the user's past care service usage history and select the optimal information collection method. For example, the data collection unit can select an information collection method preferred by the user based on their evaluation of services used in the past. The data collection unit can also prioritize the collection of information that the user needs from past usage history. For example, the data collection unit can analyze past usage history and filter out information that the user wants to avoid. In this way, the data collection unit can select the optimal information collection method by analyzing the user's past care service usage history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can take the user's past care service usage history as input and perform information collection using an AI model that selects the optimal information collection method.

[0040] The data collection unit can filter information based on the user's current lifestyle and areas of interest during the information gathering process. For example, the data collection unit can prioritize the collection of relevant information based on the user's current health status. The data collection unit can also collect information that is of interest to the user based on their areas of interest. For example, the data collection unit can filter necessary information according to the user's lifestyle. In this way, the data collection unit can provide highly relevant information by filtering information based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect information using an AI model that takes the user's current lifestyle and areas of interest as input and filters the information.

[0041] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during data collection. For example, the data collection unit can prioritize the collection of nearby care service information based on the user's current location. The data collection unit can also collect region-specific information based on the user's geographical location. For example, the data collection unit can collect relevant information based on the user's travel history. In this way, the data collection unit can provide region-specific information by collecting information while considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect information using an AI model that takes the user's geographical location as input and prioritizes the collection of highly relevant information.

[0042] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect information of interest based on the content of the user's social media posts. The data collection unit can also collect relevant information based on the activity of the user's followers and friends. For example, the data collection unit can collect information of interest based on the user's social media usage history. In this way, the data collection unit can provide information of interest by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can perform data collection using an AI model that takes the user's social media activity as input and collects relevant information.

[0043] The matching unit can improve the accuracy of service matching by considering the relationships between users. For example, the matching unit can perform matching by considering the past relationship between the user and the care service provider. The matching unit can also perform matching by referring to the opinions of the user's family and friends. For example, the matching unit can match the most suitable service based on the user's past service usage history. In this way, the matching unit can perform highly accurate service matching by considering the relationships between users. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can perform service matching using an AI model that takes the relationships between users as input and improves the accuracy of the matching.

[0044] The matching unit can perform service matching while considering the user's attribute information. For example, the matching unit can match the most suitable service based on the user's age and health condition. The matching unit can also match an appropriate service based on the user's living environment. For example, the matching unit can match the most suitable service based on the user's family structure. In this way, the matching unit can provide a service that is suitable for the user by performing matching while considering the user's attribute information. Some or all of the above processing in the matching unit may be performed using AI, for example, or without using AI. For example, the matching unit can perform service matching using an AI model that takes user attribute information as input and matches services.

[0045] The matching unit can perform service matching while considering the geographical distribution of users. For example, the matching unit can prioritize matching nearby services based on the user's current location. The matching unit can also match region-specific services based on the user's geographical distribution. For example, the matching unit can match relevant services based on the user's travel history. In this way, the matching unit can provide region-specific services by performing matching while considering the user's geographical distribution. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can perform service matching using an AI model that takes the user's geographical distribution as input and matches services.

[0046] The matching unit can improve the accuracy of service matching by referring to relevant literature. For example, the matching unit can refer to relevant research papers to match the most suitable service. The matching unit can also refer to past cases to match the most suitable service. For example, the matching unit can refer to expert opinions to match the most suitable service. As a result, the matching unit can perform highly accurate service matching by referring to relevant literature. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can take relevant literature as input and perform service matching using an AI model that improves the accuracy of the matching.

[0047] The information provision unit can adjust the level of detail provided based on the importance of the information. For example, the information provision unit can provide important information in detail to make it easy for users to understand. The information provision unit can also provide less important information concisely to reduce the burden on users. For example, the information provision unit can adjust the level of detail according to the user's interests. In this way, the information provision unit can provide important information to users in detail by adjusting the level of detail based on the importance of the information. Some or all of the above processing in the information provision unit may be performed using AI, for example, or not using AI. For example, the information provision unit can adjust the level of detail of information provided using an AI model for evaluating the importance of information.

[0048] The information provision unit can apply different provisioning algorithms depending on the information category when providing information. For example, the information provision unit can provide medical information in detail to make it easy for users to understand. The information provision unit can also provide lifestyle information concisely to reduce the burden on users. For example, the information provision unit can adjust the method of providing information according to the user's interests. In this way, the information provision unit can provide optimal information to users by applying different provisioning algorithms depending on the information category. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without using AI. For example, the information provision unit can provide information using an AI model that takes the information category as input and applies different provisioning algorithms.

[0049] The information provision department can determine the priority of information provision based on the timing of information submission. For example, the information provision department can prioritize the provision of urgent information so that users can respond quickly. The information provision department can also prioritize the provision of information with approaching deadlines so that users can respond within the deadline. For example, the information provision department can adjust the priority of information provision according to the user's interests. This allows the information provision department to enable users to respond quickly by determining the priority of information provision based on the timing of information submission. Some or all of the above processing in the information provision department may be performed using AI, for example, or not using AI. For example, the information provision department can provide information using an AI model that takes the timing of information submission as input and determines the priority of provision.

[0050] The information provision unit can adjust the order of information provision based on the relevance of the information. For example, the information provision unit may first provide the information most relevant to the user's current situation. The information provision unit may also first provide the information most relevant to the user's interests. For example, the information provision unit may prioritize providing relevant information based on the user's past usage history. In this way, the information provision unit can provide the most optimal information for the user by adjusting the order of information provision based on the relevance of the information. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without AI. For example, the information provision unit can provide information using an AI model that takes the relevance of information as input and adjusts the order of provision.

[0051] The procedure support unit can analyze the user's past procedure history to select the optimal support method during procedure support. For example, the procedure support unit can select a support method preferred by the user based on their past procedure history. The procedure support unit can also prioritize providing the support the user needs based on their past procedure history. For example, the procedure support unit can analyze the past procedure history and filter out support methods the user wants to avoid. In this way, the procedure support unit can select the optimal support method by analyzing the user's past procedure history. Some or all of the above processing in the procedure support unit may be performed using AI, for example, or without AI. For example, the procedure support unit can use an AI model that takes the user's past procedure history as input and selects the optimal support method to provide procedure support.

[0052] The procedural support unit can customize the means of support provided during procedural support based on the user's current living situation. For example, the procedural support unit may prioritize providing relevant support based on the user's current health condition. The procedural support unit can also customize the necessary support according to the user's living situation. For example, the procedural support unit may provide support that is of interest to the user based on their areas of interest. In this way, the procedural support unit can provide optimal procedural support for the user by customizing the means of support based on the user's current living situation. Some or all of the above processing in the procedural support unit may be performed using AI, for example, or without AI. For example, the procedural support unit can provide procedural support using an AI model that takes the user's current living situation as input and customizes the means of support.

[0053] The procedural support unit can select the optimal support method when providing procedural support, taking into account the user's geographical location information. For example, the procedural support unit may prioritize providing nearby support services based on the user's current location. The procedural support unit can also provide region-specific support methods based on the user's geographical location. For example, the procedural support unit may provide relevant support methods based on the user's travel history. In this way, the procedural support unit can provide region-specific support by selecting support methods while taking the user's geographical location information into consideration. Some or all of the above processing in the procedural support unit may be performed using AI, for example, or without AI. For example, the procedural support unit can provide procedural support using an AI model that takes the user's geographical location information as input and selects the optimal support method.

[0054] The procedural support unit can analyze the user's social media activity and propose support methods during procedural support. For example, the procedural support unit can propose support methods of interest based on the content of the user's social media posts. The procedural support unit can also propose relevant support methods based on the activities of the user's followers and friends. For example, the procedural support unit can propose support methods that are of interest based on the user's social media usage history. In this way, the procedural support unit can provide support methods of interest by analyzing the user's social media activity. Some or all of the above processing in the procedural support unit may be performed using AI, for example, or without AI. For example, the procedural support unit can perform procedural support using an AI model that takes the user's social media activity as input and proposes support methods.

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

[0056] The data collection unit can collect information about users' hobbies and interests. For example, it can collect information about activities that users enjoy as hobbies or areas of interest. It can also collect information about events and activities that users have participated in in the past. By collecting information about users' hobbies and interests, the data collection unit can provide more personalized services.

[0057] The matching department can suggest services based on the user's hobbies and interests. For example, the matching department can suggest care services related to the user's hobbies. The matching department can also suggest services related to areas of interest to the user. In this way, the matching department can provide services that are attractive to the user by suggesting services based on the user's hobbies and interests.

[0058] The information provision department can provide information based on the user's hobbies and interests. For example, the information provision department can provide information related to the user's hobbies. The information provision department can also provide information related to the user's areas of interest. In this way, the information provision department can provide useful information to the user by providing information based on the user's hobbies and interests.

[0059] The procedural support department can provide procedural support based on the user's hobbies and interests. For example, the procedural support department can provide support for procedures related to activities that the user enjoys as a hobby. It can also provide support for procedures related to fields that the user is interested in. In this way, the procedural support department can provide procedural support that is appealing to the user by tailoring it to their hobbies and interests.

[0060] The data collection unit can analyze the user's past care service usage history and select the most suitable information collection method. For example, it can select the information collection method preferred by the user based on their evaluation of services used in the past. It can also prioritize the collection of information that the user needs from their past usage history. In this way, the data collection unit can select the most suitable information collection method by analyzing the user's past care service usage history.

[0061] The matching unit can suggest services while considering the geographical distribution of users. For example, it can prioritize suggesting nearby services based on the user's current location. It can also suggest region-specific services based on the geographical distribution of users. In this way, the matching unit can provide region-specific services by suggesting services while considering the geographical distribution of users.

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

[0063] Step 1: The collection unit collects basic user information. For example, it collects basic information such as the user's name, address, age, and gender. It can also collect information such as the user's health status and living environment. For example, it collects information such as the user's medical history, current health status, type of residence, and family structure. The processing in the collection unit may be performed using AI or not. For example, the collection unit can provide an interface for inputting the user's basic information, and the AI ​​can analyze and collect the input information. Step 2: The matching unit performs service matching based on the information collected by the collection unit. For example, it may propose the most suitable care service using a matching algorithm based on the user's needs, or it may propose an appropriate care service based on the user's health condition and living environment. The processing in the matching unit may be performed using AI or not. For example, service matching can be performed using an AI model that takes the information collected by the collection unit as input and proposes the most suitable care service. Step 3: The Information Provision Department provides personalized information based on the services proposed by the Matching Department. For example, it provides personalized suggestions tailored to the user's family structure and living environment. If the user lives alone, it suggests nearby care services; if family members live far away, it suggests remote support. It also generates explanations that take into account any resistance to care services, ensuring the user can use the services with peace of mind. The processing in the Information Provision Department may be performed using AI or not. For example, information can be provided using an AI model that generates personalized suggestions based on the user's family structure and living environment. Step 4: The Procedure Support Department provides procedural support based on the information provided by the Information Provision Department. For example, this includes preparing necessary documents, providing guidance on submission methods, and managing the progress of the procedure. The processing in the Procedure Support Department may be performed using AI or not. For example, procedural support can be provided using an AI model that assists users in the procedures for using long-term care services.

[0064] (Example of form 2) The care service support system according to an embodiment of the present invention is a system that proposes an innovative solution to the social problem of a high percentage of people giving up on using care services, by utilizing generative AI and a hybrid system. This care service support system provides integrated basic service matching by a conventional system, personalized information provision and psychological support by generative AI, and procedural support by a hybrid system. For example, the care service support system takes the user's basic information as input and proposes appropriate care services. For example, by inputting information such as the user's age, health condition, and living environment, it proposes the optimal care service. Next, the care service support system uses generative AI to make personalized suggestions according to the user's family structure and living environment. For example, if the user lives alone, it proposes nearby care services, and if the family lives far away, it proposes remote support. In addition, the care service support system generates explanations that take into account the resistance to care services, so that users can use the service with peace of mind. Furthermore, the care service support system assists the user with the procedures for using care services. For example, it provides guidance on how to prepare and submit necessary documents and manages the progress of the procedures. As a result, users can use care services smoothly without feeling the complexity of the procedures. This will enable the long-term care service support system to create a society where people who need care can reliably access services, reducing the burden on families and ensuring they receive appropriate care services. This will remove barriers to using long-term care services, allowing users to access services smoothly.

[0065] The care service support system according to the embodiment comprises a collection unit, a matching unit, an information provision unit, and a procedure support unit. The collection unit collects basic user information. For example, the collection unit collects basic information such as the user's name, address, age, and gender. The collection unit can also collect information such as the user's health status and living environment. For example, the collection unit collects information such as the user's medical history, current health status, type of residence, and family structure. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can provide an interface for inputting the user's basic information, and the AI ​​can analyze and collect the input information. The matching unit performs service matching based on the information collected by the collection unit. For example, the matching unit proposes the optimal care service using a matching algorithm based on the user's needs. For example, the matching unit proposes an appropriate care service based on the user's health status and living environment. Some or all of the above-described processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can perform service matching using an AI model that proposes the most suitable care service, taking the information collected by the collection unit as input. The information provision unit provides personalized information based on the services proposed by the matching unit. For example, the information provision unit makes personalized suggestions according to the user's family structure and living environment. For example, if the user lives alone, the information provision unit will suggest nearby care services, and if the family lives far away, it will suggest remote support. The information provision unit also generates explanations that take into account any resistance to care services, so that users can use the services with peace of mind. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without AI. For example, the information provision unit can provide information using an AI model that generates personalized suggestions based on the user's family structure and living environment. The procedure support unit provides procedure support based on the information provided by the information provision unit.The procedural support department handles tasks such as preparing and submitting necessary documents, and managing the progress of procedures. Some or all of the above-mentioned processes in the procedural support department may be performed using AI, for example, or not. For example, the procedural support department can provide procedural support using an AI model that assists users in the procedures for using long-term care services. As a result, the long-term care service support system according to the embodiment can remove barriers to using long-term care services by collecting basic user information, performing service matching, providing personalized information, and providing procedural support.

[0066] The data collection unit collects basic user information. For example, it collects basic information such as the user's name, address, age, and gender. Specifically, when the user accesses the system, the data collection unit prompts them to input this information through a dedicated interface. The interface is designed with ease of use in mind, making it easy for the elderly and their families to operate. For example, it supports voice input and touch panel operation, accommodating users with visual or hearing impairments. The data collection unit can also collect information such as the user's health status and living environment. For example, it collects information such as the user's medical history, current health status, type of residence, and family structure. This information can be collected not only through user input but also in conjunction with data provided by medical institutions and care facilities. For example, with the user's consent, data can be acquired from electronic medical record systems and health management apps and integrated into the data collection unit. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can provide an interface for inputting the user's basic information, and AI can analyze and collect the inputted information. The AI ​​checks the accuracy of the input information and automatically generates questions to supplement any missing information. The AI ​​also analyzes user input and identifies patterns related to health status and living environment, providing guidelines for collecting more detailed information. This allows the data collection unit to efficiently and accurately collect basic user information, health status, and living environment information. Furthermore, the data collection unit securely manages the collected information and implements measures to protect privacy. For example, collected data is encrypted and can only be viewed by those with access rights. The data retention period and purpose of use are also clearly defined and managed appropriately with the user's consent. This allows the data collection unit to gain user trust and reliably collect necessary information.

[0067] The matching unit performs service matching based on the information collected by the collection unit. The matching unit proposes the most suitable care services using, for example, a matching algorithm based on the user's needs. Specifically, the matching unit proposes appropriate care services based on the user's health condition and living environment. For example, if the user has a specific medical history, it proposes specialized care services that correspond to that medical history. Also, if the user lives alone, it proposes services that allow for regular home visits and emergency response. Some or all of the above processing in the matching unit may be performed using, for example, AI, or not using AI. For example, the matching unit can perform service matching using an AI model that takes the information collected by the collection unit as input and proposes the most suitable care services. The AI ​​model learns an algorithm to identify the service best suited to the user's needs based on past data and statistical information. For example, the AI ​​analyzes data on the user's health condition and living environment and proposes the most suitable service by comparing it with data from other users with similar needs. The AI ​​can also collect user feedback and continuously improve the accuracy of its suggestions. As a result, the matching unit can quickly and accurately propose the most suitable care services that meet the user's needs. Furthermore, the matching system can flexibly respond to changes in user needs. For example, if a user's health deteriorates, the AI ​​will re-match them based on new data and propose appropriate services. Similarly, if a user's living environment changes, the AI ​​will reflect these changes and re-propose services. This allows the matching system to provide optimal care services tailored to the user's needs, thereby improving the user's quality of life.

[0068] The Information Provision Department provides personalized information based on the services proposed by the Matching Department. Specifically, the Information Provision Department makes personalized suggestions tailored to the user's family structure and living environment. For example, if the user lives alone, the Information Provision Department will suggest nearby care services, and if family members live far away, it will suggest remote support. The Information Provision Department proposes services that match the user's lifestyle and preferences, ensuring that users can use the services with peace of mind. The Information Provision Department also generates explanations that take into account any resistance to care services, ensuring that users can use the services with confidence. For example, if a user is using a care service for the first time, the Information Provision Department will provide a detailed explanation of the service's content and how to use it. Furthermore, if a user feels anxious about a particular service, the Information Provision Department will provide information to alleviate that anxiety. Some or all of the above processing in the Information Provision Department may be performed using AI, for example, or without AI. For example, the Information Provision Department can provide information using an AI model that generates personalized suggestions based on the user's family structure and living environment. The AI ​​model analyzes user data and learns algorithms to generate optimal information. For example, AI analyzes data on a user's family structure and living environment, and compares it with data from other users with similar needs to generate optimal information. Furthermore, AI can collect user feedback and continuously improve the accuracy of the information it provides. This allows the information provider to quickly and accurately deliver the most relevant information to the user's needs. Moreover, the information provider can flexibly respond to changes in user needs. For instance, if a user's health changes, the AI ​​regenerates information based on the new data and makes appropriate suggestions. Similarly, if a user's living environment changes, the AI ​​regenerates information to reflect those changes. This enables the information provider to deliver optimal information tailored to the user's needs and improve their quality of life.

[0069] The Procedure Support Department provides procedural support based on information provided by the Information Provision Department. Specifically, the Procedure Support Department handles tasks such as preparing necessary documents, providing guidance on submission methods, and managing the progress of procedures. For example, the Procedure Support Department automatically generates and provides to users the documents necessary for them to use care services. The Procedure Support Department also provides detailed guidance on how to submit documents, supporting users so that they can proceed with the procedures smoothly. Some or all of the above-mentioned processes in the Procedure Support Department may be performed using AI, for example, or not using AI. For example, the Procedure Support Department can provide procedural support using an AI model that assists users in the procedures for using care services. The AI ​​model analyzes user data and learns algorithms for managing necessary documents and the progress of procedures. For example, the AI ​​automatically generates necessary documents based on user data and provides detailed guidance on submission deadlines and methods. The AI ​​can also monitor the progress of procedures in real time and send reminders to users at appropriate times. This allows the Procedure Support Department to support users so that they can proceed with the procedures smoothly and prevent delays and errors. Furthermore, the procedural support department can collect user feedback and continuously improve the accuracy and effectiveness of its procedural support. For example, based on user feedback, it can review the content of procedural guidance and the method of generating documents, improving the system to make it more user-friendly. In addition, the procedural support department can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using a combination of email, SMS, and voice calls. As a result, the procedural support department can provide users with prompt and reliable procedural support, removing barriers to accessing care services.

[0070] The data collection unit can collect information such as the user's age, health status, and living environment. For example, the data collection unit can collect the user's age as a specific numerical value. The data collection unit can also collect the user's health status as a medical history or current health status. For example, the data collection unit can collect the user's medical history and current health status in detail. Furthermore, the data collection unit can collect the user's living environment as the type of residence and family structure. For example, the data collection unit can collect the type of residence and family structure of the user in detail. As a result, by collecting information such as the user's age, health status, and living environment, the data collection unit can enable appropriate service matching. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can provide an interface for inputting information such as the user's age, health status, and living environment, and the AI ​​can analyze and collect the input information.

[0071] The matching unit can propose the most suitable care services based on the collected information. For example, the matching unit proposes the most suitable care services based on the user's health condition and living environment. The matching unit can also propose the most suitable care services using a matching algorithm based on the user's needs. For example, the matching unit proposes appropriate care services based on the user's health condition and living environment. In this way, the matching unit can provide services that are suitable for the user by proposing the most suitable care services based on the collected information. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can perform service matching using an AI model that takes information collected by the collection unit as input and proposes the most suitable care services.

[0072] The information provision department can provide personalized suggestions tailored to the user's family structure and living environment. For example, if the user lives alone, the information provision department can suggest nearby care services. If the user's family lives far away, the information provision department can also suggest remote support. For example, the information provision department provides personalized suggestions tailored to the user's family structure and living environment. In this way, the information provision department can provide information that is suitable for the user by providing personalized suggestions tailored to the user's family structure and living environment. Some or all of the above processing in the information provision department may be performed using AI, for example, or not using AI. For example, the information provision department can provide information using an AI model that generates personalized suggestions based on the user's family structure and living environment.

[0073] The information provision unit can generate explanations that take into account users' resistance to care services. For example, the information provision unit generates explanations that take into account the anxieties and concerns that users may have about care services. The information provision unit can also generate explanations that take into account users so that they can use the services with peace of mind. For example, the information provision unit generates explanations that take into account users' resistance to care services. In this way, the information provision unit can generate explanations that take into account users' resistance to care services, so that users can use the services with peace of mind. Some or all of the above processing in the information provision unit may be performed using AI, for example, or not using AI. For example, the information provision unit can generate explanations using an AI model that generates explanations that take into account the anxieties and concerns that users may have about care services.

[0074] The procedural support department can prepare and guide users on how to submit necessary documents and manage the progress of procedures. For example, the procedural support department can assist users in preparing the documents necessary to use care services. The procedural support department can also guide users on how to submit documents and manage the progress of procedures. For example, the procedural support department prepares and guides users on how to submit necessary documents and manages the progress of procedures. In this way, the procedural support department can help users use care services smoothly by preparing and guiding users on how to submit necessary documents and managing the progress of procedures. Some or all of the above processes in the procedural support department may be performed using AI, for example, or not using AI. For example, the procedural support department can provide procedural support using an AI model that assists users in the procedures for using care services.

[0075] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is feeling stressed, the data collection unit will collect information during times when the user is relaxed. If the user is busy, the data collection unit can also collect information during times when the user is free. For example, if the user is calm, the data collection unit will collect detailed information. In this way, the data collection unit can collect information at the optimal time for the user by adjusting the timing of information collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can adjust the timing of information collection using an emotion engine to estimate the user's emotions.

[0076] The data collection unit can analyze the user's past care service usage history and select the optimal information collection method. For example, the data collection unit can select an information collection method preferred by the user based on their evaluation of services used in the past. The data collection unit can also prioritize the collection of information that the user needs from past usage history. For example, the data collection unit can analyze past usage history and filter out information that the user wants to avoid. In this way, the data collection unit can select the optimal information collection method by analyzing the user's past care service usage history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can take the user's past care service usage history as input and perform information collection using an AI model that selects the optimal information collection method.

[0077] The data collection unit can filter information based on the user's current lifestyle and areas of interest during the information gathering process. For example, the data collection unit can prioritize the collection of relevant information based on the user's current health status. The data collection unit can also collect information that is of interest to the user based on their areas of interest. For example, the data collection unit can filter necessary information according to the user's lifestyle. In this way, the data collection unit can provide highly relevant information by filtering information based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect information using an AI model that takes the user's current lifestyle and areas of interest as input and filters the information.

[0078] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting information that provides reassurance. If the user is interested in something, the data collection unit can also prioritize collecting information that is of interest to the user. For example, if the user is in a hurry, the data collection unit will prioritize collecting important information. In this way, the data collection unit can prioritize collecting information that is important to the user by determining the priority of information to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can determine the priority of information to collect using an emotion engine to estimate the user's emotions.

[0079] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during data collection. For example, the data collection unit can prioritize the collection of nearby care service information based on the user's current location. The data collection unit can also collect region-specific information based on the user's geographical location. For example, the data collection unit can collect relevant information based on the user's travel history. In this way, the data collection unit can provide region-specific information by collecting information while considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect information using an AI model that takes the user's geographical location as input and prioritizes the collection of highly relevant information.

[0080] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect information of interest based on the content of the user's social media posts. The data collection unit can also collect relevant information based on the activity of the user's followers and friends. For example, the data collection unit can collect information of interest based on the user's social media usage history. In this way, the data collection unit can provide information of interest by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can perform data collection using an AI model that takes the user's social media activity as input and collects relevant information.

[0081] The matching unit can estimate the user's emotions and adjust the service matching criteria based on the estimated emotions. For example, if the user is feeling anxious, the matching unit will prioritize matching services that provide a sense of security. If the user is interested in something, the matching unit can also prioritize matching services that the user is interested in. For example, if the user is in a hurry, the matching unit will prioritize matching services that can be delivered quickly. In this way, the matching unit can provide services that are appropriate for the user by adjusting the service matching criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can adjust the service matching criteria using an emotion engine to estimate the user's emotions.

[0082] The matching unit can improve the accuracy of service matching by considering the relationships between users. For example, the matching unit can perform matching by considering the past relationship between the user and the care service provider. The matching unit can also perform matching by referring to the opinions of the user's family and friends. For example, the matching unit can match the most suitable service based on the user's past service usage history. In this way, the matching unit can perform highly accurate service matching by considering the relationships between users. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can perform service matching using an AI model that takes the relationships between users as input and improves the accuracy of the matching.

[0083] The matching unit can perform service matching while considering the user's attribute information. For example, the matching unit can match the most suitable service based on the user's age and health condition. The matching unit can also match an appropriate service based on the user's living environment. For example, the matching unit can match the most suitable service based on the user's family structure. In this way, the matching unit can provide a service that is suitable for the user by performing matching while considering the user's attribute information. Some or all of the above processing in the matching unit may be performed using AI, for example, or without using AI. For example, the matching unit can perform service matching using an AI model that takes user attribute information as input and matches services.

[0084] The matching unit can estimate the user's emotions and adjust the order in which matching results are displayed based on the estimated emotions. For example, if the user is feeling anxious, the matching unit will first display services that provide a sense of security. The matching unit can also first display services that the user is interested in. For example, if the user is in a hurry, the matching unit will first display services that can be provided quickly. In this way, the matching unit can provide the most suitable service to the user by adjusting the order in which matching results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can adjust the order in which matching results are displayed using an emotion engine to estimate the user's emotions.

[0085] The matching unit can perform service matching while considering the geographical distribution of users. For example, the matching unit can prioritize matching nearby services based on the user's current location. The matching unit can also match region-specific services based on the user's geographical distribution. For example, the matching unit can match relevant services based on the user's travel history. In this way, the matching unit can provide region-specific services by performing matching while considering the user's geographical distribution. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can perform service matching using an AI model that takes the user's geographical distribution as input and matches services.

[0086] The matching unit can improve the accuracy of service matching by referring to relevant literature. For example, the matching unit can refer to relevant research papers to match the most suitable service. The matching unit can also refer to past cases to match the most suitable service. For example, the matching unit can refer to expert opinions to match the most suitable service. As a result, the matching unit can perform highly accurate service matching by referring to relevant literature. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can take relevant literature as input and perform service matching using an AI model that improves the accuracy of the matching.

[0087] The information delivery unit can estimate the user's emotions and adjust the way information is presented based on the estimated emotions. For example, if the user is feeling anxious, the information delivery unit will use a reassuring way of presenting the information. If the user is interested, the information delivery unit may also use an attention-grabbing way of presenting the information. For example, if the user is in a hurry, the information delivery unit will use a concise and to-the-point way of presenting the information. In this way, the information delivery unit can provide the most optimal information to the user by adjusting the way information is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can adjust the way information is presented using an emotion engine to estimate the user's emotions.

[0088] The information provision unit can adjust the level of detail provided based on the importance of the information. For example, the information provision unit can provide important information in detail to make it easy for users to understand. The information provision unit can also provide less important information concisely to reduce the burden on users. For example, the information provision unit can adjust the level of detail according to the user's interests. In this way, the information provision unit can provide important information to users in detail by adjusting the level of detail based on the importance of the information. Some or all of the above processing in the information provision unit may be performed using AI, for example, or not using AI. For example, the information provision unit can adjust the level of detail of information provided using an AI model for evaluating the importance of information.

[0089] The information provision unit can apply different provisioning algorithms depending on the information category when providing information. For example, the information provision unit can provide medical information in detail to make it easy for users to understand. The information provision unit can also provide lifestyle information concisely to reduce the burden on users. For example, the information provision unit can adjust the method of providing information according to the user's interests. In this way, the information provision unit can provide optimal information to users by applying different provisioning algorithms depending on the information category. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without using AI. For example, the information provision unit can provide information using an AI model that takes the information category as input and applies different provisioning algorithms.

[0090] The information provider can estimate the user's emotions and adjust the length of the information provided based on the estimated emotions. For example, if the user is feeling anxious, the information provider can provide detailed information to reassure them. If the user is interested, the information provider can also provide detailed information to capture their attention. For example, if the user is in a hurry, the information provider can provide concise and to-the-point information. This allows the information provider to provide optimal information to the user by adjusting the length of the information provided based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can adjust the length of the information provided using an emotion engine to estimate the user's emotions.

[0091] The information provision department can determine the priority of information provision based on the timing of information submission. For example, the information provision department can prioritize the provision of urgent information so that users can respond quickly. The information provision department can also prioritize the provision of information with approaching deadlines so that users can respond within the deadline. For example, the information provision department can adjust the priority of information provision according to the user's interests. This allows the information provision department to enable users to respond quickly by determining the priority of information provision based on the timing of information submission. Some or all of the above processing in the information provision department may be performed using AI, for example, or not using AI. For example, the information provision department can provide information using an AI model that takes the timing of information submission as input and determines the priority of provision.

[0092] The information provision unit can adjust the order of information provision based on the relevance of the information. For example, the information provision unit may first provide the information most relevant to the user's current situation. The information provision unit may also first provide the information most relevant to the user's interests. For example, the information provision unit may prioritize providing relevant information based on the user's past usage history. In this way, the information provision unit can provide the most optimal information for the user by adjusting the order of information provision based on the relevance of the information. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without AI. For example, the information provision unit can provide information using an AI model that takes the relevance of information as input and adjusts the order of provision.

[0093] The procedural support unit can estimate the user's emotions and adjust the procedural support method based on the estimated user emotions. For example, if the user is feeling anxious, the procedural support unit can provide a reassuring procedural support method. If the user is interested, the procedural support unit can also provide an engaging procedural support method. For example, if the user is in a hurry, the procedural support unit can provide a method that allows the user to proceed with the procedure quickly. In this way, the procedural support unit can provide optimal procedural support for the user by adjusting the procedural support method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the procedural support unit may be performed using AI, for example, or without AI. For example, the procedural support unit can adjust the procedural support method using an emotion engine to estimate the user's emotions.

[0094] The procedure support unit can analyze the user's past procedure history to select the optimal support method during procedure support. For example, the procedure support unit can select a support method preferred by the user based on their past procedure history. The procedure support unit can also prioritize providing the support the user needs based on their past procedure history. For example, the procedure support unit can analyze the past procedure history and filter out support methods the user wants to avoid. In this way, the procedure support unit can select the optimal support method by analyzing the user's past procedure history. Some or all of the above processing in the procedure support unit may be performed using AI, for example, or without AI. For example, the procedure support unit can use an AI model that takes the user's past procedure history as input and selects the optimal support method to provide procedure support.

[0095] The procedural support unit can customize the means of support provided during procedural support based on the user's current living situation. For example, the procedural support unit may prioritize providing relevant support based on the user's current health condition. The procedural support unit can also customize the necessary support according to the user's living situation. For example, the procedural support unit may provide support that is of interest to the user based on their areas of interest. In this way, the procedural support unit can provide optimal procedural support for the user by customizing the means of support based on the user's current living situation. Some or all of the above processing in the procedural support unit may be performed using AI, for example, or without AI. For example, the procedural support unit can provide procedural support using an AI model that takes the user's current living situation as input and customizes the means of support.

[0096] The procedural support unit can estimate the user's emotions and determine the priority of procedural support based on the estimated emotions. For example, if the user is feeling anxious, the procedural support unit will prioritize assisting with procedures that provide a sense of security. If the user is interested, the procedural support unit can also prioritize assisting with procedures that interest them. For example, if the user is in a hurry, the procedural support unit will prioritize assisting with procedures that can be completed quickly. In this way, the procedural support unit can provide optimal procedural support to the user by determining the priority of procedural support based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the procedural support unit may be performed using AI, for example, or without AI. For example, the procedural support unit can determine the priority of procedural support using an emotion engine to estimate the user's emotions.

[0097] The procedural support unit can select the optimal support method when providing procedural support, taking into account the user's geographical location information. For example, the procedural support unit may prioritize providing nearby support services based on the user's current location. The procedural support unit can also provide region-specific support methods based on the user's geographical location. For example, the procedural support unit may provide relevant support methods based on the user's travel history. In this way, the procedural support unit can provide region-specific support by selecting support methods while taking the user's geographical location information into consideration. Some or all of the above processing in the procedural support unit may be performed using AI, for example, or without AI. For example, the procedural support unit can provide procedural support using an AI model that takes the user's geographical location information as input and selects the optimal support method.

[0098] The procedural support unit can analyze the user's social media activity and propose support methods during procedural support. For example, the procedural support unit can propose support methods of interest based on the content of the user's social media posts. The procedural support unit can also propose relevant support methods based on the activities of the user's followers and friends. For example, the procedural support unit can propose support methods that are of interest based on the user's social media usage history. In this way, the procedural support unit can provide support methods of interest by analyzing the user's social media activity. Some or all of the above processing in the procedural support unit may be performed using AI, for example, or without AI. For example, the procedural support unit can perform procedural support using an AI model that takes the user's social media activity as input and proposes support methods.

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

[0100] The data collection unit can collect information about users' hobbies and interests. For example, it can collect information about activities that users enjoy as hobbies or areas of interest. It can also collect information about events and activities that users have participated in in the past. By collecting information about users' hobbies and interests, the data collection unit can provide more personalized services.

[0101] The matching department can suggest services based on the user's hobbies and interests. For example, the matching department can suggest care services related to the user's hobbies. The matching department can also suggest services related to areas of interest to the user. In this way, the matching department can provide services that are attractive to the user by suggesting services based on the user's hobbies and interests.

[0102] The information provision department can provide information based on the user's hobbies and interests. For example, the information provision department can provide information related to the user's hobbies. The information provision department can also provide information related to the user's areas of interest. In this way, the information provision department can provide useful information to the user by providing information based on the user's hobbies and interests.

[0103] The procedural support department can provide procedural support based on the user's hobbies and interests. For example, the procedural support department can provide support for procedures related to activities that the user enjoys as a hobby. It can also provide support for procedures related to fields that the user is interested in. In this way, the procedural support department can provide procedural support that is appealing to the user by tailoring it to their hobbies and interests.

[0104] The data collection unit can estimate the user's emotions and adjust the level of detail of the information it collects based on those emotions. For example, if the user is feeling anxious, collecting detailed information can provide reassurance. If the user is interested in something, it can also collect detailed information that interests them. In this way, the data collection unit can collect the most relevant information for the user by adjusting the level of detail of the information it collects based on the user's emotions.

[0105] The matching unit can estimate the user's emotions and adjust the service suggestion method based on those emotions. For example, if the user is feeling anxious, it can use a suggestion method that provides reassurance. If the user is interested, it can also use a suggestion method that attracts their attention. In this way, the matching unit can suggest the most suitable service for the user by adjusting the service suggestion method based on the user's emotions.

[0106] The information delivery unit can estimate the user's emotions and adjust the timing of information delivery based on those estimates. For example, if a user is feeling stressed, information can be delivered during a time when they can relax. If a user is busy, information can be delivered during a time when they have free time. In this way, the information delivery unit can deliver information at the optimal time for the user by adjusting the timing of information delivery based on the user's emotions.

[0107] The procedure support unit can estimate the user's emotions and adjust the pace of the procedure based on those emotions. For example, if the user is feeling anxious, it can slow down the procedure to provide reassurance. If the user is in a hurry, it can speed up the procedure. In this way, the procedure support unit can provide the user with the optimal procedure by adjusting the pace of the procedure based on the user's emotions.

[0108] The data collection unit can analyze the user's past care service usage history and select the most suitable information collection method. For example, it can select the information collection method preferred by the user based on their evaluation of services used in the past. It can also prioritize the collection of information that the user needs from their past usage history. In this way, the data collection unit can select the most suitable information collection method by analyzing the user's past care service usage history.

[0109] The matching unit can suggest services while considering the geographical distribution of users. For example, it can prioritize suggesting nearby services based on the user's current location. It can also suggest region-specific services based on the geographical distribution of users. In this way, the matching unit can provide region-specific services by suggesting services while considering the geographical distribution of users.

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

[0111] Step 1: The collection unit collects basic user information. For example, it collects basic information such as the user's name, address, age, and gender. It can also collect information such as the user's health status and living environment. For example, it collects information such as the user's medical history, current health status, type of residence, and family structure. The processing in the collection unit may be performed using AI or not. For example, the collection unit can provide an interface for inputting the user's basic information, and the AI ​​can analyze and collect the input information. Step 2: The matching unit performs service matching based on the information collected by the collection unit. For example, it may propose the most suitable care service using a matching algorithm based on the user's needs, or it may propose an appropriate care service based on the user's health condition and living environment. The processing in the matching unit may be performed using AI or not. For example, service matching can be performed using an AI model that takes the information collected by the collection unit as input and proposes the most suitable care service. Step 3: The Information Provision Department provides personalized information based on the services proposed by the Matching Department. For example, it provides personalized suggestions tailored to the user's family structure and living environment. If the user lives alone, it suggests nearby care services; if family members live far away, it suggests remote support. It also generates explanations that take into account any resistance to care services, ensuring the user can use the services with peace of mind. The processing in the Information Provision Department may be performed using AI or not. For example, information can be provided using an AI model that generates personalized suggestions based on the user's family structure and living environment. Step 4: The Procedure Support Department provides procedural support based on the information provided by the Information Provision Department. For example, this includes preparing necessary documents, providing guidance on submission methods, and managing the progress of the procedure. The processing in the Procedure Support Department may be performed using AI or not. For example, procedural support can be provided using an AI model that assists users in the procedures for using long-term care services.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements described above, including the collection unit, matching unit, information provision unit, and procedure support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and provides an interface for inputting the user's basic information. The matching unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs service matching based on the information collected by the collection unit. The information provision unit is implemented by the control unit 46A of the smart device 14 and provides personalized suggestions according to the user's family structure and living environment. The procedure support unit is implemented by the specific processing unit 290 of the data processing unit 12 and handles tasks such as preparing and submitting necessary documents and managing the progress of procedures. The correspondence between each unit and the device or control unit is not limited to the examples 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 commands and other instructions from the user by receiving voice signals. 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 data collection unit, matching unit, information provision unit, and procedure support unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for inputting the user's basic information. The matching unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs service matching based on the information collected by the data collection unit. The information provision unit is implemented by the control unit 46A of the smart glasses 214 and provides personalized suggestions according to the user's family structure and living environment. The procedure support unit is implemented by the specific processing unit 290 of the data processing unit 12 and handles tasks such as preparing and submitting necessary documents and managing the progress of procedures. The correspondence between each unit and the device or control unit is not limited to the examples 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 commands and other instructions from the user by receiving voice signals. 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 data collection unit, matching unit, information provision unit, and procedure support unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for inputting the user's basic information. The matching unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs service matching based on the information collected by the data collection unit. The information provision unit is implemented by the control unit 46A of the headset terminal 314 and provides personalized suggestions according to the user's family structure and living environment. The procedure support unit is implemented by the specific processing unit 290 of the data processing unit 12 and handles tasks such as preparing and submitting necessary documents and managing the progress of procedures. The correspondence between each unit and the device or control unit is not limited to the examples 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 commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0156] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the collection unit, matching unit, information provision unit, and procedure support unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and provides an interface for inputting the user's basic information. The matching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and performs service matching based on the information collected by the collection unit. The information provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides personalized suggestions according to the user's family structure and living environment. The procedure support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and handles tasks such as preparing and submitting necessary documents and managing the progress of procedures. The correspondence between each unit and the device or control unit is not limited to the examples 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 collection unit that collects basic user information, A matching unit that performs service matching based on the information collected by the aforementioned collection unit, An information provision unit provides personalized information based on the services proposed by the matching unit, The system includes a procedure support unit that provides procedural support based on the information provided by the aforementioned information provision unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect information such as the user's age, health status, and living environment. The system described in Appendix 1, characterized by the features described herein. (Note 3) The matching unit is Based on the collected information, we propose the most suitable care services. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned information provision unit, We provide personalized suggestions tailored to the user's family structure and living environment. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned information provision unit, Generate explanations that take into account people's resistance to care services. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned procedural support department, We provide guidance on preparing and submitting necessary documents, and manage the progress of the procedures. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past care service usage history and select the most suitable information collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When gathering information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The matching unit is The system estimates user sentiment and adjusts service matching criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The matching unit is When matching services, we improve the accuracy of matching by considering the relationships between users. The system described in Appendix 1, characterized by the features described herein. (Note 15) The matching unit is During service matching, user attribute information is taken into consideration when matching. The system described in Appendix 1, characterized by the features described herein. (Note 16) The matching unit is It estimates the user's sentiment and adjusts the order in which matching results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The matching unit is When matching services, the geographical distribution of users is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The matching unit is When matching services, we improve the accuracy of the matching process by referring to relevant literature. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned information provision unit, The system estimates the user's emotions and adjusts the way information is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned information provision unit, When providing information, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned information provision unit, When providing information, different provision algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned information provision unit, The system estimates the user's emotions and adjusts the length of information provided based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned information provision unit, When providing information, the priority of information provision will be determined based on the timing of its submission. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned information provision unit, The order in which information is provided will be adjusted based on its relevance. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned procedural support department, It estimates the user's emotions and adjusts the procedural support method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned procedural support department, When providing procedural support, the system analyzes the user's past procedural history to select the most suitable support method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned procedural support department, When providing procedural support, the means of assistance are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned procedural support department, The system estimates the user's emotions and determines the priority of procedural support based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned procedural support department, When providing procedural support, the system selects the most appropriate support method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned procedural support department, When providing procedural support, we analyze the user's social media activity and propose ways to support them. 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 collection unit that collects basic user information, A matching unit that performs service matching based on the information collected by the aforementioned collection unit, An information provision unit provides personalized information based on the services proposed by the matching unit, The system includes a procedure support unit that provides procedural support based on the information provided by the aforementioned information provision unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect information such as the user's age, health status, and living environment. The system according to feature 1.

3. The matching unit is Based on the collected information, we propose the most suitable care services. The system according to feature 1.

4. The aforementioned information provision unit, We provide personalized suggestions tailored to the user's family structure and living environment. The system according to feature 1.

5. The aforementioned information provision unit, Generate explanations that take into account people's resistance to care services. The system according to feature 1.

6. The aforementioned procedural support department, We provide guidance on preparing and submitting necessary documents, and manage the progress of the procedures. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past care service usage history and select the most suitable information collection method. The system according to feature 1.

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