system

Abstract

The system according to this embodiment aims to centrally manage information gathering regarding caregiving, proposal of care plans, progress monitoring, scheduling adjustments, and shopping assistance. [Solution] The system according to the embodiment comprises an acquisition unit, a proposal unit, a collection unit, an adjustment unit, and an agency unit. The acquisition unit acquires data on the user's family structure, health status, and economic situation. The proposal unit proposes an appropriate care plan based on the data acquired by the acquisition unit. The collection unit autonomously collects information from chat exchanges between parents and children or other related parties. The adjustment unit checks the progress based on the information collected by the collection unit and coordinates schedules among the parties involved. The agency unit performs shopping necessary for care on behalf of the user.

Description

Technical Field

[0006] , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to perform information collection related to caregiving, proposal of plans, confirmation of progress, schedule adjustment, shopping on behalf, etc. in a unified manner, and there is a problem that the burden on caregivers is large.

[0005] The system according to the embodiment aims to perform information collection related to caregiving, proposal of plans, confirmation of progress, schedule adjustment, and shopping on behalf in a unified manner.

Means for Solving the Problems

[0006] The system according to the embodiment comprises an acquisition unit, a proposal unit, a collection unit, an adjustment unit, and an agency unit. The acquisition unit acquires data on the user's family structure, health status, and economic situation. The proposal unit proposes an appropriate care plan based on the data acquired by the acquisition unit. The collection unit autonomously collects information from chat exchanges between parents and children or other related parties. The adjustment unit checks the progress based on the information collected by the collection unit and coordinates schedules among the parties involved. The agency unit performs shopping necessary for care on behalf of the user. [Effects of the Invention]

[0007] The system according to this embodiment can centrally collect information related to caregiving, propose plans, check progress, adjust schedules, and perform shopping assistance. [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 applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 2 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent service according to an embodiment of the present invention is a service for families caring for their parents in an aging society. This AI agent service acquires detailed data such as the user's family structure, health condition, and financial situation before or when parental care becomes necessary, and automatically proposes an optimal care plan. For example, the AI ​​agent service not only provides guidance on the direction of care and support systems, but also has the function of automatically collecting and sharing success stories and experiences of how other families have solved care problems. This allows users to obtain empathetic information and engage in caregiving with peace of mind. Furthermore, the AI ​​agent service autonomously collects information from interactions such as chats between parents and children and other stakeholders, checks the progress, and coordinates schedules among stakeholders (such as calendar registration). It also performs tasks such as shopping necessary for caregiving (online ordering), reducing the burden on the user. For example, when a user's parent needs care, the AI ​​agent service acquires the user's family structure, health condition, and financial situation and proposes an optimal care plan. This care plan includes guidance on the direction of care and support systems, and also shares success stories and experiences of how other families have solved care problems. This allows users to obtain empathetic information and engage in caregiving with peace of mind. Furthermore, the AI ​​agent service autonomously gathers information from chats and other interactions between parents and children, as well as other stakeholders, to check progress and coordinate schedules among those involved. For example, by registering appointments in a calendar and sharing them among stakeholders, it enables a smooth care process. In addition, it can perform tasks such as shopping for necessary care items, reducing the user's caregiving burden. For example, necessary care items can be purchased through online ordering. In this way, by utilizing the AI ​​agent service, users can perform caregiving efficiently, reducing the burden on their families. Moreover, by obtaining empathetic information, they can engage in caregiving with peace of mind. In this way, the AI ​​agent service can reduce the user's caregiving burden and enable efficient caregiving.

[0029] The AI ​​agent service according to this embodiment comprises an acquisition unit, a proposal unit, a data collection unit, a coordination unit, and an agency unit. The acquisition unit acquires data on the user's family structure, health status, and economic situation. For example, to understand the user's family structure, the acquisition unit collects information such as the number of family members, their ages, and relationships. The acquisition unit can also collect information such as medical history, current health status, and results of regular health checkups to understand the user's health status. Furthermore, the acquisition unit can also collect information such as income, expenses, savings, and debts to understand the user's economic situation. The proposal unit proposes an optimal care plan based on the data acquired by the acquisition unit. For example, the proposal unit proposes an optimal care plan to the user, taking into account the type, frequency, and cost of care services. The proposal unit can also provide guidance on the direction of care and support systems. Furthermore, the proposal unit can collect and share success stories and experiences of how other families have solved care problems. The data collection unit autonomously collects information from interactions such as chats between parents and children and other stakeholders. The data collection unit analyzes chat content, for example, to understand the progress and problems of caregiving. The data collection unit can also analyze chat content to efficiently manage communication among stakeholders. The coordination unit checks the progress based on the information collected by the data collection unit and coordinates schedules among stakeholders. The coordination unit can, for example, register appointments in a calendar and share them among stakeholders to ensure a smooth caregiving process. The coordination unit can also adjust the schedules of stakeholders to ensure the smooth progress of caregiving. The proxy unit performs tasks such as shopping necessary for caregiving. The proxy unit can, for example, purchase necessary caregiving items through online ordering. The proxy unit can also shop on behalf of the user, reducing the burden of caregiving. As a result, the AI ​​agent service according to this embodiment can reduce the user's caregiving burden and realize efficient caregiving.

[0030] The data acquisition unit acquires data on the user's family structure, health status, and financial situation. Specifically, to understand the user's family structure, it collects information such as the number of family members, their ages, and relationships. For example, family structure information is collected through questionnaires and interviews provided by the user. This allows the data acquisition unit to understand the detailed structure of the user's family and use this information to develop care plans. The data acquisition unit also collects information such as medical history, current health status, and results of regular health checkups to understand the user's health status. This includes digitizing medical records and health checkup results provided by the user and storing them in a database. Furthermore, the data acquisition unit collects information such as income, expenses, savings, and debts to understand the user's financial situation. This information plays an important role in proposing care plans that reduce the user's financial burden. For example, by understanding the details of the user's income and expenses, it is possible to appropriately estimate the cost of care services and provide the user with the most suitable plan. The data acquisition unit centrally manages this information and can collaborate with other departments as needed. For example, the acquired data is stored on a cloud server and made accessible to the proposal and coordination departments. This allows the data acquisition unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The proposal department proposes the optimal care plan based on the data acquired by the data acquisition department. Specifically, it proposes the most suitable care plan for the user, taking into account the type, frequency, and cost of care services. For example, it proposes a plan that combines services such as home care, day care, and short-term stays, taking into account the user's health condition and financial situation. The proposal department can also provide guidance on the direction of care and support systems. For example, it provides information on the long-term care insurance system and local support services, and guides the user to the support systems they can utilize. Furthermore, the proposal department can collect and share success stories and experiences of how other families have solved care problems. This allows users to refer to the experiences of other families and improve their own care plans. The proposal department can also analyze the acquired data using AI and automatically generate the optimal care plan for the user. For example, the AI ​​analyzes the user's health condition and financial situation and proposes the optimal combination of care services. The AI ​​can also utilize historical data and statistical information to develop long-term care plans. This allows the proposal department to provide users with quick and accurate care plans, reducing the burden of care.

[0032] The data collection unit autonomously gathers information from interactions such as chats between parents and children, and between other stakeholders. Specifically, it analyzes the content of chats to understand the progress and problems of caregiving. For example, the data collection unit uses natural language processing technology to analyze the content of chats and extract important information related to caregiving. This allows the data collection unit to grasp the progress and problems of caregiving in real time and take necessary actions quickly. The data collection unit can also analyze the content of chats to efficiently manage communication between stakeholders. For example, the data collection unit analyzes the content of chats to understand the frequency and content of communication between stakeholders. This allows the data collection unit to improve the quality of communication between stakeholders and facilitate the progress of caregiving. Furthermore, the data collection unit can use AI to analyze the content of chats and automatically extract important information related to caregiving. For example, the AI ​​analyzes the content of chats and identifies the progress and problems of caregiving. The AI ​​can also use past data and statistical information to predict the progress of caregiving and suggest necessary actions. This allows the data collection unit to efficiently manage communication between parents and children, and between other stakeholders, and facilitate the progress of caregiving.

[0033] The adjustment department checks the progress based on the information collected by the collection department and makes schedule adjustments among relevant parties. Specifically, by registering the schedule in the calendar and sharing it among relevant parties, a smooth care process can be realized. For example, the adjustment department grasps the schedules of relevant parties and adjusts the care schedule. This can smooth the communication among relevant parties and the progress of care. Also, the adjustment department can adjust the schedules of relevant parties to smooth the progress of care. For example, the adjustment department grasps the schedules of relevant parties and adjusts the care schedule. This can smooth the communication among relevant parties and the progress of care. Furthermore, the adjustment department can automatically adjust the schedules of relevant parties using AI. For example, AI analyzes the schedules of relevant parties and proposes an optimal care schedule. Also, AI can utilize past data and statistical information to predict the schedules of relevant parties and propose an optimal care schedule. This can smooth the communication among relevant parties and the progress of care.

[0034] The proxy department conducts proxy services such as shopping necessary for care. Specifically, it purchases the items necessary for care through online ordering. For example, the proxy department orders care supplies and pharmaceuticals online on behalf of the user and delivers them to the user's home. Also, the proxy department can conduct shopping on behalf of the user to reduce the burden of care. For example, the proxy department goes to the supermarket or pharmacy on behalf of the user and purchases the necessary items. This allows the user to save the time and effort of shopping and focus on care. Furthermore, the proxy department can use AI to predict the user's needs and automatically order the necessary items. For example, AI analyzes the user's past purchase history to predict the necessary items. Also, AI can propose the necessary items considering the user's health status and the progress of care. This enables the proxy department to efficiently conduct shopping according to the user's needs and reduce the burden of care.

[0035] The suggestion department can provide guidance on the direction of care or support systems. For example, the suggestion department can present care goals and policies to the user. It can also provide information on the types of support systems available, how to apply for them, and their eligibility requirements. This makes it easier for the user to understand an appropriate care plan. Some or all of the above processing in the suggestion department may be performed using AI, for example, or not. For example, the suggestion department can use an AI model to analyze information in order to provide guidance on the direction of care or support systems and provide the user with the most suitable guidance.

[0036] The suggestion department can collect and share success stories or experiences of how other families have solved caregiving problems. For example, the suggestion department can collect success stories of other families and share them with users. It can also collect and share experiences of other families with users. This allows users to obtain relatable information and approach caregiving with peace of mind. Some or all of the above processing in the suggestion department may be performed using AI, for example, or not. For example, the suggestion department can use an AI model to analyze information in order to collect success stories and experiences and provide users with the most relevant information.

[0037] The data collection unit can analyze the content of chats. For example, the data collection unit can analyze the content of chats between parents and children or between other stakeholders to understand the progress and problems of caregiving. The data collection unit can also analyze the content of chats in order to efficiently manage communication between stakeholders. This allows for efficient management of communication between stakeholders. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can use an AI model to analyze information in order to analyze the content of chats and provide the user with the most relevant information.

[0038] The coordination unit can register appointments in the calendar. For example, the coordination unit can register appointments between stakeholders in the calendar and share them among them. The coordination unit can also coordinate the schedules of stakeholders to ensure the smooth progress of care. This allows for smooth scheduling among stakeholders. Some or all of the above-described processes in the coordination unit may be performed using AI, for example, or not. For example, the coordination unit can use an AI model to analyze information in order to register appointments in the calendar and provide the user with the most suitable scheduling.

[0039] The proxy service can place online orders. For example, the proxy service can order necessary care items online and provide them to the user. The proxy service can also shop on behalf of the user, reducing the burden of caregiving. This allows for the efficient purchase of necessary care items. Some or all of the above processes in the proxy service may be performed using AI, for example, or not. For example, the proxy service can use an AI model to analyze information in order to place online orders and provide the user with the most suitable proxy service.

[0040] The data acquisition unit can analyze the user's past caregiving history and select the optimal data acquisition method. For example, the acquisition unit can suggest the optimal method based on the data acquisition methods the user has used in the past. Furthermore, if a particular data acquisition method was effective based on the user's past caregiving history, the acquisition unit can prioritize selecting that method. In addition, the acquisition unit can analyze the user's past caregiving history and optimize the timing and frequency of data acquisition. This allows for the selection of the optimal data acquisition method by analyzing the user's past caregiving history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can use an AI model to analyze information in order to analyze the user's past caregiving history and provide the user with the optimal data acquisition method.

[0041] The data acquisition unit can filter data based on the user's current lifestyle and areas of interest during data acquisition. For example, the acquisition unit can acquire only highly relevant data based on the user's current lifestyle. Furthermore, the acquisition unit can prioritize the acquisition of specific data based on the user's areas of interest. In addition, the acquisition unit can adjust the scope of data acquisition according to the user's lifestyle and areas of interest. This allows for the acquisition of highly relevant data by filtering the data according to the user's lifestyle and areas of interest. Some or all of the above processing in the acquisition unit may be performed using AI, or not. For example, the acquisition unit can analyze information using an AI model to analyze the user's lifestyle and areas of interest, and provide the user with the most suitable data.

[0042] The data acquisition unit can prioritize the acquisition of highly relevant data by considering the user's geographical location information during data acquisition. For example, the data acquisition unit can prioritize the acquisition of information on nearby care facilities and services based on the user's current location. The data acquisition unit can also acquire information on region-specific care support systems based on the user's geographical location information. Furthermore, the data acquisition unit can acquire data to minimize travel time and distance by considering the user's geographical location information. In this way, highly relevant data can be prioritized by considering the user's geographical location information. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can use an AI model to analyze information in order to analyze the user's geographical location information and provide the user with the most suitable data.

[0043] The data acquisition unit can analyze the user's social media activity and acquire relevant data during data acquisition. For example, the data acquisition unit can acquire information on care services and products of interest from the user's social media activity. The data acquisition unit can also acquire relevant data by referring to the care experiences of the user's friends and followers on social media. Furthermore, the data acquisition unit can analyze the user's social media activity and acquire information on trends and topics related to care. In this way, relevant data can be acquired by analyzing the user's social media activity. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or not using AI. For example, the data acquisition unit can use an AI model to analyze information in order to analyze the user's social media activity and provide the user with the most suitable data.

[0044] The proposal unit can adjust the level of detail in its care plan proposals based on the importance of the care provided. For example, it may include detailed explanations and specific procedures for high-priority care plans. Conversely, it may provide only a concise explanation and overview for low-priority care plans. Furthermore, the proposal unit can adjust the level of detail in its proposals in stages according to the importance of the care provided. This allows the system to provide users with appropriate information by adjusting the level of detail according to the importance of the care. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit may use an AI model to analyze information in order to evaluate the importance of the care and provide the user with the optimal level of detail in its proposals.

[0045] The proposal unit can apply different proposal algorithms depending on the category of care when proposing a care plan. For example, for a plan related to physical care, the proposal unit can apply an algorithm that emphasizes physical support methods. Similarly, for a plan related to mental support, the proposal unit can apply an algorithm that emphasizes psychological care methods. Furthermore, for a plan related to financial support, the proposal unit can apply an algorithm that emphasizes methods for reducing financial burdens. By applying different proposal algorithms depending on the category of care, the system can provide the user with the most suitable care plan. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can use an AI model to analyze information to analyze the category of care and provide the user with the most suitable proposal algorithm.

[0046] The proposal unit can prioritize proposals based on the expected start date of care when proposing care plans. For example, if the start date of care is approaching, the proposal unit will prioritize proposals. If the start date of care is far off, the proposal unit can also propose proposals for creating a detailed plan. Furthermore, the proposal unit can adjust the priority of proposals in stages according to the expected start date of care. This allows the system to provide information to the user at the appropriate time by prioritizing proposals based on the expected start date of care. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can use an AI model to analyze information in order to evaluate the expected start date of care and provide the user with the optimal priority of proposals.

[0047] The proposal unit can adjust the order of proposals based on the relevance of care when proposing a care plan. For example, the proposal unit can prioritize proposing items with high relevance to care. It can also postpone items with low relevance to care. Furthermore, the proposal unit can adjust the order of proposals in stages according to the relevance of care. This allows the system to prioritize providing information that is important to the user by adjusting the order of proposals based on the relevance of care. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can use an AI model to analyze information in order to evaluate the relevance of care and provide the user with the optimal order of proposals.

[0048] The data collection unit can improve the accuracy of its analysis by considering the attribute information of the parties involved when analyzing chat content. For example, the data collection unit can select an appropriate analysis method based on the age and gender of the parties involved. It can also improve the accuracy of its analysis based on the occupation and role of the parties involved. Furthermore, the data collection unit can prioritize the analysis of important information based on the relationships between the parties involved. In this way, the accuracy of the chat content analysis can be improved by considering the attribute information of the parties involved. 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 use an AI model to analyze the information in order to analyze the attribute information of the parties involved and provide the user with the most suitable method for analyzing the chat content.

[0049] The data collection unit can optimize its analysis algorithm by referring to past chat history when analyzing chat content. For example, the data collection unit can extract frequently occurring keywords from past chat history and reflect them in the analysis algorithm. The data collection unit can also analyze the communication patterns of the parties involved based on past chat history and optimize the algorithm. Furthermore, the data collection unit can prioritize the analysis of important information by referring to past chat history. This allows for the optimization of the analysis algorithm and improvement of analysis accuracy by referring to past chat 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 use an AI model to analyze information in order to analyze past chat history and provide the user with the most suitable method for analyzing chat content.

[0050] The data collection unit can analyze chat content while considering the geographical distribution of the parties involved. For example, the data collection unit can prioritize the analysis of region-specific information based on the geographical location information of the parties involved. The data collection unit can also extract important information while considering the geographical distribution of the parties involved. Furthermore, the data collection unit can analyze the geographical relationships of the parties involved to improve the efficiency of communication. This allows for the priority analysis of region-specific information by considering the geographical distribution of the parties involved. 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 use an AI model to analyze information in order to analyze the geographical distribution of the parties involved and provide the user with the most suitable method for analyzing chat content.

[0051] The data collection unit can improve the accuracy of its analysis by referring to relevant literature when analyzing chat content. For example, the data collection unit can refer to relevant literature to reflect specialized information in its analysis. Furthermore, the data collection unit can supplement background information on the chat content based on relevant literature. In addition, the data collection unit can improve the reliability of its analysis results by referring to relevant literature. This allows for the reflection of specialized information in the analysis and improved analysis accuracy by referring to relevant literature. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use an AI model to analyze information in order to refer to relevant literature and provide the user with the most suitable method for analyzing chat content.

[0052] The scheduling unit can select the optimal scheduling method by referring to the past schedule history of the parties involved when scheduling. For example, the scheduling unit can propose the optimal scheduling method based on the past schedule history of the parties involved. The scheduling unit can also prioritize scheduling frequently used time slots based on the past schedule history of the parties involved. Furthermore, the scheduling unit can improve the accuracy of scheduling by referring to the past schedule history of the parties involved. This allows the optimal scheduling method to be selected by referring to the past schedule history of the parties involved. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can use an AI model to analyze information in order to analyze the past schedule history of the parties involved and provide the user with the optimal scheduling method.

[0053] The scheduling unit can improve the accuracy of scheduling by considering the attribute information of the stakeholders. For example, the scheduling unit can select an appropriate scheduling method based on the age and gender of the stakeholders. The scheduling unit can also improve the accuracy of scheduling based on the occupation and role of the stakeholders. Furthermore, the scheduling unit can prioritize scheduling important appointments based on the relationships between the stakeholders. In this way, the accuracy of scheduling can be improved by considering the attribute information of the stakeholders. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or not using AI. For example, the scheduling unit can use an AI model to analyze the attribute information of stakeholders and provide the user with the optimal scheduling method.

[0054] The coordination unit can select the optimal coordination method when scheduling, taking into account the geographical location information of the stakeholders. For example, the coordination unit can propose the optimal coordination method based on the current location of the stakeholders. The coordination unit can also select a coordination method that minimizes travel time and distance based on the geographical location information of the stakeholders. Furthermore, the coordination unit can propose an efficient coordination method that takes into account the geographical location information of the stakeholders. This allows for the selection of a coordination method that minimizes travel time and distance by considering the geographical location information of the stakeholders. Some or all of the above processing in the coordination unit may be performed using AI, for example, or without AI. For example, the coordination unit can use an AI model to analyze the geographical location information of stakeholders and provide the user with the optimal coordination method.

[0055] The scheduling unit can analyze the social media activities of stakeholders and propose scheduling methods during the scheduling process. For example, the scheduling unit can propose scheduling methods based on events and activities of interest to stakeholders based on their social media activity. The scheduling unit can also propose scheduling methods by referring to the schedules of stakeholders' friends and followers on social media. Furthermore, the scheduling unit can analyze the social media activities of stakeholders and propose efficient scheduling methods. This allows the scheduling unit to propose scheduling methods based on events and activities of interest by analyzing the social media activities of stakeholders. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or not. For example, the scheduling unit can use an AI model to analyze information in order to analyze the social media activities of stakeholders and provide the user with the most suitable scheduling method.

[0056] The proxy service can select the optimal proxy service method by referring to the user's past order history when providing proxy services. For example, the proxy service can propose the optimal proxy service method based on the user's past order history. The proxy service can also prioritize providing frequently used services based on the user's past order history. Furthermore, the proxy service can improve the accuracy of the proxy service by referring to the user's past order history. This allows the optimal proxy service method to be selected by referring to the user's past order history. Some or all of the above processing in the proxy service may be performed using AI, for example, or not using AI. For example, the proxy service can use an AI model to analyze information in order to analyze the user's past order history and provide the user with the optimal proxy service method.

[0057] The proxy service unit can customize the means of the proxy service based on the user's current living situation when providing the proxy service. For example, the proxy service unit can provide a highly relevant proxy service based on the user's current living situation. The proxy service unit can also customize the means of the proxy service according to the user's living situation. Furthermore, the proxy service unit can adjust the scope of the proxy service based on the user's living situation. This allows for the provision of highly relevant proxy services by customizing the means of the proxy service based on the user's current living situation. Some or all of the above processing in the proxy service unit may be performed using AI, for example, or without AI. For example, the proxy service unit can use an AI model to analyze information in order to analyze the user's living situation and provide the user with the most suitable proxy service.

[0058] The proxy service can select the optimal proxy method when providing proxy services, taking into account the user's geographical location information. For example, the proxy service can propose the optimal proxy method based on the user's current location. Furthermore, the proxy service can select a proxy method that minimizes travel time and distance based on the user's geographical location information. In addition, the proxy service can propose an efficient proxy method considering the user's geographical location information. This allows for the selection of a proxy method that minimizes travel time and distance by considering the user's geographical location information. Some or all of the above processing in the proxy service may be performed using AI, for example, or without AI. For example, the proxy service can use an AI model to analyze the user's geographical location information and provide the user with the optimal proxy method.

[0059] The proxy service department can analyze the user's social media activity and propose a method for providing proxy services. For example, the proxy service department can propose a proxy method based on the user's social media activity and the services or products they are interested in. The proxy service department can also propose a proxy method by referring to the usage patterns of the user's friends and followers on social media. Furthermore, the proxy service department can analyze the user's social media activity and propose an efficient proxy method. This allows the proxy service department to propose a proxy method based on the user's social media activity and the services or products they are interested in. Some or all of the above processes in the proxy service department may be performed using AI, for example, or not. For example, the proxy service department can use an AI model to analyze information in order to analyze the user's social media activity and provide the user with the most suitable proxy method.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The data acquisition unit can collect information about the user's hobbies and interests when acquiring data on the user's family structure, health status, and economic situation. For example, it can collect information about the user's hobbies and areas of interest and incorporate them into the care plan. This allows for the proposal of a care plan based on the user's hobbies and interests, thereby improving user satisfaction. The data acquisition unit can also analyze the user's social media activity to collect information about the user's hobbies and interests. Furthermore, the data acquisition unit can provide information on care-related events and activities based on the user's hobbies and interests. This allows for the proposal of a care plan based on the user's hobbies and interests, thereby improving user satisfaction.

[0062] The proposal department can consider the user's past caregiving experience and history when proposing care plans. For example, it can analyze what care plans the user has used in the past and what problems have occurred, and then propose the most suitable care plan. This allows for the proposal of care plans based on the user's past caregiving experience, thereby improving user satisfaction. Furthermore, the proposal department can collect and analyze the user's caregiving history in order to analyze the user's past caregiving experience. In addition, the proposal department can suggest areas for improvement and points to note in the care plan based on the user's past caregiving experience. This allows for the proposal of care plans based on the user's past caregiving experience, thereby improving user satisfaction.

[0063] The coordination unit can select the optimal coordination method when coordinating schedules among stakeholders, taking into account the geographical location information of those stakeholders. For example, it can propose the optimal coordination method based on the current location of each stakeholder. It can also select a coordination method that minimizes travel time and distance based on the geographical location information of the stakeholders. Furthermore, it can propose an efficient coordination method that takes into account the geographical location information of the stakeholders. This allows for the selection of a coordination method that minimizes travel time and distance by considering the geographical location information of the stakeholders. Some or all of the above processing in the coordination unit may be performed using AI, for example, or not. For example, the coordination unit can use an AI model to analyze the geographical location information of stakeholders and provide the user with the optimal coordination method.

[0064] The data acquisition unit can collect information about the user's lifestyle and daily routines when acquiring data on the user's family structure, health status, and economic situation. For example, it can collect information such as the user's meal times and content, sleep patterns, and exercise frequency, and incorporate this into the care plan. This allows for the proposal of a care plan based on the user's lifestyle and daily routines, thereby improving user satisfaction. The data acquisition unit can also acquire data from the user's smart devices and wearable devices to collect information about the user's lifestyle and daily routines. Furthermore, the data acquisition unit can provide care-related advice and suggestions based on the user's lifestyle and daily routines. This allows for the proposal of a care plan based on the user's lifestyle and daily routines, thereby improving user satisfaction.

[0065] The data collection unit can optimize its analysis algorithm by referring to the past communication history of the parties involved when analyzing chat content between parents and children or other parties. For example, it can extract frequently occurring keywords from past chat history and reflect them in the analysis algorithm. It can also analyze the communication patterns of the parties involved based on past chat history and optimize the algorithm. Furthermore, it can prioritize the analysis of important information by referring to past chat history. In this way, by referring to past chat history, the analysis algorithm can be optimized and the accuracy of the analysis can be improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can use an AI model to analyze information in order to analyze past chat history and provide the user with the most suitable method for analyzing chat content.

[0066] When the proxy department conducts proxy services such as shopping necessary for caregiving, it can select the optimal proxy method by referring to the user's past order history. For example, based on the user's past order history, an optimal proxy method can be proposed. Also, from the user's past order history, services that are frequently used can be provided preferentially. Furthermore, by referring to the user's past order history, the accuracy of the proxy service can be improved. Thus, by referring to the user's past order history, an optimal proxy method can be selected. Some or all of the above-mentioned processes in the proxy department may be performed using, for example, AI, or may be performed without using AI. For example, the proxy department can use an AI model to analyze information in order to analyze the user's past order history and provide the user with an optimal proxy method.

[0067] The process flow of Form Example 1 will be briefly described below.

[0068] Step 1: The acquisition department acquires data on the user's family composition, health status, and economic situation. Specifically, it collects information such as the number of family members, age, relationship, medical history, current health status, results of regular health check-ups, income, expenses, savings, and debts. Step 2: The proposal department proposes an optimal care plan based on the data acquired by the acquisition department. Specifically, it considers the type, frequency, and cost of care services, and provides guidance on the direction of caregiving, support systems, and shares success stories and testimonials. Step 3: The collection department autonomously collects information from exchanges such as chats between parents and children or other related parties. Specifically, it analyzes the content of the chat, grasps the progress and problems of caregiving, and efficiently manages the communication between related parties. Step 4: The adjustment department checks the progress based on the information collected by the collection department and adjusts the schedules among related parties. Specifically, it registers the schedule in the calendar and shares it among related parties to realize a smooth caregiving process and adjusts the schedules of related parties. Step 5: The proxy department conducts proxy services such as shopping necessary for caregiving. Specifically, it purchases items necessary for caregiving through online orders and conducts shopping on behalf of the user to reduce the burden of caregiving.

[0069] (Form Example 2) The AI ​​agent service according to an embodiment of the present invention is a service for families caring for their parents in an aging society. This AI agent service acquires detailed data such as the user's family structure, health condition, and financial situation before or when parental care becomes necessary, and automatically proposes an optimal care plan. For example, the AI ​​agent service not only provides guidance on the direction of care and support systems, but also has the function of automatically collecting and sharing success stories and experiences of how other families have solved care problems. This allows users to obtain empathetic information and engage in caregiving with peace of mind. Furthermore, the AI ​​agent service autonomously collects information from interactions such as chats between parents and children and other stakeholders, checks the progress, and coordinates schedules among stakeholders (such as calendar registration). It also performs tasks such as shopping necessary for caregiving (online ordering), reducing the burden on the user. For example, when a user's parent needs care, the AI ​​agent service acquires the user's family structure, health condition, and financial situation and proposes an optimal care plan. This care plan includes guidance on the direction of care and support systems, and also shares success stories and experiences of how other families have solved care problems. This allows users to obtain empathetic information and engage in caregiving with peace of mind. Furthermore, the AI ​​agent service autonomously gathers information from chats and other interactions between parents and children, as well as other stakeholders, to check progress and coordinate schedules among those involved. For example, by registering appointments in a calendar and sharing them among stakeholders, it enables a smooth care process. In addition, it can perform tasks such as shopping for necessary care items, reducing the user's caregiving burden. For example, necessary care items can be purchased through online ordering. In this way, by utilizing the AI ​​agent service, users can perform caregiving efficiently, reducing the burden on their families. Moreover, by obtaining empathetic information, they can engage in caregiving with peace of mind. In this way, the AI ​​agent service can reduce the user's caregiving burden and enable efficient caregiving.

[0070] The AI ​​agent service according to this embodiment comprises an acquisition unit, a proposal unit, a data collection unit, a coordination unit, and an agency unit. The acquisition unit acquires data on the user's family structure, health status, and economic situation. For example, to understand the user's family structure, the acquisition unit collects information such as the number of family members, their ages, and relationships. The acquisition unit can also collect information such as medical history, current health status, and results of regular health checkups to understand the user's health status. Furthermore, the acquisition unit can also collect information such as income, expenses, savings, and debts to understand the user's economic situation. The proposal unit proposes an optimal care plan based on the data acquired by the acquisition unit. For example, the proposal unit proposes an optimal care plan to the user, taking into account the type, frequency, and cost of care services. The proposal unit can also provide guidance on the direction of care and support systems. Furthermore, the proposal unit can collect and share success stories and experiences of how other families have solved care problems. The data collection unit autonomously collects information from interactions such as chats between parents and children and other stakeholders. The data collection unit analyzes chat content, for example, to understand the progress and problems of caregiving. The data collection unit can also analyze chat content to efficiently manage communication among stakeholders. The coordination unit checks the progress based on the information collected by the data collection unit and coordinates schedules among stakeholders. The coordination unit can, for example, register appointments in a calendar and share them among stakeholders to ensure a smooth caregiving process. The coordination unit can also adjust the schedules of stakeholders to ensure the smooth progress of caregiving. The proxy unit performs tasks such as shopping necessary for caregiving. The proxy unit can, for example, purchase necessary caregiving items through online ordering. The proxy unit can also shop on behalf of the user, reducing the burden of caregiving. As a result, the AI ​​agent service according to this embodiment can reduce the user's caregiving burden and realize efficient caregiving.

[0071] The data acquisition unit acquires data on the user's family structure, health status, and financial situation. Specifically, to understand the user's family structure, it collects information such as the number of family members, their ages, and relationships. For example, family structure information is collected through questionnaires and interviews provided by the user. This allows the data acquisition unit to understand the detailed structure of the user's family and use this information to develop care plans. The data acquisition unit also collects information such as medical history, current health status, and results of regular health checkups to understand the user's health status. This includes digitizing medical records and health checkup results provided by the user and storing them in a database. Furthermore, the data acquisition unit collects information such as income, expenses, savings, and debts to understand the user's financial situation. This information plays an important role in proposing care plans that reduce the user's financial burden. For example, by understanding the details of the user's income and expenses, it is possible to appropriately estimate the cost of care services and provide the user with the most suitable plan. The data acquisition unit centrally manages this information and can collaborate with other departments as needed. For example, the acquired data is stored on a cloud server and made accessible to the proposal and coordination departments. This allows the data acquisition unit to collect data efficiently and effectively, improving the overall system performance.

[0072] The proposal department proposes the optimal care plan based on the data acquired by the data acquisition department. Specifically, it proposes the most suitable care plan for the user, taking into account the type, frequency, and cost of care services. For example, it proposes a plan that combines services such as home care, day care, and short-term stays, taking into account the user's health condition and financial situation. The proposal department can also provide guidance on the direction of care and support systems. For example, it provides information on the long-term care insurance system and local support services, and guides the user to the support systems they can utilize. Furthermore, the proposal department can collect and share success stories and experiences of how other families have solved care problems. This allows users to refer to the experiences of other families and improve their own care plans. The proposal department can also analyze the acquired data using AI and automatically generate the optimal care plan for the user. For example, the AI ​​analyzes the user's health condition and financial situation and proposes the optimal combination of care services. The AI ​​can also utilize historical data and statistical information to develop long-term care plans. This allows the proposal department to provide users with quick and accurate care plans, reducing the burden of care.

[0073] The data collection unit autonomously gathers information from interactions such as chats between parents and children, and between other stakeholders. Specifically, it analyzes the content of chats to understand the progress and problems of caregiving. For example, the data collection unit uses natural language processing technology to analyze the content of chats and extract important information related to caregiving. This allows the data collection unit to grasp the progress and problems of caregiving in real time and take necessary actions quickly. The data collection unit can also analyze the content of chats to efficiently manage communication between stakeholders. For example, the data collection unit analyzes the content of chats to understand the frequency and content of communication between stakeholders. This allows the data collection unit to improve the quality of communication between stakeholders and facilitate the progress of caregiving. Furthermore, the data collection unit can use AI to analyze the content of chats and automatically extract important information related to caregiving. For example, the AI ​​analyzes the content of chats and identifies the progress and problems of caregiving. The AI ​​can also use past data and statistical information to predict the progress of caregiving and suggest necessary actions. This allows the data collection unit to efficiently manage communication between parents and children, and between other stakeholders, and facilitate the progress of caregiving.

[0074] The adjustment department checks the progress based on the information collected by the collection department and makes schedule adjustments among relevant parties. Specifically, by registering the schedule in the calendar and sharing it among relevant parties, a smooth care process can be realized. For example, the adjustment department grasps the schedules of relevant parties and adjusts the care schedule. This can smooth the communication among relevant parties and make the progress of care smooth. Also, the adjustment department can adjust the schedules of relevant parties to smooth the progress of care. For example, the adjustment department grasps the schedules of relevant parties and adjusts the care schedule. This can smooth the communication among relevant parties and make the progress of care smooth. Furthermore, the adjustment department can automatically adjust the schedules of relevant parties using AI. For example, AI analyzes the schedules of relevant parties and proposes an optimal care schedule. Also, AI can utilize past data and statistical information to predict the schedules of relevant parties and propose an optimal care schedule. This can smooth the communication among relevant parties and make the progress of care smooth.

[0075] The proxy department conducts proxy services such as shopping necessary for care. Specifically, it purchases the items necessary for care through online ordering. For example, the proxy department orders care supplies and pharmaceuticals online on behalf of the user and delivers them to the user's home. Also, the proxy department can conduct shopping on behalf of the user to reduce the burden of care. For example, the proxy department goes to the supermarket or pharmacy on behalf of the user and purchases the necessary items. This allows the user to save the time and effort of shopping and focus on care. Furthermore, the proxy department can predict the user's needs using AI and automatically order the necessary items. For example, AI analyzes the user's past purchase history and predicts the necessary items. Also, AI can take into account the user's health status and the progress of care and propose the necessary items. This allows the proxy department to efficiently conduct shopping according to the user's needs and reduce the burden of care.

[0076] The suggestion department can provide guidance on the direction of care or support systems. For example, the suggestion department can present care goals and policies to the user. It can also provide information on the types of support systems available, how to apply for them, and their eligibility requirements. This makes it easier for the user to understand an appropriate care plan. Some or all of the above processing in the suggestion department may be performed using AI, for example, or not. For example, the suggestion department can use an AI model to analyze information in order to provide guidance on the direction of care or support systems and provide the user with the most suitable guidance.

[0077] The suggestion department can collect and share success stories or experiences of how other families have solved caregiving problems. For example, the suggestion department can collect success stories of other families and share them with users. It can also collect and share experiences of other families with users. This allows users to obtain relatable information and approach caregiving with peace of mind. Some or all of the above processing in the suggestion department may be performed using AI, for example, or not. For example, the suggestion department can use an AI model to analyze information in order to collect success stories and experiences and provide users with the most relevant information.

[0078] The data collection unit can analyze the content of chats. For example, the data collection unit can analyze the content of chats between parents and children or between other stakeholders to understand the progress and problems of caregiving. The data collection unit can also analyze the content of chats in order to efficiently manage communication between stakeholders. This allows for efficient management of communication between stakeholders. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can use an AI model to analyze information in order to analyze the content of chats and provide the user with the most relevant information.

[0079] The coordination unit can register appointments in the calendar. For example, the coordination unit can register appointments between stakeholders in the calendar and share them among them. The coordination unit can also coordinate the schedules of stakeholders to ensure the smooth progress of care. This allows for smooth scheduling among stakeholders. Some or all of the above-described processes in the coordination unit may be performed using AI, for example, or not. For example, the coordination unit can use an AI model to analyze information in order to register appointments in the calendar and provide the user with the most suitable scheduling.

[0080] The proxy service can place online orders. For example, the proxy service can order necessary care items online and provide them to the user. The proxy service can also shop on behalf of the user, reducing the burden of caregiving. This allows for the efficient purchase of necessary care items. Some or all of the above processes in the proxy service may be performed using AI, for example, or not. For example, the proxy service can use an AI model to analyze information in order to place online orders and provide the user with the most suitable proxy service.

[0081] The data acquisition unit can estimate the user's emotions and adjust the timing of data acquisition based on the estimated emotions. For example, if the user is stressed, the data acquisition unit can reduce the frequency of data acquisition to alleviate the user's burden. Conversely, if the user is relaxed, the data acquisition unit can increase the timing of detailed data acquisition to gather more accurate information. Furthermore, if the user is in a hurry, the data acquisition unit can quickly acquire only the minimum necessary data to save the user's time. In this way, the user's burden can be reduced by adjusting the timing of data acquisition according to 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 acquisition unit may be performed using AI, for example, or not using AI. For example, the data acquisition unit can use an AI model to analyze information in order to estimate the user's emotions and provide the user with the optimal timing for data acquisition.

[0082] The data acquisition unit can analyze the user's past caregiving history and select the optimal data acquisition method. For example, the acquisition unit can suggest the optimal method based on the data acquisition methods the user has used in the past. Furthermore, if a particular data acquisition method was effective based on the user's past caregiving history, the acquisition unit can prioritize selecting that method. In addition, the acquisition unit can analyze the user's past caregiving history and optimize the timing and frequency of data acquisition. This allows for the selection of the optimal data acquisition method by analyzing the user's past caregiving history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can use an AI model to analyze information in order to analyze the user's past caregiving history and provide the user with the optimal data acquisition method.

[0083] The data acquisition unit can filter data based on the user's current lifestyle and areas of interest during data acquisition. For example, the acquisition unit can acquire only highly relevant data based on the user's current lifestyle. Furthermore, the acquisition unit can prioritize the acquisition of specific data based on the user's areas of interest. In addition, the acquisition unit can adjust the scope of data acquisition according to the user's lifestyle and areas of interest. This allows for the acquisition of highly relevant data by filtering the data according to the user's lifestyle and areas of interest. Some or all of the above processing in the acquisition unit may be performed using AI, or not. For example, the acquisition unit can analyze information using an AI model to analyze the user's lifestyle and areas of interest, and provide the user with the most suitable data.

[0084] The data acquisition unit can estimate the user's emotions and determine the priority of data to acquire based on the estimated emotions. For example, if the user is stressed, the data acquisition unit will prioritize acquiring high-priority data. If the user is relaxed, the data acquisition unit can also prioritize acquiring detailed data. Furthermore, if the user is in a hurry, the data acquisition unit can prioritize acquiring data that can be retrieved quickly. This allows for the priority acquisition of important data by prioritizing data according to the user's emotions. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can use an AI model to analyze information in order to estimate the user's emotions and provide the user with the optimal data priority.

[0085] The data acquisition unit can prioritize the acquisition of highly relevant data by considering the user's geographical location information during data acquisition. For example, the data acquisition unit can prioritize the acquisition of information on nearby care facilities and services based on the user's current location. The data acquisition unit can also acquire information on region-specific care support systems based on the user's geographical location information. Furthermore, the data acquisition unit can acquire data to minimize travel time and distance by considering the user's geographical location information. In this way, highly relevant data can be prioritized by considering the user's geographical location information. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can use an AI model to analyze information in order to analyze the user's geographical location information and provide the user with the most suitable data.

[0086] The data acquisition unit can analyze the user's social media activity and acquire relevant data during data acquisition. For example, the data acquisition unit can acquire information on care services and products of interest from the user's social media activity. The data acquisition unit can also acquire relevant data by referring to the care experiences of the user's friends and followers on social media. Furthermore, the data acquisition unit can analyze the user's social media activity and acquire information on trends and topics related to care. In this way, relevant data can be acquired by analyzing the user's social media activity. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or not using AI. For example, the data acquisition unit can use an AI model to analyze information in order to analyze the user's social media activity and provide the user with the most suitable data.

[0087] The suggestion unit can estimate the user's emotions and adjust the way the care plan is presented based on those emotions. For example, if the user is feeling stressed, the suggestion unit will propose a simple and easy-to-understand care plan. If the user is relaxed, the suggestion unit may also propose a care plan that includes detailed explanations. Furthermore, if the user is in a hurry, the suggestion unit may propose a concise care plan that gets straight to the point. By adjusting the way the care plan is presented according to the user's emotions, the system can provide a plan that is easy for the user to understand. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit may use an AI model to analyze information in order to estimate the user's emotions and provide the user with the most suitable way to present the care plan.

[0088] The proposal unit can adjust the level of detail in its care plan proposals based on the importance of the care provided. For example, it may include detailed explanations and specific procedures for high-priority care plans. Conversely, it may provide only a concise explanation and overview for low-priority care plans. Furthermore, the proposal unit can adjust the level of detail in its proposals in stages according to the importance of the care provided. This allows the system to provide users with appropriate information by adjusting the level of detail according to the importance of the care. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit may use an AI model to analyze information in order to evaluate the importance of the care and provide the user with the optimal level of detail in its proposals.

[0089] The proposal unit can apply different proposal algorithms depending on the category of care when proposing a care plan. For example, for a plan related to physical care, the proposal unit can apply an algorithm that emphasizes physical support methods. Similarly, for a plan related to mental support, the proposal unit can apply an algorithm that emphasizes psychological care methods. Furthermore, for a plan related to financial support, the proposal unit can apply an algorithm that emphasizes methods for reducing financial burdens. By applying different proposal algorithms depending on the category of care, the system can provide the user with the most suitable care plan. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can use an AI model to analyze information to analyze the category of care and provide the user with the most suitable proposal algorithm.

[0090] The suggestion unit can estimate the user's emotions and adjust the length of the care plan based on those emotions. For example, if the user is stressed, the suggestion unit can suggest a short, concise care plan. If the user is relaxed, the suggestion unit can suggest a longer care plan with more detailed explanations. Furthermore, if the user is in a hurry, the suggestion unit can suggest a short, quick-to-implement care plan. By adjusting the length of the care plan according to the user's emotions, the system can provide the user with an appropriate amount of information. Some or all of the processing described above in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can use an AI model to analyze information in order to estimate the user's emotions and provide the user with the optimal care plan length.

[0091] The proposal unit can prioritize proposals based on the expected start date of care when proposing care plans. For example, if the start date of care is approaching, the proposal unit will prioritize proposals. If the start date of care is far off, the proposal unit can also propose proposals for creating a detailed plan. Furthermore, the proposal unit can adjust the priority of proposals in stages according to the expected start date of care. This allows the system to provide information to the user at the appropriate time by prioritizing proposals based on the expected start date of care. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can use an AI model to analyze information in order to evaluate the expected start date of care and provide the user with the optimal priority of proposals.

[0092] The proposal unit can adjust the order of proposals based on the relevance of care when proposing a care plan. For example, the proposal unit can prioritize proposing items with high relevance to care. It can also postpone items with low relevance to care. Furthermore, the proposal unit can adjust the order of proposals in stages according to the relevance of care. This allows the system to prioritize providing information that is important to the user by adjusting the order of proposals based on the relevance of care. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can use an AI model to analyze information in order to evaluate the relevance of care and provide the user with the optimal order of proposals.

[0093] The data collection unit can estimate the user's emotions and adjust the chat content analysis method based on the estimated emotions. For example, if the user is stressed, the data collection unit can apply a simple analysis method and extract only the important information. If the user is relaxed, the data collection unit can also apply a detailed analysis method and analyze all the information. Furthermore, if the user is in a hurry, the data collection unit can perform a rapid analysis and extract only the necessary information. In this way, by adjusting the chat content analysis method according to the user's emotions, information important to the user can be efficiently extracted. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can use an AI model to analyze information in order to estimate the user's emotions and provide the user with the most suitable chat content analysis method.

[0094] The data collection unit can improve the accuracy of its analysis by considering the attribute information of the parties involved when analyzing chat content. For example, the data collection unit can select an appropriate analysis method based on the age and gender of the parties involved. It can also improve the accuracy of its analysis based on the occupation and role of the parties involved. Furthermore, the data collection unit can prioritize the analysis of important information based on the relationships between the parties involved. In this way, the accuracy of the chat content analysis can be improved by considering the attribute information of the parties involved. 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 use an AI model to analyze the information in order to analyze the attribute information of the parties involved and provide the user with the most suitable method for analyzing the chat content.

[0095] The data collection unit can optimize its analysis algorithm by referring to past chat history when analyzing chat content. For example, the data collection unit can extract frequently occurring keywords from past chat history and reflect them in the analysis algorithm. The data collection unit can also analyze the communication patterns of the parties involved based on past chat history and optimize the algorithm. Furthermore, the data collection unit can prioritize the analysis of important information by referring to past chat history. This allows for the optimization of the analysis algorithm and improvement of analysis accuracy by referring to past chat 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 use an AI model to analyze information in order to analyze past chat history and provide the user with the most suitable method for analyzing chat content.

[0096] The data collection unit can estimate the user's emotions and adjust the order in which the analysis results of the chat content are displayed based on the estimated emotions. For example, if the user is feeling stressed, the data collection unit will prioritize displaying important information. If the user is relaxed, the data collection unit can also display detailed information in a sequential manner. Furthermore, if the user is in a hurry, the data collection unit can prioritize displaying information that can be quickly checked. In this way, by adjusting the display order of the analysis results according to the user's emotions, information that is important to the user can be displayed preferentially. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can use an AI model to analyze information in order to estimate the user's emotions and provide the user with the optimal display order of the analysis results of the chat content.

[0097] The data collection unit can analyze chat content while considering the geographical distribution of the parties involved. For example, the data collection unit can prioritize the analysis of region-specific information based on the geographical location information of the parties involved. The data collection unit can also extract important information while considering the geographical distribution of the parties involved. Furthermore, the data collection unit can analyze the geographical relationships of the parties involved to improve the efficiency of communication. This allows for the priority analysis of region-specific information by considering the geographical distribution of the parties involved. 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 use an AI model to analyze information in order to analyze the geographical distribution of the parties involved and provide the user with the most suitable method for analyzing chat content.

[0098] The data collection unit can improve the accuracy of its analysis by referring to relevant literature when analyzing chat content. For example, the data collection unit can refer to relevant literature to reflect specialized information in its analysis. Furthermore, the data collection unit can supplement background information on the chat content based on relevant literature. In addition, the data collection unit can improve the reliability of its analysis results by referring to relevant literature. This allows for the reflection of specialized information in the analysis and improved analysis accuracy by referring to relevant literature. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use an AI model to analyze information in order to refer to relevant literature and provide the user with the most suitable method for analyzing chat content.

[0099] The adjustment unit can estimate the user's emotions and adjust the scheduling method based on the estimated emotions. For example, if the user is feeling stressed, the adjustment unit can suggest a simple scheduling method. If the user is relaxed, the adjustment unit can also suggest a more detailed scheduling method. Furthermore, if the user is in a hurry, the adjustment unit can suggest a method for quickly adjusting the schedule. In this way, by adjusting the scheduling method according to the user's emotions, the system can provide the user with the most optimal scheduling method. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can use an AI model to analyze information in order to estimate the user's emotions and provide the user with the most optimal scheduling method.

[0100] The scheduling unit can select the optimal scheduling method by referring to the past schedule history of the parties involved when scheduling. For example, the scheduling unit can propose the optimal scheduling method based on the past schedule history of the parties involved. The scheduling unit can also prioritize scheduling frequently used time slots based on the past schedule history of the parties involved. Furthermore, the scheduling unit can improve the accuracy of scheduling by referring to the past schedule history of the parties involved. This allows the optimal scheduling method to be selected by referring to the past schedule history of the parties involved. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can use an AI model to analyze information in order to analyze the past schedule history of the parties involved and provide the user with the optimal scheduling method.

[0101] The scheduling unit can improve the accuracy of scheduling by considering the attribute information of the stakeholders. For example, the scheduling unit can select an appropriate scheduling method based on the age and gender of the stakeholders. The scheduling unit can also improve the accuracy of scheduling based on the occupation and role of the stakeholders. Furthermore, the scheduling unit can prioritize scheduling important appointments based on the relationships between the stakeholders. In this way, the accuracy of scheduling can be improved by considering the attribute information of the stakeholders. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or not using AI. For example, the scheduling unit can use an AI model to analyze the attribute information of stakeholders and provide the user with the optimal scheduling method.

[0102] The scheduling unit can estimate the user's emotions and determine the priority of scheduling based on those emotions. For example, if the user is feeling stressed, the scheduling unit will prioritize important appointments. It can also prioritize detailed appointments if the user is relaxed. Furthermore, if the user is in a hurry, the scheduling unit can prioritize appointments that can be quickly adjusted. This allows for prioritizing important appointments based on the user's emotions. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For instance, the scheduling unit can use an AI model to analyze information and provide the user with the optimal scheduling priority.

[0103] The coordination unit can select the optimal coordination method when scheduling, taking into account the geographical location information of the stakeholders. For example, the coordination unit can propose the optimal coordination method based on the current location of the stakeholders. The coordination unit can also select a coordination method that minimizes travel time and distance based on the geographical location information of the stakeholders. Furthermore, the coordination unit can propose an efficient coordination method that takes into account the geographical location information of the stakeholders. This allows for the selection of a coordination method that minimizes travel time and distance by considering the geographical location information of the stakeholders. Some or all of the above processing in the coordination unit may be performed using AI, for example, or without AI. For example, the coordination unit can use an AI model to analyze the geographical location information of stakeholders and provide the user with the optimal coordination method.

[0104] The scheduling unit can analyze the social media activities of stakeholders and propose scheduling methods during the scheduling process. For example, the scheduling unit can propose scheduling methods based on events and activities of interest to stakeholders based on their social media activity. The scheduling unit can also propose scheduling methods by referring to the schedules of stakeholders' friends and followers on social media. Furthermore, the scheduling unit can analyze the social media activities of stakeholders and propose efficient scheduling methods. This allows the scheduling unit to propose scheduling methods based on events and activities of interest by analyzing the social media activities of stakeholders. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or not. For example, the scheduling unit can use an AI model to analyze information in order to analyze the social media activities of stakeholders and provide the user with the most suitable scheduling method.

[0105] The proxy service can estimate the user's emotions and adjust the method of proxy service based on the estimated emotions. For example, if the user is stressed, the proxy service can provide a simple and quick proxy service. If the user is relaxed, the proxy service can also provide a proxy service that includes detailed explanations. Furthermore, if the user is in a hurry, the proxy service can provide a proxy service that can be executed quickly. In this way, by adjusting the method of proxy service according to the user's emotions, the proxy service can provide the best possible service for the user. Some or all of the above processing in the proxy service may be performed using AI, for example, or not using AI. For example, the proxy service can use an AI model to analyze information in order to estimate the user's emotions and provide the best possible proxy service to the user.

[0106] The proxy service can select the optimal proxy service method by referring to the user's past order history when providing proxy services. For example, the proxy service can propose the optimal proxy service method based on the user's past order history. The proxy service can also prioritize providing frequently used services based on the user's past order history. Furthermore, the proxy service can improve the accuracy of the proxy service by referring to the user's past order history. This allows the optimal proxy service method to be selected by referring to the user's past order history. Some or all of the above processing in the proxy service may be performed using AI, for example, or not using AI. For example, the proxy service can use an AI model to analyze information in order to analyze the user's past order history and provide the user with the optimal proxy service method.

[0107] The proxy service unit can customize the means of the proxy service based on the user's current living situation when providing the proxy service. For example, the proxy service unit can provide a highly relevant proxy service based on the user's current living situation. The proxy service unit can also customize the means of the proxy service according to the user's living situation. Furthermore, the proxy service unit can adjust the scope of the proxy service based on the user's living situation. This allows for the provision of highly relevant proxy services by customizing the means of the proxy service based on the user's current living situation. Some or all of the above processing in the proxy service unit may be performed using AI, for example, or without AI. For example, the proxy service unit can use an AI model to analyze information in order to analyze the user's living situation and provide the user with the most suitable proxy service.

[0108] The proxy service can estimate the user's emotions and prioritize proxy services based on those emotions. For example, if the user is stressed, the proxy service will prioritize important proxy services. It can also prioritize detailed proxy services if the user is relaxed. Furthermore, if the user is in a hurry, the proxy service can prioritize services that can be performed quickly. This allows for the priority of important proxy services by prioritizing them according to the user's emotions. Some or all of the above processing in the proxy service may be performed using AI, for example, or not. For instance, the proxy service could use an AI model to analyze information and provide the user with the most suitable priority proxy services.

[0109] The proxy service can select the optimal proxy method when providing proxy services, taking into account the user's geographical location information. For example, the proxy service can propose the optimal proxy method based on the user's current location. Furthermore, the proxy service can select a proxy method that minimizes travel time and distance based on the user's geographical location information. In addition, the proxy service can propose an efficient proxy method considering the user's geographical location information. This allows for the selection of a proxy method that minimizes travel time and distance by considering the user's geographical location information. Some or all of the above processing in the proxy service may be performed using AI, for example, or without AI. For example, the proxy service can use an AI model to analyze the user's geographical location information and provide the user with the optimal proxy method.

[0110] The proxy service department can analyze the user's social media activity and propose a method for providing proxy services. For example, the proxy service department can propose a proxy method based on the user's social media activity and the services or products they are interested in. The proxy service department can also propose a proxy method by referring to the usage patterns of the user's friends and followers on social media. Furthermore, the proxy service department can analyze the user's social media activity and propose an efficient proxy method. This allows the proxy service department to propose a proxy method based on the user's social media activity and the services or products they are interested in. Some or all of the above processes in the proxy service department may be performed using AI, for example, or not. For example, the proxy service department can use an AI model to analyze information in order to analyze the user's social media activity and provide the user with the most suitable proxy method.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The data acquisition unit can collect information about the user's hobbies and interests when acquiring data on the user's family structure, health status, and economic situation. For example, it can collect information about the user's hobbies and areas of interest and incorporate them into the care plan. This allows for the proposal of a care plan based on the user's hobbies and interests, thereby improving user satisfaction. The data acquisition unit can also analyze the user's social media activity to collect information about the user's hobbies and interests. Furthermore, the data acquisition unit can provide information on care-related events and activities based on the user's hobbies and interests. This allows for the proposal of a care plan based on the user's hobbies and interests, thereby improving user satisfaction.

[0113] The proposal department can consider the user's past caregiving experience and history when proposing care plans. For example, it can analyze what care plans the user has used in the past and what problems have occurred, and then propose the most suitable care plan. This allows for the proposal of care plans based on the user's past caregiving experience, thereby improving user satisfaction. Furthermore, the proposal department can collect and analyze the user's caregiving history in order to analyze the user's past caregiving experience. In addition, the proposal department can suggest areas for improvement and points to note in the care plan based on the user's past caregiving experience. This allows for the proposal of care plans based on the user's past caregiving experience, thereby improving user satisfaction.

[0114] The data collection unit can estimate the emotions of those involved when analyzing chat content between parents and children or other stakeholders, and adjust the analysis results based on the estimated emotions. For example, if the stakeholders are stressed, it can extract only the important information and provide a simple analysis result. If the stakeholders are relaxed, it can provide a detailed analysis result. Furthermore, if the stakeholders are in a hurry, it can prioritize providing information that can be quickly reviewed. In this way, by adjusting the analysis results according to the emotions of the stakeholders, important information can be efficiently provided to them. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is not limited to, but may include, text generation AI (e.g., LLM) or multimodal generation AI.

[0115] The coordination unit can select the optimal coordination method when coordinating schedules among stakeholders, taking into account the geographical location information of those stakeholders. For example, it can propose the optimal coordination method based on the current location of each stakeholder. It can also select a coordination method that minimizes travel time and distance based on the geographical location information of the stakeholders. Furthermore, it can propose an efficient coordination method that takes into account the geographical location information of the stakeholders. This allows for the selection of a coordination method that minimizes travel time and distance by considering the geographical location information of the stakeholders. Some or all of the above processing in the coordination unit may be performed using AI, for example, or not. For example, the coordination unit can use an AI model to analyze the geographical location information of stakeholders and provide the user with the optimal coordination method.

[0116] The proxy service can estimate the user's emotions when performing tasks such as shopping necessary for caregiving, and adjust the method of the proxy service based on the estimated emotions. For example, if the user is feeling stressed, a simple and quick proxy service can be provided. If the user is relaxed, a proxy service including detailed explanations can be provided. Furthermore, if the user is in a hurry, a proxy service that can be executed quickly can be provided. In this way, by adjusting the method of the proxy service according to the user's emotions, the optimal proxy service can be provided to the user. 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.

[0117] The data acquisition unit can collect information about the user's lifestyle and daily routines when acquiring data on the user's family structure, health status, and economic situation. For example, it can collect information such as the user's meal times and content, sleep patterns, and exercise frequency, and incorporate this into the care plan. This allows for the proposal of a care plan based on the user's lifestyle and daily routines, thereby improving user satisfaction. The data acquisition unit can also acquire data from the user's smart devices and wearable devices to collect information about the user's lifestyle and daily routines. Furthermore, the data acquisition unit can provide care-related advice and suggestions based on the user's lifestyle and daily routines. This allows for the proposal of a care plan based on the user's lifestyle and daily routines, thereby improving user satisfaction.

[0118] The proposal function can estimate the user's emotions when proposing a care plan and adjust the way the care plan is presented based on those emotions. For example, if the user is stressed, a simple and easy-to-understand care plan can be proposed. If the user is relaxed, a care plan with detailed explanations can be proposed. Furthermore, if the user is in a hurry, a concise care plan that gets straight to the point can be proposed. In this way, by adjusting the way the care plan is presented according to the user's emotions, a plan that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is not limited to, but may include, text generation AI (e.g., LLM) or multimodal generation AI.

[0119] The data collection unit can optimize its analysis algorithm by referring to the past communication history of the parties involved when analyzing chat content between parents and children or other parties. For example, it can extract frequently occurring keywords from past chat history and reflect them in the analysis algorithm. It can also analyze the communication patterns of the parties involved based on past chat history and optimize the algorithm. Furthermore, it can prioritize the analysis of important information by referring to past chat history. In this way, by referring to past chat history, the analysis algorithm can be optimized and the accuracy of the analysis can be improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can use an AI model to analyze information in order to analyze past chat history and provide the user with the most suitable method for analyzing chat content.

[0120] When making schedule adjustments among relevant parties, the adjustment unit can estimate the emotions of the relevant parties and adjust the method of schedule adjustment based on the estimated emotions. For example, when a relevant party is feeling stressed, a simple schedule adjustment method can be proposed. Also, when a relevant party is relaxed, a detailed schedule adjustment method can be proposed. Furthermore, when a relevant party is in a hurry, a method that can quickly adjust the schedule can be proposed. By adjusting the schedule adjustment method according to the emotions of the relevant parties, an optimal adjustment method can be provided for the relevant parties. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generative AI.

[0121] When performing proxy services such as shopping necessary for caregiving, the proxy unit can select an optimal proxy method by referring to the user's past order history. For example, based on the user's past order history, an optimal proxy method can be proposed. Also, from the user's past order history, services that are frequently used can be provided preferentially. Furthermore, by referring to the user's past order history, the accuracy of the proxy service can be improved. By referring to the user's past order history, an optimal proxy method can be selected. Some or all of the above-described processing in the proxy unit may be performed using, for example, AI or without using AI. For example, the proxy unit can analyze the user's past order history, use an AI model to analyze the information, and provide the user with an optimal proxy method.

[0122] The process flow of Form Example 2 will be briefly described below.

[0123] Step 1: The acquisition unit acquires data on the user's family composition, health status, and economic situation. Specifically, information such as the number of family members, age, relationship, medical history, current health status, results of regular medical check-ups, income, expenditure, savings, and debts is collected. Step 2: The proposal department proposes an optimal care plan based on the data acquired by the acquisition department. Specifically, it considers the type, frequency, and cost of care services, provides guidance on the direction of care and support systems, and shares success stories and testimonials. Step 3: The collection department autonomously collects information from exchanges such as chats between parents and children or other related parties. Specifically, it analyzes the content of the chat, grasps the progress and problems of care, and efficiently manages the communication between related parties. Step 4: The adjustment department checks the progress based on the information collected by the collection department and adjusts the schedules among related parties. Specifically, it registers the schedules in the calendar and shares them among related parties to realize a smooth care process and adjusts the schedules of related parties. Step 5: The agency department acts on behalf of tasks such as shopping necessary for care. Specifically, it purchases items necessary for care through online orders and acts on behalf of the user to shop, thereby reducing the burden of care.

[0124] 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 voice indicating user input with respect to the result of the specific processing. The control unit 46A transmits voice 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 voice data.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the acquisition unit, proposal unit, collection unit, adjustment unit, and proxy unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit is implemented by the control unit 46A of the smart device 14 and acquires data on the user's family structure, health status, and economic situation. The proposal unit is implemented by the specific processing unit 290 of the data processing device 12 and proposes an optimal care plan based on the data acquired by the acquisition unit. The collection unit is implemented by the control unit 46A of the smart device 14 and autonomously collects information from interactions such as chats between parents and children or other stakeholders. The adjustment unit is implemented by the specific processing unit 290 of the data processing device 12 and checks the progress based on the information collected by the acquisition unit and coordinates schedules among stakeholders. The proxy unit is implemented by the control unit 46A of the smart device 14 and performs tasks such as shopping necessary for care on behalf of the user. 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.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the acquisition unit, proposal unit, collection unit, adjustment unit, and proxy unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit is implemented by the control unit 46A of the smart glasses 214 and acquires data on the user's family structure, health status, and economic situation. The proposal unit is implemented by the specific processing unit 290 of the data processing device 12 and proposes an optimal care plan based on the data acquired by the acquisition unit. The collection unit is implemented by the control unit 46A of the smart glasses 214 and autonomously collects information from interactions such as chats between parents and children or other stakeholders. The adjustment unit is implemented by the specific processing unit 290 of the data processing device 12 and checks the progress based on the information collected by the collection unit and coordinates schedules among stakeholders. The proxy unit is implemented by the control unit 46A of the smart glasses 214 and performs tasks such as shopping necessary for care on behalf of the user. 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.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the acquisition unit, proposal unit, collection unit, adjustment unit, and proxy unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the acquisition unit is implemented by the control unit 46A of the headset terminal 314 and acquires data on the user's family structure, health status, and economic situation. The proposal unit is implemented by the specific processing unit 290 of the data processing device 12 and proposes an optimal care plan based on the data acquired by the acquisition unit. The collection unit is implemented by the control unit 46A of the headset terminal 314 and autonomously collects information from interactions such as chats between parents and children or between other stakeholders. The adjustment unit is implemented by the specific processing unit 290 of the data processing device 12 and checks the progress based on the information collected by the collection unit and coordinates schedules between stakeholders. The proxy unit is implemented by the control unit 46A of the headset terminal 314 and performs tasks such as shopping necessary for care on behalf of the user. 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.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the acquisition unit, proposal unit, collection unit, adjustment unit, and proxy unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit is implemented by the control unit 46A of the robot 414 and acquires data on the user's family structure, health status, and economic situation. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an optimal care plan based on the data acquired by the acquisition unit. The collection unit is implemented by the control unit 46A of the robot 414 and autonomously collects information from interactions such as chats between parents and children or other stakeholders. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and checks the progress based on the information collected by the acquisition unit and coordinates schedules among stakeholders. The proxy unit is implemented by the control unit 46A of the robot 414 and performs tasks such as shopping necessary for care on behalf of the user. 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) An acquisition unit that acquires data on the user's family structure, health status, and economic situation, A proposal unit proposes an appropriate care plan based on the data acquired by the acquisition unit, A collection unit that autonomously gathers information from chat exchanges between parents and children or other related parties, Based on the information collected by the aforementioned collection unit, the coordination unit confirms the progress and coordinates schedules among the relevant parties, It includes a department that provides assistance with shopping for items necessary for caregiving. A system characterized by the following features. (Note 2) The aforementioned proposal section is, To provide guidance on the direction of caregiving or support systems. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Collect and share success stories or experiences of how other families have overcome caregiving challenges. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Analyze the chat content The system described in Appendix 1, characterized by the features described herein. (Note 5) The adjustment unit is, Add an event to your calendar. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned agency unit, Place an online order The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of data acquisition based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, Analyze the user's past caregiving history and select the optimal data acquisition method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, When acquiring data, 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 acquisition unit is, It estimates the user's emotions and determines the priority of data to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When acquiring data, the system prioritizes the acquisition of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, When acquiring data, the system analyzes the user's social media activity and retrieves relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the way care plans are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When proposing a care plan, adjust the level of detail in the proposal based on the importance of the care required. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When proposing care plans, different proposal algorithms are applied depending on the category of care. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the length of the care plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When proposing a care plan, prioritize the proposals based on when care is expected to begin. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When proposing a care plan, adjust the order of suggestions based on the relevance of the care needs. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned collection unit is The system estimates the user's emotions and adjusts the chat content analysis method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned collection unit is When analyzing chat content, we improve the accuracy of the analysis by considering the attribute information of the parties involved. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned collection unit is When analyzing chat content, the analysis algorithm is optimized by referring to past chat history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned collection unit is It estimates the user's emotions and adjusts the order in which the chat content analysis results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned collection unit is When analyzing chat content, the analysis takes into account the geographical distribution of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned collection unit is When analyzing chat content, we refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 25) The adjustment unit is, It estimates the user's emotions and adjusts the scheduling method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The adjustment unit is, When coordinating schedules, refer to the past schedule history of all parties involved to select the most suitable adjustment method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The adjustment unit is, When scheduling, consider the attribute information of the stakeholders to improve the accuracy of the scheduling. The system described in Appendix 1, characterized by the features described herein. (Note 28) The adjustment unit is, It estimates the user's emotions and determines the priority of scheduling based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The adjustment unit is, When coordinating schedules, the most suitable coordination method will be selected, taking into account the geographical location information of all parties involved. The system described in Appendix 1, characterized by the features described herein. (Note 30) The adjustment unit is, When scheduling, we analyze the social media activity of those involved and propose methods for coordination. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned agency unit, The system estimates the user's emotions and adjusts the method of providing the proxy service based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned agency unit, When providing a proxy service, the system selects the most suitable proxy method by referring to the user's past order history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned agency unit, When providing a proxy service, customize the means of the proxy service based on the user's current living situation The system according to appended note 1, characterized by this. (Appended note 34) The said proxy department Estimate the user's emotions and determine the priority of the proxy service based on the estimated user's emotions The system according to appended note 1, characterized by this. (Appended note 35) The said proxy department When providing a proxy service, select the optimal proxy method considering the user's geographical location information The system according to appended note 1, characterized by this. (Appended note 36) The said proxy department When providing a proxy service, analyze the user's social media activities and propose means of the proxy service The system according to appended note 1, characterized by this.

Explanation of reference signs

[0196] 10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset-type terminal 414 Robot

Claims

1. An acquisition unit that acquires data on the user's family structure, health status, and economic situation, A proposal unit proposes an appropriate care plan based on the data acquired by the acquisition unit, A collection unit that autonomously gathers information from chat exchanges between parents and children or other related parties, Based on the information collected by the aforementioned collection unit, the coordination unit confirms the progress and coordinates schedules among the relevant parties, It includes a department that provides assistance with shopping for items necessary for caregiving. A system characterized by the following features.

2. The aforementioned proposal section is, To provide guidance on the direction of caregiving or support systems. The system according to feature 1.

3. The aforementioned proposal section is, Collect and share success stories or experiences of how other families have overcome caregiving challenges. The system according to feature 1.

4. The aforementioned collection unit is Analyze the chat content The system according to feature 1.

5. The adjustment unit is, Add an event to your calendar. The system according to feature 1.

6. The aforementioned agency unit, Place an online order The system according to feature 1.

7. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of data acquisition based on those estimated emotions. The system according to feature 1.

8. The acquisition unit is, Analyze the user's past caregiving history and select the optimal data acquisition method. The system according to feature 1.

9. The acquisition unit is, When acquiring data, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

10. The acquisition unit is, It estimates the user's emotions and determines the priority of data to acquire based on the estimated user emotions. The system according to feature 1.

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