System

Abstract

An object of a system according to an embodiment is to provide information on a nursing care service in an easy-to-understand manner and to care for a caregiver.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a unit for performing mental care. The collection unit collects a current state of a user. The analysis unit performs analysis based on the information collected by the collection unit. The suggestion unit suggests a nursing care service based on the analysis result obtained by the analysis unit. The mental care unit performs mental care based on the service proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem that information on nursing care services is difficult to understand and the procedures for receiving appropriate services are complicated.

[0005] The system according to the embodiment aims to provide information on nursing care services in an easy-to-understand manner and to provide mental care for caregivers. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and a mental care unit. The collection unit collects information on the current state of the user. The analysis unit performs analysis based on the information collected by the collection unit. The proposal unit proposes nursing care services based on the analysis results obtained by the analysis unit. The mental care unit provides mental care based on the services proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment provides information about care services in an easy-to-understand manner, and can provide mental care for caregivers. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) A care support system according to an embodiment of the present invention collects and analyzes a user's current situation, proposes appropriate care services, and provides mental care. The care support system provides advice on available services and mental care to the user. For example, in the care support system, a user interactively inputs their current situation into an app. For example, the user inputs information such as the burden of caregiving being too great and the type of services available. This information is collected by the care support system. The care support system then analyzes the input information. The care support system proposes available care services based on the user's current situation. For example, it proposes home care, day care, and how to apply for nursing care insurance. This allows the user to learn about services that are suitable for them. Furthermore, the care support system also provides mental care for caregivers. For example, it provides advice on reducing stress and anxiety. The care support system understands the user's mental state and provides appropriate care, thereby reducing the burden on caregivers. This allows caregivers to quickly receive necessary services. This allows the care support system to provide advice on available services and mental care to caregivers. For example, the care support system simplifies complex administrative procedures, allowing caregivers to quickly receive necessary services. In addition, the use of the nursing care support system can also bring benefits to the service provider, so it is beneficial for both parties. For example, by providing services through the nursing care support system, the number of users increases and profits improve.

[0029] A care support system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and a care unit. The collection unit collects information about a user's current situation. The user's current situation includes, but is not limited to, for example, a health condition, a living situation, and a need for care. The collection unit collects information about the user using, for example, a questionnaire. The collection unit can also monitor the user's health condition using a sensor. The collection unit can also understand the user's living situation through an interview. For example, the collection unit asks the user questions and records the answers. The sensor measures the user's heart rate and activity level and collects data. The interview is conducted to understand the user's living environment and need for care in detail. The analysis unit performs analysis based on the information collected by the collection unit. The analysis can be performed using, for example, data mining, statistical analysis, machine learning, or other methods, but is not limited to these examples. For example, the analysis unit extracts patterns from the user's information using data mining technology. The analysis unit can also evaluate the user's health condition and living situation using statistical analysis. The analysis unit can also predict the user's need for care using a machine learning algorithm. For example, data mining is a technology for extracting useful information from large amounts of data and identifying the user's nursing care needs based on the user's information. Statistical analysis analyzes data distributions and correlations to evaluate the user's health condition. Machine learning is a technology that learns from past data and makes predictions based on new data, thereby accurately predicting the user's nursing care needs. The suggestion unit proposes nursing care services based on the analysis results obtained by the analysis unit. Proposals include, but are not limited to, nursing care services such as home nursing care, day care services, and rehabilitation. The suggestion unit, for example, notifies the user and presents the proposal content. The suggestion unit can also select the optimal nursing care service based on the user's needs. The suggestion unit can also customize the proposal content according to the user's living situation and health condition. For example, the suggestion unit proposes home nursing care services to the user. Day care services are a service that provides nursing care during the day and reduces the user's burden.Rehabilitation is a service aimed at restoring physical functions and improving the user's health condition. The care unit provides mental care based on the service proposed by the proposal unit. Care is provided, for example, through methods such as counseling, relaxation, and mental support, but is not limited to these examples. For example, the care unit grasps the user's mental state and provides appropriate advice. The care unit can also provide support to reduce the user's stress and anxiety. The care unit can also provide programs to maintain the user's mental health. For example, counseling involves a professional counselor listening to the user's concerns and providing advice. Relaxation provides a method for relaxation and reduces the user's stress. Mental support is a program to support the user's mental health and improve the user's mental state. As a result, the care support system according to the embodiment can collect and analyze the user's current situation, propose appropriate care services, and provide mental care. For example, the care support system can collect and analyze the user's current situation, propose appropriate care services, and provide mental care. As a result, the care support system reduces the burden on caregivers and enables them to quickly receive necessary services.

[0030] The collection unit can collect information regarding the user's care burden and desired services. Examples of the care burden include, but are not limited to, physical burden, mental burden, and time burden. The collection unit can collect information regarding the user's care burden using, for example, a questionnaire. The collection unit can also monitor the user's physical burden using a sensor. The collection unit can also understand the user's mental burden through an interview. For example, the collection unit asks the user questions and records the answers. The sensor measures the user's activity level and fatigue level and collects data. The interview is conducted to understand the user's mental burden in detail. Examples of the desired services include, but are not limited to, home care, day care, and rehabilitation. The collection unit can collect information regarding the user's desired services using, for example, a questionnaire. The collection unit can also understand the user's needs through an interview. For example, the collection unit asks the user questions and records the answers. By collecting information regarding the user's care burden and desired services, the collection unit can suggest more appropriate services. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input questionnaire response data into a generation AI, which may then analyze the data.

[0031] The analysis unit can analyze the collected information and identify nursing care services available to the user. For example, an algorithm or a rule-based system can be used for the identification, but is not limited to these examples. For example, the analysis unit can analyze the collected information using an algorithm and identify nursing care services available to the user. The analysis unit can also use a rule-based system to identify nursing care services based on the user's needs. For example, the analysis unit can use a machine learning algorithm to identify optimal nursing care services from the user's information. The analysis unit can also use a rule-based system to identify nursing care services based on the user's needs. In this way, the analysis unit can identify nursing care services available to the user by analyzing the collected information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected information into a generation AI, which can then identify nursing care services.

[0032] The suggestion unit can suggest the identified nursing care service to the user. The suggestion includes, for example, a method of notifying the user and criteria for selecting the proposed content, but is not limited to these examples. For example, the suggestion unit notifies the user and suggests the identified nursing care service. The suggestion unit can also select the optimal nursing care service based on the user's needs. For example, the suggestion unit can suggest a home nursing care service to the user. The suggestion unit can also suggest day care services or rehabilitation services. As a result, the suggestion unit can suggest the identified nursing care service to the user, allowing the user to receive an appropriate service. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the identified nursing care service to a generation AI, which can then generate the proposed content.

[0033] The care unit can grasp the user's mental state and provide advice to reduce stress and anxiety. Examples of mental state assessment include, but are not limited to, psychological tests, measurements using sensors, and self-reporting. For example, the care unit can evaluate the user's mental state using a psychological test. The care unit can also use sensors to measure the user's heart rate and electrodermal activity to grasp the user's mental state. Furthermore, the care unit can evaluate the mental state based on the user's self-reporting. For example, the care unit administers a psychological test to the user and analyzes the results. The sensors measure the user's heart rate and electrodermal activity and collect data. Self-reporting involves the user reporting their own mental state, and an evaluation is made based on that information. Examples of stress and anxiety assessment include, but are not limited to, stress checklists and anxiety scales. For example, the care unit assesses the user's stress level using a stress checklist. Furthermore, the care unit can assess the user's anxiety level using an anxiety scale. For example, the care unit administers a stress checklist to the user and analyzes the results. The anxiety scale quantifies the user's anxiety level and makes an evaluation. This allows the care unit to understand the user's mental state and provide advice to reduce stress and anxiety, thereby reducing the burden on the caregiver. Some or all of the above-mentioned processing in the care unit may be performed using AI, for example, or may be performed without using AI. For example, the care unit can input the results of a psychological test into the generation AI, which can then evaluate the user's mental state.

[0034] The suggestion unit can support the user in applying for long-term care insurance and preparing the necessary documents. Examples of the method of applying for long-term care insurance include, but are not limited to, how to fill out an application form and where to submit it. For example, the suggestion unit explains to the user how to apply for long-term care insurance. The suggestion unit can also support the user in filling out the application form correctly. For example, the suggestion unit presents the user with an example of how to fill out an application form. The suggestion unit can also guide the user to where to submit the application form. Examples of required documents include, but are not limited to, a medical certificate, an identification card, and a proof of income. For example, the suggestion unit provides the user with a list of required documents. The suggestion unit can also support the user in preparing the required documents. For example, the suggestion unit guides the user on how to obtain a medical certificate. The suggestion unit can also explain how to prepare an identification card and a proof of income. By supporting the user in applying for long-term care insurance and preparing the necessary documents, the suggestion unit can quickly receive services. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information about how to apply for nursing care insurance into the generation AI, and the generation AI can explain how to apply.

[0035] The collection unit can analyze the user's past care history and select an information collection method. Examples of past care history include, but are not limited to, a history of past care service use and medical records. For example, the collection unit analyzes the user's past care service use history and prioritizes collection of related information. The collection unit can also collect detailed information about specific services from the user's past care history. Furthermore, the collection unit can analyze the user's past care history and select the most effective information collection method. For example, the collection unit prioritizes collection of related information based on the history of care services used by the user in the past. Medical records indicate the user's health condition and treatment history and serve as a reference for information collection. Examples of information collection methods include, but are not limited to, questionnaires, sensors, interviews, and the like. For example, the collection unit can collect user information using questionnaires. The collection unit can also monitor the user's health condition using sensors. Furthermore, the collection unit can understand the user's living situation through interviews. For example, the collection unit asks the user questions and records the responses. The sensors measure the user's heart rate and activity level and collect data. The interview is conducted to obtain a detailed understanding of the user's living environment and care needs. This allows the collection unit to select the optimal information collection method by analyzing the user's past care history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past care history data into a generation AI, which then selects the information collection method.

[0036] When collecting information, the collection unit can filter the information based on the user's current living situation and areas of interest. Examples of living situations include, but are not limited to, activities of daily living, living environment, and economic situation. For example, the collection unit prioritizes collecting information about related nursing care services based on the user's current living situation. The collection unit can also collect optimal information taking the user's living situation into consideration. Furthermore, the collection unit can adjust the method of collecting information according to the user's living situation. For example, the collection unit monitors the user's activities of daily living and collects related information. The living environment indicates the user's housing situation and serves as a reference for information collection. The economic situation indicates the user's income and expenses and serves as a basis for information collection. Examples of areas of interest include, but are not limited to, hobbies, interesting activities, and learning content. For example, the collection unit filters information about specific nursing care services based on the user's areas of interest. The collection unit can also collect optimal information taking the user's areas of interest into consideration. Furthermore, the collection unit can adjust the method of collecting information according to the user's areas of interest. For example, the collection unit prioritizes collecting information related to the user's hobbies. Interesting activities indicate areas of interest to the user and serve as a reference for information collection. Learning content indicates what the user wants to learn and serves as a criterion for information collection. This allows the collection unit to collect more relevant information by filtering information based on the user's current living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's living situation and areas of interest into a generation AI, which then filters the information.

[0037] When collecting information, the collection unit can select a collection means depending on the user's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, when the user uses voice input, the collection unit can collect information using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also collect information using text analysis technology. Furthermore, when the user uses image input, the collection unit can also collect information using image recognition technology. For example, the collection unit records the user's voice with a microphone and converts it into text data using voice recognition technology. Text analysis technology analyzes the text entered by the user and collects information. Image recognition technology analyzes the image provided by the user and collects information. Examples of collection means include, but are not limited to, sensors, questionnaires, interviews, and the like. For example, the collection unit monitors the user's health condition using a sensor. Furthermore, the collection unit can collect user information using a questionnaire. Furthermore, the collection unit can understand the user's living situation through interviews. For example, the collection unit measures the user's heart rate and activity level using a sensor and collects data. The questionnaire asks questions to the user and records the responses. The interview is conducted to gain a detailed understanding of the user's living environment and care needs. This allows the collection unit to select the optimal collection method depending on the user's input method, thereby improving the efficiency of information collection. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's voice data into a generation AI, which then analyzes the voice data.

[0038] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and address information. For example, the collection unit prioritizes collecting information about nearby nursing care services based on the user's current location. The collection unit can also collect information about local nursing care services by taking into account the user's geographical location information. Furthermore, the collection unit can collect information about optimal nursing care services based on the user's location information. For example, the collection unit prioritizes collecting information about nearby nursing care services based on the user's current location. Address information indicates the location of the user's residence and serves as a reference for information collection. Examples of highly relevant information include, but are not limited to, the user's areas of interest and living situation. For example, the collection unit filters information about specific nursing care services based on the user's areas of interest. The collection unit can also collect optimal information by taking into account the user's living situation. Furthermore, the collection unit can adjust the method of information collection according to the user's areas of interest and living situation. For example, the collection unit prioritizes collecting information related to the user's hobbies. The living situation indicates the user's housing situation and economic situation, and serves as a criterion for collecting information. This allows the collection unit to prioritize collection of highly relevant information by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into the generation AI, and the generation AI can collect highly relevant information.

[0039] When collecting information, the collection unit may analyze the user's social media activity and collect related information. Examples of social media activity include, but are not limited to, the content of posts, the number of likes, and the number of followers. For example, the collection unit may analyze the content of the user's social media posts and collect information on related nursing care services. The collection unit may also collect related information by referring to the activities of the user's friends on social media. Furthermore, the collection unit may collect information on related nursing care services based on the user's check-in information on social media. For example, the collection unit may analyze the content of the user's social media posts and collect information on related nursing care services. The number of likes indicates the popularity of posts that interest the user and serves as a reference for information collection. The number of followers indicates the user's influence and serves as a criterion for information collection. Examples of related information include, but are not limited to, the user's areas of interest and living situation. For example, the collection unit may filter information on specific nursing care services based on the user's areas of interest. The collection unit may also collect optimal information taking the user's living situation into consideration. Furthermore, the collection unit may adjust the method of information collection according to the user's areas of interest and living situation. For example, the collection unit prioritizes collecting information related to the user's hobbies. Living conditions indicate the user's housing situation and economic situation and serve as the basis for information collection. This allows the collection unit to collect related information by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI, which then collects related information.

[0040] When collecting information, the collection unit can customize the collection method by reflecting the user's past feedback. Past feedback includes, but is not limited to, questionnaire results, user comments, etc. For example, the collection unit improves the information collection method based on the user's past feedback. The collection unit can also adjust the type of information to be collected by reflecting the user's past feedback. Furthermore, the collection unit can select the optimal information collection method by referring to the user's past feedback. For example, the collection unit improves the information collection method based on the user's past questionnaire results. The user's comments serve as reference for information collection and are useful for customizing the collection method. Collection methods include, but are not limited to, questionnaires, sensors, interviews, etc. For example, the collection unit collects user information using questionnaires. The collection unit can also monitor the user's health condition using sensors. The collection unit can also understand the user's living situation through interviews. For example, the collection unit measures the user's heart rate and activity level using sensors and collects data. The questionnaire asks the user questions and records the answers. The interview is conducted to obtain a detailed understanding of the user's living environment and care needs. This allows the collection unit to customize the collection method by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI, and the generation AI can customize the collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. The importance of the information includes, but is not limited to, the user's level of interest, urgency, and relevance. For example, the analysis unit performs a detailed analysis of highly important information. The analysis unit can also perform a concise analysis of less important information. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the information. For example, the analysis unit performs a detailed analysis of information that is highly interesting to the user. The analysis unit quickly analyzes information that is highly urgent. The analysis unit performs a detailed analysis of highly relevant information. The level of detail of the analysis includes, but is not limited to, the granularity of the information and the depth of the analysis. For example, the analysis unit can perform a more detailed analysis by reducing the granularity of the information. The analysis unit can also adjust the depth of the analysis to perform a more detailed analysis of the information. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the information, thereby enabling efficient analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input information importance data into the generation AI, which can then adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. Information categories include, but are not limited to, health information, lifestyle information, and nursing care information, for example. The analysis unit can apply a specific analysis algorithm to information related to nursing care services, for example. The analysis unit can also apply a different analysis algorithm to information related to the user's mental care. The analysis unit can also select an optimal analysis algorithm depending on the category of information. For example, the analysis unit can apply a specific analysis algorithm to health information, a different analysis algorithm to lifestyle information, and select an optimal analysis algorithm for nursing care information. Analysis algorithms include, but are not limited to, machine learning algorithms and statistical analysis algorithms, for example. The analysis unit can analyze information using, for example, a machine learning algorithm. The analysis unit can also analyze information using a statistical analysis algorithm. As a result, the analysis unit can apply an optimal analysis algorithm depending on the category of information, thereby improving the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information category data into the generation AI, which can then select the optimal analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, past data and analysis reports. For example, the analysis unit can improve the accuracy of the current analysis based on the user's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. Furthermore, the analysis unit can dynamically improve the accuracy of the analysis by using the user's past analysis results. For example, the analysis unit improves the accuracy of the current analysis based on past data. Analysis reports indicate past analysis results and are useful for improving the accuracy of the analysis. Analysis accuracy includes, but is not limited to, data quality, algorithm improvements, and the like. For example, the analysis unit improves the accuracy of the analysis by improving the data quality. The analysis unit can also improve the accuracy of the analysis by improving the algorithm. As a result, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI, allowing the generation AI to improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the analysis priority based on the time of submission of the information. The time of submission of the information includes, but is not limited to, the latest information, past information, etc. The analysis unit, for example, prioritizes the analysis of the latest information. The analysis unit can also lower the analysis priority of information that was submitted earlier. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the time of submission of the information. For example, the analysis unit prioritizes the analysis of the latest information. The analysis unit lowers the analysis priority of past information. The analysis unit dynamically adjusts the analysis priority based on the time of submission of the information. The analysis priority includes, but is not limited to, the importance and urgency of the information. The analysis unit can determine the analysis priority based on, for example, the importance of the information. The analysis unit can also determine the analysis priority based on the urgency of the information. As a result, the analysis unit can prioritize the analysis of the latest information by determining the analysis priority based on the time of submission of the information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of information submission into the generation AI, and the generation AI can determine the priority of analysis.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. Examples of the relevance of the information include, but are not limited to, the user's areas of interest and living situation. For example, the analysis unit prioritizes the analysis of highly relevant information. Furthermore, the analysis unit can postpone the analysis order of less relevant information. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the information. For example, the analysis unit prioritizes the analysis of highly relevant information based on the user's areas of interest. For example, the analysis unit prioritizes the analysis of highly relevant information based on the living situation. For example, the analysis unit postpones the analysis order of less relevant information. Examples of the order of analysis include, but are not limited to, prioritizing highly relevant information. For example, the analysis unit prioritizes the analysis of highly relevant information. Furthermore, the analysis unit can postpone the analysis order of less relevant information. In this way, the analysis unit can prioritize the analysis of highly relevant information by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input information association data to the generation AI, and the generation AI may adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation, educational background, and experience. For example, if the user has specialized knowledge, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have specialized knowledge, the analysis unit can provide analysis results in easy-to-understand language. Furthermore, the analysis unit can dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit can adjust the use of technical terms based on the user's occupation, educational background, or experience. Examples of the use of technical terms include, but are not limited to, definitions and explanations of technical terms. For example, the analysis unit can provide definitions of technical terms to provide analysis results that are easy for the user to understand. Furthermore, the analysis unit can provide explanations of technical terms to provide analysis results that are easy for the user to understand. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's expertise level data into the generation AI, which may then adjust the use of technical terms.

[0047] The suggestion unit can adjust the level of detail of the proposal based on the importance of the service when making a proposal. The importance of the service includes, but is not limited to, user needs, urgency, and the like. For example, the suggestion unit makes a detailed proposal for a service with high importance. The suggestion unit can also make a concise proposal for a service with low importance. Furthermore, the suggestion unit can dynamically adjust the level of detail of the proposal based on the importance of the service. For example, the suggestion unit makes a detailed proposal for a service with high user needs. The suggestion unit can quickly make a proposal for a service with high urgency. The level of detail of the proposal can be dynamically adjusted based on the importance of the service. The level of detail of the proposal includes, but is not limited to, the granularity of information and the depth of the proposal. For example, the suggestion unit can make a detailed proposal by reducing the granularity of information. The suggestion unit can also adjust the depth of the proposal to make a detailed proposal. This allows the suggestion unit to adjust the level of detail of the proposal based on the importance of the service, thereby enabling efficient proposals. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input service importance data to the generation AI, which can then adjust the level of detail of the suggestions.

[0048] When making a proposal, the suggestion unit can apply different proposal algorithms depending on the service category. Service categories include, but are not limited to, home care, day care, rehabilitation, etc. For example, the suggestion unit can apply a specific proposal algorithm to proposals related to nursing care services. The suggestion unit can also apply a different proposal algorithm to proposals related to mental care for the user. The suggestion unit can also select an optimal proposal algorithm depending on the service category. For example, the suggestion unit can apply a specific proposal algorithm to home care, a different proposal algorithm to day care, and select an optimal proposal algorithm for rehabilitation. Proposal algorithms include, but are not limited to, machine learning algorithms, rule-based systems, etc., for example. The suggestion unit can propose services using, for example, a machine learning algorithm. The suggestion unit can also propose services using a rule-based system. As a result, the suggestion unit can apply an optimal proposal algorithm depending on the service category, thereby improving the accuracy of the proposal. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input service category data into the generation AI, which can then select the optimal proposal algorithm.

[0049] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. Past proposal results include, but are not limited to, past data, proposal reports, etc. The suggestion unit can improve the accuracy of the current proposal based on, for example, the user's past proposal results. The suggestion unit can also adjust the proposal algorithm by referring to the user's past proposal results. The suggestion unit can also dynamically improve the accuracy of the proposal by using the user's past proposal results. For example, the suggestion unit can improve the accuracy of the current proposal based on past data. The proposal report indicates the past proposal results and is useful for improving the accuracy of the proposal. The accuracy of the proposal can include, but is not limited to, for example, data quality, algorithm improvement, etc. The suggestion unit can improve the accuracy of the proposal by improving the data quality, for example. The suggestion unit can also improve the accuracy of the proposal by improving the algorithm. As a result, the suggestion unit improves the accuracy of the proposal by referring to the user's past proposal results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input past proposal result data into the generation AI, allowing the generation AI to improve the accuracy of proposals.

[0050] The suggestion unit may determine the priority of the proposal based on the time of submission of the service when the proposal is made. The time of submission of the service may include, but is not limited to, the latest service, the older service, etc. The suggestion unit may, for example, prioritize the latest service. The suggestion unit may also lower the priority of the proposal for a service that was submitted earlier. The suggestion unit may also dynamically adjust the priority of the proposal based on the time of submission of the service. For example, the suggestion unit may prioritize the latest service. The suggestion unit may lower the priority of the proposal for an older service. The suggestion unit may dynamically adjust the priority of the proposal based on the time of submission of the service. The priority of the proposal may include, but is not limited to, the importance and urgency of the service. The suggestion unit may, for example, determine the priority of the proposal based on the importance of the service. The suggestion unit may also determine the priority of the proposal based on the urgency of the service. As a result, the suggestion unit may prioritize the proposal based on the time of submission of the service, thereby preferentially proposing the latest service. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input service submission timing data into the generation AI, and the generation AI can determine the priority of proposals.

[0051] The suggestion unit, when suggesting services, can adjust the order of suggestions based on the relevance of the services. Examples of the relevance of the services include, but are not limited to, the user's needs, living situations, etc. The suggestion unit, for example, prioritizes suggesting highly relevant services. The suggestion unit can also postpone the order of suggestions for less relevant services. Furthermore, the suggestion unit can dynamically adjust the order of suggestions according to the relevance of the services. For example, the suggestion unit prioritizes suggesting highly relevant services based on the user's needs. The suggestion unit prioritizes suggesting highly relevant services based on living situations. The suggestion unit postpones the order of suggestions for less relevant services. Examples of the order of suggestions include, but are not limited to, prioritizing highly relevant services. For example, the suggestion unit prioritizes suggesting highly relevant services. The suggestion unit can also postpone the order of suggestions for less relevant services. In this way, the suggestion unit can prioritize highly relevant services by adjusting the order of suggestions based on the relevance of the services. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input service relevance data to the generation AI, and the generation AI can adjust the order of suggestions.

[0052] When making a proposal, the suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation, educational background, and experience. For example, if the user has specialized knowledge, the suggestion unit can provide a proposal that uses a lot of technical terms. Furthermore, if the user does not have specialized knowledge, the suggestion unit can also provide a proposal in easy-to-understand language. Furthermore, the suggestion unit can dynamically adjust the use of technical terms in the proposal according to the user's level of expertise. For example, the suggestion unit can adjust the use of technical terms based on the user's occupation, educational background, or experience. Examples of the use of technical terms include, but are not limited to, definitions of technical terms, the presence or absence of explanations, and the like. For example, the suggestion unit can provide definitions of technical terms to provide a proposal that is easy for the user to understand. Furthermore, the suggestion unit can provide explanations of technical terms to provide a proposal that is easy for the user to understand. In this way, the suggestion unit can provide a proposal that is easy for the user to understand by adjusting the use of technical terms in the proposal according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input the user's expertise level data to the generation AI, which may then adjust the use of technical terms.

[0053] During care, the care unit can analyze the user's past mental state and select an optimal care method. Examples of past mental states include, but are not limited to, past psychological test results and medical records. The care unit can select an optimal care method based on, for example, the user's past mental state. The care unit can also analyze the user's past mental state and select a care method that is effective for stress reduction. Furthermore, the care unit can also select an optimal care method by referring to the user's past mental state. For example, the care unit can select an optimal care method based on past psychological test results. Medical records indicate the user's past mental state and are useful for selecting a care method. Examples of optimal care methods include, but are not limited to, the user's needs and past care results. For example, the care unit can select an optimal care method based on the user's needs. The care unit can also select an optimal care method based on past care results. In this way, the care unit can select an optimal care method by analyzing the user's past mental state. Some or all of the above-described processing in the care unit may be performed, for example, using AI or without AI. For example, the care department can input past mental state data into the generation AI, which can then select the most appropriate care method.

[0054] During care, the care unit can customize care measures based on the user's current living situation. Examples of current living situations include, but are not limited to, activities of daily living, living environment, and economic situation. For example, the care unit provides optimal care measures based on the user's current living situation. The care unit can also customize the care measures taking the user's living situation into consideration. Furthermore, the care unit can dynamically adjust the care measures according to the user's current living situation. For example, the care unit provides optimal care measures based on the user's activities of daily living. The care unit customizes care measures based on the living environment. The care unit dynamically adjusts care measures based on economic situation. Examples of care measures include, but are not limited to, user preferences and living situation. For example, the care unit provides optimal care measures based on the user's preferences. The care unit can also customize the care measures based on the user's living situation. This allows the care unit to provide more appropriate care by customizing the care measures based on the user's current living situation. Some or all of the above-described processes in the care department may be performed using, for example, AI, or may be performed without using AI. For example, the care department can input the user's living situation data into the generation AI and use the generation AI to customize the care method.

[0055] The care unit can improve the care method by reflecting user feedback during care. Examples of feedback include, but are not limited to, survey results, user comments, and the like. For example, the care unit can improve the care method based on the user feedback. The care unit can also adjust the care method by reflecting the user feedback. Furthermore, the care unit can select the optimal care method by referring to the user feedback. For example, the care unit can improve the care method based on survey results. User comments are useful for improving the care method and adjusting the care method. Examples of care methods include, but are not limited to, user feedback, care results, and the like. For example, the care unit can improve the care method based on the user feedback. The care unit can also improve the care method based on the care results. Thus, the care unit can improve the care method by reflecting the user feedback. Some or all of the above-described processing in the care unit may be performed using, for example, AI, or may be performed without using AI. For example, the care department can input user feedback data into the generation AI, which can then improve the care method.

[0056] During care, the care unit can select an optimal care method by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and address information. For example, the care unit provides information about nearby care services based on the user's current location. The care unit can also suggest area-specific care methods by taking into account the user's geographical location information. Furthermore, the care unit can select an optimal care method based on the user's location information. For example, the care unit provides information about nearby care services based on the user's current location. Address information indicates the location of the user's residence and is useful for selecting a care method. Examples of optimal care methods include, but are not limited to, the user's needs and geographical location information. For example, the care unit selects an optimal care method based on the user's needs. The care unit can also select an optimal care method based on the geographical location information. This allows the care unit to select an optimal care method by taking into account the user's geographical location information. Some or all of the above-described processing in the care unit may be performed using, for example, AI, or without AI. For example, the care department can input the user's geographical location information data into the generation AI, which can then select the optimal care method.

[0057] The care unit can analyze the user's social media activity during care and suggest care methods. Social media activity includes, but is not limited to, examples of posts, the number of likes, the number of followers, etc. For example, the care unit can analyze the user's social media posts to suggest relevant care methods. The care unit can also suggest relevant care methods based on the user's social media check-in information. For example, the care unit can analyze the user's social media posts to suggest relevant care methods. The number of likes indicates the popularity of posts that interest the user and is useful for suggesting care methods. The number of followers indicates the user's influence and serves as a criterion for suggesting care methods. Examples of care methods include, but are not limited to, the user's social media activity and lifestyle. For example, the care unit can suggest optimal care methods based on the user's social media activity. The care unit can also suggest care methods based on the user's lifestyle. This allows the care unit to suggest relevant care methods by analyzing the user's social media activity. Some or all of the above-described processes in the care department may be performed using AI, or may be performed without AI. For example, the care department may input the user's social media data into a generation AI, which may then suggest care measures.

[0058] During care, the care unit can customize the care method by reflecting the user's past feedback. Past feedback includes, but is not limited to, survey results, user comments, and the like. The care unit customizes the care method based on, for example, the user's past feedback. The care unit can also adjust the care method by reflecting the user's past feedback. Furthermore, the care unit can select the optimal care method by referring to the user's past feedback. For example, the care unit customizes the care method based on survey results. The user's comments are useful for customizing the care method and for adjusting the care method. The care method includes, but is not limited to, user feedback, care results, and the like. For example, the care unit customizes the care method based on the user's feedback. The care unit can also customize the care method based on the care results. In this way, the care unit can customize the care method by reflecting the user's past feedback. Some or all of the above-described processing in the care unit may be performed, for example, using AI or without using AI. For example, the care department can input the user's past feedback data into the generation AI, which can then customize the care method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The collection unit can collect information about the user's lifestyle habits and dietary content and use it for analysis by the analysis unit. For example, the collection unit can record the user's daily dietary content and evaluate nutritional balance. The collection unit can also monitor the user's exercise habits and collect information useful for maintaining a healthy state. Furthermore, the collection unit can track the user's sleep patterns and provide data for evaluating sleep quality. In this way, the collection unit can provide data useful for analysis by the analysis unit by collecting information about the user's lifestyle habits and dietary content.

[0061] The suggestion unit can customize the suggestion of nursing care services based on the user's hobbies and interests. For example, if the user is interested in music, the suggestion unit can suggest music therapy. If the user is interested in gardening, the suggestion unit can also suggest horticultural therapy. Furthermore, if the user is interested in reading, the suggestion unit can also suggest book clubs or library visits. This allows the suggestion unit to make more personalized suggestions for nursing care services based on the user's hobbies and interests.

[0062] The collection unit can collect information about nursing care services specific to a region, taking into account the user's geographical location information. For example, the collection unit preferentially collects information about nursing care services in the vicinity based on the user's current location. The collection unit can also collect information about nursing care services specific to a region, taking into account the user's geographical location information. Furthermore, the collection unit can also collect information about optimal nursing care services based on the user's location information. In this way, the collection unit can preferentially collect highly relevant information by taking into account the user's geographical location information.

[0063] The analysis unit can improve the accuracy of the current analysis by referring to the user's past analysis results. For example, the analysis unit improves the accuracy of the current analysis based on past data. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. Furthermore, the analysis unit can dynamically improve the accuracy of the analysis by using the user's past analysis results. In this way, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results.

[0064] The care unit can analyze the user's past mental state and select the optimal care method. For example, the care unit selects the optimal care method based on past psychological test results. The care unit can also analyze the user's past mental state and select a care method that is effective in reducing stress. Furthermore, the care unit can also select the optimal care method by referring to the user's past mental state. In this way, the care unit can select the optimal care method by analyzing the user's past mental state.

[0065] The collection unit can analyze the user's social media activity and collect related information. For example, the collection unit can analyze the user's social media posts and collect information on related nursing care services. The collection unit can also collect related information by referring to the activities of the user's friends on social media. Furthermore, the collection unit can collect information on related nursing care services based on the user's check-in information on social media. In this way, the collection unit can collect related information by analyzing the user's social media activity.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The collection unit collects the user's current status. The user's current status includes, for example, health status, living situation, and need for care. The collection unit collects user information using questionnaires, sensors, and interviews. For example, the collection unit asks the user questions through a questionnaire and records the responses. The sensors measure the user's heart rate and activity level and collect data. The interviews are conducted to gain a detailed understanding of the user's living environment and need for care. Step 2: The analysis unit performs analysis based on the information collected by the collection unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning. For example, data mining techniques are used to extract patterns from the user's information, statistical analysis is used to evaluate the user's health condition and living situation, and machine learning algorithms are used to predict the user's need for care. Step 3: The suggestion unit proposes nursing care services based on the analysis results obtained by the analysis unit. The proposals include nursing care services such as home care, day care, and rehabilitation. The suggestion unit notifies the user and presents the proposals. It also selects the optimal nursing care service based on the user's needs and customizes the proposals according to the user's living situation and health condition. Step 4: The care department provides mental care based on the services proposed by the proposal department. Care is provided through methods such as counseling, relaxation, and mental support. The care department understands the user's mental state and provides appropriate advice. It also provides support to reduce the user's stress and anxiety and offers programs to maintain mental health.

[0068] (Example 2) A care support system according to an embodiment of the present invention collects and analyzes a user's current situation, proposes appropriate care services, and provides mental care. The care support system provides advice on available services and mental care to the user. For example, in the care support system, a user interactively inputs their current situation into an app. For example, the user inputs information such as the burden of caregiving being too great and the type of services available. This information is collected by the care support system. The care support system then analyzes the input information. The care support system proposes available care services based on the user's current situation. For example, it proposes home care, day care, and how to apply for nursing care insurance. This allows the user to learn about services that are suitable for them. Furthermore, the care support system also provides mental care for caregivers. For example, it provides advice on reducing stress and anxiety. The care support system understands the user's mental state and provides appropriate care, thereby reducing the burden on caregivers. This allows caregivers to quickly receive necessary services. This allows the care support system to provide advice on available services and mental care to caregivers. For example, the care support system simplifies complex administrative procedures, allowing caregivers to quickly receive necessary services. In addition, the use of the nursing care support system can also bring benefits to the service provider, so it is beneficial for both parties. For example, by providing services through the nursing care support system, the number of users increases and profits improve.

[0069] A care support system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and a care unit. The collection unit collects information about a user's current situation. The user's current situation includes, but is not limited to, for example, a health condition, a living situation, and a need for care. The collection unit collects information about the user using, for example, a questionnaire. The collection unit can also monitor the user's health condition using a sensor. The collection unit can also understand the user's living situation through an interview. For example, the collection unit asks the user questions and records the answers. The sensor measures the user's heart rate and activity level and collects data. The interview is conducted to understand the user's living environment and need for care in detail. The analysis unit performs analysis based on the information collected by the collection unit. The analysis can be performed using, for example, data mining, statistical analysis, machine learning, or other methods, but is not limited to these examples. For example, the analysis unit extracts patterns from the user's information using data mining technology. The analysis unit can also evaluate the user's health condition and living situation using statistical analysis. The analysis unit can also predict the user's need for care using a machine learning algorithm. For example, data mining is a technology for extracting useful information from large amounts of data and identifying the user's nursing care needs based on the user's information. Statistical analysis analyzes data distributions and correlations to evaluate the user's health condition. Machine learning is a technology that learns from past data and makes predictions based on new data, thereby accurately predicting the user's nursing care needs. The suggestion unit proposes nursing care services based on the analysis results obtained by the analysis unit. Proposals include, but are not limited to, nursing care services such as home nursing care, day care services, and rehabilitation. The suggestion unit, for example, notifies the user and presents the proposal content. The suggestion unit can also select the optimal nursing care service based on the user's needs. The suggestion unit can also customize the proposal content according to the user's living situation and health condition. For example, the suggestion unit proposes home nursing care services to the user. Day care services are a service that provides nursing care during the day and reduces the user's burden.Rehabilitation is a service aimed at restoring physical functions and improving the user's health condition. The care unit provides mental care based on the service proposed by the proposal unit. Care is provided, for example, through methods such as counseling, relaxation, and mental support, but is not limited to these examples. For example, the care unit grasps the user's mental state and provides appropriate advice. The care unit can also provide support to reduce the user's stress and anxiety. The care unit can also provide programs to maintain the user's mental health. For example, counseling involves a professional counselor listening to the user's concerns and providing advice. Relaxation provides a method for relaxation and reduces the user's stress. Mental support is a program to support the user's mental health and improve the user's mental state. As a result, the care support system according to the embodiment can collect and analyze the user's current situation, propose appropriate care services, and provide mental care. For example, the care support system can collect and analyze the user's current situation, propose appropriate care services, and provide mental care. As a result, the care support system reduces the burden on caregivers and enables them to quickly receive necessary services.

[0070] The collection unit can collect information regarding the user's care burden and desired services. Examples of the care burden include, but are not limited to, physical burden, mental burden, and time burden. The collection unit can collect information regarding the user's care burden using, for example, a questionnaire. The collection unit can also monitor the user's physical burden using a sensor. The collection unit can also understand the user's mental burden through an interview. For example, the collection unit asks the user questions and records the answers. The sensor measures the user's activity level and fatigue level and collects data. The interview is conducted to understand the user's mental burden in detail. Examples of the desired services include, but are not limited to, home care, day care, and rehabilitation. The collection unit can collect information regarding the user's desired services using, for example, a questionnaire. The collection unit can also understand the user's needs through an interview. For example, the collection unit asks the user questions and records the answers. By collecting information regarding the user's care burden and desired services, the collection unit can suggest more appropriate services. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input questionnaire response data into a generation AI, which may then analyze the data.

[0071] The analysis unit can analyze the collected information and identify nursing care services available to the user. For example, an algorithm or a rule-based system can be used for the identification, but is not limited to these examples. For example, the analysis unit can analyze the collected information using an algorithm and identify nursing care services available to the user. The analysis unit can also use a rule-based system to identify nursing care services based on the user's needs. For example, the analysis unit can use a machine learning algorithm to identify optimal nursing care services from the user's information. The analysis unit can also use a rule-based system to identify nursing care services based on the user's needs. In this way, the analysis unit can identify nursing care services available to the user by analyzing the collected information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected information into a generation AI, which can then identify nursing care services.

[0072] The suggestion unit can suggest the identified nursing care service to the user. The suggestion includes, for example, a method of notifying the user and criteria for selecting the proposed content, but is not limited to these examples. For example, the suggestion unit notifies the user and suggests the identified nursing care service. The suggestion unit can also select the optimal nursing care service based on the user's needs. For example, the suggestion unit can suggest a home nursing care service to the user. The suggestion unit can also suggest day care services or rehabilitation services. As a result, the suggestion unit can suggest the identified nursing care service to the user, allowing the user to receive an appropriate service. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the identified nursing care service to a generation AI, which can then generate the proposed content.

[0073] The care unit can grasp the user's mental state and provide advice to reduce stress and anxiety. Examples of mental state assessment include, but are not limited to, psychological tests, measurements using sensors, and self-reporting. For example, the care unit can evaluate the user's mental state using a psychological test. The care unit can also use sensors to measure the user's heart rate and electrodermal activity to grasp the user's mental state. Furthermore, the care unit can evaluate the mental state based on the user's self-reporting. For example, the care unit administers a psychological test to the user and analyzes the results. The sensors measure the user's heart rate and electrodermal activity and collect data. Self-reporting involves the user reporting their own mental state, and an evaluation is made based on that information. Examples of stress and anxiety assessment include, but are not limited to, stress checklists and anxiety scales. For example, the care unit assesses the user's stress level using a stress checklist. Furthermore, the care unit can assess the user's anxiety level using an anxiety scale. For example, the care unit administers a stress checklist to the user and analyzes the results. The anxiety scale quantifies the user's anxiety level and makes an evaluation. This allows the care unit to understand the user's mental state and provide advice to reduce stress and anxiety, thereby reducing the burden on the caregiver. Some or all of the above-mentioned processing in the care unit may be performed using AI, for example, or may be performed without using AI. For example, the care unit can input the results of a psychological test into the generation AI, which can then evaluate the user's mental state.

[0074] The suggestion unit can support the user in applying for long-term care insurance and preparing the necessary documents. Examples of the method of applying for long-term care insurance include, but are not limited to, how to fill out an application form and where to submit it. For example, the suggestion unit explains to the user how to apply for long-term care insurance. The suggestion unit can also support the user in filling out the application form correctly. For example, the suggestion unit presents the user with an example of how to fill out an application form. The suggestion unit can also guide the user to where to submit the application form. Examples of required documents include, but are not limited to, a medical certificate, an identification card, and a proof of income. For example, the suggestion unit provides the user with a list of required documents. The suggestion unit can also support the user in preparing the required documents. For example, the suggestion unit guides the user on how to obtain a medical certificate. The suggestion unit can also explain how to prepare an identification card and a proof of income. By supporting the user in applying for long-term care insurance and preparing the necessary documents, the suggestion unit can quickly receive services. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information about how to apply for nursing care insurance into the generation AI, and the generation AI can explain how to apply.

[0075] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user's emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and self-reporting. For example, the collection unit can estimate the user's emotions using facial expression recognition technology. The collection unit can also estimate the user's emotions using voice analysis technology. Furthermore, the collection unit can estimate the emotions based on the user's self-reporting. For example, the collection unit can analyze the user's facial expressions using a camera to estimate the emotions. Voice analysis technology can analyze the tone and speed of the user's voice to estimate the emotions. Self-reporting involves the user reporting their own emotions, and an evaluation is made based on that information. The timing of information collection includes, but is not limited to, periodic collection and event-based collection. For example, if the user is feeling stressed, the collection unit can collect information during a relaxed time. Furthermore, if the user is relaxed, the collection unit can engage in a long conversation to collect detailed information. Furthermore, if the user is in a hurry, the collection unit can collect necessary information in a short period of time. For example, the collection unit adjusts the timing of information collection based on the user's emotions and collects information at more appropriate timing. As a result, the collection unit can collect information at more appropriate timing by adjusting the timing of information collection based on the user's emotions. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input user emotion data into a generation AI, and the generation AI can adjust the timing of information collection.

[0076] The collection unit can analyze the user's past care history and select an information collection method. Examples of past care history include, but are not limited to, a history of past care service use and medical records. For example, the collection unit analyzes the user's past care service use history and prioritizes collection of related information. The collection unit can also collect detailed information about specific services from the user's past care history. Furthermore, the collection unit can analyze the user's past care history and select the most effective information collection method. For example, the collection unit prioritizes collection of related information based on the history of care services used by the user in the past. Medical records indicate the user's health condition and treatment history and serve as a reference for information collection. Examples of information collection methods include, but are not limited to, questionnaires, sensors, interviews, and the like. For example, the collection unit can collect user information using questionnaires. The collection unit can also monitor the user's health condition using sensors. Furthermore, the collection unit can understand the user's living situation through interviews. For example, the collection unit asks the user questions and records the responses. The sensors measure the user's heart rate and activity level and collect data. The interview is conducted to obtain a detailed understanding of the user's living environment and care needs. This allows the collection unit to select the optimal information collection method by analyzing the user's past care history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past care history data into a generation AI, which then selects the information collection method.

[0077] When collecting information, the collection unit can filter the information based on the user's current living situation and areas of interest. Examples of living situations include, but are not limited to, activities of daily living, living environment, and economic situation. For example, the collection unit prioritizes collecting information about related nursing care services based on the user's current living situation. The collection unit can also collect optimal information taking the user's living situation into consideration. Furthermore, the collection unit can adjust the method of collecting information according to the user's living situation. For example, the collection unit monitors the user's activities of daily living and collects related information. The living environment indicates the user's housing situation and serves as a reference for information collection. The economic situation indicates the user's income and expenses and serves as a basis for information collection. Examples of areas of interest include, but are not limited to, hobbies, interesting activities, and learning content. For example, the collection unit filters information about specific nursing care services based on the user's areas of interest. The collection unit can also collect optimal information taking the user's areas of interest into consideration. Furthermore, the collection unit can adjust the method of collecting information according to the user's areas of interest. For example, the collection unit prioritizes collecting information related to the user's hobbies. Interesting activities indicate areas of interest to the user and serve as a reference for information collection. Learning content indicates what the user wants to learn and serves as a criterion for information collection. This allows the collection unit to collect more relevant information by filtering information based on the user's current living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's living situation and areas of interest into a generation AI, which then filters the information.

[0078] When collecting information, the collection unit can select a collection means depending on the user's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, when the user uses voice input, the collection unit can collect information using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also collect information using text analysis technology. Furthermore, when the user uses image input, the collection unit can also collect information using image recognition technology. For example, the collection unit records the user's voice with a microphone and converts it into text data using voice recognition technology. Text analysis technology analyzes the text entered by the user and collects information. Image recognition technology analyzes the image provided by the user and collects information. Examples of collection means include, but are not limited to, sensors, questionnaires, interviews, and the like. For example, the collection unit monitors the user's health condition using a sensor. Furthermore, the collection unit can collect user information using a questionnaire. Furthermore, the collection unit can understand the user's living situation through interviews. For example, the collection unit measures the user's heart rate and activity level using a sensor and collects data. The questionnaire asks questions to the user and records the responses. The interview is conducted to gain a detailed understanding of the user's living environment and care needs. This allows the collection unit to select the optimal collection method depending on the user's input method, thereby improving the efficiency of information collection. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's voice data into a generation AI, which then analyzes the voice data.

[0079] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and self-reporting. For example, the collection unit can estimate the user's emotions using facial expression recognition technology. The collection unit can also estimate the user's emotions using voice analysis technology. Furthermore, the collection unit can estimate the emotions based on the user's self-reporting. For example, the collection unit can analyze the user's facial expressions using a camera to estimate the emotions. Voice analysis technology can analyze the tone and speed of the user's voice to estimate the emotions. Self-reporting involves the user reporting their own emotions, and an evaluation is made based on that information. The priority of information can include, but is not limited to, importance, urgency, and relevance. For example, if the user is feeling stressed, the collection unit can prioritize collecting information about stress reduction. Furthermore, if the user is relaxed, the collection unit can prioritize collecting detailed information about nursing care services. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting information about services that can be used quickly. For example, the collection unit determines the priority of information based on the user's emotions and prioritizes collecting more important information. This allows the collection unit to prioritize information based on the user's emotions and prioritize collecting more important information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's emotion data into a generation AI, and the generation AI can determine the priority of information.

[0080] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and address information. For example, the collection unit prioritizes collecting information about nearby nursing care services based on the user's current location. The collection unit can also collect information about local nursing care services by taking into account the user's geographical location information. Furthermore, the collection unit can collect information about optimal nursing care services based on the user's location information. For example, the collection unit prioritizes collecting information about nearby nursing care services based on the user's current location. Address information indicates the location of the user's residence and serves as a reference for information collection. Examples of highly relevant information include, but are not limited to, the user's areas of interest and living situation. For example, the collection unit filters information about specific nursing care services based on the user's areas of interest. The collection unit can also collect optimal information by taking into account the user's living situation. Furthermore, the collection unit can adjust the method of information collection according to the user's areas of interest and living situation. For example, the collection unit prioritizes collecting information related to the user's hobbies. The living situation indicates the user's housing situation and economic situation, and serves as a criterion for collecting information. This allows the collection unit to prioritize collection of highly relevant information by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into the generation AI, and the generation AI can collect highly relevant information.

[0081] When collecting information, the collection unit may analyze the user's social media activity and collect related information. Examples of social media activity include, but are not limited to, the content of posts, the number of likes, and the number of followers. For example, the collection unit may analyze the content of the user's social media posts and collect information on related nursing care services. The collection unit may also collect related information by referring to the activities of the user's friends on social media. Furthermore, the collection unit may collect information on related nursing care services based on the user's check-in information on social media. For example, the collection unit may analyze the content of the user's social media posts and collect information on related nursing care services. The number of likes indicates the popularity of posts that interest the user and serves as a reference for information collection. The number of followers indicates the user's influence and serves as a criterion for information collection. Examples of related information include, but are not limited to, the user's areas of interest and living situation. For example, the collection unit may filter information on specific nursing care services based on the user's areas of interest. The collection unit may also collect optimal information taking the user's living situation into consideration. Furthermore, the collection unit may adjust the method of information collection according to the user's areas of interest and living situation. For example, the collection unit prioritizes collecting information related to the user's hobbies. Living conditions indicate the user's housing situation and economic situation and serve as the basis for information collection. This allows the collection unit to collect related information by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI, which then collects related information.

[0082] When collecting information, the collection unit can customize the collection method by reflecting the user's past feedback. Past feedback includes, but is not limited to, questionnaire results, user comments, etc. For example, the collection unit improves the information collection method based on the user's past feedback. The collection unit can also adjust the type of information to be collected by reflecting the user's past feedback. Furthermore, the collection unit can select the optimal information collection method by referring to the user's past feedback. For example, the collection unit improves the information collection method based on the user's past questionnaire results. The user's comments serve as reference for information collection and are useful for customizing the collection method. Collection methods include, but are not limited to, questionnaires, sensors, interviews, etc. For example, the collection unit collects user information using questionnaires. The collection unit can also monitor the user's health condition using sensors. The collection unit can also understand the user's living situation through interviews. For example, the collection unit measures the user's heart rate and activity level using sensors and collects data. The questionnaire asks the user questions and records the answers. The interview is conducted to obtain a detailed understanding of the user's living environment and care needs. This allows the collection unit to customize the collection method by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI, and the generation AI can customize the collection method.

[0083] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and self-reporting. For example, the analysis unit can estimate the user's emotions using facial expression recognition technology. The analysis unit can also estimate the user's emotions using voice analysis technology. Furthermore, the analysis unit can estimate the emotions based on the user's self-reporting. For example, the analysis unit can analyze the user's facial expressions using a camera to estimate the emotions. Voice analysis technology can analyze the tone and speed of the user's voice to estimate the emotions. Self-reporting involves the user reporting their own emotions, and an evaluation is made based on that information. Methods for presenting the analysis include, but are not limited to, graphs, text, and voice. For example, if the user is feeling stressed, the analysis unit can use a simple and easy-to-understand presentation method. If the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. For example, the analysis unit adjusts the way the analysis is presented based on the user's emotions to provide more appropriate analysis results. This allows the analysis unit to provide more appropriate analysis results by adjusting the way the analysis is presented based on the user's emotions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's emotion data into a generation AI, which can then adjust the way the analysis is presented.

[0084] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. The importance of the information includes, but is not limited to, the user's level of interest, urgency, and relevance. For example, the analysis unit performs a detailed analysis of highly important information. The analysis unit can also perform a concise analysis of less important information. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the information. For example, the analysis unit performs a detailed analysis of information that is highly interesting to the user. The analysis unit quickly analyzes information that is highly urgent. The analysis unit performs a detailed analysis of highly relevant information. The level of detail of the analysis includes, but is not limited to, the granularity of the information and the depth of the analysis. For example, the analysis unit can perform a more detailed analysis by reducing the granularity of the information. The analysis unit can also adjust the depth of the analysis to perform a more detailed analysis of the information. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the information, thereby enabling efficient analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input information importance data into the generation AI, which can then adjust the level of detail of the analysis.

[0085] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. Information categories include, but are not limited to, health information, lifestyle information, and nursing care information, for example. The analysis unit can apply a specific analysis algorithm to information related to nursing care services, for example. The analysis unit can also apply a different analysis algorithm to information related to the user's mental care. The analysis unit can also select an optimal analysis algorithm depending on the category of information. For example, the analysis unit can apply a specific analysis algorithm to health information, a different analysis algorithm to lifestyle information, and select an optimal analysis algorithm for nursing care information. Analysis algorithms include, but are not limited to, machine learning algorithms and statistical analysis algorithms, for example. The analysis unit can analyze information using, for example, a machine learning algorithm. The analysis unit can also analyze information using a statistical analysis algorithm. As a result, the analysis unit can apply an optimal analysis algorithm depending on the category of information, thereby improving the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information category data into the generation AI, which can then select the optimal analysis algorithm.

[0086] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, past data and analysis reports. For example, the analysis unit can improve the accuracy of the current analysis based on the user's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. Furthermore, the analysis unit can dynamically improve the accuracy of the analysis by using the user's past analysis results. For example, the analysis unit improves the accuracy of the current analysis based on past data. Analysis reports indicate past analysis results and are useful for improving the accuracy of the analysis. Analysis accuracy includes, but is not limited to, data quality, algorithm improvements, and the like. For example, the analysis unit improves the accuracy of the analysis by improving the data quality. The analysis unit can also improve the accuracy of the analysis by improving the algorithm. As a result, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI, allowing the generation AI to improve the accuracy of the analysis.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and self-reporting. For example, the analysis unit can estimate the user's emotions using facial expression recognition technology. The analysis unit can also estimate the user's emotions using voice analysis technology. Furthermore, the analysis unit can estimate the emotions based on the user's self-reporting. For example, the analysis unit can analyze the user's facial expressions using a camera to estimate the emotions. Voice analysis technology can analyze the tone and speed of the user's voice to estimate the emotions. Self-reporting involves the user reporting their own emotions, and an evaluation is made based on that information. The length of the analysis can include, but is not limited to, the analysis time and the amount of data. For example, the analysis unit can provide a short and concise analysis result if the user is feeling stressed. The analysis unit can also provide a detailed analysis result if the user is relaxed. The analysis unit can also provide a concise analysis result if the user is in a hurry. For example, the analysis unit adjusts the length of the analysis based on the user's emotions to provide a more appropriate analysis result. By adjusting the length of the analysis based on the user's emotions, the analysis unit can provide a more appropriate analysis result. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's emotion data into a generation AI, and the generation AI can adjust the length of the analysis.

[0088] During analysis, the analysis unit can determine the analysis priority based on the time of submission of the information. The time of submission of the information includes, but is not limited to, the latest information, past information, etc. The analysis unit, for example, prioritizes the analysis of the latest information. The analysis unit can also lower the analysis priority of information that was submitted earlier. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the time of submission of the information. For example, the analysis unit prioritizes the analysis of the latest information. The analysis unit lowers the analysis priority of past information. The analysis unit dynamically adjusts the analysis priority based on the time of submission of the information. The analysis priority includes, but is not limited to, the importance and urgency of the information. The analysis unit can determine the analysis priority based on, for example, the importance of the information. The analysis unit can also determine the analysis priority based on the urgency of the information. As a result, the analysis unit can prioritize the analysis of the latest information by determining the analysis priority based on the time of submission of the information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of information submission into the generation AI, and the generation AI can determine the priority of analysis.

[0089] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. Examples of the relevance of the information include, but are not limited to, the user's areas of interest and living situation. For example, the analysis unit prioritizes the analysis of highly relevant information. Furthermore, the analysis unit can postpone the analysis order of less relevant information. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the information. For example, the analysis unit prioritizes the analysis of highly relevant information based on the user's areas of interest. For example, the analysis unit prioritizes the analysis of highly relevant information based on the living situation. For example, the analysis unit postpones the analysis order of less relevant information. Examples of the order of analysis include, but are not limited to, prioritizing highly relevant information. For example, the analysis unit prioritizes the analysis of highly relevant information. Furthermore, the analysis unit can postpone the analysis order of less relevant information. In this way, the analysis unit can prioritize the analysis of highly relevant information by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input information association data to the generation AI, and the generation AI may adjust the order of analysis.

[0090] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation, educational background, and experience. For example, if the user has specialized knowledge, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have specialized knowledge, the analysis unit can provide analysis results in easy-to-understand language. Furthermore, the analysis unit can dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit can adjust the use of technical terms based on the user's occupation, educational background, or experience. Examples of the use of technical terms include, but are not limited to, definitions and explanations of technical terms. For example, the analysis unit can provide definitions of technical terms to provide analysis results that are easy for the user to understand. Furthermore, the analysis unit can provide explanations of technical terms to provide analysis results that are easy for the user to understand. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's expertise level data into the generation AI, which may then adjust the use of technical terms.

[0091] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. Methods for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, and self-reporting. For example, the suggestion unit can estimate the user's emotion using facial expression recognition technology. The suggestion unit can also estimate the user's emotion using voice analysis technology. Furthermore, the suggestion unit can estimate the emotion based on the user's self-reporting. For example, the suggestion unit can analyze the user's facial expression using a camera to estimate the emotion. Voice analysis technology can analyze the tone and speed of the user's voice to estimate the emotion. Self-reporting involves the user reporting their own emotion, and the evaluation is based on that information. Methods for expressing the suggestion include, but are not limited to, text, voice, visuals, and the like. For example, if the user is feeling stressed, the suggestion unit can use a simple and easy-to-understand expression. Furthermore, if the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can provide concise suggestions that focus on the main points. For example, the suggestion unit adjusts the way the suggestion is expressed based on the user's emotions to provide a more appropriate suggestion. This allows the suggestion unit to provide a more appropriate suggestion by adjusting the way the suggestion is expressed based on the user's emotions. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input user emotion data into a generation AI, and the generation AI can adjust the way the suggestion is expressed.

[0092] The suggestion unit can adjust the level of detail of the proposal based on the importance of the service when making a proposal. The importance of the service includes, but is not limited to, user needs, urgency, and the like. For example, the suggestion unit makes a detailed proposal for a service with high importance. The suggestion unit can also make a concise proposal for a service with low importance. Furthermore, the suggestion unit can dynamically adjust the level of detail of the proposal based on the importance of the service. For example, the suggestion unit makes a detailed proposal for a service with high user needs. The suggestion unit can quickly make a proposal for a service with high urgency. The level of detail of the proposal can be dynamically adjusted based on the importance of the service. The level of detail of the proposal includes, but is not limited to, the granularity of information and the depth of the proposal. For example, the suggestion unit can make a detailed proposal by reducing the granularity of information. The suggestion unit can also adjust the depth of the proposal to make a detailed proposal. This allows the suggestion unit to adjust the level of detail of the proposal based on the importance of the service, thereby enabling efficient proposals. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input service importance data to the generation AI, which can then adjust the level of detail of the suggestions.

[0093] When making a proposal, the suggestion unit can apply different proposal algorithms depending on the service category. Service categories include, but are not limited to, home care, day care, rehabilitation, etc. For example, the suggestion unit can apply a specific proposal algorithm to proposals related to nursing care services. The suggestion unit can also apply a different proposal algorithm to proposals related to mental care for the user. The suggestion unit can also select an optimal proposal algorithm depending on the service category. For example, the suggestion unit can apply a specific proposal algorithm to home care, a different proposal algorithm to day care, and select an optimal proposal algorithm for rehabilitation. Proposal algorithms include, but are not limited to, machine learning algorithms, rule-based systems, etc., for example. The suggestion unit can propose services using, for example, a machine learning algorithm. The suggestion unit can also propose services using a rule-based system. As a result, the suggestion unit can apply an optimal proposal algorithm depending on the service category, thereby improving the accuracy of the proposal. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input service category data into the generation AI, which can then select the optimal proposal algorithm.

[0094] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. Past proposal results include, but are not limited to, past data, proposal reports, etc. The suggestion unit can improve the accuracy of the current proposal based on, for example, the user's past proposal results. The suggestion unit can also adjust the proposal algorithm by referring to the user's past proposal results. The suggestion unit can also dynamically improve the accuracy of the proposal by using the user's past proposal results. For example, the suggestion unit can improve the accuracy of the current proposal based on past data. The proposal report indicates the past proposal results and is useful for improving the accuracy of the proposal. The accuracy of the proposal can include, but is not limited to, for example, data quality, algorithm improvement, etc. The suggestion unit can improve the accuracy of the proposal by improving the data quality, for example. The suggestion unit can also improve the accuracy of the proposal by improving the algorithm. As a result, the suggestion unit improves the accuracy of the proposal by referring to the user's past proposal results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input past proposal result data into the generation AI, allowing the generation AI to improve the accuracy of proposals.

[0095] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. Methods for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, and self-reporting. For example, the suggestion unit can estimate the user's emotion using facial expression recognition technology. The suggestion unit can also estimate the user's emotion using voice analysis technology. Furthermore, the suggestion unit can estimate the emotion based on the user's self-reporting. For example, the suggestion unit can analyze the user's facial expression using a camera to estimate the emotion. Voice analysis technology can analyze the tone and speed of the user's voice to estimate the emotion. Self-reporting involves the user reporting their own emotion, and the evaluation is based on that information. The length of the suggestion can include, but is not limited to, the duration of the suggestion, the amount of information, and the like. For example, the suggestion unit can provide short and concise suggestions when the user is stressed. The suggestion unit can also provide detailed suggestions when the user is relaxed. The suggestion unit can also provide concise suggestions when the user is in a hurry. For example, the suggestion unit adjusts the length of the suggestion based on the user's emotion to provide a more appropriate suggestion. As a result, the suggestion unit can provide a more appropriate suggestion by adjusting the length of the suggestion based on the user's emotion. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input user emotion data into a generation AI, and the generation AI can adjust the length of the suggestion.

[0096] The suggestion unit may determine the priority of the proposal based on the time of submission of the service when the proposal is made. The time of submission of the service may include, but is not limited to, the latest service, the older service, etc. The suggestion unit may, for example, prioritize the latest service. The suggestion unit may also lower the priority of the proposal for a service that was submitted earlier. The suggestion unit may also dynamically adjust the priority of the proposal based on the time of submission of the service. For example, the suggestion unit may prioritize the latest service. The suggestion unit may lower the priority of the proposal for an older service. The suggestion unit may dynamically adjust the priority of the proposal based on the time of submission of the service. The priority of the proposal may include, but is not limited to, the importance and urgency of the service. The suggestion unit may, for example, determine the priority of the proposal based on the importance of the service. The suggestion unit may also determine the priority of the proposal based on the urgency of the service. As a result, the suggestion unit may prioritize the proposal based on the time of submission of the service, thereby preferentially proposing the latest service. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input service submission timing data into the generation AI, and the generation AI can determine the priority of proposals.

[0097] The suggestion unit, when suggesting services, can adjust the order of suggestions based on the relevance of the services. Examples of the relevance of the services include, but are not limited to, the user's needs, living situations, etc. The suggestion unit, for example, prioritizes suggesting highly relevant services. The suggestion unit can also postpone the order of suggestions for less relevant services. Furthermore, the suggestion unit can dynamically adjust the order of suggestions according to the relevance of the services. For example, the suggestion unit prioritizes suggesting highly relevant services based on the user's needs. The suggestion unit prioritizes suggesting highly relevant services based on living situations. The suggestion unit postpones the order of suggestions for less relevant services. Examples of the order of suggestions include, but are not limited to, prioritizing highly relevant services. For example, the suggestion unit prioritizes suggesting highly relevant services. The suggestion unit can also postpone the order of suggestions for less relevant services. In this way, the suggestion unit can prioritize highly relevant services by adjusting the order of suggestions based on the relevance of the services. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input service relevance data to the generation AI, and the generation AI can adjust the order of suggestions.

[0098] When making a proposal, the suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation, educational background, and experience. For example, if the user has specialized knowledge, the suggestion unit can provide a proposal that uses a lot of technical terms. Furthermore, if the user does not have specialized knowledge, the suggestion unit can also provide a proposal in easy-to-understand language. Furthermore, the suggestion unit can dynamically adjust the use of technical terms in the proposal according to the user's level of expertise. For example, the suggestion unit can adjust the use of technical terms based on the user's occupation, educational background, or experience. Examples of the use of technical terms include, but are not limited to, definitions of technical terms, the presence or absence of explanations, and the like. For example, the suggestion unit can provide definitions of technical terms to provide a proposal that is easy for the user to understand. Furthermore, the suggestion unit can provide explanations of technical terms to provide a proposal that is easy for the user to understand. In this way, the suggestion unit can provide a proposal that is easy for the user to understand by adjusting the use of technical terms in the proposal according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input the user's expertise level data to the generation AI, which may then adjust the use of technical terms.

[0099] The care unit can estimate the user's emotions and adjust a care method based on the estimated user's emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and self-reporting. For example, the care unit can estimate the user's emotions using facial expression recognition technology. The care unit can also estimate the user's emotions using voice analysis technology. Furthermore, the care unit can estimate emotions based on the user's self-reporting. For example, the care unit can analyze the user's facial expressions using a camera to estimate emotions. Voice analysis technology can analyze the tone and speed of the user's voice to estimate emotions. Self-reporting involves the user reporting their own emotions, and an evaluation is made based on that information. Care methods include, but are not limited to, counseling, relaxation, mental support, and the like. For example, the care unit can provide advice on how to relax if the user is feeling stressed. Furthermore, the care unit can provide detailed advice on mental care if the user is relaxed. Furthermore, the care unit can provide concise care advice if the user is in a hurry. For example, the care unit adjusts the care method based on the user's emotions to provide more appropriate care. This allows the care unit to provide more appropriate care by adjusting the care method based on the user's emotions. Some or all of the above-described processing in the care unit may be performed using AI, for example, or may be performed without using AI. For example, the care unit can input the user's emotional data into a generation AI, and the generation AI can adjust the care method.

[0100] During care, the care unit can analyze the user's past mental state and select an optimal care method. Examples of past mental states include, but are not limited to, past psychological test results and medical records. The care unit can select an optimal care method based on, for example, the user's past mental state. The care unit can also analyze the user's past mental state and select a care method that is effective for stress reduction. Furthermore, the care unit can also select an optimal care method by referring to the user's past mental state. For example, the care unit can select an optimal care method based on past psychological test results. Medical records indicate the user's past mental state and are useful for selecting a care method. Examples of optimal care methods include, but are not limited to, the user's needs and past care results. For example, the care unit can select an optimal care method based on the user's needs. The care unit can also select an optimal care method based on past care results. In this way, the care unit can select an optimal care method by analyzing the user's past mental state. Some or all of the above-described processing in the care unit may be performed, for example, using AI or without AI. For example, the care department can input past mental state data into the generation AI, which can then select the most appropriate care method.

[0101] During care, the care unit can customize care measures based on the user's current living situation. Examples of current living situations include, but are not limited to, activities of daily living, living environment, and economic situation. For example, the care unit provides optimal care measures based on the user's current living situation. The care unit can also customize the care measures taking the user's living situation into consideration. Furthermore, the care unit can dynamically adjust the care measures according to the user's current living situation. For example, the care unit provides optimal care measures based on the user's activities of daily living. The care unit customizes care measures based on the living environment. The care unit dynamically adjusts care measures based on economic situation. Examples of care measures include, but are not limited to, user preferences and living situation. For example, the care unit provides optimal care measures based on the user's preferences. The care unit can also customize the care measures based on the user's living situation. This allows the care unit to provide more appropriate care by customizing the care measures based on the user's current living situation. Some or all of the above-described processes in the care department may be performed using, for example, AI, or may be performed without using AI. For example, the care department can input the user's living situation data into the generation AI and use the generation AI to customize the care method.

[0102] The care unit can improve the care method by reflecting user feedback during care. Examples of feedback include, but are not limited to, survey results, user comments, and the like. For example, the care unit can improve the care method based on the user feedback. The care unit can also adjust the care method by reflecting the user feedback. Furthermore, the care unit can select the optimal care method by referring to the user feedback. For example, the care unit can improve the care method based on survey results. User comments are useful for improving the care method and adjusting the care method. Examples of care methods include, but are not limited to, user feedback, care results, and the like. For example, the care unit can improve the care method based on the user feedback. The care unit can also improve the care method based on the care results. Thus, the care unit can improve the care method by reflecting the user feedback. Some or all of the above-described processing in the care unit may be performed using, for example, AI, or may be performed without using AI. For example, the care department can input user feedback data into the generation AI, which can then improve the care method.

[0103] The care unit can estimate the user's emotions and determine care priorities based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and self-reporting. For example, the care unit can estimate the user's emotions using facial expression recognition technology. The care unit can also estimate the user's emotions using voice analysis technology. Furthermore, the care unit can estimate emotions based on the user's self-reporting. For example, the care unit can analyze the user's facial expressions using a camera to estimate emotions. Voice analysis technology can analyze the tone and speed of the user's voice to estimate emotions. Self-reporting involves the user reporting their own emotions, and an evaluation is made based on that information. Care priorities include, but are not limited to, the user's needs and urgency. For example, if the user is feeling stressed, the care unit can prioritize stress reduction care. If the user is relaxed, the care unit can provide detailed advice on mental care. If the user is in a hurry, the care unit can provide concise care advice. For example, the care unit determines the priority of care based on the user's emotions and provides more important care preferentially. By determining the priority of care based on the user's emotions, the care unit can provide more important care preferentially. Some or all of the above-described processing in the care unit may be performed using, for example, AI, or may be performed without using AI. For example, the care unit can input the user's emotion data into a generation AI, and the generation AI can determine the priority of care.

[0104] During care, the care unit can select an optimal care method by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and address information. For example, the care unit provides information about nearby care services based on the user's current location. The care unit can also suggest area-specific care methods by taking into account the user's geographical location information. Furthermore, the care unit can select an optimal care method based on the user's location information. For example, the care unit provides information about nearby care services based on the user's current location. Address information indicates the location of the user's residence and is useful for selecting a care method. Examples of optimal care methods include, but are not limited to, the user's needs and geographical location information. For example, the care unit selects an optimal care method based on the user's needs. The care unit can also select an optimal care method based on the geographical location information. This allows the care unit to select an optimal care method by taking into account the user's geographical location information. Some or all of the above-described processing in the care unit may be performed using, for example, AI, or without AI. For example, the care department can input the user's geographical location information data into the generation AI, which can then select the optimal care method.

[0105] The care unit can analyze the user's social media activity during care and suggest care methods. Social media activity includes, but is not limited to, examples of posts, the number of likes, the number of followers, etc. For example, the care unit can analyze the user's social media posts to suggest relevant care methods. The care unit can also suggest relevant care methods based on the user's social media check-in information. For example, the care unit can analyze the user's social media posts to suggest relevant care methods. The number of likes indicates the popularity of posts that interest the user and is useful for suggesting care methods. The number of followers indicates the user's influence and serves as a criterion for suggesting care methods. Examples of care methods include, but are not limited to, the user's social media activity and lifestyle. For example, the care unit can suggest optimal care methods based on the user's social media activity. The care unit can also suggest care methods based on the user's lifestyle. This allows the care unit to suggest relevant care methods by analyzing the user's social media activity. Some or all of the above-described processes in the care department may be performed using AI, or may be performed without AI. For example, the care department may input the user's social media data into a generation AI, which may then suggest care measures.

[0106] During care, the care unit can customize the care method by reflecting the user's past feedback. Past feedback includes, but is not limited to, survey results, user comments, and the like. The care unit customizes the care method based on, for example, the user's past feedback. The care unit can also adjust the care method by reflecting the user's past feedback. Furthermore, the care unit can select the optimal care method by referring to the user's past feedback. For example, the care unit customizes the care method based on survey results. The user's comments are useful for customizing the care method and for adjusting the care method. The care method includes, but is not limited to, user feedback, care results, and the like. For example, the care unit customizes the care method based on the user's feedback. The care unit can also customize the care method based on the care results. In this way, the care unit can customize the care method by reflecting the user's past feedback. Some or all of the above-described processing in the care unit may be performed, for example, using AI or without using AI. For example, the care department can input the user's past feedback data into the generation AI, which can then customize the care method. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and care unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect the user's current condition using the camera 42 or microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The suggestion unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and suggests nursing care services based on the analysis results. The care unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and provides mental care for the user. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and care unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect the user's current condition using the camera 42 or microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The suggestion unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and suggests care services based on the analysis results. The care unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and provides mental care for the user. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and care unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect the user's current condition using the camera 42 or microphone 238 of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The suggestion unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12 and suggests care services based on the analysis results. The care unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12 and provides mental care for the user. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and care unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect the user's current situation using the camera 42 or microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The suggestion unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and suggests nursing care services based on the analysis results. The care unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and provides mental care for the user.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The collection unit can collect information about the user's lifestyle habits and dietary content and use it for analysis by the analysis unit. For example, the collection unit can record the user's daily dietary content and evaluate nutritional balance. The collection unit can also monitor the user's exercise habits and collect information useful for maintaining a healthy state. Furthermore, the collection unit can track the user's sleep patterns and provide data for evaluating sleep quality. In this way, the collection unit can provide data useful for analysis by the analysis unit by collecting information about the user's lifestyle habits and dietary content.

[0109] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit prioritizes analysis of information related to stress reduction. The analysis unit can also perform a detailed analysis if the user is relaxed. Furthermore, the analysis unit can perform a quick analysis if the user is in a hurry. In this way, the analysis unit can provide more appropriate analysis results by determining the priority of analysis based on the user's emotions.

[0110] The suggestion unit can customize the suggestion of nursing care services based on the user's hobbies and interests. For example, if the user is interested in music, the suggestion unit can suggest music therapy. If the user is interested in gardening, the suggestion unit can also suggest horticultural therapy. Furthermore, if the user is interested in reading, the suggestion unit can also suggest book clubs or library visits. This allows the suggestion unit to make more personalized suggestions for nursing care services based on the user's hobbies and interests.

[0111] The care unit can estimate the user's emotions and adjust the care method based on the estimated user's emotions. For example, if the user is feeling stressed, the care unit can provide relaxation advice. If the user is relaxed, the care unit can also provide detailed advice on mental care. Furthermore, if the user is in a hurry, the care unit can also provide concise care advice. In this way, the care unit can provide more appropriate care by adjusting the care method based on the user's emotions.

[0112] The collection unit can collect information about nursing care services specific to a region, taking into account the user's geographical location information. For example, the collection unit preferentially collects information about nursing care services in the vicinity based on the user's current location. The collection unit can also collect information about nursing care services specific to a region, taking into account the user's geographical location information. Furthermore, the collection unit can also collect information about optimal nursing care services based on the user's location information. In this way, the collection unit can preferentially collect highly relevant information by taking into account the user's geographical location information.

[0113] The analysis unit can improve the accuracy of the current analysis by referring to the user's past analysis results. For example, the analysis unit improves the accuracy of the current analysis based on past data. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. Furthermore, the analysis unit can dynamically improve the accuracy of the analysis by using the user's past analysis results. In this way, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results.

[0114] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can use a simple and easy-to-understand way of expression. If the user is relaxed, the suggestion unit can also provide detailed suggestions. If the user is in a hurry, the suggestion unit can also provide concise suggestions that get to the point. In this way, the suggestion unit can provide more appropriate suggestions by adjusting the way suggestions are expressed based on the user's emotions.

[0115] The care unit can analyze the user's past mental state and select the optimal care method. For example, the care unit selects the optimal care method based on past psychological test results. The care unit can also analyze the user's past mental state and select a care method that is effective in reducing stress. Furthermore, the care unit can also select the optimal care method by referring to the user's past mental state. In this way, the care unit can select the optimal care method by analyzing the user's past mental state.

[0116] The collection unit can analyze the user's social media activity and collect related information. For example, the collection unit can analyze the user's social media posts and collect information on related nursing care services. The collection unit can also collect related information by referring to the activities of the user's friends on social media. Furthermore, the collection unit can collect information on related nursing care services based on the user's check-in information on social media. In this way, the collection unit can collect related information by analyzing the user's social media activity.

[0117] The care unit can estimate the user's emotions and determine the priority of care based on the estimated user's emotions. For example, if the user is feeling stressed, the care unit can prioritize care to reduce stress. Also, if the user is relaxed, the care unit can provide detailed advice on mental care. Furthermore, if the user is in a hurry, the care unit can provide concise care advice. In this way, the care unit can prioritize care based on the user's emotions, thereby providing more important care.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The collection unit collects the user's current status. The user's current status includes, for example, health status, living situation, and need for care. The collection unit collects user information using questionnaires, sensors, and interviews. For example, the collection unit asks the user questions through a questionnaire and records the responses. The sensors measure the user's heart rate and activity level and collect data. The interviews are conducted to gain a detailed understanding of the user's living environment and need for care. Step 2: The analysis unit performs analysis based on the information collected by the collection unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning. For example, data mining techniques are used to extract patterns from the user's information, statistical analysis is used to evaluate the user's health condition and living situation, and machine learning algorithms are used to predict the user's need for care. Step 3: The suggestion unit proposes nursing care services based on the analysis results obtained by the analysis unit. The proposals include nursing care services such as home care, day care, and rehabilitation. The suggestion unit notifies the user and presents the proposals. It also selects the optimal nursing care service based on the user's needs and customizes the proposals according to the user's living situation and health condition. Step 4: The care department provides mental care based on the services proposed by the proposal department. Care is provided through methods such as counseling, relaxation, and mental support. The care department understands the user's mental state and provides appropriate advice. It also provides support to reduce the user's stress and anxiety and offers programs to maintain mental health.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0123] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 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.

[0126] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 7, a 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.

[0158] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0159] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0160] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0163] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0164] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0166] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0170] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0173] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0176] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0180] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0181] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0182] 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.

[0183] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0184] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0185] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0186] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0188] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0189] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0191] [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects the current status of the user; an analysis unit that performs analysis based on the information collected by the collection unit; a proposal unit that proposes nursing care services based on the analysis results obtained by the analysis unit; a care unit that provides mental care based on the services proposed by the proposal unit; Equipped with A system characterized by:

2. The collecting unit Collect information about the user's care burden and desired services 2. The system of claim 1.

3. The analysis unit Analyze the collected information and identify the care services available to the user 2. The system of claim 1.

4. The proposal unit Propose the identified care services to the user 2. The system of claim 1.

5. The care unit includes: Understand the user's state of mind and provide advice to reduce stress and anxiety 2. The system of claim 1.

6. The proposal unit Support for applying for long-term care insurance and preparing the necessary documents 2. The system of claim 1.

7. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze the user's past care history and select the method of information collection 2. The system of claim 1.

9. The collecting unit When collecting information, filter it based on the user's current life situation and areas of interest.

2. The system of claim 1.

10. The collecting unit When collecting information, select the collection method according to the user's input method.

2. The system of claim 1.

Images