system

Abstract

The system according to this embodiment aims to provide support for finding the most suitable care services and caregivers. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a matching unit, and a support unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The matching unit performs matching based on the information analyzed by the analysis unit. The support unit provides direct support from care managers.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] [[ID=३५]]In the conventional technology, it is difficult to find optimal care services or caregivers, and there is a problem that support for the elderly who have difficulty using digital devices or online services is insufficient.

[0005] The system according to the embodiment aims to provide support for finding optimal care services or caregivers.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a matching unit, and a support unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The matching unit performs matching based on the information analyzed by the analysis unit. The support unit provides direct support from the care manager. [Effects of the Invention]

[0007] The system according to this embodiment can provide support for finding the most suitable care services and caregivers. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The care matching system according to an embodiment of the present invention is a hybrid care matching system that combines the expertise of care managers with the technology of a generating AI agent. This care matching system takes into account the digital divide among the elderly, and human care managers provide direct support to ensure that even elderly individuals unfamiliar with digital technology can use it with confidence. For example, this care matching system provides support in person or by telephone, eliminating difficulties elderly individuals face when using digital devices or online services. Meanwhile, the generating AI agent provides rapid information and matching to families and representatives, improving the overall efficiency and quality of the service. For example, families and representatives can consult with the generating AI agent regarding information gathering and procedures, enabling 24-hour support. This allows the care matching system to realize care services that meet the needs of both the elderly and their families. Furthermore, the generating AI agent reduces the burden on care managers, handling administrative tasks to understand the situation of both the family and the individual. This allows care managers to focus on more specialized support. Additionally, this care matching system addresses the issue of insufficient support during emergencies and at night by providing 24-hour support from the generating AI agent. Thus, this care matching system, through its hybrid support system, allows humans and generated AI to work together to meet the needs of both the elderly and their families. Furthermore, because the system uses technology behind the scenes, the elderly can use the service without being aware of the technology, ensuring peace of mind. For example, the system's generated AI agent collects information and handles procedures on behalf of the elderly, allowing them to receive necessary support through dialogue with care managers. This system improves the quality of life for the elderly and reduces the burden on their families. Moreover, this care matching system aims to bridge the digital divide and realize a society where all elderly people can receive care services equally.For example, this care matching system can improve the quality of life for the elderly by using a generating AI agent to quickly match them with the most suitable care services and caregivers according to their needs. Furthermore, this care matching system can establish a new care model that harmonizes technology and human interaction, addressing the labor shortage in the care industry. For instance, by having the generating AI agent handle administrative tasks for care managers, care managers can provide more specialized support to a wider range of elderly individuals. In this way, this care matching system aims to realize a society where all elderly people have equal access to care services by providing care services that meet the needs of both the elderly and their families, and by bridging the digital divide. Ultimately, this care matching system enables the provision of care services that meet the needs of both the elderly and their families.

[0029] The care matching system according to this embodiment comprises a collection unit, an analysis unit, a matching unit, and a support unit. The collection unit collects information. For example, the collection unit can collect information that meets the needs of family members or their representatives. For example, the collection unit can collect medical information, lifestyle information, information on hobbies, etc. The collection unit can also collect information using AI, for example. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze the information using methods such as data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit can also analyze the information using AI. The matching unit performs matching based on the information analyzed by the analysis unit. For example, the matching unit can perform matching based on a method for calculating the degree of fit and the algorithm used. For example, the matching unit can also perform matching using AI. The support unit allows care managers to provide direct support. For example, the support unit can provide support to elderly people in person or by telephone. For example, the support unit can provide support using methods such as home visits, telephone consultations, and emergency response. As a result, the care matching system according to this embodiment can efficiently perform information collection, analysis, matching, and support.

[0030] The data collection unit collects information. For example, the data collection unit can collect information tailored to the needs of family members or guardians. Specifically, it collects detailed information provided by family members or guardians, such as the health status, lifestyle, hobbies, preferences, and medical history of elderly individuals. This includes methods such as data acquisition from electronic medical records and health management apps, questionnaires, and interviews. The data collection unit can collect information such as medical information, lifestyle information, and information on hobbies. Medical information includes medical history, medication status, allergy information, and results of regular medical checkups, while lifestyle information includes daily routines, dietary preferences, exercise habits, and family structure. Information on hobbies includes the individual elderly person's interests and concerns, such as reading, music, handicrafts, and gardening. The data collection unit can also collect information using AI, for example. AI crawls publicly available information on the internet and social media data, automatically collecting relevant information. AI can also extract useful information from text data using natural language processing technology. This allows the data collection unit to efficiently collect a wide range of data from diverse sources, strengthening the information infrastructure of the entire system. Furthermore, the data collection unit can centrally manage the collected information and collaborate with other departments as needed. For example, collected data can be stored on a cloud server, making it accessible to the analysis and matching units. Additionally, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the information using methods such as data mining, statistical analysis, and machine learning algorithms. Specifically, it uses data mining techniques to extract patterns and trends from the collected data and understand the needs and tendencies of the elderly. Statistical analysis statistically analyzes the collected data to identify important factors and correlations. Machine learning algorithms are used to build models based on the collected data and predict future needs and behaviors. The analysis unit can also analyze information using AI, for example. AI uses deep learning techniques to generate highly accurate predictive models from complex datasets. For example, AI can predict future health risks based on the health status and lifestyle of the elderly and propose appropriate care services. Furthermore, AI can use natural language processing techniques to extract useful information from text data and reflect it in the analysis results. This allows the analysis unit to quickly and accurately analyze the collected data and build a foundation for providing optimal care services tailored to the needs of the elderly. In addition, the analysis unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past caregiving data, the system can predict fluctuations in caregiving needs in specific regions and time periods, and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and safety of the system.

[0032] The matching unit performs matching based on the information analyzed by the analysis unit. For example, the matching unit can perform matching based on the method of calculating the degree of fit and the algorithm used. Specifically, it calculates a degree of fit score to identify the care service provider best suited to the needs and characteristics of the elderly. The degree of fit score is calculated considering factors such as the elderly person's health status, lifestyle, hobbies and preferences, and the expertise and experience of the care service provider. The matching unit can also perform matching using AI. The AI ​​uses machine learning algorithms to learn from past matching data and build a model for optimal matching. For example, the AI ​​proposes the optimal match based on the elderly person's needs and the characteristics of the care service provider. Furthermore, the AI ​​can continuously modify the matching results based on real-time updated data to adapt to the latest situation. This enables the matching unit to achieve highly accurate matching and provide the most suitable care services to the elderly. In addition, the matching unit can collect feedback on the matching results and continuously improve the accuracy and effectiveness of the matching algorithm. For example, it can collect evaluations of the matching results from the elderly person and care service provider and adjust the algorithm parameters. The matching unit can also simulate multiple matching scenarios and select the most suitable one. This allows the matching department to consistently provide the best possible match, improving both the satisfaction of elderly individuals and the quality of care services.

[0033] The support department provides direct support through care managers. For example, the support department can provide support to elderly individuals in person or by telephone. Specifically, care managers visit the elderly person's home to check their health and living situation and provide necessary care services. They also regularly check in on the elderly person's situation by telephone and provide necessary support. The support department can also provide support through methods such as home visits, telephone consultations, and emergency response. In home visits, care managers regularly visit the elderly person's home to conduct health checks and provide daily living support. Telephone consultations provide appropriate advice and information to elderly individuals and their families. Emergency response ensures a swift response and necessary support when an elderly person experiences an emergency. This allows the support department to provide meticulous support to the elderly and create an environment where they can live with peace of mind. Furthermore, the support department can flexibly adjust the support content according to the elderly person's situation and needs. For example, if the elderly person's health condition changes, the support content is reviewed and necessary services are added. The support department can also collaborate with other departments and provide optimal support based on collected information and analysis results. This allows the support department to provide appropriate support tailored to the needs of the elderly, thereby improving the quality of care services.

[0034] The data collection unit can collect information tailored to the needs of family members and their representatives. For example, it can collect information such as the medical needs, daily living support needs, and psychological support needs of family members and their representatives. The data collection unit can also collect information using AI, for example. This enables the collection of information that is tailored to the needs of family members and their representatives.

[0035] The analysis unit can analyze the collected information and identify the most suitable care services and caregivers. For example, the analysis unit can analyze the collected information using methods such as data mining, statistical analysis, and machine learning algorithms to identify the most suitable care services and caregivers. The analysis unit can also analyze the information using AI, for example. This allows for the identification of the most suitable care services and caregivers.

[0036] The matching unit can match the most suitable care services and caregivers based on the analysis results. For example, the matching unit can match the most suitable care services, such as home care, day care, and rehabilitation, as well as the most suitable caregivers with the necessary qualifications, experience, and expertise, based on the analysis results. The matching unit can also perform matching using AI, for example. This allows for the matching of the most suitable care services and caregivers.

[0037] The support department can provide support to the elderly in person or by telephone. The support department can provide support to the elderly in person or by telephone using methods such as home visits, telephone consultations, and emergency response. This allows the support department to provide support to the elderly in person or by telephone.

[0038] The data collection unit can collect information necessary for emergency and nighttime support. For example, it can collect information such as emergency contact numbers, medical information, and lifestyle support information. The data collection unit can also collect information using AI, for example. This allows it to collect information necessary for emergency and nighttime support.

[0039] The analysis unit can analyze information necessary for emergency and nighttime support. For example, it can analyze information necessary for emergency and nighttime support, such as emergency contact information, medical information, and lifestyle support information, using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit can also analyze information using AI, for example. This allows it to analyze information necessary for emergency and nighttime support.

[0040] The matching unit can match care services and caregivers needed for emergency and nighttime support. For example, it can match care services such as emergency home visits and nighttime support services, as well as caregivers available for nighttime support and those with experience in emergency response. The matching unit can also use AI for matching. This allows for the matching of care services and caregivers needed for emergency and nighttime support.

[0041] The data collection unit can analyze the past needs history of family members and their representatives and select the most suitable information collection method. For example, the data collection unit can prioritize collecting information that family members have frequently requested in the past. For example, the data collection unit can prioritize selecting information collection methods (email, phone, etc.) that representatives have used in the past. For example, the data collection unit can predict the information to be collected at a specific time based on the past needs history of family members and their representatives and select the most suitable method. For example, the data collection unit can also collect information using AI. This allows it to select the most suitable information collection method based on the past needs history of family members and their representatives.

[0042] The data collection unit can filter information based on the current living situation and areas of interest of family members and their representatives. For example, the data collection unit can prioritize the collection of information necessary for family members according to their current living situation. For example, the data collection unit can filter and collect relevant information based on the areas of interest of representatives. For example, the data collection unit can select and collect the most relevant information considering the current living situation and areas of interest of family members and their representatives. For example, the data collection unit can also collect information using AI. This allows for filtering information based on the current living situation and areas of interest of family members and their representatives.

[0043] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of family members and their representatives during information gathering. For example, the data collection unit can prioritize the collection of nearby care service information based on the current location of family members and their representatives. For example, the data collection unit can prioritize the collection of information needed in emergencies by considering geographical location information. For example, the data collection unit can select and collect highly relevant information based on the geographical location information of family members and their representatives. For example, the data collection unit can also collect information using AI. This allows for the priority collection of highly relevant information by considering the geographical location information of family members and their representatives.

[0044] The data collection unit can analyze the social media activities of family members and their representatives during information gathering and collect relevant information. For example, the data collection unit can collect information on care services of interest from the social media activities of family members and their representatives. For example, the data collection unit can analyze social media activities and prioritize the collection of necessary information. For example, the data collection unit can select and collect relevant information based on the social media activities of family members and their representatives. For example, the data collection unit can also collect information using AI. This allows it to analyze the social media activities of family members and their representatives and collect relevant information.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. For example, the analysis unit can perform a concise analysis on information of low importance. The analysis unit can adjust the level of detail of the analysis according to the importance of the collected information. The analysis unit can also analyze information using AI, for example. This allows the level of detail of the analysis to be adjusted based on the importance of the collected information.

[0046] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a specific analysis algorithm to care service information. For example, the analysis unit can apply an algorithm that allows for rapid analysis to emergency information. For example, the analysis unit can select and apply the optimal analysis algorithm depending on the category of information. For example, the analysis unit can also analyze information using AI. This allows the optimal analysis algorithm to be applied depending on the category of information.

[0047] The analysis unit can determine the priority of analysis based on when the information was collected. For example, the analysis unit can prioritize the analysis of the most recent information. For example, the analysis unit can lower the priority of analysis for older information. For example, the analysis unit can adjust the priority of analysis based on when the information was collected. For example, the analysis unit can also analyze information using AI. This allows the analysis priority to be determined based on when the information was collected.

[0048] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant information. For example, the analysis unit can postpone the analysis of less relevant information. The analysis unit can adjust the order of analysis based on the relevance of the information. The analysis unit can also analyze information using AI, for example. This allows the analysis order to be adjusted based on the relevance of the information.

[0049] The matching unit can improve the accuracy of matching by considering the interrelationships of the analyzed information during the matching process. For example, the matching unit can perform optimal matching based on the interrelationships of the analyzed information. The matching unit can improve the accuracy of matching by considering the interrelationships of the information. For example, the matching unit can analyze the interrelationships of the analyzed information and perform optimal matching. The matching unit can also perform matching using AI, for example. This allows for improved matching accuracy by considering the interrelationships of the analyzed information.

[0050] The matching unit can perform matching while considering the attribute information of family members and their representatives. For example, the matching unit can match the most suitable care services and caregivers based on the attribute information of family members and their representatives. For example, the matching unit can improve the accuracy of matching by considering attribute information. For example, the matching unit can analyze the attribute information of family members and their representatives to perform optimal matching. For example, the matching unit can also perform matching using AI. This allows for optimal matching while considering the attribute information of family members and their representatives.

[0051] The matching unit can perform matching while considering the geographical distribution of information. For example, the matching unit can prioritize matching with geographically close care services or caregivers. For example, the matching unit can perform optimal matching by considering geographical distribution. For example, the matching unit can analyze the geographical distribution of information and perform optimal matching. For example, the matching unit can perform matching using AI. This allows for optimal matching while considering the geographical distribution of information.

[0052] The matching unit can improve the accuracy of matching by referring to related literature during the matching process. For example, the matching unit can perform optimal matching based on related literature. For example, the matching unit can improve the accuracy of matching by referring to related literature. For example, the matching unit can analyze related literature and perform optimal matching. The matching unit can also perform matching using AI, for example. This allows for improved matching accuracy by referring to related literature.

[0053] The support unit can select the optimal support method by referring to the elderly person's past support history during support. For example, the support unit can select the optimal support method based on the elderly person's past support history. For example, the support unit can improve the accuracy of support by referring to past support history. For example, the support unit can analyze the elderly person's past support history and select the optimal support method. This allows the optimal support method to be selected based on the elderly person's past support history.

[0054] The support unit can customize the means of support provided based on the elderly person's current living situation. For example, the support unit can provide the most suitable means of support, taking into account the elderly person's current living situation. For example, the support unit can customize the content of support based on the current living situation. For example, the support unit can analyze the elderly person's current living situation and select the most suitable means of support. This allows the means of support to be customized based on the elderly person's current living situation.

[0055] The support department can select the optimal support method when providing support, taking into account the elderly person's geographical location. For example, the support department can prioritize providing nearby support services based on the elderly person's current location. For example, the support department can prioritize providing necessary support in emergencies, taking into account geographical location information. For example, the support department can select the optimal support method based on the elderly person's geographical location information. This allows the selection of the optimal support method to be made while considering the elderly person's geographical location information.

[0056] The support department can analyze the social media activities of elderly individuals during support sessions and propose appropriate support methods. For example, the support department can suggest support methods of interest based on the elderly individual's social media activities. For example, the support department can analyze social media activities and prioritize the provision of necessary support methods. For example, the support department can select the most suitable support method based on the elderly individual's social media activities. This allows the support department to analyze the social media activities of elderly individuals and propose the most appropriate support methods.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. For example, it can perform a detailed analysis on highly important information, and a concise analysis on less important information. Furthermore, it can adjust the level of detail of the analysis according to the importance of the collected information. This allows the level of detail of the analysis to be adjusted based on the importance of the collected information.

[0059] The information collection unit can analyze the past needs history of family members and their representatives and select the most suitable information collection method. For example, it can prioritize collecting information that family members have frequently requested in the past. It can also prioritize information collection methods (email, telephone, etc.) that representatives have used in the past. Furthermore, it can predict the information to be collected at specific times based on the past needs history of family members and their representatives and select the most suitable method. This allows for the selection of the most suitable information collection method based on the past needs history of family members and their representatives.

[0060] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, a specific analysis algorithm can be applied to care service information. Furthermore, an algorithm that allows for rapid analysis can be applied to emergency information. In addition, the system can select and apply the most suitable analysis algorithm depending on the category of information. This ensures that the most appropriate analysis algorithm is applied according to the information category.

[0061] The matching unit can perform matching while considering the geographical distribution of information. For example, it can prioritize matching with care services and caregivers that are geographically close. It can also perform optimal matching by considering geographical distribution. Furthermore, it can analyze the geographical distribution of information and perform optimal matching. This allows for optimal matching while considering the geographical distribution of information.

[0062] The support department can select the optimal support method by referring to the elderly person's past support history during support. For example, it can select the optimal support method based on the elderly person's past support history. Furthermore, it can improve the accuracy of support by referring to past support history. In addition, it can analyze the elderly person's past support history to select the optimal support method. This allows for the selection of the optimal support method based on the elderly person's past support history.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The collection unit collects information. The collection unit can collect information tailored to the needs of family members or their representatives, for example. The collection unit can collect information such as medical information, lifestyle information, and information about hobbies. The collection unit can also collect information using AI, for example. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the information using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit can also analyze the information using AI, for example. Step 3: The matching unit performs matching based on the information analyzed by the analysis unit. The matching unit can perform matching based, for example, on the method of calculating the goodness of fit or the algorithm used. The matching unit can also perform matching using, for example, AI. Step 4: The support department provides direct support through care managers. The support department can, for example, provide support to elderly individuals in person or by telephone. The support department can also provide support using methods such as home visits, telephone consultations, and emergency response.

[0065] (Example of form 2) The care matching system according to an embodiment of the present invention is a hybrid care matching system that combines the expertise of care managers with the technology of a generating AI agent. This care matching system takes into account the digital divide among the elderly, and human care managers provide direct support to ensure that even elderly individuals unfamiliar with digital technology can use it with confidence. For example, this care matching system provides support in person or by telephone, eliminating difficulties elderly individuals face when using digital devices or online services. Meanwhile, the generating AI agent provides rapid information and matching to families and representatives, improving the overall efficiency and quality of the service. For example, families and representatives can consult with the generating AI agent regarding information gathering and procedures, enabling 24-hour support. This allows the care matching system to realize care services that meet the needs of both the elderly and their families. Furthermore, the generating AI agent reduces the burden on care managers, handling administrative tasks to understand the situation of both the family and the individual. This allows care managers to focus on more specialized support. Additionally, this care matching system addresses the issue of insufficient support during emergencies and at night by providing 24-hour support from the generating AI agent. Thus, this care matching system, through its hybrid support system, allows humans and generated AI to work together to meet the needs of both the elderly and their families. Furthermore, because the system uses technology behind the scenes, the elderly can use the service without being aware of the technology, ensuring peace of mind. For example, the system's generated AI agent collects information and handles procedures on behalf of the elderly, allowing them to receive necessary support through dialogue with care managers. This system improves the quality of life for the elderly and reduces the burden on their families. Moreover, this care matching system aims to bridge the digital divide and realize a society where all elderly people can receive care services equally.For example, this care matching system can improve the quality of life for the elderly by using a generating AI agent to quickly match them with the most suitable care services and caregivers according to their needs. Furthermore, this care matching system can establish a new care model that harmonizes technology and human interaction, addressing the labor shortage in the care industry. For instance, by having the generating AI agent handle administrative tasks for care managers, care managers can provide more specialized support to a wider range of elderly individuals. In this way, this care matching system aims to realize a society where all elderly people have equal access to care services by providing care services that meet the needs of both the elderly and their families, and by bridging the digital divide. Ultimately, this care matching system enables the provision of care services that meet the needs of both the elderly and their families.

[0066] The care matching system according to this embodiment comprises a collection unit, an analysis unit, a matching unit, and a support unit. The collection unit collects information. For example, the collection unit can collect information that meets the needs of family members or their representatives. For example, the collection unit can collect medical information, lifestyle information, information on hobbies, etc. The collection unit can also collect information using AI, for example. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze the information using methods such as data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit can also analyze the information using AI. The matching unit performs matching based on the information analyzed by the analysis unit. For example, the matching unit can perform matching based on a method for calculating the degree of fit and the algorithm used. For example, the matching unit can also perform matching using AI. The support unit allows care managers to provide direct support. For example, the support unit can provide support to elderly people in person or by telephone. For example, the support unit can provide support using methods such as home visits, telephone consultations, and emergency response. As a result, the care matching system according to this embodiment can efficiently perform information collection, analysis, matching, and support.

[0067] The data collection unit collects information. For example, the data collection unit can collect information tailored to the needs of family members or guardians. Specifically, it collects detailed information provided by family members or guardians, such as the health status, lifestyle, hobbies, preferences, and medical history of elderly individuals. This includes methods such as data acquisition from electronic medical records and health management apps, questionnaires, and interviews. The data collection unit can collect information such as medical information, lifestyle information, and information on hobbies. Medical information includes medical history, medication status, allergy information, and results of regular medical checkups, while lifestyle information includes daily routines, dietary preferences, exercise habits, and family structure. Information on hobbies includes the individual elderly person's interests and concerns, such as reading, music, handicrafts, and gardening. The data collection unit can also collect information using AI, for example. AI crawls publicly available information on the internet and social media data, automatically collecting relevant information. AI can also extract useful information from text data using natural language processing technology. This allows the data collection unit to efficiently collect a wide range of data from diverse sources, strengthening the information infrastructure of the entire system. Furthermore, the data collection unit can centrally manage the collected information and collaborate with other departments as needed. For example, collected data can be stored on a cloud server, making it accessible to the analysis and matching units. Additionally, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0068] The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the information using methods such as data mining, statistical analysis, and machine learning algorithms. Specifically, it uses data mining techniques to extract patterns and trends from the collected data and understand the needs and tendencies of the elderly. Statistical analysis statistically analyzes the collected data to identify important factors and correlations. Machine learning algorithms are used to build models based on the collected data and predict future needs and behaviors. The analysis unit can also analyze information using AI, for example. AI uses deep learning techniques to generate highly accurate predictive models from complex datasets. For example, AI can predict future health risks based on the health status and lifestyle of the elderly and propose appropriate care services. Furthermore, AI can use natural language processing techniques to extract useful information from text data and reflect it in the analysis results. This allows the analysis unit to quickly and accurately analyze the collected data and build a foundation for providing optimal care services tailored to the needs of the elderly. In addition, the analysis unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past caregiving data, the system can predict fluctuations in caregiving needs in specific regions and time periods, and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and safety of the system.

[0069] The matching unit performs matching based on the information analyzed by the analysis unit. For example, the matching unit can perform matching based on the method of calculating the degree of fit and the algorithm used. Specifically, it calculates a degree of fit score to identify the care service provider best suited to the needs and characteristics of the elderly. The degree of fit score is calculated considering factors such as the elderly person's health status, lifestyle, hobbies and preferences, and the expertise and experience of the care service provider. The matching unit can also perform matching using AI. The AI ​​uses machine learning algorithms to learn from past matching data and build a model for optimal matching. For example, the AI ​​proposes the optimal match based on the elderly person's needs and the characteristics of the care service provider. Furthermore, the AI ​​can continuously modify the matching results based on real-time updated data to adapt to the latest situation. This enables the matching unit to achieve highly accurate matching and provide the most suitable care services to the elderly. In addition, the matching unit can collect feedback on the matching results and continuously improve the accuracy and effectiveness of the matching algorithm. For example, it can collect evaluations of the matching results from the elderly person and care service provider and adjust the algorithm parameters. The matching unit can also simulate multiple matching scenarios and select the most suitable one. This allows the matching department to consistently provide the best possible match, improving both the satisfaction of elderly individuals and the quality of care services.

[0070] The support department provides direct support through care managers. For example, the support department can provide support to elderly individuals in person or by telephone. Specifically, care managers visit the elderly person's home to check their health and living situation and provide necessary care services. They also regularly check in on the elderly person's situation by telephone and provide necessary support. The support department can also provide support through methods such as home visits, telephone consultations, and emergency response. In home visits, care managers regularly visit the elderly person's home to conduct health checks and provide daily living support. Telephone consultations provide appropriate advice and information to elderly individuals and their families. Emergency response ensures a swift response and necessary support when an elderly person experiences an emergency. This allows the support department to provide meticulous support to the elderly and create an environment where they can live with peace of mind. Furthermore, the support department can flexibly adjust the support content according to the elderly person's situation and needs. For example, if the elderly person's health condition changes, the support content is reviewed and necessary services are added. The support department can also collaborate with other departments and provide optimal support based on collected information and analysis results. This allows the support department to provide appropriate support tailored to the needs of the elderly, thereby improving the quality of care services.

[0071] The data collection unit can collect information tailored to the needs of family members and their representatives. For example, it can collect information such as the medical needs, daily living support needs, and psychological support needs of family members and their representatives. The data collection unit can also collect information using AI, for example. This enables the collection of information that is tailored to the needs of family members and their representatives.

[0072] The analysis unit can analyze the collected information and identify the most suitable care services and caregivers. For example, the analysis unit can analyze the collected information using methods such as data mining, statistical analysis, and machine learning algorithms to identify the most suitable care services and caregivers. The analysis unit can also analyze the information using AI, for example. This allows for the identification of the most suitable care services and caregivers.

[0073] The matching unit can match the most suitable care services and caregivers based on the analysis results. For example, the matching unit can match the most suitable care services, such as home care, day care, and rehabilitation, as well as the most suitable caregivers with the necessary qualifications, experience, and expertise, based on the analysis results. The matching unit can also perform matching using AI, for example. This allows for the matching of the most suitable care services and caregivers.

[0074] The support department can provide support to the elderly in person or by telephone. The support department can provide support to the elderly in person or by telephone using methods such as home visits, telephone consultations, and emergency response. This allows the support department to provide support to the elderly in person or by telephone.

[0075] The data collection unit can collect information necessary for emergency and nighttime support. For example, it can collect information such as emergency contact numbers, medical information, and lifestyle support information. The data collection unit can also collect information using AI, for example. This allows it to collect information necessary for emergency and nighttime support.

[0076] The analysis unit can analyze information necessary for emergency and nighttime support. For example, it can analyze information necessary for emergency and nighttime support, such as emergency contact information, medical information, and lifestyle support information, using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit can also analyze information using AI, for example. This allows it to analyze information necessary for emergency and nighttime support.

[0077] The matching unit can match care services and caregivers needed for emergency and nighttime support. For example, it can match care services such as emergency home visits and nighttime support services, as well as caregivers available for nighttime support and those with experience in emergency response. The matching unit can also use AI for matching. This allows for the matching of care services and caregivers needed for emergency and nighttime support.

[0078] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of information collection and collect information when the user is relaxed. For example, if the user is in a hurry, the data collection unit can quickly collect and immediately provide information. For example, if the user is anxious, the data collection unit can collect and provide information at an appropriate time to provide reassurance. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can estimate the user's emotions using technologies such as facial recognition, voice analysis, and behavioral pattern analysis. This allows the timing of information collection to be adjusted according to the user's emotions.

[0079] The data collection unit can analyze the past needs history of family members and their representatives and select the most suitable information collection method. For example, the data collection unit can prioritize collecting information that family members have frequently requested in the past. For example, the data collection unit can prioritize selecting information collection methods (email, phone, etc.) that representatives have used in the past. For example, the data collection unit can predict the information to be collected at a specific time based on the past needs history of family members and their representatives and select the most suitable method. For example, the data collection unit can also collect information using AI. This allows it to select the most suitable information collection method based on the past needs history of family members and their representatives.

[0080] The data collection unit can filter information based on the current living situation and areas of interest of family members and their representatives. For example, the data collection unit can prioritize the collection of information necessary for family members according to their current living situation. For example, the data collection unit can filter and collect relevant information based on the areas of interest of representatives. For example, the data collection unit can select and collect the most relevant information considering the current living situation and areas of interest of family members and their representatives. For example, the data collection unit can also collect information using AI. This allows for filtering information based on the current living situation and areas of interest of family members and their representatives.

[0081] The data collection unit can estimate the user's emotions and prioritize the information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting information of high importance. For example, if the user is relaxed, the data collection unit can collect detailed information. For example, if the user is in a hurry, the data collection unit can prioritize collecting information that needs to be provided quickly. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can estimate the user's emotions using technologies such as facial recognition, voice analysis, and behavioral pattern analysis. This allows the system to prioritize the information to collect according to the user's emotions.

[0082] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of family members and their representatives during information gathering. For example, the data collection unit can prioritize the collection of nearby care service information based on the current location of family members and their representatives. For example, the data collection unit can prioritize the collection of information needed in emergencies by considering geographical location information. For example, the data collection unit can select and collect highly relevant information based on the geographical location information of family members and their representatives. For example, the data collection unit can also collect information using AI. This allows for the priority collection of highly relevant information by considering the geographical location information of family members and their representatives.

[0083] The data collection unit can analyze the social media activities of family members and their representatives during information gathering and collect relevant information. For example, the data collection unit can collect information on care services of interest from the social media activities of family members and their representatives. For example, the data collection unit can analyze social media activities and prioritize the collection of necessary information. For example, the data collection unit can select and collect relevant information based on the social media activities of family members and their representatives. For example, the data collection unit can also collect information using AI. This allows it to analyze the social media activities of family members and their representatives and collect relevant information.

[0084] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can use a simple and easy-to-understand presentation. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can estimate the user's emotions using technologies such as facial recognition, voice analysis, and behavioral pattern analysis. This allows the presentation of the analysis to be adjusted according to the user's emotions.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. For example, the analysis unit can perform a concise analysis on information of low importance. The analysis unit can adjust the level of detail of the analysis according to the importance of the collected information. The analysis unit can also analyze information using AI, for example. This allows the level of detail of the analysis to be adjusted based on the importance of the collected information.

[0086] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a specific analysis algorithm to care service information. For example, the analysis unit can apply an algorithm that allows for rapid analysis to emergency information. For example, the analysis unit can select and apply the optimal analysis algorithm depending on the category of information. For example, the analysis unit can also analyze information using AI. This allows the optimal analysis algorithm to be applied depending on the category of information.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is excited, the analysis unit can provide a visually stimulating analysis result. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can estimate the user's emotions using technologies such as facial recognition, voice analysis, and behavioral pattern analysis. This allows the length of the analysis to be adjusted according to the user's emotions.

[0088] The analysis unit can determine the priority of analysis based on when the information was collected. For example, the analysis unit can prioritize the analysis of the most recent information. For example, the analysis unit can lower the priority of analysis for older information. For example, the analysis unit can adjust the priority of analysis based on when the information was collected. For example, the analysis unit can also analyze information using AI. This allows the analysis priority to be determined based on when the information was collected.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant information. For example, the analysis unit can postpone the analysis of less relevant information. The analysis unit can adjust the order of analysis based on the relevance of the information. The analysis unit can also analyze information using AI, for example. This allows the analysis order to be adjusted based on the relevance of the information.

[0090] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated emotions. For example, if the user is stressed, the matching unit can perform a quick match. For example, if the user is relaxed, the matching unit can apply detailed matching criteria. For example, if the user is in a hurry, the matching unit can apply concise matching criteria. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can estimate the user's emotions using technologies such as facial recognition, voice analysis, and behavioral pattern analysis. This allows the matching criteria to be adjusted according to the user's emotions.

[0091] The matching unit can improve the accuracy of matching by considering the interrelationships of the analyzed information during the matching process. For example, the matching unit can perform optimal matching based on the interrelationships of the analyzed information. The matching unit can improve the accuracy of matching by considering the interrelationships of the information. For example, the matching unit can analyze the interrelationships of the analyzed information and perform optimal matching. The matching unit can also perform matching using AI, for example. This allows for improved matching accuracy by considering the interrelationships of the analyzed information.

[0092] The matching unit can perform matching while considering the attribute information of family members and their representatives. For example, the matching unit can match the most suitable care services and caregivers based on the attribute information of family members and their representatives. For example, the matching unit can improve the accuracy of matching by considering attribute information. For example, the matching unit can analyze the attribute information of family members and their representatives to perform optimal matching. For example, the matching unit can also perform matching using AI. This allows for optimal matching while considering the attribute information of family members and their representatives.

[0093] The matching unit can estimate the user's emotions and adjust the order in which matching results are displayed based on the estimated emotions. For example, if the user is stressed, the matching unit can prioritize displaying results of high importance. For example, if the user is relaxed, the matching unit can prioritize displaying detailed results. For example, if the user is in a hurry, the matching unit can prioritize displaying concise results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can estimate the user's emotions using technologies such as facial recognition, voice analysis, and behavioral pattern analysis. This allows the order in which matching results are displayed to be adjusted according to the user's emotions.

[0094] The matching unit can perform matching while considering the geographical distribution of information. For example, the matching unit can prioritize matching with geographically close care services or caregivers. For example, the matching unit can perform optimal matching by considering geographical distribution. For example, the matching unit can analyze the geographical distribution of information and perform optimal matching. For example, the matching unit can perform matching using AI. This allows for optimal matching while considering the geographical distribution of information.

[0095] The matching unit can improve the accuracy of matching by referring to related literature during the matching process. For example, the matching unit can perform optimal matching based on related literature. For example, the matching unit can improve the accuracy of matching by referring to related literature. For example, the matching unit can analyze related literature and perform optimal matching. The matching unit can also perform matching using AI, for example. This allows for improved matching accuracy by referring to related literature.

[0096] The support unit can estimate the user's emotions and adjust its support methods based on those emotions. For example, if the user is stressed, the support unit can provide support in a calm voice. If the user is relaxed, the support unit can provide detailed explanations. If the user is in a hurry, the support unit can provide quick and concise support. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can estimate the user's emotions using technologies such as facial recognition, voice analysis, and behavioral pattern analysis. This allows the support methods to be adjusted according to the user's emotions.

[0097] The support unit can select the optimal support method by referring to the elderly person's past support history during support. For example, the support unit can select the optimal support method based on the elderly person's past support history. For example, the support unit can improve the accuracy of support by referring to past support history. For example, the support unit can analyze the elderly person's past support history and select the optimal support method. This allows the optimal support method to be selected based on the elderly person's past support history.

[0098] The support unit can customize the means of support provided based on the elderly person's current living situation. For example, the support unit can provide the most suitable means of support, taking into account the elderly person's current living situation. For example, the support unit can customize the content of support based on the current living situation. For example, the support unit can analyze the elderly person's current living situation and select the most suitable means of support. This allows the means of support to be customized based on the elderly person's current living situation.

[0099] The support unit can estimate the user's emotions and prioritize support based on those emotions. For example, if the user is stressed, the support unit can prioritize providing high-priority support. For example, if the user is relaxed, the support unit can provide detailed support. For example, if the user is in a hurry, the support unit can prioritize providing support that needs to be delivered quickly. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can estimate the user's emotions using technologies such as facial recognition, voice analysis, and behavioral pattern analysis. This allows the support unit to prioritize support according to the user's emotions.

[0100] The support department can select the optimal support method when providing support, taking into account the elderly person's geographical location. For example, the support department can prioritize providing nearby support services based on the elderly person's current location. For example, the support department can prioritize providing necessary support in emergencies, taking into account geographical location information. For example, the support department can select the optimal support method based on the elderly person's geographical location information. This allows the selection of the optimal support method to be made while considering the elderly person's geographical location information.

[0101] The support department can analyze the social media activities of elderly individuals during support sessions and propose appropriate support methods. For example, the support department can suggest support methods of interest based on the elderly individual's social media activities. For example, the support department can analyze social media activities and prioritize the provision of necessary support methods. For example, the support department can select the most suitable support method based on the elderly individual's social media activities. This allows the support department to analyze the social media activities of elderly individuals and propose the most appropriate support methods.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on those emotions. For example, if the user is stressed, the frequency of information collection can be reduced, and collection can be performed when the user is relaxed. If the user is in a hurry, information can be collected quickly and provided immediately. Furthermore, if the user is feeling anxious, information can be collected and provided at an appropriate time to provide reassurance. Emotion estimation is achieved using an emotion engine or generative AI, which allows the timing of information collection to be adjusted according to the user's emotions.

[0104] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. For example, it can perform a detailed analysis on highly important information, and a concise analysis on less important information. Furthermore, it can adjust the level of detail of the analysis according to the importance of the collected information. This allows the level of detail of the analysis to be adjusted based on the importance of the collected information.

[0105] The matching unit can estimate the user's emotions and adjust the matching criteria based on those emotions. For example, if the user is stressed, it can perform a quick match. If the user is relaxed, it can apply detailed matching criteria. Furthermore, if the user is in a hurry, it can apply concise matching criteria. Emotion estimation is achieved using an emotion engine or generative AI, which allows the matching criteria to be adjusted according to the user's emotions.

[0106] The support unit can estimate the user's emotions and adjust its support methods based on those estimates. For example, if the user is stressed, it can provide support in a calm voice. If the user is relaxed, it can provide detailed explanations. Furthermore, if the user is in a hurry, it can provide quick and concise support. Emotion estimation is achieved using an emotion engine or generative AI, allowing the support method to be adjusted according to the user's emotions.

[0107] The information collection unit can analyze the past needs history of family members and their representatives and select the most suitable information collection method. For example, it can prioritize collecting information that family members have frequently requested in the past. It can also prioritize information collection methods (email, telephone, etc.) that representatives have used in the past. Furthermore, it can predict the information to be collected at specific times based on the past needs history of family members and their representatives and select the most suitable method. This allows for the selection of the most suitable information collection method based on the past needs history of family members and their representatives.

[0108] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, a specific analysis algorithm can be applied to care service information. Furthermore, an algorithm that allows for rapid analysis can be applied to emergency information. In addition, the system can select and apply the most suitable analysis algorithm depending on the category of information. This ensures that the most appropriate analysis algorithm is applied according to the information category.

[0109] The matching unit can perform matching while considering the geographical distribution of information. For example, it can prioritize matching with care services and caregivers that are geographically close. It can also perform optimal matching by considering geographical distribution. Furthermore, it can analyze the geographical distribution of information and perform optimal matching. This allows for optimal matching while considering the geographical distribution of information.

[0110] The support department can select the optimal support method by referring to the elderly person's past support history during support. For example, it can select the optimal support method based on the elderly person's past support history. Furthermore, it can improve the accuracy of support by referring to past support history. In addition, it can analyze the elderly person's past support history to select the optimal support method. This allows for the selection of the optimal support method based on the elderly person's past support history.

[0111] The data collection unit can estimate the user's emotions and prioritize the information to collect based on those emotions. For example, if the user is stressed, it can prioritize collecting high-priority information. If the user is relaxed, it can collect detailed information. Furthermore, if the user is in a hurry, it can prioritize collecting information that needs to be provided quickly. Emotion estimation is achieved using an emotion engine or generative AI, which allows the system to prioritize the information to collect according to the user's emotions.

[0112] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, a simple and easy-to-understand presentation can be used. If the user is relaxed, detailed analysis results can be provided. Furthermore, if the user is in a hurry, concise analysis results that get straight to the point can be provided. Emotion estimation is achieved using an emotion engine or generative AI, which allows the presentation of the analysis to be adjusted according to the user's emotions.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The collection unit collects information. The collection unit can collect information tailored to the needs of family members or their representatives, for example. The collection unit can collect information such as medical information, lifestyle information, and information about hobbies. The collection unit can also collect information using AI, for example. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the information using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit can also analyze the information using AI, for example. Step 3: The matching unit performs matching based on the information analyzed by the analysis unit. The matching unit can perform matching based, for example, on the method of calculating the goodness of fit or the algorithm used. The matching unit can also perform matching using, for example, AI. Step 4: The support department provides direct support through care managers. The support department can, for example, provide support to elderly individuals in person or by telephone. The support department can also provide support using methods such as home visits, telephone consultations, and emergency response.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0117] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the data collection unit, analysis unit, matching unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects information using the camera 42 and microphone 38B of the smart device 14 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The matching unit is implemented in the specific processing unit 290 of the data processing unit 12 and performs matching based on the analyzed information. The support unit is implemented in the specific processing unit 46A of the smart device 14 and provides support to the elderly in person or by telephone. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the data collection unit, analysis unit, matching unit, and support unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects information using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected information. The matching unit is implemented in the identification processing unit 290 of the data processing unit 12 and performs matching based on the analyzed information. The support unit is implemented in the control unit 46A of the smart glasses 214 and provides support to the elderly in person or by telephone. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the data collection unit, analysis unit, matching unit, and support unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects information using the camera 42 and microphone 238 of the headset terminal 314 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The matching unit is implemented in the specific processing unit 290 of the data processing unit 12 and performs matching based on the analyzed information. The support unit is implemented in the specific processing unit 46A of the headset terminal 314 and provides support to the elderly in person or by telephone. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0159] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the data collection unit, analysis unit, matching unit, and support unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects information using the camera 42 and microphone 238 of the robot 414 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The matching unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12 and performs matching based on the analyzed information. The support unit is implemented in, for example, the control unit 46A of the robot 414 and provides support to the elderly in person or by telephone. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0168] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0170] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0171] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0172] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0176] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0177] 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.

[0178] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0179] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0180] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0181] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0183] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0184] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0185] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0186] (Note 1) The information collection unit, An analysis unit analyzes the information collected by the aforementioned collection unit, A matching unit performs matching based on the information analyzed by the aforementioned analysis unit, It includes a support department where care managers provide direct support. A system characterized by the following features. (Note 2) The aforementioned collection unit is Gather information tailored to the needs of family members and their representatives. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected information is analyzed to identify the most suitable care services and caregivers. The system described in Appendix 1, characterized by the features described herein. (Note 4) The matching unit is Based on the analysis results, the system matches the most suitable care services and caregivers. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned support unit is Providing support to the elderly through face-to-face interactions and telephone calls. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Gather information necessary for emergency and nighttime support. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, Analyze the information needed for emergency and nighttime support. The system described in Appendix 1, characterized by the features described herein. (Note 8) The matching unit is Matching care services and caregivers needed for emergency and nighttime support. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze the past needs and history of family members and their representatives to select the most suitable method for gathering information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When gathering information, filtering is performed based on the current living situation and areas of interest of family members and their representatives. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When gathering information, prioritize collecting highly relevant information by considering the geographical location of family members and their representatives. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When gathering information, we analyze the social media activities of family members and their representatives and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The matching unit is It estimates the user's emotions and adjusts the matching criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The matching unit is During the matching process, the accuracy of the matching is improved by considering the interrelationships of the analyzed information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The matching unit is During the matching process, the attribute information of family members and their representatives will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The matching unit is It estimates the user's sentiment and adjusts the order in which matching results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The matching unit is During the matching process, the geographical distribution of information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 26) The matching unit is During the matching process, we refer to relevant literature to improve the accuracy of the matching. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned support unit is It estimates the user's emotions and adjusts the support method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support unit is When providing support, the most suitable support method is selected by referring to the elderly person's past support history. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned support unit is During support, customize the support methods based on the elderly person's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned support unit is The system estimates the user's emotions and determines support priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned support unit is When providing support, the most suitable support method is selected considering the geographical location of the elderly person. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned support unit is During support sessions, we analyze the social media activity of elderly individuals and propose support methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The information collection unit, An analysis unit analyzes the information collected by the aforementioned collection unit, A matching unit performs matching based on the information analyzed by the aforementioned analysis unit, It includes a support department where care managers provide direct support. A system characterized by the following features.

2. The aforementioned collection unit is Gather information tailored to the needs of family members and their representatives. The system according to feature 1.

3. The aforementioned analysis unit, The collected information is analyzed to identify the most suitable care services and caregivers. The system according to feature 1.

4. The matching unit is Based on the analysis results, the system matches the most suitable care services and caregivers. The system according to feature 1.

5. The aforementioned support unit is Providing support to the elderly through face-to-face interactions and telephone calls. The system according to feature 1.

6. The aforementioned collection unit is Gather information necessary for emergency and nighttime support. The system according to feature 1.

7. The aforementioned analysis unit, Analyze the information needed for emergency and nighttime support. The system according to feature 1.

8. The matching unit is Matching care services and caregivers needed for emergency and nighttime support. The system according to feature 1.

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