system

Abstract

The system according to the embodiment aims to provide comprehensive support to single women to help them cope with various life stages and challenges. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects user data. The analysis unit analyzes the data collected by the collection unit. The provision unit provides various types of support based on the analysis results obtained by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult for single women to receive comprehensive support to deal with various life stages and challenges.

[0005] The system according to the embodiment aims to provide comprehensive support to single women to help them cope with various life stages and challenges. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects user data. The analysis unit analyzes the data collected by the collection unit. The provision unit provides various types of support based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide comprehensive support to single women to help them cope with various life stages and challenges. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A comprehensive support system according to an embodiment of the present invention provides comprehensive services to help single women cope with various life stages and challenges. This comprehensive support system supports single women in selecting appropriate insurance products and enrollment procedures, and provides advice on health management and preventive medicine. It also provides support for career advancement and skill development, advice on estates and inheritance, and support for preparing wills. It also provides advice on savings, investments, and budget management, and provides family care and a support network. This system provides comprehensive support tailored to the user's needs, helping single women live a richer life. For example, a system is constructed that collects user data, analyzes it using AI, and provides personalized advice. Health management support involves collecting health data using a wearable device, which is then analyzed by AI to provide advice. Furthermore, estate and inheritance advice involves using legal tools to support the preparation of wills. This allows the comprehensive support system to provide comprehensive support to help single women cope with various life stages and challenges.

[0029] The comprehensive support system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects user data. The user data includes, but is not limited to, behavioral data, health data, and purchase data. The collection unit collects the user's health data using, for example, a wearable device. The collection unit can also collect the user's online activity data. For example, the collection unit can collect the user's website browsing history and social media posting data. The collection unit can also collect the user's purchase history data. For example, the collection unit can collect data on products purchased by the user through online shopping. The analysis unit analyzes the data collected by the collection unit. The analysis can be performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to, examples. For example, the analysis unit analyzes the user's health data and evaluates the user's health condition. The analysis unit can also analyze the user's behavioral data and identify behavioral patterns. The analysis unit can also analyze the user's purchase data and understand purchasing trends. The provision unit provides various types of support based on the analysis results obtained by the analysis unit. The various types of support include, but are not limited to, health advice, financial advice, and legal advice. For example, the providing unit may provide health advice based on the user's health data. The providing unit may also provide financial advice based on the user's financial data. Furthermore, the providing unit may also provide legal advice based on the user's legal data. As a result, the comprehensive support system according to the embodiment can provide personalized support tailored to the user's needs by collecting and analyzing user data and providing various types of support.

[0030] The comprehensive support system includes a health data collection unit that collects health data. The health data collection unit collects the user's health data. The health data includes, but is not limited to, heart rate, blood pressure, and exercise volume. For example, the health data collection unit collects the user's heart rate using a wearable device. The health data collection unit can also collect the user's blood pressure using a blood pressure monitor. The health data collection unit can also collect the user's exercise volume using a fitness tracker. For example, the health data collection unit collects heart rate data in real time from a wearable device worn by the user. The health data collection unit can also periodically collect blood pressure data from a blood pressure monitor used by the user at home. The health data collection unit can also collect exercise volume data from a fitness tracker used by the user on a daily basis. This allows the health data collection unit to understand the user's health condition and provide appropriate support.

[0031] The comprehensive support system includes a health advice providing unit that provides health advice. The health advice providing unit provides health advice based on the user's health data. The health advice includes, but is not limited to, dietary guidance, exercise guidance, and suggestions for improving lifestyle habits. For example, the health advice providing unit evaluates the user's diet and suggests a balanced meal plan. The health advice providing unit can also evaluate the user's exercise habits and suggest an appropriate exercise plan. Furthermore, the health advice providing unit can evaluate the user's lifestyle habits and suggest areas for improvement. For example, the health advice providing unit can analyze the user's dietary data and provide a nutritionally balanced meal plan. The health advice providing unit can also analyze the user's exercise data and provide an effective exercise plan. Furthermore, the health advice providing unit can analyze the user's lifestyle habit data and suggest healthy lifestyle habits. In this way, the health advice providing unit can support the user's health management.

[0032] The comprehensive support system includes a legal advice providing unit that provides specific legal advice regarding inheritance and inheritance. The legal advice providing unit provides specific legal advice to resolve issues regarding the user's inheritance and inheritance. Specific legal advice includes, but is not limited to, methods for dividing the inheritance and measures for inheritance tax. For example, the legal advice providing unit evaluates the user's inheritance and proposes an appropriate method for dividing the inheritance. The legal advice providing unit can also propose measures to reduce the user's inheritance tax burden. Furthermore, the legal advice providing unit can support the user in preparing a will. For example, the legal advice providing unit analyzes the user's inheritance data and proposes an optimal method for dividing the inheritance. The legal advice providing unit can also analyze the user's inheritance tax data and propose effective measures for inheritance tax. Furthermore, the legal advice providing unit can support the user in preparing a will and prepare a will that meets legal requirements. In this way, the legal advice providing unit can resolve issues regarding the user's inheritance and inheritance.

[0033] The comprehensive support system includes a will creation support unit that supports the creation of a will. The will creation support unit supports the user in creating an appropriate will. The will creation support includes, but is not limited to, for example, providing a will template and checking legal requirements. For example, the will creation support unit provides a will template to the user. The will creation support unit can also check whether the will created by the user meets legal requirements. Furthermore, the will creation support unit can provide advice to the user when creating a will. For example, the will creation support unit provides a will template to the user and instructs the user to enter necessary information. The will creation support unit can also have a legal expert review the will created by the user to check whether it meets legal requirements. Furthermore, the will creation support unit can provide advice to the user when creating a will and support the user in creating an appropriate will. In this way, the will creation support unit enables the user to create an appropriate will.

[0034] The comprehensive support system includes an economic advice providing unit that provides specific advice on savings, investments, and budget management. The economic advice providing unit provides specific advice to support the user's financial management. Examples of specific advice include, but are not limited to, investment strategies, savings plans, and budget management methods. For example, the economic advice providing unit evaluates the user's investment portfolio and proposes an optimal investment strategy. The economic advice providing unit can also set the user's savings goal and propose a savings plan. Furthermore, the economic advice providing unit can evaluate the user's income and expenses and propose an effective budget management method. For example, the economic advice providing unit can analyze the user's investment data and propose an investment strategy that takes into account the balance between risk and return. The economic advice providing unit can also analyze the user's savings data and propose a savings plan aimed at achieving the goal. Furthermore, the economic advice providing unit can analyze the user's income and expenditure data and propose a budget management method to improve the income and expenditure balance. In this way, the economic advice providing unit can support the user's financial management.

[0035] The comprehensive support system includes a care information providing unit that provides information about family care. The care information providing unit provides information to support the user's care of their family. The care information includes, but is not limited to, information about care facilities, how to use nursing care insurance, and introductions to care techniques. For example, the care information providing unit provides the user with information about care facilities. The care information providing unit can also provide information about how to use nursing care insurance. Furthermore, the care information providing unit can also introduce care techniques. For example, the care information providing unit provides the user with information about local nursing care facilities and supports the user in selecting an available facility. The care information providing unit can also provide a detailed guide on the application procedures and how to use nursing care insurance. Furthermore, the care information providing unit can provide training and practical advice on care techniques to help the user provide effective care at home. In this way, the care information providing unit can support the user's care of their family.

[0036] The comprehensive support system includes a support network providing unit that provides a support network. The support network providing unit provides the support network so that the user can receive the support they need. Examples of the support network include, but are not limited to, introductions to experts and information on community groups. For example, the support network providing unit introduces experts to the user. The support network providing unit can also provide information on community groups that the user can join. Furthermore, the support network providing unit can also provide information on support services that the user can use. For example, the support network providing unit introduces an appropriate expert based on the user's needs and helps the user receive the necessary support. The support network providing unit can also provide information on local community groups and online forums that the user can join, allowing the user to interact with other people and receive support. Furthermore, the support network providing unit can provide information on various support services that the user can use, allowing the user to receive the necessary support. In this way, the support network providing unit can ensure that the user receives the necessary support.

[0037] The collection unit can analyze the user's past data collection history and select the most appropriate collection method. For example, the collection unit preferentially selects a data collection method that the user has used favorably in the past. The collection unit can also select the most efficient collection method from the user's past data collection history. Furthermore, the collection unit can analyze the user's past data collection history and customize the collection method. For example, the collection unit analyzes data collection methods that the user has used in the past and selects the optimal collection method. The collection unit can also suggest an efficient collection method based on the user's data collection history. Furthermore, the collection unit can analyze the user's data collection history and customize the collection method to provide the user with the optimal data collection method. In this way, the collection unit can select the optimal collection method by analyzing the past data collection history.

[0038] When collecting data, the collection unit can filter the data based on the user's current living situation and areas of interest. For example, if the user is interested in health, the collection unit can prioritize collecting health-related data. Furthermore, if the user is interested in career advancement, the collection unit can prioritize collecting career-related data. Furthermore, if the user is interested in caring for a family member, the collection unit can prioritize collecting care-related data. For example, the collection unit filters data based on the user's areas of interest to collect highly relevant data. Furthermore, the collection unit can filter data based on the user's living situation to collect appropriate data. Furthermore, the collection unit can filter data based on the user's areas of interest and living situation to collect data that is useful to the user. In this way, the collection unit can filter data based on the user's areas of interest to collect highly relevant data.

[0039] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting data around the user's home. For example, the collection unit collects highly relevant data based on the user's geographical location information. Furthermore, the collection unit can analyze the user's location information and collect appropriate data. Furthermore, the collection unit can collect data that is useful to the user by taking into account the user's geographical location information. In this way, the collection unit can collect highly relevant data by taking into account the user's geographical location information.

[0040] When collecting data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit collects related data based on information shared by the user on social media. The collection unit can also collect related data based on information about accounts the user follows on social media. Furthermore, the collection unit can collect related data based on information about groups the user participates in on social media. For example, the collection unit analyzes the user's social media posts and collects highly relevant data. The collection unit can also analyze information about accounts the user follows and collect appropriate data. Furthermore, the collection unit can analyze information about social media groups the user participates in and collect data that is useful to the user. In this way, the collection unit can analyze the user's social media activities and collect related data.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can adjust the depth of the analysis based on the importance of the data. For example, the analysis unit evaluates the importance of the data and performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can adjust the depth of the analysis based on the importance of the data and perform an efficient analysis. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data and perform an efficient analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies a health-related analysis algorithm to health data. The analysis unit can also apply an economics-related analysis algorithm to economic data. The analysis unit can also apply a nursing care-related analysis algorithm to nursing care data. For example, the analysis unit applies a health-related analysis algorithm to health data to evaluate the health condition. The analysis unit can also apply an economics-related analysis algorithm to economic data to evaluate the economic situation. The analysis unit can also apply a nursing care-related analysis algorithm to nursing care data to evaluate the nursing care situation. This allows the analysis unit to apply an analysis algorithm depending on the category of data and perform a more accurate analysis.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of data that has been submitted earlier. Furthermore, the analysis unit can adjust the order of analysis based on the time of submission. For example, the analysis unit evaluates the time of data submission and prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of data that has been submitted earlier. Furthermore, the analysis unit can adjust the order of analysis based on the time of submission to perform efficient analysis. This allows the analysis unit to determine the priority of analysis based on the time of data submission and prioritize analysis of the most recent data.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit evaluates the relevance of the data and prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the data and perform efficient analysis. In this way, the analysis unit can adjust the order of analysis based on the relevance of the data and prioritize analysis of highly relevant data.

[0045] The providing unit can adjust the level of detail of the support provided based on the importance of the support when providing the support. For example, the providing unit provides detailed information for support with a high level of importance. The providing unit can also provide simplified information for support with a low level of importance. Furthermore, the providing unit can adjust the depth of the provision based on the importance of the support. For example, the providing unit evaluates the importance of the support and provides detailed information for support with a high level of importance. The providing unit can also provide simplified information for support with a low level of importance. Furthermore, the providing unit can adjust the depth of the provision based on the importance of the support and provide efficient support. In this way, the providing unit can adjust the level of detail of the provision based on the importance of the support and provide efficient support.

[0046] The providing unit can apply different providing algorithms depending on the category of support when providing the support. For example, the providing unit applies a health-related providing algorithm to health support. The providing unit can also apply an economics-related providing algorithm to financial support. Furthermore, the providing unit can apply a nursing care-related providing algorithm to nursing care support. For example, the providing unit applies a health-related providing algorithm to health support to provide health advice. The providing unit can also apply an economics-related providing algorithm to financial support to provide financial advice. Furthermore, the providing unit can apply a nursing care-related providing algorithm to nursing care support to provide nursing care information. In this way, the providing unit can apply a providing algorithm according to the category of support to provide more accurate support.

[0047] The providing unit can determine the priority of provision based on the time of submission of the support when providing the support. For example, the providing unit can provide the most recent support preferentially. The providing unit can also provide support that has been submitted earlier at a later date. Furthermore, the providing unit can adjust the order of provision based on the time of submission. For example, the providing unit evaluates the time of submission of the support and provides the most recent support preferentially. The providing unit can also provide support that has been submitted earlier at a later date. Furthermore, the providing unit can adjust the order of provision based on the time of submission to provide efficient support. In this way, the providing unit can determine the priority of provision based on the time of submission of the support and provide the most recent support preferentially.

[0048] The providing unit can adjust the order of provision based on the relevance of the support when providing the support. For example, the providing unit provides highly relevant support preferentially. The providing unit can also postpone providing less relevant support. Furthermore, the providing unit can adjust the order of provision based on the relevance of the support. For example, the providing unit evaluates the relevance of the support and provides highly relevant support preferentially. The providing unit can also postpone providing less relevant support. Furthermore, the providing unit can adjust the order of provision based on the relevance of the support to provide efficient support. In this way, the providing unit can adjust the order of provision based on the relevance of the support and prioritize providing highly relevant support.

[0049] When collecting health data, the health data collection unit can analyze the user's past health data and select the optimal collection method. For example, the health data collection unit prioritizes the selection of a health data collection method that the user has previously preferred. The health data collection unit can also select the most efficient collection method based on the user's past health data collection history. Furthermore, the health data collection unit can analyze the user's past health data collection history and customize the collection method. For example, the health data collection unit analyzes the health data collection methods the user has previously used and selects the optimal collection method. The health data collection unit can also suggest an efficient collection method based on the user's health data collection history. Furthermore, the health data collection unit can analyze the user's health data collection history and customize the collection method to provide the user with the optimal health data collection method. In this way, the health data collection unit can select the optimal collection method by analyzing the past health data.

[0050] When collecting health data, the health data collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the health data collection unit prioritizes collecting health data related to that area. Furthermore, when the user is traveling, the health data collection unit can prioritize collecting health data related to the user's travel destination. Furthermore, when the user is at home, the health data collection unit can prioritize collecting health data around the user's home. For example, the health data collection unit collects highly relevant health data based on the user's geographical location information. Furthermore, the health data collection unit can analyze the user's location information and collect appropriate health data. Furthermore, the health data collection unit can collect health data that is useful to the user by taking into account the user's geographical location information. In this way, the health data collection unit can collect highly relevant health data by taking into account the user's geographical location information.

[0051] When providing health advice, the health advice providing unit can analyze the user's past health data and provide optimal advice. The health advice providing unit, for example, proposes an optimal exercise plan based on the user's past health data. The health advice providing unit can also propose an optimal meal plan based on the user's past health data. Furthermore, the health advice providing unit can also propose an optimal stress management method based on the user's past health data. For example, the health advice providing unit analyzes the user's health data and provides an optimal exercise plan. The health advice providing unit can also analyze the user's health data and provide an optimal meal plan. Furthermore, the health advice providing unit can analyze the user's health data and provide an optimal stress management method. In this way, the health advice providing unit can provide optimal health advice by analyzing past health data.

[0052] When providing health advice, the health advice providing unit can provide optimal advice by taking into account the user's geographical location information. For example, when the user is in a specific area, the health advice providing unit can provide health advice related to the area. Furthermore, when the user is traveling, the health advice providing unit can also provide health advice related to the user's travel destination. Furthermore, when the user is at home, the health advice providing unit can also provide health advice related to the area around the user's home. For example, the health advice providing unit can provide highly relevant health advice based on the user's geographical location information. Furthermore, the health advice providing unit can analyze the user's location information and provide appropriate health advice. Furthermore, the health advice providing unit can provide health advice that is beneficial to the user by taking into account the user's geographical location information. In this way, the health advice providing unit can provide optimal health advice by taking into account the user's geographical location information.

[0053] When providing legal advice, the legal advice providing unit can analyze the user's past legal data and provide optimal advice. The legal advice providing unit, for example, suggests an optimal method for creating a will based on the user's past legal data. The legal advice providing unit can also suggest optimal inheritance procedures based on the user's past legal data. Furthermore, the legal advice providing unit can also suggest an optimal method for creating a contract based on the user's past legal data. For example, the legal advice providing unit analyzes the user's legal data and provides an optimal method for creating a will. The legal advice providing unit can also analyze the user's legal data and provide optimal inheritance procedures. Furthermore, the legal advice providing unit can analyze the user's legal data and provide an optimal method for creating a contract. In this way, the legal advice providing unit can provide optimal legal advice by analyzing past legal data.

[0054] When providing legal advice, the legal advice providing unit can provide optimal advice by taking into account the user's geographical location information. For example, if the user is in a specific area, the legal advice providing unit can provide legal advice related to the area. Furthermore, if the user is traveling, the legal advice providing unit can also provide legal advice related to the user's travel destination. Furthermore, if the user is at home, the legal advice providing unit can also provide legal advice around the user's home. For example, the legal advice providing unit can provide highly relevant legal advice based on the user's geographical location information. Furthermore, the legal advice providing unit can analyze the user's location information and provide appropriate legal advice. Furthermore, the legal advice providing unit can provide legal advice that is beneficial to the user by taking into account the user's geographical location information. In this way, the legal advice providing unit can provide optimal legal advice by taking into account the user's geographical location information.

[0055] When assisting in the creation of a will, the will creation support unit can analyze the user's past legal data and support the creation of an optimal will. The will creation support unit, for example, suggests an optimal will creation method based on the user's past legal data. The will creation support unit can also suggest optimal inheritance procedures based on the user's past legal data. Furthermore, the will creation support unit can also suggest an optimal contract creation method based on the user's past legal data. For example, the will creation support unit analyzes the user's legal data and provides an optimal will creation method. The will creation support unit can also analyze the user's legal data and provide optimal inheritance procedures. Furthermore, the will creation support unit can analyze the user's legal data and provide an optimal contract creation method. In this way, the will creation support unit can support the creation of an optimal will by analyzing past legal data.

[0056] When assisting in the creation of a will, the will creation support unit can support the creation of an optimal will by taking into account the user's geographical location information. For example, if the user is in a specific area, the will creation support unit can provide a will creation method related to that area. Furthermore, if the user is traveling, the will creation support unit can also provide a will creation method related to the user's travel destination. Furthermore, if the user is at home, the will creation support unit can also provide a will creation method near the user's home. For example, the will creation support unit can provide a highly relevant will creation method based on the user's geographical location information. Furthermore, the will creation support unit can analyze the user's location information and provide an appropriate will creation method. Furthermore, the will creation support unit can provide a will creation method that is beneficial to the user by taking into account the user's geographical location information. In this way, the will creation support unit can support the creation of an optimal will by taking into account the user's geographical location information.

[0057] When providing economic advice, the economic advice providing unit can analyze the user's past economic data and provide optimal advice. The economic advice providing unit, for example, proposes an optimal savings plan based on the user's past economic data. The economic advice providing unit can also propose an optimal investment plan based on the user's past economic data. Furthermore, the economic advice providing unit can also propose an optimal budget management method based on the user's past economic data. For example, the economic advice providing unit analyzes the user's economic data and provides an optimal savings plan. The economic advice providing unit can also analyze the user's economic data and provide an optimal investment plan. Furthermore, the economic advice providing unit can analyze the user's economic data and provide an optimal budget management method. In this way, the economic advice providing unit can provide optimal economic advice by analyzing past economic data.

[0058] When providing economic advice, the economic advice providing unit can provide optimal advice by taking into account the user's geographical location information. For example, if the user is in a specific area, the economic advice providing unit can provide economic advice related to that area. Furthermore, if the user is traveling, the economic advice providing unit can also provide economic advice related to the user's travel destination. Furthermore, if the user is at home, the economic advice providing unit can also provide economic advice related to the area around the user's home. For example, the economic advice providing unit can provide highly relevant economic advice based on the user's geographical location information. Furthermore, the economic advice providing unit can analyze the user's location information and provide appropriate economic advice. Furthermore, the economic advice providing unit can provide economic advice that is useful to the user by taking into account the user's geographical location information. In this way, the economic advice providing unit can provide optimal economic advice by taking into account the user's geographical location information.

[0059] When providing care information, the care information providing unit can analyze the user's past care data and provide optimal information. The care information providing unit, for example, proposes an optimal care plan based on the user's past care data. The care information providing unit can also propose an optimal care facility based on the user's past care data. Furthermore, the care information providing unit can also propose optimal care services based on the user's past care data. For example, the care information providing unit analyzes the user's care data and provides an optimal care plan. The care information providing unit can also analyze the user's care data and provide an optimal care facility. Furthermore, the care information providing unit can analyze the user's care data and provide optimal care services. In this way, the care information providing unit can provide optimal care information by analyzing the past care data.

[0060] When providing care information, the care information providing unit can provide optimal information by taking into account the user's geographical location information. For example, when the user is in a specific area, the care information providing unit provides care information related to that area. Furthermore, when the user is traveling, the care information providing unit can also provide care information related to the user's travel destination. Furthermore, when the user is at home, the care information providing unit can also provide care information around the user's home. For example, the care information providing unit provides highly relevant care information based on the user's geographical location information. Furthermore, the care information providing unit can analyze the user's location information and provide appropriate care information. Furthermore, the care information providing unit can provide care information that is useful to the user by taking into account the user's geographical location information. In this way, the care information providing unit can provide optimal care information by taking into account the user's geographical location information.

[0061] When providing a support network, the support network providing unit can analyze the user's past network usage data and provide the optimal network. The support network providing unit, for example, proposes the optimal support network based on the user's past network usage data. The support network providing unit can also propose the optimal support group based on the user's past network usage data. Furthermore, the support network providing unit can also propose the optimal support service based on the user's past network usage data. For example, the support network providing unit analyzes the user's network usage data and provides the optimal support network. The support network providing unit can also analyze the user's network usage data and provide the optimal support group. Furthermore, the support network providing unit can analyze the user's network usage data and provide the optimal support service. In this way, the support network providing unit can provide the optimal support network by analyzing the past network usage data.

[0062] When providing a support network, the support network providing unit can provide an optimal network by taking into account the user's geographical location information. For example, when the user is in a specific area, the support network providing unit can provide a support network related to that area. Furthermore, when the user is traveling, the support network providing unit can also provide a support network related to the user's travel destination. Furthermore, when the user is at home, the support network providing unit can also provide a support network in the vicinity of the user's home. For example, the support network providing unit provides a highly relevant support network based on the user's geographical location information. Furthermore, the support network providing unit can analyze the user's location information and provide an appropriate support network. Furthermore, the support network providing unit can provide a support network that is useful to the user by taking into account the user's geographical location information. In this way, the support network providing unit can provide an optimal support network by taking into account the user's geographical location information.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] When collecting user data, the collection unit can grasp the user's current activity status in real time and collect data at appropriate times. For example, when the user is exercising, exercise data can be collected preferentially, and when the user is resting, data related to relaxation can be collected. The collection unit can also adjust the frequency of data collection according to the user's activity status to reduce the burden on the user. Furthermore, the collection unit can analyze the user's activity status and suggest the optimal data collection method. This allows the collection unit to achieve flexible data collection according to the user's activity status.

[0065] When collecting a user's health data, the health data collection unit can collect the data taking into account the user's lifestyle habits and environmental factors. For example, if the user is in a hot and humid environment, data related to the risk of heatstroke can be collected, and if the user is in a cold region, data related to the risk of hypothermia can be collected. The health data collection unit can also collect data taking into account the user's lifestyle habits (e.g., smoking and drinking habits) and evaluate health risks. Furthermore, the health data collection unit can collect data taking into account the user's environmental factors (e.g., air quality and noise level) and evaluate the impact on health. This allows the health data collection unit to achieve comprehensive health data collection that takes into account the user's lifestyle habits and environmental factors.

[0066] The health advice providing unit can provide health advice according to the season and climate based on the user's health data. For example, in the summer, it can provide advice on hydration and appropriate clothing to prevent heatstroke, and in the winter, it can provide advice on nutritional intake and cold weather measures to prevent colds and influenza. The health advice providing unit can also provide advice on region-specific health risks by taking into account the climatic characteristics of the user's region of residence. Furthermore, the health advice providing unit can provide advice on health management during travel based on climatic information of the user's travel destination. This allows the health advice providing unit to provide appropriate health advice according to the season and climate.

[0067] When providing advice to a user regarding a legal issue, the legal advice providing unit can take into consideration the user's past legal trouble history. For example, if the user has previously experienced trouble with an inheritance issue, the legal advice providing unit can provide detailed advice regarding inheritance, and if the user has previously experienced trouble with a contract, the legal advice providing unit can provide advice regarding points to be careful about when drafting a contract. The legal advice providing unit can also analyze the user's past legal trouble history and provide advice to prevent future trouble. Furthermore, the legal advice providing unit can provide optimal legal support based on the user's legal trouble history. This allows the legal advice providing unit to provide appropriate legal advice that takes into consideration the user's past legal trouble history.

[0068] When a user creates a will, the will creation support unit can propose the optimal will creation method taking into consideration the user's family structure and financial situation. For example, if the user has multiple heirs, it can provide specific advice on how to divide the inheritance, and if the user wants to bequeath specific assets to specific heirs, it can provide advice on how to do so. The will creation support unit can also propose the optimal will creation method taking into consideration the user's financial situation (e.g., types of real estate and financial assets). Furthermore, the will creation support unit can provide advice to prevent future inheritance disputes based on the user's family structure and financial situation. In this way, the will creation support unit can provide appropriate will creation support taking into consideration the user's family structure and financial situation.

[0069] The processing flow of the first embodiment will be briefly explained below.

[0070] Step 1: The collection unit collects user data. The user data includes behavioral data, health data, purchase data, etc. For example, the collection unit collects the user's health data using a wearable device, as well as the user's online activity data and purchase history data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms, and includes assessing the user's health status, identifying behavioral patterns, and understanding purchasing trends. Step 3: The providing unit provides various types of support based on the analysis results obtained by the analyzing unit. The various types of support include health advice, economic advice, legal advice, etc. For example, the providing unit provides health advice based on the user's health data, economic advice based on the economic data, and legal advice based on the legal data.

[0071] (Example 2) A comprehensive support system according to an embodiment of the present invention provides comprehensive services to help single women cope with various life stages and challenges. This comprehensive support system supports single women in selecting appropriate insurance products and enrollment procedures, and provides advice on health management and preventive medicine. It also provides support for career advancement and skill development, advice on estates and inheritance, and support for preparing wills. It also provides advice on savings, investments, and budget management, and provides family care and a support network. This system provides comprehensive support tailored to the user's needs, helping single women live a richer life. For example, a system is constructed that collects user data, analyzes it using AI, and provides personalized advice. Health management support involves collecting health data using a wearable device, which is then analyzed by AI to provide advice. Furthermore, estate and inheritance advice involves using legal tools to support the preparation of wills. This allows the comprehensive support system to provide comprehensive support to help single women cope with various life stages and challenges.

[0072] The comprehensive support system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects user data. The user data includes, but is not limited to, behavioral data, health data, and purchase data. The collection unit collects the user's health data using, for example, a wearable device. The collection unit can also collect the user's online activity data. For example, the collection unit can collect the user's website browsing history and social media posting data. The collection unit can also collect the user's purchase history data. For example, the collection unit can collect data on products purchased by the user through online shopping. The analysis unit analyzes the data collected by the collection unit. The analysis can be performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to, examples. For example, the analysis unit analyzes the user's health data and evaluates the user's health condition. The analysis unit can also analyze the user's behavioral data and identify behavioral patterns. The analysis unit can also analyze the user's purchase data and understand purchasing trends. The provision unit provides various types of support based on the analysis results obtained by the analysis unit. The various types of support include, but are not limited to, health advice, financial advice, and legal advice. For example, the providing unit may provide health advice based on the user's health data. The providing unit may also provide financial advice based on the user's financial data. Furthermore, the providing unit may also provide legal advice based on the user's legal data. As a result, the comprehensive support system according to the embodiment can provide personalized support tailored to the user's needs by collecting and analyzing user data and providing various types of support.

[0073] The comprehensive support system includes a health data collection unit that collects health data. The health data collection unit collects the user's health data. The health data includes, but is not limited to, heart rate, blood pressure, and exercise volume. For example, the health data collection unit collects the user's heart rate using a wearable device. The health data collection unit can also collect the user's blood pressure using a blood pressure monitor. The health data collection unit can also collect the user's exercise volume using a fitness tracker. For example, the health data collection unit collects heart rate data in real time from a wearable device worn by the user. The health data collection unit can also periodically collect blood pressure data from a blood pressure monitor used by the user at home. The health data collection unit can also collect exercise volume data from a fitness tracker used by the user on a daily basis. This allows the health data collection unit to understand the user's health condition and provide appropriate support.

[0074] The comprehensive support system includes a health advice providing unit that provides health advice. The health advice providing unit provides health advice based on the user's health data. The health advice includes, but is not limited to, dietary guidance, exercise guidance, and suggestions for improving lifestyle habits. For example, the health advice providing unit evaluates the user's diet and suggests a balanced meal plan. The health advice providing unit can also evaluate the user's exercise habits and suggest an appropriate exercise plan. Furthermore, the health advice providing unit can evaluate the user's lifestyle habits and suggest areas for improvement. For example, the health advice providing unit can analyze the user's dietary data and provide a nutritionally balanced meal plan. The health advice providing unit can also analyze the user's exercise data and provide an effective exercise plan. Furthermore, the health advice providing unit can analyze the user's lifestyle habit data and suggest healthy lifestyle habits. In this way, the health advice providing unit can support the user's health management.

[0075] The comprehensive support system includes a legal advice providing unit that provides specific legal advice regarding inheritance and inheritance. The legal advice providing unit provides specific legal advice to resolve issues regarding the user's inheritance and inheritance. Specific legal advice includes, but is not limited to, methods for dividing the inheritance and measures for inheritance tax. For example, the legal advice providing unit evaluates the user's inheritance and proposes an appropriate method for dividing the inheritance. The legal advice providing unit can also propose measures to reduce the user's inheritance tax burden. Furthermore, the legal advice providing unit can support the user in preparing a will. For example, the legal advice providing unit analyzes the user's inheritance data and proposes an optimal method for dividing the inheritance. The legal advice providing unit can also analyze the user's inheritance tax data and propose effective measures for inheritance tax. Furthermore, the legal advice providing unit can support the user in preparing a will and prepare a will that meets legal requirements. In this way, the legal advice providing unit can resolve issues regarding the user's inheritance and inheritance.

[0076] The comprehensive support system includes a will creation support unit that supports the creation of a will. The will creation support unit supports the user in creating an appropriate will. The will creation support includes, but is not limited to, for example, providing a will template and checking legal requirements. For example, the will creation support unit provides a will template to the user. The will creation support unit can also check whether the will created by the user meets legal requirements. Furthermore, the will creation support unit can provide advice to the user when creating a will. For example, the will creation support unit provides a will template to the user and instructs the user to enter necessary information. The will creation support unit can also have a legal expert review the will created by the user to check whether it meets legal requirements. Furthermore, the will creation support unit can provide advice to the user when creating a will and support the user in creating an appropriate will. In this way, the will creation support unit enables the user to create an appropriate will.

[0077] The comprehensive support system includes an economic advice providing unit that provides specific advice on savings, investments, and budget management. The economic advice providing unit provides specific advice to support the user's financial management. Examples of specific advice include, but are not limited to, investment strategies, savings plans, and budget management methods. For example, the economic advice providing unit evaluates the user's investment portfolio and proposes an optimal investment strategy. The economic advice providing unit can also set the user's savings goal and propose a savings plan. Furthermore, the economic advice providing unit can evaluate the user's income and expenses and propose an effective budget management method. For example, the economic advice providing unit can analyze the user's investment data and propose an investment strategy that takes into account the balance between risk and return. The economic advice providing unit can also analyze the user's savings data and propose a savings plan aimed at achieving the goal. Furthermore, the economic advice providing unit can analyze the user's income and expenditure data and propose a budget management method to improve the income and expenditure balance. In this way, the economic advice providing unit can support the user's financial management.

[0078] The comprehensive support system includes a care information providing unit that provides information about family care. The care information providing unit provides information to support the user's care of their family. The care information includes, but is not limited to, information about care facilities, how to use nursing care insurance, and introductions to care techniques. For example, the care information providing unit provides the user with information about care facilities. The care information providing unit can also provide information about how to use nursing care insurance. Furthermore, the care information providing unit can also introduce care techniques. For example, the care information providing unit provides the user with information about local nursing care facilities and supports the user in selecting an available facility. The care information providing unit can also provide a detailed guide on the application procedures and how to use nursing care insurance. Furthermore, the care information providing unit can provide training and practical advice on care techniques to help the user provide effective care at home. In this way, the care information providing unit can support the user's care of their family.

[0079] The comprehensive support system includes a support network providing unit that provides a support network. The support network providing unit provides the support network so that the user can receive the support they need. Examples of the support network include, but are not limited to, introductions to experts and information on community groups. For example, the support network providing unit introduces experts to the user. The support network providing unit can also provide information on community groups that the user can join. Furthermore, the support network providing unit can also provide information on support services that the user can use. For example, the support network providing unit introduces an appropriate expert based on the user's needs and helps the user receive the necessary support. The support network providing unit can also provide information on local community groups and online forums that the user can join, allowing the user to interact with other people and receive support. Furthermore, the support network providing unit can provide information on various support services that the user can use, allowing the user to receive the necessary support. In this way, the support network providing unit can ensure that the user receives the necessary support.

[0080] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the collection unit can temporarily stop data collection and resume it later. For example, the collection unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. The collection unit can also record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows the collection unit to adjust the timing of data collection according to the user's emotions and reduce the user's burden. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] The collection unit can analyze the user's past data collection history and select the most appropriate collection method. For example, the collection unit preferentially selects a data collection method that the user has used favorably in the past. The collection unit can also select the most efficient collection method from the user's past data collection history. Furthermore, the collection unit can analyze the user's past data collection history and customize the collection method. For example, the collection unit analyzes data collection methods that the user has used in the past and selects the optimal collection method. The collection unit can also suggest an efficient collection method based on the user's data collection history. Furthermore, the collection unit can analyze the user's data collection history and customize the collection method to provide the user with the optimal data collection method. In this way, the collection unit can select the optimal collection method by analyzing the past data collection history.

[0082] When collecting data, the collection unit can filter the data based on the user's current living situation and areas of interest. For example, if the user is interested in health, the collection unit can prioritize collecting health-related data. Furthermore, if the user is interested in career advancement, the collection unit can prioritize collecting career-related data. Furthermore, if the user is interested in caring for a family member, the collection unit can prioritize collecting care-related data. For example, the collection unit filters data based on the user's areas of interest to collect highly relevant data. Furthermore, the collection unit can filter data based on the user's living situation to collect appropriate data. Furthermore, the collection unit can filter data based on the user's areas of interest and living situation to collect data that is useful to the user. In this way, the collection unit can filter data based on the user's areas of interest to collect highly relevant data.

[0083] The collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting data related to relaxation. Furthermore, if the user is relaxed, the collection unit can also prioritize collecting data related to health and career. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting data of high importance. For example, the collection unit can estimate the user's emotions and prioritize data based on the emotions. Furthermore, the collection unit can analyze the user's emotion data and prioritize collecting important data. Furthermore, the collection unit can adjust the data priority based on the user's emotions and collect data that is important to the user. In this way, the collection unit can prioritize data according to the user's emotions and prioritize collecting important data. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0084] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting data around the user's home. For example, the collection unit collects highly relevant data based on the user's geographical location information. Furthermore, the collection unit can analyze the user's location information and collect appropriate data. Furthermore, the collection unit can collect data that is useful to the user by taking into account the user's geographical location information. In this way, the collection unit can collect highly relevant data by taking into account the user's geographical location information.

[0085] When collecting data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit collects related data based on information shared by the user on social media. The collection unit can also collect related data based on information about accounts the user follows on social media. Furthermore, the collection unit can collect related data based on information about groups the user participates in on social media. For example, the collection unit analyzes the user's social media posts and collects highly relevant data. The collection unit can also analyze information about accounts the user follows and collect appropriate data. Furthermore, the collection unit can analyze information about social media groups the user participates in and collect data that is useful to the user. In this way, the collection unit can analyze the user's social media activities and collect related data.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. For example, the analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the emotions. Furthermore, the analysis unit can analyze the user's emotion data and select an appropriate presentation method. Furthermore, the analysis unit can adjust the presentation method of the analysis results based on the user's emotions to provide analysis results that are easy for the user to understand. In this way, the analysis unit can adjust the presentation method of the analysis according to the user's emotions and provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can adjust the depth of the analysis based on the importance of the data. For example, the analysis unit evaluates the importance of the data and performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can adjust the depth of the analysis based on the importance of the data and perform an efficient analysis. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data and perform an efficient analysis.

[0088] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies a health-related analysis algorithm to health data. The analysis unit can also apply an economics-related analysis algorithm to economic data. The analysis unit can also apply a nursing care-related analysis algorithm to nursing care data. For example, the analysis unit applies a health-related analysis algorithm to health data to evaluate the health condition. The analysis unit can also apply an economics-related analysis algorithm to economic data to evaluate the economic situation. The analysis unit can also apply a nursing care-related analysis algorithm to nursing care data to evaluate the nursing care situation. This allows the analysis unit to apply an analysis algorithm depending on the category of data and perform a more accurate analysis.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with a visually stimulating effect. For example, the analysis unit can estimate the user's emotions and adjust the length of the analysis result based on the emotions. Furthermore, the analysis unit can analyze the user's emotion data and provide an analysis result of an appropriate length. Furthermore, the analysis unit can adjust the length of the analysis result based on the user's emotions and provide an analysis result of an appropriate length for the user. Thus, the analysis unit can adjust the length of the analysis according to the user's emotions and provide an analysis result of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0090] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of data that has been submitted earlier. Furthermore, the analysis unit can adjust the order of analysis based on the time of submission. For example, the analysis unit evaluates the time of data submission and prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of data that has been submitted earlier. Furthermore, the analysis unit can adjust the order of analysis based on the time of submission to perform efficient analysis. This allows the analysis unit to determine the priority of analysis based on the time of data submission and prioritize analysis of the most recent data.

[0091] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit evaluates the relevance of the data and prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the data and perform efficient analysis. In this way, the analysis unit can adjust the order of analysis based on the relevance of the data and prioritize analysis of highly relevant data.

[0092] The providing unit can estimate the user's emotions and adjust the way in which support is expressed based on the estimated user emotions. For example, when the user is feeling stressed, the providing unit can provide simple, highly visible support. Furthermore, when the user is relaxed, the providing unit can provide detailed support. Furthermore, when the user is in a hurry, the providing unit can provide support that focuses on the key points. For example, the providing unit can estimate the user's emotions and adjust the way in which support is expressed based on the emotions. Furthermore, the providing unit can analyze the user's emotion data and select an appropriate way of expression. Furthermore, the providing unit can adjust the way in which support is expressed based on the user's emotions to provide support that is easy for the user to understand. In this way, the providing unit can adjust the way in which support is expressed according to the user's emotions and provide support that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0093] The providing unit can adjust the level of detail of the support provided based on the importance of the support when providing the support. For example, the providing unit provides detailed information for support with a high level of importance. The providing unit can also provide simplified information for support with a low level of importance. Furthermore, the providing unit can adjust the depth of the provision based on the importance of the support. For example, the providing unit evaluates the importance of the support and provides detailed information for support with a high level of importance. The providing unit can also provide simplified information for support with a low level of importance. Furthermore, the providing unit can adjust the depth of the provision based on the importance of the support and provide efficient support. In this way, the providing unit can adjust the level of detail of the provision based on the importance of the support and provide efficient support.

[0094] The providing unit can apply different providing algorithms depending on the category of support when providing the support. For example, the providing unit applies a health-related providing algorithm to health support. The providing unit can also apply an economics-related providing algorithm to financial support. Furthermore, the providing unit can apply a nursing care-related providing algorithm to nursing care support. For example, the providing unit applies a health-related providing algorithm to health support to provide health advice. The providing unit can also apply an economics-related providing algorithm to financial support to provide financial advice. Furthermore, the providing unit can apply a nursing care-related providing algorithm to nursing care support to provide nursing care information. In this way, the providing unit can apply a providing algorithm according to the category of support to provide more accurate support.

[0095] The providing unit can estimate the user's emotions and adjust the length of the support provided based on the estimated user's emotions. For example, if the user is in a hurry, the providing unit can provide short, to-the-point support. The providing unit can also provide detailed support if the user is relaxed. Furthermore, if the user is excited, the providing unit can provide support with visually stimulating effects. For example, the providing unit can estimate the user's emotions and adjust the length of the support based on the emotions. The providing unit can also analyze the user's emotion data and provide support of an appropriate length. Furthermore, the providing unit can adjust the length of the support based on the user's emotions and provide support of an appropriate length for the user. Thus, the providing unit can adjust the length of the support according to the user's emotions and provide support of an appropriate length for the user. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0096] The providing unit can determine the priority of provision based on the time of submission of the support when providing the support. For example, the providing unit can provide the most recent support preferentially. The providing unit can also provide support that has been submitted earlier at a later date. Furthermore, the providing unit can adjust the order of provision based on the time of submission. For example, the providing unit evaluates the time of submission of the support and provides the most recent support preferentially. The providing unit can also provide support that has been submitted earlier at a later date. Furthermore, the providing unit can adjust the order of provision based on the time of submission to provide efficient support. In this way, the providing unit can determine the priority of provision based on the time of submission of the support and provide the most recent support preferentially.

[0097] The providing unit can adjust the order of provision based on the relevance of the support when providing the support. For example, the providing unit provides highly relevant support preferentially. The providing unit can also postpone providing less relevant support. Furthermore, the providing unit can adjust the order of provision based on the relevance of the support. For example, the providing unit evaluates the relevance of the support and provides highly relevant support preferentially. The providing unit can also postpone providing less relevant support. Furthermore, the providing unit can adjust the order of provision based on the relevance of the support to provide efficient support. In this way, the providing unit can adjust the order of provision based on the relevance of the support and prioritize providing highly relevant support.

[0098] The health data collection unit can estimate the user's emotions and adjust the timing of health data collection based on the estimated user emotions. For example, if the user is feeling stressed, the health data collection unit can reduce the frequency of health data collection to reduce the user's burden. Furthermore, if the user is relaxed, the health data collection unit can increase the frequency of health data collection and collect more detailed data. Furthermore, if the user is in a hurry, the health data collection unit can temporarily stop health data collection and resume it later. For example, the health data collection unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. The health data collection unit can also record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, the health data collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows the health data collection unit to adjust the timing of health data collection according to the user's emotions and reduce the user's burden. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0099] When collecting health data, the health data collection unit can analyze the user's past health data and select the optimal collection method. For example, the health data collection unit prioritizes the selection of a health data collection method that the user has previously preferred. The health data collection unit can also select the most efficient collection method based on the user's past health data collection history. Furthermore, the health data collection unit can analyze the user's past health data collection history and customize the collection method. For example, the health data collection unit analyzes the health data collection methods the user has previously used and selects the optimal collection method. The health data collection unit can also suggest an efficient collection method based on the user's health data collection history. Furthermore, the health data collection unit can analyze the user's health data collection history and customize the collection method to provide the user with the optimal health data collection method. In this way, the health data collection unit can select the optimal collection method by analyzing the past health data.

[0100] The health data collection unit can estimate the user's emotions and prioritize the health data to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, the health data collection unit can prioritize collecting health data related to relaxation. Furthermore, if the user is relaxed, the health data collection unit can prioritize collecting health data related to exercise and nutrition. Furthermore, if the user is in a hurry, the health data collection unit can prioritize collecting health data of high importance. For example, the health data collection unit can estimate the user's emotions and prioritize the health data based on the emotions. Furthermore, the health data collection unit can analyze the user's emotional data and prioritize collecting important health data. Furthermore, the health data collection unit can adjust the priority of health data based on the user's emotions and collect health data important to the user. In this way, the health data collection unit can prioritize the health data according to the user's emotions and prioritize collecting important health data. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0101] When collecting health data, the health data collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the health data collection unit prioritizes collecting health data related to that area. Furthermore, when the user is traveling, the health data collection unit can prioritize collecting health data related to the user's travel destination. Furthermore, when the user is at home, the health data collection unit can prioritize collecting health data around the user's home. For example, the health data collection unit collects highly relevant health data based on the user's geographical location information. Furthermore, the health data collection unit can analyze the user's location information and collect appropriate health data. Furthermore, the health data collection unit can collect health data that is useful to the user by taking into account the user's geographical location information. In this way, the health data collection unit can collect highly relevant health data by taking into account the user's geographical location information.

[0102] The health advice providing unit can estimate the user's emotions and adjust the way health advice is presented based on the estimated user's emotions. For example, when the user is feeling stressed, the health advice providing unit can provide simple, highly visible health advice. Furthermore, when the user is relaxed, the health advice providing unit can provide detailed health advice. Furthermore, when the user is in a hurry, the health advice providing unit can provide health advice that focuses on the key points. For example, the health advice providing unit can estimate the user's emotions and adjust the way health advice is presented based on the emotions. Furthermore, the health advice providing unit can analyze the user's emotion data and select an appropriate way of presenting the health advice. Furthermore, the health advice providing unit can adjust the way health advice is presented based on the user's emotions to provide health advice that is easy for the user to understand. In this way, the health advice providing unit can adjust the way health advice is presented according to the user's emotions and provide health advice that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0103] When providing health advice, the health advice providing unit can analyze the user's past health data and provide optimal advice. The health advice providing unit, for example, proposes an optimal exercise plan based on the user's past health data. The health advice providing unit can also propose an optimal meal plan based on the user's past health data. Furthermore, the health advice providing unit can also propose an optimal stress management method based on the user's past health data. For example, the health advice providing unit analyzes the user's health data and provides an optimal exercise plan. The health advice providing unit can also analyze the user's health data and provide an optimal meal plan. Furthermore, the health advice providing unit can analyze the user's health data and provide an optimal stress management method. In this way, the health advice providing unit can provide optimal health advice by analyzing past health data.

[0104] The health advice providing unit can estimate the user's emotions and prioritize health advice based on the estimated user emotions. For example, if the user is feeling stressed, the health advice providing unit can prioritize providing advice related to stress management. Furthermore, if the user is relaxed, the health advice providing unit can prioritize providing advice related to exercise and nutrition. Furthermore, if the user is in a hurry, the health advice providing unit can prioritize providing advice of high importance. For example, the health advice providing unit can estimate the user's emotions and prioritize health advice based on the emotions. Furthermore, the health advice providing unit can analyze the user's emotion data and prioritize providing important health advice. Furthermore, the health advice providing unit can adjust the priority of health advice based on the user's emotions and provide health advice that is important to the user. In this way, the health advice providing unit can prioritize health advice according to the user's emotions and prioritize providing important health advice. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0105] When providing health advice, the health advice providing unit can provide optimal advice by taking into account the user's geographical location information. For example, when the user is in a specific area, the health advice providing unit can provide health advice related to the area. Furthermore, when the user is traveling, the health advice providing unit can also provide health advice related to the user's travel destination. Furthermore, when the user is at home, the health advice providing unit can also provide health advice related to the area around the user's home. For example, the health advice providing unit can provide highly relevant health advice based on the user's geographical location information. Furthermore, the health advice providing unit can analyze the user's location information and provide appropriate health advice. Furthermore, the health advice providing unit can provide health advice that is beneficial to the user by taking into account the user's geographical location information. In this way, the health advice providing unit can provide optimal health advice by taking into account the user's geographical location information.

[0106] The legal advice providing unit can estimate the user's emotions and adjust the manner in which legal advice is presented based on the estimated user emotions. For example, when the user is feeling stressed, the legal advice providing unit can provide simple, highly visible legal advice. Furthermore, when the user is relaxed, the legal advice providing unit can provide detailed legal advice. Furthermore, when the user is in a hurry, the legal advice providing unit can provide legal advice that focuses on the key points. For example, the legal advice providing unit can estimate the user's emotions and adjust the manner in which legal advice is presented based on the emotions. Furthermore, the legal advice providing unit can analyze the user's emotion data and select an appropriate manner of presentation. Furthermore, the legal advice providing unit can adjust the manner in which legal advice is presented based on the user's emotions to provide legal advice that is easy for the user to understand. In this way, the legal advice providing unit can adjust the manner in which legal advice is presented according to the user's emotions and provide legal advice that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0107] When providing legal advice, the legal advice providing unit can analyze the user's past legal data and provide optimal advice. The legal advice providing unit, for example, suggests an optimal method for creating a will based on the user's past legal data. The legal advice providing unit can also suggest optimal inheritance procedures based on the user's past legal data. Furthermore, the legal advice providing unit can also suggest an optimal method for creating a contract based on the user's past legal data. For example, the legal advice providing unit analyzes the user's legal data and provides an optimal method for creating a will. The legal advice providing unit can also analyze the user's legal data and provide optimal inheritance procedures. Furthermore, the legal advice providing unit can analyze the user's legal data and provide an optimal method for creating a contract. In this way, the legal advice providing unit can provide optimal legal advice by analyzing past legal data.

[0108] The legal advice providing unit can estimate the user's emotions and determine the priority of legal advice based on the estimated user emotions. For example, if the user is feeling stressed, the legal advice providing unit can prioritize providing legal advice that reduces stress. Furthermore, if the user is relaxed, the legal advice providing unit can prioritize providing detailed legal advice. Furthermore, if the user is in a hurry, the legal advice providing unit can prioritize providing important legal advice. For example, the legal advice providing unit can estimate the user's emotions and determine the priority of legal advice based on the emotions. Furthermore, the legal advice providing unit can analyze the user's emotion data and prioritize providing important legal advice. Furthermore, the legal advice providing unit can adjust the priority of legal advice based on the user's emotions and provide important legal advice to the user. In this way, the legal advice providing unit can prioritize legal advice according to the user's emotions and prioritize providing important legal advice. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0109] When providing legal advice, the legal advice providing unit can provide optimal advice by taking into account the user's geographical location information. For example, if the user is in a specific area, the legal advice providing unit can provide legal advice related to the area. Furthermore, if the user is traveling, the legal advice providing unit can also provide legal advice related to the user's travel destination. Furthermore, if the user is at home, the legal advice providing unit can also provide legal advice around the user's home. For example, the legal advice providing unit can provide highly relevant legal advice based on the user's geographical location information. Furthermore, the legal advice providing unit can analyze the user's location information and provide appropriate legal advice. Furthermore, the legal advice providing unit can provide legal advice that is beneficial to the user by taking into account the user's geographical location information. In this way, the legal advice providing unit can provide optimal legal advice by taking into account the user's geographical location information.

[0110] The will creation support unit can estimate the user's emotions and adjust the expression method for creating a will based on the estimated user's emotions. For example, if the user is feeling stressed, the will creation support unit can provide a simple and highly visible will creation method. Furthermore, if the user is relaxed, the will creation support unit can provide a detailed will creation method. Furthermore, if the user is in a hurry, the will creation support unit can provide a will creation method that focuses on the main points. For example, the will creation support unit can estimate the user's emotions and adjust the expression method for creating a will based on the emotions. Furthermore, the will creation support unit can analyze the user's emotion data and select an appropriate expression method. Furthermore, the will creation support unit can adjust the expression method for creating a will based on the user's emotions and provide a will creation method that is easy for the user to understand. In this way, the will creation support unit can adjust the expression method for creating a will according to the user's emotions and provide a will creation method that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0111] When assisting in the creation of a will, the will creation support unit can analyze the user's past legal data and support the creation of an optimal will. The will creation support unit, for example, suggests an optimal will creation method based on the user's past legal data. The will creation support unit can also suggest optimal inheritance procedures based on the user's past legal data. Furthermore, the will creation support unit can also suggest an optimal contract creation method based on the user's past legal data. For example, the will creation support unit analyzes the user's legal data and provides an optimal will creation method. The will creation support unit can also analyze the user's legal data and provide optimal inheritance procedures. Furthermore, the will creation support unit can analyze the user's legal data and provide an optimal contract creation method. In this way, the will creation support unit can support the creation of an optimal will by analyzing past legal data.

[0112] The will writing support unit can estimate the user's emotions and determine the priority of will writing based on the estimated user's emotions. For example, if the user is feeling stressed, the will writing support unit can prioritize providing a will writing method that reduces stress. Furthermore, if the user is relaxed, the will writing support unit can prioritize providing a detailed will writing method. Furthermore, if the user is in a hurry, the will writing support unit can prioritize providing a will writing method that is more important. For example, the will writing support unit can estimate the user's emotions and determine the priority of will writing based on the emotions. Furthermore, the will writing support unit can analyze the user's emotion data and prioritize providing an important will writing method. Furthermore, the will writing support unit can adjust the priority of will writing based on the user's emotions and provide a will writing method that is important to the user. In this way, the will writing support unit can determine the priority of will writing according to the user's emotions and prioritize providing an important will writing method. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0113] When assisting in the creation of a will, the will creation support unit can support the creation of an optimal will by taking into account the user's geographical location information. For example, if the user is in a specific area, the will creation support unit can provide a will creation method related to that area. Furthermore, if the user is traveling, the will creation support unit can also provide a will creation method related to the user's travel destination. Furthermore, if the user is at home, the will creation support unit can also provide a will creation method near the user's home. For example, the will creation support unit can provide a highly relevant will creation method based on the user's geographical location information. Furthermore, the will creation support unit can analyze the user's location information and provide an appropriate will creation method. Furthermore, the will creation support unit can provide a will creation method that is beneficial to the user by taking into account the user's geographical location information. In this way, the will creation support unit can support the creation of an optimal will by taking into account the user's geographical location information.

[0114] The economic advice providing unit can estimate the user's emotions and adjust the way in which economic advice is presented based on the estimated user emotions. For example, when the user is feeling stressed, the economic advice providing unit can provide simple, highly visible economic advice. Furthermore, when the user is relaxed, the economic advice providing unit can provide detailed economic advice. Furthermore, when the user is in a hurry, the economic advice providing unit can provide economic advice that focuses on the main points. For example, the economic advice providing unit can estimate the user's emotions and adjust the way in which economic advice is presented based on the emotions. Furthermore, the economic advice providing unit can analyze the user's emotion data and select an appropriate way of presenting the advice. Furthermore, the economic advice providing unit can adjust the way in which economic advice is presented based on the user's emotions to provide economic advice that is easy for the user to understand. In this way, the economic advice providing unit can adjust the way in which economic advice is presented according to the user's emotions and provide economic advice that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0115] When providing economic advice, the economic advice providing unit can analyze the user's past economic data and provide optimal advice. The economic advice providing unit, for example, proposes an optimal savings plan based on the user's past economic data. The economic advice providing unit can also propose an optimal investment plan based on the user's past economic data. Furthermore, the economic advice providing unit can also propose an optimal budget management method based on the user's past economic data. For example, the economic advice providing unit analyzes the user's economic data and provides an optimal savings plan. The economic advice providing unit can also analyze the user's economic data and provide an optimal investment plan. Furthermore, the economic advice providing unit can analyze the user's economic data and provide an optimal budget management method. In this way, the economic advice providing unit can provide optimal economic advice by analyzing past economic data.

[0116] The economic advice providing unit can estimate the user's emotions and prioritize economic advice based on the estimated user emotions. For example, if the user is feeling stressed, the economic advice providing unit can prioritize providing stress-relieving economic advice. Furthermore, if the user is relaxed, the economic advice providing unit can prioritize providing detailed economic advice. Furthermore, if the user is in a hurry, the economic advice providing unit can prioritize providing important economic advice. For example, the economic advice providing unit can estimate the user's emotions and prioritize economic advice based on the emotions. Furthermore, the economic advice providing unit can analyze the user's emotion data and prioritize providing important economic advice. Furthermore, the economic advice providing unit can adjust the priority of economic advice based on the user's emotions and provide important economic advice to the user. In this way, the economic advice providing unit can prioritize economic advice according to the user's emotions and prioritize providing important economic advice. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0117] When providing economic advice, the economic advice providing unit can provide optimal advice by taking into account the user's geographical location information. For example, if the user is in a specific area, the economic advice providing unit can provide economic advice related to that area. Furthermore, if the user is traveling, the economic advice providing unit can also provide economic advice related to the user's travel destination. Furthermore, if the user is at home, the economic advice providing unit can also provide economic advice related to the area around the user's home. For example, the economic advice providing unit can provide highly relevant economic advice based on the user's geographical location information. Furthermore, the economic advice providing unit can analyze the user's location information and provide appropriate economic advice. Furthermore, the economic advice providing unit can provide economic advice that is useful to the user by taking into account the user's geographical location information. In this way, the economic advice providing unit can provide optimal economic advice by taking into account the user's geographical location information.

[0118] The care information providing unit can estimate the user's emotions and adjust the presentation method of the care information based on the estimated user's emotions. For example, when the user is feeling stressed, the care information providing unit can provide simple, highly visible care information. Furthermore, when the user is relaxed, the care information providing unit can provide detailed care information. Furthermore, when the user is in a hurry, the care information providing unit can provide care information that focuses on the main points. For example, the care information providing unit can estimate the user's emotions and adjust the presentation method of the care information based on the emotions. Furthermore, the care information providing unit can analyze the user's emotion data and select an appropriate presentation method. Furthermore, the care information providing unit can adjust the presentation method of the care information based on the user's emotions to provide care information that is easy for the user to understand. In this way, the care information providing unit can adjust the presentation method of the care information according to the user's emotions and provide care information that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0119] When providing care information, the care information providing unit can analyze the user's past care data and provide optimal information. The care information providing unit, for example, proposes an optimal care plan based on the user's past care data. The care information providing unit can also propose an optimal care facility based on the user's past care data. Furthermore, the care information providing unit can also propose optimal care services based on the user's past care data. For example, the care information providing unit analyzes the user's care data and provides an optimal care plan. The care information providing unit can also analyze the user's care data and provide an optimal care facility. Furthermore, the care information providing unit can analyze the user's care data and provide optimal care services. In this way, the care information providing unit can provide optimal care information by analyzing the past care data.

[0120] The care information providing unit can estimate the user's emotions and determine the priority of care information based on the estimated user's emotions. For example, when the user is feeling stressed, the care information providing unit can prioritize providing care information that alleviates stress. Furthermore, when the user is relaxed, the care information providing unit can prioritize providing detailed care information. Furthermore, when the user is in a hurry, the care information providing unit can prioritize providing important care information. For example, the care information providing unit can estimate the user's emotions and determine the priority of care information based on the emotions. Furthermore, the care information providing unit can analyze the user's emotion data and prioritize providing important care information. Furthermore, the care information providing unit can adjust the priority of care information based on the user's emotions and provide important care information for the user. In this way, the care information providing unit can prioritize the care information according to the user's emotions and prioritize providing important care information. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0121] When providing care information, the care information providing unit can provide optimal information by taking into account the user's geographical location information. For example, when the user is in a specific area, the care information providing unit provides care information related to that area. Furthermore, when the user is traveling, the care information providing unit can also provide care information related to the user's travel destination. Furthermore, when the user is at home, the care information providing unit can also provide care information around the user's home. For example, the care information providing unit provides highly relevant care information based on the user's geographical location information. Furthermore, the care information providing unit can analyze the user's location information and provide appropriate care information. Furthermore, the care information providing unit can provide care information that is useful to the user by taking into account the user's geographical location information. In this way, the care information providing unit can provide optimal care information by taking into account the user's geographical location information.

[0122] The support network providing unit can estimate the user's emotions and adjust the method of providing the support network based on the estimated user emotions. For example, when the user is feeling stressed, the support network providing unit can provide a simple, highly visible support network. Furthermore, when the user is relaxed, the support network providing unit can provide a detailed support network. Furthermore, when the user is in a hurry, the support network providing unit can provide a support network that focuses on the key points. For example, the support network providing unit can estimate the user's emotions and adjust the method of providing the support network based on the emotions. Furthermore, the support network providing unit can analyze the user's emotion data and select an appropriate method of providing the support network. Furthermore, the support network providing unit can adjust the method of providing the support network based on the user's emotions to provide a support network that is easy for the user to understand. In this way, the support network providing unit can adjust the method of providing the support network according to the user's emotions and provide a support network that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0123] When providing a support network, the support network providing unit can analyze the user's past network usage data and provide the optimal network. The support network providing unit, for example, proposes the optimal support network based on the user's past network usage data. The support network providing unit can also propose the optimal support group based on the user's past network usage data. Furthermore, the support network providing unit can also propose the optimal support service based on the user's past network usage data. For example, the support network providing unit analyzes the user's network usage data and provides the optimal support network. The support network providing unit can also analyze the user's network usage data and provide the optimal support group. Furthermore, the support network providing unit can analyze the user's network usage data and provide the optimal support service. In this way, the support network providing unit can provide the optimal support network by analyzing the past network usage data.

[0124] The support network providing unit can estimate the user's emotions and prioritize support networks based on the estimated user emotions. For example, if the user is feeling stressed, the support network providing unit can prioritize providing support networks that alleviate stress. Furthermore, if the user is relaxed, the support network providing unit can prioritize providing detailed support networks. Furthermore, if the user is in a hurry, the support network providing unit can prioritize providing support networks with high importance. For example, the support network providing unit can estimate the user's emotions and prioritize support networks based on the emotions. Furthermore, the support network providing unit can analyze the user's emotion data and prioritize providing important support networks. Furthermore, the support network providing unit can adjust the priority of support networks based on the user's emotions and provide support networks that are important to the user. In this way, the support network providing unit can prioritize support networks according to the user's emotions and prioritize providing important support networks. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0125] When providing a support network, the support network providing unit can provide an optimal network by taking into account the user's geographical location information. For example, when the user is in a specific area, the support network providing unit can provide a support network related to that area. Furthermore, when the user is traveling, the support network providing unit can also provide a support network related to the user's travel destination. Furthermore, when the user is at home, the support network providing unit can also provide a support network in the vicinity of the user's home. For example, the support network providing unit provides a highly relevant support network based on the user's geographical location information. Furthermore, the support network providing unit can analyze the user's location information and provide an appropriate support network. Furthermore, the support network providing unit can provide a support network that is useful to the user by taking into account the user's geographical location information. In this way, the support network providing unit can provide an optimal support network by taking into account the user's geographical location information. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, health data collection unit, health advice provision unit, legal advice provision unit, will preparation support unit, financial advice provision unit, care information provision unit, and support network provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects user data using the camera 42 and microphone 38B of the smart device 14 and processes the data using the control unit 46A. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing device 12. The provision unit provides various types of support based on the analysis results using the specific processing unit 290 of the data processing device 12. The health data collection unit collects health data using the wearable device of the smart device 14. The health advice provision unit provides health advice using the specific processing unit 290 of the data processing device 12. The legal advice provision unit provides legal advice using the specific processing unit 290 of the data processing device 12. The will preparation support unit supports will preparation using the specific processing unit 290 of the data processing device 12. The economic advice providing unit provides economic advice by the specific processing unit 290 of the data processing device 12. The care information providing unit provides care information by the specific processing unit 290 of the data processing device 12. The support network providing unit provides a support network by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, health data collection unit, health advice provision unit, legal advice provision unit, will preparation support unit, financial advice provision unit, care information provision unit, and support network provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects user data using the camera 42 and microphone 238 of the smart glasses 214 and processes the data using the control unit 46A. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing device 12. The provision unit provides various types of support based on the analysis results using the specific processing unit 290 of the data processing device 12. The health data collection unit collects health data using the wearable device of the smart glasses 214. The health advice provision unit provides health advice using the specific processing unit 290 of the data processing device 12. The legal advice provision unit provides legal advice using the specific processing unit 290 of the data processing device 12. The will preparation support unit supports will preparation using the specific processing unit 290 of the data processing device 12. The economic advice providing unit provides economic advice by the specific processing unit 290 of the data processing device 12. The care information providing unit provides care information by the specific processing unit 290 of the data processing device 12. The support network providing unit provides a support network by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, health data collection unit, health advice provision unit, legal advice provision unit, will preparation support unit, financial advice provision unit, care information provision unit, and support network provision unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects user data using the camera 42 and microphone 238 of the headset-type terminal 314 and processes the data using the control unit 46A. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing device 12. The provision unit provides various types of support based on the analysis results using the specific processing unit 290 of the data processing device 12. The health data collection unit collects health data using the wearable device of the headset-type terminal 314. The health advice provision unit provides health advice using the specific processing unit 290 of the data processing device 12. The legal advice provision unit provides legal advice using the specific processing unit 290 of the data processing device 12. The will writing support unit supports the writing of a will by the specific processing unit 290 of the data processing device 12. The economic advice providing unit provides economic advice by the specific processing unit 290 of the data processing device 12. The care information providing unit provides care information by the specific processing unit 290 of the data processing device 12. The support network providing unit provides a support network by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, health data collection unit, health advice provision unit, legal advice provision unit, will preparation support unit, financial advice provision unit, care information provision unit, and support network provision unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects user data using the camera 42 and microphone 238 of the robot 414 and processes the data using the control unit 46A. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing device 12. The provision unit provides various types of support based on the analysis results using the specific processing unit 290 of the data processing device 12. The health data collection unit collects health data using the wearable device of the robot 414. The health advice provision unit provides health advice using the specific processing unit 290 of the data processing device 12. The legal advice provision unit provides legal advice using the specific processing unit 290 of the data processing device 12. The will preparation support unit supports will preparation using the specific processing unit 290 of the data processing device 12. The economic advice providing unit provides economic advice by the specific processing unit 290 of the data processing device 12. The care information providing unit provides care information by the specific processing unit 290 of the data processing device 12. The support network providing unit provides a support network by the specific processing unit 290 of the data processing device 12.

[0126] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0127] When collecting user data, the collection unit can grasp the user's current activity status in real time and collect data at appropriate times. For example, when the user is exercising, exercise data can be collected preferentially, and when the user is resting, data related to relaxation can be collected. The collection unit can also adjust the frequency of data collection according to the user's activity status to reduce the burden on the user. Furthermore, the collection unit can analyze the user's activity status and suggest the optimal data collection method. This allows the collection unit to achieve flexible data collection according to the user's activity status.

[0128] When collecting a user's health data, the health data collection unit can collect the data taking into account the user's lifestyle habits and environmental factors. For example, if the user is in a hot and humid environment, data related to the risk of heatstroke can be collected, and if the user is in a cold region, data related to the risk of hypothermia can be collected. The health data collection unit can also collect data taking into account the user's lifestyle habits (e.g., smoking and drinking habits) and evaluate health risks. Furthermore, the health data collection unit can collect data taking into account the user's environmental factors (e.g., air quality and noise level) and evaluate the impact on health. This allows the health data collection unit to achieve comprehensive health data collection that takes into account the user's lifestyle habits and environmental factors.

[0129] The health advice providing unit can provide health advice according to the season and climate based on the user's health data. For example, in the summer, it can provide advice on hydration and appropriate clothing to prevent heatstroke, and in the winter, it can provide advice on nutritional intake and cold weather measures to prevent colds and influenza. The health advice providing unit can also provide advice on region-specific health risks by taking into account the climatic characteristics of the user's region of residence. Furthermore, the health advice providing unit can provide advice on health management during travel based on climatic information of the user's travel destination. This allows the health advice providing unit to provide appropriate health advice according to the season and climate.

[0130] When providing advice to a user regarding a legal issue, the legal advice providing unit can take into consideration the user's past legal trouble history. For example, if the user has previously experienced trouble with an inheritance issue, the legal advice providing unit can provide detailed advice regarding inheritance, and if the user has previously experienced trouble with a contract, the legal advice providing unit can provide advice regarding points to be careful about when drafting a contract. The legal advice providing unit can also analyze the user's past legal trouble history and provide advice to prevent future trouble. Furthermore, the legal advice providing unit can provide optimal legal support based on the user's legal trouble history. This allows the legal advice providing unit to provide appropriate legal advice that takes into consideration the user's past legal trouble history.

[0131] When a user creates a will, the will creation support unit can propose the optimal will creation method taking into consideration the user's family structure and financial situation. For example, if the user has multiple heirs, it can provide specific advice on how to divide the inheritance, and if the user wants to bequeath specific assets to specific heirs, it can provide advice on how to do so. The will creation support unit can also propose the optimal will creation method taking into consideration the user's financial situation (e.g., types of real estate and financial assets). Furthermore, the will creation support unit can provide advice to prevent future inheritance disputes based on the user's family structure and financial situation. In this way, the will creation support unit can provide appropriate will creation support taking into consideration the user's family structure and financial situation.

[0132] The collection unit can estimate the user's emotions and adjust the data collection method based on the estimated user emotions. For example, if the user is feeling stressed, the data collection method can be simplified, reducing the burden on the user. Furthermore, if the user is relaxed, the collection unit can collect more detailed data and collect more accurate data. Furthermore, if the user is in a hurry, the collection unit can temporarily stop data collection and resume it later. For example, the collection unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The collection unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the collection unit to adjust the data collection method according to the user's emotions and reduce the burden on the user.

[0133] The analysis unit can estimate the user's emotions and adjust the timing of analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis can be delayed, and if the user is relaxed, the analysis can be accelerated. The analysis unit can also temporarily stop the analysis if the user is in a hurry and resume it later. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the analysis unit to adjust the timing of analysis according to the user's emotions and reduce the burden on the user.

[0134] The providing unit can estimate the user's emotions and adjust the content of the support provided based on the estimated user's emotions. For example, if the user is feeling stressed, support related to relaxation can be provided preferentially, and if the user is relaxed, support related to career advancement can be provided. The providing unit can also provide support that focuses on the main points when the user is in a hurry, and provide detailed support when the user has time. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. In this way, the providing unit can adjust the content of the support provided according to the user's emotions and provide optimal support for the user.

[0135] The health data collection unit can estimate the user's emotions and adjust the health data collection method based on the estimated user emotions. For example, if the user is feeling stressed, the health data collection method can be simplified, reducing the user's burden. Furthermore, if the user is relaxed, the health data collection unit can collect more detailed health data and collect more accurate data. Furthermore, if the user is in a hurry, the health data collection unit can temporarily stop health data collection and resume it later. For example, the health data collection unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. The health data collection unit can also record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, the health data collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows the health data collection unit to adjust the health data collection method according to the user's emotions and reduce the user's burden.

[0136] The health advice providing unit can estimate the user's emotions and adjust the content of health advice based on the estimated user's emotions. For example, if the user is feeling stressed, it can prioritize providing advice on stress management, and if the user is relaxed, it can provide advice on exercise and nutrition. The health advice providing unit can also provide advice that focuses on the main points if the user is in a hurry, and provide detailed advice if the user has time. For example, the health advice providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The health advice providing unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the health advice providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. In this way, the health advice providing unit can adjust the content of health advice according to the user's emotions and provide the optimal health advice for the user.

[0137] The processing flow of the second embodiment will be briefly explained below.

[0138] Step 1: The collection unit collects user data. The user data includes behavioral data, health data, purchase data, etc. For example, the collection unit collects the user's health data using a wearable device, as well as the user's online activity data and purchase history data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms, and includes assessing the user's health status, identifying behavioral patterns, and understanding purchasing trends. Step 3: The providing unit provides various types of support based on the analysis results obtained by the analyzing unit. The various types of support include health advice, economic advice, legal advice, etc. For example, the providing unit provides health advice based on the user's health data, economic advice based on the economic data, and legal advice based on the legal data.

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0141] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0144] 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.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0150] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0157] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0160] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0161] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0162] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0163] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0165] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0166] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0167] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0168] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0169] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0170] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0171] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0172] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0173] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0176] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0177] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0178] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0179] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0180] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0181] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0182] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0183] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0184] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0185] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0186] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0187] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0188] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0189] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0190] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0191] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0192] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0193] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0194] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0195] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0196] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0197] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0198] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0199] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0200] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0201] 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.

[0202] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0203] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0204] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0205] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0206] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0207] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0208] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0209] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0210] [Explanation of symbols]

[0211] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects user data; an analysis unit that analyzes the data collected by the collection unit; a providing unit that provides various types of support based on the analysis results obtained by the analysis unit. A system characterized by:

2. Equipped with a health data collection unit that collects health data 2. The system of claim 1.

3. Equipped with a health advice providing department that provides health advice 2. The system of claim 1.

4. We have a legal advice department that provides specific legal advice on estates and inheritances.

2. The system of claim 1.

5. Have a will writing support department to assist with the writing of wills 2. The system of claim 1.

6. A financial advice department that provides specific advice on saving, investing, and budgeting 2. The system of claim 1.

7. Have a care information department that provides information on family caregiving 2. The system of claim 1.

8. Equipped with a support network provision department that provides a support network 2. The system of claim 1.

9. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

10. The collecting unit Analyze the user's past data collection history and select the most appropriate collection method 2. The system of claim 1.

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