System

Abstract

An object of a system according to an embodiment is to support a general user to efficiently utilize subsidies and administrative services.SOLUTION: A system according to an embodiment includes an information providing unit, an application support unit, and an advice providing unit. The information providing unit provides information on subsidies and administrative services. The application supporter supports an application procedure based on the information provided by the information provider. The advice providing unit provides advice and support in accordance with individual needs based on the application procedure supported by the application support unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that it is difficult for general users to efficiently collect and appropriately use information on subsidies and government services.

[0005] The system according to the embodiment aims to support general users in efficiently utilizing subsidies and government services. [Means for solving the problem]

[0006] The system according to the embodiment includes an information providing unit, an application support unit, and an advice providing unit. The information providing unit provides information on subsidies and administrative services. The application support unit supports application procedures based on the information provided by the information providing unit. The advice providing unit provides advice and support tailored to individual needs based on the application procedures supported by the application support unit. [Effects of the Invention]

[0007] The system according to the embodiment can support general users in efficiently utilizing subsidies and government services. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 support platform according to an embodiment of the present invention is a system that supports general users in efficiently utilizing subsidies and government services available in their daily lives. This system provides information on and supports applications for subsidies and services related to various areas of life, such as housing subsidies, childcare support, and health promotion programs. It also provides advice and support in areas of life, such as home renovations, childcare, and health promotion, tailored to individual needs. This allows the support platform to efficiently utilize subsidies and government services available in users' daily lives.

[0029] The support platform according to the embodiment includes an information providing unit, an application support unit, and an advice providing unit. The information providing unit provides information on subsidies and government services. For example, the information providing unit uses a generation AI to collect and provide information on subsidies and government services available to the user. The generation AI analyzes information on, for example, housing subsidies, child care support, and health promotion programs, and provides information appropriate to the user. The generation AI receives input from prompts containing the user's basic information and needs, and the generation AI provides information based on the prompts. The application support unit supports application procedures based on the information provided by the information providing unit. For example, the generation AI supports application procedures for subsidies and government services. The generation AI supports, for example, the creation of application documents and automatically enters required information. The application support unit also has a function to track the progress of the application procedures and notify the user. The generation AI receives input from prompts containing the user's application details and required document information, and the generation AI supports the application procedures based on the prompts. The advice providing unit provides advice and support tailored to the user's individual needs based on the application procedures supported by the application support unit. For example, the generation AI provides advice and support tailored to the user's individual needs. For example, the generative AI can provide advice on home renovations, support for child-rearing, and suggest health promotion programs. The input to the generative AI is prompts that include the user's specific needs and circumstances, and the generative AI provides advice and support based on those prompts. This allows the support platform to efficiently utilize subsidies and government services available to users in their daily lives. For example, users can smoothly apply for subsidies for home renovations and easily complete the procedures for receiving child-rearing support. Furthermore, users can quickly obtain information on participating in health promotion programs and receive support with the application process, helping them live healthier lives.

[0030] The information provision unit uses the generation AI to analyze the user's past application history and usage history, and can predict and provide the most appropriate subsidies and services. For example, the information provision unit uses the generation AI to analyze the user's past application history and automatically suggest similar subsidies and services. For example, a user who has applied for a housing subsidy in the past can be suggested a renovation subsidy or an eco-housing subsidy. The information provision unit also uses the generation AI to analyze the user's usage history, and predict and provide the most appropriate subsidies and services. For example, a user who has used a health promotion program in the past can be suggested a new health program or fitness subsidy. This makes it possible to provide the most appropriate subsidies and services to the user.

[0031] The information provision unit uses the generation AI to analyze the characteristics and trends of each region and provide information on subsidies and services specific to that region. For example, the generation AI in the information provision unit analyzes the characteristics of each region and provides information on subsidies and services specific to that region. For example, agricultural subsidies and regional development subsidies are proposed for rural regions. The generation AI in the information provision unit also analyzes regional trends and provides information on subsidies and services specific to that region. For example, eco-housing subsidies and smart city-related subsidies are proposed for urban areas. This makes it possible to provide information on subsidies and services specific to that region.

[0032] The information provision unit can use the generation AI to predict the user's life events and notify the user in advance of corresponding subsidies and services. For example, the information provision unit uses the generation AI to predict the user's life events and notify the user in advance of marriage-related subsidies and services. For example, it can suggest wedding subsidies and newlywed life support subsidies. The information provision unit also uses the generation AI to predict the user's life events and notify the user in advance of childbirth-related subsidies and services. For example, it can suggest maternity lump-sum payments and childcare leave benefits. This makes it possible to notify the user in advance of subsidies and services corresponding to the user's life events.

[0033] The information provision unit can use the generation AI to collect success stories of other users and provide customized information to users with similar needs. For example, the information provision unit uses the generation AI to collect success stories of other users and provide customized subsidy information to users with similar needs. For example, the information provision unit proposes renovation subsidies based on successful renovation cases. The information provision unit also uses the generation AI to collect success stories of other users and provide customized service information to users with similar needs. For example, the information provision unit proposes childcare support programs based on successful childcare support cases. This makes it possible to provide customized information to users with similar needs.

[0034] The application support unit uses the generation AI to detect input errors and omissions made by the user in real time and make suggestions for corrections. For example, the application support unit uses the generation AI to detect input errors in application documents in real time and make suggestions for corrections. For example, it automatically corrects incorrect dates and incomplete address information. The application support unit also uses the generation AI to detect omissions in application documents in real time and make suggestions for corrections. For example, it automatically completes missing required documents or incomplete information. This allows the application support unit to detect input errors and omissions made by the user in real time and make suggestions for corrections.

[0035] The application support unit uses the generation AI to track the progress of the application procedures in detail and can provide specific instructions to the user on the next step. For example, the application support unit uses the generation AI to track the progress of the application procedures in real time and provide specific instructions to the user on the next step. For example, it notifies the user of the next documents to be submitted and deadlines. The application support unit also uses the generation AI to track the progress of the application procedures in detail and provide specific instructions to the user on the next step. For example, it notifies the user of status updates and required actions for the application procedures. This allows the application support unit to track the progress of the application procedures in detail and provide specific instructions on the next step.

[0036] The application support unit uses the generation AI to integrate application procedures for different subsidies and services so that they can be completed in one go, reducing the workload for users. For example, the application support unit integrates application procedures for different subsidies and services so that they can be completed in one go, reducing the workload for users. For example, a system is provided that allows multiple subsidy applications to be submitted at once. The application support unit also integrates application procedures for different services so that they can be completed in one go, reducing the workload for users. For example, a system is provided that allows applications for education subsidies and medical subsidies to be submitted at once. This allows application procedures for different subsidies and services to be integrated so that they can be completed in one go, reducing the workload for users.

[0037] The application support unit uses the generation AI to automatically divert the user's application content to other related services and subsidies, allowing multiple applications to be submitted simultaneously. For example, the application support unit uses the generation AI to analyze the user's application content and automatically divert it to other related services and subsidies. For example, based on the application content for a housing subsidy, an application for a renovation subsidy is automatically submitted. In addition, the application support unit uses the generation AI to analyze the user's application content and submit multiple applications simultaneously. For example, an application for a childcare support subsidy and an education subsidy is submitted simultaneously. This allows the user's application content to automatically divert to other related services and subsidies, allowing multiple applications to be submitted simultaneously.

[0038] The advice providing unit can use the generation AI to analyze the user's lifestyle habits and health condition in detail and propose an optimal health promotion program. For example, the generation AI in the advice providing unit can analyze the user's lifestyle habits in detail and propose an optimal health promotion program. For example, it can provide a customized fitness plan based on food records and exercise habits. The advice providing unit can also use the generation AI to analyze the user's health condition in detail and propose an optimal health promotion program. For example, it can provide an individual health program based on health checkup results. This allows the user's lifestyle habits and health condition to be analyzed in detail and the optimal health promotion program to be proposed.

[0039] The advice providing unit can use the generation AI to provide customized child-rearing support and renovation advice, taking into consideration the user's family structure and lifestyle. For example, the generation AI analyzes the user's family structure and provides customized child-rearing support. For example, it proposes a child-rearing support program based on the age and number of children. The advice providing unit also analyzes the user's lifestyle and provides customized renovation advice. For example, it proposes a renovation plan based on the type of residence and daily activity patterns. This makes it possible to provide customized child-rearing support and renovation advice, taking into consideration the user's family structure and lifestyle.

[0040] The advice providing unit can use the generation AI to collaborate with experts in different fields according to the user's needs and provide comprehensive support. For example, the generation AI analyzes the user's needs and collaborates with experts in different fields to provide comprehensive support. For example, it can introduce a doctor for health consultations and an architect for renovation consultations. The advice providing unit can also analyze the user's needs and collaborate with experts in different fields to provide comprehensive support. For example, it can introduce a childcare advisor for childcare consultations and a lawyer for legal consultations. This allows it to collaborate with experts in different fields according to the user's needs and provide comprehensive support.

[0041] The advice providing unit uses the generation AI to match the user with other users based on the user's needs, and can promote information exchange and support. For example, the generation AI in the advice providing unit analyzes the user's needs and matches the user with other users who have similar needs. For example, it connects users who have the same renovation plan. The advice providing unit also analyzes the user's needs and promotes information exchange and support with other users. For example, it connects users who need the same childcare support. This makes it possible to match the user with other users based on the user's needs, and promote information exchange and support.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The assistance platform can further suggest customized recreational activities based on the user's hobbies and interests. For example, the information providing unit can provide information on local events and workshops based on the user's hobbies. If the user is interested in art, the information providing unit can provide information on local art classes and exhibitions. If the user likes outdoor activities, the information providing unit can provide information on hiking and camping events. If the user is interested in music, the information providing unit can provide information on concerts and music festivals. This makes it possible to suggest recreational activities to enrich daily life based on the user's hobbies and interests.

[0044] The assistance platform can further monitor the user's health condition, predict health risks, and suggest preventive measures. For example, the information provision unit can analyze the user's health data and predict future health risks. If the user is at risk of high blood pressure, advice on diet and exercise can be provided. If the user is at risk of diabetes, a blood sugar management program can be suggested. Furthermore, if the user is feeling stressed, relaxation techniques and mental health support can be provided. This makes it possible to monitor the user's health condition, predict health risks, and suggest preventive measures.

[0045] The support platform can further suggest community activities to strengthen the user's social connections. For example, the information providing unit can provide information on local volunteer activities and club activities. If the user is interested in environmental protection, information on local cleanup activities and recycling programs can be provided. If the user is interested in sports, information on local sports clubs and teams can be provided. Furthermore, if the user is interested in cultural activities, information on local cultural events and workshops can be provided. This allows the user to strengthen their social connections and live a fulfilling life through community activities.

[0046] The assistance platform can further suggest eco-friendly lifestyle habits based on the user's lifestyle. For example, the information providing unit can analyze the user's consumption patterns and suggest eco-friendly products and services. It can suggest environmentally friendly products as alternatives to products the user uses on a daily basis. It can also suggest ways for the user to reduce energy consumption. For example, it can suggest energy-efficient home appliances and the use of renewable energy. It can also provide advice for the user to adopt recycling and reuse habits. In this way, it can suggest eco-friendly lifestyle habits based on the user's lifestyle.

[0047] The support platform can further suggest educational programs and training courses to support the user's career development. For example, the information providing unit can suggest appropriate educational programs and training courses based on the user's career goals. If the user wants to acquire new skills, information on online courses and workshops can be provided. Also, if the user is considering a career change, related training programs and qualification courses can be suggested. Furthermore, if the user is aiming to advance their career, leadership training and management courses can be suggested. In this way, educational programs and training courses can be suggested to support the user's career development.

[0048] The support platform can also collect user feedback and use it to improve the service. For example, the information providing unit can collect user feedback and provide data to improve the quality of the service. It can evaluate whether the user is satisfied with the information and support provided and identify areas for improvement. In addition, if the user requests new functions or services, it can collect these requests and reflect them in the development of the service. Furthermore, it can enable the user to report any problems or inconveniences they experience while using the service, and the service can be improved based on this information. In this way, it is possible to collect user feedback and use it to improve the service.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The information provider provides information on subsidies and government services. For example, the generator uses a generation AI to collect and provide information on subsidies and government services available to users. The generator AI analyzes information on housing subsidies, childcare support, health promotion programs, etc., and provides information appropriate for the user. The generator AI receives input from prompts containing the user's basic information and needs, and the generator AI provides information based on those prompts. Step 2: The application support unit supports the application process based on the information provided by the information provision unit. For example, the generation AI is used to support the application process for subsidies and government services. The generation AI assists in the creation of application documents and automatically enters the required information. It also has the function of tracking the progress of the application process and notifying the user. The input to the generation AI is a prompt containing the user's application details and required document information, and the generation AI supports the application process based on the prompt. Step 3: The advice providing unit provides advice and support tailored to individual needs based on the application procedures supported by the application support unit. For example, the generation AI is used to provide advice and support tailored to the user's individual needs. The generation AI provides advice on renovations, support for child-rearing, and suggestions for health promotion programs. The input to the generation AI is a prompt that includes the user's specific needs and situation, and the generation AI provides advice and support based on the prompt.

[0051] (Example 2) A support platform according to an embodiment of the present invention is a system that supports general users in efficiently utilizing subsidies and government services available in their daily lives. This system provides information on and supports applications for subsidies and services related to various areas of life, such as housing subsidies, childcare support, and health promotion programs. It also provides advice and support in areas of life, such as home renovations, childcare, and health promotion, tailored to individual needs. This allows the support platform to efficiently utilize subsidies and government services available in users' daily lives.

[0052] The support platform according to the embodiment includes an information providing unit, an application support unit, and an advice providing unit. The information providing unit provides information on subsidies and government services. For example, the information providing unit uses a generation AI to collect and provide information on subsidies and government services available to the user. The generation AI analyzes information on, for example, housing subsidies, child care support, and health promotion programs, and provides information appropriate to the user. The generation AI receives input from prompts containing the user's basic information and needs, and the generation AI provides information based on the prompts. The application support unit supports application procedures based on the information provided by the information providing unit. For example, the generation AI supports application procedures for subsidies and government services. The generation AI supports, for example, the creation of application documents and automatically enters required information. The application support unit also has a function to track the progress of the application procedures and notify the user. The generation AI receives input from prompts containing the user's application details and required document information, and the generation AI supports the application procedures based on the prompts. The advice providing unit provides advice and support tailored to the user's individual needs based on the application procedures supported by the application support unit. For example, the generation AI provides advice and support tailored to the user's individual needs. For example, the generative AI can provide advice on home renovations, support for child-rearing, and suggest health promotion programs. The input to the generative AI is prompts that include the user's specific needs and circumstances, and the generative AI provides advice and support based on those prompts. This allows the support platform to efficiently utilize subsidies and government services available to users in their daily lives. For example, users can smoothly apply for subsidies for home renovations and easily complete the procedures for receiving child-rearing support. Furthermore, users can quickly obtain information on participating in health promotion programs and receive support with the application process, helping them live healthier lives.

[0053] The information provision unit uses the generation AI to analyze the user's past application history and usage history, and can predict and provide the most appropriate subsidies and services. For example, the information provision unit uses the generation AI to analyze the user's past application history and automatically suggest similar subsidies and services. For example, a user who has applied for a housing subsidy in the past can be suggested a renovation subsidy or an eco-housing subsidy. The information provision unit also uses the generation AI to analyze the user's usage history, and predict and provide the most appropriate subsidies and services. For example, a user who has used a health promotion program in the past can be suggested a new health program or fitness subsidy. This makes it possible to provide the most appropriate subsidies and services to the user.

[0054] The information provision unit uses the generation AI to analyze the characteristics and trends of each region and provide information on subsidies and services specific to that region. For example, the generation AI in the information provision unit analyzes the characteristics of each region and provides information on subsidies and services specific to that region. For example, agricultural subsidies and regional development subsidies are proposed for rural regions. The generation AI in the information provision unit also analyzes regional trends and provides information on subsidies and services specific to that region. For example, eco-housing subsidies and smart city-related subsidies are proposed for urban areas. This makes it possible to provide information on subsidies and services specific to that region.

[0055] The information providing unit can use the emotion estimation function to analyze the user's emotional state and suggest subsidies and services to reduce stress. For example, the information providing unit can use the emotion estimation function to analyze the user's stress level and suggest subsidies and services to provide a relaxing environment. For example, the information providing unit can suggest relaxation programs and mental health support subsidies. The information providing unit can also use the emotion estimation function to analyze the user's emotional state and suggest services to reduce stress. For example, the information providing unit can suggest yoga classes and mindfulness programs. This makes it possible to suggest subsidies and services to reduce the user's stress.

[0056] The information provision unit can use the generation AI to predict the user's life events and notify the user in advance of corresponding subsidies and services. For example, the information provision unit uses the generation AI to predict the user's life events and notify the user in advance of marriage-related subsidies and services. For example, it can suggest wedding subsidies and newlywed life support subsidies. The information provision unit also uses the generation AI to predict the user's life events and notify the user in advance of childbirth-related subsidies and services. For example, it can suggest maternity lump-sum payments and childcare leave benefits. This makes it possible to notify the user in advance of subsidies and services corresponding to the user's life events.

[0057] The information provision unit can use the generation AI to collect success stories of other users and provide customized information to users with similar needs. For example, the information provision unit uses the generation AI to collect success stories of other users and provide customized subsidy information to users with similar needs. For example, the information provision unit proposes renovation subsidies based on successful renovation cases. The information provision unit also uses the generation AI to collect success stories of other users and provide customized service information to users with similar needs. For example, the information provision unit proposes childcare support programs based on successful childcare support cases. This makes it possible to provide customized information to users with similar needs.

[0058] The information providing unit can use the emotion estimation function to identify subsidies and services in which the user is most interested and display that information preferentially. The information providing unit, for example, uses the emotion estimation function to identify subsidies and services in which the user is most interested and display that information preferentially. For example, subsidy information with a high emotion score is displayed at the top. The information providing unit also uses the emotion estimation function to identify services in which the user is most interested and display that information preferentially. For example, health promotion programs with a high emotion score are displayed at the top. This allows subsidies and services in which the user is most interested to be displayed preferentially.

[0059] The application support unit uses the generation AI to detect input errors and omissions made by the user in real time and make suggestions for corrections. For example, the application support unit uses the generation AI to detect input errors in application documents in real time and make suggestions for corrections. For example, it automatically corrects incorrect dates and incomplete address information. The application support unit also uses the generation AI to detect omissions in application documents in real time and make suggestions for corrections. For example, it automatically completes missing required documents or incomplete information. This allows the application support unit to detect input errors and omissions made by the user in real time and make suggestions for corrections.

[0060] The application support unit uses the generation AI to track the progress of the application procedures in detail and can provide specific instructions to the user on the next step. For example, the application support unit uses the generation AI to track the progress of the application procedures in real time and provide specific instructions to the user on the next step. For example, it notifies the user of the next documents to be submitted and deadlines. The application support unit also uses the generation AI to track the progress of the application procedures in detail and provide specific instructions to the user on the next step. For example, it notifies the user of status updates and required actions for the application procedures. This allows the application support unit to track the progress of the application procedures in detail and provide specific instructions on the next step.

[0061] The application support unit can use the emotion estimation function to analyze the user's stress level and suggest application procedures in a relaxing environment. The application support unit, for example, uses the emotion estimation function to analyze the user's stress level and suggest application procedures in a relaxing environment. For example, it can suggest a quiet place or a time of day when it is easy to relax. The application support unit can also use the emotion estimation function to analyze the user's stress level and suggest application procedures in a relaxing environment. For example, it can suggest an environment using relaxing music or aromas. In this way, it is possible to analyze the user's stress level and suggest application procedures in a relaxing environment.

[0062] The application support unit uses the generation AI to integrate application procedures for different subsidies and services so that they can be completed in one go, reducing the workload for users. For example, the application support unit integrates application procedures for different subsidies and services so that they can be completed in one go, reducing the workload for users. For example, a system is provided that allows multiple subsidy applications to be submitted at once. The application support unit also integrates application procedures for different services so that they can be completed in one go, reducing the workload for users. For example, a system is provided that allows applications for education subsidies and medical subsidies to be submitted at once. This allows application procedures for different subsidies and services to be integrated so that they can be completed in one go, reducing the workload for users.

[0063] The application support unit uses the generation AI to automatically divert the user's application content to other related services and subsidies, allowing multiple applications to be submitted simultaneously. For example, the application support unit uses the generation AI to analyze the user's application content and automatically divert it to other related services and subsidies. For example, based on the application content for a housing subsidy, an application for a renovation subsidy is automatically submitted. In addition, the application support unit uses the generation AI to analyze the user's application content and submit multiple applications simultaneously. For example, an application for a childcare support subsidy and an education subsidy is submitted simultaneously. This allows the user's application content to automatically divert to other related services and subsidies, allowing multiple applications to be submitted simultaneously.

[0064] The application support unit can use the emotion estimation function to suggest a schedule so that the application procedure is carried out during a time period when the user is most relaxed. The application support unit, for example, uses the emotion estimation function to suggest a schedule so that the application procedure is carried out during a time period when the user is most relaxed. For example, the application support unit notifies the user of the optimal time period based on the user's emotion data. The application support unit also uses the emotion estimation function to suggest a schedule so that the application procedure is carried out during a time period when the user is most relaxed. For example, the application support unit notifies the user to carry out the application procedure during a time period when the user is most relaxed. This makes it possible to suggest a schedule so that the application procedure is carried out during a time period when the user is most relaxed.

[0065] The advice providing unit can use the generation AI to analyze the user's lifestyle habits and health condition in detail and propose an optimal health promotion program. For example, the generation AI in the advice providing unit can analyze the user's lifestyle habits in detail and propose an optimal health promotion program. For example, it can provide a customized fitness plan based on food records and exercise habits. The advice providing unit can also use the generation AI to analyze the user's health condition in detail and propose an optimal health promotion program. For example, it can provide an individual health program based on health checkup results. This allows the user's lifestyle habits and health condition to be analyzed in detail and the optimal health promotion program to be proposed.

[0066] The advice providing unit can use the generation AI to provide customized child-rearing support and renovation advice, taking into consideration the user's family structure and lifestyle. For example, the generation AI analyzes the user's family structure and provides customized child-rearing support. For example, it proposes a child-rearing support program based on the age and number of children. The advice providing unit also analyzes the user's lifestyle and provides customized renovation advice. For example, it proposes a renovation plan based on the type of residence and daily activity patterns. This makes it possible to provide customized child-rearing support and renovation advice, taking into consideration the user's family structure and lifestyle.

[0067] The advice providing unit can use the emotion estimation function to analyze the user's emotional state and provide advice to elicit positive emotions. The advice providing unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and provide advice to elicit positive emotions. For example, it can suggest relaxing activities or hobbies. The advice providing unit also uses the emotion estimation function to analyze the user's emotional state and provide advice to elicit positive emotions. For example, it can suggest a lifestyle to increase a sense of happiness. This makes it possible to analyze the user's emotional state and provide advice to elicit positive emotions.

[0068] The advice providing unit can use the generation AI to collaborate with experts in different fields according to the user's needs and provide comprehensive support. For example, the generation AI analyzes the user's needs and collaborates with experts in different fields to provide comprehensive support. For example, it can introduce a doctor for health consultations and an architect for renovation consultations. The advice providing unit can also analyze the user's needs and collaborate with experts in different fields to provide comprehensive support. For example, it can introduce a childcare advisor for childcare consultations and a lawyer for legal consultations. This allows it to collaborate with experts in different fields according to the user's needs and provide comprehensive support.

[0069] The advice providing unit uses the generation AI to match the user with other users based on the user's needs, and can promote information exchange and support. For example, the generation AI in the advice providing unit analyzes the user's needs and matches the user with other users who have similar needs. For example, it connects users who have the same renovation plan. The advice providing unit also analyzes the user's needs and promotes information exchange and support with other users. For example, it connects users who need the same childcare support. This makes it possible to match the user with other users based on the user's needs, and promote information exchange and support.

[0070] The advice providing unit can use the emotion estimation function to adjust the schedule so that advice and support is provided to the user during a time period when the user is most relaxed. The advice providing unit can, for example, use the emotion estimation function to adjust the schedule so that advice and support is provided to the user during a time period when the user is most relaxed. For example, health advice is provided during a time period when the user can relax. The advice providing unit can also use the emotion estimation function to adjust the schedule so that advice and support is provided to the user during a time period when the user is most relaxed. For example, childcare advice is provided during a time period when the user can relax. This allows the schedule to be adjusted so that advice and support is provided to the user during a time period when the user is most relaxed.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The assistance platform can further suggest customized recreational activities based on the user's hobbies and interests. For example, the information providing unit can provide information on local events and workshops based on the user's hobbies. If the user is interested in art, the information providing unit can provide information on local art classes and exhibitions. If the user likes outdoor activities, the information providing unit can provide information on hiking and camping events. If the user is interested in music, the information providing unit can provide information on concerts and music festivals. This makes it possible to suggest recreational activities to enrich daily life based on the user's hobbies and interests.

[0073] The assistance platform can further monitor the user's health condition, predict health risks, and suggest preventive measures. For example, the information provision unit can analyze the user's health data and predict future health risks. If the user is at risk of high blood pressure, advice on diet and exercise can be provided. If the user is at risk of diabetes, a blood sugar management program can be suggested. Furthermore, if the user is feeling stressed, relaxation techniques and mental health support can be provided. This makes it possible to monitor the user's health condition, predict health risks, and suggest preventive measures.

[0074] The support platform can further suggest community activities to strengthen the user's social connections. For example, the information providing unit can provide information on local volunteer activities and club activities. If the user is interested in environmental protection, information on local cleanup activities and recycling programs can be provided. If the user is interested in sports, information on local sports clubs and teams can be provided. Furthermore, if the user is interested in cultural activities, information on local cultural events and workshops can be provided. This allows the user to strengthen their social connections and live a fulfilling life through community activities.

[0075] The assistance platform can further analyze the user's emotional state and suggest entertainment content that will elicit positive emotions. For example, the information providing unit can use the emotion estimation function to suggest movies and music that will help the user relax. If the user is feeling stressed, the information providing unit can suggest relaxing movies and music. If the user wants to increase their sense of happiness, the information providing unit can suggest movies and music with positive messages. If the user wants to increase their energy, the information providing unit can suggest lively music or action movies. In this way, the information providing unit can analyze the user's emotional state and suggest entertainment content that will elicit positive emotions.

[0076] The assistance platform can further analyze the user's emotional state and provide feedback according to the emotion. For example, the information providing unit can use the emotion estimation function to suggest relaxing activities if the user is feeling stressed. If the user is feeling anxious, it can suggest mental health support or counseling services. If the user is feeling happy, it can suggest positive activities to maintain that emotion. Furthermore, if the user wants to increase their energy, it can suggest exercise or outdoor activities. This makes it possible to analyze the user's emotional state and provide feedback according to the emotion.

[0077] The support platform can further analyze the user's emotional state and provide mental health support according to the emotion. For example, the information providing unit can use the emotion estimation function to suggest mental health counseling or relaxation techniques if the user is feeling stressed. If the user is feeling anxious, it can suggest mental health support groups or online counseling. If the user is feeling happy, it can suggest positive activities to maintain that emotion. Furthermore, if the user wants to increase their energy, it can suggest exercise or outdoor activities. In this way, the user's emotional state can be analyzed and mental health support according to the emotion can be provided.

[0078] The assistance platform can further analyze the user's emotional state and suggest relaxation techniques according to the emotion. For example, the information providing unit can use the emotion estimation function to suggest deep breathing or meditation techniques if the user is feeling stressed. If the user is feeling anxious, it can suggest mindfulness or yoga techniques. If the user is feeling happy, it can suggest relaxation techniques to maintain that emotion. Furthermore, if the user wants to increase their energy, it can suggest active relaxation techniques. This makes it possible to analyze the user's emotional state and suggest relaxation techniques according to the emotion.

[0079] The assistance platform can further suggest eco-friendly lifestyle habits based on the user's lifestyle. For example, the information providing unit can analyze the user's consumption patterns and suggest eco-friendly products and services. It can suggest environmentally friendly products as alternatives to products the user uses on a daily basis. It can also suggest ways for the user to reduce energy consumption. For example, it can suggest energy-efficient home appliances and the use of renewable energy. It can also provide advice for the user to adopt recycling and reuse habits. In this way, it can suggest eco-friendly lifestyle habits based on the user's lifestyle.

[0080] The support platform can further suggest educational programs and training courses to support the user's career development. For example, the information providing unit can suggest appropriate educational programs and training courses based on the user's career goals. If the user wants to acquire new skills, information on online courses and workshops can be provided. Also, if the user is considering a career change, related training programs and qualification courses can be suggested. Furthermore, if the user is aiming to advance their career, leadership training and management courses can be suggested. In this way, educational programs and training courses can be suggested to support the user's career development.

[0081] The support platform can also collect user feedback and use it to improve the service. For example, the information providing unit can collect user feedback and provide data to improve the quality of the service. It can evaluate whether the user is satisfied with the information and support provided and identify areas for improvement. In addition, if the user requests new functions or services, it can collect these requests and reflect them in the development of the service. Furthermore, it can enable the user to report any problems or inconveniences they experience while using the service, and the service can be improved based on this information. In this way, it is possible to collect user feedback and use it to improve the service.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The information provider provides information on subsidies and government services. For example, the generator uses a generation AI to collect and provide information on subsidies and government services available to users. The generator AI analyzes information on housing subsidies, childcare support, health promotion programs, etc., and provides information appropriate for the user. The generator AI receives input from prompts containing the user's basic information and needs, and the generator AI provides information based on those prompts. Step 2: The application support unit supports the application process based on the information provided by the information provision unit. For example, the generation AI is used to support the application process for subsidies and government services. The generation AI assists in the creation of application documents and automatically enters the required information. It also has the function of tracking the progress of the application process and notifying the user. The input to the generation AI is a prompt containing the user's application details and required document information, and the generation AI supports the application process based on the prompt. Step 3: The advice providing unit provides advice and support tailored to individual needs based on the application procedures supported by the application support unit. For example, the generation AI is used to provide advice and support tailored to the user's individual needs. The generation AI provides advice on renovations, support for child-rearing, and suggestions for health promotion programs. The input to the generation AI is a prompt that includes the user's specific needs and situation, and the generation AI provides advice and support based on the prompt.

[0084] 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.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] 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.

[0099] 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.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] 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.

[0111] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] 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.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] 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.

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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. [Explanation of symbols]

[0151] 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. An information department that provides information on subsidies and government services; an application support unit that supports application procedures based on the information provided by the information providing unit; an advice providing unit that provides advice and support tailored to individual needs based on the application procedure supported by the application support unit; A system characterized by:

2. The information providing unit Using generative AI, we analyze users' past application and usage history to predict and provide optimal subsidies and services.

2. The system of claim 1.

3. The information providing unit Using generative AI to predict users' life events and notify them in advance of corresponding subsidies and services 2. The system of claim 1.

4. The application support department Using generative AI, it detects user input errors and omissions in real time and suggests corrections.

2. The system of claim 1.

5. The advice providing unit Using generative AI, the system analyzes users' lifestyle habits and health conditions in detail and suggests optimal health promotion programs.

2. The system of claim 1.

6. The information providing unit Using emotion estimation, the system analyzes the user's emotional state and suggests subsidies and services to reduce stress.

2. The system of claim 1.

7. The application support department Using emotion estimation, the app analyzes the user's stress level and suggests a relaxing environment for the application process.

2. The system of claim 1.

8. The advice providing unit Using emotion estimation function, analyze the user's emotional state and provide advice to elicit positive emotions 2. The system of claim 1.

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