System

Abstract

An object of a system according to an embodiment is to efficiently collect information on a subsidy system and notify the information at an appropriate timing.SOLUTION: A system according to an embodiment includes a list generation unit, a schedule analysis unit, a notification unit, and an application support unit. The list generation unit collects information on subsidy systems and aggregates the information into a list. A schedule analysis part analyzes the schedule of the user based on the information of the subsidy system summarized by the list generation part. The notification unit notifies the information of the subsidy system at an appropriate timing based on the schedule analyzed by the schedule analysis unit. The application support unit provides support when applying to the subsidy system.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has faced the challenge of making it difficult to efficiently collect information on the many subsidy programs available and notify users in a timely manner.

[0005] The system according to the embodiment aims to efficiently collect information on subsidy programs and notify the information at an appropriate time. [Means for solving the problem]

[0006] The system according to the embodiment includes a list generation unit, a schedule analysis unit, a notification unit, and an application support unit. The list generation unit collects information on subsidy programs and compiles it into a list. The schedule analysis unit analyzes the user's schedule based on the information on subsidy programs compiled by the list generation unit. The notification unit notifies the user of the information on the subsidy programs at an appropriate time based on the schedule analyzed by the schedule analysis unit. The application support unit provides support when applying for the subsidy programs. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect information on subsidy programs and notify the information at an appropriate time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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) The subsidy system utilization support system according to an embodiment of the present invention is a system for effectively utilizing the numerous government support and subsidy systems that exist. This system compiles target subsidy and support systems into a list and supports applications. As a result, the subsidy system utilization support system allows users to effectively utilize subsidy systems that are suitable for them without overlooking them.

[0029] A subsidy program utilization support system according to an embodiment includes a list generation unit, a schedule analysis unit, a notification unit, and an application support unit. The list generation unit collects information about subsidy programs and compiles it into a list. For example, the generation AI collects information about subsidy programs from publicly available information on the Internet and official government websites, organizes it, and creates a list. The generation AI receives prompts containing instructions to collect information about subsidy programs, collects information based on the prompts, and generates a list. The schedule analysis unit analyzes a user's schedule based on the information about subsidy programs compiled by the list generation unit. For example, the generation AI analyzes the user's diary and schedule information and suggests appropriate subsidy programs. The generation AI receives the user's diary and schedule information as input, analyzes them, and suggests appropriate subsidy programs. The notification unit notifies the user of information about subsidy programs at appropriate times based on the schedule analyzed by the schedule analysis unit. For example, if a user writes in their diary that they want to build a new house, the generation AI notifies the user of information about the "Children's Eco-Housing Support Project" based on that information. The application support unit provides support when applying for subsidy programs. For example, the generation AI generates application documents based on information about the subsidy program for which the user wants to apply. The generation AI receives information about the subsidy program for which the user wants to apply as input, and generates application documents based on that information. As a result, the subsidy program utilization support system according to the embodiment allows the user to receive information about the subsidy program at the appropriate time, allowing for a smooth application process.

[0030] The list generation unit can prioritize and list information about subsidy programs based on the user's past application history and interests. For example, the list generation unit uses a generation AI to analyze the user's past application history and prioritize and list similar subsidy programs. For example, new housing-related subsidy programs are displayed preferentially to a user who has applied for housing-related subsidies in the past. The list generation unit also uses a generation AI to analyze the user's interests and prioritize and list information about subsidy programs based on that analysis. For example, if the user is interested in "environmental protection," environmental subsidy programs are displayed preferentially. This makes it possible to display the most suitable subsidy programs preferentially based on the user's past application history and interests.

[0031] The list generation unit can filter information on subsidy programs based on region or specific conditions, and provide the user with the most suitable list. For example, the generation AI in the list generation unit analyzes the user's region of residence and prioritizes listing subsidy programs applicable to that region. For example, for a user living in Tokyo, Tokyo's subsidy programs are displayed preferentially. The generation AI in the list generation unit also analyzes the user's income level and family structure, and filters information on subsidy programs based on that. For example, subsidy programs for low-income earners and subsidy programs for households with children are displayed preferentially. This makes it possible to provide the user with the most suitable subsidy program based on region or specific conditions.

[0032] The list generation unit can customize the list of subsidy programs based on the user's occupation and life stage. For example, the generation AI in the list generation unit analyzes the user's occupation and lists the subsidy programs that apply to that occupation. For example, subsidy programs for teachers are displayed preferentially. The list generation unit also analyzes the user's life stage and customizes the list of subsidy programs based on that analysis. For example, subsidy programs for newlyweds are displayed preferentially to a newlywed user, and subsidy programs for childcare support are displayed preferentially to a user raising children. This makes it possible to provide the optimal subsidy program based on the user's occupation and life stage.

[0033] The list generation unit can share the information on subsidy programs collected by the generation AI with other users, promoting community-based information exchange. The list generation unit, for example, builds a platform where users can share the information on subsidy programs collected by the generation AI. For example, a user posts information about subsidy programs and shares it with other users. The list generation unit also provides the information on subsidy programs collected by the generation AI to promote community-based information exchange. For example, a user posts a question about a subsidy program, and other users answer it. This allows the information on the subsidy program to be shared with other users, promoting community-based information exchange.

[0034] The schedule analysis unit can analyze the user's schedule and notify them of the optimal subsidy program to coincide with important events. For example, the schedule analysis unit uses a generation AI to analyze the user's schedule and notify them of housing-related subsidy programs to coincide with the timing of moving. For example, information about the "Children's Eco Living Support Project" is notified one month before the planned moving date. The schedule analysis unit also uses a generation AI to analyze the user's schedule and notify them of child-rearing support subsidy programs to coincide with the timing of childbirth. For example, information about the "Child-rearing Support Program" is notified before the expected date of birth. This makes it possible to notify them of the optimal subsidy program to coincide with important events for the user.

[0035] The schedule analysis unit analyzes the user's diary and schedule information, predicts future needs based on past behavioral patterns, and can notify them at the appropriate time. In the schedule analysis unit, for example, the generation AI analyzes the user's diary and predicts future needs based on past behavioral patterns. For example, a user who moves every spring is notified of housing-related subsidy programs to coincide with the spring move. In addition, the schedule analysis unit analyzes the user's schedule information and predicts future needs based on past behavioral patterns. For example, a user who travels every summer is notified of travel-related subsidy programs to coincide with the summer trip. In this way, future needs can be predicted based on past behavioral patterns and notified at the appropriate time.

[0036] The schedule analysis unit can notify information about subsidy programs by linking with the user's schedule and taking into account the schedules of family and friends. For example, the schedule analysis unit uses a generation AI to link the user's schedule with the schedules of family members and notify the user of subsidy programs that apply to the entire family. For example, it notifies the user of information about the "family support program" that can be used by the entire family. The schedule analysis unit also uses a generation AI to link the user's schedule with the schedules of friends and notify the user of subsidy programs that can be used jointly with friends. For example, it notifies the user of information about the "group purchasing support program" that can be used jointly with friends. This makes it possible to notify information about subsidy programs by taking into account the schedules of family and friends.

[0037] The schedule analysis unit can analyze the user's schedule and notify information about subsidy programs in line with specific seasons or events. For example, the generation AI in the schedule analysis unit analyzes the user's schedule and notifies information about subsidy programs in line with the end of the fiscal year. For example, it notifies information about the "end-of-fiscal-year support program" that can be used at the end of the fiscal year. Furthermore, the generation AI in the schedule analysis unit analyzes the user's schedule and notifies information about subsidy programs in line with summer vacation. For example, it notifies information about the "summer vacation support program" that can be used during summer vacation. This makes it possible to notify information about subsidy programs in line with specific seasons or events.

[0038] The application support unit can analyze the user's input in real time and automatically detect and notify any missing required information or documents. For example, the application support unit uses a generation AI to analyze the user's input in real time and automatically detect and notify any missing required information or documents. For example, it notifies the user if required information is missing from the application documents. The application support unit also builds a system in which the generation AI analyzes the user's input in real time and detects and notifies any missing required information or documents. For example, it notifies the user of missing information before the application documents are submitted. This allows the application support unit to analyze the user's input in real time and automatically detect and notify any missing required information or documents.

[0039] The application support unit can collect success stories of other users and provide useful application documents and advice. For example, the generation AI can collect success stories of other users and provide useful application documents and advice. For example, it can provide samples of successful application documents. The application support unit can also collect success stories of other users and provide advice based on them. For example, it can provide advice based on successful application cases. This allows the generation AI to collect success stories of other users and provide useful application documents and advice.

[0040] The application support unit can analyze the user's application process and provide expert advice at the optimal timing. For example, the application support unit uses a generation AI to analyze the user's application process and provide expert advice at the optimal timing. For example, expert advice is provided before the application documents are submitted. The application support unit also builds a system in which the generation AI analyzes the user's application process and provides expert advice at the optimal timing. For example, expert advice is provided while the application documents are being prepared. This makes it possible to analyze the user's application process and provide expert advice at the optimal timing.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The subsidy program utilization support system can also include a health monitoring unit that monitors the user's health condition and suggests health-related subsidy programs. For example, if the user uses a fitness tracker, the system analyzes the data and suggests subsidy programs related to health promotion. Also, if the user regularly visits a medical institution, the system can suggest medical expense subsidy programs based on that information. Furthermore, if the user has a specific health problem, the system can prioritize and display subsidy programs that address that problem. This allows the system to provide the optimal subsidy program based on the user's health condition.

[0043] The subsidy system utilization support system can further include a hobby analysis unit that suggests subsidy systems based on the user's hobbies and lifestyle. For example, if the user's hobby is gardening, gardening-related subsidy systems can be suggested. If the user's hobby is traveling, travel-related subsidy systems can be suggested. Furthermore, if the user is active in volunteer activities, subsidy systems related to volunteer activities can be displayed preferentially. This makes it possible to provide the optimal subsidy system based on the user's hobbies and lifestyle.

[0044] The subsidy program usage support system can also be equipped with a career analysis unit that suggests subsidy programs based on the user's educational background and work history. For example, if the user has a specific degree, subsidy programs related to that degree will be suggested. Also, if the user is engaged in a specific occupation, subsidy programs related to that occupation will be suggested. Furthermore, if the user is considering a career change, subsidy programs that will be useful for that career change can be preferentially displayed. This makes it possible to provide the optimal subsidy program based on the user's educational background and work history.

[0045] The subsidy program utilization support system can further include a purchase analysis unit that analyzes the user's purchasing history and suggests subsidy programs based on purchasing behavior. For example, if the user frequently purchases eco-friendly products, eco-related subsidy programs can be suggested. If the user purchases education-related products, subsidy programs for educational support can be suggested. Furthermore, if the user purchases products from a specific brand, subsidy programs related to that brand can be displayed preferentially. This allows the system to provide the optimal subsidy program based on the user's purchasing history.

[0046] The subsidy program utilization support system can also include a social media analysis unit that analyzes the user's social media activity and suggests subsidy programs based on their interests. For example, if the user frequently posts about environmental protection, the system can suggest environment-related subsidy programs. If the user posts about education, the system can suggest education support subsidy programs. Furthermore, if the user is participating in a specific event, the system can preferentially display subsidy programs related to that event. This allows the system to provide the optimal subsidy program based on the user's social media activity.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The list generation unit collects information about subsidy programs and compiles it into a list. For example, the generation AI collects information about subsidy programs from publicly available information on the Internet and official government websites, and organizes and lists it. The generation AI receives prompts containing instructions to collect information about subsidy programs, collects information based on the prompts, and generates a list. Step 2: The schedule analysis unit analyzes the user's schedule based on the information on subsidy programs collected by the list generation unit. For example, the generation AI analyzes the user's diary and schedule information and suggests appropriate subsidy programs. The generation AI receives the user's diary and schedule information as input, analyzes it, and suggests appropriate subsidy programs. Step 3: The notification unit notifies the user of information about subsidy programs at appropriate times based on the schedule analyzed by the schedule analysis unit. For example, if a user writes in their diary that they want to build a new house, the generation AI will notify them of information about the Children's Eco-Housing Support Project. Step 4: The application support unit provides support when applying for a subsidy program. For example, the generation AI generates application documents based on information about the subsidy program the user wants to apply for. The generation AI receives information about the subsidy program the user wants to apply for as input and generates application documents based on that information.

[0049] (Example 2) The subsidy system utilization support system according to an embodiment of the present invention is a system for effectively utilizing the numerous government support and subsidy systems that exist. This system compiles target subsidy and support systems into a list and supports applications. As a result, the subsidy system utilization support system allows users to effectively utilize subsidy systems that are suitable for them without overlooking them.

[0050] A subsidy program utilization support system according to an embodiment includes a list generation unit, a schedule analysis unit, a notification unit, and an application support unit. The list generation unit collects information about subsidy programs and compiles it into a list. For example, the generation AI collects information about subsidy programs from publicly available information on the Internet and official government websites, organizes it, and creates a list. The generation AI receives prompts containing instructions to collect information about subsidy programs, collects information based on the prompts, and generates a list. The schedule analysis unit analyzes a user's schedule based on the information about subsidy programs compiled by the list generation unit. For example, the generation AI analyzes the user's diary and schedule information and suggests appropriate subsidy programs. The generation AI receives the user's diary and schedule information as input, analyzes them, and suggests appropriate subsidy programs. The notification unit notifies the user of information about subsidy programs at appropriate times based on the schedule analyzed by the schedule analysis unit. For example, if a user writes in their diary that they want to build a new house, the generation AI notifies the user of information about the "Children's Eco-Housing Support Project" based on that information. The application support unit provides support when applying for subsidy programs. For example, the generation AI generates application documents based on information about the subsidy program for which the user wants to apply. The generation AI receives information about the subsidy program for which the user wants to apply as input, and generates application documents based on that information. As a result, the subsidy program utilization support system according to the embodiment allows the user to receive information about the subsidy program at the appropriate time, allowing for a smooth application process.

[0051] The list generation unit can prioritize and list information about subsidy programs based on the user's past application history and interests. For example, the list generation unit uses a generation AI to analyze the user's past application history and prioritize and list similar subsidy programs. For example, new housing-related subsidy programs are displayed preferentially to a user who has applied for housing-related subsidies in the past. The list generation unit also uses a generation AI to analyze the user's interests and prioritize and list information about subsidy programs based on that analysis. For example, if the user is interested in "environmental protection," environmental subsidy programs are displayed preferentially. This makes it possible to display the most suitable subsidy programs preferentially based on the user's past application history and interests.

[0052] The list generation unit can filter information on subsidy programs based on region or specific conditions, and provide the user with the most suitable list. For example, the generation AI in the list generation unit analyzes the user's region of residence and prioritizes listing subsidy programs applicable to that region. For example, for a user living in Tokyo, Tokyo's subsidy programs are displayed preferentially. The generation AI in the list generation unit also analyzes the user's income level and family structure, and filters information on subsidy programs based on that. For example, subsidy programs for low-income earners and subsidy programs for households with children are displayed preferentially. This makes it possible to provide the user with the most suitable subsidy program based on region or specific conditions.

[0053] The list generation unit can use the emotion estimation function to prioritize the display of subsidy programs for which the user has previously expressed positive emotions. For example, the list generation unit uses a generation AI to analyze the user's past emotion data and prioritize listing subsidy programs for which the user has previously expressed positive emotions. For example, subsidy programs for which the user has previously expressed high satisfaction are displayed preferentially. The list generation unit also uses a generation AI to analyze the user's emotion data in real time and prioritize listing subsidy programs for which the user has previously expressed positive emotions. For example, if the user feels that "this subsidy program is great," that subsidy program is displayed preferentially. This improves user satisfaction by prioritize displaying subsidy programs for which the user has previously expressed positive emotions.

[0054] The list generation unit can customize the list of subsidy programs based on the user's occupation and life stage. For example, the generation AI in the list generation unit analyzes the user's occupation and lists the subsidy programs that apply to that occupation. For example, subsidy programs for teachers are displayed preferentially. The list generation unit also analyzes the user's life stage and customizes the list of subsidy programs based on that analysis. For example, subsidy programs for newlyweds are displayed preferentially to a newlywed user, and subsidy programs for childcare support are displayed preferentially to a user raising children. This makes it possible to provide the optimal subsidy program based on the user's occupation and life stage.

[0055] The list generation unit can share the information on subsidy programs collected by the generation AI with other users, promoting community-based information exchange. The list generation unit, for example, builds a platform where users can share the information on subsidy programs collected by the generation AI. For example, a user posts information about subsidy programs and shares it with other users. The list generation unit also provides the information on subsidy programs collected by the generation AI to promote community-based information exchange. For example, a user posts a question about a subsidy program, and other users answer it. This allows the information on the subsidy program to be shared with other users, promoting community-based information exchange.

[0056] The schedule analysis unit can analyze the user's schedule and notify them of the optimal subsidy program to coincide with important events. For example, the schedule analysis unit uses a generation AI to analyze the user's schedule and notify them of housing-related subsidy programs to coincide with the timing of moving. For example, information about the "Children's Eco Living Support Project" is notified one month before the planned moving date. The schedule analysis unit also uses a generation AI to analyze the user's schedule and notify them of child-rearing support subsidy programs to coincide with the timing of childbirth. For example, information about the "Child-rearing Support Program" is notified before the expected date of birth. This makes it possible to notify them of the optimal subsidy program to coincide with important events for the user.

[0057] The schedule analysis unit analyzes the user's diary and schedule information, predicts future needs based on past behavioral patterns, and can notify them at the appropriate time. In the schedule analysis unit, for example, the generation AI analyzes the user's diary and predicts future needs based on past behavioral patterns. For example, a user who moves every spring is notified of housing-related subsidy programs to coincide with the spring move. In addition, the schedule analysis unit analyzes the user's schedule information and predicts future needs based on past behavioral patterns. For example, a user who travels every summer is notified of travel-related subsidy programs to coincide with the summer trip. In this way, future needs can be predicted based on past behavioral patterns and notified at the appropriate time.

[0058] The schedule analysis unit can use the emotion estimation function to notify the user of additional information and success stories related to a specific subsidy program when the user expresses positive feelings toward the program. For example, the schedule analysis unit can use the emotion estimation function to notify the user of additional information related to the program when the user expresses positive feelings toward the program. For example, a user who expresses positive feelings toward the "Children's Eco-Housing Support Program" is notified of additional application procedure information. Furthermore, the schedule analysis unit can use the emotion estimation function to notify the user of success stories related to the program when the user expresses positive feelings toward the program. For example, a user who expresses positive feelings toward the "Provision of Housing and Temporary Housing in the Event of a Disaster" program is notified of success stories. This makes it possible to notify the user of additional information and success stories related to the subsidy program about which the user expressed positive feelings.

[0059] The schedule analysis unit can notify information about subsidy programs by linking with the user's schedule and taking into account the schedules of family and friends. For example, the schedule analysis unit uses a generation AI to link the user's schedule with the schedules of family members and notify the user of subsidy programs that apply to the entire family. For example, it notifies the user of information about the "family support program" that can be used by the entire family. The schedule analysis unit also uses a generation AI to link the user's schedule with the schedules of friends and notify the user of subsidy programs that can be used jointly with friends. For example, it notifies the user of information about the "group purchasing support program" that can be used jointly with friends. This makes it possible to notify information about subsidy programs by taking into account the schedules of family and friends.

[0060] The schedule analysis unit can analyze the user's schedule and notify information about subsidy programs in line with specific seasons or events. For example, the generation AI in the schedule analysis unit analyzes the user's schedule and notifies information about subsidy programs in line with the end of the fiscal year. For example, it notifies information about the "end-of-fiscal-year support program" that can be used at the end of the fiscal year. Furthermore, the generation AI in the schedule analysis unit analyzes the user's schedule and notifies information about subsidy programs in line with summer vacation. For example, it notifies information about the "summer vacation support program" that can be used during summer vacation. This makes it possible to notify information about subsidy programs in line with specific seasons or events.

[0061] The application support unit can analyze the user's input in real time and automatically detect and notify any missing required information or documents. For example, the application support unit uses a generation AI to analyze the user's input in real time and automatically detect and notify any missing required information or documents. For example, it notifies the user if required information is missing from the application documents. The application support unit also builds a system in which the generation AI analyzes the user's input in real time and detects and notifies any missing required information or documents. For example, it notifies the user of missing information before the application documents are submitted. This allows the application support unit to analyze the user's input in real time and automatically detect and notify any missing required information or documents.

[0062] The application support unit can use the emotion estimation function to provide advice and support to reduce the stress and anxiety the user feels during the application process. For example, the application support unit can use the emotion estimation function to provide advice and support to reduce the stress and anxiety the user feels during the application process. For example, the application support unit can suggest relaxation methods if the user feels stressed. The application support unit can also use the emotion estimation function to provide support to reduce the anxiety the user feels during the application process. For example, the application support unit can provide expert advice if the user feels anxious. This makes it possible to provide advice and support to reduce the stress and anxiety the user feels during the application process.

[0063] The application support unit can collect success stories of other users and provide useful application documents and advice. For example, the generation AI can collect success stories of other users and provide useful application documents and advice. For example, it can provide samples of successful application documents. The application support unit can also collect success stories of other users and provide advice based on them. For example, it can provide advice based on successful application cases. This allows the generation AI to collect success stories of other users and provide useful application documents and advice.

[0064] The application support unit can analyze the user's application process and provide expert advice at the optimal timing. For example, the application support unit uses a generation AI to analyze the user's application process and provide expert advice at the optimal timing. For example, expert advice is provided before the application documents are submitted. The application support unit also builds a system in which the generation AI analyzes the user's application process and provides expert advice at the optimal timing. For example, expert advice is provided while the application documents are being prepared. This makes it possible to analyze the user's application process and provide expert advice at the optimal timing.

[0065] The application support unit can use the emotion estimation function to provide success stories and encouraging messages to reinforce the positive emotions felt by the user during the application process. For example, the application support unit can use the emotion estimation function to provide success stories to reinforce the positive emotions felt by the user during the application process. For example, the application support unit can introduce successful application cases. The application support unit can also use the emotion estimation function to provide encouraging messages to reinforce the positive emotions felt by the user during the application process. For example, the application support unit can provide messages to increase motivation. This makes it possible to provide success stories and encouraging messages to reinforce the positive emotions felt by the user during the application process.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The subsidy program utilization support system can also include a health monitoring unit that monitors the user's health condition and suggests health-related subsidy programs. For example, if the user uses a fitness tracker, the system analyzes the data and suggests subsidy programs related to health promotion. Also, if the user regularly visits a medical institution, the system can suggest medical expense subsidy programs based on that information. Furthermore, if the user has a specific health problem, the system can prioritize and display subsidy programs that address that problem. This allows the system to provide the optimal subsidy program based on the user's health condition.

[0068] The subsidy system utilization support system can further include a hobby analysis unit that suggests subsidy systems based on the user's hobbies and lifestyle. For example, if the user's hobby is gardening, gardening-related subsidy systems can be suggested. If the user's hobby is traveling, travel-related subsidy systems can be suggested. Furthermore, if the user is active in volunteer activities, subsidy systems related to volunteer activities can be displayed preferentially. This makes it possible to provide the optimal subsidy system based on the user's hobbies and lifestyle.

[0069] The subsidy program usage support system can also be equipped with a career analysis unit that suggests subsidy programs based on the user's educational background and work history. For example, if the user has a specific degree, subsidy programs related to that degree will be suggested. Also, if the user is engaged in a specific occupation, subsidy programs related to that occupation will be suggested. Furthermore, if the user is considering a career change, subsidy programs that will be useful for that career change can be preferentially displayed. This makes it possible to provide the optimal subsidy program based on the user's educational background and work history.

[0070] The subsidy program utilization support system can further include a purchase analysis unit that analyzes the user's purchasing history and suggests subsidy programs based on purchasing behavior. For example, if the user frequently purchases eco-friendly products, eco-related subsidy programs can be suggested. If the user purchases education-related products, subsidy programs for educational support can be suggested. Furthermore, if the user purchases products from a specific brand, subsidy programs related to that brand can be displayed preferentially. This allows the system to provide the optimal subsidy program based on the user's purchasing history.

[0071] The subsidy program utilization support system can also include a social media analysis unit that analyzes the user's social media activity and suggests subsidy programs based on their interests. For example, if the user frequently posts about environmental protection, the system can suggest environment-related subsidy programs. If the user posts about education, the system can suggest education support subsidy programs. Furthermore, if the user is participating in a specific event, the system can preferentially display subsidy programs related to that event. This allows the system to provide the optimal subsidy program based on the user's social media activity.

[0072] The subsidy program usage support system can further include an emotion analysis unit that estimates the user's emotions and suggests subsidy programs based on the estimated emotions. For example, if the user is feeling stressed, subsidy programs that will help reduce stress can be suggested. Also, if the user is expressing positive emotions, subsidy programs that will further enhance those emotions can be suggested. Furthermore, if the user is feeling anxious, subsidy programs that will reduce that anxiety can be preferentially displayed. This makes it possible to provide the optimal subsidy program based on the user's emotions.

[0073] The subsidy program utilization support system may further include an emotion support unit that estimates the user's emotions and supports the subsidy program application process based on the estimated emotions. For example, if the user feels stressed during the application process, advice to reduce the stress is provided. Also, if the user shows positive emotions, an encouraging message to maintain the positive emotions is provided. Furthermore, if the user feels anxious, specific support to reduce the anxiety can be provided. In this way, the application process can be supported based on the user's emotions.

[0074] The subsidy program usage support system may further include an emotion customization unit that estimates the user's emotions and customizes information about subsidy programs based on the estimated emotions. For example, if the user has positive emotions about a specific subsidy program, additional information related to that program is provided. If the user has negative emotions, alternatives to alleviate those emotions are suggested. Furthermore, success stories and specific usage methods can be provided for subsidy programs in which the user is interested. This allows information about subsidy programs to be customized based on the user's emotions.

[0075] The subsidy program usage support system can also include an emotion filtering unit that estimates the user's emotions and narrows down the options for subsidy programs based on the estimated emotions. For example, if the user is feeling stressed, subsidy programs that will reduce that stress can be displayed preferentially. Also, if the user is expressing positive emotions, subsidy programs that will further enhance those emotions can be suggested. Furthermore, if the user is feeling anxious, subsidy programs that will reduce that anxiety can be displayed preferentially. This allows the optimal subsidy program to be selected based on the user's emotions.

[0076] The subsidy program utilization support system may further include an emotion document customization unit that estimates the user's emotions and customizes the application documents for the subsidy program based on the estimated emotions. For example, if the user is feeling stressed, the system may provide simplified application documents to reduce the stress. If the user is showing positive emotions, the system may provide application documents based on success stories to maintain the emotions. Furthermore, if the user is feeling anxious, the system may provide detailed guidelines to reduce the anxiety. In this way, the application documents can be customized based on the user's emotions.

[0077] The processing flow of the second embodiment will be briefly explained below.

[0078] Step 1: The list generation unit collects information about subsidy programs and compiles it into a list. For example, the generation AI collects information about subsidy programs from publicly available information on the Internet and official government websites, and organizes and lists it. The generation AI receives prompts containing instructions to collect information about subsidy programs, collects information based on the prompts, and generates a list. Step 2: The schedule analysis unit analyzes the user's schedule based on the information on subsidy programs collected by the list generation unit. For example, the generation AI analyzes the user's diary and schedule information and suggests appropriate subsidy programs. The generation AI receives the user's diary and schedule information as input, analyzes it, and suggests appropriate subsidy programs. Step 3: The notification unit notifies the user of information about subsidy programs at appropriate times based on the schedule analyzed by the schedule analysis unit. For example, if a user writes in their diary that they want to build a new house, the generation AI will notify them of information about the Children's Eco-Housing Support Project. Step 4: The application support unit provides support when applying for a subsidy program. For example, the generation AI generates application documents based on information about the subsidy program the user wants to apply for. The generation AI receives information about the subsidy program the user wants to apply for as input and generates application documents based on that information.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0083] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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).

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0098] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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."

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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]

[0146] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a list generation unit that collects information on subsidy programs and compiles it into a list; a schedule analysis unit that analyzes a user's schedule based on the information on the subsidy programs collected by the list generation unit; a notification unit that notifies information about the subsidy program at an appropriate timing based on the schedule analyzed by the schedule analysis unit; An application support unit that provides support when applying for the subsidy system. A system characterized by:

2. The list generation unit The information on the subsidy programs is prioritized and listed based on the user's past application history and interests.

2. The system of claim 1.

3. The list generation unit Filtering the subsidy information by region or specific criteria to provide the user with the most appropriate list 2. The system of claim 1.

4. a list generation unit that collects information on subsidy programs and compiles it into a list; a schedule analysis unit that analyzes a user's schedule based on the information on the subsidy programs collected by the list generation unit; a notification unit that notifies information about the subsidy program at an appropriate timing based on the schedule analyzed by the schedule analysis unit; An application support unit that provides support when applying for the subsidy system. A system characterized by:

5. The list generation unit The subsidy programs for which the user has expressed positive feelings in the past are preferentially displayed in the list.

2. The system of claim 1.

6. The schedule analysis unit If the user expresses positive feelings toward a particular subsidy program, the notification unit notifies the user of additional information and success stories related to the program.

2. The system of claim 1.

7. The application support department The notification unit provides advice and support to reduce stress and anxiety felt by the user during the application process.

2. The system of claim 1.

8. The application support department The notification unit provides success stories and encouraging messages to reinforce the positive feelings the user feels during the application process.

2. The system of claim 1.

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