System

Abstract

An object of the system according to the embodiment is to allow a user to easily find out an administrative system corresponding to the user and to understand a usage method and a procedure of the administrative system.SOLUTION: A system according to an embodiment includes a reception unit, a search unit, and a provision unit. The receiving unit receives information of a user. A retrieval part analyzes the information inputted by the reception part and retrieves the corresponding administrative system. The provision part teaches a method and a procedure for utilizing the administrative system retrieved by the retrieval part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult for users to find the administrative system that applies to them and understand how to use it and the procedures.

[0005] The system according to the embodiment aims to enable users to easily find the administrative system that applies to them and understand how to use it and the procedures involved. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a search unit, and a provision unit. The reception unit inputs user information. The search unit analyzes the information input by the reception unit and searches for the relevant administrative system. The provision unit provides instructions on how to use and procedures for the administrative system searched for by the search unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily find the administrative system that applies to them and understand how to use it and the procedures involved. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention automatically compiles relevant government programs and provides easy-to-understand instructions on how to use them and the procedures involved. In this system, a user inputs their information, and a generation AI analyzes the information and automatically compiles relevant government programs. Furthermore, the generation AI also provides easy-to-understand instructions on how to use them and the procedures involved. For example, when a user inputs basic information such as their name, address, age, occupation, family composition, income, and health status, the generation AI searches for relevant government programs based on that information and provides specific explanations of application procedures, required documents, and the procedure flow. This allows users to easily find the government programs that are appropriate for them and complete the procedures smoothly. This allows the system to automatically compile relevant government programs based on the user's information and provide easy-to-understand instructions on how to use them and the procedures involved. For example, if a user wants to use the childcare support program, the generation AI searches for the relevant program and provides instructions on how to apply and the necessary documents. This allows users to complete the procedures smoothly.

[0029] The administrative system support system according to the embodiment includes a reception unit, a search unit, and a provision unit. The reception unit inputs user information. The user information includes, but is not limited to, for example, name, address, age, occupation, family composition, income, and health status. The reception unit can input information using methods such as text input, multiple-choice format, and numerical input. The search unit uses a generation AI to analyze the information input by the reception unit and search for relevant administrative systems. The generation AI analyzes the information using techniques such as text analysis, data mining, and machine learning algorithms. The search unit searches for relevant administrative systems using methods such as keyword search, filtering, and ranking. The provision unit uses the generation AI to teach the user how to use and the procedures for the administrative systems found by the search unit. The provision unit provides specific explanations, such as application methods, required documents, and the procedure flow. The provision unit can provide information using methods such as text explanations, illustrations, and videos. As a result, the administrative system support system according to the embodiment can automatically compile relevant administrative systems based on the user's information and clearly explain how to use and the procedures. For example, if a user wants to use a childcare support system, the AI ​​will search for the relevant system and provide instructions on how to apply and the necessary documents, allowing the user to go through the process smoothly.

[0030] The reception unit can input information including the user's name, address, age, occupation, family structure, income, and health condition. The reception unit inputs, for example, information such as the user's name, address, age, occupation, family structure, income, and health condition. For example, a user inputs information such as "I live in Tokyo, I'm 30 years old, I'm an office worker, my family consists of my wife and one child, my annual income is 5 million yen, and I'm in good health." The reception unit can input information using methods such as text input, multiple-choice input, and numeric input. This allows the user's basic information to be input in detail. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's input information to a generation AI and have the generation AI analyze the information.

[0031] The search unit can search for a relevant administrative system based on user information. For example, the search unit searches for a relevant administrative system based on user information. For example, if a user inputs information such as "living in Tokyo, 30 years old, office worker, family structure: wife and one child, annual income of 5 million yen, good health," the search unit searches for administrative systems in Tokyo and systems suitable for a 30-year-old office worker. The search unit searches for relevant administrative systems using methods such as keyword search, filtering, and ranking. This makes it possible to search for an appropriate administrative system based on the user information. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit may input user information into a generation AI and cause the generation AI to search for relevant administrative systems.

[0032] The provision unit can instruct the user on how to apply for the relevant administrative system, the necessary documents, and the procedure flow. The provision unit, for example, can instruct the user on how to apply for the relevant administrative system, the necessary documents, and the procedure flow. For example, the provision unit can specifically explain how to apply for the relevant administrative system. The provision unit can also list the necessary documents and illustrate the procedure flow. For example, the provision unit can provide information using text explanations, illustrations, videos, etc. This makes it possible to instruct the user on how to use the administrative system and the procedures in an easy-to-understand manner. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can use a generation AI to instruct the user on how to apply for the relevant administrative system, the necessary documents, and the procedure flow.

[0033] The reception unit can analyze the user's past input history and select the optimal input method. For example, the reception unit can automatically display information that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest information that will be used in a specific time period based on the user's past input history. This makes it possible to provide the optimal input method based on the user's past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input the user's past input history into a generation AI and have the generation AI select the optimal input method.

[0034] The reception unit can customize input items based on the user's current living situation and areas of interest when inputting information. For example, if the user is raising a child, the reception unit can prioritize displaying information input items related to childcare. Furthermore, if the user is elderly, the reception unit can also prioritize displaying information input items for elderly people. Furthermore, if the user has a specific area of ​​interest, the reception unit can also display information input items related to that area. This enables information input according to the user's living situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's current living situation and areas of interest into the generation AI and have the generation AI customize the input items.

[0035] When inputting information, the reception unit can select an appropriate input means according to the user's input method. For example, if the user desires voice input, the reception unit can provide a voice input interface. Furthermore, if the user desires text input, the reception unit can also provide a text input interface. Furthermore, if the user desires image input, the reception unit can also provide an image upload function. This makes it possible to provide the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input method to the generation AI and cause the generation AI to select an appropriate input means.

[0036] When inputting information, the reception unit can prioritize inputting highly relevant information taking into account the user's geographical location information. For example, if the user lives in a specific area, the reception unit can cause the user to prioritize inputting information related to that area. Furthermore, if the user is traveling, the reception unit can also cause the user to prioritize inputting information related to the travel destination. Furthermore, if the user is planning to move, the reception unit can also cause the user to prioritize inputting information related to the user's new address. This makes it possible to prioritize inputting highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize inputting highly relevant information.

[0037] The reception unit can analyze the user's social media activity and input related information when inputting information. For example, the reception unit can input related information based on information shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and input related information. The reception unit can also input related information by referring to the activity of the user's friends on social media. This makes it possible to input related information based on the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's social media activity to the generation AI and cause the generation AI to input related information.

[0038] The reception unit can customize the input method by reflecting the user's past feedback when inputting information. The reception unit can, for example, suggest an optimal input method based on feedback provided by the user in the past. The reception unit can also improve the input interface based on the user's past feedback. The reception unit can also simplify the input procedure by referring to the user's past feedback. This makes it possible to provide an optimal input method based on the user's past feedback. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past feedback into a generation AI and have the generation AI customize the input method.

[0039] When searching, the search unit can optimize the search algorithm by referring to the user's past search history. For example, the search unit can prioritize displaying related search results based on keywords the user has previously searched for. The search unit can also suggest search results that the user is likely to be interested in based on the user's past search history. The search unit can also analyze the user's past search history and apply an optimal search algorithm. This makes it possible to provide optimal search results based on the user's past search history. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the user's past search history into a generation AI and cause the generation AI to optimize the search algorithm.

[0040] The search unit can customize search results based on the user's attribute information during a search. For example, the search unit can prioritize displaying appropriate administrative systems based on the user's age. The search unit can also prioritize displaying relevant administrative systems based on the user's occupation. The search unit can also prioritize displaying appropriate administrative systems based on the user's family structure. This makes it possible to provide optimal search results based on the user's attribute information. Some or all of the above-described processing in the search unit may be performed using AI, for example, or may be performed without using AI. For example, the search unit can input the user's attribute information into a generation AI and cause the generation AI to customize the search results.

[0041] During a search, the search unit can adjust the priority of search results based on the user's current living situation. For example, if the user is raising a child, the search unit can prioritize displaying childcare support systems. Furthermore, if the user is elderly, the search unit can also prioritize displaying administrative systems for the elderly. Furthermore, if the user has a specific area of ​​interest, the search unit can also prioritize displaying administrative systems related to that area. This makes it possible to provide optimal search results according to the user's living situation. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the user's current living situation into the generation AI and have the generation AI adjust the priority of the search results.

[0042] During a search, the search unit can prioritize displaying highly relevant search results by taking into account the user's geographical location information. For example, if the user lives in a specific area, the search unit can prioritize displaying administrative systems related to that area. Furthermore, if the user is traveling, the search unit can prioritize displaying administrative systems related to the user's travel destination. Furthermore, if the user is planning to move, the search unit can prioritize displaying administrative systems related to the user's new address. This makes it possible to provide highly relevant search results based on the user's geographical location information. Some or all of the above-described processing in the search unit may be performed using, or without, AI. For example, the search unit can input the user's geographical location information into the generation AI and cause the generation AI to prioritize displaying highly relevant search results.

[0043] The search unit can analyze the user's social media activity during a search and display relevant search results. The search unit can, for example, display relevant administrative systems based on information shared by the user on social media. The search unit can also analyze the content of the user's social media posts and display relevant administrative systems. The search unit can also display relevant administrative systems based on the activity of the user's friends on social media. This makes it possible to provide relevant search results based on the user's social media activity. Some or all of the above-described processing in the search unit can be performed using, for example, AI, or can be performed without using AI. For example, the search unit can input the user's social media activity into a generation AI and cause the generation AI to display relevant search results.

[0044] The search unit can customize the search algorithm by reflecting the user's past feedback when searching. The search unit can apply an optimal search algorithm based on, for example, feedback provided by the user in the past. The search unit can also improve the way search results are displayed based on the user's past feedback. The search unit can also adjust the priority of search results by referring to the user's past feedback. This makes it possible to provide an optimal search algorithm based on the user's past feedback. Some or all of the above-mentioned processing in the search unit can be performed using, for example, AI, or can be performed without using AI. For example, the search unit can input the user's past feedback into a generation AI and cause the generation AI to customize the search algorithm.

[0045] The providing unit can adjust the level of detail of the information to be provided by referring to the user's past usage history when providing the information. The providing unit, for example, provides detailed information based on information used by the user in the past. The providing unit can also provide information that is likely to be of interest to the user based on the user's past usage history. The providing unit can also analyze the user's past usage history and provide optimal information. This makes it possible to provide optimal information based on the user's past usage history. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past usage history into the generating AI and cause the generating AI to adjust the level of detail of the information.

[0046] The providing unit can customize the information taking into account the user's attribute information when providing the information. The providing unit can provide information on appropriate administrative systems, for example, depending on the user's age. The providing unit can also provide information on related administrative systems depending on the user's occupation. The providing unit can also provide information on appropriate administrative systems depending on the user's family structure. This makes it possible to provide optimal information based on the user's attribute information. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input the user's attribute information into a generating AI and cause the generating AI to customize the information.

[0047] The providing unit can adjust the priority of information based on the user's current living situation when providing the information. For example, if the user is raising a child, the providing unit can prioritize providing information on childcare support systems. Furthermore, if the user is elderly, the providing unit can prioritize providing information on administrative systems for the elderly. Furthermore, if the user has a specific area of ​​interest, the providing unit can prioritize providing information on administrative systems related to that area. This makes it possible to provide optimal information according to the user's living situation. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's current living situation into the generating AI and cause the generating AI to adjust the priority of information.

[0048] When providing information, the providing unit can prioritize providing highly relevant information taking into account the user's geographical location information. For example, if the user lives in a specific area, the providing unit can prioritize providing information on administrative systems related to that area. Furthermore, if the user is traveling, the providing unit can prioritize providing information on administrative systems related to the user's travel destination. Furthermore, if the user is planning to move, the providing unit can prioritize providing information on administrative systems related to the user's new address. This makes it possible to provide highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize providing highly relevant information.

[0049] The providing unit can analyze the user's social media activity and provide related information at the time of providing. For example, the providing unit can provide information on related administrative systems based on information shared by the user on social media. The providing unit can also analyze the content of the user's social media posts and provide information on related administrative systems. The providing unit can also provide information on related administrative systems based on the activity of the user's friends on social media. This makes it possible to provide related information based on the user's social media activity. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's social media activity into a generation AI and cause the generation AI to provide related information.

[0050] The providing unit can customize the information provision method by reflecting the user's past feedback when providing information. The providing unit can, for example, suggest an optimal information provision method based on feedback provided by the user in the past. The providing unit can also improve the information provision interface based on the user's past feedback. The providing unit can also simplify the information provision procedure by referring to the user's past feedback. This makes it possible to provide optimal information based on the user's past feedback. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input the user's past feedback into the generating AI and cause the generating AI to customize the information provision method.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The reception unit can analyze the user's lifestyle habits and behavioral patterns based on the user's input information and suggest the optimal timing for inputting information. For example, if the user tends to input information at night, the reception unit can send a notification at night to prompt the user to input information. Also, if the user spends a lot of time on weekends, the reception unit can send a notification prompting the user to input information on weekends. Furthermore, if the user's concentration increases during a specific time period, the reception unit can also prompt the user to input information during that time period. This makes it possible to suggest the optimal timing for inputting information based on the user's lifestyle habits and behavioral patterns.

[0053] The search unit can estimate the user's interests and concerns based on the information input by the user and suggest related administrative systems. For example, if the user has input a lot of information about childcare, it can prioritize suggesting childcare support systems. Also, if the user has input a lot of information for the elderly, it can suggest administrative systems for the elderly. Furthermore, if the user has input a lot of information about a specific region, it can suggest administrative systems related to that region. This makes it possible to suggest the most suitable administrative system based on the user's interests and concerns.

[0054] The reception unit can estimate the user's health condition based on the information input by the user and provide health advice. For example, if the user tends to sit for long periods of time, it can send a notification encouraging the user to stand up periodically. Also, if the user has irregular eating habits, it can suggest a balanced diet. Furthermore, if the user is not getting enough exercise, it can suggest appropriate exercise. In this way, it is possible to provide appropriate advice based on the user's health condition.

[0055] The providing unit can analyze the user's past usage history and suggest the optimal timing for providing information. For example, if the user has used a lot of information during a specific time period in the past, the information can be provided during that time period. Also, if the user has used a lot of information on weekends, the information can be provided on weekends. Furthermore, if the user has used a lot of information related to a specific event, the information can be provided in accordance with that event. In this way, the optimal timing for providing information can be suggested based on the user's past usage history.

[0056] The search unit can predict the user's future needs based on the information input by the user and suggest relevant administrative systems. For example, if the user has input a lot of information about their current occupation, it can suggest vocational training systems that may be needed in the future. Also, if the user has input a lot of information about childcare, it can suggest educational support systems that may be needed in the future. Furthermore, if the user has input a lot of information for the elderly, it can suggest nursing care support systems that may be needed in the future. This makes it possible to suggest the most appropriate administrative systems based on the user's future needs.

[0057] The reception unit can estimate the user's hobbies and preferences based on the information input by the user and provide related information. For example, if the user has input a lot of information about sports, it can suggest sports-related administrative systems. If the user has input a lot of information about cultural activities, it can suggest cultural activity support systems. Furthermore, if the user is interested in environmental protection, it can suggest environmental protection-related administrative systems. This makes it possible to provide optimal information based on the user's hobbies and preferences.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The reception unit inputs user information, including name, address, age, occupation, family composition, income, health status, etc. The reception unit can input information using methods such as text input, multiple-choice format, and numeric input. Step 2: The search unit uses the generation AI to analyze the information entered by the reception unit and search for the relevant administrative system. The generation AI analyzes the information using techniques such as text analysis, data mining, and machine learning algorithms. The search unit searches for the relevant administrative system using methods such as keyword search, filtering, and ranking. Step 3: The provision unit uses the generation AI to teach the user how to use the administrative system and procedures found by the search unit. The provision unit provides specific explanations of application procedures, required documents, procedure flow, etc. The provision unit can provide information using methods such as text explanations, illustrations, and videos.

[0060] (Example 2) A system according to an embodiment of the present invention automatically compiles relevant government programs and provides easy-to-understand instructions on how to use them and the procedures involved. In this system, a user inputs their information, and a generation AI analyzes the information and automatically compiles relevant government programs. Furthermore, the generation AI also provides easy-to-understand instructions on how to use them and the procedures involved. For example, when a user inputs basic information such as their name, address, age, occupation, family composition, income, and health status, the generation AI searches for relevant government programs based on that information and provides specific explanations of application procedures, required documents, and the procedure flow. This allows users to easily find the government programs that are appropriate for them and complete the procedures smoothly. This allows the system to automatically compile relevant government programs based on the user's information and provide easy-to-understand instructions on how to use them and the procedures involved. For example, if a user wants to use the childcare support program, the generation AI searches for the relevant program and provides instructions on how to apply and the necessary documents. This allows users to complete the procedures smoothly.

[0061] The administrative system support system according to the embodiment includes a reception unit, a search unit, and a provision unit. The reception unit inputs user information. The user information includes, but is not limited to, for example, name, address, age, occupation, family composition, income, and health status. The reception unit can input information using methods such as text input, multiple-choice format, and numerical input. The search unit uses a generation AI to analyze the information input by the reception unit and search for relevant administrative systems. The generation AI analyzes the information using techniques such as text analysis, data mining, and machine learning algorithms. The search unit searches for relevant administrative systems using methods such as keyword search, filtering, and ranking. The provision unit uses the generation AI to teach the user how to use and the procedures for the administrative systems found by the search unit. The provision unit provides specific explanations, such as application methods, required documents, and the procedure flow. The provision unit can provide information using methods such as text explanations, illustrations, and videos. As a result, the administrative system support system according to the embodiment can automatically compile relevant administrative systems based on the user's information and clearly explain how to use and the procedures. For example, if a user wants to use a childcare support system, the AI ​​will search for the relevant system and provide instructions on how to apply and the necessary documents, allowing the user to go through the process smoothly.

[0062] The reception unit can input information including the user's name, address, age, occupation, family structure, income, and health condition. The reception unit inputs, for example, information such as the user's name, address, age, occupation, family structure, income, and health condition. For example, a user inputs information such as "I live in Tokyo, I'm 30 years old, I'm an office worker, my family consists of my wife and one child, my annual income is 5 million yen, and I'm in good health." The reception unit can input information using methods such as text input, multiple-choice input, and numeric input. This allows the user's basic information to be input in detail. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's input information to a generation AI and have the generation AI analyze the information.

[0063] The search unit can search for a relevant administrative system based on user information. For example, the search unit searches for a relevant administrative system based on user information. For example, if a user inputs information such as "living in Tokyo, 30 years old, office worker, family structure: wife and one child, annual income of 5 million yen, good health," the search unit searches for administrative systems in Tokyo and systems suitable for a 30-year-old office worker. The search unit searches for relevant administrative systems using methods such as keyword search, filtering, and ranking. This makes it possible to search for an appropriate administrative system based on the user information. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit may input user information into a generation AI and cause the generation AI to search for relevant administrative systems.

[0064] The provision unit can instruct the user on how to apply for the relevant administrative system, the necessary documents, and the procedure flow. The provision unit, for example, can instruct the user on how to apply for the relevant administrative system, the necessary documents, and the procedure flow. For example, the provision unit can specifically explain how to apply for the relevant administrative system. The provision unit can also list the necessary documents and illustrate the procedure flow. For example, the provision unit can provide information using text explanations, illustrations, videos, etc. This makes it possible to instruct the user on how to use the administrative system and the procedures in an easy-to-understand manner. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can use a generation AI to instruct the user on how to apply for the relevant administrative system, the necessary documents, and the procedure flow.

[0065] The reception unit can estimate the user's emotions and adjust the timing of information input based on the emotion data. For example, if the user is feeling stressed, the reception unit can pause input and display a relaxing interface. The reception unit can also allow the user to input information continuously when the user is relaxed. The reception unit can also allow the user to quickly complete input when the user is in a hurry. This allows more appropriate information input by adjusting the timing of information input according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit can be performed using an AI, for example, or without an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of information input.

[0066] The reception unit can analyze the user's past input history and select the optimal input method. For example, the reception unit can automatically display information that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest information that will be used in a specific time period based on the user's past input history. This makes it possible to provide the optimal input method based on the user's past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input the user's past input history into a generation AI and have the generation AI select the optimal input method.

[0067] The reception unit can customize input items based on the user's current living situation and areas of interest when inputting information. For example, if the user is raising a child, the reception unit can prioritize displaying information input items related to childcare. Furthermore, if the user is elderly, the reception unit can also prioritize displaying information input items for elderly people. Furthermore, if the user has a specific area of ​​interest, the reception unit can also display information input items related to that area. This enables information input according to the user's living situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's current living situation and areas of interest into the generation AI and have the generation AI customize the input items.

[0068] When inputting information, the reception unit can select an appropriate input means according to the user's input method. For example, if the user desires voice input, the reception unit can provide a voice input interface. Furthermore, if the user desires text input, the reception unit can also provide a text input interface. Furthermore, if the user desires image input, the reception unit can also provide an image upload function. This makes it possible to provide the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input method to the generation AI and cause the generation AI to select an appropriate input means.

[0069] The reception unit can estimate the user's emotions and determine the priority of information to be input based on the emotion data. For example, when the user is feeling stressed, the reception unit can prioritize input of important information. Furthermore, when the user is relaxed, the reception unit can also prompt the user to input detailed information. Furthermore, when the user is in a hurry, the reception unit can also prompt the user to input minimal information. This allows for more appropriate information input by determining the priority of information to be input according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of information.

[0070] When inputting information, the reception unit can prioritize inputting highly relevant information taking into account the user's geographical location information. For example, if the user lives in a specific area, the reception unit can cause the user to prioritize inputting information related to that area. Furthermore, if the user is traveling, the reception unit can also cause the user to prioritize inputting information related to the travel destination. Furthermore, if the user is planning to move, the reception unit can also cause the user to prioritize inputting information related to the user's new address. This makes it possible to prioritize inputting highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize inputting highly relevant information.

[0071] The reception unit can analyze the user's social media activity and input related information when inputting information. For example, the reception unit can input related information based on information shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and input related information. The reception unit can also input related information by referring to the activity of the user's friends on social media. This makes it possible to input related information based on the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's social media activity to the generation AI and cause the generation AI to input related information.

[0072] The reception unit can customize the input method by reflecting the user's past feedback when inputting information. The reception unit can, for example, suggest an optimal input method based on feedback provided by the user in the past. The reception unit can also improve the input interface based on the user's past feedback. The reception unit can also simplify the input procedure by referring to the user's past feedback. This makes it possible to provide an optimal input method based on the user's past feedback. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past feedback into a generation AI and have the generation AI customize the input method.

[0073] The search unit can estimate the user's emotions and adjust the display method of search results based on the emotion data. For example, if the user is feeling stressed, the search unit can display simple, highly visible search results. Furthermore, if the user is relaxed, the search unit can display detailed search results. Furthermore, if the user is in a hurry, the search unit can display search results that focus on the main points. This allows for more appropriate search results to be provided by adjusting the display method of search results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the search unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the search unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the search results.

[0074] When searching, the search unit can optimize the search algorithm by referring to the user's past search history. For example, the search unit can prioritize displaying related search results based on keywords the user has previously searched for. The search unit can also suggest search results that the user is likely to be interested in based on the user's past search history. The search unit can also analyze the user's past search history and apply an optimal search algorithm. This makes it possible to provide optimal search results based on the user's past search history. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the user's past search history into a generation AI and cause the generation AI to optimize the search algorithm.

[0075] The search unit can customize search results based on the user's attribute information during a search. For example, the search unit can prioritize displaying appropriate administrative systems based on the user's age. The search unit can also prioritize displaying relevant administrative systems based on the user's occupation. The search unit can also prioritize displaying appropriate administrative systems based on the user's family structure. This makes it possible to provide optimal search results based on the user's attribute information. Some or all of the above-described processing in the search unit may be performed using AI, for example, or may be performed without using AI. For example, the search unit can input the user's attribute information into a generation AI and cause the generation AI to customize the search results.

[0076] During a search, the search unit can adjust the priority of search results based on the user's current living situation. For example, if the user is raising a child, the search unit can prioritize displaying childcare support systems. Furthermore, if the user is elderly, the search unit can also prioritize displaying administrative systems for the elderly. Furthermore, if the user has a specific area of ​​interest, the search unit can also prioritize displaying administrative systems related to that area. This makes it possible to provide optimal search results according to the user's living situation. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the user's current living situation into the generation AI and have the generation AI adjust the priority of the search results.

[0077] The search unit can estimate the user's emotions and adjust the display order of search results based on the emotion data. For example, when the user is stressed, the search unit can prioritize displaying important search results. Furthermore, when the user is relaxed, the search unit can also display detailed search results. Furthermore, when the user is in a hurry, the search unit can prioritize displaying search results that highlight the main points. This allows for adjusting the display order of search results according to the user's emotions, thereby providing more appropriate search results. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the search unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the search unit can input the user's emotion data into the generation AI and have the generation AI adjust the display order of the search results.

[0078] During a search, the search unit can prioritize displaying highly relevant search results by taking into account the user's geographical location information. For example, if the user lives in a specific area, the search unit can prioritize displaying administrative systems related to that area. Furthermore, if the user is traveling, the search unit can prioritize displaying administrative systems related to the user's travel destination. Furthermore, if the user is planning to move, the search unit can prioritize displaying administrative systems related to the user's new address. This makes it possible to provide highly relevant search results based on the user's geographical location information. Some or all of the above-described processing in the search unit may be performed using, or without, AI. For example, the search unit can input the user's geographical location information into the generation AI and cause the generation AI to prioritize displaying highly relevant search results.

[0079] The search unit can analyze the user's social media activity during a search and display relevant search results. The search unit can, for example, display relevant administrative systems based on information shared by the user on social media. The search unit can also analyze the content of the user's social media posts and display relevant administrative systems. The search unit can also display relevant administrative systems based on the activity of the user's friends on social media. This makes it possible to provide relevant search results based on the user's social media activity. Some or all of the above-described processing in the search unit can be performed using, for example, AI, or can be performed without using AI. For example, the search unit can input the user's social media activity into a generation AI and cause the generation AI to display relevant search results.

[0080] The search unit can customize the search algorithm by reflecting the user's past feedback when searching. The search unit can apply an optimal search algorithm based on, for example, feedback provided by the user in the past. The search unit can also improve the way search results are displayed based on the user's past feedback. The search unit can also adjust the priority of search results by referring to the user's past feedback. This makes it possible to provide an optimal search algorithm based on the user's past feedback. Some or all of the above-mentioned processing in the search unit can be performed using, for example, AI, or can be performed without using AI. For example, the search unit can input the user's past feedback into a generation AI and cause the generation AI to customize the search algorithm.

[0081] The providing unit can estimate the user's emotions and adjust the way the information is presented based on the emotion data. For example, if the user is feeling stressed, the providing unit can provide simple, highly visible information. Furthermore, if the user is relaxed, the providing unit can provide detailed information. Furthermore, if the user is in a hurry, the providing unit can provide information that focuses on the main points. This allows for more appropriate information provision by adjusting the way information is presented according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the information is presented.

[0082] The providing unit can adjust the level of detail of the information to be provided by referring to the user's past usage history when providing the information. The providing unit, for example, provides detailed information based on information used by the user in the past. The providing unit can also provide information that is likely to be of interest to the user based on the user's past usage history. The providing unit can also analyze the user's past usage history and provide optimal information. This makes it possible to provide optimal information based on the user's past usage history. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past usage history into the generating AI and cause the generating AI to adjust the level of detail of the information.

[0083] The providing unit can customize the information taking into account the user's attribute information when providing the information. The providing unit can provide information on appropriate administrative systems, for example, depending on the user's age. The providing unit can also provide information on related administrative systems depending on the user's occupation. The providing unit can also provide information on appropriate administrative systems depending on the user's family structure. This makes it possible to provide optimal information based on the user's attribute information. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input the user's attribute information into a generating AI and cause the generating AI to customize the information.

[0084] The providing unit can adjust the priority of information based on the user's current living situation when providing the information. For example, if the user is raising a child, the providing unit can prioritize providing information on childcare support systems. Furthermore, if the user is elderly, the providing unit can prioritize providing information on administrative systems for the elderly. Furthermore, if the user has a specific area of ​​interest, the providing unit can prioritize providing information on administrative systems related to that area. This makes it possible to provide optimal information according to the user's living situation. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's current living situation into the generating AI and cause the generating AI to adjust the priority of information.

[0085] The providing unit can estimate the user's emotions and adjust the length of the information to be provided based on the emotion data. For example, if the user is feeling stressed, the providing unit can provide short, concise information. Furthermore, if the user is relaxed, the providing unit can provide detailed information. Furthermore, if the user is in a hurry, the providing unit can provide concise information. This allows for more appropriate information provision by adjusting the length of information according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the information.

[0086] When providing information, the providing unit can prioritize providing highly relevant information taking into account the user's geographical location information. For example, if the user lives in a specific area, the providing unit can prioritize providing information on administrative systems related to that area. Furthermore, if the user is traveling, the providing unit can prioritize providing information on administrative systems related to the user's travel destination. Furthermore, if the user is planning to move, the providing unit can prioritize providing information on administrative systems related to the user's new address. This makes it possible to provide highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize providing highly relevant information.

[0087] The providing unit can analyze the user's social media activity and provide related information at the time of providing. For example, the providing unit can provide information on related administrative systems based on information shared by the user on social media. The providing unit can also analyze the content of the user's social media posts and provide information on related administrative systems. The providing unit can also provide information on related administrative systems based on the activity of the user's friends on social media. This makes it possible to provide related information based on the user's social media activity. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's social media activity into a generation AI and cause the generation AI to provide related information.

[0088] The providing unit can customize the information provision method by reflecting the user's past feedback when providing information. The providing unit can, for example, suggest an optimal information provision method based on feedback provided by the user in the past. The providing unit can also improve the information provision interface based on the user's past feedback. The providing unit can also simplify the information provision procedure by referring to the user's past feedback. This makes it possible to provide optimal information based on the user's past feedback. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input the user's past feedback into the generating AI and cause the generating AI to customize the information provision method. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, search unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can input user information using the touch panel 38A or microphone 38B of the smart device 14. The search unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the user information using a generation AI to search for the relevant administrative system. The provision unit is realized by the specific processing unit 290 of the data processing device 12, and teaches how to use and procedures for the searched administrative system. The provision unit can provide information using the display 40A or speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, search unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can input user information using the microphone 238 of the smart glasses 214. The search unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user information using a generation AI to search for the relevant administrative system. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides instructions on how to use and procedures for the searched administrative system. The provision unit can provide information using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, search unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can input user information using the microphone 238 of the headset-type terminal 314. The search unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the user information using a generation AI and searches for the relevant administrative system. The provision unit is realized by the specific processing unit 290 of the data processing device 12, and teaches how to use and the procedures for the searched administrative system. The provision unit can provide information using the speaker 240 or display 343 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the reception unit, search unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can input user information using the microphone 238 of the robot 414. The search unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the user information using a generation AI and searches for the relevant administrative system. The provision unit is realized by the specific processing unit 290 of the data processing device 12, and teaches how to use and the procedures for the searched administrative system. The provision unit can provide information using the speaker 240 or display device of the robot 414.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The reception unit can analyze the user's lifestyle habits and behavioral patterns based on the user's input information and suggest the optimal timing for inputting information. For example, if the user tends to input information at night, the reception unit can send a notification at night to prompt the user to input information. Also, if the user spends a lot of time on weekends, the reception unit can send a notification prompting the user to input information on weekends. Furthermore, if the user's concentration increases during a specific time period, the reception unit can also prompt the user to input information during that time period. This makes it possible to suggest the optimal timing for inputting information based on the user's lifestyle habits and behavioral patterns.

[0091] The search unit can estimate the user's interests and concerns based on the information input by the user and suggest related administrative systems. For example, if the user has input a lot of information about childcare, it can prioritize suggesting childcare support systems. Also, if the user has input a lot of information for the elderly, it can suggest administrative systems for the elderly. Furthermore, if the user has input a lot of information about a specific region, it can suggest administrative systems related to that region. This makes it possible to suggest the most suitable administrative system based on the user's interests and concerns.

[0092] The providing unit can estimate the user's emotions and adjust the method of providing information based on the emotion data. For example, if the user is feeling stressed, simple, highly visible information can be provided. If the user is relaxed, detailed information can be provided. Furthermore, if the user is in a hurry, information that focuses on the main points can be provided. In this way, by adjusting the method of providing information according to the user's emotions, more appropriate information can be provided.

[0093] The reception unit can estimate the user's health condition based on the information input by the user and provide health advice. For example, if the user tends to sit for long periods of time, it can send a notification encouraging the user to stand up periodically. Also, if the user has irregular eating habits, it can suggest a balanced diet. Furthermore, if the user is not getting enough exercise, it can suggest appropriate exercise. In this way, it is possible to provide appropriate advice based on the user's health condition.

[0094] The search unit can estimate the user's emotions and adjust the way search results are displayed based on the emotion data. For example, if the user is feeling stressed, simple, highly visible search results can be displayed. If the user is relaxed, detailed search results can be displayed. Furthermore, if the user is in a hurry, search results that focus on the main points can be displayed. In this way, by adjusting the way search results are displayed according to the user's emotions, more appropriate search results can be provided.

[0095] The providing unit can analyze the user's past usage history and suggest the optimal timing for providing information. For example, if the user has used a lot of information during a specific time period in the past, the information can be provided during that time period. Also, if the user has used a lot of information on weekends, the information can be provided on weekends. Furthermore, if the user has used a lot of information related to a specific event, the information can be provided in accordance with that event. In this way, the optimal timing for providing information can be suggested based on the user's past usage history.

[0096] The reception unit can estimate the user's emotions and determine the priority of information to be input based on the emotion data. For example, if the user is feeling stressed, the reception unit can prompt the user to input important information first. If the user is relaxed, the reception unit can prompt the user to input detailed information. If the user is in a hurry, the reception unit can prompt the user to input the minimum amount of information. In this way, the reception unit can determine the priority of information to be input based on the user's emotions, thereby enabling more appropriate information input.

[0097] The search unit can predict the user's future needs based on the information input by the user and suggest relevant administrative systems. For example, if the user has input a lot of information about their current occupation, it can suggest vocational training systems that may be needed in the future. Also, if the user has input a lot of information about childcare, it can suggest educational support systems that may be needed in the future. Furthermore, if the user has input a lot of information for the elderly, it can suggest nursing care support systems that may be needed in the future. This makes it possible to suggest the most appropriate administrative systems based on the user's future needs.

[0098] The providing unit can estimate the user's emotions and adjust the length of the information to be provided based on the emotion data. For example, if the user is feeling stressed, short, to-the-point information can be provided. If the user is relaxed, detailed information can be provided. Furthermore, if the user is in a hurry, concise information can be provided. In this way, by adjusting the length of information according to the user's emotions, more appropriate information can be provided.

[0099] The reception unit can estimate the user's hobbies and preferences based on the information input by the user and provide related information. For example, if the user has input a lot of information about sports, it can suggest sports-related administrative systems. If the user has input a lot of information about cultural activities, it can suggest cultural activity support systems. Furthermore, if the user is interested in environmental protection, it can suggest environmental protection-related administrative systems. This makes it possible to provide optimal information based on the user's hobbies and preferences.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The reception unit inputs user information, including name, address, age, occupation, family composition, income, health status, etc. The reception unit can input information using methods such as text input, multiple-choice format, and numeric input. Step 2: The search unit uses the generation AI to analyze the information entered by the reception unit and search for the relevant administrative system. The generation AI analyzes the information using techniques such as text analysis, data mining, and machine learning algorithms. The search unit searches for the relevant administrative system using methods such as keyword search, filtering, and ranking. Step 3: The provision unit uses the generation AI to teach the user how to use the administrative system and procedures found by the search unit. The provision unit provides specific explanations of application procedures, required documents, procedure flow, etc. The provision unit can provide information using methods such as text explanations, illustrations, and videos.

[0102] 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.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] 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.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] [Explanation of symbols]

[0174] 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 reception unit for inputting user information; a search unit that analyzes the information input by the reception unit and searches for a corresponding administrative system; a providing unit that provides instruction on how to use and procedures for the administrative system searched by the search unit; Equipped with A system characterized by:

2. The reception unit Enter information including the user's name, address, age, occupation, family composition, income, and health status 2. The system of claim 1.

3. The search unit Search for the relevant administrative system based on the user's information 2. The system of claim 1.

4. The providing unit We will teach you how to apply for the relevant administrative system, the necessary documents, and the procedure.

2. The system of claim 1.

5. The reception unit Estimates the user's emotions and adjusts the timing of information input based on the estimated user emotions.

2. The system of claim 1.

6. The reception unit Analyze the user's past input history and select the appropriate input method 2. The system of claim 1.

7. The reception unit As you enter information, customize the input based on your current life situation and interests.

2. The system of claim 1.

8. The reception unit When entering information, select the appropriate input method depending on the user's input method.

2. The system of claim 1.

9. The reception unit Estimate the user's emotions and prioritize the information to be input based on the estimated user emotions.

2. The system of claim 1.

10. The reception unit When entering information, the system takes into account the user's geographic location information and prioritizes entering the most relevant information.

2. The system of claim 1.

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