System

Abstract

To provide a system allowing citizens to easily grasp applicable institutions and subsidies.SOLUTION: A system according to an embodiment comprises a registration unit, a collection unit, an analysis unit, and a provision unit. The registration unit registers user information. The collection unit collects institution and subsidy information on the basis of the information registered by the registration unit. The analysis unit analyzes the information collected by the collection unit, and identifies applicable institutions and subsidies. The provision unit provides information of the institutions and subsidies identified by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, there was an issue that the scope of local government systems and subsidies was complex, making it difficult for citizens to understand the systems and subsidies to which they were eligible.

[0005] The system according to the embodiment aims to enable citizens to easily understand applicable systems and subsidies. [Means for solving the problem]

[0006] The system according to the embodiment includes a registration unit, a collection unit, an analysis unit, and a provision unit. The registration unit registers user information. The collection unit collects information on systems and subsidies based on the information registered by the registration unit. The analysis unit analyzes the information collected by the collection unit and identifies applicable systems and subsidies. The provision unit provides information on systems and subsidies identified by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can enable citizens to easily understand applicable systems and subsidies. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The local government procedure support system according to the embodiment of the present invention is a system that compiles information on systems and subsidies provided by the country, prefectures, cities, towns, villages, and tax offices, and provides guidance on application decisions to individuals. In this local government procedure support system, a user registers his / her personal information, the system collects information on systems and subsidies provided by each local government and tax office based on the registered information, and the system analyzes the information to identify systems and subsidies that are applicable to the user, provides the identified information to the user, and provides guidance on procedures. For example, if a user registers information such as "resident of Tokyo, 30 years old, company employee, annual income 5 million yen," the system collects information such as housing subsidies provided by Tokyo and childcare support systems provided by the country, and the AI ​​analyzes the information to identify applicable systems and subsidies and provide them to the user. This reduces the burden on local government officials and simplifies procedures for citizens. As a result, the local government procedure support system identifies and provides applicable systems and subsidies based on user information, thereby reducing the burden on local government officials and simplifying procedures for citizens.

[0029] The local government procedure support system according to the embodiment includes a registration unit, a collection unit, an analysis unit, and a provision unit. The registration unit registers user information. The user information includes, for example, basic information such as name, address, age, occupation, and income, but is not limited to such examples. The registration unit enables the user to input information such as "living in Tokyo, 30 years old, company employee, annual income 5 million yen". The collection unit collects information on systems and subsidies provided by each local government and tax office based on the information registered by the registration unit. The collection unit collects information such as housing subsidies provided by Tokyo and childcare support systems provided by the country. The collection unit can acquire information from the official websites of each local government and tax office via the Internet. The analysis unit analyzes the information collected by the collection unit and identifies systems and subsidies applicable to the user. The analysis unit, for example, uses AI to match the user information with the application criteria of each system and identifies the applicable system and subsidy. The AI, for example, uses a text generation AI (for example, LLM) to analyze the application criteria of each system and identify the system that is most suitable for the user. The providing unit provides the user with information on the systems and subsidies identified by the analyzing unit and provides guidance on the procedures. For example, if the user is eligible for a housing subsidy, the providing unit provides information such as how to apply for the subsidy and necessary documents. The providing unit can provide the information through a web application or a mobile application. As a result, the local government procedure support system according to the embodiment can identify and provide applicable systems and subsidies based on user information, thereby reducing the burden on local government officials and simplifying procedures for citizens.

[0030] The registration unit registers user information. The user information includes, but is not limited to, basic information such as name, address, age, occupation, and income. For example, the registration unit enables the user to input information such as "resident in Tokyo, 30 years old, company employee, annual income 5 million yen". Specifically, the registration unit provides an interface for the user to input information through a web form or a mobile application. The user inputs the necessary information according to the form and presses the send button, and the information is registered in the system. The registration unit saves the input information in a database so that it can be used for subsequent processing. Furthermore, the registration unit also has a function for checking the accuracy of the information input by the user. For example, when inputting an address, an auto-completion function is provided based on the postal code to prevent input errors. In addition, range checks and format checks are performed on information such as age and income to ensure that accurate data is registered. In this way, the registration unit can efficiently and accurately collect basic information about the user and improve the reliability of the entire system. Furthermore, the registration unit also provides a function for the user to update information later. For example, if the user moves or their income changes, they can log in again to update their information. This ensures that the system always contains the latest information, helping to identify applicable schemes and subsidies.

[0031] The collection unit collects information on systems and subsidies provided by each local government and tax office based on the information registered by the registration unit. For example, the collection unit collects information such as housing subsidies provided by the Tokyo Metropolitan Government and childcare support systems provided by the national government. Specifically, the collection unit can obtain information from the official websites of each local government and tax office via the Internet. The collection unit uses web scraping technology to automatically extract the necessary information from each website. For example, a web scraping tool is used to periodically check the subsidy information page of each local government, and when new information is added, it is automatically incorporated into the database. The collection unit can also obtain information using APIs. Many local governments and government agencies provide public APIs, which can be used to efficiently collect information. The collection unit centrally manages this information and makes it accessible to the analysis unit. In addition, the collection unit also has a function for checking the accuracy and recency of the collected information. For example, when the collected information becomes outdated, it is automatically updated, and the latest information is always kept. The collection unit also provides a data cleansing function for detecting duplication and errors in information and maintaining the quality of data. This enables the collection unit to collect information efficiently and accurately, improving the performance of the entire system.

[0032] The analysis unit analyzes the information collected by the collection unit and identifies systems and subsidies that are applicable to the user. The analysis unit, for example, uses AI to match the user's information with the application criteria of each system and identify applicable systems and subsidies. Specifically, the AI ​​uses a text generation AI (for example, LLM) to analyze the application criteria of each system and identify the system that is most suitable for the user. The AI ​​uses natural language processing technology to understand the application criteria of each system and match them with the user's information. For example, if a user registers information such as "living in Tokyo, 30 years old, company employee, annual income 5 million yen," the AI ​​analyzes the application criteria of the housing subsidy and childcare support system provided by the Tokyo Metropolitan Government and determines whether the user is applicable to these systems. Furthermore, the AI ​​can also make optimal proposals that combine multiple systems and subsidies based on the user's information. For example, by combining a housing subsidy and a childcare support system, the user can be provided with the maximum benefit. The analysis unit can also improve the accuracy of proposals to users by utilizing past data and statistical information. For example, a proposal with a high success rate is made based on past cases where applications were approved under similar conditions. This enables the analysis unit to quickly and accurately identify and provide optimal systems and subsidies to users. Furthermore, the analysis unit can collect user feedback and use it as learning data for the AI ​​to continuously improve the accuracy of its suggestions.

[0033] The provision unit provides the user with information on the system or subsidy identified by the analysis unit and guides the user through the procedure. For example, if the user is eligible for a housing subsidy, the provision unit provides information such as how to apply for the subsidy and necessary documents. Specifically, the provision unit can provide the information through a web application or a mobile application. The user can log in to the application and check the information on the system or subsidy identified by the analysis unit. The provision unit provides the user with a step-by-step guide to the application procedure and provides detailed information such as the necessary documents and the place to submit the application. For example, for the application procedure for a housing subsidy, the provision unit provides a download link for the application form, an example of how to fill it out, and the address and contact information of the place to submit the application. In addition, the provision unit also provides an online application support function so that the user can proceed with the procedure smoothly. For example, the provision unit uses a chatbot to answer the user's questions in real time and support the procedure. Furthermore, the provision unit has a function of tracking the progress even after the user has completed the application procedure and sending reminders as necessary. This allows the user to always understand the progress of the procedure and take the necessary actions in a timely manner. The service provider will maximize user convenience and simplify and streamline procedures, thereby reducing the burden on local government officials and simplifying procedures for citizens.

[0034] The registration unit can register basic information such as the user's name, address, age, occupation, and income. The basic information includes, but is not limited to, for example, name, address, age, occupation, and income. The registration unit enables the user to input information such as "resident of Tokyo, 30 years old, company employee, annual income 5 million yen." By registering the user's basic information, basic data for identifying applicable systems and subsidies can be provided. Some or all of the above-mentioned processing in the registration unit may be performed, for example, using AI, or may be performed without using AI. For example, the registration unit can input the user's input data into AI and check the consistency of the data.

[0035] The collection unit can collect information on systems and subsidies provided by each local government and tax office. Information on systems and subsidies includes, for example, the target person, the application method, the payment amount, etc., but is not limited to such examples. The collection unit collects information, for example, on housing subsidies provided by the Tokyo Metropolitan Government and childcare support systems provided by the national government. The collection unit can acquire information from the official websites of each local government and tax office via the Internet. In this way, by collecting information on each local government and tax office, data for identifying systems and subsidies applicable to the user can be provided. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can automatically collect information from the websites of each local government and tax office using web scraping technology.

[0036] The analysis unit can analyze the collected information and identify systems and subsidies that are applicable to the user. Applicable systems and subsidies include, but are not limited to, criteria such as income restrictions and age restrictions. The analysis unit, for example, uses AI to match the user's information with the application criteria of each system and identify applicable systems and subsidies. The AI, for example, uses a text generation AI (for example, LLM) to analyze the application criteria of each system and identify the system that is most suitable for the user. In this way, the collected information can be analyzed to identify the system and subsidy that is most suitable for the user. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's information into AI and perform analysis to identify applicable systems and subsidies.

[0037] The provision unit can provide the user with information on the identified system or subsidy and guide the user through the procedure. The procedure guidance includes, for example, necessary documents, application procedures, etc., but is not limited to such examples. For example, if the user is eligible for a housing subsidy, the provision unit provides information such as a method of applying for the subsidy and necessary documents. The provision unit can provide information through a web application or a mobile application. As a result, by providing information on the identified system or subsidy, the user can smoothly proceed with the procedure. A part 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 input information to be provided to the user into AI and select an optimal provision method.

[0038] The registration unit can analyze the user's past registration history and select the optimal registration method. For example, the registration unit automatically displays information that the user frequently input in the past as a candidate. The registration unit can also preferentially suggest an input method (such as voice or text) that the user has used in the past. The registration unit can also predict and suggest information to be used in a specific time period from the user's past registration history. In this way, by analyzing the past registration history, the optimal registration method can be provided to the user, and input efficiency can be improved. A part or all of the above-mentioned processing in the registration unit may be performed, for example, using AI, or may be performed without using AI. For example, the registration unit can input the user's past input data to the generation AI and have the generation AI select the optimal registration method.

[0039] The registration unit can customize the input items based on the user's current living situation and areas of interest at the time of registration. For example, if the user is raising a child, the registration unit can display childcare-related input items with priority. In addition, if the user is an elderly person, the registration unit can also display input items related to systems and subsidies for the elderly. In addition, if the user is a student, the registration unit can also display education-related input items with priority. This allows the user to efficiently input information that is highly relevant to the user by customizing the input items based on the user's living situation and areas of interest. Part or all of the above-mentioned processing in the registration unit may be performed, for example, using AI, or may be performed without using AI. For example, the registration unit can input the user's living situation data to the generation AI and cause the generation AI to customize the input items.

[0040] The registration unit can preferentially register highly relevant information in consideration of the geographical location information of the user at the time of registration. For example, when the user lives in a specific area, the registration unit allows the user to preferentially input information related to the area. In addition, when the user is moving, the registration unit can also preferentially input information based on the current location. In addition, when the user is traveling, the registration unit can also preferentially input information related to the travel destination. In this way, highly relevant information can be efficiently registered by considering the geographical location information of the user. Part or all of the above-mentioned processing in the registration unit may be performed, for example, using AI, or may be performed without using AI. For example, the registration unit inputs the geographical location data of the user to the generation AI and allows the generation AI to select highly relevant information.

[0041] The registration unit can analyze the user's social media activity at the time of registration and automatically complete the related information. The registration unit automatically completes the registration information based on, for example, information shared by the user on social media. The registration unit can also analyze the content of the user's social media posts and automatically input the related information. The registration unit can also automatically complete the related information based on the user's social media friendships. In this way, by analyzing social media activity, the user's registration information can be automatically completed and the effort of inputting can be reduced. A part or all of the above-mentioned processing in the registration unit may be performed, for example, using AI or may be performed without using AI. For example, the registration unit can input the user's social media data to the generation AI and cause the generation AI to complete the related information.

[0042] The collection unit can acquire the latest information of each local government and tax office in real time at the time of collection. For example, the collection unit acquires the latest system information from the official website of each local government in real time. The collection unit can also acquire the latest subsidy information from the official website of the tax office in real time. The collection unit can also acquire the latest information in real time by using the API of each local government and tax office. As a result, by acquiring the latest information in real time, the latest system and subsidy information can always be provided. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input API data of each local government and tax office to the generation AI and cause the generation AI to acquire the latest information.

[0043] The collection unit can evaluate the reliability of information at the time of collection and preferentially collect reliable information. For example, the collection unit preferentially collects official information from each local government or tax office. The collection unit can also preferentially collect information from a reliable third party organization. The collection unit can also evaluate the source of information and preferentially collect reliable information. In this way, by evaluating the reliability of information, reliable information can be preferentially collected and provided. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input reliability data of the information source to the generation AI and cause the generation AI to perform the reliability evaluation.

[0044] The collection unit can preferentially collect highly relevant information in consideration of the geographical location information of the user at the time of collection. For example, if the user lives in a specific area, the collection unit preferentially collects information related to that area. In addition, if the user is moving, the collection unit can also preferentially collect information based on the current location. In addition, if the user is traveling, the collection unit can also preferentially collect information related to the travel destination. In this way, highly relevant information can be efficiently collected by considering the geographical location information of the user. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the geographical location data of the user to the generation AI and cause the generation AI to select highly relevant information.

[0045] The collection unit can analyze the user's social media activity and collect related information at the time of collection. The collection unit can collect related information based on, for example, information shared by the user on social media. The collection unit can also analyze the content of the user's social media posts and collect related information. The collection unit can also collect related information based on the user's social media friendships. This makes it possible to efficiently collect information related to the user by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without using AI. For example, the collection unit can input the user's social media data to the generation AI and cause the generation AI to collect related information.

[0046] The analysis unit can optimize the analysis algorithm by referring to past analysis data during analysis. The analysis unit, for example, selects an optimal algorithm based on past analysis data. The analysis unit can also analyze past analysis data and improve the accuracy of the algorithm. The analysis unit can also improve the reliability of the analysis result by referring to past analysis data. As a result, the accuracy of the analysis algorithm can be improved by referring to past analysis data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis data to the generation AI and cause the generation AI to optimize the algorithm.

[0047] During the analysis, the analysis unit can analyze the application criteria of each system and subsidy in detail and identify the optimal system. The analysis unit can, for example, analyze the application criteria of each system in detail and identify the optimal system. The analysis unit can also analyze the application criteria of each subsidy in detail and identify the optimal subsidy. The analysis unit can also compare the application criteria of each system and subsidy and identify the optimal option. As a result, the application criteria of each system and subsidy can be analyzed in detail to identify the optimal system for the user. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input application criteria data of each system to the generation AI and cause the generation AI to identify the optimal system.

[0048] The analysis unit can perform the analysis while taking into consideration the geographical location information of the user. For example, if the user lives in a specific area, the analysis unit preferentially analyzes information related to that area. In addition, if the user is moving, the analysis unit can also preferentially analyze information based on the current location. In addition, if the user is traveling, the analysis unit can also preferentially analyze information related to the travel destination. In this way, by taking into consideration the geographical location information of the user, it is possible to provide a highly relevant analysis result. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the geographical location data of the user to the generation AI and cause the generation AI to perform a highly relevant analysis.

[0049] The analysis unit can analyze the user's social media activities during the analysis and reflect related information in the analysis. The analysis unit can adjust the analysis result based on, for example, information shared by the user on social media. The analysis unit can also analyze the content of the user's social media posts and reflect related information in the analysis. The analysis unit can also reflect related information in the analysis based on the user's social media friendships. In this way, by analyzing social media activities, information related to the user can be reflected in the analysis and accuracy can be improved. A part or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI or can be performed without using AI. For example, the analysis unit can input the user's social media data to the generation AI and cause the generation AI to execute an analysis of the related information.

[0050] The providing unit can select the optimal information providing method by referring to the user's past usage history at the time of providing. For example, the providing unit preferentially suggests the information providing method (email, notification, etc.) that the user has used in the past. The providing unit can also provide information during a specific time period from the user's past usage history. The providing unit can also analyze the user's past usage history and select the most efficient information providing method. As a result, by referring to the past usage history, the optimal information providing method for the user can be selected and information can be provided efficiently. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the user's past usage data to the generating AI and cause the generating AI to select the optimal information providing method.

[0051] The providing unit can customize the means of providing information based on the user's current living situation at the time of providing. For example, when the user is raising a child, the providing unit can provide childcare-related information preferentially. In addition, when the user is an elderly person, the providing unit can also provide information for the elderly preferentially. In addition, when the user is a student, the providing unit can also provide education-related information preferentially. In this way, by customizing the means of providing information based on the user's living situation, highly relevant information can be provided. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's living situation data to the generating AI and have the generating AI customize the means of providing information.

[0052] The providing unit can select the optimal information providing method in consideration of the geographical location information of the user at the time of providing. For example, when the user lives in a specific area, the providing unit preferentially provides information related to the area. In addition, when the user is moving, the providing unit can also preferentially provide information based on the current location. In addition, when the user is traveling, the providing unit can also preferentially provide information related to the travel destination. In this way, by considering the geographical location information of the user, highly relevant information can be efficiently provided. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location data of the user to the generating AI and cause the generating AI to select the optimal information providing method.

[0053] The providing unit can analyze the user's social media activity and provide related information at the time of providing. The providing unit can provide related information based on, for example, 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 related information. The providing unit can also provide related information based on the user's social media friendships. In this way, by analyzing social media activity, it is possible to efficiently provide information related to the user. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media data to the generating AI and cause the generating AI to provide related information.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The local government procedure support system can further identify applicable systems and subsidies by taking into account the user's health condition. For example, if the user has a chronic illness, information on medical expense subsidies and specific medical services can be provided preferentially. In addition, if the user is pregnant, support systems and subsidies for pregnant women can be identified. Furthermore, if the user is elderly, information on nursing care services and subsidies for elderly people can be provided. This makes it possible to provide more personalized support by providing optimal support information according to the user's health condition.

[0056] The registration unit can customize the input items based on the user's hobbies and interests. For example, if the user is interested in sports, sports-related subsidy and event information can be displayed preferentially. If the user is interested in art and culture, art-related subsidy and event information can be provided. Furthermore, if the user is interested in environmental protection, information on systems and subsidies related to environmental protection can be displayed. This allows the user to efficiently input highly relevant information based on the user's hobbies and interests.

[0057] The collection unit can analyze the user's past search history and prioritize collection of related information. For example, if the user has frequently searched for information about housing subsidies in the past, the latest information about housing subsidies is prioritized. Also, if the user has searched for information about childcare support systems in the past, the latest information about childcare support systems can be collected. Furthermore, if the user has searched for education-related information in the past, the latest education-related information can be collected. This makes it possible to efficiently collect highly relevant information based on the user's past search history.

[0058] The analysis unit can perform analysis taking into account the user's lifestyle. For example, if the user is a nocturnal person, the analysis unit prioritizes the analysis of systems and subsidies available at night. In addition, if the user is mostly active during the day, the analysis unit can also analyze systems and subsidies available during the day. Furthermore, if the user is mostly active on weekends, the analysis unit can also analyze systems and subsidies available on weekends. This makes it possible to provide optimal support information according to the user's lifestyle.

[0059] The providing unit can adjust the method of providing information based on the device usage status of the user. For example, if the user mainly uses a smartphone, a mobile-friendly display method can be provided. Also, if the user uses a tablet, a display method optimized for the tablet can be provided. Furthermore, if the user uses a desktop, a display method optimized for the desktop can be provided. This makes it possible to realize an optimal information providing method according to the device usage status of the user.

[0060] The process flow of the first embodiment will be briefly described below.

[0061] Step 1: The registration unit registers user information. User information includes basic information such as name, address, age, occupation, and income. For example, a user can enter information such as "resident of Tokyo, 30 years old, company employee, annual income 5 million yen." Step 2: The Collection Department collects information on systems and subsidies offered by each local government and tax office based on the information registered by the Registration Department. For example, the Collection Department collects information on housing subsidies offered by the Tokyo Metropolitan Government and childcare support systems offered by the national government. The Collection Department can obtain information from the official websites of each local government and tax office via the Internet. Step 3: The analysis unit analyzes the information collected by the collection unit and identifies systems and subsidies that are applicable to the user. For example, using AI, it matches the user's information with the application criteria of each system and identifies the applicable systems and subsidies. The AI ​​uses a text generation AI (e.g., LLM) to analyze the application criteria of each system and identify the system that is most suitable for the user. Step 4: The provision unit provides the user with information on the systems and subsidies identified by the analysis unit and guides the user through the procedures. For example, if the user is eligible for a housing subsidy, the provision unit provides information on how to apply for the subsidy and the necessary documents. The provision unit can provide the information through a web application or a mobile application.

[0062] (Example 2) The local government procedure support system according to the embodiment of the present invention is a system that compiles information on systems and subsidies provided by the country, prefectures, cities, towns, villages, and tax offices, and provides guidance on application decisions to individuals. In this local government procedure support system, a user registers his / her personal information, the system collects information on systems and subsidies provided by each local government and tax office based on the registered information, and the system analyzes the information to identify systems and subsidies that are applicable to the user, provides the identified information to the user, and provides guidance on procedures. For example, if a user registers information such as "resident of Tokyo, 30 years old, company employee, annual income 5 million yen," the system collects information such as housing subsidies provided by Tokyo and childcare support systems provided by the country, and the AI ​​analyzes the information to identify applicable systems and subsidies and provide them to the user. This reduces the burden on local government officials and simplifies procedures for citizens. As a result, the local government procedure support system identifies and provides applicable systems and subsidies based on user information, thereby reducing the burden on local government officials and simplifying procedures for citizens.

[0063] The local government procedure support system according to the embodiment includes a registration unit, a collection unit, an analysis unit, and a provision unit. The registration unit registers user information. The user information includes, for example, basic information such as name, address, age, occupation, and income, but is not limited to such examples. The registration unit enables the user to input information such as "living in Tokyo, 30 years old, company employee, annual income 5 million yen". The collection unit collects information on systems and subsidies provided by each local government and tax office based on the information registered by the registration unit. The collection unit collects information such as housing subsidies provided by Tokyo and childcare support systems provided by the country. The collection unit can acquire information from the official websites of each local government and tax office via the Internet. The analysis unit analyzes the information collected by the collection unit and identifies systems and subsidies applicable to the user. The analysis unit, for example, uses AI to match the user information with the application criteria of each system and identifies the applicable system and subsidy. The AI, for example, uses a text generation AI (for example, LLM) to analyze the application criteria of each system and identify the system that is most suitable for the user. The providing unit provides the user with information on the systems and subsidies identified by the analyzing unit and provides guidance on the procedures. For example, if the user is eligible for a housing subsidy, the providing unit provides information such as how to apply for the subsidy and necessary documents. The providing unit can provide the information through a web application or a mobile application. As a result, the local government procedure support system according to the embodiment can identify and provide applicable systems and subsidies based on user information, thereby reducing the burden on local government officials and simplifying procedures for citizens.

[0064] The registration unit registers user information. The user information includes, but is not limited to, basic information such as name, address, age, occupation, and income. For example, the registration unit enables the user to input information such as "resident in Tokyo, 30 years old, company employee, annual income 5 million yen". Specifically, the registration unit provides an interface for the user to input information through a web form or a mobile application. The user inputs the necessary information according to the form and presses the send button, and the information is registered in the system. The registration unit saves the input information in a database so that it can be used for subsequent processing. Furthermore, the registration unit also has a function for checking the accuracy of the information input by the user. For example, when inputting an address, an auto-completion function is provided based on the postal code to prevent input errors. In addition, range checks and format checks are performed on information such as age and income to ensure that accurate data is registered. In this way, the registration unit can efficiently and accurately collect basic information about the user and improve the reliability of the entire system. Furthermore, the registration unit also provides a function for the user to update information later. For example, if the user moves or their income changes, they can log in again to update their information. This ensures that the system always contains the latest information, helping to identify applicable schemes and subsidies.

[0065] The collection unit collects information on systems and subsidies provided by each local government and tax office based on the information registered by the registration unit. For example, the collection unit collects information such as housing subsidies provided by the Tokyo Metropolitan Government and childcare support systems provided by the national government. Specifically, the collection unit can obtain information from the official websites of each local government and tax office via the Internet. The collection unit uses web scraping technology to automatically extract the necessary information from each website. For example, a web scraping tool is used to periodically check the subsidy information page of each local government, and when new information is added, it is automatically incorporated into the database. The collection unit can also obtain information using APIs. Many local governments and government agencies provide public APIs, which can be used to efficiently collect information. The collection unit centrally manages this information and makes it accessible to the analysis unit. In addition, the collection unit also has a function for checking the accuracy and recency of the collected information. For example, when the collected information becomes outdated, it is automatically updated, and the latest information is always kept. The collection unit also provides a data cleansing function for detecting duplication and errors in information and maintaining the quality of data. This enables the collection unit to collect information efficiently and accurately, improving the performance of the entire system.

[0066] The analysis unit analyzes the information collected by the collection unit and identifies systems and subsidies that are applicable to the user. The analysis unit, for example, uses AI to match the user's information with the application criteria of each system and identify applicable systems and subsidies. Specifically, the AI ​​uses a text generation AI (for example, LLM) to analyze the application criteria of each system and identify the system that is most suitable for the user. The AI ​​uses natural language processing technology to understand the application criteria of each system and match them with the user's information. For example, if a user registers information such as "living in Tokyo, 30 years old, company employee, annual income 5 million yen," the AI ​​analyzes the application criteria of the housing subsidy and childcare support system provided by the Tokyo Metropolitan Government and determines whether the user is applicable to these systems. Furthermore, the AI ​​can also make optimal proposals that combine multiple systems and subsidies based on the user's information. For example, by combining a housing subsidy and a childcare support system, the user can be provided with the maximum benefit. The analysis unit can also improve the accuracy of proposals to users by utilizing past data and statistical information. For example, a proposal with a high success rate is made based on past cases where applications were approved under similar conditions. This enables the analysis unit to quickly and accurately identify and provide optimal systems and subsidies to users. Furthermore, the analysis unit can collect user feedback and use it as learning data for the AI ​​to continuously improve the accuracy of its suggestions.

[0067] The provision unit provides the user with information on the system or subsidy identified by the analysis unit and guides the user through the procedure. For example, if the user is eligible for a housing subsidy, the provision unit provides information such as how to apply for the subsidy and necessary documents. Specifically, the provision unit can provide the information through a web application or a mobile application. The user can log in to the application and check the information on the system or subsidy identified by the analysis unit. The provision unit provides the user with a step-by-step guide to the application procedure and provides detailed information such as the necessary documents and the place to submit the application. For example, for the application procedure for a housing subsidy, the provision unit provides a download link for the application form, an example of how to fill it out, and the address and contact information of the place to submit the application. In addition, the provision unit also provides an online application support function so that the user can proceed with the procedure smoothly. For example, the provision unit uses a chatbot to answer the user's questions in real time and support the procedure. Furthermore, the provision unit has a function of tracking the progress even after the user has completed the application procedure and sending reminders as necessary. This allows the user to always understand the progress of the procedure and take the necessary actions in a timely manner. The service provider will maximize user convenience and simplify and streamline procedures, thereby reducing the burden on local government officials and simplifying procedures for citizens.

[0068] The registration unit can register basic information such as the user's name, address, age, occupation, and income. The basic information includes, but is not limited to, for example, name, address, age, occupation, and income. The registration unit enables the user to input information such as "resident of Tokyo, 30 years old, company employee, annual income 5 million yen." By registering the user's basic information, basic data for identifying applicable systems and subsidies can be provided. Some or all of the above-mentioned processing in the registration unit may be performed, for example, using AI, or may be performed without using AI. For example, the registration unit can input the user's input data into AI and check the consistency of the data.

[0069] The collection unit can collect information on systems and subsidies provided by each local government and tax office. Information on systems and subsidies includes, for example, the target person, the application method, the payment amount, etc., but is not limited to such examples. The collection unit collects information, for example, on housing subsidies provided by the Tokyo Metropolitan Government and childcare support systems provided by the national government. The collection unit can acquire information from the official websites of each local government and tax office via the Internet. In this way, by collecting information on each local government and tax office, data for identifying systems and subsidies applicable to the user can be provided. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can automatically collect information from the websites of each local government and tax office using web scraping technology.

[0070] The analysis unit can analyze the collected information and identify systems and subsidies that are applicable to the user. Applicable systems and subsidies include, but are not limited to, criteria such as income restrictions and age restrictions. The analysis unit, for example, uses AI to match the user's information with the application criteria of each system and identify applicable systems and subsidies. The AI, for example, uses a text generation AI (for example, LLM) to analyze the application criteria of each system and identify the system that is most suitable for the user. In this way, the collected information can be analyzed to identify the system and subsidy that is most suitable for the user. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's information into AI and perform analysis to identify applicable systems and subsidies.

[0071] The provision unit can provide the user with information on the identified system or subsidy and guide the user through the procedure. The procedure guidance includes, for example, necessary documents, application procedures, etc., but is not limited to such examples. For example, if the user is eligible for a housing subsidy, the provision unit provides information such as a method of applying for the subsidy and necessary documents. The provision unit can provide information through a web application or a mobile application. As a result, by providing information on the identified system or subsidy, the user can smoothly proceed with the procedure. A part 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 input information to be provided to the user into AI and select an optimal provision method.

[0072] The registration unit can estimate the user's emotion and adjust the display method of the registration form based on the estimated user's emotion. For example, when the user is stressed, the registration unit provides a simple interface and minimizes the input procedure. In addition, when the user is relaxed, the registration unit can provide detailed input options and suggest a customizable input method. In addition, when the user is in a hurry, the registration unit can prioritize voice input and enable the user to quickly register information. In this way, by adjusting the display method of the registration form according to the user's emotion, the user's stress can be reduced and the input procedure can be optimized. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the registration unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the registration unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0073] The registration unit can analyze the user's past registration history and select the optimal registration method. For example, the registration unit automatically displays information that the user frequently input in the past as a candidate. The registration unit can also preferentially suggest an input method (such as voice or text) that the user has used in the past. The registration unit can also predict and suggest information to be used in a specific time period from the user's past registration history. In this way, by analyzing the past registration history, the optimal registration method can be provided to the user, and input efficiency can be improved. A part or all of the above-mentioned processing in the registration unit may be performed, for example, using AI, or may be performed without using AI. For example, the registration unit can input the user's past input data to the generation AI and have the generation AI select the optimal registration method.

[0074] The registration unit can customize the input items based on the user's current living situation and areas of interest at the time of registration. For example, if the user is raising a child, the registration unit can display childcare-related input items with priority. In addition, if the user is an elderly person, the registration unit can also display input items related to systems and subsidies for the elderly. In addition, if the user is a student, the registration unit can also display education-related input items with priority. This allows the user to efficiently input information that is highly relevant to the user by customizing the input items based on the user's living situation and areas of interest. Part or all of the above-mentioned processing in the registration unit may be performed, for example, using AI, or may be performed without using AI. For example, the registration unit can input the user's living situation data to the generation AI and cause the generation AI to customize the input items.

[0075] The registration unit can estimate the user's emotion and determine the priority of information to be registered based on the estimated user's emotion. For example, when the user is feeling stressed, the registration unit allows the user to input important information with priority. Also, when the user is relaxed, the registration unit can also allow the user to input detailed information with priority. Also, when the user is in a hurry, the registration unit can also allow the user to input the minimum amount of information with priority. In this way, by determining the priority of information according to the user's emotion, important information can be input with priority. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the registration unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the registration unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0076] The registration unit can preferentially register highly relevant information in consideration of the geographical location information of the user at the time of registration. For example, when the user lives in a specific area, the registration unit allows the user to preferentially input information related to the area. In addition, when the user is moving, the registration unit can also preferentially input information based on the current location. In addition, when the user is traveling, the registration unit can also preferentially input information related to the travel destination. In this way, highly relevant information can be efficiently registered by considering the geographical location information of the user. Part or all of the above-mentioned processing in the registration unit may be performed, for example, using AI, or may be performed without using AI. For example, the registration unit inputs the geographical location data of the user to the generation AI and allows the generation AI to select highly relevant information.

[0077] The registration unit can analyze the user's social media activity at the time of registration and automatically complete the related information. The registration unit automatically completes the registration information based on, for example, information shared by the user on social media. The registration unit can also analyze the content of the user's social media posts and automatically input the related information. The registration unit can also automatically complete the related information based on the user's social media friendships. In this way, by analyzing social media activity, the user's registration information can be automatically completed and the effort of inputting can be reduced. A part or all of the above-mentioned processing in the registration unit may be performed, for example, using AI or may be performed without using AI. For example, the registration unit can input the user's social media data to the generation AI and cause the generation AI to complete the related information.

[0078] The collection unit can estimate the user's emotion and adjust the range of information to be collected based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit collects only important information. Also, when the user is relaxed, the collection unit can collect detailed information. Also, when the user is in a hurry, the collection unit can collect the minimum amount of information. In this way, by adjusting the range of information to be collected according to the user's emotion, important information can be preferentially collected. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0079] The collection unit can acquire the latest information of each local government and tax office in real time at the time of collection. For example, the collection unit acquires the latest system information from the official website of each local government in real time. The collection unit can also acquire the latest subsidy information from the official website of the tax office in real time. The collection unit can also acquire the latest information in real time by using the API of each local government and tax office. As a result, by acquiring the latest information in real time, the latest system and subsidy information can always be provided. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input API data of each local government and tax office to the generation AI and cause the generation AI to acquire the latest information.

[0080] The collection unit can evaluate the reliability of information at the time of collection and preferentially collect reliable information. For example, the collection unit preferentially collects official information from each local government or tax office. The collection unit can also preferentially collect information from a reliable third party organization. The collection unit can also evaluate the source of information and preferentially collect reliable information. In this way, by evaluating the reliability of information, reliable information can be preferentially collected and provided. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input reliability data of the information source to the generation AI and cause the generation AI to perform the reliability evaluation.

[0081] The collection unit can estimate the user's emotion and determine the priority of information to be collected based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit preferentially collects important information. In addition, when the user is relaxed, the collection unit can also preferentially collect detailed information. In addition, when the user is in a hurry, the collection unit can also preferentially collect the minimum amount of information. In this way, important information can be preferentially collected by determining the priority of information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0082] The collection unit can preferentially collect highly relevant information in consideration of the geographical location information of the user at the time of collection. For example, if the user lives in a specific area, the collection unit preferentially collects information related to that area. In addition, if the user is moving, the collection unit can also preferentially collect information based on the current location. In addition, if the user is traveling, the collection unit can also preferentially collect information related to the travel destination. In this way, highly relevant information can be efficiently collected by considering the geographical location information of the user. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the geographical location data of the user to the generation AI and cause the generation AI to select highly relevant information.

[0083] The collection unit can analyze the user's social media activity and collect related information at the time of collection. The collection unit can collect related information based on, for example, information shared by the user on social media. The collection unit can also analyze the content of the user's social media posts and collect related information. The collection unit can also collect related information based on the user's social media friendships. This makes it possible to efficiently collect information related to the user by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without using AI. For example, the collection unit can input the user's social media data to the generation AI and cause the generation AI to collect related information.

[0084] The analysis unit can estimate the user's emotion and adjust the analysis algorithm based on the estimated user's emotion. For example, when the user is stressed, the analysis unit performs the analysis using a simple algorithm. Also, when the user is relaxed, the analysis unit can perform the analysis using a detailed algorithm. Also, when the user is in a hurry, the analysis unit can use an algorithm that performs the analysis quickly. This allows the analysis result to be improved in accuracy by adjusting the analysis algorithm according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0085] The analysis unit can optimize the analysis algorithm by referring to past analysis data during analysis. The analysis unit, for example, selects an optimal algorithm based on past analysis data. The analysis unit can also analyze past analysis data and improve the accuracy of the algorithm. The analysis unit can also improve the reliability of the analysis result by referring to past analysis data. As a result, the accuracy of the analysis algorithm can be improved by referring to past analysis data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis data to the generation AI and cause the generation AI to optimize the algorithm.

[0086] During the analysis, the analysis unit can analyze the application criteria of each system and subsidy in detail and identify the optimal system. The analysis unit can, for example, analyze the application criteria of each system in detail and identify the optimal system. The analysis unit can also analyze the application criteria of each subsidy in detail and identify the optimal subsidy. The analysis unit can also compare the application criteria of each system and subsidy and identify the optimal option. As a result, the application criteria of each system and subsidy can be analyzed in detail to identify the optimal system for the user. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input application criteria data of each system to the generation AI and cause the generation AI to identify the optimal system.

[0087] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. For example, when the user is stressed, the analysis unit provides a simple and highly visible display method. In addition, when the user is relaxed, the analysis unit can also provide a display method including detailed information. In addition, when the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. In this way, by adjusting the display method of the analysis result according to the user's emotion, it is possible to provide a display that is easy for the user to see. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0088] The analysis unit can perform the analysis while taking into consideration the geographical location information of the user. For example, if the user lives in a specific area, the analysis unit preferentially analyzes information related to that area. In addition, if the user is moving, the analysis unit can also preferentially analyze information based on the current location. In addition, if the user is traveling, the analysis unit can also preferentially analyze information related to the travel destination. In this way, by taking into consideration the geographical location information of the user, it is possible to provide a highly relevant analysis result. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the geographical location data of the user to the generation AI and cause the generation AI to perform a highly relevant analysis.

[0089] The analysis unit can analyze the user's social media activities during the analysis and reflect related information in the analysis. The analysis unit can adjust the analysis result based on, for example, information shared by the user on social media. The analysis unit can also analyze the content of the user's social media posts and reflect related information in the analysis. The analysis unit can also reflect related information in the analysis based on the user's social media friendships. In this way, by analyzing social media activities, information related to the user can be reflected in the analysis and accuracy can be improved. A part or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI or can be performed without using AI. For example, the analysis unit can input the user's social media data to the generation AI and cause the generation AI to execute an analysis of the related information.

[0090] The providing unit can estimate the user's emotion and adjust the display method of the information to be provided based on the estimated user's emotion. For example, when the user is feeling stressed, the providing unit provides a simple and highly visible display method. In addition, when the user is relaxed, the providing unit can also provide a display method including detailed information. In addition, when the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. In this way, by adjusting the display method of information according to the user's emotion, it is possible to provide a display that is easy for the user to see. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned 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 facial expression data to the generation AI and cause the generation AI to execute the emotion estimation.

[0091] The providing unit can select the optimal information providing method by referring to the user's past usage history at the time of providing. For example, the providing unit preferentially suggests the information providing method (email, notification, etc.) that the user has used in the past. The providing unit can also provide information during a specific time period from the user's past usage history. The providing unit can also analyze the user's past usage history and select the most efficient information providing method. As a result, by referring to the past usage history, the optimal information providing method for the user can be selected and information can be provided efficiently. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the user's past usage data to the generating AI and cause the generating AI to select the optimal information providing method.

[0092] The providing unit can customize the means of providing information based on the user's current living situation at the time of providing. For example, when the user is raising a child, the providing unit can provide childcare-related information preferentially. In addition, when the user is an elderly person, the providing unit can also provide information for the elderly preferentially. In addition, when the user is a student, the providing unit can also provide education-related information preferentially. In this way, by customizing the means of providing information based on the user's living situation, highly relevant information can be provided. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's living situation data to the generating AI and have the generating AI customize the means of providing information.

[0093] The providing unit can estimate the user's emotion and determine the priority of information to be provided based on the estimated user's emotion. For example, when the user is feeling stressed, the providing unit provides important information with priority. In addition, the providing unit can also provide detailed information when the user is relaxed. In addition, the providing unit can also provide the minimum amount of information with priority when the user is in a hurry. In this way, important information can be provided with priority by determining the priority of information according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0094] The providing unit can select the optimal information providing method in consideration of the geographical location information of the user at the time of providing. For example, when the user lives in a specific area, the providing unit preferentially provides information related to the area. In addition, when the user is moving, the providing unit can also preferentially provide information based on the current location. In addition, when the user is traveling, the providing unit can also preferentially provide information related to the travel destination. In this way, by considering the geographical location information of the user, highly relevant information can be efficiently provided. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location data of the user to the generating AI and cause the generating AI to select the optimal information providing method.

[0095] The providing unit can analyze the user's social media activity and provide related information at the time of providing. The providing unit can provide related information based on, for example, 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 related information. The providing unit can also provide related information based on the user's social media friendships. In this way, by analyzing social media activity, it is possible to efficiently provide information related to the user. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media data to the generating AI and cause the generating AI to provide related information.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The local government procedure support system can further identify applicable systems and subsidies by taking into account the user's health condition. For example, if the user has a chronic illness, information on medical expense subsidies and specific medical services can be provided preferentially. In addition, if the user is pregnant, support systems and subsidies for pregnant women can be identified. Furthermore, if the user is elderly, information on nursing care services and subsidies for elderly people can be provided. This makes it possible to provide more personalized support by providing optimal support information according to the user's health condition.

[0098] The registration unit can customize the input items based on the user's hobbies and interests. For example, if the user is interested in sports, sports-related subsidy and event information can be displayed preferentially. If the user is interested in art and culture, art-related subsidy and event information can be provided. Furthermore, if the user is interested in environmental protection, information on systems and subsidies related to environmental protection can be displayed. This allows the user to efficiently input highly relevant information based on the user's hobbies and interests.

[0099] The collection unit can analyze the user's past search history and prioritize collection of related information. For example, if the user has frequently searched for information about housing subsidies in the past, the latest information about housing subsidies is prioritized. Also, if the user has searched for information about childcare support systems in the past, the latest information about childcare support systems can be collected. Furthermore, if the user has searched for education-related information in the past, the latest education-related information can be collected. This makes it possible to efficiently collect highly relevant information based on the user's past search history.

[0100] The analysis unit can perform analysis taking into account the user's lifestyle. For example, if the user is a nocturnal person, the analysis unit prioritizes the analysis of systems and subsidies available at night. In addition, if the user is mostly active during the day, the analysis unit can also analyze systems and subsidies available during the day. Furthermore, if the user is mostly active on weekends, the analysis unit can also analyze systems and subsidies available on weekends. This makes it possible to provide optimal support information according to the user's lifestyle.

[0101] The providing unit can adjust the method of providing information based on the device usage status of the user. For example, if the user mainly uses a smartphone, a mobile-friendly display method can be provided. Also, if the user uses a tablet, a display method optimized for the tablet can be provided. Furthermore, if the user uses a desktop, a display method optimized for the desktop can be provided. This makes it possible to realize an optimal information providing method according to the device usage status of the user.

[0102] The registration unit can estimate the user's emotions and adjust the color and design of the registration form based on the estimated user's emotions. For example, if the user is feeling stressed, a design with calm colors can be provided to create a relaxing environment. Also, if the user is relaxed, a design with bright colors can be provided to create a fun atmosphere. Furthermore, if the user is in a hurry, a simple and intuitive design can be provided to enable the user to input information quickly. In this way, the input experience can be improved by providing an optimal design according to the user's emotions.

[0103] The collection unit can estimate the user's emotions and adjust the level of detail of the information to be collected based on the estimated user's emotions. For example, when the user is feeling stressed, concise information that focuses on the main points can be collected. When the user is relaxed, detailed information can be collected. Furthermore, when the user is in a hurry, the minimum necessary information can be collected. In this way, the level of detail of the information can be adjusted according to the user's emotions, making it possible to efficiently collect necessary information.

[0104] The analysis unit can estimate the user's emotions and determine the priority order of analysis results based on the estimated user's emotions. For example, if the user is feeling stressed, the most important information is displayed with priority. Also, if the user is relaxed, detailed analysis results can be displayed. Furthermore, if the user is in a hurry, concise analysis results that focus on the main points can be displayed. In this way, the priority order of analysis results can be adjusted according to the user's emotions, making it possible to provide the user with the most suitable information.

[0105] The providing unit can estimate the user's emotion and adjust the timing of providing information based on the estimated user's emotion. For example, if the user is feeling stressed, information is provided during a time period when the user can relax. Also, if the user is relaxed, information can be provided at any time. Furthermore, if the user is in a hurry, information can be provided quickly. In this way, by adjusting the timing of providing information according to the user's emotion, information can be provided at the optimal timing.

[0106] The providing unit can estimate the user's emotion and adjust the format of information provision based on the estimated user's emotion. For example, when the user is feeling stressed, information can be provided using visually easy-to-understand graphs and charts. When the user is relaxed, detailed text information can be provided. Furthermore, when the user is in a hurry, information can be provided in a bullet-point format that summarizes the main points. In this way, by adjusting the format of information provision according to the user's emotion, information can be provided in a format that is easy for the user to understand.

[0107] The process flow of the second embodiment will be briefly described below.

[0108] Step 1: The registration unit registers user information. User information includes basic information such as name, address, age, occupation, and income. For example, a user can enter information such as "resident of Tokyo, 30 years old, company employee, annual income 5 million yen." Step 2: The Collection Department collects information on systems and subsidies offered by each local government and tax office based on the information registered by the Registration Department. For example, the Collection Department collects information on housing subsidies offered by the Tokyo Metropolitan Government and childcare support systems offered by the national government. The Collection Department can obtain information from the official websites of each local government and tax office via the Internet. Step 3: The analysis unit analyzes the information collected by the collection unit and identifies systems and subsidies that are applicable to the user. For example, using AI, it matches the user's information with the application criteria of each system and identifies the applicable systems and subsidies. The AI ​​uses a text generation AI (e.g., LLM) to analyze the application criteria of each system and identify the system that is most suitable for the user. Step 4: The provision unit provides the user with information on the systems and subsidies identified by the analysis unit and guides the user through the procedures. For example, if the user is eligible for a housing subsidy, the provision unit provides information on how to apply for the subsidy and the necessary documents. The provision unit can provide the information through a web application or a mobile application.

[0109] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0110] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0111] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements including the above-mentioned registration unit, collection unit, analysis unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the smart device 14, and the user can input information using the smart device 14. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and acquires information from the official websites of each local government and tax office via the Internet. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses AI to match the user's information with the application criteria of each system and identify applicable systems and subsidies. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the user with information on the identified systems and subsidies and guides them through the procedures. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0116] 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.

[0117] 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 the voice according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0119] 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 performed in a secure state.

[0120] Fig. 4 shows an example of 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.

[0121] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0125] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0127] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements including the above-mentioned registration unit, collection unit, analysis unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the smart glasses 214, and the user can input information using the smart glasses 214. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and acquires information from the official websites of each local government and tax office via the Internet. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses AI to match the user's information with the application criteria of each system and identify applicable systems and subsidies. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the user with information on the identified systems and subsidies and guides the user through the procedures. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 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.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0132] 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.

[0133] 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 the voice according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0135] 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 performed in a secure state.

[0136] 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.

[0137] 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.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0139] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0140] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0141] 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 voice indicating a user input for 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.

[0142] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0143] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements including the above-mentioned registration unit, collection unit, analysis 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 registration unit is realized by the control unit 46A of the headset type terminal 314, and the user can input information using the headset type terminal 314. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and acquires information from the official websites of each local government and tax office via the Internet. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses AI to match the user's information with the application criteria of each system and identify applicable systems and subsidies. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides the user with information on the identified systems and subsidies and guides them through the procedures. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0148] 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.

[0149] 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 the voice according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0151] 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 performed in a secure state.

[0152] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0153] 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.

[0154] 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.

[0155] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0156] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0157] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0158] 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 a voice indicating a user input for 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.

[0159] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0160] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0161] Each of the multiple elements including the above-mentioned registration unit, collection unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the robot 414, and the user can input information using the robot 414. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and acquires information from the official websites of each local government and tax office via the Internet. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses AI to match the user's information with the application criteria of each system and identify applicable systems and subsidies. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the user with information on the identified systems and subsidies and guides them through the procedures. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0162] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0163] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0165] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0166] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0167] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0169] 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.

[0170] 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 Universal Serial Bus (USB) 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.

[0171] In addition, 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 upon request from the data processing device 12.

[0172] 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.

[0173] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0174] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0175] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0176] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The above specific processes are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0178] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0180] (Appendix 1) a registration unit for registering user information; a collection unit that collects information on systems and subsidies based on the information registered by the registration unit; an analysis unit that analyzes the information collected by the collection unit and identifies applicable systems and subsidies; A provision unit that provides information on the system or subsidy identified by the analysis unit. A system characterized by: (Appendix 2) The registration unit is Register basic information such as user's name, address, age, occupation, and income 2. The system of claim 1, (Appendix 3) The collecting unit includes: Collect information on systems and subsidies offered by local governments and tax offices 2. The system of claim 1, (Appendix 4) The analysis unit is Analyze the collected information and identify programs and subsidies for users 2. The system of claim 1, (Appendix 5) The providing unit is Provide users with information on identified programs and subsidies, and guide them through the procedures 2. The system of claim 1, (Appendix 6) The registration unit is Infer user emotions and adjust the way the registration form is displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 7) The registration unit is Analyze the user's past registration history and select the registration method 2. The system of claim 1, (Appendix 8) The registration unit is Customize the registration process based on the user's current life situation and interests 2. The system of claim 1, (Appendix 9) The registration unit is Estimate the user's emotions and determine the priority of the information to be registered based on the estimated user emotions. 2. The system of claim 1, (Appendix 10) The registration unit is When registering, the system takes into account the user's geographic location information to prioritize the registration of relevant information. 2. The system of claim 1, (Appendix 11) The registration unit is Upon registration, we analyze your social media activity and automatically complete relevant information 2. The system of claim 1, (Appendix 12) The collecting unit includes: Estimate user emotions and adjust the range of information to be collected based on the estimated user emotions 2. The system of claim 1, (Appendix 13) The collecting unit includes: Obtain real-time updates from local governments and tax offices at the time of collection 2. The system of claim 1, (Appendix 14) The collecting unit includes: When collecting information, evaluate its reliability and prioritize collection of reliable information. 2. The system of claim 1, (Appendix 15) The collecting unit includes: Estimate user emotions and prioritize information to be collected based on the estimated user emotions. 2. The system of claim 1, (Appendix 16) The collecting unit includes: When collecting information, the most relevant information is collected with priority given to the user's geographic location. 2. The system of claim 1, (Appendix 17) The collecting unit includes: At the time of collection, we analyze your social media activity and collect related information. 2. The system of claim 1, (Appendix 18) The analysis unit is Infer user sentiment and adjust analysis algorithms based on the inferred user sentiment 2. The system of claim 1, (Appendix 19) The analysis unit is During analysis, the analysis algorithm is optimized by referencing past analysis data. 2. The system of claim 1, (Appendix 20) The analysis unit is During the analysis, we will analyze in detail the application criteria for each system and subsidy and identify the system. 2. The system of claim 1, (Appendix 21) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 22) The analysis unit is When analyzing, the geographic location of the user is taken into account. 2. The system of claim 1, (Appendix 23) The analysis unit is During analysis, we analyze users' social media activities and incorporate relevant information into the analysis. 2. The system of claim 1, (Appendix 24) The providing unit is Estimate the user's emotions and adjust the way information is displayed based on the estimated user's emotions 2. The system of claim 1, (Appendix 25) The providing unit is When providing information, the optimal method of providing information is selected by referring to the user's past usage history. 2. The system of claim 1, (Appendix 26) The providing unit is At the time of delivery, customize the information delivery method based on the user's current life situation 2. The system of claim 1, (Appendix 27) The providing unit is Estimate the user's emotions and prioritize the information to be provided based on the estimated user's emotions. 2. The system of claim 1, (Appendix 28) The providing unit is When providing information, the optimal information delivery method is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 29) The providing unit is When provided, to analyze your social media activity and provide you with relevant information 2. The system of claim 1, [Explanation of symbols]

[0181] 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 registration unit for registering user information; a collection unit that collects information on systems and subsidies based on the information registered by the registration unit; an analysis unit that analyzes the information collected by the collection unit and identifies applicable systems and subsidies; A provision unit that provides information on the system or subsidy identified by the analysis unit. A system characterized by:

2. The registration unit is Register basic information such as user's name, address, age, occupation, and income 2. The system of claim 1 .

3. The collecting unit includes: Collect information on systems and subsidies offered by local governments and tax offices 2. The system of claim 1 .

4. The providing unit is Provide users with information on identified programs and subsidies, and guide them through the procedures 2. The system of claim 1 .

5. The registration unit is Infer user emotions and adjust the way the registration form is displayed based on the inferred user emotions 2. The system of claim 1 .

6. The registration unit is Analyze the user's past registration history and select the registration method 2. The system of claim 1 .

7. The registration unit is Customize the registration process based on the user's current life situation and interests 2. The system of claim 1 .

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