system

Abstract

The system according to this embodiment aims to streamline family register management operations and enable even inexperienced staff to respond quickly and appropriately. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a linking unit, an inquiry unit, and a management unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The linking unit links the data analyzed by the analysis unit. The inquiry unit performs a family register inquiry based on the data linked by the linking unit. The management unit manages the cloud-based system.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the household register management work is complicated and it is difficult for inexperienced staff to make a quick and appropriate response.

[0005] The system according to the embodiment aims to improve the efficiency of household register management work and enable inexperienced staff to make a quick and appropriate response.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a linking unit, an inquiry unit, and a management unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The linking unit links the data analyzed by the analysis unit. The inquiry unit performs a family register inquiry based on the data linked by the linking unit. The management unit manages the cloud-based system. [Effects of the Invention]

[0007] The system according to this embodiment streamlines family register management operations, enabling even inexperienced staff to respond quickly and appropriately. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The next-generation family register management system according to an embodiment of the present invention is an AI-powered "smart family register navigator." This system provides an AI agent service specialized in family register management, enabling high-speed and accurate information management, searching, and a series of various processes. The next-generation family register management system streamlines complex family register operations and supports even inexperienced local government officials in providing quick and appropriate responses to residents. Furthermore, the next-generation family register management system provides a comprehensive solution through advanced data analysis and prediction functions, multilingual support, real-time response functions with residents, and integration of region-specific databases. For example, the next-generation family register management system digitizes paper family registers using high-precision OCR technology with AI. This technology employs a learning model that can handle old and cursive characters. Next, the next-generation family register management system utilizes RAG (Relative Aggregation Technology) to automatically track and associate life event data such as legal revisions, new and old kanji characters, municipal mergers, relocation history, marriage, and death. This streamlines the process of linking complex data. In addition, the next-generation family register management system provides a family register inquiry function using an AI assistant, which analyzes and visualizes complex kinship relationships. This will enable local government officials to respond to residents quickly and accurately. Furthermore, the next-generation family register management system employs a secure cloud-based system, ensuring operation in an environment with strict security measures. Access rights can also be managed for each local government. The next-generation family register management system integrates region-specific legal and historical databases to strengthen the ability to respond to inquiries specific to a region. In addition, the next-generation family register management system utilizes advanced data analysis and prediction functions using generating AI to analyze past cases and predict future resident migration, marriage, and birth rates, enabling long-term business planning. Moreover, the next-generation family register management system provides inquiry response via a 24-hour AI chatbot and real-time response services to residents via telephone and chat. The next-generation family register management system aims to enable local governments nationwide to provide efficient and high-quality family register management services, simultaneously achieving improved resident satisfaction and the modernization of administration.This will enable the next-generation family register management system to provide efficient and high-quality family register management services to municipalities nationwide, simultaneously achieving improved resident satisfaction and the modernization of administration.

[0029] The next-generation family register management system according to this embodiment comprises a collection unit, an analysis unit, a linking unit, an inquiry unit, and a management unit. The collection unit collects information. The collection unit collects information such as personal information, family register information, and legal information. The collection unit can collect information using sensors or cameras, for example. The collection unit can also collect information via the internet. Furthermore, the collection unit can collect information from local government databases. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the information using machine learning algorithms, for example. The analysis unit can also analyze the information using data mining techniques, for example. The analysis unit can also analyze the information using natural language processing techniques. Furthermore, the analysis unit can also analyze the information using image analysis techniques. The linking unit links the data analyzed by the analysis unit. The linking unit associates database records, for example. The linking unit can also integrate data from different data sources, for example. Furthermore, the linking unit can check the integrity of the data to maintain data consistency. The inquiry unit performs family register inquiries based on data linked by the linking unit. The inquiry unit can, for example, analyze the kinship relationships of residents. The inquiry unit can also, for example, inquire about the relocation history of residents. Furthermore, the inquiry unit can also inquire about the marriage history of residents. In addition, the inquiry unit can also inquire about the death information of residents. The management unit manages the cloud-based system. The management unit can, for example, monitor the operation of the cloud server. The management unit can, for example, back up data. Furthermore, the management unit can manage access rights. In addition, the management unit can implement security measures. As a result, the next-generation family register management system according to this embodiment can efficiently collect, analyze, link, inquire about, and manage information.

[0030] The data collection unit collects information. For example, it collects personal information, family register information, and legal information. Specifically, personal information includes basic information such as name, address, date of birth, and gender. Family register information includes information about life events such as birth, marriage, divorce, and death. Legal information includes information about laws and regulations related to family registers. The data collection unit can collect information using sensors and cameras, for example. Sensors are used to identify individuals using biometric authentication technologies such as fingerprint recognition and facial recognition. Cameras are used to digitize documents such as copies of resident registration certificates and family register transcripts. The data collection unit can also collect information via the internet. For example, residents can update their information through online forms. Furthermore, the data collection unit can collect information from municipal databases. Municipal databases store basic information and family register information of residents, and this information can be obtained in real time. By collecting data from these diverse sources and managing it centrally, the data collection unit can maintain the accuracy and timeliness of the information. Furthermore, the data collection unit can ensure information security by encrypting and storing the collected data. This allows the data collection unit to efficiently and securely collect information and build the foundation for a next-generation family register management system.

[0031] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit uses machine learning algorithms to analyze the information. Specifically, it can use machine learning algorithms to extract patterns and trends from the collected data, enabling future predictions and anomaly detection. For instance, analyzing residents' relocation history can predict changes in population dynamics in a specific area. The analysis unit can also analyze information using data mining techniques. Data mining techniques allow for the extraction of useful information from large amounts of data, revealing correlations and causal relationships. Furthermore, the analysis unit can analyze information using natural language processing techniques. Natural language processing techniques can extract meaning from text data, analyzing residents' opinions and requests. Additionally, the analysis unit can analyze information using image analysis techniques. Image analysis techniques allow for the automatic reading of digitized documents and registration in a database. This enables the analysis unit to analyze collected data from multiple perspectives, improving the accuracy and efficiency of the next-generation family register management system. Moreover, the analysis unit can visualize the analysis results, making them easily understandable to stakeholders. For example, analysis results can be displayed using graphs and charts, allowing for an intuitive understanding of data trends and anomalies. This enables the analysis unit to effectively utilize the collected data and maximize the value of the next-generation family register management system.

[0032] The linking unit links the data analyzed by the analysis unit. For example, the linking unit associates records in a database. Specifically, it integrates data collected from different data sources such as personal information, family register information, and legal information to build a consistent database. For example, it can associate records from different databases using residents' names and dates of birth as keys. The linking unit can also integrate data from different data sources. For example, it can integrate resident information collected from a municipal database with residents' opinions and requests collected via the internet. The linking unit can also check the integrity of the data to maintain consistency. For example, it can eliminate data redundancy by detecting and merging duplicate records. Furthermore, the linking unit can manage the data update history and automatically update the database when changes occur. This allows the linking unit to efficiently manage the database of the next-generation family register management system while maintaining data integrity and consistency. In addition, the linking unit can manage data access rights and grant appropriate access rights to specific users or departments. This allows the linking unit to achieve efficient data management while ensuring data security.

[0033] The inquiry unit performs family register inquiries based on data linked by the linking unit. For example, the inquiry unit analyzes the kinship relationships of residents. Specifically, it can identify kinship relationships and create a family tree based on the resident's name and date of birth. The inquiry unit can also inquire about a resident's relocation history. Based on a resident's relocation history, it can understand changes in population dynamics in a specific area. The inquiry unit can also inquire about a resident's marriage history. Based on the marriage history, it can understand a resident's family structure and marital status. Furthermore, the inquiry unit can also inquire about a resident's death information. Based on the death information, it can identify the date and cause of death of a resident and quickly carry out related procedures. Based on this information, the inquiry unit can provide detailed information about a resident's life events. Furthermore, the inquiry unit can visualize the inquiry results so that stakeholders can easily understand them. For example, family trees and relocation history can be displayed in graphs and charts, allowing for an intuitive understanding of data trends and relationships. This allows the inquiry unit to provide detailed information about residents quickly and accurately, improving the usability of the next-generation family register management system. Furthermore, the inquiry unit can automatically update inquiry results to provide the latest information. This ensures that the inquiry unit always provides accurate inquiry results based on the most up-to-date information, improving the reliability of the next-generation family register management system.

[0034] The management department manages the cloud-based system. For example, the management department monitors the operation of the cloud servers. Specifically, it monitors the operational status of the cloud servers in real time and can respond immediately if an anomaly occurs. The management department can also perform data backups. By regularly backing up data, they can prepare for the possibility of data loss. The management department can also manage access rights. They can grant appropriate access rights to specific users and departments to prevent unauthorized access. Furthermore, the management department can implement security measures. For example, they can install firewalls and antivirus software to strengthen system security. This allows the management department to efficiently manage the operation of the cloud-based system and improve the reliability and security of the next-generation family register management system. In addition, the management department can monitor resource usage and adjust resource allocation as needed to optimize system performance. This allows the management department to achieve efficient system operation and maximize the performance of the next-generation family register management system. Furthermore, the management department can regularly update and maintain the system, introducing the latest technologies and security measures. This allows the management department to always operate the system in an up-to-date state and maintain the reliability and security of the next-generation family register management system.

[0035] The data collection unit can digitize paper family registers using high-precision OCR technology. For example, the data collection unit scans the paper family register and saves it as image data. Then, the data collection unit converts the image data into text data using OCR technology. The data collection unit also employs a learning model that can handle old characters and cursive characters. For example, the data collection unit can recognize old characters and cursive characters using high-precision OCR technology and digitize them. Furthermore, the data collection unit can also digitize handwritten notes and annotations. For example, the data collection unit reads handwritten notes using OCR technology and saves them as digital data. This makes information management easier by digitizing paper family registers. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the image data scanned by the scanner into a generating AI and have the generating AI perform the conversion from image data to text data.

[0036] The linking unit can use RAG to automatically track and associate life event data such as legal revisions, old and new kanji characters, municipal mergers, relocation history, marriage, and death. For example, the linking unit can automatically track and associate data changes due to legal revisions. The linking unit can also automatically convert old and new kanji characters and associate the data. Furthermore, the linking unit can automatically track and associate data changes due to municipal mergers. In addition, the linking unit can automatically track and associate life event data such as relocation history, marriage, and death. For example, the linking unit can automatically track residents' relocation history and associate the data. Furthermore, the linking unit can automatically track residents' marriage history and associate the data. In addition, the linking unit can automatically track residents' death information and associate the data. This makes data linking more efficient by automatically tracking and associating life event data. Some or all of the above processing in the linking unit may be performed using AI, for example, or without using AI. For example, the linking unit can input data related to legal revisions and conversions between old and new kanji characters into the generating AI, and have the generating AI perform the data association.

[0037] The inquiry unit can perform analysis and visualization of complex family relationships using an AI assistant. For example, the inquiry unit can analyze and visualize the family relationships of residents. For example, the inquiry unit can generate and visually display a family relationship tree diagram. The inquiry unit can also display detailed information about family relationships. For example, the inquiry unit can analyze the family relationships of residents and display the names and relationships of relatives. Furthermore, the inquiry unit can display the changes in family relationships over time. For example, the inquiry unit can display the changes in family relationships based on life events such as marriage, divorce, and birth of residents. This enables the analysis and visualization of complex family relationships. Some or all of the above-described processes in the inquiry unit may be performed using AI, for example, or without AI. For example, the inquiry unit can input resident family relationship data into a generating AI and have the generating AI perform the analysis and visualization of family relationships.

[0038] The management department can adopt a secure cloud-based system and manage access rights for each local government. For example, the management department can monitor the operation of the cloud server and implement security measures. For example, the management department can perform regular data backups. The management department can also set and manage access rights for each local government. For example, the management department can grant appropriate access rights to employees of each local government to prevent unauthorized access to data. Furthermore, the management department can monitor and optimize the performance of the cloud-based system. For example, the management department can distribute the load on the cloud server and ensure system stability. This makes access rights management possible by adopting a secure cloud-based system. Some or all of the above processes performed by the management department may be carried out using AI, for example, or not. For example, the management department can input cloud server operation data into a generating AI and have the generating AI execute security measures and performance optimizations.

[0039] The analysis unit can integrate region-specific legal and historical databases to enhance its ability to handle region-specific inquiries. For example, the analysis unit can collect legal and historical data related to a specific region and integrate it into the database. The analysis unit can also add, for example, a history of local historical events and legal amendments to the database. Furthermore, the analysis unit can provide answers to region-specific inquiries based on relevant legal and historical data. For example, the analysis unit can provide information about the scope of application and historical background of laws in a specific region. In addition, the analysis unit can use region-specific databases to provide region-specific statistical data and analysis results. For example, the analysis unit can provide statistical data about demographics and economic conditions in a specific region. This enhances the ability to handle region-specific inquiries. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input region-specific legal and historical data into a generating AI and have the generating AI perform data integration and inquiry handling.

[0040] The analysis unit can use a generating AI to analyze past cases and predict future population migration, marriage, and birth rates. For example, the analysis unit can analyze past population migration cases to predict future population migration trends. The analysis unit can also analyze past marriage cases to predict future marriage rates. Furthermore, the analysis unit can analyze past birth rate data to predict future birth rates. For example, the analysis unit can input past population migration data into the generating AI to predict future population migration trends. The analysis unit can also input past marriage data into the generating AI to predict future marriage rates. In addition, the analysis unit can input past birth rate data into the generating AI to predict future birth rates. This enables the analysis of past cases and future predictions. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past population migration, marriage, and birth rate data into the generating AI and have the generating AI perform data analysis and predictions.

[0041] The inquiry department can provide 24-hour AI chatbot support for inquiries and real-time support services for residents via telephone and chat. For example, the inquiry department can use an AI chatbot to respond to inquiries from residents 24 hours a day. The inquiry department can, for example, automatically provide answers to residents' questions. The inquiry department can also provide real-time support services for residents via telephone and chat. For example, when a resident makes an inquiry by telephone, the AI ​​assistant will respond in real time. Furthermore, when a resident makes an inquiry via chat, the AI ​​assistant can respond in real time. This provides 24-hour inquiry support and real-time support services. Some or all of the above processing in the inquiry department may be performed using AI, or not using AI. For example, the inquiry department can input inquiry data from residents into a generating AI and have the generating AI execute the inquiry response.

[0042] The analysis unit can provide multilingual support functionality using LLM. For example, the analysis unit can support multiple languages ​​using LLM. For example, the analysis unit can support multiple languages ​​such as Japanese, English, Chinese, and French. Furthermore, the analysis unit can perform real-time translation using LLM. For example, the analysis unit can translate and respond to inquiries from residents in real time. In addition, the analysis unit can provide a multilingual chatbot using LLM. For example, when a resident makes an inquiry in a different language, the chatbot automatically translates and responds. This provides multilingual support functionality. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input resident inquiry data into a generating AI and have the generating AI perform translation and multilingual support.

[0043] The data collection unit can use high-precision OCR technology to digitize not only old-style and cursive characters, but also handwritten notes and annotations. For example, the data collection unit can read handwritten notes using OCR technology and save them as digital data. The data collection unit can also recognize old-style and cursive characters using high-precision OCR technology and digitize them. Furthermore, the data collection unit can read handwritten annotations using OCR technology and save them as digital data. For example, the data collection unit can scan handwritten notes and annotations and convert them into text data using OCR technology. This makes information management easier by digitizing handwritten notes and annotations. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input image data of handwritten notes and annotations into a generating AI and have the generating AI perform the conversion from image data to text data.

[0044] The collection unit can select the optimal collection method when collecting family register information by referring to past collection history. For example, the collection unit can analyze past collection history and select the optimal collection method. The collection unit can also select an efficient collection method by referring to past collection history. Furthermore, the collection unit can select the optimal collection method based on past collection history. For example, the collection unit can store past collection history in a database and refer to it during collection. This allows the collection unit to select the optimal collection method by referring to past collection history. Some or all of the above processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input past collection history data into a generating AI and have the generating AI select the optimal collection method.

[0045] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting family register information. For example, the data collection unit can prioritize the collection of highly relevant information based on the user's current location. The data collection unit can also efficiently collect information by considering the user's geographical location. Furthermore, the data collection unit can select the optimal collection method based on the user's location information. For example, the data collection unit prioritizes the collection of highly relevant information based on the user's current location. This allows for the efficient collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0046] The data collection unit can analyze the user's social media activity and collect relevant information when collecting family register information. For example, the data collection unit can analyze the user's social media activity and collect relevant information. The data collection unit can also collect relevant information based on the content of the user's social media posts. Furthermore, the data collection unit can analyze the user's social media activity history and select the optimal collection method. For example, the data collection unit can analyze the user's social media activity and collect relevant information. This allows for the efficient collection of relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant information.

[0047] The analysis unit can use generative AI to analyze past cases and predict future population migration, marriage, and birth rates. For example, the analysis unit can use generative AI to analyze past population migration cases. The analysis unit can also use generative AI to predict future marriage rates. Furthermore, the analysis unit can use generative AI to predict future birth rates. For example, the analysis unit can input past population migration data into the generative AI to predict future population migration trends. The analysis unit can also input past marriage data into the generative AI to predict future marriage rates. In addition, the analysis unit can input past birth rate data into the generative AI to predict future birth rates. Thus, by using generative AI, it becomes possible to analyze past cases and make future predictions. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past population migration, marriage, and birth rate data into the generative AI and have the generative AI perform data analysis and predictions.

[0048] The linking unit can improve the accuracy of linking by considering the interrelationships of family register information during the linking process. The linking unit can improve the accuracy of linking by considering the interrelationships of family register information, for example. The linking unit can also improve the accuracy of linking based on the interrelationships, for example. Furthermore, the linking unit can improve the accuracy of linking by considering the interrelationships of family register information. For example, the linking unit can improve the accuracy of linking by considering the interrelationships of family register information. As a result, the accuracy of linking is improved by considering the interrelationships of family register information. Some or all of the above processing in the linking unit may be performed using AI, for example, or without using AI. For example, the linking unit can input interrelationship data of family register information into a generating AI and have the generating AI perform the improvement of linking accuracy.

[0049] The inquiry unit can adjust the order of inquiries based on the relevance of the family register information during the inquiry process. For example, the inquiry unit adjusts the order of inquiries based on the relevance of the family register information. The inquiry unit can also, for example, prioritize inquiries for highly relevant information. Furthermore, the inquiry unit can postpone inquiries for less relevant information. For example, the inquiry unit adjusts the order of inquiries based on the relevance of the family register information. This allows for efficient inquiries by adjusting the order of inquiries based on the relevance of the family register information. Some or all of the above processing in the inquiry unit may be performed using AI, for example, or without AI. For example, the inquiry unit can input the relevance data of the family register information into a generating AI and have the generating AI perform the adjustment of the inquiry order.

[0050] The management department can select the optimal management method by referring to past management history when managing a cloud-based system. For example, the management department can select the optimal management method by referring to past management history. The management department can also select an efficient management method based on management history. Furthermore, the management department can select the optimal management method by analyzing past management history. For example, the management department can store past management history in a database and refer to it during management. This allows the management department to select the optimal management method by referring to past management history. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input past management history data into a generating AI and have the generating AI select the optimal management method.

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

[0052] The data collection unit can select the optimal collection method by referring to the user's past behavioral history when collecting family register information. For example, the data collection unit can analyze past behavioral history and select the optimal collection method. The data collection unit can also select an efficient collection method based on past behavioral history. Furthermore, the data collection unit can store past behavioral history in a database and refer to it during collection. This allows the optimal collection method to be selected by referring to past behavioral history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past behavioral history data into a generating AI and have the generating AI select the optimal collection method.

[0053] The analysis unit can customize the analysis results when analyzing family register information, taking into account the user's occupation and lifestyle. For example, the analysis unit prioritizes analyzing information relevant to the user's occupation. The analysis unit can also customize the analysis results based on the user's lifestyle. Furthermore, the analysis unit stores the user's occupation and lifestyle in a database and references it during analysis. This allows the analysis unit to provide customized analysis results by considering the user's occupation and lifestyle. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's occupation and lifestyle data into a generating AI and have the generating AI perform the customization of the analysis results.

[0054] The inquiry unit can select the optimal inquiry method by referring to the user's past inquiry history when inquiring about family register information. For example, the inquiry unit can analyze past inquiry history and select the optimal inquiry method. The inquiry unit can also select an efficient inquiry method based on past inquiry history. Furthermore, the inquiry unit can store past inquiry history in a database and refer to it during inquiries. This allows the optimal inquiry method to be selected by referring to past inquiry history. Some or all of the above processing in the inquiry unit may be performed using AI, or it may be performed without AI. For example, the inquiry unit can input past inquiry history data into a generating AI and have the generating AI perform the selection of the optimal inquiry method.

[0055] The management department can select the optimal management method when managing the cloud-based system, taking into account the user's device information. For example, the management department can analyze the user's device information and select the optimal management method. The management department can also select an efficient management method based on the user's device information. Furthermore, the management department can store the user's device information in a database and refer to it during management. This allows the management department to select the optimal management method by considering the user's device information. Some or all of the above processes in the management department may be performed using AI, or they may not. For example, the management department can input user device information data into a generating AI and have the generating AI select the optimal management method.

[0056] The linking unit can select the optimal linking method by referring to the user's past linking history when linking family register information. For example, the linking unit can analyze past linking history and select the optimal linking method. The linking unit can also select an efficient linking method based on past linking history. Furthermore, the linking unit can store past linking history in a database and refer to it during linking. This allows the optimal linking method to be selected by referring to past linking history. Some or all of the above processes in the linking unit may be performed using AI or not. For example, the linking unit can input past linking history data into a generating AI and have the generating AI select the optimal linking method.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The collection unit collects information. The collection unit collects information such as personal information, family register information, and legal information. The collection unit can collect information using sensors and cameras, and can also collect information via the internet. It can also collect information from local government databases. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the information using machine learning algorithms, data mining techniques, natural language processing techniques, and image analysis techniques. Step 3: The linking unit links the data analyzed by the analysis unit. The linking unit can associate database records and integrate data from different data sources. It can also check data integrity to maintain data consistency. Step 4: The inquiry unit performs a family register inquiry based on the data linked by the linking unit. The inquiry unit can inquire about the resident's family relationships, relocation history, marriage history, death information, etc. Step 5: The administration department manages the cloud-based system. The administration department can monitor the operation of the cloud servers, back up data, manage access permissions, and implement security measures.

[0059] (Example of form 2) The next-generation family register management system according to an embodiment of the present invention is an AI-powered "smart family register navigator." This system provides an AI agent service specialized in family register management, enabling high-speed and accurate information management, searching, and a series of various processes. The next-generation family register management system streamlines complex family register operations and supports even inexperienced local government officials in providing quick and appropriate responses to residents. Furthermore, the next-generation family register management system provides a comprehensive solution through advanced data analysis and prediction functions, multilingual support, real-time response functions with residents, and integration of region-specific databases. For example, the next-generation family register management system digitizes paper family registers using high-precision OCR technology with AI. This technology employs a learning model that can handle old and cursive characters. Next, the next-generation family register management system utilizes RAG (Relative Aggregation Technology) to automatically track and associate life event data such as legal revisions, new and old kanji characters, municipal mergers, relocation history, marriage, and death. This streamlines the process of linking complex data. In addition, the next-generation family register management system provides a family register inquiry function using an AI assistant, which analyzes and visualizes complex kinship relationships. This will enable local government officials to respond to residents quickly and accurately. Furthermore, the next-generation family register management system employs a secure cloud-based system, ensuring operation in an environment with strict security measures. Access rights can also be managed for each local government. The next-generation family register management system integrates region-specific legal and historical databases to strengthen the ability to respond to inquiries specific to a region. In addition, the next-generation family register management system utilizes advanced data analysis and prediction functions using generating AI to analyze past cases and predict future resident migration, marriage, and birth rates, enabling long-term business planning. Moreover, the next-generation family register management system provides inquiry response via a 24-hour AI chatbot and real-time response services to residents via telephone and chat. The next-generation family register management system aims to enable local governments nationwide to provide efficient and high-quality family register management services, simultaneously achieving improved resident satisfaction and the modernization of administration.This will enable the next-generation family register management system to provide efficient and high-quality family register management services to municipalities nationwide, simultaneously achieving improved resident satisfaction and the modernization of administration.

[0060] The next-generation family register management system according to this embodiment comprises a collection unit, an analysis unit, a linking unit, an inquiry unit, and a management unit. The collection unit collects information. The collection unit collects information such as personal information, family register information, and legal information. The collection unit can collect information using sensors or cameras, for example. The collection unit can also collect information via the internet. Furthermore, the collection unit can collect information from local government databases. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the information using machine learning algorithms, for example. The analysis unit can also analyze the information using data mining techniques, for example. The analysis unit can also analyze the information using natural language processing techniques. Furthermore, the analysis unit can also analyze the information using image analysis techniques. The linking unit links the data analyzed by the analysis unit. The linking unit associates database records, for example. The linking unit can also integrate data from different data sources, for example. Furthermore, the linking unit can check the integrity of the data to maintain data consistency. The inquiry unit performs family register inquiries based on data linked by the linking unit. The inquiry unit can, for example, analyze the kinship relationships of residents. The inquiry unit can also, for example, inquire about the relocation history of residents. Furthermore, the inquiry unit can also inquire about the marriage history of residents. In addition, the inquiry unit can also inquire about the death information of residents. The management unit manages the cloud-based system. The management unit can, for example, monitor the operation of the cloud server. The management unit can, for example, back up data. Furthermore, the management unit can manage access rights. In addition, the management unit can implement security measures. As a result, the next-generation family register management system according to this embodiment can efficiently collect, analyze, link, inquire about, and manage information.

[0061] The data collection unit collects information. For example, it collects personal information, family register information, and legal information. Specifically, personal information includes basic information such as name, address, date of birth, and gender. Family register information includes information about life events such as birth, marriage, divorce, and death. Legal information includes information about laws and regulations related to family registers. The data collection unit can collect information using sensors and cameras, for example. Sensors are used to identify individuals using biometric authentication technologies such as fingerprint recognition and facial recognition. Cameras are used to digitize documents such as copies of resident registration certificates and family register transcripts. The data collection unit can also collect information via the internet. For example, residents can update their information through online forms. Furthermore, the data collection unit can collect information from municipal databases. Municipal databases store basic information and family register information of residents, and this information can be obtained in real time. By collecting data from these diverse sources and managing it centrally, the data collection unit can maintain the accuracy and timeliness of the information. Furthermore, the data collection unit can ensure information security by encrypting and storing the collected data. This allows the data collection unit to efficiently and securely collect information and build the foundation for a next-generation family register management system.

[0062] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit uses machine learning algorithms to analyze the information. Specifically, it can use machine learning algorithms to extract patterns and trends from the collected data, enabling future predictions and anomaly detection. For instance, analyzing residents' relocation history can predict changes in population dynamics in a specific area. The analysis unit can also analyze information using data mining techniques. Data mining techniques allow for the extraction of useful information from large amounts of data, revealing correlations and causal relationships. Furthermore, the analysis unit can analyze information using natural language processing techniques. Natural language processing techniques can extract meaning from text data, analyzing residents' opinions and requests. Additionally, the analysis unit can analyze information using image analysis techniques. Image analysis techniques allow for the automatic reading of digitized documents and registration in a database. This enables the analysis unit to analyze collected data from multiple perspectives, improving the accuracy and efficiency of the next-generation family register management system. Moreover, the analysis unit can visualize the analysis results, making them easily understandable to stakeholders. For example, analysis results can be displayed using graphs and charts, allowing for an intuitive understanding of data trends and anomalies. This enables the analysis unit to effectively utilize the collected data and maximize the value of the next-generation family register management system.

[0063] The linking unit links the data analyzed by the analysis unit. For example, the linking unit associates records in a database. Specifically, it integrates data collected from different data sources such as personal information, family register information, and legal information to build a consistent database. For example, it can associate records from different databases using residents' names and dates of birth as keys. The linking unit can also integrate data from different data sources. For example, it can integrate resident information collected from a municipal database with residents' opinions and requests collected via the internet. The linking unit can also check the integrity of the data to maintain consistency. For example, it can eliminate data redundancy by detecting and merging duplicate records. Furthermore, the linking unit can manage the data update history and automatically update the database when changes occur. This allows the linking unit to efficiently manage the database of the next-generation family register management system while maintaining data integrity and consistency. In addition, the linking unit can manage data access rights and grant appropriate access rights to specific users or departments. This allows the linking unit to achieve efficient data management while ensuring data security.

[0064] The inquiry unit performs family register inquiries based on data linked by the linking unit. For example, the inquiry unit analyzes the kinship relationships of residents. Specifically, it can identify kinship relationships and create a family tree based on the resident's name and date of birth. The inquiry unit can also inquire about a resident's relocation history. Based on a resident's relocation history, it can understand changes in population dynamics in a specific area. The inquiry unit can also inquire about a resident's marriage history. Based on the marriage history, it can understand a resident's family structure and marital status. Furthermore, the inquiry unit can also inquire about a resident's death information. Based on the death information, it can identify the date and cause of death of a resident and quickly carry out related procedures. Based on this information, the inquiry unit can provide detailed information about a resident's life events. Furthermore, the inquiry unit can visualize the inquiry results so that stakeholders can easily understand them. For example, family trees and relocation history can be displayed in graphs and charts, allowing for an intuitive understanding of data trends and relationships. This allows the inquiry unit to provide detailed information about residents quickly and accurately, improving the usability of the next-generation family register management system. Furthermore, the inquiry unit can automatically update inquiry results to provide the latest information. This ensures that the inquiry unit always provides accurate inquiry results based on the most up-to-date information, improving the reliability of the next-generation family register management system.

[0065] The management department manages the cloud-based system. For example, the management department monitors the operation of the cloud servers. Specifically, it monitors the operational status of the cloud servers in real time and can respond immediately if an anomaly occurs. The management department can also perform data backups. By regularly backing up data, they can prepare for the possibility of data loss. The management department can also manage access rights. They can grant appropriate access rights to specific users and departments to prevent unauthorized access. Furthermore, the management department can implement security measures. For example, they can install firewalls and antivirus software to strengthen system security. This allows the management department to efficiently manage the operation of the cloud-based system and improve the reliability and security of the next-generation family register management system. In addition, the management department can monitor resource usage and adjust resource allocation as needed to optimize system performance. This allows the management department to achieve efficient system operation and maximize the performance of the next-generation family register management system. Furthermore, the management department can regularly update and maintain the system, introducing the latest technologies and security measures. This allows the management department to always operate the system in an up-to-date state and maintain the reliability and security of the next-generation family register management system.

[0066] The data collection unit can digitize paper family registers using high-precision OCR technology. For example, the data collection unit scans the paper family register and saves it as image data. Then, the data collection unit converts the image data into text data using OCR technology. The data collection unit also employs a learning model that can handle old characters and cursive characters. For example, the data collection unit can recognize old characters and cursive characters using high-precision OCR technology and digitize them. Furthermore, the data collection unit can also digitize handwritten notes and annotations. For example, the data collection unit reads handwritten notes using OCR technology and saves them as digital data. This makes information management easier by digitizing paper family registers. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the image data scanned by the scanner into a generating AI and have the generating AI perform the conversion from image data to text data.

[0067] The linking unit can use RAG to automatically track and associate life event data such as legal revisions, old and new kanji characters, municipal mergers, relocation history, marriage, and death. For example, the linking unit can automatically track and associate data changes due to legal revisions. The linking unit can also automatically convert old and new kanji characters and associate the data. Furthermore, the linking unit can automatically track and associate data changes due to municipal mergers. In addition, the linking unit can automatically track and associate life event data such as relocation history, marriage, and death. For example, the linking unit can automatically track residents' relocation history and associate the data. Furthermore, the linking unit can automatically track residents' marriage history and associate the data. In addition, the linking unit can automatically track residents' death information and associate the data. This makes data linking more efficient by automatically tracking and associating life event data. Some or all of the above processing in the linking unit may be performed using AI, for example, or without using AI. For example, the linking unit can input data related to legal revisions and conversions between old and new kanji characters into the generating AI, and have the generating AI perform the data association.

[0068] The inquiry unit can perform analysis and visualization of complex family relationships using an AI assistant. For example, the inquiry unit can analyze and visualize the family relationships of residents. For example, the inquiry unit can generate and visually display a family relationship tree diagram. The inquiry unit can also display detailed information about family relationships. For example, the inquiry unit can analyze the family relationships of residents and display the names and relationships of relatives. Furthermore, the inquiry unit can display the changes in family relationships over time. For example, the inquiry unit can display the changes in family relationships based on life events such as marriage, divorce, and birth of residents. This enables the analysis and visualization of complex family relationships. Some or all of the above-described processes in the inquiry unit may be performed using AI, for example, or without AI. For example, the inquiry unit can input resident family relationship data into a generating AI and have the generating AI perform the analysis and visualization of family relationships.

[0069] The management department can adopt a secure cloud-based system and manage access rights for each local government. For example, the management department can monitor the operation of the cloud server and implement security measures. For example, the management department can perform regular data backups. The management department can also set and manage access rights for each local government. For example, the management department can grant appropriate access rights to employees of each local government to prevent unauthorized access to data. Furthermore, the management department can monitor and optimize the performance of the cloud-based system. For example, the management department can distribute the load on the cloud server and ensure system stability. This makes access rights management possible by adopting a secure cloud-based system. Some or all of the above processes performed by the management department may be carried out using AI, for example, or not. For example, the management department can input cloud server operation data into a generating AI and have the generating AI execute security measures and performance optimizations.

[0070] The analysis unit can integrate region-specific legal and historical databases to enhance its ability to handle region-specific inquiries. For example, the analysis unit can collect legal and historical data related to a specific region and integrate it into the database. The analysis unit can also add, for example, a history of local historical events and legal amendments to the database. Furthermore, the analysis unit can provide answers to region-specific inquiries based on relevant legal and historical data. For example, the analysis unit can provide information about the scope of application and historical background of laws in a specific region. In addition, the analysis unit can use region-specific databases to provide region-specific statistical data and analysis results. For example, the analysis unit can provide statistical data about demographics and economic conditions in a specific region. This enhances the ability to handle region-specific inquiries. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input region-specific legal and historical data into a generating AI and have the generating AI perform data integration and inquiry handling.

[0071] The analysis unit can use a generating AI to analyze past cases and predict future population migration, marriage, and birth rates. For example, the analysis unit can analyze past population migration cases to predict future population migration trends. The analysis unit can also analyze past marriage cases to predict future marriage rates. Furthermore, the analysis unit can analyze past birth rate data to predict future birth rates. For example, the analysis unit can input past population migration data into the generating AI to predict future population migration trends. The analysis unit can also input past marriage data into the generating AI to predict future marriage rates. In addition, the analysis unit can input past birth rate data into the generating AI to predict future birth rates. This enables the analysis of past cases and future predictions. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past population migration, marriage, and birth rate data into the generating AI and have the generating AI perform data analysis and predictions.

[0072] The inquiry department can provide 24-hour AI chatbot support for inquiries and real-time support services for residents via telephone and chat. For example, the inquiry department can use an AI chatbot to respond to inquiries from residents 24 hours a day. The inquiry department can, for example, automatically provide answers to residents' questions. The inquiry department can also provide real-time support services for residents via telephone and chat. For example, when a resident makes an inquiry by telephone, the AI ​​assistant will respond in real time. Furthermore, when a resident makes an inquiry via chat, the AI ​​assistant can respond in real time. This provides 24-hour inquiry support and real-time support services. Some or all of the above processing in the inquiry department may be performed using AI, or not using AI. For example, the inquiry department can input inquiry data from residents into a generating AI and have the generating AI execute the inquiry response.

[0073] The analysis unit can provide multilingual support functionality using LLM. For example, the analysis unit can support multiple languages ​​using LLM. For example, the analysis unit can support multiple languages ​​such as Japanese, English, Chinese, and French. Furthermore, the analysis unit can perform real-time translation using LLM. For example, the analysis unit can translate and respond to inquiries from residents in real time. In addition, the analysis unit can provide a multilingual chatbot using LLM. For example, when a resident makes an inquiry in a different language, the chatbot automatically translates and responds. This provides multilingual support functionality. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input resident inquiry data into a generating AI and have the generating AI perform translation and multilingual support.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to reduce the user's burden. For example, if the user is relaxed, the data collection unit can also accelerate the collection timing to collect information more efficiently. Furthermore, if the user is in a hurry, the data collection unit can instantly set the collection timing to collect information quickly. For example, the data collection unit can monitor the user's emotions in real time and adjust the collection timing according to changes in emotions. This reduces the user's burden by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's emotion data into the generative AI and have the generative AI perform emotion estimation and adjustment of the collection timing.

[0075] The data collection unit can use high-precision OCR technology to digitize not only old-style and cursive characters, but also handwritten notes and annotations. For example, the data collection unit can read handwritten notes using OCR technology and save them as digital data. The data collection unit can also recognize old-style and cursive characters using high-precision OCR technology and digitize them. Furthermore, the data collection unit can read handwritten annotations using OCR technology and save them as digital data. For example, the data collection unit can scan handwritten notes and annotations and convert them into text data using OCR technology. This makes information management easier by digitizing handwritten notes and annotations. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input image data of handwritten notes and annotations into a generating AI and have the generating AI perform the conversion from image data to text data.

[0076] The collection unit can select the optimal collection method when collecting family register information by referring to past collection history. For example, the collection unit can analyze past collection history and select the optimal collection method. The collection unit can also select an efficient collection method by referring to past collection history. Furthermore, the collection unit can select the optimal collection method based on past collection history. For example, the collection unit can store past collection history in a database and refer to it during collection. This allows the collection unit to select the optimal collection method by referring to past collection history. Some or all of the above processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input past collection history data into a generating AI and have the generating AI select the optimal collection method.

[0077] The data collection unit can estimate the user's emotions and determine the priority of the data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone the collection of less important information. For example, if the user is relaxed, the data collection unit can prioritize the collection of highly important information. Also, if the user is in a hurry, the data collection unit can prioritize the collection of the most important information. For example, the data collection unit can monitor the user's emotions in real time and determine the priority of the information to collect according to changes in emotions. This enables efficient information collection by determining the priority of the information to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and determine the priority of information.

[0078] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting family register information. For example, the data collection unit can prioritize the collection of highly relevant information based on the user's current location. The data collection unit can also efficiently collect information by considering the user's geographical location. Furthermore, the data collection unit can select the optimal collection method based on the user's location information. For example, the data collection unit prioritizes the collection of highly relevant information based on the user's current location. This allows for the efficient collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0079] The data collection unit can analyze the user's social media activity and collect relevant information when collecting family register information. For example, the data collection unit can analyze the user's social media activity and collect relevant information. The data collection unit can also collect relevant information based on the content of the user's social media posts. Furthermore, the data collection unit can analyze the user's social media activity history and select the optimal collection method. For example, the data collection unit can analyze the user's social media activity and collect relevant information. This allows for the efficient collection of relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant information.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit may use a simple presentation. If the user is relaxed, the analysis unit may use a detailed presentation. If the user is in a hurry, the analysis unit may use a concise presentation. For example, the analysis unit can monitor the user's emotions in real time and adjust the presentation of the analysis according to changes in emotions. By adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the presentation of the analysis.

[0081] The analysis unit can use generative AI to analyze past cases and predict future population migration, marriage, and birth rates. For example, the analysis unit can use generative AI to analyze past population migration cases. The analysis unit can also use generative AI to predict future marriage rates. Furthermore, the analysis unit can use generative AI to predict future birth rates. For example, the analysis unit can input past population migration data into the generative AI to predict future population migration trends. The analysis unit can also input past marriage data into the generative AI to predict future marriage rates. In addition, the analysis unit can input past birth rate data into the generative AI to predict future birth rates. Thus, by using generative AI, it becomes possible to analyze past cases and make future predictions. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past population migration, marriage, and birth rate data into the generative AI and have the generative AI perform data analysis and predictions.

[0082] The analysis unit can estimate the user's emotions and determine the priority of analyses based on the estimated emotions. For example, if the user is stressed, the analysis unit may postpone less important analyses. If the user is relaxed, the analysis unit may prioritize more important analyses. If the user is in a hurry, the analysis unit may prioritize the most important analyses. For example, the analysis unit may monitor the user's emotions in real time and determine the priority of analyses according to changes in emotions. This enables efficient analysis by determining the priority of analyses according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and determination of analysis priorities.

[0083] The linking unit can estimate the user's emotions and adjust the linking criteria based on the estimated emotions. For example, if the user is stressed, the linking unit can perform linking using simple criteria. If the user is relaxed, the linking unit can also perform linking using detailed criteria. Furthermore, if the user is in a hurry, the linking unit can perform linking using concise criteria. For example, the linking unit can monitor the user's emotions in real time and adjust the linking criteria according to changes in emotions. This allows for efficient linking by adjusting the linking criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the linking unit may be performed using AI, for example, or without AI. For example, the linking unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the linking criteria.

[0084] The linking unit can improve the accuracy of linking by considering the interrelationships of family register information during the linking process. The linking unit can improve the accuracy of linking by considering the interrelationships of family register information, for example. The linking unit can also improve the accuracy of linking based on the interrelationships, for example. Furthermore, the linking unit can improve the accuracy of linking by considering the interrelationships of family register information. For example, the linking unit can improve the accuracy of linking by considering the interrelationships of family register information. As a result, the accuracy of linking is improved by considering the interrelationships of family register information. Some or all of the above processing in the linking unit may be performed using AI, for example, or without using AI. For example, the linking unit can input interrelationship data of family register information into a generating AI and have the generating AI perform the improvement of linking accuracy.

[0085] The query unit can estimate the user's emotions and adjust the query display method based on the estimated emotions. For example, if the user is stressed, the query unit may use a simple display method. If the user is relaxed, the query unit may use a detailed display method. If the user is in a hurry, the query unit may use a concise display method. For example, the query unit can monitor the user's emotions in real time and adjust the query display method according to changes in emotions. This allows for query results that are easy for the user to understand by adjusting the query display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the query unit may be performed using AI, for example, or without AI. For example, the query unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the query display method.

[0086] The inquiry unit can adjust the order of inquiries based on the relevance of the family register information during the inquiry process. For example, the inquiry unit adjusts the order of inquiries based on the relevance of the family register information. The inquiry unit can also, for example, prioritize inquiries for highly relevant information. Furthermore, the inquiry unit can postpone inquiries for less relevant information. For example, the inquiry unit adjusts the order of inquiries based on the relevance of the family register information. This allows for efficient inquiries by adjusting the order of inquiries based on the relevance of the family register information. Some or all of the above processing in the inquiry unit may be performed using AI, for example, or without AI. For example, the inquiry unit can input the relevance data of the family register information into a generating AI and have the generating AI perform the adjustment of the inquiry order.

[0087] The management unit can estimate the user's emotions and adjust the management methods of the cloud-based system based on the estimated user emotions. For example, if the user is stressed, the management unit may use a simple management method. For example, if the user is relaxed, the management unit may use a more detailed management method. Furthermore, if the user is in a hurry, the management unit may use a more concise management method. For example, the management unit can monitor the user's emotions in real time and adjust the management methods of the cloud-based system in response to changes in emotions. This enables efficient management by adjusting the management method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the management method.

[0088] The management department can select the optimal management method by referring to past management history when managing a cloud-based system. For example, the management department can select the optimal management method by referring to past management history. The management department can also select an efficient management method based on management history. Furthermore, the management department can select the optimal management method by analyzing past management history. For example, the management department can store past management history in a database and refer to it during management. This allows the management department to select the optimal management method by referring to past management history. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input past management history data into a generating AI and have the generating AI select the optimal management method.

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

[0090] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated emotions. For example, if the user is stressed, it can send a notification that concisely summarizes the analysis results. If the user is relaxed, it can also send a notification that includes detailed analysis results. If the user is in a hurry, it can send a notification that gets straight to the point. By adjusting the notification method of the analysis results according to the user's emotions, it becomes possible to provide the user with the most optimal information. Emotion estimation is achieved using an emotion engine or a generative AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the notification method.

[0091] The data collection unit can estimate the user's emotions and adjust the format of the information it collects based on the estimated emotions. For example, if the user is stressed, it can collect information in a concise format. If the user is relaxed, it can collect information in a detailed format. If the user is in a hurry, it can collect information in a concise format. By adjusting the format of the information collected according to the user's emotions, it becomes possible to collect information that is optimal for the user. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation and information format adjustment.

[0092] The inquiry unit can estimate the user's emotions and adjust the timing of displaying the query results based on the estimated emotions. For example, if the user is stressed, the display of the query results can be delayed to reduce the user's burden. If the user is relaxed, the display of the query results can be accelerated to provide information efficiently. If the user is in a hurry, the query results can be displayed immediately. In this way, the user's burden can be reduced by adjusting the timing of query result display according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above processing in the inquiry unit may be performed using AI or not. For example, the inquiry unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of display timing.

[0093] The management department can estimate the user's emotions and adjust the system maintenance schedule based on those emotions. For example, if the user is stressed, maintenance can be delayed to reduce the user's burden. If the user is relaxed, maintenance can be accelerated to maintain the system efficiently. If the user is in a hurry, maintenance can be performed immediately. In this way, the user's burden can be reduced by adjusting the maintenance schedule according to their emotions. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above processing in the management department may be performed using AI or not. For example, the management department can input user emotion data into a generative AI and have the generative AI perform emotion estimation and maintenance schedule adjustment.

[0094] The linking unit can estimate the user's emotions and adjust the priority of linking based on the estimated emotions. For example, if the user is stressed, less important links can be postponed. If the user is relaxed, more important links can be prioritized. Also, if the user is in a hurry, the most important links can be prioritized. This allows for efficient linking by adjusting the priority of links according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above processing in the linking unit may be performed using AI or not. For example, the linking unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of linking priority.

[0095] The data collection unit can select the optimal collection method by referring to the user's past behavioral history when collecting family register information. For example, the data collection unit can analyze past behavioral history and select the optimal collection method. The data collection unit can also select an efficient collection method based on past behavioral history. Furthermore, the data collection unit can store past behavioral history in a database and refer to it during collection. This allows the optimal collection method to be selected by referring to past behavioral history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past behavioral history data into a generating AI and have the generating AI select the optimal collection method.

[0096] The analysis unit can customize the analysis results when analyzing family register information, taking into account the user's occupation and lifestyle. For example, the analysis unit prioritizes analyzing information relevant to the user's occupation. The analysis unit can also customize the analysis results based on the user's lifestyle. Furthermore, the analysis unit stores the user's occupation and lifestyle in a database and references it during analysis. This allows the analysis unit to provide customized analysis results by considering the user's occupation and lifestyle. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's occupation and lifestyle data into a generating AI and have the generating AI perform the customization of the analysis results.

[0097] The inquiry unit can select the optimal inquiry method by referring to the user's past inquiry history when inquiring about family register information. For example, the inquiry unit can analyze past inquiry history and select the optimal inquiry method. The inquiry unit can also select an efficient inquiry method based on past inquiry history. Furthermore, the inquiry unit can store past inquiry history in a database and refer to it during inquiries. This allows the optimal inquiry method to be selected by referring to past inquiry history. Some or all of the above processing in the inquiry unit may be performed using AI, or it may be performed without AI. For example, the inquiry unit can input past inquiry history data into a generating AI and have the generating AI perform the selection of the optimal inquiry method.

[0098] The management department can select the optimal management method when managing the cloud-based system, taking into account the user's device information. For example, the management department can analyze the user's device information and select the optimal management method. The management department can also select an efficient management method based on the user's device information. Furthermore, the management department can store the user's device information in a database and refer to it during management. This allows the management department to select the optimal management method by considering the user's device information. Some or all of the above processes in the management department may be performed using AI, or they may not. For example, the management department can input user device information data into a generating AI and have the generating AI select the optimal management method.

[0099] The linking unit can select the optimal linking method by referring to the user's past linking history when linking family register information. For example, the linking unit can analyze past linking history and select the optimal linking method. The linking unit can also select an efficient linking method based on past linking history. Furthermore, the linking unit can store past linking history in a database and refer to it during linking. This allows the optimal linking method to be selected by referring to past linking history. Some or all of the above processes in the linking unit may be performed using AI or not. For example, the linking unit can input past linking history data into a generating AI and have the generating AI select the optimal linking method.

[0100] The following briefly describes the processing flow for example form 2.

[0101] Step 1: The collection unit collects information. The collection unit collects information such as personal information, family register information, and legal information. The collection unit can collect information using sensors and cameras, and can also collect information via the internet. It can also collect information from local government databases. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the information using machine learning algorithms, data mining techniques, natural language processing techniques, and image analysis techniques. Step 3: The linking unit links the data analyzed by the analysis unit. The linking unit can associate database records and integrate data from different data sources. It can also check data integrity to maintain data consistency. Step 4: The inquiry unit performs a family register inquiry based on the data linked by the linking unit. The inquiry unit can inquire about the resident's family relationships, relocation history, marriage history, death information, etc. Step 5: The administration department manages the cloud-based system. The administration department can monitor the operation of the cloud servers, back up data, manage access permissions, and implement security measures.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0104] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] Each of the multiple elements described above, including the collection unit, analysis unit, linking unit, inquiry unit, and management unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and sensors of the smart device 14, and the specific processing unit 290 of the data processing unit 12 collects information from the internet and local government databases. The analysis unit analyzes the information using machine learning algorithms and data mining techniques, for example, by the specific processing unit 290 of the data processing unit 12. The linking unit associates database records and integrates data from different data sources, for example, by the specific processing unit 290 of the data processing unit 12. The inquiry unit analyzes residents' family relationships and relocation history, for example, by the control unit 46A of the smart device 14, and queries marriage history and death information, for example, by the specific processing unit 290 of the data processing unit 12. The management unit monitors the operation of the cloud server, implements data backups, manages access rights, and implements security measures, for example, by the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

[0107] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0112] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0113] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0114] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0116] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] Each of the multiple elements described above, including the collection unit, analysis unit, linking unit, inquiry unit, and management unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and sensors of the smart glasses 214, and the specific processing unit 290 of the data processing unit 12 collects information from the internet and local government databases. The analysis unit analyzes the information using machine learning algorithms and data mining techniques, for example, by the specific processing unit 290 of the data processing unit 12. The linking unit associates database records and integrates data from different data sources, for example, by the specific processing unit 290 of the data processing unit 12. The inquiry unit analyzes residents' family relationships and relocation history, for example, by the control unit 46A of the smart glasses 214, and queries marriage history and death information, for example, by the specific processing unit 290 of the data processing unit 12. The management unit monitors the operation of the cloud server, implements data backups, manages access rights, and implements security measures, for example, by the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

[0123] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0125] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0126] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0128] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0129] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0130] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0132] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] Each of the multiple elements described above, including the collection unit, analysis unit, linking unit, inquiry unit, and management unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and sensors of the headset terminal 314, and the specific processing unit 290 of the data processing unit 12 collects information from the internet and local government databases. The analysis unit analyzes the information using machine learning algorithms and data mining techniques, for example, the specific processing unit 290 of the data processing unit 12. The linking unit associates database records and integrates data from different data sources, for example, the specific processing unit 290 of the data processing unit 12. The inquiry unit analyzes residents' family relationships and relocation history, for example, the control unit 46A of the headset terminal 314, and queries marriage history and death information, for example, the specific processing unit 290 of the data processing unit 12. The management unit monitors the operation of the cloud server, implements data backups, manages access rights, and implements security measures, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

[0139] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0142] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0144] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0145] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0146] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0149] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements described above, including the collection unit, analysis unit, linking unit, inquiry unit, and management unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and sensors of the robot 414, and the specific processing unit 290 of the data processing unit 12 collects information from the internet and local government databases. The analysis unit analyzes the information using machine learning algorithms and data mining techniques, for example, the specific processing unit 290 of the data processing unit 12. The linking unit associates database records and integrates data from different data sources, for example, the specific processing unit 290 of the data processing unit 12. The inquiry unit analyzes residents' family relationships and relocation history, for example, the control unit 46A of the robot 414, and queries marriage history and death information, for example, the specific processing unit 290 of the data processing unit 12. The management unit monitors the operation of the cloud server, implements data backups, manages access rights, and implements security measures, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0155] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0156] Figure 9 shows the emotion map 400, in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0157] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0158] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0159] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0160] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0161] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0162] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0163] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0164] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0165] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0166] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0167] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0168] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0169] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0170] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0171] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0172] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0173] (Note 1) The information collection unit, An analysis unit analyzes the information collected by the aforementioned collection unit, A linking unit that links the data analyzed by the aforementioned analysis unit, An inquiry unit that performs a family register inquiry based on the data linked by the aforementioned linking unit, It comprises a management department that manages the cloud-based system. A system characterized by the following features. (Note 2) The aforementioned collection unit is Using high-precision OCR technology to digitize paper family registers. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned tying part is, Using RAG, we automatically track and associate life event data such as legal revisions, new and old kanji characters, municipal mergers, relocation history, marriage, and death. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned inquiry unit is, Using an AI assistant to analyze and visualize complex family relationships. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned management department, A secure cloud-based system is adopted to manage access rights for each local government. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, We will integrate regionally specific legal and historical databases to strengthen our ability to handle inquiries specific to a particular region. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, We use generative AI to analyze past data and predict future population migration, marriage, and birth rates. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned inquiry unit is, We offer 24 / 7 AI chatbot support for inquiries, as well as real-time support services for residents via phone and chat. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, We provide multilingual support using LLM. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting family register information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is Using high-precision OCR technology, not only old-style and cursive characters, but also handwritten notes and annotations are converted into digital data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting family register information, the most suitable collection method is selected by referring to past collection history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is The system estimates the user's emotions and determines the priority of the family register information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting family register information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is When collecting family register information, we analyze the user's social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, We use generative AI to analyze past data and predict future population migration, marriage, and birth rates. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned tying part is, It estimates the user's emotions and adjusts the linking criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned tying part is, When linking data, the accuracy of the linking process is improved by considering the interrelationships between family register information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned inquiry unit is, It estimates the user's sentiment and adjusts how queries are displayed based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned inquiry unit is, When making an inquiry, the order of inquiries will be adjusted based on the relevance of the family register information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned management department, It estimates user sentiment and adjusts how the cloud-based system is managed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned management department, When managing cloud-based systems, refer to past management history to select the optimal management method. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The information collection unit, An analysis unit analyzes the information collected by the aforementioned collection unit, A linking unit that links the data analyzed by the aforementioned analysis unit, An inquiry unit that performs a family register inquiry based on the data linked by the aforementioned linking unit, It comprises a management department that manages the cloud-based system. A system characterized by the following features.

2. The aforementioned collection unit is Using high-precision OCR technology to digitize paper family registers. The system according to feature 1.

3. The aforementioned tying part is, Using RAG, we automatically track and associate life event data such as legal revisions, new and old kanji characters, municipal mergers, relocation history, marriage, and death. The system according to feature 1.

4. The aforementioned inquiry unit is, Using an AI assistant to analyze and visualize complex family relationships. The system according to feature 1.

5. The aforementioned management department, A secure cloud-based system is adopted to manage access rights for each local government. The system according to feature 1.

6. The aforementioned analysis unit, We will integrate regionally specific legal and historical databases to strengthen our ability to handle inquiries specific to a particular region. The system according to feature 1.

7. The aforementioned analysis unit, Using generative AI, we analyze past cases and predict future population migration, marriage, and birth rates. The system according to feature 1.

8. The aforementioned inquiry unit is, We offer 24 / 7 AI chatbot support for inquiries, as well as real-time support services for residents via phone and chat. The system according to feature 1.

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