system

Abstract

The system according to this embodiment aims to reduce the burden on the elderly and people with disabilities in particular by streamlining the application process and providing information. [Solution] The system according to the embodiment comprises a verification unit, a provision unit, and an access unit. The verification unit verifies the applicant's requirements and recommends appropriate support. The provision unit provides information based on the requirements verified by the verification unit. The access unit makes the information provided by the provision unit accessible online.

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, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there are complexities in the application procedures, insufficient information, and difficulties in physical movement, which particularly impose a heavy burden on the elderly and the physically disabled.

[0005] The system according to the embodiment aims to reduce the burden on the elderly and the physically disabled, particularly through the improvement of application procedures and information provision.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a verification unit, a provision unit, and an access unit. The verification unit verifies the applicant's requirements and recommends appropriate support. The provision unit provides information based on the requirements verified by the verification unit. The access unit makes the information provided by the provision unit accessible online. [Effects of the Invention]

[0007] The system according to this embodiment can reduce the burden on elderly people and people with disabilities in particular by streamlining the application process and providing information. [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, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applicable 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 receiving 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 receiving 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 AI-Social Security Optimization System (AI-SOS) according to an embodiment of the present invention is a system particularly targeted at the elderly, people with disabilities, and those requiring long-term medical care. This system is designed to solve problems such as the complexity of application procedures, lack of information, and difficulties in physical travel. AI-SOS streamlines the application process by using AI-based automation and data analysis to confirm applicant requirements and recommend appropriate support. Next, it has information provision and education functions, using an AI chatbot function to provide users with information about available support, services, and procedures. Furthermore, AI-SOS is accessible online, allowing users with physical travel difficulties to apply from home. In this way, the AI-Social Security Optimization System can streamline the application process, enhance information provision, and eliminate difficulties in physical travel.

[0029] The AI-based social security optimization system according to this embodiment comprises a verification unit, a provision unit, and an access unit. The verification unit verifies the applicant's requirements and recommends appropriate support. For example, the verification unit uses AI to automatically analyze the requirements based on the information entered by the applicant and propose the optimal support. The verification unit can also refer to the applicant's past application history and select the optimal support based on past successes and failures. Furthermore, the verification unit can filter the necessary support based on the applicant's current living situation and areas of interest. The provision unit provides information based on the requirements verified by the verification unit. For example, the provision unit uses an AI chatbot function to provide information to the applicant in real time. The provision unit can also estimate the applicant's emotions and provide information in an expression that matches those emotions. Furthermore, the provision unit adjusts the level of detail of the information based on the applicant's importance and provides the necessary information in an appropriate format. The access unit makes the information provided by the provision unit accessible online. For example, the access unit allows the applicant to complete the application process online from home. The access unit can also provide the optimal access method considering the applicant's device information. Furthermore, the access unit can also estimate the applicant's emotions and provide an access method that corresponds to those emotions. As a result, the AI-social security optimization system according to this embodiment can efficiently verify the applicant's requirements, provide information, and provide online access.

[0030] The verification unit checks the applicant's requirements and recommends appropriate support. For example, the verification unit uses AI to automatically analyze the requirements based on the information entered by the applicant and propose the most suitable support. Specifically, the information entered by the applicant includes detailed data such as age, income, family structure, and health status. This data is analyzed by AI to determine what social security services the applicant is eligible for. The AI ​​uses natural language processing technology to understand the applicant's input and extract appropriate keywords and phrases. Furthermore, the verification unit can also refer to the applicant's past application history and select the most suitable support based on past successes and failures. For example, by referring to cases where applications were successful under similar conditions in the past, the most effective support can be provided to the applicant. Analyzing failures can also help prevent the same mistakes from being repeated. In addition, the verification unit can filter the necessary support based on the applicant's current living situation and areas of interest. For example, if the applicant is elderly, information on health management and care services will be prioritized. If the applicant is a parent raising children, information on childcare support and education will be provided. This allows the verification unit to provide optimal support tailored to the individual needs of each applicant.

[0031] The provision department provides information based on the requirements confirmed by the verification department. The provision department provides information to applicants in real time, for example, by using AI chatbot functionality. The chatbot can generate appropriate answers to applicants' questions using natural language processing technology and respond quickly. For example, if an applicant asks about a specific social security service, the chatbot will provide information about the service details and application procedures. The provision department can also estimate the applicant's emotions and provide information in an emotionally appropriate manner. For example, if an applicant is feeling stressed, the chatbot will provide information using gentle language and encouraging messages. On the other hand, if the applicant is calm, it will provide concise and direct information. Furthermore, the provision department adjusts the level of detail of the information based on the importance of the applicant and provides the necessary information in an appropriate format. For example, it will provide basic information to first-time applicants and detailed procedural information to users who have applied many times before. This allows the provision department to provide information tailored to the applicant's needs and facilitate the application process.

[0032] The Access Department makes services accessible online based on information provided by the Service Provider Department. For example, the Access Department allows applicants to complete the application process online from home. Specifically, applicants can upload necessary documents and complete the application process through a dedicated web portal or mobile app. The Access Department can also provide the optimal access method considering the applicant's device information. For example, if the applicant is using a smartphone, it provides a mobile-friendly interface and adopts a design optimized for touch operation. On the other hand, if the applicant is using a desktop computer, it provides a layout suitable for a large screen. Furthermore, the Access Department can estimate the applicant's emotions and provide an access method that matches those emotions. For example, if the applicant is feeling anxious, the Access Department provides step-by-step guides and help functions to support the application process. On the other hand, if the applicant is confident, it provides a simple interface to allow them to complete the process quickly. In this way, the Access Department can ensure that applicants can complete the application process smoothly in any situation and promote the use of social security services.

[0033] The verification unit includes an automation unit to streamline the application process. For example, the verification unit uses AI to automatically analyze requirements based on information entered by the applicant and propose the most suitable support. The verification unit can also refer to the applicant's past application history and select the most suitable support based on past successes and failures. The verification unit can also filter the necessary support based on the applicant's current living situation and areas of interest. This streamlines the application process. Some or all of the above-described processes in the verification unit may be performed using AI or not. For example, the verification unit can input the applicant's input information into a generating AI and have the generating AI perform requirements analysis and support proposals.

[0034] The information provision department includes an education department to enhance the provision of information. The information provision department can, for example, use an AI chatbot function to provide information to applicants in real time. The information provision department can also estimate the applicant's emotions and provide information in an expression that matches those emotions. The information provision department adjusts the level of detail of the information based on the applicant's importance and provides the necessary information in an appropriate format. This enhances the provision of information. Some or all of the above processes in the information provision department may be performed using AI or not. For example, the information provision department can input the applicant's emotional data into a generating AI and have the generating AI execute an expression of information provision that matches those emotions.

[0035] The access unit includes a remote unit to overcome the difficulties of physical travel. For example, the access unit allows applicants to complete the application process online from their homes. The access unit can also provide the optimal access method by considering the applicant's device information. The access unit can also estimate the applicant's emotions and provide an access method that corresponds to those emotions. This eliminates the difficulties of physical travel. Some or all of the above-described processes in the access unit may be performed using AI or not. For example, the access unit can input the applicant's device information into a generating AI and have the generating AI execute the optimal access method.

[0036] The verification unit analyzes the applicant's past application history and selects the optimal verification method. For example, the verification unit may prioritize suggesting application methods that the applicant has succeeded with in the past. The verification unit can also avoid application methods that the applicant has failed with in the past. The verification unit can also select the most efficient verification method from the applicant's past application history. In this way, the optimal verification method is selected based on the past application history. Some or all of the above processing in the verification unit may be performed using AI or not. For example, the verification unit can input the applicant's past application history data into a generating AI and have the generating AI perform the selection of the optimal verification method.

[0037] The verification unit filters the requirements based on the applicant's current living situation and areas of interest. For example, the verification unit prioritizes checking requirements that are relevant to the applicant's current living situation. The verification unit can also prioritize checking requirements related to the applicant's areas of interest. The verification unit can also filter out unnecessary requirements based on the applicant's living situation and areas of interest. This allows for requirement verification tailored to the applicant's living situation and areas of interest. Some or all of the above processing in the verification unit may be performed using AI or not. For example, the verification unit can input the applicant's living situation data and areas of interest data into a generating AI and have the generating AI perform the requirement filtering.

[0038] The verification unit, when verifying requirements, prioritizes the verification of highly relevant requirements, taking into account the applicant's geographical location information. For example, the verification unit may prioritize requirements related to the area where the applicant lives. The verification unit may also prioritize requirements related to the applicant's current location. The verification unit may also prioritize the most relevant requirements based on the applicant's geographical location information. This ensures that highly relevant requirements are prioritized based on geographical location information. Some or all of the above processing in the verification unit may be performed using AI or not. For example, the verification unit may input the applicant's geographical location information into a generating AI and have the generating AI prioritize highly relevant requirements.

[0039] The verification unit analyzes the applicant's social media activity and identifies relevant requirements during requirements verification. For example, the verification unit prioritizes identifying requirements of interest from the applicant's social media activity. The verification unit can also identify requirements relevant to the current situation from the applicant's social media activity. The verification unit can also analyze the applicant's social media activity and identify the most relevant requirements. This ensures that relevant requirements are identified based on social media activity. Some or all of the above processing in the verification unit may be performed using AI or not. For example, the verification unit can input the applicant's social media activity data into a generating AI and have the generating AI perform the verification of relevant requirements.

[0040] The information provider adjusts the level of detail of the information based on the applicant's importance when providing the information. For example, if the applicant needs important information, the provider will provide detailed information. If the applicant needs general information, the provider may also provide concise information. The provider can also adjust the level of detail of the information based on the applicant's importance. This ensures that information is provided with a level of detail appropriate to the applicant's importance. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider can input applicant importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the information.

[0041] The information provision unit applies different information provision algorithms depending on the applicant's category when providing information. For example, the information provision unit may apply an information provision algorithm for the elderly. The information provision unit may also apply an information provision algorithm for people with disabilities. The information provision unit may also apply an information provision algorithm for people who require long-term care. This allows for information provision tailored to the applicant's category. Some or all of the above processing in the information provision unit may be performed using AI or not. For example, the information provision unit may input the applicant's category data into a generating AI and have the generating AI execute the application of the information provision algorithm.

[0042] The information provision unit determines the priority of information provision based on the applicant's submission timing. For example, if an applicant submits early, the information provision unit will provide information preferentially. The information provision unit may also provide information quickly if the applicant is close to the submission deadline. The information provision unit can also determine the priority of information provision based on the applicant's submission timing. This ensures that the priority of information provision is determined based on the submission timing. Some or all of the above processes in the information provision unit may be performed using AI or not. For example, the information provision unit can input applicant submission timing data into a generating AI and have the generating AI perform the determination of the priority of information provision.

[0043] The information provider adjusts the order of information provision based on the applicant's relevance. For example, the provider may prioritize providing information of the applicant's greatest interest. The provider may also prioritize providing information that the applicant needs. The provider may also adjust the order of information provision based on the applicant's relevance. This adjusts the order of information provision based on relevance. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider may input applicant relevance data into a generating AI and have the generating AI perform the adjustment of the order of information provision.

[0044] The access unit, upon access, selects the optimal access method by referring to the applicant's past access history. For example, the access unit may prioritize providing access methods previously used by the applicant. The access unit may also avoid access methods that the applicant has previously failed to use. The access unit may also select the most efficient access method from the applicant's past access history. In this way, the optimal access method is selected based on past access history. Some or all of the above processing in the access unit may be performed using AI or not. For example, the access unit may input the applicant's past access history data into a generating AI and have the generating AI perform the selection of the optimal access method.

[0045] The access unit provides the optimal access method when accessing the application, taking into account the applicant's device information. For example, if the applicant is using a smartphone, the access unit provides an access method optimized for smartphones. If the applicant is using a tablet, the access unit can also provide an access method optimized for tablets. If the applicant is using a personal computer, the access unit can also provide an access method optimized for personal computers. This ensures that the optimal access method is provided based on device information. Some or all of the above processing in the access unit may be performed using AI or not. For example, the access unit can input the applicant's device information into a generating AI and have the generating AI perform the task of providing the optimal access method.

[0046] The access unit provides the optimal access method when an applicant accesses the system, taking into account the applicant's geographical location. For example, the access unit may prioritize providing information related to the area where the applicant lives. It may also prioritize providing information related to the applicant's current location. The access unit may also prioritize the most relevant information based on the applicant's geographical location. This ensures that the optimal access method is provided based on geographical location. Some or all of the above processing in the access unit may be performed using AI or not. For example, the access unit can input the applicant's geographical location into a generating AI and have the generating AI perform the task of providing the optimal access method.

[0047] The access unit analyzes the applicant's social media activity at the time of access and provides the optimal access method. For example, the access unit prioritizes providing information of interest based on the applicant's social media activity. The access unit can also provide information relevant to the current situation based on the applicant's social media activity. The access unit can also analyze the applicant's social media activity and provide the most relevant information. This ensures that the optimal access method is provided based on social media activity. Some or all of the above processing in the access unit may be performed using AI or not. For example, the access unit can input the applicant's social media activity data into a generating AI and have the generating AI perform the task of providing the optimal access method.

[0048] The automation unit analyzes the applicant's past application history to select the optimal automation method during the automation process. For example, the automation unit may prioritize suggesting automation methods that have been successful for the applicant in the past. The automation unit can also avoid automation methods that have failed for the applicant in the past. The automation unit can also select the most efficient automation method from the applicant's past application history. This ensures that the optimal automation method is selected based on past application history. Some or all of the above processes in the automation unit may be performed using AI or not. For example, the automation unit can input the applicant's past application history data into a generating AI and have the generating AI select the optimal automation method.

[0049] The automation unit performs filtering based on the applicant's current living situation and areas of interest during the automation process. For example, the automation unit may prioritize providing automation methods that are appropriate to the applicant's current living situation. The automation unit may also prioritize providing automation methods related to the applicant's areas of interest. The automation unit may also filter out unnecessary automation methods based on the applicant's living situation and areas of interest. This allows for automation tailored to the applicant's living situation and areas of interest. Some or all of the above-described processes in the automation unit may be performed using AI or not. For example, the automation unit may input the applicant's living situation data and areas of interest data into a generating AI and have the generating AI perform the filtering of automation methods.

[0050] The automation unit provides the optimal automation method during automation, taking into account the applicant's geographical location information. For example, the automation unit may prioritize providing automation methods related to the area where the applicant lives. The automation unit may also prioritize providing automation methods related to the applicant's current location. The automation unit may also provide the most relevant automation method based on the applicant's geographical location information. This ensures that the optimal automation method is provided based on geographical location information. Some or all of the above processing in the automation unit may be performed using AI or not. For example, the automation unit may input the applicant's geographical location information into a generating AI and have the generating AI perform the task of providing the optimal automation method.

[0051] The Ministry of Education selects the most suitable teaching method during the educational process by referring to the applicant's past learning history. For example, the Ministry of Education may prioritize providing learning methods that the applicant has succeeded with in the past. The Ministry of Education may also avoid learning methods that the applicant has failed with in the past. The Ministry of Education may also select the most efficient teaching method based on the applicant's past learning history. In this way, the most suitable teaching method is selected based on past learning history. Some or all of the above processes by the Ministry of Education may be performed using AI or not. For example, the Ministry of Education may input the applicant's past learning history data into a generating AI and have the generating AI perform the selection of the most suitable teaching method.

[0052] The Ministry of Education filters educational materials based on the applicant's current living situation and areas of interest. For example, the Ministry of Education may prioritize providing educational content that is relevant to the applicant's current living situation. The Ministry of Education may also prioritize providing educational content related to the applicant's areas of interest. The Ministry of Education may also filter out unnecessary educational content based on the applicant's living situation and areas of interest. This ensures that educational content is tailored to the applicant's living situation and areas of interest. Some or all of the above processes by the Ministry of Education may be performed using AI or not. For example, the Ministry of Education may input the applicant's living situation data and areas of interest data into a generating AI and have the generating AI perform the filtering of educational content.

[0053] The Ministry of Education will provide the most suitable educational methods during education, taking into account the applicant's geographical location. For example, the Ministry of Education may prioritize providing educational content related to the area where the applicant lives. The Ministry of Education may also prioritize providing educational content related to the place where the applicant is currently located. The Ministry of Education may also provide the most relevant educational content based on the applicant's geographical location. This ensures that the most suitable educational methods are provided based on geographical location. Some or all of the above processes by the Ministry of Education may be performed using AI or not. For example, the Ministry of Education may input the applicant's geographical location into a generating AI and have the generating AI provide the most suitable educational methods.

[0054] The Ministry of Education analyzes applicants' social media activity during education to provide the most suitable teaching methods. For example, the Ministry of Education may prioritize providing educational content that interests the applicant based on their social media activity. The Ministry of Education may also provide educational content relevant to the applicant's current situation based on their social media activity. The Ministry of Education may also analyze the applicant's social media activity and provide the most relevant educational content. This ensures that the most suitable teaching methods are provided based on social media activity. Some or all of the above processes by the Ministry of Education may be performed using AI or not. For example, the Ministry of Education may input the applicant's social media activity data into a generating AI and have the generating AI provide the most suitable teaching methods.

[0055] The remote access unit selects the optimal remote access method by referring to the applicant's past remote access history when remote access is requested. For example, the remote access unit may prioritize providing remote access methods that the applicant has used in the past. The remote access unit may also avoid remote access methods that the applicant has failed to use in the past. The remote access unit may also select the most efficient remote access method from the applicant's past remote access history. In this way, the optimal remote access method is selected based on past remote access history. Some or all of the above processing in the remote access unit may be performed using AI or not. For example, the remote access unit may input the applicant's past remote access history data into a generating AI and have the generating AI perform the selection of the optimal remote access method.

[0056] The remote unit provides the optimal remote access method when remote access is performed, taking into account the applicant's device information. For example, if the applicant is using a smartphone, the remote unit provides a remote access method optimized for smartphones. If the applicant is using a tablet, the remote unit can also provide a remote access method optimized for tablets. If the applicant is using a personal computer, the remote unit can also provide a remote access method optimized for personal computers. This ensures that the optimal remote access method is provided based on device information. Some or all of the above processing in the remote unit may be performed using AI or not. For example, the remote unit can input the applicant's device information into a generating AI and have the generating AI perform the task of providing the optimal remote access method.

[0057] The remote access unit provides the optimal remote access method when an applicant accesses the system remotely, taking into account the applicant's geographical location. For example, the remote access unit may prioritize providing information related to the area where the applicant lives. It may also prioritize providing information related to the applicant's current location. The remote access unit may also prioritize the most relevant information based on the applicant's geographical location. This ensures that the optimal remote access method is provided based on geographical location. Some or all of the above processing in the remote access unit may be performed using AI or not. For example, the remote access unit can input the applicant's geographical location into a generating AI and have the generating AI provide the optimal remote access method.

[0058] The remote access unit analyzes the applicant's social media activity during remote access to provide the optimal remote access method. For example, the remote access unit prioritizes providing information of interest based on the applicant's social media activity. The remote access unit can also provide information relevant to the current situation based on the applicant's social media activity. The remote access unit can analyze the applicant's social media activity and provide the most relevant information. This ensures that the optimal remote access method is provided based on social media activity. Some or all of the above processing in the remote access unit may be performed using AI or not. For example, the remote access unit can input the applicant's social media activity data into a generating AI and have the generating AI perform the task of providing the optimal remote access method.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The verification unit can monitor the applicant's health data in real time and adjust the priority of requirement verification based on their health status. For example, if the applicant's health is deteriorating, it will prioritize checking for urgent support. If the applicant's health is stable, it can perform a normal requirement verification. The verification unit can also analyze the applicant's health data and suggest preventative support. This allows for requirement verification tailored to the applicant's health status.

[0061] The information delivery department can analyze applicants' past feedback and optimize its information delivery methods. For example, it can prioritize using information delivery methods that have received positive feedback from applicants in the past, and avoid methods that have received negative feedback. Furthermore, the information delivery department can collect applicant feedback in real time and continuously improve its information delivery methods. This allows for optimal information delivery based on applicant feedback.

[0062] The access unit can monitor the applicant's internet connection status in real time and provide the optimal access method. For example, if the applicant is using a slow internet connection, it can provide a lightweight access method. If the applicant is using a high-speed internet connection, it can also provide a detailed access method. Furthermore, the access unit can adjust the access priority based on the applicant's internet connection status. This ensures that the optimal access method is provided according to the internet connection status.

[0063] The verification unit can prioritize the verification of highly relevant requirements by considering the applicant's geographical location information. For example, it can prioritize the verification of requirements related to the area where the applicant lives. It can also prioritize the verification of requirements related to the applicant's current location. Furthermore, it can prioritize the most relevant requirements based on the applicant's geographical location information. As a result, highly relevant requirements are prioritized based on geographical location information.

[0064] The information provision department can apply different information provision algorithms depending on the applicant's category. For example, it can apply an information provision algorithm for the elderly. It can also apply an information provision algorithm for people with disabilities. Furthermore, it can apply an information provision algorithm for people requiring long-term medical care. This allows for the provision of information tailored to the applicant's category.

[0065] The Access Department can analyze the applicant's social media activity and identify relevant requirements. For example, it can prioritize identifying requirements of interest based on the applicant's social media activity. It can also identify requirements relevant to the applicant's current situation based on their social media activity. Furthermore, it can analyze the applicant's social media activity to identify the most relevant requirements. This ensures that relevant requirements are identified based on social media activity.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The verification unit confirms the applicant's requirements and recommends appropriate support. Based on the information entered by the applicant, the verification unit's AI automatically analyzes the requirements and proposes the most suitable support. It can also refer to the applicant's past application history and select the best support based on past successes and failures. Furthermore, it can filter the necessary support based on the applicant's current living situation and areas of interest. Step 2: The provisioning department provides information based on the requirements confirmed by the verification department. The provisioning department uses an AI chatbot function to provide information to applicants in real time. It can also estimate the applicant's emotions and provide information in an emotionally appropriate manner. Furthermore, it adjusts the level of detail of the information based on the applicant's importance and provides the necessary information in the appropriate format. Step 3: The Access Unit makes the information available online based on the information provided by the Provider Unit. The Access Unit enables applicants to complete the application process online from their homes. It can also provide the optimal access method considering the applicant's device information. Furthermore, it can estimate the applicant's emotions and provide an access method that responds accordingly.

[0068] (Example of form 2) The AI-Social Security Optimization System (AI-SOS) according to an embodiment of the present invention is a system particularly targeted at the elderly, people with disabilities, and those requiring long-term medical care. This system is designed to solve problems such as the complexity of application procedures, lack of information, and difficulties in physical travel. AI-SOS streamlines the application process by using AI-based automation and data analysis to confirm applicant requirements and recommend appropriate support. Next, it has information provision and education functions, using an AI chatbot function to provide users with information about available support, services, and procedures. Furthermore, AI-SOS is accessible online, allowing users with physical travel difficulties to apply from home. In this way, the AI-Social Security Optimization System can streamline the application process, enhance information provision, and eliminate difficulties in physical travel.

[0069] The AI-based social security optimization system according to this embodiment comprises a verification unit, a provision unit, and an access unit. The verification unit verifies the applicant's requirements and recommends appropriate support. For example, the verification unit uses AI to automatically analyze the requirements based on the information entered by the applicant and propose the optimal support. The verification unit can also refer to the applicant's past application history and select the optimal support based on past successes and failures. Furthermore, the verification unit can filter the necessary support based on the applicant's current living situation and areas of interest. The provision unit provides information based on the requirements verified by the verification unit. For example, the provision unit uses an AI chatbot function to provide information to the applicant in real time. The provision unit can also estimate the applicant's emotions and provide information in an expression that matches those emotions. Furthermore, the provision unit adjusts the level of detail of the information based on the applicant's importance and provides the necessary information in an appropriate format. The access unit makes the information provided by the provision unit accessible online. For example, the access unit allows the applicant to complete the application process online from home. The access unit can also provide the optimal access method considering the applicant's device information. Furthermore, the access unit can also estimate the applicant's emotions and provide an access method that corresponds to those emotions. As a result, the AI-social security optimization system according to this embodiment can efficiently verify the applicant's requirements, provide information, and provide online access.

[0070] The verification unit checks the applicant's requirements and recommends appropriate support. For example, the verification unit uses AI to automatically analyze the requirements based on the information entered by the applicant and propose the most suitable support. Specifically, the information entered by the applicant includes detailed data such as age, income, family structure, and health status. This data is analyzed by AI to determine what social security services the applicant is eligible for. The AI ​​uses natural language processing technology to understand the applicant's input and extract appropriate keywords and phrases. Furthermore, the verification unit can also refer to the applicant's past application history and select the most suitable support based on past successes and failures. For example, by referring to cases where applications were successful under similar conditions in the past, the most effective support can be provided to the applicant. Analyzing failures can also help prevent the same mistakes from being repeated. In addition, the verification unit can filter the necessary support based on the applicant's current living situation and areas of interest. For example, if the applicant is elderly, information on health management and care services will be prioritized. If the applicant is a parent raising children, information on childcare support and education will be provided. This allows the verification unit to provide optimal support tailored to the individual needs of each applicant.

[0071] The provision department provides information based on the requirements confirmed by the verification department. The provision department provides information to applicants in real time, for example, by using AI chatbot functionality. The chatbot can generate appropriate answers to applicants' questions using natural language processing technology and respond quickly. For example, if an applicant asks about a specific social security service, the chatbot will provide information about the service details and application procedures. The provision department can also estimate the applicant's emotions and provide information in an emotionally appropriate manner. For example, if an applicant is feeling stressed, the chatbot will provide information using gentle language and encouraging messages. On the other hand, if the applicant is calm, it will provide concise and direct information. Furthermore, the provision department adjusts the level of detail of the information based on the importance of the applicant and provides the necessary information in an appropriate format. For example, it will provide basic information to first-time applicants and detailed procedural information to users who have applied many times before. This allows the provision department to provide information tailored to the applicant's needs and facilitate the application process.

[0072] The Access Department makes services accessible online based on information provided by the Service Provider Department. For example, the Access Department allows applicants to complete the application process online from home. Specifically, applicants can upload necessary documents and complete the application process through a dedicated web portal or mobile app. The Access Department can also provide the optimal access method considering the applicant's device information. For example, if the applicant is using a smartphone, it provides a mobile-friendly interface and adopts a design optimized for touch operation. On the other hand, if the applicant is using a desktop computer, it provides a layout suitable for a large screen. Furthermore, the Access Department can estimate the applicant's emotions and provide an access method that matches those emotions. For example, if the applicant is feeling anxious, the Access Department provides step-by-step guides and help functions to support the application process. On the other hand, if the applicant is confident, it provides a simple interface to allow them to complete the process quickly. In this way, the Access Department can ensure that applicants can complete the application process smoothly in any situation and promote the use of social security services.

[0073] The verification unit includes an automation unit to streamline the application process. For example, the verification unit uses AI to automatically analyze requirements based on information entered by the applicant and propose the most suitable support. The verification unit can also refer to the applicant's past application history and select the most suitable support based on past successes and failures. The verification unit can also filter the necessary support based on the applicant's current living situation and areas of interest. This streamlines the application process. Some or all of the above-described processes in the verification unit may be performed using AI or not. For example, the verification unit can input the applicant's input information into a generating AI and have the generating AI perform requirements analysis and support proposals.

[0074] The information provision department includes an education department to enhance the provision of information. The information provision department can, for example, use an AI chatbot function to provide information to applicants in real time. The information provision department can also estimate the applicant's emotions and provide information in an expression that matches those emotions. The information provision department adjusts the level of detail of the information based on the applicant's importance and provides the necessary information in an appropriate format. This enhances the provision of information. Some or all of the above processes in the information provision department may be performed using AI or not. For example, the information provision department can input the applicant's emotional data into a generating AI and have the generating AI execute an expression of information provision that matches those emotions.

[0075] The access unit includes a remote unit to overcome the difficulties of physical travel. For example, the access unit allows applicants to complete the application process online from their homes. The access unit can also provide the optimal access method by considering the applicant's device information. The access unit can also estimate the applicant's emotions and provide an access method that corresponds to those emotions. This eliminates the difficulties of physical travel. Some or all of the above-described processes in the access unit may be performed using AI or not. For example, the access unit can input the applicant's device information into a generating AI and have the generating AI execute the optimal access method.

[0076] The verification unit estimates the applicant's emotions and adjusts the timing of requirement verification based on the estimated emotions. For example, if the applicant is feeling stressed, the verification unit will perform requirement verification at a time when the applicant can relax. If the applicant is in a hurry, the verification unit can also perform requirement verification quickly. If the applicant is feeling anxious, the verification unit can also perform requirement verification at a time that will provide reassurance. This allows requirement verification to be performed at a time that matches the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the verification unit may be performed using AI or not. For example, the verification unit can input the applicant's emotion data into the generative AI and have the generative AI adjust the timing of requirement verification.

[0077] The verification unit analyzes the applicant's past application history and selects the optimal verification method. For example, the verification unit may prioritize suggesting application methods that the applicant has succeeded with in the past. The verification unit can also avoid application methods that the applicant has failed with in the past. The verification unit can also select the most efficient verification method from the applicant's past application history. In this way, the optimal verification method is selected based on the past application history. Some or all of the above processing in the verification unit may be performed using AI or not. For example, the verification unit can input the applicant's past application history data into a generating AI and have the generating AI perform the selection of the optimal verification method.

[0078] The verification unit filters the requirements based on the applicant's current living situation and areas of interest. For example, the verification unit prioritizes checking requirements that are relevant to the applicant's current living situation. The verification unit can also prioritize checking requirements related to the applicant's areas of interest. The verification unit can also filter out unnecessary requirements based on the applicant's living situation and areas of interest. This allows for requirement verification tailored to the applicant's living situation and areas of interest. Some or all of the above processing in the verification unit may be performed using AI or not. For example, the verification unit can input the applicant's living situation data and areas of interest data into a generating AI and have the generating AI perform the requirement filtering.

[0079] The verification unit estimates the applicant's emotions and determines the priority of the requirements to be verified based on the estimated emotions. For example, if the applicant is stressed, the verification unit will prioritize verifying important requirements. If the applicant is relaxed, the verification unit may also verify detailed requirements. If the applicant is in a hurry, the verification unit may also prioritize requirements that need to be verified quickly. This determines the priority of requirements according to the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the verification unit may be performed using AI or not. For example, the verification unit can input the applicant's emotion data into a generative AI and have the generative AI perform the determination of requirement priorities.

[0080] The verification unit, when verifying requirements, prioritizes the verification of highly relevant requirements, taking into account the applicant's geographical location information. For example, the verification unit may prioritize requirements related to the area where the applicant lives. The verification unit may also prioritize requirements related to the applicant's current location. The verification unit may also prioritize the most relevant requirements based on the applicant's geographical location information. This ensures that highly relevant requirements are prioritized based on geographical location information. Some or all of the above processing in the verification unit may be performed using AI or not. For example, the verification unit may input the applicant's geographical location information into a generating AI and have the generating AI prioritize highly relevant requirements.

[0081] The verification unit analyzes the applicant's social media activity and identifies relevant requirements during requirements verification. For example, the verification unit prioritizes identifying requirements of interest from the applicant's social media activity. The verification unit can also identify requirements relevant to the current situation from the applicant's social media activity. The verification unit can also analyze the applicant's social media activity and identify the most relevant requirements. This ensures that relevant requirements are identified based on social media activity. Some or all of the above processing in the verification unit may be performed using AI or not. For example, the verification unit can input the applicant's social media activity data into a generating AI and have the generating AI perform the verification of relevant requirements.

[0082] The information provider estimates the applicant's emotions and adjusts the way information is presented based on the estimated emotions. For example, if the applicant is stressed, the provider uses a concise and easy-to-understand method of expression. If the applicant is relaxed, the provider may also provide detailed information. If the applicant is anxious, the provider may also use a reassuring method of expression. This ensures that information is presented in a way that is appropriate to the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input the applicant's emotion data into a generative AI and have the generative AI adjust the way information is presented.

[0083] The information provider adjusts the level of detail of the information based on the applicant's importance when providing the information. For example, if the applicant needs important information, the provider will provide detailed information. If the applicant needs general information, the provider may also provide concise information. The provider can also adjust the level of detail of the information based on the applicant's importance. This ensures that information is provided with a level of detail appropriate to the applicant's importance. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider can input applicant importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the information.

[0084] The information provision unit applies different information provision algorithms depending on the applicant's category when providing information. For example, the information provision unit may apply an information provision algorithm for the elderly. The information provision unit may also apply an information provision algorithm for people with disabilities. The information provision unit may also apply an information provision algorithm for people who require long-term care. This allows for information provision tailored to the applicant's category. Some or all of the above processing in the information provision unit may be performed using AI or not. For example, the information provision unit may input the applicant's category data into a generating AI and have the generating AI execute the application of the information provision algorithm.

[0085] The information provider estimates the applicant's emotions and adjusts the length of the information provided based on the estimated emotions. For example, if the applicant is in a hurry, the provider provides short, concise information. If the applicant is relaxed, the provider can also provide detailed information. If the applicant is feeling anxious, the provider can also provide detailed information to reassure them. This ensures that the information is provided at a length appropriate to the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input the applicant's emotion data into a generative AI and have the generative AI adjust the length of the information provided.

[0086] The information provision unit determines the priority of information provision based on the applicant's submission timing. For example, if an applicant submits early, the information provision unit will provide information preferentially. The information provision unit may also provide information quickly if the applicant is close to the submission deadline. The information provision unit can also determine the priority of information provision based on the applicant's submission timing. This ensures that the priority of information provision is determined based on the submission timing. Some or all of the above processes in the information provision unit may be performed using AI or not. For example, the information provision unit can input applicant submission timing data into a generating AI and have the generating AI perform the determination of the priority of information provision.

[0087] The information provider adjusts the order of information provision based on the applicant's relevance. For example, the provider may prioritize providing information of the applicant's greatest interest. The provider may also prioritize providing information that the applicant needs. The provider may also adjust the order of information provision based on the applicant's relevance. This adjusts the order of information provision based on relevance. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider may input applicant relevance data into a generating AI and have the generating AI perform the adjustment of the order of information provision.

[0088] The access unit estimates the applicant's emotions and adjusts the access method based on the estimated emotions. For example, if the applicant is stressed, the access unit provides a simple access method. If the applicant is relaxed, the access unit may also provide a more detailed access method. If the applicant is anxious, the access unit may also provide a reassuring access method. This ensures that an access method is provided that is appropriate to the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the processing described above in the access unit may be performed using AI or not. For example, the access unit can input the applicant's emotion data into a generative AI and have the generative AI adjust the access method.

[0089] The access unit, upon access, selects the optimal access method by referring to the applicant's past access history. For example, the access unit may prioritize providing access methods previously used by the applicant. The access unit may also avoid access methods that the applicant has previously failed to use. The access unit may also select the most efficient access method from the applicant's past access history. In this way, the optimal access method is selected based on past access history. Some or all of the above processing in the access unit may be performed using AI or not. For example, the access unit may input the applicant's past access history data into a generating AI and have the generating AI perform the selection of the optimal access method.

[0090] The access unit provides the optimal access method when accessing the application, taking into account the applicant's device information. For example, if the applicant is using a smartphone, the access unit provides an access method optimized for smartphones. If the applicant is using a tablet, the access unit can also provide an access method optimized for tablets. If the applicant is using a personal computer, the access unit can also provide an access method optimized for personal computers. This ensures that the optimal access method is provided based on device information. Some or all of the above processing in the access unit may be performed using AI or not. For example, the access unit can input the applicant's device information into a generating AI and have the generating AI perform the task of providing the optimal access method.

[0091] The access unit estimates the applicant's emotions and determines access priorities based on the estimated emotions. For example, if the applicant is stressed, the access unit will prioritize important access. If the applicant is relaxed, the access unit may also provide detailed access. If the applicant is in a hurry, the access unit may also prioritize information that needs to be accessed quickly. This determines access priorities according to the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the access unit may be performed using AI or not. For example, the access unit can input the applicant's emotion data into a generative AI and have the generative AI determine the access priorities.

[0092] The access unit provides the optimal access method when an applicant accesses the system, taking into account the applicant's geographical location. For example, the access unit may prioritize providing information related to the area where the applicant lives. It may also prioritize providing information related to the applicant's current location. The access unit may also prioritize the most relevant information based on the applicant's geographical location. This ensures that the optimal access method is provided based on geographical location. Some or all of the above processing in the access unit may be performed using AI or not. For example, the access unit can input the applicant's geographical location into a generating AI and have the generating AI perform the task of providing the optimal access method.

[0093] The access unit analyzes the applicant's social media activity at the time of access and provides the optimal access method. For example, the access unit prioritizes providing information of interest based on the applicant's social media activity. The access unit can also provide information relevant to the current situation based on the applicant's social media activity. The access unit can also analyze the applicant's social media activity and provide the most relevant information. This ensures that the optimal access method is provided based on social media activity. Some or all of the above processing in the access unit may be performed using AI or not. For example, the access unit can input the applicant's social media activity data into a generating AI and have the generating AI perform the task of providing the optimal access method.

[0094] The automation unit estimates the applicant's emotions and adjusts the timing of automation based on the estimated emotions. For example, if the applicant is feeling stressed, the automation unit will perform automation during a time when the applicant can relax. If the applicant is in a hurry, the automation unit can also perform automation quickly. If the applicant is feeling anxious, the automation unit can also perform automation at a time that provides reassurance. This allows automation to be performed at a time that matches the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the automation unit may be performed using AI or not. For example, the automation unit can input the applicant's emotion data into the generative AI and have the generative AI adjust the timing of automation.

[0095] The automation unit analyzes the applicant's past application history to select the optimal automation method during the automation process. For example, the automation unit may prioritize suggesting automation methods that have been successful for the applicant in the past. The automation unit can also avoid automation methods that have failed for the applicant in the past. The automation unit can also select the most efficient automation method from the applicant's past application history. This ensures that the optimal automation method is selected based on past application history. Some or all of the above processes in the automation unit may be performed using AI or not. For example, the automation unit can input the applicant's past application history data into a generating AI and have the generating AI select the optimal automation method.

[0096] The automation unit performs filtering based on the applicant's current living situation and areas of interest during the automation process. For example, the automation unit may prioritize providing automation methods that are appropriate to the applicant's current living situation. The automation unit may also prioritize providing automation methods related to the applicant's areas of interest. The automation unit may also filter out unnecessary automation methods based on the applicant's living situation and areas of interest. This allows for automation tailored to the applicant's living situation and areas of interest. Some or all of the above-described processes in the automation unit may be performed using AI or not. For example, the automation unit may input the applicant's living situation data and areas of interest data into a generating AI and have the generating AI perform the filtering of automation methods.

[0097] The automation unit estimates the applicant's emotions and determines the priority of automation based on the estimated emotions. For example, if the applicant is stressed, the automation unit will prioritize important automation tasks. If the applicant is relaxed, the automation unit may also perform detailed automation tasks. If the applicant is in a hurry, the automation unit may also prioritize tasks that require rapid automation. This determines the priority of automation according to the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the automation unit may be performed using AI or not. For example, the automation unit can input the applicant's emotion data into a generative AI and have the generative AI determine the priority of automation.

[0098] The automation unit provides the optimal automation method during automation, taking into account the applicant's geographical location information. For example, the automation unit may prioritize providing automation methods related to the area where the applicant lives. The automation unit may also prioritize providing automation methods related to the applicant's current location. The automation unit may also provide the most relevant automation method based on the applicant's geographical location information. This ensures that the optimal automation method is provided based on geographical location information. Some or all of the above processing in the automation unit may be performed using AI or not. For example, the automation unit may input the applicant's geographical location information into a generating AI and have the generating AI perform the task of providing the optimal automation method.

[0099] The Ministry of Education estimates the applicant's emotions and adjusts the educational content based on the estimated emotions. For example, if the applicant is stressed, the Ministry of Education provides concise and easy-to-understand educational content. If the applicant is relaxed, the Ministry of Education may also provide detailed educational content. If the applicant is anxious, the Ministry of Education may also provide reassuring educational content. This ensures that the educational content is tailored to the applicant's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing by the Ministry of Education may be performed using AI or not. For example, the Ministry of Education can input the applicant's emotion data into a generative AI and have the generative AI adjust the educational content.

[0100] The Ministry of Education selects the most suitable teaching method during the educational process by referring to the applicant's past learning history. For example, the Ministry of Education may prioritize providing learning methods that the applicant has succeeded with in the past. The Ministry of Education may also avoid learning methods that the applicant has failed with in the past. The Ministry of Education may also select the most efficient teaching method based on the applicant's past learning history. In this way, the most suitable teaching method is selected based on past learning history. Some or all of the above processes by the Ministry of Education may be performed using AI or not. For example, the Ministry of Education may input the applicant's past learning history data into a generating AI and have the generating AI perform the selection of the most suitable teaching method.

[0101] The Ministry of Education filters educational materials based on the applicant's current living situation and areas of interest. For example, the Ministry of Education may prioritize providing educational content that is relevant to the applicant's current living situation. The Ministry of Education may also prioritize providing educational content related to the applicant's areas of interest. The Ministry of Education may also filter out unnecessary educational content based on the applicant's living situation and areas of interest. This ensures that educational content is tailored to the applicant's living situation and areas of interest. Some or all of the above processes by the Ministry of Education may be performed using AI or not. For example, the Ministry of Education may input the applicant's living situation data and areas of interest data into a generating AI and have the generating AI perform the filtering of educational content.

[0102] The Ministry of Education estimates the applicant's emotions and determines educational priorities based on the estimated emotions. For example, if the applicant is stressed, the Ministry of Education may prioritize providing important educational content. If the applicant is relaxed, the Ministry of Education may also provide detailed educational content. If the applicant is in a hurry, the Ministry of Education may also prioritize content that needs to be taught quickly. This determines educational priorities according to the applicant's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing by the Ministry of Education may be performed using AI or not. For example, the Ministry of Education may input the applicant's emotion data into a generative AI and have the generative AI determine the educational priorities.

[0103] The Ministry of Education will provide the most suitable educational methods during education, taking into account the applicant's geographical location. For example, the Ministry of Education may prioritize providing educational content related to the area where the applicant lives. The Ministry of Education may also prioritize providing educational content related to the place where the applicant is currently located. The Ministry of Education may also provide the most relevant educational content based on the applicant's geographical location. This ensures that the most suitable educational methods are provided based on geographical location. Some or all of the above processes by the Ministry of Education may be performed using AI or not. For example, the Ministry of Education may input the applicant's geographical location into a generating AI and have the generating AI provide the most suitable educational methods.

[0104] The Ministry of Education analyzes applicants' social media activity during education to provide the most suitable teaching methods. For example, the Ministry of Education may prioritize providing educational content that interests the applicant based on their social media activity. The Ministry of Education may also provide educational content relevant to the applicant's current situation based on their social media activity. The Ministry of Education may also analyze the applicant's social media activity and provide the most relevant educational content. This ensures that the most suitable teaching methods are provided based on social media activity. Some or all of the above processes by the Ministry of Education may be performed using AI or not. For example, the Ministry of Education may input the applicant's social media activity data into a generating AI and have the generating AI provide the most suitable teaching methods.

[0105] The remote unit estimates the applicant's emotions and adjusts the remote access method based on the estimated emotions. For example, if the applicant is stressed, the remote unit provides a simple remote access method. If the applicant is relaxed, the remote unit may also provide a more detailed remote access method. If the applicant is anxious, the remote unit may also provide a reassuring remote access method. This ensures that a remote access method is provided that is appropriate to the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the remote unit may be performed using AI or not. For example, the remote unit can input the applicant's emotion data into a generative AI and have the generative AI adjust the remote access method.

[0106] The remote access unit selects the optimal remote access method by referring to the applicant's past remote access history when remote access is requested. For example, the remote access unit may prioritize providing remote access methods that the applicant has used in the past. The remote access unit may also avoid remote access methods that the applicant has failed to use in the past. The remote access unit may also select the most efficient remote access method from the applicant's past remote access history. In this way, the optimal remote access method is selected based on past remote access history. Some or all of the above processing in the remote access unit may be performed using AI or not. For example, the remote access unit may input the applicant's past remote access history data into a generating AI and have the generating AI perform the selection of the optimal remote access method.

[0107] The remote unit provides the optimal remote access method when remote access is performed, taking into account the applicant's device information. For example, if the applicant is using a smartphone, the remote unit provides a remote access method optimized for smartphones. If the applicant is using a tablet, the remote unit can also provide a remote access method optimized for tablets. If the applicant is using a personal computer, the remote unit can also provide a remote access method optimized for personal computers. This ensures that the optimal remote access method is provided based on device information. Some or all of the above processing in the remote unit may be performed using AI or not. For example, the remote unit can input the applicant's device information into a generating AI and have the generating AI perform the task of providing the optimal remote access method.

[0108] The remote access unit estimates the applicant's emotions and determines the priority of remote access based on the estimated emotions. For example, if the applicant is stressed, the remote access unit may prioritize important remote access. If the applicant is relaxed, the remote access unit may also provide detailed remote access. If the applicant is in a hurry, the remote access unit may also prioritize information that requires quick remote access. This determines the priority of remote access according to the applicant's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the remote access unit may be performed using AI or not. For example, the remote access unit can input the applicant's emotion data into a generative AI and have the generative AI perform the determination of remote access priorities.

[0109] The remote access unit provides the optimal remote access method when an applicant accesses the system remotely, taking into account the applicant's geographical location. For example, the remote access unit may prioritize providing information related to the area where the applicant lives. It may also prioritize providing information related to the applicant's current location. The remote access unit may also prioritize the most relevant information based on the applicant's geographical location. This ensures that the optimal remote access method is provided based on geographical location. Some or all of the above processing in the remote access unit may be performed using AI or not. For example, the remote access unit can input the applicant's geographical location into a generating AI and have the generating AI provide the optimal remote access method.

[0110] The remote access unit analyzes the applicant's social media activity during remote access to provide the optimal remote access method. For example, the remote access unit prioritizes providing information of interest based on the applicant's social media activity. The remote access unit can also provide information relevant to the current situation based on the applicant's social media activity. The remote access unit can analyze the applicant's social media activity and provide the most relevant information. This ensures that the optimal remote access method is provided based on social media activity. Some or all of the above processing in the remote access unit may be performed using AI or not. For example, the remote access unit can input the applicant's social media activity data into a generating AI and have the generating AI perform the task of providing the optimal remote access method.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The verification unit can monitor the applicant's health data in real time and adjust the priority of requirement verification based on their health status. For example, if the applicant's health is deteriorating, it will prioritize checking for urgent support. If the applicant's health is stable, it can perform a normal requirement verification. The verification unit can also analyze the applicant's health data and suggest preventative support. This allows for requirement verification tailored to the applicant's health status.

[0113] The information delivery department can analyze applicants' past feedback and optimize its information delivery methods. For example, it can prioritize using information delivery methods that have received positive feedback from applicants in the past, and avoid methods that have received negative feedback. Furthermore, the information delivery department can collect applicant feedback in real time and continuously improve its information delivery methods. This allows for optimal information delivery based on applicant feedback.

[0114] The access unit can monitor the applicant's internet connection status in real time and provide the optimal access method. For example, if the applicant is using a slow internet connection, it can provide a lightweight access method. If the applicant is using a high-speed internet connection, it can also provide a detailed access method. Furthermore, the access unit can adjust the access priority based on the applicant's internet connection status. This ensures that the optimal access method is provided according to the internet connection status.

[0115] The verification unit can estimate the applicant's emotions and determine the priority of the requirements to be verified based on those emotions. For example, if the applicant is stressed, important requirements will be prioritized. If the applicant is relaxed, detailed requirements can also be reviewed. If the applicant is in a hurry, requirements that need to be reviewed quickly can be prioritized. This ensures that the priority of requirements is determined in accordance with the applicant's emotions.

[0116] The information provider can estimate the applicant's emotions and adjust the way information is presented based on those estimates. For example, if the applicant is stressed, a concise and easy-to-understand approach is used. If the applicant is relaxed, detailed information can be provided. If the applicant is anxious, a reassuring approach can be used. This ensures that information is presented in a way that is appropriate to the applicant's emotions.

[0117] The access unit can estimate the applicant's emotions and adjust the access method based on those estimates. For example, if the applicant is feeling stressed, it can provide a simple access method. If the applicant is relaxed, it can provide a more detailed access method. Furthermore, if the applicant is feeling anxious, it can provide a reassuring access method. This ensures that the access method is tailored to the applicant's emotions.

[0118] The verification unit can prioritize the verification of highly relevant requirements by considering the applicant's geographical location information. For example, it can prioritize the verification of requirements related to the area where the applicant lives. It can also prioritize the verification of requirements related to the applicant's current location. Furthermore, it can prioritize the most relevant requirements based on the applicant's geographical location information. As a result, highly relevant requirements are prioritized based on geographical location information.

[0119] The information provision department can apply different information provision algorithms depending on the applicant's category. For example, it can apply an information provision algorithm for the elderly. It can also apply an information provision algorithm for people with disabilities. Furthermore, it can apply an information provision algorithm for people requiring long-term medical care. This allows for the provision of information tailored to the applicant's category.

[0120] The Access Department can analyze the applicant's social media activity and identify relevant requirements. For example, it can prioritize identifying requirements of interest based on the applicant's social media activity. It can also identify requirements relevant to the applicant's current situation based on their social media activity. Furthermore, it can analyze the applicant's social media activity to identify the most relevant requirements. This ensures that relevant requirements are identified based on social media activity.

[0121] The information provider can estimate the applicant's emotions and adjust the length of the information provided based on those estimates. For example, if the applicant is in a hurry, short, concise information can be provided. If the applicant is relaxed, detailed information can be provided. Furthermore, if the applicant is feeling anxious, detailed information can be provided to reassure them. This ensures that the information is provided at a length appropriate to the applicant's emotions.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The verification unit confirms the applicant's requirements and recommends appropriate support. Based on the information entered by the applicant, the verification unit's AI automatically analyzes the requirements and proposes the most suitable support. It can also refer to the applicant's past application history and select the best support based on past successes and failures. Furthermore, it can filter the necessary support based on the applicant's current living situation and areas of interest. Step 2: The provisioning department provides information based on the requirements confirmed by the verification department. The provisioning department uses an AI chatbot function to provide information to applicants in real time. It can also estimate the applicant's emotions and provide information in an emotionally appropriate manner. Furthermore, it adjusts the level of detail of the information based on the applicant's importance and provides the necessary information in the appropriate format. Step 3: The Access Unit makes the information available online based on the information provided by the Provider Unit. The Access Unit enables applicants to complete the application process online from their homes. It can also provide the optimal access method considering the applicant's device information. Furthermore, it can estimate the applicant's emotions and provide an access method that responds accordingly.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the verification unit, provision unit, and access unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the verification unit is implemented by the control unit 46A of the smart device 14, which analyzes the applicant's input information and proposes optimal support. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides information in real time using an AI chatbot function. The access unit is implemented by the control unit 46A of the smart device 14, which enables the applicant to complete the application process online from home. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the verification unit, provision unit, and access unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the verification unit is implemented by the control unit 46A of the smart glasses 214, which analyzes the applicant's input information and proposes optimal support. The provision unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which provides information in real time using an AI chatbot function. The access unit is implemented, for example, by the control unit 46A of the smart glasses 214, which enables the applicant to complete the application process online from home. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the verification unit, provision unit, and access unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the verification unit is implemented by the control unit 46A of the headset terminal 314, which analyzes the applicant's input information and proposes optimal support. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides information in real time using an AI chatbot function. The access unit is implemented by the control unit 46A of the headset terminal 314, which enables the applicant to complete the application process online from home. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the verification unit, provision unit, and access unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the verification unit is implemented by the control unit 46A of the robot 414, which analyzes the applicant's input information and proposes optimal support. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides information in real time using an AI chatbot function. The access unit is implemented by, for example, the control unit 46A of the robot 414, which enables the applicant to complete the application process online from home. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) A verification unit that checks the applicant's requirements and recommends appropriate support, A providing unit that provides information based on the requirements confirmed by the aforementioned verification unit, The system includes an access unit that makes information provided by the aforementioned provision unit accessible online. A system characterized by the following features. (Note 2) The aforementioned verification unit is It is equipped with an automation section to streamline the application process. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, It will have an education department to enhance the provision of information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned access unit is It is equipped with a remote unit to overcome the difficulty of physical movement. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned verification unit is We estimate the applicant's emotions and adjust the timing of requirement verification based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned verification unit is Analyze the applicant's past application history and select the most appropriate verification method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned verification unit is During the requirements check, filtering will be performed based on the applicant's current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned verification unit is Estimate the applicant's emotions and determine the priority of the requirements to be verified based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned verification unit is When reviewing requirements, the applicant's geographical location will be taken into consideration to prioritize the review of the most relevant requirements. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned verification unit is During the requirements review process, we will analyze the applicant's social media activity and confirm the relevant requirements. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned supply unit is, We estimate the applicant's emotions and adjust the way information is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned supply unit is, When providing information, adjust the level of detail based on the applicant's importance. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned supply unit is, When providing information, different information provision algorithms are applied depending on the applicant's category. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, The system estimates the applicant's emotions and adjusts the length of the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, When providing information, the priority of information provision will be determined based on the timing of the applicant's submission. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, When providing information, the order in which information is provided will be adjusted based on the applicant's relevance. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned access unit is The system estimates the applicant's emotions and adjusts the access method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned access unit is Upon access, the system will select the most suitable access method by referring to the applicant's past access history. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned access unit is When accessing the site, the system will provide the optimal access method, taking into account the applicant's device information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned access unit is The system estimates the applicant's emotions and determines access priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned access unit is When accessing the site, the system will provide the most suitable access method, taking into account the applicant's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned access unit is When accessing the site, the system analyzes the applicant's social media activity to provide the most suitable access method. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned automation unit, The system estimates the applicant's emotions and adjusts the timing of automation based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 24) The aforementioned automation unit, During automation, the system analyzes the applicant's past application history to select the most suitable automation method. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned automation unit, During the automated process, filtering is performed based on the applicant's current living situation and areas of interest. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned automation unit, The system estimates the applicant's emotions and determines automation priorities based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned automation unit, When automating the process, the system provides the optimal automation method, taking into account the applicant's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned Ministry of Education, The system estimates the applicant's emotions and adjusts the educational content based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 29) The aforementioned Ministry of Education, During the educational process, the most suitable teaching method will be selected by referring to the applicant's past learning history. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned Ministry of Education, During the educational process, applicants will be filtered based on their current living situation and areas of interest. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned Ministry of Education, The system estimates the applicant's emotions and determines educational priorities based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned Ministry of Education, During education, we provide the most suitable teaching method, taking into account the applicant's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned Ministry of Education, During the educational process, we analyze the applicant's social media activity to provide the most suitable teaching methods. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned remote unit is The system estimates the applicant's emotions and adjusts the remote access method based on the estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 35) The aforementioned remote unit is When remote access is requested, the system will select the most suitable remote access method by referring to the applicant's past remote access history. The system described in Appendix 4, characterized by the features described herein. (Note 36) The aforementioned remote unit is When providing remote access, the system will provide the optimal remote access method, taking into account the applicant's device information. The system described in Appendix 4, characterized by the features described herein. (Note 37) The aforementioned remote unit is The system estimates the applicant's emotions and prioritizes remote access based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned remote unit is When providing remote access, the system will provide the optimal remote access method, taking into account the applicant's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned remote unit is During remote access, the system analyzes the applicant's social media activity to provide the optimal remote access method. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0196] 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. A verification unit that checks the applicant's requirements and recommends appropriate support, A providing unit that provides information based on the requirements confirmed by the aforementioned verification unit, The system includes an access unit that makes information provided by the aforementioned provision unit accessible online. A system characterized by the following features.

2. The aforementioned verification unit is It is equipped with an automation section to streamline the application process. The system according to feature 1.

3. The aforementioned supply unit is, It will have an education department to enhance the provision of information. The system according to feature 1.

4. The aforementioned access unit is It is equipped with a remote unit to overcome the difficulty of physical movement. The system according to feature 1.

5. The aforementioned verification unit is We estimate the applicant's emotions and adjust the timing of requirement verification based on those estimated emotions. The system according to feature 1.

6. The aforementioned verification unit is Analyze the applicant's past application history and select the most appropriate verification method. The system according to feature 1.

7. The aforementioned verification unit is During the requirements check, filtering will be performed based on the applicant's current living situation and areas of interest. The system according to feature 1.

8. The aforementioned verification unit is Estimate the applicant's emotions and determine the priority of the requirements to be verified based on those estimated emotions. The system according to feature 1.

9. The aforementioned verification unit is When reviewing requirements, the applicant's geographical location will be taken into consideration to prioritize the review of the most relevant requirements. The system according to feature 1.

10. The aforementioned verification unit is During the requirements review process, we will analyze the applicant's social media activity and confirm the relevant requirements. The system according to feature 1.

Images