system

Abstract

The system according to this embodiment aims to allow users to easily find advantageous methods for pensions, taxes, and other related matters and proceed with the necessary procedures. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, and a procedure unit. The reception unit inputs information on the user's income and expenses. The analysis unit analyzes the information input by the reception unit and proposes advantageous methods for pensions, taxes, etc. The procedure unit proceeds with the procedures based on the methods proposed by the analysis unit.

Description

Technical Field

[0004] ,

[0006] , , , ,

[0005] , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003] ​​​​​​​​​​​​​​​​​​​​​​​​​​​[Effects of the Invention]

[0007] The system according to this embodiment allows users to easily find advantageous methods for pensions, taxes, and other benefits, and to proceed with the necessary procedures. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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) An AI agent system according to an embodiment of the present invention is a system that allows users to easily find advantageous ways to manage their pensions, taxes, utilities, food expenses, etc., based on their income and expenses, and to proceed with the necessary procedures. The AI ​​agent system receives input from the user regarding their current income and expenses, and the AI ​​agent analyzes the input information to suggest advantageous ways to manage pensions, taxes, etc. Furthermore, the AI ​​agent system presents the user with the most suitable method, taking into account what many people are doing. Finally, the AI ​​agent system can proceed with the actual procedures. For example, the user inputs information such as their current income and expenses, such as monthly income, rent, utilities, and food expenses. This information is input into the AI ​​agent and becomes the basis for analysis. Next, the AI ​​agent system analyzes the input information. Based on the income and expense data, the AI ​​agent suggests advantageous ways to manage pensions, taxes, utilities, food expenses, etc. For example, it suggests specific tax benefits, pension payment methods, and ways to save on utilities. Furthermore, the AI ​​agent system presents the user with the most suitable method, taking into account what many people are doing. For example, it analyzes what methods people with similar incomes and expenses are using and suggests the best method for the user. Finally, the AI ​​agent system can handle the actual procedures. For example, the AI ​​agent can handle procedures such as applying for tax incentives or receiving pension benefits. This allows users to implement cost-effective methods without hassle. In this way, the AI ​​agent system helps users increase the amount of money they have left over and live a more economical life. For example, they can save on taxes by taking advantage of tax incentives or reduce expenses by implementing methods to save on utility costs. Furthermore, because the AI ​​agent handles the procedures, users can implement cost-effective methods without hassle. In this way, the AI ​​agent system can suggest cost-effective methods for pensions, taxes, etc., based on the user's income and expense information, and can handle the procedures.

[0029] The AI ​​agent system according to this embodiment comprises a reception unit, an analysis unit, and a procedure unit. The reception unit inputs information on the user's income and expenses. This information includes, but is not limited to, salary, rent, utilities, and food expenses. The reception unit receives information from the user, such as monthly income, rent, utilities, and food expenses. The reception unit transmits the information entered by the user to the analysis unit. The analysis unit analyzes the information entered by the reception unit and proposes advantageous methods for pensions, taxes, etc. The analysis unit proposes, for example, specific tax incentives, methods for receiving pensions, and methods for saving on utilities, based on income and expense data. The analysis unit can propose, for example, specific tax incentives. The analysis unit can propose, for example, methods for receiving pensions. The analysis unit can propose, for example, methods for saving on utilities. The analysis department further proposes the best method for the user, taking into account what many people are doing. For example, the analysis department analyzes what methods people with similar income and expenditures are using and proposes the best method for the user. For example, the analysis department can perform analysis based on statistical data. For example, the analysis department can perform analysis based on statistical data. For example, the analysis department can perform analysis based on survey results. For example, the analysis department can perform analysis based on survey results. For example, the analysis department can perform analysis based on past cases. For example, the analysis department can perform analysis based on past cases. The procedures department proceeds with procedures based on the methods proposed by the analysis department. For example, the procedures department can perform application procedures for tax incentives. For example, the procedures department can perform application procedures for tax incentives. For example, the procedures department can perform application procedures for pension benefits. For example, the procedures department can perform application procedures for pension benefits. The procedures department proceeds with procedures to enable users to implement advantageous methods without hassle. For example, the procedures department can proceed with procedures to enable users to implement advantageous methods without hassle.As a result, the AI ​​agent system according to this embodiment can suggest advantageous methods for pensions, taxes, and other matters based on the user's income and expenditure information, and even proceed with the necessary procedures.

[0030] The reception desk inputs the user's income and expense information. This information includes, but is not limited to, salary, rent, utilities, and food expenses. For example, the reception desk takes information such as the user's monthly income, rent, utilities, and food expenses. The reception desk then transmits the information entered by the user to the analysis department. The reception desk provides an intuitive interface to make it easy for users to input information. For example, users can use their smartphones or computers to access a dedicated application or website and input their income and expense information. The input forms display guide messages and input examples to ensure that users enter all the necessary information. The reception desk also automatically saves the information entered by the user, allowing for later modification or addition. Furthermore, the reception desk encrypts the information entered by the user and securely transmits it to the analysis department. This allows for the collection of accurate information while protecting the user's privacy.

[0031] The analysis department analyzes the information entered by the reception department and proposes advantageous methods for pensions, taxes, etc. For example, based on income and expenditure data, the analysis department proposes specific tax benefits, pension payment methods, and ways to save on utility costs. For example, the analysis department can propose specific tax benefits. For example, the analysis department can propose specific tax benefits. For example, the analysis department can propose pension payment methods. For example, the analysis department can propose pension payment methods. For example, the analysis department can propose ways to save on utility costs. For example, the analysis department can propose ways to save on utility costs. The analysis department can further propose the most suitable method for the user by referring to what many people are doing. For example, the analysis department analyzes what methods people with similar income and expenditure are using and proposes the most suitable method for the user. For example, the analysis department can perform analysis based on statistical data. For example, the analysis department can perform analysis based on statistical data. For example, the analysis department can perform analysis based on survey results. For example, the analysis department can perform analysis based on survey results. The analysis department performs analysis based on past cases, for example. The analysis department uses AI to analyze user income and expenditure data. The AI ​​uses machine learning algorithms to analyze user data and generate optimal suggestions. For example, the AI ​​learns user income and expenditure patterns and suggests specific tax benefits, pension payment methods, and ways to save on utility bills. The AI ​​suggests the best methods for users based on past data and statistical information. For example, the AI ​​analyzes what methods people with similar income and expenditure are using and suggests the best methods for users. The AI ​​performs analysis based on statistical data, survey results, and past cases. For example, the AI ​​can perform analysis based on statistical data. The AI ​​can perform analysis based on survey results. The AI ​​can perform analysis based on past cases. This allows the analysis department to suggest advantageous methods for pensions, taxation, etc., based on user income and expenditure data.

[0032] The Procedures Department proceeds with procedures based on the methods proposed by the Analysis Department. For example, the Procedures Department can handle the application process for tax incentives. For example, the Procedures Department can handle the application process for tax incentives. For example, the Procedures Department can handle the application process for receiving pension benefits. For example, the Procedures Department can handle the application process for receiving pension benefits. The Procedures Department proceeds with procedures to enable users to obtain advantageous methods without hassle. For example, the Procedures Department can handle procedures to enable users to obtain advantageous methods without hassle. The Procedures Department proceeds with procedures to enable users to obtain advantageous methods without hassle. To enable users to obtain advantageous methods without hassle, the Procedures Department automatically generates necessary documents based on user information and supports online application procedures. For example, in the application process for tax incentives, it identifies applicable tax incentives based on the user's income and expense information and automatically generates the necessary application documents. The Procedures Department supports the use of electronic signatures and digital certificates so that users can submit application documents online. The Procedures Department also notifies users of the progress of the application process in real time and prompts them to submit any additional information or documents as needed. In the pension application process, the system proposes the most suitable application method based on the user's age and income, and automatically generates the necessary application documents. The application department provides a guide for users to enter the necessary information so that they can apply for pensions online, and notifies them of the application process's progress in real time. This allows the application department to efficiently manage the process so that users can find the most advantageous method without hassle. Furthermore, the application department can collect user feedback and continuously improve the accuracy and efficiency of the process. For example, it collects inconveniences and problems experienced by users during the process and incorporates them into system improvements. The application department also provides a dashboard where users can check the progress of their application and centrally manage necessary information and documents. This allows the application department to smoothly manage the process so that users can find the most advantageous method without hassle.

[0033] The reception desk can input information such as the user's monthly income, rent, utilities, and food expenses. The reception desk can input information such as the user's monthly income, rent, utilities, and food expenses. The reception desk can input information such as the user's breakdown of monthly income. The reception desk can input information such as the user's rent payment method. The reception desk can input information such as the user's type of utilities. The reception desk can input information such as the user's details of food expenses. The reception desk can input information such as the user's details of food expenses. In this way, the reception desk can obtain detailed income and expenditure information by inputting information such as the user's monthly income, rent, utilities, and food expenses. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the information entered by the user into a generating AI, which can analyze the information.

[0034] The analysis unit can propose specific tax benefits, pension eligibility methods, and utility cost-saving methods based on income and expenditure data. For example, the analysis unit can propose specific tax benefits based on income and expenditure data. For example, the analysis unit can propose specific tax benefits based on income and expenditure data. For example, the analysis unit can propose pension eligibility methods based on income and expenditure data. For example, the analysis unit can propose pension eligibility methods. For example, the analysis unit can propose utility cost-saving methods based on income and expenditure data. For example, the analysis unit can propose utility cost-saving methods. In this way, the analysis unit can provide the user with the most suitable method by proposing specific tax benefits, pension eligibility methods, and utility cost-saving methods based on income and expenditure data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit inputs income and expenditure data into a generating AI, which can then suggest specific tax incentives, pension payment methods, and ways to save on utility bills.

[0035] The analysis unit can suggest the best method for the user by referring to what many people are doing. For example, the analysis unit can analyze what methods people with similar income and expenditures are using and suggest the best method for the user. The analysis unit can perform analysis based on statistical data, for example. The analysis unit can perform analysis based on survey results, for example. The analysis unit can perform analysis based on survey results, for example. The analysis unit can perform analysis based on past cases, for example. The analysis unit can perform analysis based on past cases. In this way, the analysis unit can suggest the best method for the user by referring to what many people are doing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input statistical data, survey results, and past cases into a generating AI, and the generating AI can suggest the best method for the user.

[0036] The procedural unit can perform procedures such as applying for tax incentives and receiving pension benefits on behalf of users. For example, the procedural unit can perform application procedures for tax incentives. For example, the procedural unit can perform application procedures for tax incentives. For example, the procedural unit can perform procedures such as receiving pension benefits. For example, the procedural unit can perform procedures such as applying for tax incentives and receiving pension benefits on behalf of users, thereby saving users time and effort. Some or all of the above-mentioned processes in the procedural unit may be performed using AI, for example, or not using AI. For example, the procedural unit can input application procedures for tax incentives and pension benefit procedures into a generating AI, and the generating AI can perform the procedures on behalf of users.

[0037] The procedure unit can carry out procedures to enable users to achieve advantageous methods without requiring them to expend effort. The procedure unit can, for example, carry out procedures to enable users to achieve advantageous methods without requiring them to expend effort. The procedure unit can, for example, carry out procedures to enable users to achieve advantageous methods without requiring them to expend effort. In this way, the procedure unit can reduce the burden on users by carrying out procedures to enable users to achieve advantageous methods without requiring them to expend effort. Some or all of the above-mentioned processing in the procedure unit may be carried out using AI, for example, or not using AI. For example, the procedure unit can input procedures for enabling users to achieve advantageous methods without expending effort into a generating AI, and the generating AI can carry out the procedures.

[0038] The reception desk can analyze the user's past income and expenditure history and select the optimal input method. For example, the reception desk can automatically display as suggestions income and expenditure items that the user has frequently entered in the past. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest income and expenditure items to be used during specific time periods based on the user's past input history. In this way, the reception desk can select the optimal input method by analyzing the user's past income and expenditure history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past income and expenditure history into a generating AI, which can then select the optimal input method.

[0039] The reception unit can filter the input of income and expenditure information based on the user's current living situation and areas of interest. For example, the reception unit can prioritize displaying relevant income and expenditure items according to the user's current living situation. For example, the reception unit can suggest relevant income and expenditure items based on the user's areas of interest. For example, the reception unit can customize input fields based on the user's current living situation and areas of interest. This allows the reception unit to prioritize input of highly relevant information by filtering based on the user's current living situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input data on the user's current living situation and areas of interest into a generating AI, which can then perform the filtering.

[0040] The reception unit can prioritize inputting highly relevant information when users enter income and expense information, taking into account the user's geographical location. For example, the reception unit can prioritize displaying relevant income and expense items based on the user's current location. For example, the reception unit can suggest region-specific income and expense items based on the user's geographical location. For example, the reception unit can suggest the optimal input method, taking into account the user's geographical location. In this way, the reception unit can prioritize inputting highly relevant information by taking into account the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI, which can then prioritize inputting highly relevant information.

[0041] The reception desk can analyze the user's social media activity and input relevant information when inputting income and expenditure information. For example, the reception desk can suggest relevant income and expenditure items based on the user's social media activity. For example, the reception desk can analyze the user's social media activity and suggest the optimal input method. For example, the reception desk can customize input fields based on the user's social media activity. This allows the reception desk to input relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input data on the user's social media activity into a generating AI, and the generating AI can input relevant information.

[0042] The analysis unit can adjust the level of detail of its suggestions based on the importance of income and expenses during analysis. For example, the analysis unit provides detailed suggestions for important income and expense items. For example, it provides concise suggestions for less important income and expense items. The analysis unit customizes the level of detail of its suggestions according to the importance of income and expenses. This allows the analysis unit to provide detailed suggestions for important information by adjusting the level of detail of its suggestions based on the importance of income and expenses. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the importance of income and expenses into a generating AI, which can then adjust the level of detail of its suggestions.

[0043] The analysis unit can apply different analysis algorithms depending on the income and expenditure categories during analysis. For example, the analysis unit can make suggestions for increasing income for the income category. For example, the analysis unit can make suggestions for reducing expenditures for the expenditure category. The analysis unit can apply the most suitable analysis algorithm depending on the income and expenditure categories. In this way, the analysis unit can make optimal suggestions by applying different analysis algorithms depending on the income and expenditure categories. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input income and expenditure category data into a generating AI, and the generating AI can apply the most suitable analysis algorithm.

[0044] The analysis unit can determine the priority of proposals based on the submission timing of income and expenses during the analysis. For example, the analysis unit will prioritize proposals for income and expense items with approaching submission deadlines. For example, the analysis unit will postpone proposals for income and expense items with ample time for submission. The analysis unit can customize the priority of proposals based on the submission timing of income and expenses. This allows the analysis unit to prioritize proposals based on the submission timing of income and expenses, ensuring that proposals are submitted on time. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input data on the submission timing of income and expenses into a generating AI, which can then determine the priority of proposals.

[0045] The analysis unit can adjust the order of suggestions based on the relevance of income and expenses during analysis. For example, the analysis unit will prioritize suggesting highly relevant income and expense items. For example, it will postpone suggesting less relevant income and expense items. The analysis unit will customize the order of suggestions based on the relevance of income and expenses. This allows the analysis unit to prioritize suggesting highly relevant information by adjusting the order of suggestions based on the relevance of income and expenses. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the relevance of income and expenses into a generating AI, which can then adjust the order of suggestions.

[0046] The procedure unit can analyze the user's past procedure history and select the optimal procedure method during a procedure. For example, the procedure unit can propose the optimal procedure method based on the procedures the user has performed in the past. For example, the procedure unit can propose a simplification of the procedure based on the user's past procedure history. For example, the procedure unit can analyze the user's past procedure history and propose the most efficient procedure method. In this way, the procedure unit can select the optimal procedure method by analyzing the user's past procedure history. Some or all of the above processing in the procedure unit may be performed using AI, for example, or without AI. For example, the procedure unit can input data of the user's past procedure history into a generating AI, and the generating AI can select the optimal procedure method.

[0047] The procedure unit can customize the means of the procedure based on the user's current living situation during the procedure. For example, the procedure unit proposes the optimal procedure method according to the user's current living situation. For example, the procedure unit customizes the means of the procedure based on the user's living situation. For example, the procedure unit proposes the means of the procedure considering the user's current living situation. In this way, the procedure unit can provide the user with the optimal procedure method by customizing the means of the procedure based on the user's current living situation. Some or all of the above processing in the procedure unit may be performed using AI, for example, or without AI. For example, the procedure unit can input data on the user's current living situation into a generating AI, and the generating AI can customize the means of the procedure.

[0048] The procedure unit can select the optimal procedure method while considering the user's geographical location information. For example, the procedure unit may propose the optimal procedure method based on the user's current location. For example, the procedure unit may propose a region-specific procedure method based on the user's geographical location information. For example, the procedure unit may customize the means of the procedure while considering the user's geographical location information. In this way, the procedure unit can provide a region-specific optimal procedure method by considering the user's geographical location information. Some or all of the above processing in the procedure unit may be performed using AI, for example, or without AI. For example, the procedure unit may input the user's geographical location data into a generating AI, which can then select the optimal procedure method.

[0049] The procedure unit can analyze the user's social media activity during a procedure and propose a means of procedure. For example, the procedure unit proposes the optimal procedure method based on the user's social media activity. For example, the procedure unit analyzes the user's social media activity and customizes the means of procedure. For example, the procedure unit proposes a means of procedure based on the user's social media activity. In this way, the procedure unit can propose the optimal means of procedure by analyzing the user's social media activity. Some or all of the above processing in the procedure unit may be performed using AI, for example, or without AI. For example, the procedure unit can input data on the user's social media activity into a generating AI, and the generating AI can propose the optimal means of procedure.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The reception desk can reduce the effort required for users to input income and expense information by referring to their past input history. For example, the reception desk can automatically display income and expense items that the user has entered in the past as suggestions. Furthermore, the reception desk can prioritize suggesting input methods that the user has used in the past (such as voice input or text input). In addition, the reception desk can predict and suggest income and expense items that will be used during specific time periods based on the user's past input history. In this way, the reception desk can reduce the effort required for users to input information by utilizing their past input history.

[0052] The analytics department can provide personalized suggestions based on income and expenditure data. For example, if a user is health-conscious, the analytics department can prioritize suggesting spending on health foods and fitness-related items. If a user enjoys traveling, the analytics department can suggest travel-related saving methods and deals. Furthermore, if a user spends a significant amount on hobbies, the analytics department can suggest saving methods and deals related to those hobbies. In this way, the analytics department can provide personalized suggestions tailored to the user's lifestyle.

[0053] The procedures department can track the progress of users' procedures in real time and send reminders as needed. For example, if a user is applying for tax benefits, the department can send a reminder as the application deadline approaches. Similarly, if a user is applying for pension benefits, the department can remind them of the deadline for submitting necessary documents. Furthermore, if a user is implementing energy-saving measures, the department can periodically check their progress and provide advice as needed. This allows the procedures department to track the progress of users' procedures in real time and send necessary reminders.

[0054] The reception desk can optimize the input of income and expense information by taking into account the user's geographical location. For example, the reception desk can prioritize displaying relevant income and expense items based on the user's current location. Furthermore, the reception desk can suggest region-specific income and expense items based on the user's geographical location. In addition, the reception desk can suggest the optimal input method, taking into account the user's geographical location. As a result, the reception desk can prioritize the input of highly relevant information by considering the user's geographical location.

[0055] The analytics department can predict a user's future income and expenses based on income and expense data and provide long-term recommendations. For example, the analytics department can analyze a user's current income and expense trends and predict the possibility of future income increases or expense decreases. Furthermore, the analytics department can predict future fluctuations in income and expenses by considering the user's life events (marriage, childbirth, job change, etc.). In addition, the analytics department can propose long-term financial plans based on the user's future goals (purchasing a home, saving for education, etc.). In this way, the analytics department can predict a user's future income and expenses and provide long-term recommendations.

[0056] The procedures department can analyze users' social media activity and share the progress of their procedures. For example, if a user wishes to share the progress of their procedures on social media, the procedures department can automatically post the progress. Furthermore, the procedures department can analyze users' social media activity and provide advice and support regarding the procedures. In addition, the procedures department can customize the progress of procedures based on the user's social media activity. This allows the procedures department to support users' procedures by analyzing their social media activity and sharing the progress of their procedures.

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

[0058] Step 1: The reception department inputs the user's income and expense information. This information includes, for example, salary, rent, utilities, and food expenses. The reception department then sends the information entered by the user to the analysis department. Step 2: The analysis department analyzes the information entered by the reception department and proposes advantageous methods for pensions, taxes, etc. Based on income and expenditure data, the analysis department proposes specific tax benefits, pension receiving methods, and ways to save on utility costs. Furthermore, the analysis department performs analysis based on statistical data, survey results, and past cases to propose the most suitable method for the user. Step 3: The Procedures Department proceeds with the procedures based on the methods proposed by the Analysis Department. For example, the Procedures Department handles procedures such as applying for tax incentives or receiving pension benefits. This allows users to proceed with procedures that enable them to obtain advantageous methods without any hassle.

[0059] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that allows users to easily find advantageous ways to manage their pensions, taxes, utilities, food expenses, etc., based on their income and expenses, and to proceed with the necessary procedures. The AI ​​agent system receives input from the user regarding their current income and expenses, and the AI ​​agent analyzes the input information to suggest advantageous ways to manage pensions, taxes, etc. Furthermore, the AI ​​agent system presents the user with the most suitable method, taking into account what many people are doing. Finally, the AI ​​agent system can proceed with the actual procedures. For example, the user inputs information such as their current income and expenses, such as monthly income, rent, utilities, and food expenses. This information is input into the AI ​​agent and becomes the basis for analysis. Next, the AI ​​agent system analyzes the input information. Based on the income and expense data, the AI ​​agent suggests advantageous ways to manage pensions, taxes, utilities, food expenses, etc. For example, it suggests specific tax benefits, pension payment methods, and ways to save on utilities. Furthermore, the AI ​​agent system presents the user with the most suitable method, taking into account what many people are doing. For example, it analyzes what methods people with similar incomes and expenses are using and suggests the best method for the user. Finally, the AI ​​agent system can handle the actual procedures. For example, the AI ​​agent can handle procedures such as applying for tax incentives or receiving pension benefits. This allows users to implement cost-effective methods without hassle. In this way, the AI ​​agent system helps users increase the amount of money they have left over and live a more economical life. For example, they can save on taxes by taking advantage of tax incentives or reduce expenses by implementing methods to save on utility costs. Furthermore, because the AI ​​agent handles the procedures, users can implement cost-effective methods without hassle. In this way, the AI ​​agent system can suggest cost-effective methods for pensions, taxes, etc., based on the user's income and expense information, and can handle the procedures.

[0060] The AI ​​agent system according to this embodiment comprises a reception unit, an analysis unit, and a procedure unit. The reception unit inputs information on the user's income and expenses. This information includes, but is not limited to, salary, rent, utilities, and food expenses. The reception unit receives information from the user, such as monthly income, rent, utilities, and food expenses. The reception unit transmits the information entered by the user to the analysis unit. The analysis unit analyzes the information entered by the reception unit and proposes advantageous methods for pensions, taxes, etc. The analysis unit proposes, for example, specific tax incentives, methods for receiving pensions, and methods for saving on utilities, based on income and expense data. The analysis unit can propose, for example, specific tax incentives. The analysis unit can propose, for example, methods for receiving pensions. The analysis unit can propose, for example, methods for saving on utilities. The analysis department further proposes the best method for the user, taking into account what many people are doing. For example, the analysis department analyzes what methods people with similar income and expenditures are using and proposes the best method for the user. For example, the analysis department can perform analysis based on statistical data. For example, the analysis department can perform analysis based on statistical data. For example, the analysis department can perform analysis based on survey results. For example, the analysis department can perform analysis based on survey results. For example, the analysis department can perform analysis based on past cases. For example, the analysis department can perform analysis based on past cases. The procedures department proceeds with procedures based on the methods proposed by the analysis department. For example, the procedures department can perform application procedures for tax incentives. For example, the procedures department can perform application procedures for tax incentives. For example, the procedures department can perform application procedures for pension benefits. For example, the procedures department can perform application procedures for pension benefits. The procedures department proceeds with procedures to enable users to implement advantageous methods without hassle. For example, the procedures department can proceed with procedures to enable users to implement advantageous methods without hassle.As a result, the AI ​​agent system according to this embodiment can suggest advantageous methods for pensions, taxes, and other matters based on the user's income and expenditure information, and even proceed with the necessary procedures.

[0061] The reception desk inputs the user's income and expense information. This information includes, but is not limited to, salary, rent, utilities, and food expenses. For example, the reception desk takes information such as the user's monthly income, rent, utilities, and food expenses. The reception desk then transmits the information entered by the user to the analysis department. The reception desk provides an intuitive interface to make it easy for users to input information. For example, users can use their smartphones or computers to access a dedicated application or website and input their income and expense information. The input forms display guide messages and input examples to ensure that users enter all the necessary information. The reception desk also automatically saves the information entered by the user, allowing for later modification or addition. Furthermore, the reception desk encrypts the information entered by the user and securely transmits it to the analysis department. This allows for the collection of accurate information while protecting the user's privacy.

[0062] The analysis department analyzes the information entered by the reception department and proposes advantageous methods for pensions, taxes, etc. For example, based on income and expenditure data, the analysis department proposes specific tax benefits, pension payment methods, and ways to save on utility costs. For example, the analysis department can propose specific tax benefits. For example, the analysis department can propose specific tax benefits. For example, the analysis department can propose pension payment methods. For example, the analysis department can propose pension payment methods. For example, the analysis department can propose ways to save on utility costs. For example, the analysis department can propose ways to save on utility costs. The analysis department can further propose the most suitable method for the user by referring to what many people are doing. For example, the analysis department analyzes what methods people with similar income and expenditure are using and proposes the most suitable method for the user. For example, the analysis department can perform analysis based on statistical data. For example, the analysis department can perform analysis based on statistical data. For example, the analysis department can perform analysis based on survey results. For example, the analysis department can perform analysis based on survey results. The analysis department performs analysis based on past cases, for example. The analysis department uses AI to analyze user income and expenditure data. The AI ​​uses machine learning algorithms to analyze user data and generate optimal suggestions. For example, the AI ​​learns user income and expenditure patterns and suggests specific tax benefits, pension payment methods, and ways to save on utility bills. The AI ​​suggests the best methods for users based on past data and statistical information. For example, the AI ​​analyzes what methods people with similar income and expenditure are using and suggests the best methods for users. The AI ​​performs analysis based on statistical data, survey results, and past cases. For example, the AI ​​can perform analysis based on statistical data. The AI ​​can perform analysis based on survey results. The AI ​​can perform analysis based on past cases. This allows the analysis department to suggest advantageous methods for pensions, taxation, etc., based on user income and expenditure data.

[0063] The Procedures Department proceeds with procedures based on the methods proposed by the Analysis Department. For example, the Procedures Department can handle the application process for tax incentives. For example, the Procedures Department can handle the application process for tax incentives. For example, the Procedures Department can handle the application process for receiving pension benefits. For example, the Procedures Department can handle the application process for receiving pension benefits. The Procedures Department proceeds with procedures to enable users to obtain advantageous methods without hassle. For example, the Procedures Department can handle procedures to enable users to obtain advantageous methods without hassle. The Procedures Department proceeds with procedures to enable users to obtain advantageous methods without hassle. To enable users to obtain advantageous methods without hassle, the Procedures Department automatically generates necessary documents based on user information and supports online application procedures. For example, in the application process for tax incentives, it identifies applicable tax incentives based on the user's income and expense information and automatically generates the necessary application documents. The Procedures Department supports the use of electronic signatures and digital certificates so that users can submit application documents online. The Procedures Department also notifies users of the progress of the application process in real time and prompts them to submit any additional information or documents as needed. In the pension application process, the system proposes the most suitable application method based on the user's age and income, and automatically generates the necessary application documents. The application department provides a guide for users to enter the necessary information so that they can apply for pensions online, and notifies them of the application process's progress in real time. This allows the application department to efficiently manage the process so that users can find the most advantageous method without hassle. Furthermore, the application department can collect user feedback and continuously improve the accuracy and efficiency of the process. For example, it collects inconveniences and problems experienced by users during the process and incorporates them into system improvements. The application department also provides a dashboard where users can check the progress of their application and centrally manage necessary information and documents. This allows the application department to smoothly manage the process so that users can find the most advantageous method without hassle.

[0064] The reception desk can input information such as the user's monthly income, rent, utilities, and food expenses. The reception desk can input information such as the user's monthly income, rent, utilities, and food expenses. The reception desk can input information such as the user's breakdown of monthly income. The reception desk can input information such as the user's rent payment method. The reception desk can input information such as the user's type of utilities. The reception desk can input information such as the user's details of food expenses. The reception desk can input information such as the user's details of food expenses. In this way, the reception desk can obtain detailed income and expenditure information by inputting information such as the user's monthly income, rent, utilities, and food expenses. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the information entered by the user into a generating AI, which can analyze the information.

[0065] The analysis unit can propose specific tax benefits, pension eligibility methods, and utility cost-saving methods based on income and expenditure data. For example, the analysis unit can propose specific tax benefits based on income and expenditure data. For example, the analysis unit can propose specific tax benefits based on income and expenditure data. For example, the analysis unit can propose pension eligibility methods based on income and expenditure data. For example, the analysis unit can propose pension eligibility methods. For example, the analysis unit can propose utility cost-saving methods based on income and expenditure data. For example, the analysis unit can propose utility cost-saving methods. In this way, the analysis unit can provide the user with the most suitable method by proposing specific tax benefits, pension eligibility methods, and utility cost-saving methods based on income and expenditure data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit inputs income and expenditure data into a generating AI, which can then suggest specific tax incentives, pension payment methods, and ways to save on utility bills.

[0066] The analysis unit can suggest the best method for the user by referring to what many people are doing. For example, the analysis unit can analyze what methods people with similar income and expenditures are using and suggest the best method for the user. The analysis unit can perform analysis based on statistical data, for example. The analysis unit can perform analysis based on survey results, for example. The analysis unit can perform analysis based on survey results, for example. The analysis unit can perform analysis based on past cases, for example. The analysis unit can perform analysis based on past cases. In this way, the analysis unit can suggest the best method for the user by referring to what many people are doing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input statistical data, survey results, and past cases into a generating AI, and the generating AI can suggest the best method for the user.

[0067] The procedural unit can perform procedures such as applying for tax incentives and receiving pension benefits on behalf of users. For example, the procedural unit can perform application procedures for tax incentives. For example, the procedural unit can perform application procedures for tax incentives. For example, the procedural unit can perform procedures such as receiving pension benefits. For example, the procedural unit can perform procedures such as applying for tax incentives and receiving pension benefits on behalf of users, thereby saving users time and effort. Some or all of the above-mentioned processes in the procedural unit may be performed using AI, for example, or not using AI. For example, the procedural unit can input application procedures for tax incentives and pension benefit procedures into a generating AI, and the generating AI can perform the procedures on behalf of users.

[0068] The procedure unit can carry out procedures to enable users to achieve advantageous methods without requiring them to expend effort. The procedure unit can, for example, carry out procedures to enable users to achieve advantageous methods without requiring them to expend effort. The procedure unit can, for example, carry out procedures to enable users to achieve advantageous methods without requiring them to expend effort. In this way, the procedure unit can reduce the burden on users by carrying out procedures to enable users to achieve advantageous methods without requiring them to expend effort. Some or all of the above-mentioned processing in the procedure unit may be carried out using AI, for example, or not using AI. For example, the procedure unit can input procedures for enabling users to achieve advantageous methods without expending effort into a generating AI, and the generating AI can carry out the procedures.

[0069] The reception desk can estimate the user's emotions and adjust the timing of income and expenditure information input based on the estimated emotions. For example, if the user is stressed, the reception desk will simplify the input and request only minimal information. For example, if the user is relaxed, the reception desk will provide detailed input options and suggest a customizable input method. For example, if the user is in a hurry, the reception desk will prioritize voice input to allow for quick input of income and expenditure information. In this way, the reception desk can reduce the user's burden by adjusting the input timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's emotion data into a generative AI, which can estimate the emotion and adjust the input timing.

[0070] The reception desk can analyze the user's past income and expenditure history and select the optimal input method. For example, the reception desk can automatically display as suggestions income and expenditure items that the user has frequently entered in the past. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest income and expenditure items to be used during specific time periods based on the user's past input history. In this way, the reception desk can select the optimal input method by analyzing the user's past income and expenditure history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past income and expenditure history into a generating AI, which can then select the optimal input method.

[0071] The reception unit can filter the input of income and expenditure information based on the user's current living situation and areas of interest. For example, the reception unit can prioritize displaying relevant income and expenditure items according to the user's current living situation. For example, the reception unit can suggest relevant income and expenditure items based on the user's areas of interest. For example, the reception unit can customize input fields based on the user's current living situation and areas of interest. This allows the reception unit to prioritize input of highly relevant information by filtering based on the user's current living situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input data on the user's current living situation and areas of interest into a generating AI, which can then perform the filtering.

[0072] The reception desk can estimate the user's emotions and determine the priority of the information to be entered based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize inputting only important information. If the user is relaxed, the reception desk will prioritize inputting detailed information. If the user is in a hurry, the reception desk will prioritize inputting only the most important information. In this way, the reception desk can prioritize inputting important information by determining the priority of the information to be entered based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of the information to be entered.

[0073] The reception unit can prioritize inputting highly relevant information when users enter income and expense information, taking into account the user's geographical location. For example, the reception unit can prioritize displaying relevant income and expense items based on the user's current location. For example, the reception unit can suggest region-specific income and expense items based on the user's geographical location. For example, the reception unit can suggest the optimal input method, taking into account the user's geographical location. In this way, the reception unit can prioritize inputting highly relevant information by taking into account the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI, which can then prioritize inputting highly relevant information.

[0074] The reception desk can analyze the user's social media activity and input relevant information when inputting income and expenditure information. For example, the reception desk can suggest relevant income and expenditure items based on the user's social media activity. For example, the reception desk can analyze the user's social media activity and suggest the optimal input method. For example, the reception desk can customize input fields based on the user's social media activity. This allows the reception desk to input relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input data on the user's social media activity into a generating AI, and the generating AI can input relevant information.

[0075] The analysis unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated emotions. For example, if the user is relaxed, the analysis unit will provide suggestions that include detailed explanations. If the user is in a hurry, the analysis unit will provide concise and to-the-point suggestions. If the user is stressed, the analysis unit will provide simple and easy-to-understand suggestions. In this way, the analysis unit can provide suggestions that are easy for the user to understand by adjusting the way suggestions are presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the way suggestions are presented.

[0076] The analysis unit can adjust the level of detail of its suggestions based on the importance of income and expenses during analysis. For example, the analysis unit provides detailed suggestions for important income and expense items. For example, it provides concise suggestions for less important income and expense items. The analysis unit customizes the level of detail of its suggestions according to the importance of income and expenses. This allows the analysis unit to provide detailed suggestions for important information by adjusting the level of detail of its suggestions based on the importance of income and expenses. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the importance of income and expenses into a generating AI, which can then adjust the level of detail of its suggestions.

[0077] The analysis unit can apply different analysis algorithms depending on the income and expenditure categories during analysis. For example, the analysis unit can make suggestions for increasing income for the income category. For example, the analysis unit can make suggestions for reducing expenditures for the expenditure category. The analysis unit can apply the most suitable analysis algorithm depending on the income and expenditure categories. In this way, the analysis unit can make optimal suggestions by applying different analysis algorithms depending on the income and expenditure categories. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input income and expenditure category data into a generating AI, and the generating AI can apply the most suitable analysis algorithm.

[0078] The analysis unit can estimate the user's emotions and adjust the length of suggestions based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will provide short, concise suggestions. If the user is relaxed, the analysis unit will provide longer suggestions with detailed explanations. If the user is stressed, the analysis unit will provide simple and easy-to-understand suggestions. In this way, the analysis unit can provide suggestions of an appropriate length for the user by adjusting the length based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the length of suggestions.

[0079] The analysis unit can determine the priority of proposals based on the submission timing of income and expenses during the analysis. For example, the analysis unit will prioritize proposals for income and expense items with approaching submission deadlines. For example, the analysis unit will postpone proposals for income and expense items with ample time for submission. The analysis unit can customize the priority of proposals based on the submission timing of income and expenses. This allows the analysis unit to prioritize proposals based on the submission timing of income and expenses, ensuring that proposals are submitted on time. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input data on the submission timing of income and expenses into a generating AI, which can then determine the priority of proposals.

[0080] The analysis unit can adjust the order of suggestions based on the relevance of income and expenses during analysis. For example, the analysis unit will prioritize suggesting highly relevant income and expense items. For example, it will postpone suggesting less relevant income and expense items. The analysis unit will customize the order of suggestions based on the relevance of income and expenses. This allows the analysis unit to prioritize suggesting highly relevant information by adjusting the order of suggestions based on the relevance of income and expenses. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the relevance of income and expenses into a generating AI, which can then adjust the order of suggestions.

[0081] The procedure unit can estimate the user's emotions and adjust the procedure based on the estimated emotions. For example, if the user is stressed, the procedure unit may simplify the procedure and proceed with the minimum number of steps. For example, if the user is relaxed, the procedure unit may provide a detailed procedure and suggest a customizable procedure. For example, if the user is in a hurry, the procedure unit may suggest a way to proceed with the procedure quickly. In this way, the procedure unit can reduce the burden on the user by adjusting the procedure based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the procedure unit may be performed using AI or not using AI. For example, the procedure unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the procedure.

[0082] The procedure unit can analyze the user's past procedure history and select the optimal procedure method during a procedure. For example, the procedure unit can propose the optimal procedure method based on the procedures the user has performed in the past. For example, the procedure unit can propose a simplification of the procedure based on the user's past procedure history. For example, the procedure unit can analyze the user's past procedure history and propose the most efficient procedure method. In this way, the procedure unit can select the optimal procedure method by analyzing the user's past procedure history. Some or all of the above processing in the procedure unit may be performed using AI, for example, or without AI. For example, the procedure unit can input data of the user's past procedure history into a generating AI, and the generating AI can select the optimal procedure method.

[0083] The procedure unit can customize the means of the procedure based on the user's current living situation during the procedure. For example, the procedure unit proposes the optimal procedure method according to the user's current living situation. For example, the procedure unit customizes the means of the procedure based on the user's living situation. For example, the procedure unit proposes the means of the procedure considering the user's current living situation. In this way, the procedure unit can provide the user with the optimal procedure method by customizing the means of the procedure based on the user's current living situation. Some or all of the above processing in the procedure unit may be performed using AI, for example, or without AI. For example, the procedure unit can input data on the user's current living situation into a generating AI, and the generating AI can customize the means of the procedure.

[0084] The procedure unit can estimate the user's emotions and determine the priority of procedures based on the estimated user emotions. For example, if the user is stressed, the procedure unit will prioritize important procedures. For example, if the user is relaxed, the procedure unit will provide detailed procedures. For example, if the user is in a hurry, the procedure unit will prioritize procedures that should be completed quickly. In this way, the procedure unit can prioritize important procedures by determining the priority of procedures based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the procedure unit may be performed using AI or not using AI. For example, the procedure unit can input user emotion data into a generative AI, which can estimate emotions and determine the priority of procedures.

[0085] The procedure unit can select the optimal procedure method while considering the user's geographical location information. For example, the procedure unit may propose the optimal procedure method based on the user's current location. For example, the procedure unit may propose a region-specific procedure method based on the user's geographical location information. For example, the procedure unit may customize the means of the procedure while considering the user's geographical location information. In this way, the procedure unit can provide a region-specific optimal procedure method by considering the user's geographical location information. Some or all of the above processing in the procedure unit may be performed using AI, for example, or without AI. For example, the procedure unit may input the user's geographical location data into a generating AI, which can then select the optimal procedure method.

[0086] The procedure unit can analyze the user's social media activity during a procedure and propose a means of procedure. For example, the procedure unit proposes the optimal procedure method based on the user's social media activity. For example, the procedure unit analyzes the user's social media activity and customizes the means of procedure. For example, the procedure unit proposes a means of procedure based on the user's social media activity. In this way, the procedure unit can propose the optimal means of procedure by analyzing the user's social media activity. Some or all of the above processing in the procedure unit may be performed using AI, for example, or without AI. For example, the procedure unit can input data on the user's social media activity into a generating AI, and the generating AI can propose the optimal means of procedure.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The reception desk can reduce the effort required for users to input income and expense information by referring to their past input history. For example, the reception desk can automatically display income and expense items that the user has entered in the past as suggestions. Furthermore, the reception desk can prioritize suggesting input methods that the user has used in the past (such as voice input or text input). In addition, the reception desk can predict and suggest income and expense items that will be used during specific time periods based on the user's past input history. In this way, the reception desk can reduce the effort required for users to input information by utilizing their past input history.

[0089] The analytics department can provide personalized suggestions based on income and expenditure data. For example, if a user is health-conscious, the analytics department can prioritize suggesting spending on health foods and fitness-related items. If a user enjoys traveling, the analytics department can suggest travel-related saving methods and deals. Furthermore, if a user spends a significant amount on hobbies, the analytics department can suggest saving methods and deals related to those hobbies. In this way, the analytics department can provide personalized suggestions tailored to the user's lifestyle.

[0090] The procedures department can track the progress of users' procedures in real time and send reminders as needed. For example, if a user is applying for tax benefits, the department can send a reminder as the application deadline approaches. Similarly, if a user is applying for pension benefits, the department can remind them of the deadline for submitting necessary documents. Furthermore, if a user is implementing energy-saving measures, the department can periodically check their progress and provide advice as needed. This allows the procedures department to track the progress of users' procedures in real time and send necessary reminders.

[0091] The reception desk can estimate the user's emotions and customize the input interface based on those emotions. For example, if the user is stressed, the reception desk can provide a simple and intuitive interface. If the user is relaxed, the reception desk can offer detailed input options and suggest a customizable interface. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick entry of income and expense information. In this way, the reception desk can reduce the user's burden by customizing the input interface based on the user's emotions.

[0092] The analysis unit can estimate the user's emotions and adjust the content of suggestions based on those emotions. For example, if the user is relaxed, the analysis unit can provide suggestions that include detailed explanations. If the user is in a hurry, the analysis unit can provide concise and to-the-point suggestions. Furthermore, if the user is stressed, the analysis unit can provide simple and easy-to-understand suggestions. In this way, by adjusting the content of suggestions based on the user's emotions, the analysis unit can provide suggestions that are easy for the user to understand.

[0093] The procedure unit can estimate the user's emotions and adjust the procedure based on those emotions. For example, if the user is stressed, the procedure unit can simplify the procedure and proceed with the minimum number of steps. If the user is relaxed, the procedure unit can provide detailed instructions and suggest customizable procedures. Furthermore, if the user is in a hurry, the procedure unit can suggest ways to expedite the procedure. In this way, the procedure unit can reduce the user's burden by adjusting the procedure based on their emotions.

[0094] The reception desk can optimize the input of income and expense information by taking into account the user's geographical location. For example, the reception desk can prioritize displaying relevant income and expense items based on the user's current location. Furthermore, the reception desk can suggest region-specific income and expense items based on the user's geographical location. In addition, the reception desk can suggest the optimal input method, taking into account the user's geographical location. As a result, the reception desk can prioritize the input of highly relevant information by considering the user's geographical location.

[0095] The analytics department can predict a user's future income and expenses based on income and expense data and provide long-term recommendations. For example, the analytics department can analyze a user's current income and expense trends and predict the possibility of future income increases or expense decreases. Furthermore, the analytics department can predict future fluctuations in income and expenses by considering the user's life events (marriage, childbirth, job change, etc.). In addition, the analytics department can propose long-term financial plans based on the user's future goals (purchasing a home, saving for education, etc.). In this way, the analytics department can predict a user's future income and expenses and provide long-term recommendations.

[0096] The procedures department can analyze users' social media activity and share the progress of their procedures. For example, if a user wishes to share the progress of their procedures on social media, the procedures department can automatically post the progress. Furthermore, the procedures department can analyze users' social media activity and provide advice and support regarding the procedures. In addition, the procedures department can customize the progress of procedures based on the user's social media activity. This allows the procedures department to support users' procedures by analyzing their social media activity and sharing the progress of their procedures.

[0097] The analysis unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the analysis unit can prioritize important suggestions. If the user is relaxed, the analysis unit can provide more detailed suggestions. Furthermore, if the user is in a hurry, the analysis unit can prioritize suggestions that require immediate attention. In this way, by prioritizing suggestions based on the user's emotions, the analysis unit can provide the most important suggestions to the user at the right time.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The reception department inputs the user's income and expense information. This information includes, for example, salary, rent, utilities, and food expenses. The reception department then sends the information entered by the user to the analysis department. Step 2: The analysis department analyzes the information entered by the reception department and proposes advantageous methods for pensions, taxes, etc. Based on income and expenditure data, the analysis department proposes specific tax benefits, pension receiving methods, and ways to save on utility costs. Furthermore, the analysis department performs analysis based on statistical data, survey results, and past cases to propose the most suitable method for the user. Step 3: The Procedures Department proceeds with the procedures based on the methods proposed by the Analysis Department. For example, the Procedures Department handles procedures such as applying for tax incentives or receiving pension benefits. This allows users to proceed with procedures that enable them to obtain advantageous methods without any hassle.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] Each of the multiple elements described above, including the reception unit, analysis unit, and procedure unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, where the user inputs information such as monthly income, rent, utilities, and food expenses. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where it proposes advantageous methods for pensions, taxes, etc., based on income and expenditure data. The procedure unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where it proceeds with the procedure based on the proposed method. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] Each of the multiple elements described above, including the reception unit, analysis unit, and procedure unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, where the user inputs information such as monthly income, rent, utilities, and food expenses. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where it proposes advantageous methods for pensions, taxes, etc., based on income and expenditure data. The procedure unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where it proceeds with the procedure based on the proposed method. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements described above, including the reception unit, analysis unit, and procedure unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, where the user inputs information such as monthly income, rent, utilities, and food expenses. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where it proposes advantageous methods for pensions, taxes, etc., based on income and expenditure data. The procedure unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where it proceeds with the procedure based on the proposed method. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the reception unit, analysis unit, and procedure unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, where the user inputs information such as monthly income, rent, utilities, and food expenses. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where it proposes advantageous methods for pensions, taxes, etc., based on income and expenditure data. The procedure unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where it proceeds with procedures based on the proposed methods. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] (Note 1) A reception area where users input their income and expense information, The analysis unit analyzes the information entered by the reception unit and proposes advantageous methods regarding pensions, tax systems, etc. The system comprises a procedure unit that carries out procedures based on the method proposed by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is Enter information such as the user's monthly income, rent, utilities, and food expenses. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on income and expenditure data, we propose specific tax benefits, pension payment methods, and ways to save on utility costs. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, We propose the best approach for the user, taking into account how many people are doing it. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned procedural department, We handle procedures such as applying for tax incentives and receiving pension benefits on your behalf. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned procedural department, We will proceed with the process to enable users to obtain advantageous methods without any hassle. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of income and expenditure information input based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past income and expenditure history to select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users enter income and expense information, the system filters the data based on their current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes the information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering income and expense information, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users enter income and expense information, the system analyzes their social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail of the suggestions based on the importance of income and expenses. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the income and expenditure categories. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, proposals are prioritized based on the timing of income and expenditure submissions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of suggestions will be adjusted based on the relationship between income and expenses. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned procedural department, It estimates the user's emotions and adjusts the procedure based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned procedural department, During the process, the system analyzes the user's past procedure history to select the most suitable procedure. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned procedural department, During the process, the procedure methods are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned procedural department, The system estimates the user's emotions and determines the priority of procedures based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned procedural department, During the process, the system will select the most appropriate procedure based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned procedural department, During the process, we analyze the user's social media activity and suggest appropriate procedures. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0172] 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 reception area where users input their income and expense information, The analysis unit analyzes the information entered by the reception unit and proposes advantageous methods regarding pensions, tax systems, etc. The system comprises a procedure unit that carries out procedures based on the method proposed by the analysis unit. A system characterized by the following features.

2. The aforementioned reception unit is Enter information such as the user's monthly income, rent, utilities, and food expenses. The system according to feature 1.

3. The aforementioned analysis unit, Based on income and expenditure data, we propose specific tax benefits, pension payment methods, and ways to save on utility costs. The system according to feature 1.

4. The aforementioned analysis unit, We propose the best approach for the user, taking into account how many people are doing it. The system according to feature 1.

5. The aforementioned procedural department, We handle procedures such as applying for tax incentives and receiving pension benefits on your behalf. The system according to feature 1.

6. The aforementioned procedural department, We will proceed with the process to enable users to obtain advantageous methods without any hassle. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of income and expenditure information input based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past income and expenditure history to select the optimal input method. The system according to feature 1.

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