system

Abstract

The system according to this embodiment aims to efficiently collect and analyze financial data of individuals and companies and propose optimal tax strategies. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and a generation unit. The collection unit collects financial data in conjunction with personal identification numbers. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes specific tax strategies based on the analysis results obtained by the analysis unit. The generation unit automatically generates tax returns based on the tax strategies proposed by the proposal unit.

Description

Technical Field

[0006] , ,

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to efficiently collect and analyze personal and corporate financial data and propose an optimal tax strategy.

[0005] The system according to the embodiment aims to efficiently collect and analyze personal and corporate financial data and propose an optimal tax strategy.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a generation unit. The collection unit collects financial data in conjunction with personal identification numbers. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes specific tax strategies based on the analysis results obtained by the analysis unit. The generation unit automatically generates tax returns based on the tax strategies proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently collect and analyze financial data of individuals and companies and propose optimal tax strategies. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​system according to an embodiment of the present invention is an innovative system that automatically collects and analyzes personal and corporate financial data in real time in conjunction with personal identification numbers. This system automatically collects financial data in real time in conjunction with personal identification numbers. Next, it analyzes the collected data using machine learning algorithms and proposes the optimal tax strategy tailored to each user's situation. For example, during tax filing or year-end adjustments, it proposes the optimal tax-saving method considering the user's income, expenses, and investment status. Furthermore, it automatically generates AI-driven optimized tax returns, allowing users to easily create tax returns without cumbersome procedures. It also provides a 24 / 7 AI chatbot consultation function, enabling tax consultations at any time. It also features predictive analysis and future strategy proposal functions, suggesting future tax measures and asset building methods. This system employs blockchain security, protecting personal information with advanced encryption technology. This allows users to use the system with peace of mind. The AI ​​system leverages strong partnerships with fintech companies and financial institutions to provide comprehensive AI solutions. This enables the system to realize a comprehensive tax service ahead of competitors and accurately respond to customer needs. This system frees users from time-consuming tax filing and cumbersome procedures, allowing them to focus on value creation. Furthermore, by ensuring appropriate tax savings and accurate filing, it contributes to a fairer tax society and economic revitalization. The AI ​​system automatically collects and analyzes personal and corporate financial data in real time, proposes optimal tax strategies, and automatically generates tax returns.

[0029] The AI ​​system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a generation unit. The collection unit collects financial data in conjunction with a personal identification number. The collection unit can, for example, automatically collect financial data such as bank transaction history, investment information, and credit card usage history in real time using the personal identification number. The collection unit can, for example, obtain data from financial institutions via an API. The collection unit can also implement security protocols for securely collecting financial data with the user's permission. The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, analyze the data using machine learning algorithms to evaluate the user's income, expenses, investment status, etc. The analysis unit can, for example, predict income trends using regression analysis. The analysis unit can also classify spending patterns using clustering. Furthermore, the analysis unit can also evaluate investment risk using a neural network. The proposal unit proposes the optimal tax strategy based on the analysis results obtained by the analysis unit. The proposal unit, for example, proposes tax-saving measures and tax deductions. The proposal unit can, for example, calculate the optimal deduction amount according to the user's income. Furthermore, the proposal unit can suggest optimal investment strategies based on the user's investment status. It can also suggest saving methods based on the user's spending patterns. The generation unit automatically generates tax returns based on the tax strategies proposed by the proposal unit. The generation unit, for example, generates AI-driven, efficient tax returns. It can also automatically generate the content of tax returns using natural language processing technology. Additionally, it can automatically attach necessary documents using image recognition technology. Furthermore, the generation unit can suggest future tax strategies using predictive analytics. This allows the AI ​​system to streamline tax procedures by collecting, analyzing, proposing, and automatically generating tax returns based on financial data linked to personal identification numbers.

[0030] The data collection unit collects financial data in conjunction with personal identification numbers. Specifically, the data collection unit can use personal identification numbers to automatically collect financial data such as bank transaction history, investment information, and credit card usage history in real time. For example, when acquiring data from financial institutions via APIs, the unit performs appropriate authentication procedures according to the API specifications of each financial institution before acquiring the data. With the user's permission, the data collection unit can also implement security protocols to securely collect financial data. This includes data encryption, access control, and recording of audit logs. For example, it uses TLS (Transport Layer Security) to encrypt data communications and protect user privacy. The data collection unit also provides an interface that allows users to check their data collection status, ensuring transparency. Furthermore, the data collection unit is equipped with a monitoring system to detect abnormal data collection activity, minimizing the risk of unauthorized access and data breaches. As a result, the data collection unit can efficiently and securely collect users' financial data and improve the reliability of the entire system.

[0031] The Analysis Department analyzes the data collected by the Data Collection Department. Specifically, it uses machine learning algorithms to analyze the data and evaluate the user's income, expenses, and investment status. For example, it can use regression analysis to predict income trends and estimate the user's future income. It can also use clustering to classify spending patterns and understand the user's consumption behavior. Furthermore, it can use neural networks to assess investment risk and determine the health of the user's investment portfolio. Based on these analysis results, the Analysis Department comprehensively evaluates the user's financial situation and identifies potential risks and opportunities. For example, it can use anomaly detection algorithms to detect unusual spending patterns or fluctuations in income and issue early warnings. In addition, the Analysis Department utilizes historical data and statistical information to analyze long-term financial trends and support the development of future financial plans. This allows the Analysis Department to gain a detailed understanding of the user's financial situation and build a foundation for providing appropriate advice.

[0032] The Proposal Department proposes optimal tax strategies based on the analysis results obtained by the Analysis Department. Specifically, it proposes tax-saving measures and tax deductions. For example, it calculates the optimal deduction amount based on the user's income and proposes specific methods to reduce the tax burden. It also proposes optimal investment strategies based on the user's investment situation and provides advice to maximize returns while minimizing risk. Furthermore, the Proposal Department can propose saving methods based on the user's spending patterns and provide specific action plans to reduce unnecessary spending. To present these proposals clearly to users, the Proposal Department provides interactive dashboards and reports, allowing users to intuitively understand their financial situation. In addition, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. In this way, the Proposal Department can provide users with optimal tax strategies and support the efficiency and optimization of their financial management.

[0033] The generation unit automatically generates tax returns based on the tax strategies proposed by the proposal unit. Specifically, it generates efficient, AI-driven tax returns. For example, it uses natural language processing technology to automatically generate the content of the tax return, making it easy for users to input the necessary information. It also uses image recognition technology to automatically attach necessary documents, simplifying the tax return preparation process. Furthermore, the generation unit uses predictive analytics to suggest future tax strategies, providing users with reference information when creating long-term financial plans. Through these functions, the generation unit helps users complete tax procedures quickly and accurately. For example, in the automated tax return generation process, it improves the accuracy of the tax return by automatically filling in necessary fields based on the user's financial data and performing error checks. The generation unit also guides users through the tax return submission process, supporting the submission and verification of necessary documents. In this way, the generation unit streamlines the user's tax procedures and provides consistent support from tax return preparation to submission.

[0034] The data collection unit can automatically collect financial data in real time in conjunction with personal identification numbers. For example, the data collection unit can automatically collect financial data such as bank transaction history, investment information, and credit card usage history in real time using personal identification numbers. The data collection unit can obtain data from financial institutions, for example, through APIs. The data collection unit can also implement security protocols to securely collect financial data with the user's permission. This ensures that the latest data is always available by automatically collecting financial data in real time in conjunction with personal identification numbers. Specific definitions and criteria for "real time" include, for example, the frequency and latency of data collection. Some or all of the above-described processes in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can have a generative AI execute the process of collecting financial data using personal identification numbers.

[0035] The analysis department can analyze collected data using machine learning algorithms and propose optimal tax strategies tailored to each user's situation. For example, the analysis department can analyze data using machine learning algorithms to evaluate users' income, expenses, and investment status. For example, the analysis department can predict income trends using regression analysis. The analysis department can also classify expense patterns using clustering. Furthermore, the analysis department can evaluate investment risk using neural networks. In this way, by analyzing data using machine learning algorithms, it is possible to propose optimal tax strategies for each user. Specific types and implementation methods of machine learning algorithms include, for example, regression analysis, clustering, and neural networks. Some or all of the above processing in the analysis department may be performed using, for example, AI, or not using AI. For example, the analysis department can input collected data into a generating AI and have the generating AI perform the data analysis.

[0036] The generation unit can automatically generate AI-driven, efficient tax returns. For example, the generation unit can automatically generate the content of the tax return using natural language processing technology. The generation unit can also automatically attach necessary documents using image recognition technology. Furthermore, the generation unit can suggest future tax strategies using predictive analytics. This allows users to easily prepare tax returns by automatically generating AI-driven, optimized tax returns. Specific AI-driven technologies and methods include, for example, natural language processing, image recognition, and predictive analytics. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can have a generation AI execute the tax return generation process.

[0037] The proposal unit is equipped with predictive analysis and future strategy proposal functions, enabling it to propose future tax strategies and asset building methods. For example, the proposal unit can propose future tax strategies using predictive analysis. The proposal unit can also propose future tax strategies using simulations, for example. Furthermore, the proposal unit can propose future asset building methods using scenario analysis. Thus, by incorporating predictive analysis and future strategy proposal functions, it can propose future tax strategies and asset building methods. Specific methods and criteria for predictive analysis include, for example, time series analysis and regression analysis. Specific content and methods of the future strategy proposal function include, for example, simulations and scenario analysis. Some or all of the above-described processes in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal unit can have a generation AI execute the process of proposing future tax strategies and asset building methods.

[0038] The system employs blockchain-based security and can protect personal information with specific encryption technologies. For example, the system can prevent data tampering using blockchain technology. The system can ensure data transparency using, for example, a public blockchain. Furthermore, the system can control data access using a private blockchain. In addition, the system can protect data using encryption technologies such as AES and RSA. Thus, by employing blockchain-based security, personal information can be highly protected. Specific blockchain technologies and implementation methods include, for example, public blockchains and private blockchains. Specific types and methods of encryption technologies include, for example, AES, RSA, and SHA-256. Some or all of the above processes in the system may be performed using, for example, AI, or not. For example, the system can have a generating AI execute security processes using blockchain technology.

[0039] The system can provide comprehensive AI solutions by leveraging effective partnerships with fintech companies and financial institutions. For example, the system can introduce the latest financial technologies through partnerships with fintech companies. The system can also provide comprehensive financial services to users through collaboration with financial institutions. Furthermore, the system can develop new financial solutions through joint projects with fintech companies and financial institutions. This allows the system to provide comprehensive AI solutions by leveraging strong partnerships with fintech companies and financial institutions. Specific details and criteria for effective partnerships include, for example, contract terms and scope of cooperation. Some or all of the above-mentioned processes in the system may be performed using AI, or not. For example, the system can have a generation AI execute the partnership proposal process.

[0040] The data collection unit can analyze the user's past financial data collection history and select the optimal collection method. For example, the data collection unit may prioritize collection methods that the user has frequently used in the past. The data collection unit can also suggest the most efficient collection method based on the user's past collection history. Furthermore, the data collection unit can analyze the user's past collection history and customize the collection method. This allows for the selection of the optimal collection method by analyzing past collection history. Specific criteria and methods for the optimal collection method include, for example, the frequency of data collection and the means of collection. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past financial data collection history into a generating AI and have the generating AI select the optimal collection method.

[0041] The data collection unit can filter financial data based on the user's current economic situation and areas of interest. For example, the data collection unit can prioritize the collection of relevant financial data based on the user's current income. The data collection unit can also filter and collect specific financial data based on the user's areas of interest. Furthermore, the data collection unit can adjust the scope of data to be collected, taking into account the user's economic situation. This allows for the collection of highly relevant data by filtering data based on the user's economic situation and areas of interest. Specific filtering methods and criteria include, for example, filtering conditions and algorithms. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can have a generating AI perform the process of filtering based on the user's economic situation and areas of interest.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting financial data. For example, if the user is in a specific region, the data collection unit will prioritize the collection of financial data related to that region. The data collection unit can also filter and collect highly relevant data based on the user's geographical location. Furthermore, the data collection unit can select the optimal collection method based on the user's current location. This allows for the priority collection of highly relevant data by considering the user's geographical location. Specific types and methods of acquiring geographical location information include, for example, GPS data and address information. Some or all of the above-described processes in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can have a generating AI execute the process of collecting highly relevant data based on the user's geographical location.

[0043] The data collection unit can analyze a user's social media activity and collect relevant data when collecting financial data. For example, the data collection unit can collect financial data of interest from a user's social media activity. The data collection unit can also analyze the content of a user's social media posts and prioritize the collection of relevant data. Furthermore, the data collection unit can select data to collect by referring to the activities of the user's social media followers and friends. This allows for the collection of relevant data by analyzing the user's social media activity. Specific types of social media activity and methods of analysis include, for example, post content, number of likes, and number of followers. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can have a generative AI perform the process of analyzing a user's social media activity.

[0044] The analysis unit can adjust the level of detail of its analysis based on the importance of the financial data during the analysis process. For example, the analysis unit can perform a detailed analysis on high-importance data, or a simplified analysis on low-importance data. Furthermore, the analysis unit can dynamically adjust the level of detail of its analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail based on the importance of the financial data. Specific criteria and methods for adjusting the level of detail include, for example, data granularity and the number of displayed items. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have a generating AI perform the process of adjusting the level of detail of its analysis based on the importance of the financial data.

[0045] The analysis unit can apply different analysis algorithms depending on the category of financial data during analysis. For example, the analysis unit can apply an income-specific analysis algorithm to income data. It can also apply an expenditure-specific analysis algorithm to expenditure data. Furthermore, it can apply an investment-specific analysis algorithm to investment data. This allows for highly accurate analysis by applying different analysis algorithms depending on the category of financial data. Specific categories and classification criteria include, for example, income categories, expenditure categories, and investment categories. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have a generating AI execute the process of applying different analysis algorithms depending on the category of financial data.

[0046] The analysis department can prioritize analyses based on the submission timing of financial data. For example, the analysis department might prioritize analyzing data with an approaching submission deadline. It might also postpone analyzing data with a distant submission deadline. Furthermore, the analysis department can dynamically adjust the analysis priority based on the submission timing. This enables efficient analysis by prioritizing analyses based on the submission timing of financial data. Specific criteria and methods for determining submission timing include, for example, legal deadlines and user-requested dates. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department could have a generating AI perform the process of prioritizing analyses based on the submission timing of financial data.

[0047] The analysis unit can adjust the order of analysis based on the relevance of financial data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of financial data. Specific criteria and evaluation methods for relevance include, for example, data correlation and co-occurrence frequency. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can have a generative AI perform the process of adjusting the order of analysis based on the relevance of financial data.

[0048] The proposal department can adjust the level of detail in its proposals based on the importance of the tax strategies. For example, it can provide detailed proposals for highly important tax strategies, and simplified proposals for less important ones. Furthermore, the proposal department can dynamically adjust the level of detail in its proposals according to the importance of the tax strategies. This allows for more efficient proposals by adjusting the level of detail based on the importance of the tax strategies. Specific evaluation criteria and methods for determining importance include, for example, impact and urgency. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can have a generating AI perform the process of adjusting the level of detail in its proposals based on the importance of the tax strategies.

[0049] The proposal unit can apply different proposal algorithms depending on the category of tax strategy when making a proposal. For example, the proposal unit can apply an income-specific proposal algorithm to a tax strategy related to income. For example, the proposal unit can also apply an expense-specific proposal algorithm to a tax strategy related to expenses. Furthermore, the proposal unit can apply an investment-specific proposal algorithm to a tax strategy related to investments. This allows for highly accurate proposals by applying different proposal algorithms depending on the category of tax strategy. Specific types and implementation methods of proposal algorithms include, for example, rule-based and machine learning-based algorithms. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal unit can have a generating AI execute the process of applying different proposal algorithms depending on the category of tax strategy.

[0050] The proposal department can prioritize proposals based on the submission timing of tax strategies. For example, the proposal department will prioritize tax strategies with approaching submission deadlines. It can also postpone tax strategies with later submission deadlines. Furthermore, the proposal department can dynamically adjust the priority of proposals based on submission timing. This enables efficient proposals by prioritizing proposals based on the submission timing of tax strategies. Specific criteria and methods for determining submission timing include, for example, legal deadlines and user-requested dates. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not. For example, the proposal department can have a generating AI perform the process of determining the priority of proposals based on the submission timing of tax strategies.

[0051] The proposal unit can adjust the order of proposals based on the relevance of the tax strategies during the proposal process. For example, the proposal unit may prioritize proposing highly relevant tax strategies. For example, it may postpone proposing less relevant tax strategies. The proposal unit can also dynamically adjust the order of proposals based on the relevance of the tax strategies. This allows for more efficient proposals by adjusting the order of proposals based on the relevance of the tax strategies. Specific criteria and evaluation methods for relevance include, for example, data correlation and co-occurrence frequency. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal unit can have a generating AI perform the process of adjusting the order of proposals based on the relevance of the tax strategies.

[0052] The generation unit can analyze the user's past tax return history to select the optimal generation method when generating a tax return. For example, the generation unit can select the optimal generation method based on the format of tax returns previously used by the user. For example, the generation unit can also suggest the most efficient generation method based on the user's past tax return history. Furthermore, the generation unit can analyze the user's past tax return history and customize the generation method. This allows the optimal generation method to be selected by analyzing past tax return history. Specific criteria and selection methods for the optimal generation method include, for example, past history and user preferences. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past tax return history into a generation AI and have the generation AI select the optimal generation method.

[0053] The generation unit can customize the content of the tax return based on the user's current financial situation when generating the return. For example, the generation unit can customize the content of the return based on the user's current income. The generation unit can also customize the content of the return based on the user's current spending. Furthermore, the generation unit can customize the content of the return based on the user's current investment status. This allows for the generation of a more appropriate tax return by customizing the content based on the user's current financial situation. Specific methods and criteria for customization include, for example, adding or deleting items and changing the layout. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can have a generation AI perform the process of customizing the content of the tax return based on the user's current financial situation.

[0054] The generation unit can generate the most suitable tax return by considering the user's geographical location information during the tax return generation process. For example, if the user is in a specific region, the generation unit will prioritize generating tax returns relevant to that region. The generation unit can also filter and generate highly relevant tax returns based on the user's geographical location information. Furthermore, the generation unit can select the most suitable generation method based on the user's current location. This allows for the generation of the most suitable tax return by considering the user's geographical location information. Specific types and methods of acquiring geographical location information include, for example, GPS data and address information. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can have a generation AI execute the process of generating the most suitable tax return based on the user's geographical location information.

[0055] The generation unit can analyze the user's social media activity and suggest content for the declaration form when generating it. For example, the generation unit can suggest content for the declaration form that the user is interested in based on their social media activity. The generation unit can also analyze the user's social media posts and prioritize suggesting relevant content for the declaration form. Furthermore, the generation unit can select content for the declaration form by referring to the activities of the user's social media followers and friends. In this way, by analyzing the user's social media activity, it can suggest relevant content for the declaration form. Specific types of social media activity and methods of analysis include, for example, post content, number of likes, and number of followers. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can have a generation AI perform the process of analyzing the user's social media activity.

[0056] The security department can analyze past security incidents and select the optimal security measures during security operations. For example, the security department can select the optimal security measures based on past security incidents. The security department can also propose measures to prevent recurrence based on past security incidents. Furthermore, the security department can analyze past security incidents and customize security measures. This allows for the selection of optimal security measures by analyzing past security incidents. Specific types of security incidents and methods of analysis include, for example, data breaches and hacking. Some or all of the above processes in the security department may be performed using, for example, AI, or not. For example, the security department can have a generative AI perform the process of analyzing past security incidents.

[0057] The security department can select the optimal security measures during security operations, taking into account the user's geographical location. For example, if the user is in a specific region, the security department will prioritize implementing security measures relevant to that region. The security department can also filter and implement highly relevant security measures based on the user's geographical location. Furthermore, the security department can select the optimal security measures based on the user's current location. This allows for the selection of optimal security measures by considering the user's geographical location. Specific types and methods of acquiring geographical location information include, for example, GPS data and address information. Some or all of the above-described processes in the security department may be performed using, for example, AI, or not. For example, the security department can have a generating AI execute the process of selecting the optimal security measures based on the user's geographical location.

[0058] The Partnership Department can analyze past partnership history to select the optimal proposal method when proposing a partnership. For example, the Partnership Department can select the optimal proposal method based on past partnership history. The Partnership Department can also propose successful proposal methods based on past partnership history. Furthermore, the Partnership Department can analyze past partnership history and customize the proposal method. This allows for the selection of the optimal proposal method by analyzing past partnership history. Specific criteria and selection methods for the optimal proposal method include, for example, past history and user preferences. Some or all of the above processes in the Partnership Department may be performed using AI, or not. For example, the Partnership Department can input past partnership history into a generating AI and have the generating AI select the optimal proposal method.

[0059] The Partnership Department can provide optimal partnership proposals by considering the user's geographical location information. For example, if a user is in a specific region, the Partnership Department will prioritize proposing partnerships related to that region. The Partnership Department can also filter and propose highly relevant partnerships based on the user's geographical location information. Furthermore, the Partnership Department can select the optimal proposal method based on the user's current location. This allows the department to provide optimal partnership proposals by considering the user's geographical location information. Specific types and methods of acquiring geographical location information include, for example, GPS data and address information. Some or all of the above processing in the Partnership Department may be performed using, for example, AI, or not. For example, the Partnership Department can have a generating AI execute the process of providing optimal proposals based on the user's geographical location information.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The data collection unit can analyze the user's past financial data collection history and select the optimal collection method. For example, it can prioritize selecting collection methods that the user has frequently used in the past. It can also suggest the most efficient collection method based on the user's past collection history. Furthermore, it can analyze the user's past collection history and customize the collection method. This allows for the selection of the optimal collection method by analyzing past collection history. Specific criteria and methods for the optimal collection method include, for example, the frequency of data collection and the means of collection. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past financial data collection history into a generating AI and have the generating AI select the optimal collection method.

[0062] The analysis unit can adjust the level of detail of the analysis based on the importance of the financial data during the analysis process. For example, it can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. Furthermore, it can dynamically adjust the level of detail of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail based on the importance of the financial data. Specific criteria and methods for adjusting the level of detail include, for example, the granularity of the data and the number of items displayed. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can have a generating AI perform the process of adjusting the level of detail of the analysis based on the importance of the financial data.

[0063] The proposal department can adjust the level of detail in a proposal based on the importance of the tax strategy. For example, it can provide detailed proposals for highly important tax strategies and simplified proposals for less important ones. Furthermore, it can dynamically adjust the level of detail in a proposal according to the importance of the tax strategy. This allows for more efficient proposals by adjusting the level of detail based on the importance of the tax strategy. Specific evaluation criteria and methods for determining importance include, for example, impact and urgency. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can have a generating AI perform the process of adjusting the level of detail in a proposal based on the importance of the tax strategy.

[0064] The generation unit can analyze the user's past tax return history to select the optimal generation method when generating a tax return. For example, it can select the optimal generation method based on the format of tax returns the user has used in the past. It can also suggest the most efficient generation method based on the user's past tax return history. Furthermore, it can analyze the user's past tax return history and customize the generation method. This allows for the selection of the optimal generation method by analyzing past tax return history. Specific criteria and selection methods for the optimal generation method include, for example, past history and user preferences. Some or all of the above-described processes in the generation unit may be performed using AI, or they may not. For example, the generation unit can input the user's past tax return history into a generation AI and have the generation AI select the optimal generation method.

[0065] The security department can analyze past security incidents and select the optimal security measures during security operations. For example, it can select the optimal security measures based on past security incidents. It can also propose measures to prevent recurrence based on past security incidents. Furthermore, it can analyze past security incidents and customize security measures. This allows for the selection of optimal security measures by analyzing past security incidents. Specific types of security incidents and methods of analysis include, for example, data breaches and hacking. Some or all of the above processes in the security department may be performed using AI, or not. For example, the security department can have a generating AI perform the process of analyzing past security incidents.

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

[0067] Step 1: The collection unit collects financial data in conjunction with personal identification numbers. For example, personal identification numbers can be used to automatically collect financial data such as bank transaction history, investment information, and credit card usage history in real time. The collection unit obtains data from financial institutions via APIs, obtains user permission, and implements security protocols to securely collect financial data. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it uses machine learning algorithms to analyze the data and evaluate the user's income, expenses, and investment status. It uses regression analysis to predict income trends, clustering to classify spending patterns, and neural networks to assess investment risk. Step 3: The proposal department proposes the optimal tax strategy based on the analysis results obtained by the analysis department. For example, it proposes tax-saving measures and tax deductions, calculates the optimal deduction amount according to the user's income, proposes the optimal investment strategy based on the user's investment situation, and proposes saving methods based on the user's spending patterns. Step 4: The generation unit automatically generates the tax return based on the tax strategy proposed by the proposal unit. For example, it generates an AI-driven, efficient tax return, automatically generates the contents of the tax return using natural language processing technology, automatically attaches necessary documents using image recognition technology, and proposes future tax strategies using predictive analytics.

[0068] (Example of form 2) The AI ​​system according to an embodiment of the present invention is an innovative system that automatically collects and analyzes personal and corporate financial data in real time in conjunction with personal identification numbers. This system automatically collects financial data in real time in conjunction with personal identification numbers. Next, it analyzes the collected data using machine learning algorithms and proposes the optimal tax strategy tailored to each user's situation. For example, during tax filing or year-end adjustments, it proposes the optimal tax-saving method considering the user's income, expenses, and investment status. Furthermore, it automatically generates AI-driven optimized tax returns, allowing users to easily create tax returns without cumbersome procedures. It also provides a 24 / 7 AI chatbot consultation function, enabling tax consultations at any time. It also features predictive analysis and future strategy proposal functions, suggesting future tax measures and asset building methods. This system employs blockchain security, protecting personal information with advanced encryption technology. This allows users to use the system with peace of mind. The AI ​​system leverages strong partnerships with fintech companies and financial institutions to provide comprehensive AI solutions. This enables the system to realize a comprehensive tax service ahead of competitors and accurately respond to customer needs. This system frees users from time-consuming tax filing and cumbersome procedures, allowing them to focus on value creation. Furthermore, by ensuring appropriate tax savings and accurate filing, it contributes to a fairer tax society and economic revitalization. The AI ​​system automatically collects and analyzes personal and corporate financial data in real time, proposes optimal tax strategies, and automatically generates tax returns.

[0069] The AI ​​system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a generation unit. The collection unit collects financial data in conjunction with a personal identification number. The collection unit can, for example, automatically collect financial data such as bank transaction history, investment information, and credit card usage history in real time using the personal identification number. The collection unit can, for example, obtain data from financial institutions via an API. The collection unit can also implement security protocols for securely collecting financial data with the user's permission. The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, analyze the data using machine learning algorithms to evaluate the user's income, expenses, investment status, etc. The analysis unit can, for example, predict income trends using regression analysis. The analysis unit can also classify spending patterns using clustering. Furthermore, the analysis unit can also evaluate investment risk using a neural network. The proposal unit proposes the optimal tax strategy based on the analysis results obtained by the analysis unit. The proposal unit, for example, proposes tax-saving measures and tax deductions. The proposal unit can, for example, calculate the optimal deduction amount according to the user's income. Furthermore, the proposal unit can suggest optimal investment strategies based on the user's investment status. It can also suggest saving methods based on the user's spending patterns. The generation unit automatically generates tax returns based on the tax strategies proposed by the proposal unit. The generation unit, for example, generates AI-driven, efficient tax returns. It can also automatically generate the content of tax returns using natural language processing technology. Additionally, it can automatically attach necessary documents using image recognition technology. Furthermore, the generation unit can suggest future tax strategies using predictive analytics. This allows the AI ​​system to streamline tax procedures by collecting, analyzing, proposing, and automatically generating tax returns based on financial data linked to personal identification numbers.

[0070] The data collection unit collects financial data in conjunction with personal identification numbers. Specifically, the data collection unit can use personal identification numbers to automatically collect financial data such as bank transaction history, investment information, and credit card usage history in real time. For example, when acquiring data from financial institutions via APIs, the unit performs appropriate authentication procedures according to the API specifications of each financial institution before acquiring the data. With the user's permission, the data collection unit can also implement security protocols to securely collect financial data. This includes data encryption, access control, and recording of audit logs. For example, it uses TLS (Transport Layer Security) to encrypt data communications and protect user privacy. The data collection unit also provides an interface that allows users to check their data collection status, ensuring transparency. Furthermore, the data collection unit is equipped with a monitoring system to detect abnormal data collection activity, minimizing the risk of unauthorized access and data breaches. As a result, the data collection unit can efficiently and securely collect users' financial data and improve the reliability of the entire system.

[0071] The Analysis Department analyzes the data collected by the Data Collection Department. Specifically, it uses machine learning algorithms to analyze the data and evaluate the user's income, expenses, and investment status. For example, it can use regression analysis to predict income trends and estimate the user's future income. It can also use clustering to classify spending patterns and understand the user's consumption behavior. Furthermore, it can use neural networks to assess investment risk and determine the health of the user's investment portfolio. Based on these analysis results, the Analysis Department comprehensively evaluates the user's financial situation and identifies potential risks and opportunities. For example, it can use anomaly detection algorithms to detect unusual spending patterns or fluctuations in income and issue early warnings. In addition, the Analysis Department utilizes historical data and statistical information to analyze long-term financial trends and support the development of future financial plans. This allows the Analysis Department to gain a detailed understanding of the user's financial situation and build a foundation for providing appropriate advice.

[0072] The Proposal Department proposes optimal tax strategies based on the analysis results obtained by the Analysis Department. Specifically, it proposes tax-saving measures and tax deductions. For example, it calculates the optimal deduction amount based on the user's income and proposes specific methods to reduce the tax burden. It also proposes optimal investment strategies based on the user's investment situation and provides advice to maximize returns while minimizing risk. Furthermore, the Proposal Department can propose saving methods based on the user's spending patterns and provide specific action plans to reduce unnecessary spending. To present these proposals clearly to users, the Proposal Department provides interactive dashboards and reports, allowing users to intuitively understand their financial situation. In addition, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. In this way, the Proposal Department can provide users with optimal tax strategies and support the efficiency and optimization of their financial management.

[0073] The generation unit automatically generates tax returns based on the tax strategies proposed by the proposal unit. Specifically, it generates efficient, AI-driven tax returns. For example, it uses natural language processing technology to automatically generate the content of the tax return, making it easy for users to input the necessary information. It also uses image recognition technology to automatically attach necessary documents, simplifying the tax return preparation process. Furthermore, the generation unit uses predictive analytics to suggest future tax strategies, providing users with reference information when creating long-term financial plans. Through these functions, the generation unit helps users complete tax procedures quickly and accurately. For example, in the automated tax return generation process, it improves the accuracy of the tax return by automatically filling in necessary fields based on the user's financial data and performing error checks. The generation unit also guides users through the tax return submission process, supporting the submission and verification of necessary documents. In this way, the generation unit streamlines the user's tax procedures and provides consistent support from tax return preparation to submission.

[0074] The data collection unit can automatically collect financial data in real time in conjunction with personal identification numbers. For example, the data collection unit can automatically collect financial data such as bank transaction history, investment information, and credit card usage history in real time using personal identification numbers. The data collection unit can obtain data from financial institutions, for example, through APIs. The data collection unit can also implement security protocols to securely collect financial data with the user's permission. This ensures that the latest data is always available by automatically collecting financial data in real time in conjunction with personal identification numbers. Specific definitions and criteria for "real time" include, for example, the frequency and latency of data collection. Some or all of the above-described processes in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can have a generative AI execute the process of collecting financial data using personal identification numbers.

[0075] The analysis department can analyze collected data using machine learning algorithms and propose optimal tax strategies tailored to each user's situation. For example, the analysis department can analyze data using machine learning algorithms to evaluate users' income, expenses, and investment status. For example, the analysis department can predict income trends using regression analysis. The analysis department can also classify expense patterns using clustering. Furthermore, the analysis department can evaluate investment risk using neural networks. In this way, by analyzing data using machine learning algorithms, it is possible to propose optimal tax strategies for each user. Specific types and implementation methods of machine learning algorithms include, for example, regression analysis, clustering, and neural networks. Some or all of the above processing in the analysis department may be performed using, for example, AI, or not using AI. For example, the analysis department can input collected data into a generating AI and have the generating AI perform the data analysis.

[0076] The generation unit can automatically generate AI-driven, efficient tax returns. For example, the generation unit can automatically generate the content of the tax return using natural language processing technology. The generation unit can also automatically attach necessary documents using image recognition technology. Furthermore, the generation unit can suggest future tax strategies using predictive analytics. This allows users to easily prepare tax returns by automatically generating AI-driven, optimized tax returns. Specific AI-driven technologies and methods include, for example, natural language processing, image recognition, and predictive analytics. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can have a generation AI execute the tax return generation process.

[0077] The proposal unit is equipped with predictive analysis and future strategy proposal functions, enabling it to propose future tax strategies and asset building methods. For example, the proposal unit can propose future tax strategies using predictive analysis. The proposal unit can also propose future tax strategies using simulations, for example. Furthermore, the proposal unit can propose future asset building methods using scenario analysis. Thus, by incorporating predictive analysis and future strategy proposal functions, it can propose future tax strategies and asset building methods. Specific methods and criteria for predictive analysis include, for example, time series analysis and regression analysis. Specific content and methods of the future strategy proposal function include, for example, simulations and scenario analysis. Some or all of the above-described processes in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal unit can have a generation AI execute the process of proposing future tax strategies and asset building methods.

[0078] The system employs blockchain-based security and can protect personal information with specific encryption technologies. For example, the system can prevent data tampering using blockchain technology. The system can ensure data transparency using, for example, a public blockchain. Furthermore, the system can control data access using a private blockchain. In addition, the system can protect data using encryption technologies such as AES and RSA. Thus, by employing blockchain-based security, personal information can be highly protected. Specific blockchain technologies and implementation methods include, for example, public blockchains and private blockchains. Specific types and methods of encryption technologies include, for example, AES, RSA, and SHA-256. Some or all of the above processes in the system may be performed using, for example, AI, or not. For example, the system can have a generating AI execute security processes using blockchain technology.

[0079] The system can provide comprehensive AI solutions by leveraging effective partnerships with fintech companies and financial institutions. For example, the system can introduce the latest financial technologies through partnerships with fintech companies. The system can also provide comprehensive financial services to users through collaboration with financial institutions. Furthermore, the system can develop new financial solutions through joint projects with fintech companies and financial institutions. This allows the system to provide comprehensive AI solutions by leveraging strong partnerships with fintech companies and financial institutions. Specific details and criteria for effective partnerships include, for example, contract terms and scope of cooperation. Some or all of the above-mentioned processes in the system may be performed using AI, or not. For example, the system can have a generation AI execute the partnership proposal process.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of financial data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing and collect data when the user is relaxed. For example, if the user is relaxed, the data collection unit can also advance the collection timing to collect data more efficiently. Furthermore, if the user is in a hurry, the data collection unit can adjust the collection timing to reduce the user's burden. In this way, the user's burden can be reduced by adjusting the collection timing based on the user's emotions. Specific methods and criteria for estimating emotions include, for example, facial recognition, voice analysis, and text analysis. Some or all of the above processing in the data collection unit is implemented using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. The data collection unit can estimate the user's emotions using an emotion estimation algorithm and adjust the collection timing based on the estimated emotions.

[0081] The data collection unit can analyze the user's past financial data collection history and select the optimal collection method. For example, the data collection unit may prioritize collection methods that the user has frequently used in the past. The data collection unit can also suggest the most efficient collection method based on the user's past collection history. Furthermore, the data collection unit can analyze the user's past collection history and customize the collection method. This allows for the selection of the optimal collection method by analyzing past collection history. Specific criteria and methods for the optimal collection method include, for example, the frequency of data collection and the means of collection. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past financial data collection history into a generating AI and have the generating AI select the optimal collection method.

[0082] The data collection unit can filter financial data based on the user's current economic situation and areas of interest. For example, the data collection unit can prioritize the collection of relevant financial data based on the user's current income. The data collection unit can also filter and collect specific financial data based on the user's areas of interest. Furthermore, the data collection unit can adjust the scope of data to be collected, taking into account the user's economic situation. This allows for the collection of highly relevant data by filtering data based on the user's economic situation and areas of interest. Specific filtering methods and criteria include, for example, filtering conditions and algorithms. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can have a generating AI perform the process of filtering based on the user's economic situation and areas of interest.

[0083] The data collection unit can estimate the user's emotions and prioritize the financial data to collect based on those emotions. For example, if the user is stressed, the data collection unit will postpone collecting less important data. For example, if the user is relaxed, the data collection unit can prioritize collecting more important data. The data collection unit can also adjust the priority of the data to be collected to collect it quickly if the user is in a hurry. This allows for efficient data collection by prioritizing data based on the user's emotions. Specific criteria and methods for determining priority include, for example, importance and urgency. Some or all of the above processing in the data collection unit is implemented using emotion estimation functions, such as using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The data collection unit can estimate the user's emotions using an emotion estimation algorithm and prioritize the financial data to collect based on those emotions.

[0084] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting financial data. For example, if the user is in a specific region, the data collection unit will prioritize the collection of financial data related to that region. The data collection unit can also filter and collect highly relevant data based on the user's geographical location. Furthermore, the data collection unit can select the optimal collection method based on the user's current location. This allows for the priority collection of highly relevant data by considering the user's geographical location. Specific types and methods of acquiring geographical location information include, for example, GPS data and address information. Some or all of the above-described processes in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can have a generating AI execute the process of collecting highly relevant data based on the user's geographical location.

[0085] The data collection unit can analyze a user's social media activity and collect relevant data when collecting financial data. For example, the data collection unit can collect financial data of interest from a user's social media activity. The data collection unit can also analyze the content of a user's social media posts and prioritize the collection of relevant data. Furthermore, the data collection unit can select data to collect by referring to the activities of the user's social media followers and friends. This allows for the collection of relevant data by analyzing the user's social media activity. Specific types of social media activity and methods of analysis include, for example, post content, number of likes, and number of followers. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can have a generative AI perform the process of analyzing a user's social media activity.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. In this way, by adjusting the presentation of the analysis based on the user's emotions, the analysis results can be provided in a way that is easy for the user to understand. Specific types of presentation methods and adjustment methods include, for example, graph display, text display, and audio output. Some or all of the above processing in the analysis unit is implemented using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. The analysis unit can estimate the user's emotions using an emotion estimation algorithm and adjust the presentation of the analysis based on the estimated emotions.

[0087] The analysis unit can adjust the level of detail of its analysis based on the importance of the financial data during the analysis process. For example, the analysis unit can perform a detailed analysis on high-importance data, or a simplified analysis on low-importance data. Furthermore, the analysis unit can dynamically adjust the level of detail of its analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail based on the importance of the financial data. Specific criteria and methods for adjusting the level of detail include, for example, data granularity and the number of displayed items. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have a generating AI perform the process of adjusting the level of detail of its analysis based on the importance of the financial data.

[0088] The analysis unit can apply different analysis algorithms depending on the category of financial data during analysis. For example, the analysis unit can apply an income-specific analysis algorithm to income data. It can also apply an expenditure-specific analysis algorithm to expenditure data. Furthermore, it can apply an investment-specific analysis algorithm to investment data. This allows for highly accurate analysis by applying different analysis algorithms depending on the category of financial data. Specific categories and classification criteria include, for example, income categories, expenditure categories, and investment categories. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have a generating AI execute the process of applying different analysis algorithms depending on the category of financial data.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, to-the-point analysis. If the user is relaxed, the analysis unit can also provide a longer analysis with detailed explanations. Furthermore, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. In this way, by adjusting the length of the analysis based on the user's emotions, analysis results can be provided that are appropriate to the user's situation. Specific criteria and methods for adjusting the length include, for example, the length of the text and the display time. Some or all of the above processing in the analysis unit is implemented using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. The analysis unit can estimate the user's emotions using an emotion estimation algorithm and adjust the length of the analysis based on the estimated emotions.

[0090] The analysis department can prioritize analyses based on the submission timing of financial data. For example, the analysis department might prioritize analyzing data with an approaching submission deadline. It might also postpone analyzing data with a distant submission deadline. Furthermore, the analysis department can dynamically adjust the analysis priority based on the submission timing. This enables efficient analysis by prioritizing analyses based on the submission timing of financial data. Specific criteria and methods for determining submission timing include, for example, legal deadlines and user-requested dates. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department could have a generating AI perform the process of prioritizing analyses based on the submission timing of financial data.

[0091] The analysis unit can adjust the order of analysis based on the relevance of financial data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of financial data. Specific criteria and evaluation methods for relevance include, for example, data correlation and co-occurrence frequency. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can have a generative AI perform the process of adjusting the order of analysis based on the relevance of financial data.

[0092] The suggestion unit can estimate the user's emotions and adjust the presentation of suggestions based on those emotions. For example, if the user is nervous, the suggestion unit can provide simple and highly visible suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can provide concise suggestions. By adjusting the presentation of suggestions based on the user's emotions, the suggestion unit can provide suggestions that are easy for the user to understand. Specific types of suggestion presentation and adjustment methods include, for example, graph displays, text displays, and audio output. Some or all of the above processing in the suggestion unit is implemented using emotion estimation functions, such as using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. The suggestion unit can estimate the user's emotions using an emotion estimation algorithm and adjust the presentation of suggestions based on those emotions.

[0093] The proposal department can adjust the level of detail in its proposals based on the importance of the tax strategies. For example, it can provide detailed proposals for highly important tax strategies, and simplified proposals for less important ones. Furthermore, the proposal department can dynamically adjust the level of detail in its proposals according to the importance of the tax strategies. This allows for more efficient proposals by adjusting the level of detail based on the importance of the tax strategies. Specific evaluation criteria and methods for determining importance include, for example, impact and urgency. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can have a generating AI perform the process of adjusting the level of detail in its proposals based on the importance of the tax strategies.

[0094] The proposal unit can apply different proposal algorithms depending on the category of tax strategy when making a proposal. For example, the proposal unit can apply an income-specific proposal algorithm to a tax strategy related to income. For example, the proposal unit can also apply an expense-specific proposal algorithm to a tax strategy related to expenses. Furthermore, the proposal unit can apply an investment-specific proposal algorithm to a tax strategy related to investments. This allows for highly accurate proposals by applying different proposal algorithms depending on the category of tax strategy. Specific types and implementation methods of proposal algorithms include, for example, rule-based and machine learning-based algorithms. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal unit can have a generating AI execute the process of applying different proposal algorithms depending on the category of tax strategy.

[0095] The suggestion section can estimate the user's emotions and adjust the length of suggestions based on those emotions. For example, if the user is in a hurry, the suggestion section can provide short, concise suggestions. If the user is relaxed, the suggestion section can provide longer suggestions with detailed explanations. If the user is excited, the suggestion section can also provide suggestions with visually stimulating effects. By adjusting the length of suggestions based on the user's emotions, suggestions can be tailored to the user's situation. Specific criteria and methods for adjusting the length of suggestions include, for example, the length of the text and the display time. Some or all of the above processing in the suggestion section is implemented using emotion estimation functions, such as using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The suggestion section can estimate the user's emotions using an emotion estimation algorithm and adjust the length of suggestions based on those emotions.

[0096] The proposal department can prioritize proposals based on the submission timing of tax strategies. For example, the proposal department will prioritize tax strategies with approaching submission deadlines. It can also postpone tax strategies with later submission deadlines. Furthermore, the proposal department can dynamically adjust the priority of proposals based on submission timing. This enables efficient proposals by prioritizing proposals based on the submission timing of tax strategies. Specific criteria and methods for determining submission timing include, for example, legal deadlines and user-requested dates. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not. For example, the proposal department can have a generating AI perform the process of determining the priority of proposals based on the submission timing of tax strategies.

[0097] The proposal unit can adjust the order of proposals based on the relevance of the tax strategies during the proposal process. For example, the proposal unit may prioritize proposing highly relevant tax strategies. For example, it may postpone proposing less relevant tax strategies. The proposal unit can also dynamically adjust the order of proposals based on the relevance of the tax strategies. This allows for more efficient proposals by adjusting the order of proposals based on the relevance of the tax strategies. Specific criteria and evaluation methods for relevance include, for example, data correlation and co-occurrence frequency. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal unit can have a generating AI perform the process of adjusting the order of proposals based on the relevance of the tax strategies.

[0098] The generation unit can estimate the user's emotions and adjust the declaration generation method based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a declaration that proceeds at a relaxed pace. If the user is in a hurry, the generation unit can also generate a declaration that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a declaration with visually stimulating effects. In this way, by adjusting the declaration generation method based on the user's emotions, a user-friendly declaration can be generated. Specific types of generation methods and adjustment methods include, for example, template-based and AI generation. Some or all of the above processing in the generation unit is implemented using emotion estimation functions, for example, using an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The generation unit can estimate the user's emotions using an emotion estimation algorithm and adjust the declaration generation method based on the estimated emotions.

[0099] The generation unit can analyze the user's past tax return history to select the optimal generation method when generating a tax return. For example, the generation unit can select the optimal generation method based on the format of tax returns previously used by the user. For example, the generation unit can also suggest the most efficient generation method based on the user's past tax return history. Furthermore, the generation unit can analyze the user's past tax return history and customize the generation method. This allows the optimal generation method to be selected by analyzing past tax return history. Specific criteria and selection methods for the optimal generation method include, for example, past history and user preferences. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past tax return history into a generation AI and have the generation AI select the optimal generation method.

[0100] The generation unit can customize the content of the tax return based on the user's current financial situation when generating the return. For example, the generation unit can customize the content of the return based on the user's current income. The generation unit can also customize the content of the return based on the user's current spending. Furthermore, the generation unit can customize the content of the return based on the user's current investment status. This allows for the generation of a more appropriate tax return by customizing the content based on the user's current financial situation. Specific methods and criteria for customization include, for example, adding or deleting items and changing the layout. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can have a generation AI perform the process of customizing the content of the tax return based on the user's current financial situation.

[0101] The generation unit can estimate the user's emotions and determine the priority of declarations based on those emotions. For example, if the user is stressed, the generation unit will postpone less important declarations. For example, if the user is relaxed, the generation unit can prioritize the generation of more important declarations. Furthermore, if the user is in a hurry, the generation unit can adjust the priority of declarations to generate them quickly. This allows for efficient declaration generation by determining the priority of declarations based on the user's emotions. Specific criteria and methods for determining priority include, for example, importance and urgency. Some or all of the above processing in the generation unit is implemented using emotion estimation functions, such as using an emotion engine or generation AI. Generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The generation unit can estimate the user's emotions using an emotion estimation algorithm and determine the priority of declarations based on those emotions.

[0102] The generation unit can generate the most suitable tax return by considering the user's geographical location information during the tax return generation process. For example, if the user is in a specific region, the generation unit will prioritize generating tax returns relevant to that region. The generation unit can also filter and generate highly relevant tax returns based on the user's geographical location information. Furthermore, the generation unit can select the most suitable generation method based on the user's current location. This allows for the generation of the most suitable tax return by considering the user's geographical location information. Specific types and methods of acquiring geographical location information include, for example, GPS data and address information. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can have a generation AI execute the process of generating the most suitable tax return based on the user's geographical location information.

[0103] The generation unit can analyze the user's social media activity and suggest content for the declaration form when generating it. For example, the generation unit can suggest content for the declaration form that the user is interested in based on their social media activity. The generation unit can also analyze the user's social media posts and prioritize suggesting relevant content for the declaration form. Furthermore, the generation unit can select content for the declaration form by referring to the activities of the user's social media followers and friends. In this way, by analyzing the user's social media activity, it can suggest relevant content for the declaration form. Specific types of social media activity and methods of analysis include, for example, post content, number of likes, and number of followers. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can have a generation AI perform the process of analyzing the user's social media activity.

[0104] The security unit can estimate the user's emotions and adjust the security level based on those emotions. For example, if the user is feeling anxious, the security unit can increase the security level. For example, if the user is relaxed, the security unit can return the security level to normal. Also, if the user is in a hurry, the security unit can adjust the security level to expedite processing. In this way, adjusting the security level based on the user's emotions can increase the user's sense of security. Specific criteria and adjustment methods for security levels include, for example, encryption strength and access control. Some or all of the above processing in the security unit is implemented using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The security unit can estimate the user's emotions using an emotion estimation algorithm and adjust the security level based on those emotions.

[0105] The security department can analyze past security incidents and select the optimal security measures during security operations. For example, the security department can select the optimal security measures based on past security incidents. The security department can also propose measures to prevent recurrence based on past security incidents. Furthermore, the security department can analyze past security incidents and customize security measures. This allows for the selection of optimal security measures by analyzing past security incidents. Specific types of security incidents and methods of analysis include, for example, data breaches and hacking. Some or all of the above processes in the security department may be performed using, for example, AI, or not. For example, the security department can have a generative AI perform the process of analyzing past security incidents.

[0106] The security department can estimate the user's emotions and determine security priorities based on those estimated emotions. For example, if the user is feeling anxious, the security department may postpone less important security measures. For example, if the user is relaxed, the security department may prioritize more important security measures. Furthermore, if the user is in a hurry, the security department can adjust the priority of security measures to ensure rapid implementation. This allows for efficient implementation of security measures by determining security priorities based on the user's emotions. Specific criteria and methods for determining priorities include, for example, importance and urgency. Some or all of the above processing in the security department is implemented using emotion estimation functions, such as using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The security department can estimate the user's emotions using an emotion estimation algorithm and determine security priorities based on those estimated emotions.

[0107] The security department can select the optimal security measures during security operations, taking into account the user's geographical location. For example, if the user is in a specific region, the security department will prioritize implementing security measures relevant to that region. The security department can also filter and implement highly relevant security measures based on the user's geographical location. Furthermore, the security department can select the optimal security measures based on the user's current location. This allows for the selection of optimal security measures by considering the user's geographical location. Specific types and methods of acquiring geographical location information include, for example, GPS data and address information. Some or all of the above-described processes in the security department may be performed using, for example, AI, or not. For example, the security department can have a generating AI execute the process of selecting the optimal security measures based on the user's geographical location.

[0108] The partnership unit can estimate the user's emotions and adjust the partnership proposal method based on the estimated emotions. For example, if the user is nervous, the partnership unit can provide simple and highly visual proposals. If the user is relaxed, the partnership unit can also provide detailed proposals. Furthermore, if the user is in a hurry, the partnership unit can provide concise proposals. In this way, by adjusting the proposal method based on the user's emotions, proposals that are easy for the user to understand can be provided. Specific types of proposal methods and adjustment methods include, for example, graph displays, text displays, and audio output. Some or all of the above processing in the partnership unit is implemented using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. The partnership unit can estimate the user's emotions using an emotion estimation algorithm and adjust the partnership proposal method based on the estimated emotions.

[0109] The Partnership Department can analyze past partnership history to select the optimal proposal method when proposing a partnership. For example, the Partnership Department can select the optimal proposal method based on past partnership history. The Partnership Department can also propose successful proposal methods based on past partnership history. Furthermore, the Partnership Department can analyze past partnership history and customize the proposal method. This allows for the selection of the optimal proposal method by analyzing past partnership history. Specific criteria and selection methods for the optimal proposal method include, for example, past history and user preferences. Some or all of the above processes in the Partnership Department may be performed using AI, or not. For example, the Partnership Department can input past partnership history into a generating AI and have the generating AI select the optimal proposal method.

[0110] The partnership department can estimate the user's emotions and determine the priority of partnerships based on those emotions. For example, if the user is feeling anxious, the partnership department may postpone less important partnerships. For example, if the user is relaxed, the partnership department may prioritize suggesting more important partnerships. Furthermore, if the user is in a hurry, the partnership department can adjust the priority of partnerships and make suggestions quickly. This allows for efficient partnership suggestions by determining the priority of partnerships based on the user's emotions. Specific criteria and methods for determining priority include, for example, importance and urgency. Some or all of the above processing in the partnership department is implemented using emotion estimation functions, such as using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The partnership department can estimate the user's emotions using an emotion estimation algorithm and determine the priority of partnerships based on those emotions.

[0111] The Partnership Department can provide optimal partnership proposals by considering the user's geographical location information. For example, if a user is in a specific region, the Partnership Department will prioritize proposing partnerships related to that region. The Partnership Department can also filter and propose highly relevant partnerships based on the user's geographical location information. Furthermore, the Partnership Department can select the optimal proposal method based on the user's current location. This allows the department to provide optimal partnership proposals by considering the user's geographical location information. Specific types and methods of acquiring geographical location information include, for example, GPS data and address information. Some or all of the above processing in the Partnership Department may be performed using, for example, AI, or not. For example, the Partnership Department can have a generating AI execute the process of providing optimal proposals based on the user's geographical location information.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The data collection unit can estimate the user's emotions and adjust the timing of financial data collection based on the estimated emotions. For example, if the user is stressed, the data collection timing can be delayed until the user is relaxed. Similarly, if the user is in a hurry, the data collection timing can be adjusted to reduce the user's burden. This reduces the user's burden by adjusting the data collection timing based on their emotions. Specific methods and criteria for estimating emotions include, for example, facial recognition, voice analysis, and text analysis. Some or all of the above processing in the data collection unit is implemented using emotion estimation functions with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. The data collection unit can estimate the user's emotions using an emotion estimation algorithm and adjust the data collection timing based on the estimated emotions.

[0114] The data collection unit can analyze the user's past financial data collection history and select the optimal collection method. For example, it can prioritize selecting collection methods that the user has frequently used in the past. It can also suggest the most efficient collection method based on the user's past collection history. Furthermore, it can analyze the user's past collection history and customize the collection method. This allows for the selection of the optimal collection method by analyzing past collection history. Specific criteria and methods for the optimal collection method include, for example, the frequency of data collection and the means of collection. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past financial data collection history into a generating AI and have the generating AI select the optimal collection method.

[0115] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, it can provide simple and easy-to-understand analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide concise analysis results. In this way, by adjusting the presentation of the analysis based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Specific types of presentation methods and adjustment methods include, for example, graph display, text display, and audio output. Some or all of the above processing in the analysis unit is implemented using emotion estimation functions with emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. The analysis unit can estimate the user's emotions using emotion estimation algorithms and adjust the presentation of the analysis based on the estimated emotions.

[0116] The analysis unit can adjust the level of detail of the analysis based on the importance of the financial data during the analysis process. For example, it can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. Furthermore, it can dynamically adjust the level of detail of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail based on the importance of the financial data. Specific criteria and methods for adjusting the level of detail include, for example, the granularity of the data and the number of items displayed. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can have a generating AI perform the process of adjusting the level of detail of the analysis based on the importance of the financial data.

[0117] The suggestion unit can estimate the user's emotions and adjust the presentation of suggestions based on those emotions. For example, if the user is nervous, it can provide simple and highly visual suggestions. If the user is relaxed, it can provide detailed suggestions. Furthermore, if the user is in a hurry, it can provide concise suggestions. By adjusting the presentation of suggestions based on the user's emotions, it can provide suggestions that are easy for the user to understand. Specific types of suggestion presentation and adjustment methods include, for example, graph displays, text displays, and audio output. Some or all of the above processing in the suggestion unit is implemented using emotion estimation functions with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. The suggestion unit can estimate the user's emotions using an emotion estimation algorithm and adjust the presentation of suggestions based on those emotions.

[0118] The proposal department can adjust the level of detail in a proposal based on the importance of the tax strategy. For example, it can provide detailed proposals for highly important tax strategies and simplified proposals for less important ones. Furthermore, it can dynamically adjust the level of detail in a proposal according to the importance of the tax strategy. This allows for more efficient proposals by adjusting the level of detail based on the importance of the tax strategy. Specific evaluation criteria and methods for determining importance include, for example, impact and urgency. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can have a generating AI perform the process of adjusting the level of detail in a proposal based on the importance of the tax strategy.

[0119] The generation unit can estimate the user's emotions and adjust the declaration generation method based on the estimated emotions. For example, if the user is relaxed, it can generate a declaration that proceeds at a leisurely pace. If the user is in a hurry, it can generate a declaration that emphasizes the shortest route. Furthermore, if the user is excited, it can generate a declaration with visually stimulating effects. In this way, by adjusting the declaration generation method based on the user's emotions, a declaration that is easy for the user to use can be generated. Specific types of generation methods and adjustment methods include, for example, template-based and AI generation. Some or all of the above processing in the generation unit is implemented using emotion estimation functions with an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The generation unit can estimate the user's emotions using an emotion estimation algorithm and adjust the declaration generation method based on the estimated emotions.

[0120] The generation unit can analyze the user's past tax return history to select the optimal generation method when generating a tax return. For example, it can select the optimal generation method based on the format of tax returns the user has used in the past. It can also suggest the most efficient generation method based on the user's past tax return history. Furthermore, it can analyze the user's past tax return history and customize the generation method. This allows for the selection of the optimal generation method by analyzing past tax return history. Specific criteria and selection methods for the optimal generation method include, for example, past history and user preferences. Some or all of the above-described processes in the generation unit may be performed using AI, or they may not. For example, the generation unit can input the user's past tax return history into a generation AI and have the generation AI select the optimal generation method.

[0121] The security unit can estimate the user's emotions and adjust the security level based on those emotions. For example, if the user is feeling anxious, the security level can be increased. Conversely, if the user is relaxed, the security level can be returned to normal. Furthermore, if the user is in a hurry, the security level can be adjusted to expedite processing. In this way, adjusting the security level based on the user's emotions can enhance the user's sense of security. Specific criteria and adjustment methods for security levels include, for example, encryption strength and access control. Some or all of the above processing in the security unit is implemented using emotion estimation functions with emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. The security unit can estimate the user's emotions using emotion estimation algorithms and adjust the security level based on those emotions.

[0122] The security department can analyze past security incidents and select the optimal security measures during security operations. For example, it can select the optimal security measures based on past security incidents. It can also propose measures to prevent recurrence based on past security incidents. Furthermore, it can analyze past security incidents and customize security measures. This allows for the selection of optimal security measures by analyzing past security incidents. Specific types of security incidents and methods of analysis include, for example, data breaches and hacking. Some or all of the above processes in the security department may be performed using AI, or not. For example, the security department can have a generating AI perform the process of analyzing past security incidents.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The collection unit collects financial data in conjunction with personal identification numbers. For example, personal identification numbers can be used to automatically collect financial data such as bank transaction history, investment information, and credit card usage history in real time. The collection unit obtains data from financial institutions via APIs, obtains user permission, and implements security protocols to securely collect financial data. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it uses machine learning algorithms to analyze the data and evaluate the user's income, expenses, and investment status. It uses regression analysis to predict income trends, clustering to classify spending patterns, and neural networks to assess investment risk. Step 3: The proposal department proposes the optimal tax strategy based on the analysis results obtained by the analysis department. For example, it proposes tax-saving measures and tax deductions, calculates the optimal deduction amount according to the user's income, proposes the optimal investment strategy based on the user's investment situation, and proposes saving methods based on the user's spending patterns. Step 4: The generation unit automatically generates the tax return based on the tax strategy proposed by the proposal unit. For example, it generates an AI-driven, efficient tax return, automatically generates the contents of the tax return using natural language processing technology, automatically attaches necessary documents using image recognition technology, and proposes future tax strategies using predictive analytics.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and generation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart device 14 and automatically collects financial data in real time using personal identification numbers. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using machine learning algorithms. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes the optimal tax strategy based on the analysis results. The generation unit is implemented by the control unit 46A of the smart device 14 and automatically generates an AI-driven, efficient tax return. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and generation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart glasses 214 and automatically collects financial data in real time using a personal identification number. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using a machine learning algorithm. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes an optimal tax strategy based on the analysis results. The generation unit is implemented by the control unit 46A of the smart glasses 214 and automatically generates an AI-driven, efficient tax return. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the headset terminal 314 and automatically collects financial data in real time using personal identification numbers. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using machine learning algorithms. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes the optimal tax strategy based on the analysis results. The generation unit is implemented by the control unit 46A of the headset terminal 314 and automatically generates an AI-driven, efficient tax return. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and generation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the robot 414 and automatically collects financial data in real time using personal identification numbers. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using machine learning algorithms. The proposal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and proposes the optimal tax strategy based on the analysis results. The generation unit is implemented by, for example, the control unit 46A of the robot 414 and automatically generates an AI-driven, efficient tax return. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) The collection department collects financial data in conjunction with personal identification numbers, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes specific tax strategies. The system comprises a generation unit that automatically generates a tax return based on the tax strategy proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Financial data is automatically collected in real time in conjunction with personal identification numbers. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is We analyze the collected data using machine learning algorithms and propose specific tax strategies tailored to each user's situation. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is AI-driven, efficient automatic generation of tax returns. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, It features predictive analytics and future strategy proposals, offering suggestions for future tax planning and wealth building strategies. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned system, We employ blockchain-based security and protect personal information with specific encryption technologies. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned system, We leverage effective partnerships with fintech companies and financial institutions to provide comprehensive AI solutions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate user sentiment and adjust the timing of financial data collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past financial data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting financial data, filtering is performed based on the user's current economic situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates user sentiment and prioritizes the financial data to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting financial data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting financial data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During the analysis, adjust the level of detail based on the importance of the financial data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the category of financial data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During the analysis, prioritize the analysis based on when the financial data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of financial data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, 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 21) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the tax strategy. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the tax strategy category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, 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 24) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the tax strategy will be submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on their relevance to the tax strategy. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is The system estimates the user's emotions and adjusts the declaration generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is When generating a tax return, the system analyzes the user's past tax return history to select the optimal generation method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is When generating a tax return, the contents of the return are customized based on the user's current financial situation. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is The system estimates user sentiment and determines the priority of declarations based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The generating unit is When generating a tax return, the system takes the user's geographical location into consideration to generate the most suitable return. The system described in Appendix 1, characterized by the features described herein. (Note 31) The generating unit is When generating a tax return, the system analyzes the user's social media activity and suggests content for the return. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned security unit is It estimates the user's emotions and adjusts the security level based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned security unit is During security checks, past security incidents are analyzed to select the most appropriate security measures. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned security unit is It estimates user sentiment and determines security priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned security unit is During security checks, the optimal security measures are selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned partnership department, We estimate the user's emotions and adjust how we propose partnerships based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned partnership department, When proposing a partnership, we analyze past partnership history to select the most suitable proposal method. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned partnership department, It estimates user sentiment and prioritizes partnerships based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned partnership department, When proposing partnerships, we provide optimal proposals by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects financial data in conjunction with personal identification numbers, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes specific tax strategies. The system comprises a generation unit that automatically generates a tax return based on the tax strategy proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is Financial data is automatically collected in real time in conjunction with personal identification numbers. The system according to feature 1.

3. The aforementioned analysis unit is We analyze the collected data using machine learning algorithms and propose specific tax strategies tailored to each user's situation. The system according to feature 1.

4. The generating unit is AI-driven, efficient automatic generation of tax returns. The system according to feature 1.

5. The aforementioned proposal section is, It features predictive analytics and future strategy proposals, offering suggestions for future tax planning and wealth building strategies. The system according to feature 1.

6. The aforementioned system, We employ blockchain-based security and protect personal information with specific encryption technologies. The system according to feature 1.

7. The aforementioned system, We leverage effective partnerships with fintech companies and financial institutions to provide comprehensive AI solutions. The system according to feature 1.

8. The aforementioned collection unit is We estimate user sentiment and adjust the timing of financial data collection based on the estimated user sentiment. The system according to feature 1.

9. The aforementioned collection unit is Analyze the user's past financial data collection history and select the optimal collection method. The system according to feature 1.

10. The aforementioned collection unit is When collecting financial data, filtering is performed based on the user's current economic situation and areas of interest. The system according to feature 1.

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