system

Abstract

The system according to this embodiment aims to simplify and streamline the tax return filing process. [Solution] The system according to the embodiment comprises a reception unit, an extraction unit, a proposal unit, and a management unit. The reception unit receives user inquiries. The extraction unit extracts text data from documents received by the reception unit. The proposal unit analyzes the data extracted by the extraction unit and proposes the optimal tax strategy. The management unit manages the data proposed by the proposal unit on the cloud.

Description

Technical Field

[0005]

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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 a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that the process of final tax return is complicated and difficult to perform efficiently.

[0005] The system according to the embodiment aims to simplify and efficiently perform the process of final tax return.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an extraction unit, a proposal unit, and a management unit. The reception unit receives user inquiries. The extraction unit extracts text data from documents received by the reception unit. The proposal unit analyzes the data extracted by the extraction unit and proposes the optimal tax strategy. The management unit manages the data proposed by the proposal unit on the cloud. [Effects of the Invention]

[0007] The system according to this embodiment can simplify and streamline the tax filing process. [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 manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The fully automated support system according to an embodiment of the present invention is a system that simplifies and streamlines the tax return process using AI technology. This fully automated support system begins with the user making an inquiry in natural language via voice recognition or a chatbot. Next, it extracts text data from documents using OCR technology and automatically inputs it into the tax return form. Furthermore, it analyzes past data using machine learning and data analysis to propose the optimal tax strategy. Finally, cloud-based information management securely manages user data and makes it accessible anytime, anywhere. This system can significantly reduce the time spent on tax returns and alleviate user stress. For example, a user can make an inquiry via voice recognition or a chatbot, such as "Please tell me how to file my tax return this year." This inquiry is analyzed by an AI agent, which provides appropriate information and guidance. Next, it extracts text data from documents using OCR technology and automatically inputs it into the tax return form. For example, documents such as receipts and invoices are scanned, and text data is extracted using OCR technology. This text data is automatically input into the tax return form, reducing data entry errors and improving processing speed. Furthermore, it analyzes past data using machine learning and data analysis to propose the optimal tax strategy. For example, it analyzes past filing data and suggests the optimal filing method to ensure users receive the maximum possible tax deductions. It also provides personalized advice based on users' behavioral patterns. Finally, cloud-based information management securely manages user data and makes it accessible anytime, anywhere. For example, users can access it from home or the office to check the progress of their tax filing. This allows users to proceed with the tax filing process simply and efficiently. As a result, the fully automated support system can significantly reduce the time spent on tax filing and alleviate user stress. For example, it can reduce the average tax filing time from 20 hours to 2 hours. In addition, 24 / 7 accessibility, automated document generation, and important deadline alerts significantly reduce the stress and time users spend on tax filing.Furthermore, AI-powered analysis of past tax return data and optimization suggestions ensure users receive the maximum possible tax deductions. This fully automated support system simplifies and streamlines the tax filing process.

[0029] The fully automated support system according to this embodiment comprises a reception unit, an extraction unit, a proposal unit, and a management unit. The reception unit receives user inquiries. The reception unit can, for example, analyze the user's voice using speech recognition technology to understand the content of the inquiry. The reception unit can also interact with users using a chatbot and accept inquiries in natural language. For example, if a user asks the reception unit, "Please tell me how to file my tax return this year," the reception unit will analyze the content using speech recognition technology and provide appropriate information. Furthermore, the reception unit can also interact with users using a chatbot and accept inquiries in natural language. For example, if a user asks the chatbot, "When is the deadline for filing my tax return?", the chatbot will provide an appropriate answer to that question. The extraction unit extracts text data from documents using OCR technology. The extraction unit can, for example, scan documents such as receipts and invoices and convert them into text data using OCR technology. The extraction unit can also scan handwritten documents and convert them into text data using OCR technology. For example, the extraction unit scans handwritten receipts and converts them into text data using OCR technology. Furthermore, the extraction unit can directly read digital documents and convert them into text data. For example, the extraction unit reads PDF invoices and converts them into text data using OCR technology. The proposal unit analyzes historical data using machine learning and data analytics to propose optimal tax strategies. For example, the proposal unit can analyze past filing data and propose the best filing method to ensure users receive maximum tax deductions. The proposal unit can also analyze user behavior patterns and provide personalized advice. For example, the proposal unit analyzes users' past filing data and proposes optimal tax strategies. Furthermore, the proposal unit can analyze user behavior patterns and provide personalized advice. The management unit securely manages user data through cloud-based information management. For example, the management unit can store user data in the cloud and protect it using encryption technology.Furthermore, the management department can manage access permissions to ensure that users can access data anytime, anywhere. For example, the management department can enable users to access and check the progress of their tax return from home or the office. This allows the fully automated support system according to the embodiment to simplify and streamline the tax return process.

[0030] The reception desk receives user inquiries. For example, the reception desk can analyze user speech using speech recognition technology to understand the content of inquiries. Specifically, speech recognition technology converts user speech into text in real time, and then analyzes that text data using natural language processing (NLP) technology. This allows for an accurate understanding of the user's intent and the content of their question. The reception desk can also interact with users using a chatbot to accept inquiries in natural language. The chatbot generates appropriate responses to user input based on a pre-trained dialogue model. For example, if a user asks, "How do I file my tax return this year?", the chatbot analyzes the content using speech recognition technology and provides appropriate information. Furthermore, if a user asks the chatbot, "When is the deadline for filing my tax return?", the chatbot will provide an appropriate answer. The chatbot can refer to a predefined FAQ database and knowledge base to respond quickly and accurately to user questions. This allows the reception desk to respond quickly and accurately to user inquiries, improving user convenience. Furthermore, the reception department can record user inquiry history and use it to handle future inquiries. For example, by analyzing user trends and needs based on past inquiries, it can provide more personalized responses. This allows the reception department to improve user satisfaction and increase the overall efficiency of the system.

[0031] The extraction unit extracts text data from documents using OCR technology. For example, the extraction unit can scan documents such as receipts and invoices and convert them into text data using OCR technology. Specifically, the image data scanned is analyzed by OCR software, and characters and numbers are recognized. Because OCR technology can recognize not only printed characters but also handwritten characters, it is also possible to scan handwritten documents and convert them into text data. For example, the extraction unit scans a handwritten receipt and converts it into text data using OCR technology. Furthermore, the extraction unit can directly read digital documents and convert them into text data. For example, the extraction unit reads a PDF invoice and converts it into text data using OCR technology. This allows the extraction unit to efficiently extract text data not only from paper documents but also from digital documents. The extracted text data is used for subsequent processing and analysis. For example, the text data extracted by the extraction unit is analyzed by the proposal unit and used to propose the optimal tax strategy. Furthermore, the extraction unit can continuously improve its OCR model using machine learning algorithms to enhance the accuracy of the extracted text data. This allows the extraction unit to consistently provide highly accurate text data, thereby improving the overall system performance.

[0032] The proposal department uses machine learning and data analysis to analyze historical data and propose the optimal tax strategy. For example, the proposal department can analyze past filing data and propose the optimal filing method to ensure users receive the maximum tax deduction. Specifically, the proposal department inputs past filing data into a machine learning algorithm to learn patterns and trends. This allows the proposal department to propose the optimal tax strategy based on the user's past filing data. The proposal department can also analyze the user's behavior patterns and provide personalized advice. For example, the proposal department analyzes the user's past filing data and proposes the optimal tax strategy. Furthermore, the proposal department can analyze the user's behavior patterns and provide personalized advice. The proposal department uses machine learning algorithms to analyze the user's behavior patterns and past filing data and proposes the optimal tax strategy. This allows the proposal department to propose the optimal tax strategy to the user and maximize their tax deductions. Furthermore, the proposal department can analyze the user's behavior patterns and provide personalized advice. For example, the proposal department analyzes the user's past filing data and proposes the optimal tax strategy. This allows the proposal department to propose the optimal tax strategy to the user and maximize their tax deductions.

[0033] The management department securely manages user data through cloud-based information management. For example, the management department can store user data in the cloud and protect it using encryption technology. Specifically, user data is stored on cloud servers and protected using encryption technology. This protects user data from unauthorized access and data breaches. The management department can also manage access rights to ensure that users can access their data anytime, anywhere. For example, the management department can allow users to access their tax return progress from home or the office. This allows the management department to securely manage user data and ensure that users can access their data anytime, anywhere. Furthermore, the management department can regularly back up data to prepare for data loss or corruption. This allows the management department to securely manage user data and improve the reliability of the entire system.

[0034] The reception desk can accept inquiries in natural language through speech recognition or chatbots. For example, the reception desk can analyze the user's voice using speech recognition technology to understand the content of the inquiry. For instance, if a user asks, "Please tell me how to file my tax return this year," the reception desk can analyze the content using speech recognition technology and provide appropriate information. The reception desk can also interact with users using chatbots to accept inquiries in natural language. For example, if a user asks the chatbot, "When is the deadline for filing my tax return?", the chatbot can provide an appropriate answer. This improves user convenience by accepting inquiries in natural language through speech recognition or chatbots. Speech recognition technology includes, for example, deep learning-based speech recognition technology. Chatbots include, for example, rule-based chatbots and AI-based chatbots. Some or all of the above-described processes in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's voice data into a generating AI and have the generating AI perform the analysis of the inquiry content.

[0035] The extraction unit can extract text data from documents using OCR technology. For example, the extraction unit can scan documents such as receipts and invoices and convert them into text data using OCR technology. For example, the extraction unit can scan handwritten receipts and convert them into text data using OCR technology. The extraction unit can also directly read digital documents and convert them into text data. For example, the extraction unit can read PDF invoices and convert them into text data using OCR technology. This improves the accuracy of text data extraction from documents by using OCR technology. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input document data scanned by a scanner into a generating AI and have the generating AI perform the text data extraction.

[0036] The proposal department can analyze historical data using machine learning and data analysis to propose the optimal tax strategy. For example, the proposal department can analyze past filing data and propose the optimal filing method to ensure the user receives the maximum possible tax deductions. For example, the proposal department can analyze a user's past filing data and propose the optimal tax strategy. The proposal department can also analyze user behavior patterns and provide personalized advice. For example, the proposal department can analyze user behavior patterns and provide personalized advice. This improves the accuracy of proposing the optimal tax strategy by using machine learning and data analysis. Machine learning includes, for example, regression analysis and neural networks. Data analysis includes, for example, statistical analysis and data mining. Some or all of the above processes in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input historical filing data into a generating AI and have the generating AI generate a proposal for the optimal tax strategy.

[0037] The management department can securely manage user data through cloud-based information management. For example, the management department can store user data in the cloud and protect it using encryption technology. The management department can also manage access permissions to ensure users can access their data anytime, anywhere. For example, the management department can allow users to access their tax return progress from home or the office. This enables the secure management and accessibility of user data through cloud-based information management. Cloud-based information management includes, for example, data encryption and backup methods. Some or all of the above processes in the management department may be performed using, for example, AI, or not. For example, the management department can have a generating AI store user data in the cloud and protect it using encryption technology.

[0038] The reception department can analyze a user's past inquiry history and select the most appropriate response. For example, if a user has made a similar inquiry in the past, the reception department can refer to the response method used then to respond quickly. For example, if a user has made a similar inquiry in the past, the reception department can refer to the response method used then to respond quickly. The reception department can also prioritize selecting a response method that the user has been satisfied with in the past. For example, the reception department can prioritize selecting a response method that the user has been satisfied with in the past. The reception department can also select the most effective response method from a user's past inquiry history. For example, the reception department can select the most effective response method from a user's past inquiry history. This allows for the selection of the most appropriate response method and a quick response by analyzing a user's past inquiry history. Past inquiry history includes, for example, the content of the inquiry and the response history. The most appropriate response method includes, for example, providing an FAQ or escalating the issue to an expert. Some or all of the above processing in the reception department may be performed using, for example, AI, or not using AI. For example, the reception department can input data on the user's past inquiry history into a generating AI, and have the AI ​​select the most appropriate response method.

[0039] The reception desk can generate customized responses to provide appropriate guidance depending on the content of the inquiry. For example, if the user asks a basic question about filing a tax return, the reception desk can provide concise guidance. For example, if the user asks a basic question about filing a tax return, the reception desk can provide concise guidance. The reception desk can also provide detailed guidance if the user asks about a complex tax issue. For example, if the reception desk asks about a specific tax document, the reception desk can generate a customized response regarding that document. For example, if the reception desk asks about a specific tax document, the reception desk can generate a customized response regarding that document. This allows the reception desk to provide appropriate guidance to the user by generating customized responses according to the content of the inquiry. Customized responses may include, for example, responses based on the user's profile or responses based on past inquiry history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's inquiry into a generating AI and have the generating AI perform the generation of a customized response.

[0040] The reception desk can provide highly relevant information when an inquiry is made, taking into account the user's geographical location. For example, if the user lives in a specific region, the reception desk can provide tax information relevant to that region. For example, if the user lives in a specific region, the reception desk can provide tax information relevant to that region. The reception desk can also provide tax information based on the user's current location if the user is traveling. For example, if the reception desk is planning to move, the reception desk can provide tax information relevant to the new address if the user is planning to move. For example, if the reception desk is planning to move, the reception desk can provide tax information relevant to the new address. This allows the reception desk to provide highly relevant information by taking into account the user's geographical location. Geographical location information includes, for example, the use of GPS data or location estimation from IP addresses. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing highly relevant information.

[0041] The reception desk can analyze a user's social media activity and provide relevant information when an inquiry is made. For example, if a user has made tax-related posts on social media, the reception desk can provide information related to those posts. The reception desk can also provide information related to a specific tax issue if the user is discussing that issue on social media. The reception desk can also provide information about a specific tax document if the user is asking about that document on social media. This allows the reception desk to provide relevant information by analyzing the user's social media activity. Social media activity includes, for example, analysis of post content and analysis of followers. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI provide the relevant information.

[0042] The extraction unit can improve extraction accuracy by applying different OCR algorithms depending on the type of document. For example, in the case of a receipt, the extraction unit can apply an OCR algorithm that corresponds to a specific format. For example, in the case of an invoice, the extraction unit can apply an OCR algorithm that corresponds to a different format. For example, in the case of a contract, the extraction unit can apply an OCR algorithm that corresponds to a different format. For example, in the case of a contract, the extraction unit can apply an OCR algorithm that extracts detailed text data. This improves extraction accuracy by applying an OCR algorithm appropriate to the type of document. Different OCR algorithms include, for example, algorithms for handwritten characters and algorithms for printed characters. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the type of document into a generating AI and cause the generating AI to apply an appropriate OCR algorithm.

[0043] The extraction unit can select the optimal extraction method by considering the document's layout and format during extraction. For example, if the document has a complex layout, the extraction unit can analyze the layout and select the optimal extraction method. The extraction unit can also select a method to quickly extract text data if the document has a simple format. The extraction unit can also select an extraction method specifically for handwritten character recognition if the document is handwritten. This allows the extraction unit to select the optimal extraction method by considering the document's layout and format. The document's layout and format include, for example, analysis of column layouts and font recognition. Some or all of the above-described processes in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the document's layout and format into a generating AI and have the generating AI select the optimal extraction method.

[0044] The extraction unit can determine the priority of extracted data based on the document submission date during the extraction process. For example, the extraction unit can prioritize the extraction of text data from documents with approaching submission deadlines. The extraction unit can also prioritize the extraction of text data from documents with distant submission deadlines. The extraction unit can also postpone the extraction of text data from documents whose submission deadlines have passed. By determining the priority of extracted data based on the document submission date, documents with approaching submission deadlines can be processed preferentially. The document submission date includes, for example, analysis of the submission date and setting of deadlines. Some or all of the above processing in the extraction unit may be performed using, for example, AI, or not using AI. For example, the extraction unit can input document submission date data into a generating AI and have the generating AI determine the priority of the extracted data.

[0045] The extraction unit can adjust the order of extracted data based on the relevance of the documents during extraction. For example, the extraction unit can prioritize the extraction of text data from highly relevant documents. The extraction unit can also postpone the extraction of text data from less relevant documents. The extraction unit can also extract text data from documents of moderate relevance with normal priority. This allows for the priority extraction of highly relevant data by adjusting the order of extracted data based on the relevance of the documents. Document relevance includes, for example, similarity of content and frequency of relevant keywords. Some or all of the above processing in the extraction unit may be performed using, for example, AI, or not using AI. For example, the extraction unit can input document relevance data into a generating AI and have the generating AI adjust the order of the extracted data.

[0046] The proposal unit can select the optimal tax strategy by referring to past tax data when making a proposal. For example, the proposal unit can analyze the user's past tax return data and propose the optimal tax strategy. For example, the proposal unit can analyze the user's past tax return data and propose the optimal tax strategy. The proposal unit can also refer to the user's past tax history and propose the maximum possible tax deductions. For example, the proposal unit can refer to the user's past tax history and propose the maximum possible tax deductions. The proposal unit can also analyze the user's past behavioral patterns and propose a personalized tax strategy. For example, the proposal unit can analyze the user's past behavioral patterns and propose a personalized tax strategy. This allows the optimal tax strategy to be selected by referring to past tax data. Past tax data includes, for example, past tax return details and tax investigation results. 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 input past tax data into a generating AI and have the generating AI select the optimal tax strategy.

[0047] The proposal department can analyze the user's behavior patterns and provide personalized advice when making a proposal. For example, the proposal department can analyze the user's past behavior patterns and propose the optimal tax strategy. For example, the proposal department can analyze the user's past behavior patterns and propose the optimal tax strategy. The proposal department can also propose the maximum possible tax deductions based on the user's behavior patterns. For example, the proposal department can propose the maximum possible tax deductions based on the user's behavior patterns. The proposal department can also provide personalized advice based on the user's behavior patterns. For example, the proposal department can provide personalized advice based on the user's behavior patterns. This allows for the provision of personalized advice by analyzing the user's behavior patterns. User behavior patterns include, for example, past behavior history and frequency of use. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input user behavior pattern data into a generating AI and have the generating AI provide personalized advice.

[0048] The proposal unit can propose the optimal tax strategy by considering the user's geographical location information. For example, if the user lives in a specific region, the proposal unit can propose a tax strategy relevant to that region. For example, if the user lives in a specific region, the proposal unit can propose a tax strategy relevant to that region. For example, if the user is traveling, the proposal unit can propose a tax strategy based on the user's current location. For example, if the user is planning to move, the proposal unit can propose a tax strategy relevant to the new address. For example, if the user is planning to move, the proposal unit can propose a tax strategy relevant to the new address. This allows the proposal to propose the optimal tax strategy by considering the user's geographical location information. Geographical location information includes, for example, the use of GPS data or location estimation from IP addresses. 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 input the user's geographical location information into a generating AI and have the generating AI execute the proposal of the optimal tax strategy.

[0049] The proposal unit can analyze a user's social media activity and propose relevant tax strategies when making a proposal. For example, if a user posts about tax matters on social media, the proposal unit can propose tax strategies related to those posts. The proposal unit can also propose tax strategies related to specific tax issues if a user discusses them on social media. The proposal unit can also propose tax strategies related to specific tax documents if a user asks about them on social media. This allows the proposal unit to suggest relevant tax strategies by analyzing a user's social media activity. Social media activity includes, for example, analysis of post content and analysis of followers. 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 department can input user social media activity data into a generating AI and have the AI ​​generate proposals for relevant tax strategies.

[0050] The management department can select the optimal management method by referring to past data management history when managing data. For example, the management department can select the optimal management method by referring to data management methods previously used by users. For example, the management department can select the optimal management method by referring to data management methods previously used by users. The management department can also prioritize selecting data management methods that users have been satisfied with in the past. For example, the management department can prioritize selecting data management methods that users have been satisfied with in the past. The management department can also select the most effective management method from the user's past data management history. For example, the management department can select the most effective management method from the user's past data management history. This allows the optimal management method to be selected by referring to past data management history. Past data management history includes, for example, past access logs and data change history. Some or all of the above processes in the management department may be performed using, for example, AI, or not using AI. For example, the management department can input past data management history into a generating AI and have the generating AI select the optimal management method.

[0051] The management department can analyze user access history during data management to provide the optimal data management method. For example, the management department can prioritize the management of data that users frequently access. The management department can also provide the optimal data management method based on user access history. The management department can also analyze user access history to provide an efficient data management method. For example, the management department can analyze user access history to provide an efficient data management method. This allows the management department to provide the optimal data management method by analyzing user access history. User access history includes, for example, access frequency and access time. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can input user access history data into a generating AI and have the generating AI perform the task of providing the optimal data management method.

[0052] The management department can select the optimal data management method when managing data, taking into account the user's geographical location information. For example, if the user lives in a specific region, the management department can provide a data management method related to that region. For example, if the user lives in a specific region, the management department can provide a data management method related to that region. For example, if the user is traveling, the management department can provide a data management method based on the user's current location. For example, if the user is planning to move, the management department can provide a data management method related to the new address. For example, if the user is planning to move, the management department can provide a data management method related to the new address. This allows the management department to provide the optimal data management method by taking into account the user's geographical location information. Geographical location information includes, for example, the use of GPS data or location estimation from IP addresses. Some or all of the above processing in the management department may be performed using, for example, AI, or not using AI. For example, the management department can input the user's geographical location information into a generating AI and have the generating AI select the optimal data management method.

[0053] The management department can analyze users' social media activity and provide relevant data management tools when managing data. For example, if a user makes a post on social media about data management, the management department can provide data management tools related to that post. The management department can also provide data management tools related to a specific data management issue if a user is discussing that issue on social media. The management department can also provide data management tools related to a specific data management document if a user is asking about that document on social media. In this way, by analyzing users' social media activity, relevant data management tools can be provided. Social media activity includes, for example, analysis of post content and analysis of followers. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can input user social media activity data into a generating AI and have the AI ​​perform the task of providing related data management methods.

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

[0055] The reception department can analyze a user's past inquiry history and select the most appropriate response. For example, if a user has made a similar inquiry in the past, the response method from that inquiry can be used as a reference to provide a quick response. It can also prioritize selecting a response method that the user was satisfied with in the past. Furthermore, it can select the most effective response method from the user's past inquiry history. In this way, by analyzing a user's past inquiry history, the most appropriate response method can be selected, enabling a quick response. Past inquiry history includes the content of the inquiry and the response history. The most appropriate response method includes providing FAQs and escalating to experts. Some or all of the above processes in the reception department may be performed using AI, or they may not be performed using AI.

[0056] The extraction unit can improve extraction accuracy by applying different OCR algorithms depending on the type of document. For example, in the case of a receipt, an OCR algorithm corresponding to a specific format can be applied. In the case of an invoice, an OCR algorithm corresponding to a different format can be applied. Furthermore, in the case of a contract, an OCR algorithm for extracting detailed text data can be applied. In this way, extraction accuracy is improved by applying an OCR algorithm appropriate to the type of document. Different OCR algorithms include algorithms for handwritten characters and algorithms for printed characters. Some or all of the above processing in the extraction unit may be performed using AI, or it may be performed without using AI.

[0057] The proposal department can select the optimal tax strategy by referring to past tax data when making a proposal. For example, it can analyze the user's past tax return data and propose the optimal tax strategy. It can also propose the maximum possible tax deductions by referring to the user's past tax history. Furthermore, it can analyze the user's past behavioral patterns and propose a personalized tax strategy. This allows for the selection of the optimal tax strategy by referring to past tax data. Past tax data includes past tax returns and tax investigation results. Some or all of the above processing in the proposal department may be performed using AI or not.

[0058] The management department can select the optimal management method by referring to past data management history when managing data. For example, it can select the optimal management method by referring to data management methods previously used by users. It can also prioritize selecting data management methods that users have been satisfied with in the past. Furthermore, it can select the most effective management method from the user's past data management history. In this way, the optimal management method can be selected by referring to past data management history. Past data management history includes past access logs and data change history. Some or all of the above processes in the management department may be performed using AI, or they may not be performed using AI.

[0059] The management department can analyze user access history during data management to provide optimal data management methods. For example, it can prioritize the management of data that users frequently access. It can also provide optimal data management methods based on user access history. Furthermore, it can analyze user access history to provide efficient data management methods. Thus, by analyzing user access history, it is possible to provide optimal data management methods. User access history includes access frequency and access time. Some or all of the above processes in the management department may be performed using AI, or they may be performed without using AI.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk receives user inquiries. For example, it can use speech recognition technology to analyze the user's voice and understand the content of the inquiry. It can also use a chatbot to interact with users and accept inquiries in natural language. Step 2: The extraction unit extracts text data from the document using OCR technology. For example, documents such as receipts and invoices can be scanned and converted into text data using OCR technology. Handwritten documents can also be scanned and converted into text data using OCR technology. Furthermore, digital documents can be directly read and converted into text data. Step 3: The proposal team uses machine learning and data analysis to analyze historical data and propose the optimal tax strategy. For example, they can analyze past filing data and suggest the best filing method to ensure users receive the maximum possible tax deductions. They can also analyze user behavior patterns and provide personalized advice. Step 4: The management department securely manages user data using cloud-based information management. For example, user data can be stored in the cloud and protected using encryption technology. Access rights management can also be implemented to ensure users can access their data anytime, anywhere.

[0062] (Example of form 2) The fully automated support system according to an embodiment of the present invention is a system that simplifies and streamlines the tax return process using AI technology. This fully automated support system begins with the user making an inquiry in natural language via voice recognition or a chatbot. Next, it extracts text data from documents using OCR technology and automatically inputs it into the tax return form. Furthermore, it analyzes past data using machine learning and data analysis to propose the optimal tax strategy. Finally, cloud-based information management securely manages user data and makes it accessible anytime, anywhere. This system can significantly reduce the time spent on tax returns and alleviate user stress. For example, a user can make an inquiry via voice recognition or a chatbot, such as "Please tell me how to file my tax return this year." This inquiry is analyzed by an AI agent, which provides appropriate information and guidance. Next, it extracts text data from documents using OCR technology and automatically inputs it into the tax return form. For example, documents such as receipts and invoices are scanned, and text data is extracted using OCR technology. This text data is automatically input into the tax return form, reducing data entry errors and improving processing speed. Furthermore, it analyzes past data using machine learning and data analysis to propose the optimal tax strategy. For example, it analyzes past filing data and suggests the optimal filing method to ensure users receive the maximum possible tax deductions. It also provides personalized advice based on users' behavioral patterns. Finally, cloud-based information management securely manages user data and makes it accessible anytime, anywhere. For example, users can access it from home or the office to check the progress of their tax filing. This allows users to proceed with the tax filing process simply and efficiently. As a result, the fully automated support system can significantly reduce the time spent on tax filing and alleviate user stress. For example, it can reduce the average tax filing time from 20 hours to 2 hours. In addition, 24 / 7 accessibility, automated document generation, and important deadline alerts significantly reduce the stress and time users spend on tax filing.Furthermore, AI-powered analysis of past tax return data and optimization suggestions ensure users receive the maximum possible tax deductions. This fully automated support system simplifies and streamlines the tax filing process.

[0063] The fully automated support system according to this embodiment comprises a reception unit, an extraction unit, a proposal unit, and a management unit. The reception unit receives user inquiries. The reception unit can, for example, analyze the user's voice using speech recognition technology to understand the content of the inquiry. The reception unit can also interact with users using a chatbot and accept inquiries in natural language. For example, if a user asks the reception unit, "Please tell me how to file my tax return this year," the reception unit will analyze the content using speech recognition technology and provide appropriate information. Furthermore, the reception unit can also interact with users using a chatbot and accept inquiries in natural language. For example, if a user asks the chatbot, "When is the deadline for filing my tax return?", the chatbot will provide an appropriate answer to that question. The extraction unit extracts text data from documents using OCR technology. The extraction unit can, for example, scan documents such as receipts and invoices and convert them into text data using OCR technology. The extraction unit can also scan handwritten documents and convert them into text data using OCR technology. For example, the extraction unit scans handwritten receipts and converts them into text data using OCR technology. Furthermore, the extraction unit can directly read digital documents and convert them into text data. For example, the extraction unit reads PDF invoices and converts them into text data using OCR technology. The proposal unit analyzes historical data using machine learning and data analytics to propose optimal tax strategies. For example, the proposal unit can analyze past filing data and propose the best filing method to ensure users receive maximum tax deductions. The proposal unit can also analyze user behavior patterns and provide personalized advice. For example, the proposal unit analyzes users' past filing data and proposes optimal tax strategies. Furthermore, the proposal unit can analyze user behavior patterns and provide personalized advice. The management unit securely manages user data through cloud-based information management. For example, the management unit can store user data in the cloud and protect it using encryption technology.Furthermore, the management department can manage access permissions to ensure that users can access data anytime, anywhere. For example, the management department can enable users to access and check the progress of their tax return from home or the office. This allows the fully automated support system according to the embodiment to simplify and streamline the tax return process.

[0064] The reception desk receives user inquiries. For example, the reception desk can analyze user speech using speech recognition technology to understand the content of inquiries. Specifically, speech recognition technology converts user speech into text in real time, and then analyzes that text data using natural language processing (NLP) technology. This allows for an accurate understanding of the user's intent and the content of their question. The reception desk can also interact with users using a chatbot to accept inquiries in natural language. The chatbot generates appropriate responses to user input based on a pre-trained dialogue model. For example, if a user asks, "How do I file my tax return this year?", the chatbot analyzes the content using speech recognition technology and provides appropriate information. Furthermore, if a user asks the chatbot, "When is the deadline for filing my tax return?", the chatbot will provide an appropriate answer. The chatbot can refer to a predefined FAQ database and knowledge base to respond quickly and accurately to user questions. This allows the reception desk to respond quickly and accurately to user inquiries, improving user convenience. Furthermore, the reception department can record user inquiry history and use it to handle future inquiries. For example, by analyzing user trends and needs based on past inquiries, it can provide more personalized responses. This allows the reception department to improve user satisfaction and increase the overall efficiency of the system.

[0065] The extraction unit extracts text data from documents using OCR technology. For example, the extraction unit can scan documents such as receipts and invoices and convert them into text data using OCR technology. Specifically, the image data scanned is analyzed by OCR software, and characters and numbers are recognized. Because OCR technology can recognize not only printed characters but also handwritten characters, it is also possible to scan handwritten documents and convert them into text data. For example, the extraction unit scans a handwritten receipt and converts it into text data using OCR technology. Furthermore, the extraction unit can directly read digital documents and convert them into text data. For example, the extraction unit reads a PDF invoice and converts it into text data using OCR technology. This allows the extraction unit to efficiently extract text data not only from paper documents but also from digital documents. The extracted text data is used for subsequent processing and analysis. For example, the text data extracted by the extraction unit is analyzed by the proposal unit and used to propose the optimal tax strategy. Furthermore, the extraction unit can continuously improve its OCR model using machine learning algorithms to enhance the accuracy of the extracted text data. This allows the extraction unit to consistently provide highly accurate text data, thereby improving the overall system performance.

[0066] The proposal department uses machine learning and data analysis to analyze historical data and propose the optimal tax strategy. For example, the proposal department can analyze past filing data and propose the optimal filing method to ensure users receive the maximum tax deduction. Specifically, the proposal department inputs past filing data into a machine learning algorithm to learn patterns and trends. This allows the proposal department to propose the optimal tax strategy based on the user's past filing data. The proposal department can also analyze the user's behavior patterns and provide personalized advice. For example, the proposal department analyzes the user's past filing data and proposes the optimal tax strategy. Furthermore, the proposal department can analyze the user's behavior patterns and provide personalized advice. The proposal department uses machine learning algorithms to analyze the user's behavior patterns and past filing data and proposes the optimal tax strategy. This allows the proposal department to propose the optimal tax strategy to the user and maximize their tax deductions. Furthermore, the proposal department can analyze the user's behavior patterns and provide personalized advice. For example, the proposal department analyzes the user's past filing data and proposes the optimal tax strategy. This allows the proposal department to propose the optimal tax strategy to the user and maximize their tax deductions.

[0067] The management department securely manages user data through cloud-based information management. For example, the management department can store user data in the cloud and protect it using encryption technology. Specifically, user data is stored on cloud servers and protected using encryption technology. This protects user data from unauthorized access and data breaches. The management department can also manage access rights to ensure that users can access their data anytime, anywhere. For example, the management department can allow users to access their tax return progress from home or the office. This allows the management department to securely manage user data and ensure that users can access their data anytime, anywhere. Furthermore, the management department can regularly back up data to prepare for data loss or corruption. This allows the management department to securely manage user data and improve the reliability of the entire system.

[0068] The reception desk can accept inquiries in natural language through speech recognition or chatbots. For example, the reception desk can analyze the user's voice using speech recognition technology to understand the content of the inquiry. For instance, if a user asks, "Please tell me how to file my tax return this year," the reception desk can analyze the content using speech recognition technology and provide appropriate information. The reception desk can also interact with users using chatbots to accept inquiries in natural language. For example, if a user asks the chatbot, "When is the deadline for filing my tax return?", the chatbot can provide an appropriate answer. This improves user convenience by accepting inquiries in natural language through speech recognition or chatbots. Speech recognition technology includes, for example, deep learning-based speech recognition technology. Chatbots include, for example, rule-based chatbots and AI-based chatbots. Some or all of the above-described processes in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's voice data into a generating AI and have the generating AI perform the analysis of the inquiry content.

[0069] The extraction unit can extract text data from documents using OCR technology. For example, the extraction unit can scan documents such as receipts and invoices and convert them into text data using OCR technology. For example, the extraction unit can scan handwritten receipts and convert them into text data using OCR technology. The extraction unit can also directly read digital documents and convert them into text data. For example, the extraction unit can read PDF invoices and convert them into text data using OCR technology. This improves the accuracy of text data extraction from documents by using OCR technology. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input document data scanned by a scanner into a generating AI and have the generating AI perform the text data extraction.

[0070] The proposal department can analyze historical data using machine learning and data analysis to propose the optimal tax strategy. For example, the proposal department can analyze past filing data and propose the optimal filing method to ensure the user receives the maximum possible tax deductions. For example, the proposal department can analyze a user's past filing data and propose the optimal tax strategy. The proposal department can also analyze user behavior patterns and provide personalized advice. For example, the proposal department can analyze user behavior patterns and provide personalized advice. This improves the accuracy of proposing the optimal tax strategy by using machine learning and data analysis. Machine learning includes, for example, regression analysis and neural networks. Data analysis includes, for example, statistical analysis and data mining. Some or all of the above processes in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input historical filing data into a generating AI and have the generating AI generate a proposal for the optimal tax strategy.

[0071] The management department can securely manage user data through cloud-based information management. For example, the management department can store user data in the cloud and protect it using encryption technology. The management department can also manage access permissions to ensure users can access their data anytime, anywhere. For example, the management department can allow users to access their tax return progress from home or the office. This enables the secure management and accessibility of user data through cloud-based information management. Cloud-based information management includes, for example, data encryption and backup methods. Some or all of the above processes in the management department may be performed using, for example, AI, or not. For example, the management department can have a generating AI store user data in the cloud and protect it using encryption technology.

[0072] The reception desk can estimate the user's emotions and determine the priority of inquiries based on the estimated emotions. For example, if the user is stressed, the reception desk can prioritize the inquiry as an urgent matter. The reception desk can also process inquiries with normal priority if the user is relaxed. The reception desk can also raise the priority of inquiries to ensure a quick response if the user is in a hurry. This allows for more appropriate responses by prioritizing inquiries based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's voice data into a generating AI and have the AI ​​perform emotion estimation.

[0073] The reception department can analyze a user's past inquiry history and select the most appropriate response. For example, if a user has made a similar inquiry in the past, the reception department can refer to the response method used then to respond quickly. For example, if a user has made a similar inquiry in the past, the reception department can refer to the response method used then to respond quickly. The reception department can also prioritize selecting a response method that the user has been satisfied with in the past. For example, the reception department can prioritize selecting a response method that the user has been satisfied with in the past. The reception department can also select the most effective response method from a user's past inquiry history. For example, the reception department can select the most effective response method from a user's past inquiry history. This allows for the selection of the most appropriate response method and a quick response by analyzing a user's past inquiry history. Past inquiry history includes, for example, the content of the inquiry and the response history. The most appropriate response method includes, for example, providing an FAQ or escalating the issue to an expert. Some or all of the above processing in the reception department may be performed using, for example, AI, or not using AI. For example, the reception department can input data on the user's past inquiry history into a generating AI, and have the AI ​​select the most appropriate response method.

[0074] The reception desk can generate customized responses to provide appropriate guidance depending on the content of the inquiry. For example, if the user asks a basic question about filing a tax return, the reception desk can provide concise guidance. For example, if the user asks a basic question about filing a tax return, the reception desk can provide concise guidance. The reception desk can also provide detailed guidance if the user asks about a complex tax issue. For example, if the reception desk asks about a specific tax document, the reception desk can generate a customized response regarding that document. For example, if the reception desk asks about a specific tax document, the reception desk can generate a customized response regarding that document. This allows the reception desk to provide appropriate guidance to the user by generating customized responses according to the content of the inquiry. Customized responses may include, for example, responses based on the user's profile or responses based on past inquiry history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's inquiry into a generating AI and have the generating AI perform the generation of a customized response.

[0075] The reception desk can estimate the user's emotions and adjust the content of the inquiry based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a concise and easy-to-understand answer. For example, if the user is stressed, the reception desk can provide a concise and easy-to-understand answer. For example, if the user is relaxed, the reception desk can provide a detailed answer. For example, if the user is relaxed, the reception desk can provide a detailed answer. For example, if the user is in a hurry, the reception desk can provide a quick answer. For example, if the user is in a hurry, the reception desk can provide a quick answer. This allows for more appropriate answers to be provided by adjusting the content of the inquiry based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0076] The reception desk can provide highly relevant information when an inquiry is made, taking into account the user's geographical location. For example, if the user lives in a specific region, the reception desk can provide tax information relevant to that region. For example, if the user lives in a specific region, the reception desk can provide tax information relevant to that region. The reception desk can also provide tax information based on the user's current location if the user is traveling. For example, if the reception desk is planning to move, the reception desk can provide tax information relevant to the new address if the user is planning to move. For example, if the reception desk is planning to move, the reception desk can provide tax information relevant to the new address. This allows the reception desk to provide highly relevant information by taking into account the user's geographical location. Geographical location information includes, for example, the use of GPS data or location estimation from IP addresses. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing highly relevant information.

[0077] The reception desk can analyze a user's social media activity and provide relevant information when an inquiry is made. For example, if a user has made tax-related posts on social media, the reception desk can provide information related to those posts. The reception desk can also provide information related to a specific tax issue if the user is discussing that issue on social media. The reception desk can also provide information about a specific tax document if the user is asking about that document on social media. This allows the reception desk to provide relevant information by analyzing the user's social media activity. Social media activity includes, for example, analysis of post content and analysis of followers. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI provide the relevant information.

[0078] The extraction unit can estimate the user's emotions and determine the priority of text data to extract based on the estimated emotions. For example, if the user is stressed, the extraction unit can prioritize the extraction of important text data. For example, if the user is relaxed, the extraction unit can prioritize the extraction of important text data. For example, if the user is relaxed, the extraction unit can prioritize the extraction of text data. For example, if the user is in a hurry, the extraction unit can quickly extract the necessary text data. For example, if the user is in a hurry, the extraction unit can quickly extract the necessary text data. In this way, important data can be prioritized by determining the priority of text data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input user emotion data into a generating AI and have the generating AI determine the priority of the text data.

[0079] The extraction unit can improve extraction accuracy by applying different OCR algorithms depending on the type of document. For example, in the case of a receipt, the extraction unit can apply an OCR algorithm that corresponds to a specific format. For example, in the case of an invoice, the extraction unit can apply an OCR algorithm that corresponds to a different format. For example, in the case of a contract, the extraction unit can apply an OCR algorithm that corresponds to a different format. For example, in the case of a contract, the extraction unit can apply an OCR algorithm that extracts detailed text data. This improves extraction accuracy by applying an OCR algorithm appropriate to the type of document. Different OCR algorithms include, for example, algorithms for handwritten characters and algorithms for printed characters. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the type of document into a generating AI and cause the generating AI to apply an appropriate OCR algorithm.

[0080] The extraction unit can select the optimal extraction method by considering the document's layout and format during extraction. For example, if the document has a complex layout, the extraction unit can analyze the layout and select the optimal extraction method. The extraction unit can also select a method to quickly extract text data if the document has a simple format. The extraction unit can also select an extraction method specifically for handwritten character recognition if the document is handwritten. This allows the extraction unit to select the optimal extraction method by considering the document's layout and format. The document's layout and format include, for example, analysis of column layouts and font recognition. Some or all of the above-described processes in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the document's layout and format into a generating AI and have the generating AI select the optimal extraction method.

[0081] The extraction unit can estimate the user's emotions and adjust how the extracted text data is displayed based on the estimated emotions. For example, if the user is stressed, the extraction unit can highlight important text data. The extraction unit can also display the text data in the normal way if the user is relaxed. The extraction unit can also quickly display the necessary text data if the user is in a hurry. This allows for the highlighting of important data by adjusting how the text data is displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input user emotion data into the generating AI and have the generating AI adjust how the text data is displayed.

[0082] The extraction unit can determine the priority of extracted data based on the document submission date during the extraction process. For example, the extraction unit can prioritize the extraction of text data from documents with approaching submission deadlines. The extraction unit can also prioritize the extraction of text data from documents with distant submission deadlines. The extraction unit can also postpone the extraction of text data from documents whose submission deadlines have passed. By determining the priority of extracted data based on the document submission date, documents with approaching submission deadlines can be processed preferentially. The document submission date includes, for example, analysis of the submission date and setting of deadlines. Some or all of the above processing in the extraction unit may be performed using, for example, AI, or not using AI. For example, the extraction unit can input document submission date data into a generating AI and have the generating AI determine the priority of the extracted data.

[0083] The extraction unit can adjust the order of extracted data based on the relevance of the documents during extraction. For example, the extraction unit can prioritize the extraction of text data from highly relevant documents. The extraction unit can also postpone the extraction of text data from less relevant documents. The extraction unit can also extract text data from documents of moderate relevance with normal priority. This allows for the priority extraction of highly relevant data by adjusting the order of extracted data based on the relevance of the documents. Document relevance includes, for example, similarity of content and frequency of relevant keywords. Some or all of the above processing in the extraction unit may be performed using, for example, AI, or not using AI. For example, the extraction unit can input document relevance data into a generating AI and have the generating AI adjust the order of the extracted data.

[0084] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can provide concise and easy-to-understand suggestions. For example, if the user is relaxed, the suggestion unit can provide suggestions that include detailed information. For example, if the user is relaxed, the suggestion unit can provide suggestions that include detailed information. For example, if the user is in a hurry, the suggestion unit can provide suggestions quickly. For example, if the user is in a hurry, the suggestion unit can provide suggestions quickly. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI, for example, or without AI. For example, the proposal department can input user emotion data into a generation AI and have the generation AI adjust the way the proposal is expressed.

[0085] The proposal unit can select the optimal tax strategy by referring to past tax data when making a proposal. For example, the proposal unit can analyze the user's past tax return data and propose the optimal tax strategy. For example, the proposal unit can analyze the user's past tax return data and propose the optimal tax strategy. The proposal unit can also refer to the user's past tax history and propose the maximum possible tax deductions. For example, the proposal unit can refer to the user's past tax history and propose the maximum possible tax deductions. The proposal unit can also analyze the user's past behavioral patterns and propose a personalized tax strategy. For example, the proposal unit can analyze the user's past behavioral patterns and propose a personalized tax strategy. This allows the optimal tax strategy to be selected by referring to past tax data. Past tax data includes, for example, past tax return details and tax investigation results. 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 input past tax data into a generating AI and have the generating AI select the optimal tax strategy.

[0086] The proposal department can analyze the user's behavior patterns and provide personalized advice when making a proposal. For example, the proposal department can analyze the user's past behavior patterns and propose the optimal tax strategy. For example, the proposal department can analyze the user's past behavior patterns and propose the optimal tax strategy. The proposal department can also propose the maximum possible tax deductions based on the user's behavior patterns. For example, the proposal department can propose the maximum possible tax deductions based on the user's behavior patterns. The proposal department can also provide personalized advice based on the user's behavior patterns. For example, the proposal department can provide personalized advice based on the user's behavior patterns. This allows for the provision of personalized advice by analyzing the user's behavior patterns. User behavior patterns include, for example, past behavior history and frequency of use. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input user behavior pattern data into a generating AI and have the generating AI provide personalized advice.

[0087] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit can prioritize important suggestions. For example, if the user is stressed, the suggestion unit can prioritize important suggestions. The suggestion unit can also provide suggestions with normal priority if the user is relaxed. For example, if the suggestion unit is relaxed, the suggestion unit can provide suggestions with normal priority if the user is in a hurry. For example, if the suggestion unit is in a hurry, the suggestion unit can provide suggestions quickly. In this way, by determining the priority of suggestions based on the user's emotions, important suggestions can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the proposal department can input user emotion data into a generation AI and have the generation AI determine the priority of proposals.

[0088] The proposal unit can propose the optimal tax strategy by considering the user's geographical location information. For example, if the user lives in a specific region, the proposal unit can propose a tax strategy relevant to that region. For example, if the user lives in a specific region, the proposal unit can propose a tax strategy relevant to that region. For example, if the user is traveling, the proposal unit can propose a tax strategy based on the user's current location. For example, if the user is planning to move, the proposal unit can propose a tax strategy relevant to the new address. For example, if the user is planning to move, the proposal unit can propose a tax strategy relevant to the new address. This allows the proposal to propose the optimal tax strategy by considering the user's geographical location information. Geographical location information includes, for example, the use of GPS data or location estimation from IP addresses. 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 input the user's geographical location information into a generating AI and have the generating AI execute the proposal of the optimal tax strategy.

[0089] The proposal unit can analyze a user's social media activity and propose relevant tax strategies when making a proposal. For example, if a user posts about tax matters on social media, the proposal unit can propose tax strategies related to those posts. The proposal unit can also propose tax strategies related to specific tax issues if a user discusses them on social media. The proposal unit can also propose tax strategies related to specific tax documents if a user asks about them on social media. This allows the proposal unit to suggest relevant tax strategies by analyzing a user's social media activity. Social media activity includes, for example, analysis of post content and analysis of followers. 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 department can input user social media activity data into a generating AI and have the AI ​​generate proposals for relevant tax strategies.

[0090] The management unit can estimate the user's emotions and adjust the data management method based on the estimated user emotions. For example, if the user is stressed, the management unit can provide a simple and easy-to-understand data management method. For example, if the user is stressed, the management unit can provide a simple and easy-to-understand data management method. For example, if the user is relaxed, the management unit can provide a data management method that includes detailed information. For example, if the user is relaxed, the management unit can provide a data management method that includes detailed information. For example, if the user is in a hurry, the management unit can provide a method for managing data quickly. For example, if the user is in a hurry, the management unit can provide a method for managing data quickly. This allows for more appropriate data management by adjusting the data management method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI, for example, or without AI. For example, the management department can input user emotion data into a generating AI and have the AI ​​adjust the data management method.

[0091] The management department can select the optimal management method by referring to past data management history when managing data. For example, the management department can select the optimal management method by referring to data management methods previously used by users. For example, the management department can select the optimal management method by referring to data management methods previously used by users. The management department can also prioritize selecting data management methods that users have been satisfied with in the past. For example, the management department can prioritize selecting data management methods that users have been satisfied with in the past. The management department can also select the most effective management method from the user's past data management history. For example, the management department can select the most effective management method from the user's past data management history. This allows the optimal management method to be selected by referring to past data management history. Past data management history includes, for example, past access logs and data change history. Some or all of the above processes in the management department may be performed using, for example, AI, or not using AI. For example, the management department can input past data management history into a generating AI and have the generating AI select the optimal management method.

[0092] The management department can analyze user access history during data management to provide the optimal data management method. For example, the management department can prioritize the management of data that users frequently access. The management department can also provide the optimal data management method based on user access history. The management department can also analyze user access history to provide an efficient data management method. For example, the management department can analyze user access history to provide an efficient data management method. This allows the management department to provide the optimal data management method by analyzing user access history. User access history includes, for example, access frequency and access time. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can input user access history data into a generating AI and have the generating AI perform the task of providing the optimal data management method.

[0093] The management unit can estimate a user's emotions and adjust data access permissions based on the estimated emotions. For example, if a user is stressed, the management unit can provide easy access permissions. For example, if a user is stressed, the management unit can provide easy access permissions. The management unit can also provide normal access permissions if a user is relaxed. For example, if a user is relaxed, the management unit can provide normal access permissions. The management unit can also provide quick access permissions if a user is in a hurry. For example, if a user is in a hurry, the management unit can provide quick access permissions. This allows for more appropriate access permissions to be provided by adjusting data access permissions based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI, for example, or without AI. For example, the management department can input user emotion data into a generating AI and have the AI ​​adjust data access permissions.

[0094] The management department can select the optimal data management method when managing data, taking into account the user's geographical location information. For example, if the user lives in a specific region, the management department can provide a data management method related to that region. For example, if the user lives in a specific region, the management department can provide a data management method related to that region. For example, if the user is traveling, the management department can provide a data management method based on the user's current location. For example, if the user is planning to move, the management department can provide a data management method related to the new address. For example, if the user is planning to move, the management department can provide a data management method related to the new address. This allows the management department to provide the optimal data management method by taking into account the user's geographical location information. Geographical location information includes, for example, the use of GPS data or location estimation from IP addresses. Some or all of the above processing in the management department may be performed using, for example, AI, or not using AI. For example, the management department can input the user's geographical location information into a generating AI and have the generating AI select the optimal data management method.

[0095] The management department can analyze users' social media activity and provide relevant data management tools when managing data. For example, if a user makes a post on social media about data management, the management department can provide data management tools related to that post. The management department can also provide data management tools related to a specific data management issue if a user is discussing that issue on social media. The management department can also provide data management tools related to a specific data management document if a user is asking about that document on social media. In this way, by analyzing users' social media activity, relevant data management tools can be provided. Social media activity includes, for example, analysis of post content and analysis of followers. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can input user social media activity data into a generating AI and have the AI ​​perform the task of providing related data management methods.

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

[0097] The reception desk can estimate the user's emotions and prioritize inquiries based on those emotions. For example, if a user is stressed, the inquiry can be prioritized as an urgent matter. If the user is relaxed, the inquiry can be processed with the normal priority. Furthermore, if the user is in a hurry, the priority can be increased to ensure a quick response. This allows for more appropriate responses by prioritizing inquiries based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above processing in the reception desk may be performed using AI or not.

[0098] The reception department can analyze a user's past inquiry history and select the most appropriate response. For example, if a user has made a similar inquiry in the past, the response method from that inquiry can be used as a reference to provide a quick response. It can also prioritize selecting a response method that the user was satisfied with in the past. Furthermore, it can select the most effective response method from the user's past inquiry history. In this way, by analyzing a user's past inquiry history, the most appropriate response method can be selected, enabling a quick response. Past inquiry history includes the content of the inquiry and the response history. The most appropriate response method includes providing FAQs and escalating to experts. Some or all of the above processes in the reception department may be performed using AI, or they may not be performed using AI.

[0099] The extraction unit can estimate the user's emotions and determine the priority of text data to extract based on the estimated emotions. For example, if the user is stressed, important text data can be extracted preferentially. If the user is relaxed, text data can be extracted with normal priority. Furthermore, if the user is in a hurry, necessary text data can be extracted quickly. In this way, important data can be extracted preferentially by determining the priority of text data based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Some or all of the above processing in the extraction unit may be performed using AI or not.

[0100] The extraction unit can improve extraction accuracy by applying different OCR algorithms depending on the type of document. For example, in the case of a receipt, an OCR algorithm corresponding to a specific format can be applied. In the case of an invoice, an OCR algorithm corresponding to a different format can be applied. Furthermore, in the case of a contract, an OCR algorithm for extracting detailed text data can be applied. In this way, extraction accuracy is improved by applying an OCR algorithm appropriate to the type of document. Different OCR algorithms include algorithms for handwritten characters and algorithms for printed characters. Some or all of the above processing in the extraction unit may be performed using AI, or it may be performed without using AI.

[0101] The suggestion section can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, it can provide concise and easy-to-understand suggestions. If the user is relaxed, it can provide suggestions that include more detailed information. Furthermore, if the user is in a hurry, it can provide suggestions quickly. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the processing described above in the suggestion section may be performed using AI or not.

[0102] The proposal department can select the optimal tax strategy by referring to past tax data when making a proposal. For example, it can analyze the user's past tax return data and propose the optimal tax strategy. It can also propose the maximum possible tax deductions by referring to the user's past tax history. Furthermore, it can analyze the user's past behavioral patterns and propose a personalized tax strategy. This allows for the selection of the optimal tax strategy by referring to past tax data. Past tax data includes past tax returns and tax investigation results. Some or all of the above processing in the proposal department may be performed using AI or not.

[0103] The management department can estimate the user's emotions and adjust data management methods based on those estimated emotions. For example, if the user is stressed, a simple and easy-to-understand data management method can be provided. If the user is relaxed, a data management method including detailed information can be provided. Furthermore, if the user is in a hurry, a method for quickly managing data can be provided. This allows for more appropriate data management by adjusting data management methods based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above processing in the management department may be performed using AI or not.

[0104] The management department can select the optimal management method by referring to past data management history when managing data. For example, it can select the optimal management method by referring to data management methods previously used by users. It can also prioritize selecting data management methods that users have been satisfied with in the past. Furthermore, it can select the most effective management method from the user's past data management history. In this way, the optimal management method can be selected by referring to past data management history. Past data management history includes past access logs and data change history. Some or all of the above processes in the management department may be performed using AI, or they may not be performed using AI.

[0105] The management department can analyze user access history during data management to provide optimal data management methods. For example, it can prioritize the management of data that users frequently access. It can also provide optimal data management methods based on user access history. Furthermore, it can analyze user access history to provide efficient data management methods. Thus, by analyzing user access history, it is possible to provide optimal data management methods. User access history includes access frequency and access time. Some or all of the above processes in the management department may be performed using AI, or they may be performed without using AI.

[0106] The management department can estimate the user's emotions and adjust data access permissions based on those emotions. For example, if a user is stressed, they can be given easy access permissions. If a user is relaxed, they can be given normal access permissions. Furthermore, if a user is in a hurry, they can be given quick access permissions. This allows for more appropriate access permissions to be provided by adjusting data access permissions based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Some or all of the above processing in the management department may be performed using AI or not.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The reception desk receives user inquiries. For example, it can use speech recognition technology to analyze the user's voice and understand the content of the inquiry. It can also use a chatbot to interact with users and accept inquiries in natural language. Step 2: The extraction unit extracts text data from the document using OCR technology. For example, documents such as receipts and invoices can be scanned and converted into text data using OCR technology. Handwritten documents can also be scanned and converted into text data using OCR technology. Furthermore, digital documents can be directly read and converted into text data. Step 3: The proposal team uses machine learning and data analysis to analyze historical data and propose the optimal tax strategy. For example, they can analyze past filing data and suggest the best filing method to ensure users receive the maximum possible tax deductions. They can also analyze user behavior patterns and provide personalized advice. Step 4: The management department securely manages user data using cloud-based information management. For example, user data can be stored in the cloud and protected using encryption technology. Access rights management can also be implemented to ensure users can access their data anytime, anywhere.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the reception unit, extraction unit, proposal unit, and management unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives user inquiries using the voice recognition technology or chatbot of the smart device 14. The extraction unit extracts text data from documents using the OCR technology of the smart device 14. The proposal unit proposes the optimal tax strategy using machine learning and data analysis by the identification processing unit 290 of the data processing unit 12. The management unit securely manages user data through the cloud-based information management of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires 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.

[0126] 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.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0128] Each of the multiple elements described above, including the reception unit, extraction unit, proposal unit, and management unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives user inquiries using the voice recognition technology and chatbot of the smart glasses 214. The extraction unit extracts text data from documents using the OCR technology of the smart glasses 214. The proposal unit proposes the optimal tax strategy using machine learning and data analysis by the identification processing unit 290 of the data processing unit 12. The management unit securely manages user data through the cloud-based information management of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

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

[0130] 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.

[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 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.

[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 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.

[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 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.

[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 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.

[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 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.

[0144] Each of the multiple elements described above, including the reception unit, extraction unit, proposal unit, and management unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives user inquiries using the voice recognition technology or chatbot of the headset terminal 314. The extraction unit extracts text data from documents using the OCR technology of the headset terminal 314. The proposal unit proposes the optimal tax strategy using machine learning and data analysis by the identification processing unit 290 of the data processing unit 12. The management unit securely manages user data through the cloud-based information management of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

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

[0146] 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.

[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 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.

[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 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).

[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] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0159] 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.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0161] Each of the multiple elements described above, including the reception unit, extraction unit, proposal unit, and management unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives user inquiries using the robot 414's voice recognition technology or chatbot. The extraction unit extracts text data from documents using the robot 414's OCR technology. The proposal unit proposes the optimal tax strategy using machine learning and data analysis via the identification processing unit 290 of the data processing unit 12. The management unit securely manages user data through the cloud-based information management of the data processing unit 12. The correspondence between each unit and the devices or control units is not limited to the example described above and can be modified in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A reception desk that handles user inquiries, An extraction unit that extracts text data from documents received by the aforementioned reception unit, A proposal unit analyzes the data extracted by the extraction unit and proposes the optimal tax strategy, The system comprises a management unit that manages the data proposed by the proposal unit on the cloud. A system characterized by the following features. (Note 2) The aforementioned reception unit is Inquiries are accepted in natural language through speech recognition and chatbots. The system described in Appendix 1, characterized by the features described herein. (Note 3) The extraction unit is Extract text data from documents using OCR technology. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We use machine learning and data analysis to analyze historical data and propose the optimal tax strategy. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned management department, Cloud-based information management ensures the secure management of user data. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and prioritizes inquiries based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past inquiry history and select the most appropriate response method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Based on the nature of the inquiry, we generate customized responses to provide appropriate guidance. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and adjusts the content of the inquiry based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When an inquiry is made, we will provide highly relevant information by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When you contact us, we analyze your social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The extraction unit is It estimates the user's emotions and determines the priority of text data to extract based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The extraction unit is Depending on the type of document, different OCR algorithms are applied to improve extraction accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 14) The extraction unit is During extraction, the optimal extraction method is selected considering the document's layout and format. The system described in Appendix 1, characterized by the features described herein. (Note 15) The extraction unit is It estimates the user's emotions and adjusts how the extracted text data is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The extraction unit is During extraction, the priority of extracted data is determined based on the submission date of the documents. The system described in Appendix 1, characterized by the features described herein. (Note 17) The extraction unit is During extraction, the order of extracted data is adjusted based on the relevance of the documents. The system described in Appendix 1, characterized by the features described herein. (Note 18) 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 19) The aforementioned proposal section is, When making a proposal, we select the optimal tax strategy by referring to past tax data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, we analyze the user's behavior patterns to provide personalized advice. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, we will suggest the optimal tax strategy taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and suggest relevant tax strategies. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned management department, We estimate user sentiment and adjust data management methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned management department, When managing data, refer to past data management history to select the optimal management method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned management department, When managing data, we analyze user access history to provide the optimal data management method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned management department, It estimates the user's emotions and adjusts data access permissions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned management department, When managing data, the optimal data management method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned management department, During data management, analyze users' social media activity and provide relevant data management tools. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that handles user inquiries, An extraction unit that extracts text data from documents received by the aforementioned reception unit, A proposal unit analyzes the data extracted by the extraction unit and proposes the optimal tax strategy, The system comprises a management unit that manages the data proposed by the proposal unit on the cloud. A system characterized by the following features.

2. The aforementioned reception unit is Inquiries are accepted in natural language through speech recognition and chatbots. The system according to feature 1.

3. The extraction unit is Extract text data from documents using OCR technology. The system according to feature 1.

4. The aforementioned proposal section is, We use machine learning and data analysis to analyze historical data and propose the optimal tax strategy. The system according to feature 1.

5. The aforementioned management department, Cloud-based information management ensures the secure management of user data. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and prioritizes inquiries based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past inquiry history and select the most appropriate response method. The system according to feature 1.

8. The aforementioned reception unit is Based on the nature of the inquiry, we generate customized responses to provide appropriate guidance. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and adjusts the content of the inquiry based on the estimated user emotions. The system according to feature 1.

10. The aforementioned reception unit is When an inquiry is made, we will provide highly relevant information by taking into account the user's geographical location. The system according to feature 1.

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