system

Abstract

The system according to this embodiment aims to efficiently collect and examine the information necessary for year-end tax adjustments and to create appropriate year-end tax adjustment sheets. [Solution] The system according to this embodiment comprises a scanning unit, a review unit, and a creation unit. The scanning unit scans and stores expense information and other information necessary for year-end tax adjustments. The review unit reviews the information scanned by the scanning unit. The creation unit creates a year-end tax adjustment sheet based on the information reviewed by the review unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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 conventional technology, there is a problem that it is difficult to efficiently collect and examine information necessary for year-end adjustment and create an appropriate year-end adjustment sheet.

[0005] The system according to the embodiment aims to efficiently collect and examine information necessary for year-end adjustment and create an appropriate year-end adjustment sheet.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a scanning unit, a review unit, and a creation unit. The scanning unit scans and stores expense information and other information necessary for year-end tax adjustments. The review unit reviews the information scanned by the scanning unit. The creation unit creates a year-end tax adjustment sheet based on the information reviewed by the review unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently collect and examine the information necessary for year-end tax adjustments and create an appropriate year-end tax adjustment sheet. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An application for automatically creating year-end tax adjustments according to an embodiment of the present invention is a system that automatically scans, scrutinizes, and creates information necessary for year-end tax adjustments, including expenses and other relevant data. The application allows users to scan and store documents daily that they are unsure whether they are necessary for year-end tax adjustments. Users use their smartphone cameras to photograph documents such as receipts and invoices and upload them to the application. For example, a user might photograph a receipt for a meal at a restaurant and store it in the application. This information is stored in the application's database. Next, the application scrutinizes this information. Using AI, the application analyzes the uploaded documents and extracts the information necessary for year-end tax adjustments. For example, the application automatically reads information such as the amount, date, and payee from a receipt and identifies the items required for year-end tax adjustments. This eliminates the need for users to manually enter information. Furthermore, the application creates a year-end tax adjustment sheet based on the scrutinized information. The application automatically organizes the necessary documents and information for year-end tax adjustments and generates a submission format. For example, the application calculates the total expense amount and deduction amount and reflects them in the year-end tax adjustment sheet. This sheet can be downloaded and submitted by the user. This application significantly reduces the time and effort users spend on year-end tax adjustments. Users simply scan their daily expenses and store them in the app, and their year-end tax adjustment preparations are complete. Furthermore, the app automatically scrutinizes the information and creates the year-end tax adjustment sheet, eliminating the need for manual data entry. This simplifies the year-end tax adjustment process and reduces the burden on users. For example, by using the "Easy Year-End Tax Adjustment App," users can easily complete their year-end tax adjustments without getting tired during the annual tax adjustment season. Users simply download the app and scan and store their expenses and other necessary documents for year-end tax adjustments. The app automatically scrutinizes the information and creates the year-end tax adjustment sheet, allowing users to complete the process effortlessly. This automatic year-end tax adjustment creation application significantly reduces the time and effort users spend on year-end tax adjustments.

[0029] The application for automatically creating year-end tax adjustments according to this embodiment comprises a scanning unit, a review unit, and a creation unit. The scanning unit scans and stores expenses and other information necessary for year-end tax adjustments. The scanning unit, for example, uses a smartphone camera to photograph documents such as receipts and invoices and uploads them to the app. The scanning unit, for example, takes a photograph of a receipt for a meal at a restaurant and stores it in the app. This information is stored in a database within the app. The review unit reviews the information scanned by the scanning unit. The review unit uses AI to analyze the uploaded documents and extract the information necessary for year-end tax adjustments. For example, the review unit automatically reads information such as the amount, date, and payee of a receipt and identifies the items necessary for year-end tax adjustments. The review unit can, for example, use AI to analyze the contents of uploaded documents and extract the necessary information. The creation unit creates a year-end tax adjustment sheet based on the information reviewed by the review unit. The creation unit automatically organizes the documents and information necessary for year-end tax adjustments and generates a format for submission. For example, the creation unit calculates the total amount of expenses and deductions, and reflects them in the year-end tax adjustment sheet. The creation unit also creates the year-end tax adjustment sheet based on the scrutinized information and generates a format for the user to download and submit. As a result, the automated year-end tax adjustment creation application according to this embodiment can significantly reduce the effort involved in year-end tax adjustments by automatically scanning, scrutinizing, and creating expenses and other information necessary for year-end tax adjustments.

[0030] The scanning unit scans and stores expense and other information necessary for year-end tax adjustments. For example, the scanning unit uses a smartphone camera to photograph documents such as receipts and invoices and uploads them to the app. Specifically, a user might photograph a receipt for a meal at a restaurant and store it in the app. This information is stored in the app's database. The scanning unit uses OCR (Optical Character Recognition) technology to convert the text information of the photographed documents into digital data. This allows for accurate reading of handwritten receipts and printed invoices. Furthermore, the scanning unit can scan multiple documents at once, enabling users to efficiently process large volumes of documents. For example, a user can photograph multiple receipts at once and upload them all at once. The scanning unit also features automatic cropping and distortion correction for scanned document images. This improves the quality of the scanned images and enhances the accuracy of analysis in the subsequent review unit. Additionally, the scanning unit provides a function to organize scanned documents by category. For example, saving documents categorized by food and beverage expenses, transportation expenses, and accommodation expenses makes subsequent processing easier. This allows the scanning unit to efficiently scan and organize the information users need for year-end tax adjustments.

[0031] The Verification Department scrutinizes the information scanned by the Scanning Department. The Verification Department uses AI to analyze uploaded documents and extract the information necessary for year-end tax adjustments. Specifically, the Verification Department uses OCR technology to analyze the text information of scanned documents, automatically reading information such as amounts, dates, and payees from receipts. The AI ​​uses natural language processing (NLP) technology to understand the content of the documents and identify the items necessary for year-end tax adjustments. For example, the AI ​​automatically identifies categories such as "food and beverage expenses" and "transportation expenses" from receipts and extracts the amounts for each. Furthermore, the AI ​​is trained to handle documents in different formats, and can accurately extract information from handwritten receipts and invoices with different layouts. The Verification Department also has a function to verify the accuracy of the extracted information. For example, the AI ​​checks whether the extracted amounts match the total amounts on the actual receipts and notifies the user if there are errors. The Verification Department also has a function to detect anomalies based on past data. For example, if an unusually high amount is entered compared to past expense data, it prompts the user for confirmation. This allows the scrutiny unit to accurately and efficiently examine the scanned information and extract the information necessary for year-end tax adjustments.

[0032] The creation department creates the year-end tax adjustment sheet based on the information reviewed by the review department. The creation department automatically organizes the necessary documents and information for year-end tax adjustments and generates a submission format. Specifically, the creation department calculates the total amount of expenses and deductions based on the reviewed information and reflects them in the year-end tax adjustment sheet. For example, the creation department aggregates expenses by category, such as food and beverage expenses, transportation expenses, and accommodation expenses, and calculates the grand total. The creation department also automatically calculates deductions based on tax laws. For example, for expenses that meet certain conditions, it calculates the deduction amount and reflects it in the year-end tax adjustment sheet. Furthermore, the creation department has a function to generate a format for user submission. For example, it can generate the year-end tax adjustment sheet in PDF format so that users can download and print it. The creation department can also generate a format compatible with electronic applications, allowing users to submit year-end tax adjustments online. In addition, the creation department has a function to automatically update the contents of the year-end tax adjustment sheet based on information entered by the user and information extracted by the review department. For example, if a user adds new expenses, the creation department automatically updates the contents of the sheet to reflect the latest information. This allows the creation department to efficiently organize the information necessary for year-end tax adjustments and create accurate year-end tax adjustment sheets.

[0033] The scrutiny department can analyze uploaded documents using AI and extract the information necessary for year-end tax adjustments. For example, the scrutiny department can use AI to analyze the contents of uploaded documents and extract the necessary information. For example, the scrutiny department can automatically read information such as the amount, date, and payee of receipts and identify the items necessary for year-end tax adjustments. For example, the scrutiny department can use AI to analyze the contents of uploaded documents and extract the necessary information. This allows for the accurate extraction of information necessary for year-end tax adjustments by using AI. The AI ​​is implemented using technologies such as machine learning, deep learning, and natural language processing. Some or all of the above-described processes in the scrutiny department may be performed using AI or not. For example, the scrutiny department can extract information necessary for year-end tax adjustments using an AI model that analyzes the contents of uploaded documents and extracts the necessary information.

[0034] The creation unit can create year-end tax adjustment sheets based on the scrutinized information. For example, the creation unit creates year-end tax adjustment sheets based on the scrutinized information and generates a format for users to download and submit. The creation unit calculates, for example, the total amount of expenses and deductions and reflects them in the year-end tax adjustment sheet. The creation unit creates year-end tax adjustment sheets based on the scrutinized information and generates a format for users to download and submit. This reduces the effort required from users by automatically creating year-end tax adjustment sheets based on the scrutinized information. Scrutinized information includes, for example, information whose accuracy has been confirmed and information for which error checks have been completed. Some or all of the above processes in the creation unit may be performed using AI or not. For example, the creation unit can create year-end tax adjustment sheets using an AI model that creates year-end tax adjustment sheets based on scrutinized information.

[0035] The creation unit can calculate the total amount of expenses and deductions and reflect them in the year-end tax adjustment sheet. For example, the creation unit can calculate the total amount of expenses and deductions and reflect them in the year-end tax adjustment sheet. For example, the creation unit can create a year-end tax adjustment sheet based on the scrutinized information and generate a format for the user to download and submit. For example, the creation unit can calculate the total amount of expenses and deductions and reflect them in the year-end tax adjustment sheet. This enables accurate year-end tax adjustments by automatically calculating the total amount of expenses and deductions and reflecting them in the year-end tax adjustment sheet. The total amount of expenses and deductions are calculated based on, for example, a calculation formula, applicable tax laws, etc. Some or all of the above processing in the creation unit may be performed using AI or not. For example, the creation unit can use an AI model to calculate the total amount of expenses and deductions and reflect them in the year-end tax adjustment sheet.

[0036] The creation unit can generate a format for users to download and submit. For example, the creation unit creates a year-end tax adjustment sheet based on scrutinized information and generates a format for users to download and submit. For example, the creation unit calculates the total amount of expenses and deductions and reflects them in the year-end tax adjustment sheet. For example, the creation unit creates a year-end tax adjustment sheet based on scrutinized information and generates a format for users to download and submit. This makes it easier for users to submit year-end tax adjustments by generating a format that they can download and submit. The format is generated based on, for example, PDF, Excel, and the requirements of the submission destination. Some or all of the above processes in the creation unit may be performed using AI or not. For example, the creation unit can create a year-end tax adjustment sheet using an AI model that generates a format for users to download and submit.

[0037] The scanning unit can use a smartphone camera to photograph documents such as receipts and invoices and upload them to the app. For example, the scanning unit can use a smartphone camera to photograph documents such as receipts and invoices and upload them to the app. For example, the scanning unit can use a smartphone camera to photograph a receipt for a meal at a restaurant and store it in the app. This information is stored in the app's database. The scanning unit can use a smartphone camera to photograph documents such as receipts and invoices and upload them to the app. This makes it easy to scan documents using a smartphone camera and upload them to the app. The smartphone camera is used based on, for example, the corresponding app, camera resolution, etc. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit can input image data captured using a smartphone camera into a generating AI and have the generating AI perform the conversion of image data into text data.

[0038] The scanning unit can automatically select the optimal scanning settings according to the type of document during scanning. For example, in the case of a receipt, the scanning unit scans at high resolution to improve character recognition accuracy. For example, in the case of an invoice, the scanning unit scans in color to preserve the overall layout. For example, in the case of a handwritten memo, the scanning unit applies a specific filter to enhance handwritten character recognition. This improves scanning accuracy by automatically selecting the optimal scanning settings according to the type of document. The optimal scanning settings are selected based on, for example, resolution, color mode, and scanning speed. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit can improve scanning accuracy by using an AI model that identifies the type of document and selects the optimal scanning settings.

[0039] The scanning unit can detect the condition of a document during scanning and perform optimal correction processing. For example, the scanning unit can automatically fill in torn areas to make the document readable. For example, the scanning unit can detect soiled areas and apply a digital filter to create a clear image. For example, the scanning unit can detect distortion in a document and automatically correct it to perform an accurate scan. In this way, the accuracy of the scan is improved by detecting the condition of the document and performing optimal correction processing. The condition of the document is detected based on, for example, tears, soiling, distortion, etc. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit can improve the accuracy of the scan by using an AI model that detects the condition of the document and performs optimal correction processing.

[0040] The scanning unit can prioritize scanning documents that are highly relevant based on the user's geographical location information. For example, if the user is on a business trip, the scanning unit will prioritize scanning business trip-related receipts. If the user is at home, the scanning unit will prioritize scanning documents related to daily expenses. If the user is attending a specific event, the scanning unit will prioritize scanning documents related to that event. This enables efficient scanning by prioritizing the scanning of highly relevant documents based on the user's geographical location information. Geographical location information is obtained, for example, from GPS data or location services. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit can improve scanning efficiency by using an AI model that prioritizes scanning highly relevant documents while considering the user's geographical location information.

[0041] The scanning unit can analyze the user's social media activity during scanning and scan relevant documents. For example, if the user has posted about a business trip on social media, the scanning unit will prioritize scanning documents related to that trip. For example, if the user has posted about attending an event, the scanning unit will prioritize scanning documents related to that event. For example, if the user has posted about a specific project, the scanning unit will prioritize scanning documents related to that project. This enables efficient scanning by analyzing the user's social media activity and scanning relevant documents. Social media activity is analyzed based on, for example, the content of posts and the reactions of followers. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit can improve scanning efficiency by using an AI model that analyzes the user's social media activity and scans relevant documents.

[0042] The scrutiny unit can apply different scrutiny algorithms based on the content of the document during the scrutiny process. For example, in the case of a receipt, the scrutiny unit might apply an algorithm that focuses on scrutinizing the amount, date, and payee. For example, in the case of an invoice, the scrutiny unit might apply an algorithm that scrutinizes the amount and payment terms for each item. For example, in the case of a handwritten memo, the scrutiny unit might apply an algorithm that enhances handwriting recognition. This improves the accuracy of the scrutiny by applying different scrutiny algorithms based on the content of the document. The scrutiny algorithms are implemented using technologies such as rule-based or machine learning-based methods. Some or all of the above-described processes in the scrutiny unit may be performed using AI or not. For example, the scrutiny unit can improve the accuracy of the scrutiny by using an AI model that applies different scrutiny algorithms based on the content of the document.

[0043] The review department can perform the review while considering the attribute information of the document submitter. For example, if the submitter is a company, the review department will focus on reviewing the company name and the name of the person in charge. For example, if the submitter is an individual, the review department will focus on reviewing the individual's name and address. For example, if the submitter belongs to a specific industry, the review department will review items specific to that industry. This improves the accuracy of the review by considering the attribute information of the document submitter. The attribute information of the submitter is obtained based on, for example, age, occupation, and department. Some or all of the above processing in the review department may be performed using AI or not. For example, the review department can improve the accuracy of the review by using an AI model that performs the review while considering the attribute information of the document submitter.

[0044] The review unit can perform a review while considering the geographical distribution of the documents. For example, if the documents are concentrated in a particular region, the review unit will focus its review on the information in that region. For example, if the documents are dispersed across multiple regions, the review unit will perform a review region by region. For example, if the documents are international, the review unit will perform a review while considering the regulations and rules of each country. This improves the accuracy of the review by considering the geographical distribution of the documents. Geographical distribution is considered based on, for example, regional regulations and geographical characteristics. Some or all of the above processing in the review unit may be performed using AI or not. For example, the review unit can improve the accuracy of the review by using an AI model that performs a review while considering the geographical distribution of the documents.

[0045] The review department can improve the accuracy of its review by referring to relevant literature related to the document during the review process. For example, the review department may refer to laws and regulations related to the document during the review. For example, the review department may refer to past cases related to the document during the review. For example, the review department may refer to industry guidelines related to the document during the review. This improves the accuracy of the review by referring to relevant literature related to the document. Relevant literature may be referenced based on, for example, citations, relevant research papers, etc. Some or all of the above processes in the review department may be performed using AI or not. For example, the review department can improve the accuracy of its review by using an AI model that performs a review by referring to relevant literature related to the document.

[0046] The creation unit can adjust the level of detail in the sheet based on the importance of the information reviewed during creation. For example, the creation unit can prioritize the display of important information and add detailed explanations. For example, the creation unit can display less important information concisely and add only the necessary explanations. For example, the creation unit can adjust the display order of information according to its importance. This allows for efficient year-end tax adjustments by adjusting the level of detail in the sheet based on the importance of the reviewed information. The importance of information is evaluated based on, for example, the accuracy of the information, the submission deadline, and legal requirements. Some or all of the above processes in the creation unit may be performed using AI or not. For example, the creation unit can create year-end tax adjustment sheets using an AI model that adjusts the level of detail in the sheet based on the importance of the reviewed information.

[0047] The creation unit can determine the priority of sheets based on the submission timing of the reviewed information during creation. For example, the creation unit can prioritize displaying information with an approaching submission deadline. For example, the creation unit can postpone displaying information with a distant submission deadline. For example, the creation unit can adjust the display order of information according to the submission deadline. This enables efficient year-end tax adjustments by prioritizing sheets based on submission timing. Submission timing is evaluated based on, for example, the submission deadline, legal requirements, and the user's schedule. Some or all of the above processing in the creation unit may be performed using AI or not. For example, the creation unit can create year-end tax adjustment sheets using an AI model that determines the priority of sheets based on the submission timing of the reviewed information.

[0048] The creation unit can adjust the order of sheets based on the relevance of the scrutinized information during creation. For example, the creation unit can prioritize displaying highly relevant information. For example, the creation unit can postpone displaying less relevant information. For example, the creation unit can adjust the display order of information according to its relevance. This allows for efficient year-end tax adjustments by adjusting the order of sheets based on the relevance of the information. The relevance of the information is evaluated based on, for example, the interrelationships between information, its importance, and its submission deadline. Some or all of the above processing in the creation unit may be performed using AI or not. For example, the creation unit can create year-end tax adjustment sheets using an AI model that adjusts the order of sheets based on the relevance of the scrutinized information.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The automated year-end tax adjustment application can create year-end tax adjustment sheets that comply with regional tax laws and regulations based on the user's geographical location. For example, if the user lives in a different state or country, the application customizes the year-end tax adjustment sheet based on the tax laws and regulations of that region. If the user is on a business trip, the application processes expenses in accordance with the tax laws and regulations of the destination country. If the user moves, the application updates the year-end tax adjustment sheet based on the new address. This enables accurate year-end tax adjustments by creating year-end tax adjustment sheets that comply with regional tax laws and regulations based on the user's geographical location. Geographical location information is obtained, for example, from GPS data or location services.

[0051] The year-end tax adjustment automatic creation application can analyze a user's social media activity and automatically extract relevant expense information. For example, if a user posts about a business trip on social media, it will automatically extract expense information related to that trip. If a user posts about attending an event, it will extract expense information related to that event. If a user posts about a specific project, it will extract expense information related to that project. This enables efficient expense management by analyzing a user's social media activity and automatically extracting relevant expense information. Social media activity is analyzed based on factors such as the content of posts and follower reactions.

[0052] The year-end tax adjustment automated creation application can analyze a user's past expense data and predict future expenses. For example, it can predict expenses for the following year and create a budget based on past expense data. It analyzes past expense data and provides advice on how to reduce unnecessary expenses. Based on past expense data, it can understand expense trends over specific periods and use this information to improve future expense management. This enables efficient expense management by analyzing the user's past expense data and predicting future expenses. The analysis of expense data is performed based on methods such as statistical analysis and machine learning models.

[0053] The year-end tax adjustment automatic creation application can integrate user expense data with other financial management tools. For example, it can integrate with the user's accounting software to automatically synchronize expense data, integrate with the user's bank account to automatically retrieve expense information, and integrate with the user's credit card to automatically import expense details. This enables efficient expense management by integrating user expense data with other financial management tools. Integration is achieved using methods such as APIs and data import / export functions.

[0054] The year-end tax adjustment automated creation application can analyze the user's expense data and provide tax-saving advice. For example, it can suggest the optimal processing method for each expense category to reduce taxes. Based on past expense data, it can advise on how to use expenses to maximize tax savings. It also provides the latest tax-saving information in response to changes in tax laws. By analyzing the user's expense data and providing tax-saving advice, it enables efficient expense management. The advice is provided based on, for example, statistical analysis, machine learning models, and a knowledge base of tax laws.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The scanning unit scans and stores expense and other information necessary for year-end tax adjustments. For example, a user can use their smartphone camera to take pictures of documents such as receipts and invoices and upload them to the app. A user can take a picture of a receipt for a meal at a restaurant and store it in the app. This information is stored in the app's database. Step 2: The review unit reviews the information scanned by the scanning unit. The review unit uses AI to analyze the uploaded documents and extract the information necessary for year-end tax adjustments. For example, it automatically reads information such as the amount, date, and payee of receipts to identify the items required for year-end tax adjustments. Step 3: The creation department creates the year-end tax adjustment sheet based on the information reviewed by the verification department. The creation department automatically organizes the necessary documents and information for year-end tax adjustment and generates a format for submission. For example, it calculates the total amount of expenses and deductions and reflects them in the year-end tax adjustment sheet.

[0057] (Example of form 2) An application for automatically creating year-end tax adjustments according to an embodiment of the present invention is a system that automatically scans, scrutinizes, and creates information necessary for year-end tax adjustments, including expenses and other relevant data. The application allows users to scan and store documents daily that they are unsure whether they are necessary for year-end tax adjustments. Users use their smartphone cameras to photograph documents such as receipts and invoices and upload them to the application. For example, a user might photograph a receipt for a meal at a restaurant and store it in the application. This information is stored in the application's database. Next, the application scrutinizes this information. Using AI, the application analyzes the uploaded documents and extracts the information necessary for year-end tax adjustments. For example, the application automatically reads information such as the amount, date, and payee from a receipt and identifies the items required for year-end tax adjustments. This eliminates the need for users to manually enter information. Furthermore, the application creates a year-end tax adjustment sheet based on the scrutinized information. The application automatically organizes the necessary documents and information for year-end tax adjustments and generates a submission format. For example, the application calculates the total expense amount and deduction amount and reflects them in the year-end tax adjustment sheet. This sheet can be downloaded and submitted by the user. This application significantly reduces the time and effort users spend on year-end tax adjustments. Users simply scan their daily expenses and store them in the app, and their year-end tax adjustment preparations are complete. Furthermore, the app automatically scrutinizes the information and creates the year-end tax adjustment sheet, eliminating the need for manual data entry. This simplifies the year-end tax adjustment process and reduces the burden on users. For example, by using the "Easy Year-End Tax Adjustment App," users can easily complete their year-end tax adjustments without getting tired during the annual tax adjustment season. Users simply download the app and scan and store their expenses and other necessary documents for year-end tax adjustments. The app automatically scrutinizes the information and creates the year-end tax adjustment sheet, allowing users to complete the process effortlessly. This automatic year-end tax adjustment creation application significantly reduces the time and effort users spend on year-end tax adjustments.

[0058] The application for automatically creating year-end tax adjustments according to this embodiment comprises a scanning unit, a review unit, and a creation unit. The scanning unit scans and stores expenses and other information necessary for year-end tax adjustments. The scanning unit, for example, uses a smartphone camera to photograph documents such as receipts and invoices and uploads them to the app. The scanning unit, for example, takes a photograph of a receipt for a meal at a restaurant and stores it in the app. This information is stored in a database within the app. The review unit reviews the information scanned by the scanning unit. The review unit uses AI to analyze the uploaded documents and extract the information necessary for year-end tax adjustments. For example, the review unit automatically reads information such as the amount, date, and payee of a receipt and identifies the items necessary for year-end tax adjustments. The review unit can, for example, use AI to analyze the contents of uploaded documents and extract the necessary information. The creation unit creates a year-end tax adjustment sheet based on the information reviewed by the review unit. The creation unit automatically organizes the documents and information necessary for year-end tax adjustments and generates a format for submission. For example, the creation unit calculates the total amount of expenses and deductions, and reflects them in the year-end tax adjustment sheet. The creation unit also creates the year-end tax adjustment sheet based on the scrutinized information and generates a format for the user to download and submit. As a result, the automated year-end tax adjustment creation application according to this embodiment can significantly reduce the effort involved in year-end tax adjustments by automatically scanning, scrutinizing, and creating expenses and other information necessary for year-end tax adjustments.

[0059] The scanning unit scans and stores expense and other information necessary for year-end tax adjustments. For example, the scanning unit uses a smartphone camera to photograph documents such as receipts and invoices and uploads them to the app. Specifically, a user might photograph a receipt for a meal at a restaurant and store it in the app. This information is stored in the app's database. The scanning unit uses OCR (Optical Character Recognition) technology to convert the text information of the photographed documents into digital data. This allows for accurate reading of handwritten receipts and printed invoices. Furthermore, the scanning unit can scan multiple documents at once, enabling users to efficiently process large volumes of documents. For example, a user can photograph multiple receipts at once and upload them all at once. The scanning unit also features automatic cropping and distortion correction for scanned document images. This improves the quality of the scanned images and enhances the accuracy of analysis in the subsequent review unit. Additionally, the scanning unit provides a function to organize scanned documents by category. For example, saving documents categorized by food and beverage expenses, transportation expenses, and accommodation expenses makes subsequent processing easier. This allows the scanning unit to efficiently scan and organize the information users need for year-end tax adjustments.

[0060] The Verification Department scrutinizes the information scanned by the Scanning Department. The Verification Department uses AI to analyze uploaded documents and extract the information necessary for year-end tax adjustments. Specifically, the Verification Department uses OCR technology to analyze the text information of scanned documents, automatically reading information such as amounts, dates, and payees from receipts. The AI ​​uses natural language processing (NLP) technology to understand the content of the documents and identify the items necessary for year-end tax adjustments. For example, the AI ​​automatically identifies categories such as "food and beverage expenses" and "transportation expenses" from receipts and extracts the amounts for each. Furthermore, the AI ​​is trained to handle documents in different formats, and can accurately extract information from handwritten receipts and invoices with different layouts. The Verification Department also has a function to verify the accuracy of the extracted information. For example, the AI ​​checks whether the extracted amounts match the total amounts on the actual receipts and notifies the user if there are errors. The Verification Department also has a function to detect anomalies based on past data. For example, if an unusually high amount is entered compared to past expense data, it prompts the user for confirmation. This allows the scrutiny unit to accurately and efficiently examine the scanned information and extract the information necessary for year-end tax adjustments.

[0061] The creation department creates the year-end tax adjustment sheet based on the information reviewed by the review department. The creation department automatically organizes the necessary documents and information for year-end tax adjustments and generates a submission format. Specifically, the creation department calculates the total amount of expenses and deductions based on the reviewed information and reflects them in the year-end tax adjustment sheet. For example, the creation department aggregates expenses by category, such as food and beverage expenses, transportation expenses, and accommodation expenses, and calculates the grand total. The creation department also automatically calculates deductions based on tax laws. For example, for expenses that meet certain conditions, it calculates the deduction amount and reflects it in the year-end tax adjustment sheet. Furthermore, the creation department has a function to generate a format for user submission. For example, it can generate the year-end tax adjustment sheet in PDF format so that users can download and print it. The creation department can also generate a format compatible with electronic applications, allowing users to submit year-end tax adjustments online. In addition, the creation department has a function to automatically update the contents of the year-end tax adjustment sheet based on information entered by the user and information extracted by the review department. For example, if a user adds new expenses, the creation department automatically updates the contents of the sheet to reflect the latest information. This allows the creation department to efficiently organize the information necessary for year-end tax adjustments and create accurate year-end tax adjustment sheets.

[0062] The scrutiny department can analyze uploaded documents using AI and extract the information necessary for year-end tax adjustments. For example, the scrutiny department can use AI to analyze the contents of uploaded documents and extract the necessary information. For example, the scrutiny department can automatically read information such as the amount, date, and payee of receipts and identify the items necessary for year-end tax adjustments. For example, the scrutiny department can use AI to analyze the contents of uploaded documents and extract the necessary information. This allows for the accurate extraction of information necessary for year-end tax adjustments by using AI. The AI ​​is implemented using technologies such as machine learning, deep learning, and natural language processing. Some or all of the above-described processes in the scrutiny department may be performed using AI or not. For example, the scrutiny department can extract information necessary for year-end tax adjustments using an AI model that analyzes the contents of uploaded documents and extracts the necessary information.

[0063] The creation unit can create year-end tax adjustment sheets based on the scrutinized information. For example, the creation unit creates year-end tax adjustment sheets based on the scrutinized information and generates a format for users to download and submit. The creation unit calculates, for example, the total amount of expenses and deductions and reflects them in the year-end tax adjustment sheet. The creation unit creates year-end tax adjustment sheets based on the scrutinized information and generates a format for users to download and submit. This reduces the effort required from users by automatically creating year-end tax adjustment sheets based on the scrutinized information. Scrutinized information includes, for example, information whose accuracy has been confirmed and information for which error checks have been completed. Some or all of the above processes in the creation unit may be performed using AI or not. For example, the creation unit can create year-end tax adjustment sheets using an AI model that creates year-end tax adjustment sheets based on scrutinized information.

[0064] The creation unit can calculate the total amount of expenses and deductions and reflect them in the year-end tax adjustment sheet. For example, the creation unit can calculate the total amount of expenses and deductions and reflect them in the year-end tax adjustment sheet. For example, the creation unit can create a year-end tax adjustment sheet based on the scrutinized information and generate a format for the user to download and submit. For example, the creation unit can calculate the total amount of expenses and deductions and reflect them in the year-end tax adjustment sheet. This enables accurate year-end tax adjustments by automatically calculating the total amount of expenses and deductions and reflecting them in the year-end tax adjustment sheet. The total amount of expenses and deductions are calculated based on, for example, a calculation formula, applicable tax laws, etc. Some or all of the above processing in the creation unit may be performed using AI or not. For example, the creation unit can use an AI model to calculate the total amount of expenses and deductions and reflect them in the year-end tax adjustment sheet.

[0065] The creation unit can generate a format for users to download and submit. For example, the creation unit creates a year-end tax adjustment sheet based on scrutinized information and generates a format for users to download and submit. For example, the creation unit calculates the total amount of expenses and deductions and reflects them in the year-end tax adjustment sheet. For example, the creation unit creates a year-end tax adjustment sheet based on scrutinized information and generates a format for users to download and submit. This makes it easier for users to submit year-end tax adjustments by generating a format that they can download and submit. The format is generated based on, for example, PDF, Excel, and the requirements of the submission destination. Some or all of the above processes in the creation unit may be performed using AI or not. For example, the creation unit can create a year-end tax adjustment sheet using an AI model that generates a format for users to download and submit.

[0066] The scanning unit can use a smartphone camera to photograph documents such as receipts and invoices and upload them to the app. For example, the scanning unit can use a smartphone camera to photograph documents such as receipts and invoices and upload them to the app. For example, the scanning unit can use a smartphone camera to photograph a receipt for a meal at a restaurant and store it in the app. This information is stored in the app's database. The scanning unit can use a smartphone camera to photograph documents such as receipts and invoices and upload them to the app. This makes it easy to scan documents using a smartphone camera and upload them to the app. The smartphone camera is used based on, for example, the corresponding app, camera resolution, etc. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit can input image data captured using a smartphone camera into a generating AI and have the generating AI perform the conversion of image data into text data.

[0067] The scanning unit can estimate the user's emotions and adjust the timing of scans based on the estimated emotions. For example, if the user is stressed, the scanning unit reduces the frequency of scans and prompts scans at times when the user is relaxed. For example, if the user is relaxed, the scanning unit increases the frequency of scans and efficiently collects information. For example, if the user is in a hurry, the scanning unit performs scans quickly and obtains only the minimum necessary information. In this way, the user's burden can be reduced by adjusting the timing of scans according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0068] The scanning unit can automatically select the optimal scanning settings according to the type of document during scanning. For example, in the case of a receipt, the scanning unit scans at high resolution to improve character recognition accuracy. For example, in the case of an invoice, the scanning unit scans in color to preserve the overall layout. For example, in the case of a handwritten memo, the scanning unit applies a specific filter to enhance handwritten character recognition. This improves scanning accuracy by automatically selecting the optimal scanning settings according to the type of document. The optimal scanning settings are selected based on, for example, resolution, color mode, and scanning speed. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit can improve scanning accuracy by using an AI model that identifies the type of document and selects the optimal scanning settings.

[0069] The scanning unit can detect the condition of a document during scanning and perform optimal correction processing. For example, the scanning unit can automatically fill in torn areas to make the document readable. For example, the scanning unit can detect soiled areas and apply a digital filter to create a clear image. For example, the scanning unit can detect distortion in a document and automatically correct it to perform an accurate scan. In this way, the accuracy of the scan is improved by detecting the condition of the document and performing optimal correction processing. The condition of the document is detected based on, for example, tears, soiling, distortion, etc. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit can improve the accuracy of the scan by using an AI model that detects the condition of the document and performs optimal correction processing.

[0070] The scanning unit can estimate the user's emotions and prioritize the documents to scan based on the estimated emotions. For example, if the user is stressed, the scanning unit will postpone scanning less important documents. If the user is relaxed, the scanning unit will prioritize scanning more important documents. If the user is in a hurry, the scanning unit will quickly scan the most important documents. This enables efficient scanning by prioritizing documents according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0071] The scanning unit can prioritize scanning documents that are highly relevant based on the user's geographical location information. For example, if the user is on a business trip, the scanning unit will prioritize scanning business trip-related receipts. If the user is at home, the scanning unit will prioritize scanning documents related to daily expenses. If the user is attending a specific event, the scanning unit will prioritize scanning documents related to that event. This enables efficient scanning by prioritizing the scanning of highly relevant documents based on the user's geographical location information. Geographical location information is obtained, for example, from GPS data or location services. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit can improve scanning efficiency by using an AI model that prioritizes scanning highly relevant documents while considering the user's geographical location information.

[0072] The scanning unit can analyze the user's social media activity during scanning and scan relevant documents. For example, if the user has posted about a business trip on social media, the scanning unit will prioritize scanning documents related to that trip. For example, if the user has posted about attending an event, the scanning unit will prioritize scanning documents related to that event. For example, if the user has posted about a specific project, the scanning unit will prioritize scanning documents related to that project. This enables efficient scanning by analyzing the user's social media activity and scanning relevant documents. Social media activity is analyzed based on, for example, the content of posts and the reactions of followers. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit can improve scanning efficiency by using an AI model that analyzes the user's social media activity and scans relevant documents.

[0073] The scrutiny unit can estimate the user's emotions and adjust the scrutiny criteria based on the estimated emotions. For example, if the user is stressed, the scrutiny unit will loosen the scrutiny criteria and process the information quickly. For example, if the user is relaxed, the scrutiny unit will tighten the scrutiny criteria and review the information in detail. For example, if the user is in a hurry, the scrutiny unit will only review important items and process them quickly. This allows for efficient scrutiny by adjusting the scrutiny criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scrutiny unit may be performed using AI or not. For example, the scrutiny unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0074] The scrutiny unit can apply different scrutiny algorithms based on the content of the document during the scrutiny process. For example, in the case of a receipt, the scrutiny unit might apply an algorithm that focuses on scrutinizing the amount, date, and payee. For example, in the case of an invoice, the scrutiny unit might apply an algorithm that scrutinizes the amount and payment terms for each item. For example, in the case of a handwritten memo, the scrutiny unit might apply an algorithm that enhances handwriting recognition. This improves the accuracy of the scrutiny by applying different scrutiny algorithms based on the content of the document. The scrutiny algorithms are implemented using technologies such as rule-based or machine learning-based methods. Some or all of the above-described processes in the scrutiny unit may be performed using AI or not. For example, the scrutiny unit can improve the accuracy of the scrutiny by using an AI model that applies different scrutiny algorithms based on the content of the document.

[0075] The review department can perform the review while considering the attribute information of the document submitter. For example, if the submitter is a company, the review department will focus on reviewing the company name and the name of the person in charge. For example, if the submitter is an individual, the review department will focus on reviewing the individual's name and address. For example, if the submitter belongs to a specific industry, the review department will review items specific to that industry. This improves the accuracy of the review by considering the attribute information of the document submitter. The attribute information of the submitter is obtained based on, for example, age, occupation, and department. Some or all of the above processing in the review department may be performed using AI or not. For example, the review department can improve the accuracy of the review by using an AI model that performs the review while considering the attribute information of the document submitter.

[0076] The analysis unit can estimate the user's emotions and adjust the order in which the analysis results are displayed based on the estimated emotions. For example, if the user is stressed, the analysis unit will display important results first. If the user is relaxed, the analysis unit will display detailed results sequentially. If the user is in a hurry, the analysis unit will display concise results first. This allows for efficient analysis by adjusting the order in which the analysis results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0077] The review unit can perform a review while considering the geographical distribution of the documents. For example, if the documents are concentrated in a particular region, the review unit will focus its review on the information in that region. For example, if the documents are dispersed across multiple regions, the review unit will perform a review region by region. For example, if the documents are international, the review unit will perform a review while considering the regulations and rules of each country. This improves the accuracy of the review by considering the geographical distribution of the documents. Geographical distribution is considered based on, for example, regional regulations and geographical characteristics. Some or all of the above processing in the review unit may be performed using AI or not. For example, the review unit can improve the accuracy of the review by using an AI model that performs a review while considering the geographical distribution of the documents.

[0078] The review department can improve the accuracy of its review by referring to relevant literature related to the document during the review process. For example, the review department may refer to laws and regulations related to the document during the review. For example, the review department may refer to past cases related to the document during the review. For example, the review department may refer to industry guidelines related to the document during the review. This improves the accuracy of the review by referring to relevant literature related to the document. Relevant literature may be referenced based on, for example, citations, relevant research papers, etc. Some or all of the above processes in the review department may be performed using AI or not. For example, the review department can improve the accuracy of its review by using an AI model that performs a review by referring to relevant literature related to the document.

[0079] The creation unit can estimate the user's emotions and adjust the method of creating the year-end tax adjustment sheet based on the estimated emotions. For example, if the user is stressed, the creation unit will create the year-end tax adjustment sheet in a simple format. For example, if the user is relaxed, the creation unit will create the year-end tax adjustment sheet in a format that includes detailed information. For example, if the user is in a hurry, the creation unit will create the year-end tax adjustment sheet in a format that includes only the minimum necessary information. This allows for efficient year-end tax adjustments by adjusting the method of creating the year-end tax adjustment sheet according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the creation unit may be performed using AI or not. For example, the creation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The creation unit can adjust the level of detail in the sheet based on the importance of the information reviewed during creation. For example, the creation unit can prioritize the display of important information and add detailed explanations. For example, the creation unit can display less important information concisely and add only the necessary explanations. For example, the creation unit can adjust the display order of information according to its importance. This allows for efficient year-end tax adjustments by adjusting the level of detail in the sheet based on the importance of the reviewed information. The importance of information is evaluated based on, for example, the accuracy of the information, the submission deadline, and legal requirements. Some or all of the above processes in the creation unit may be performed using AI or not. For example, the creation unit can create year-end tax adjustment sheets using an AI model that adjusts the level of detail in the sheet based on the importance of the reviewed information.

[0081] The creation unit can determine the priority of sheets based on the submission timing of the reviewed information during creation. For example, the creation unit can prioritize displaying information with an approaching submission deadline. For example, the creation unit can postpone displaying information with a distant submission deadline. For example, the creation unit can adjust the display order of information according to the submission deadline. This enables efficient year-end tax adjustments by prioritizing sheets based on submission timing. Submission timing is evaluated based on, for example, the submission deadline, legal requirements, and the user's schedule. Some or all of the above processing in the creation unit may be performed using AI or not. For example, the creation unit can create year-end tax adjustment sheets using an AI model that determines the priority of sheets based on the submission timing of the reviewed information.

[0082] The creation unit can adjust the order of sheets based on the relevance of the scrutinized information during creation. For example, the creation unit can prioritize displaying highly relevant information. For example, the creation unit can postpone displaying less relevant information. For example, the creation unit can adjust the display order of information according to its relevance. This allows for efficient year-end tax adjustments by adjusting the order of sheets based on the relevance of the information. The relevance of the information is evaluated based on, for example, the interrelationships between information, its importance, and its submission deadline. Some or all of the above processing in the creation unit may be performed using AI or not. For example, the creation unit can create year-end tax adjustment sheets using an AI model that adjusts the order of sheets based on the relevance of the scrutinized information.

[0083] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0084] The year-end tax adjustment automatic creation application can estimate the user's emotions and dynamically change the application interface based on the estimated emotions. For example, if the user is stressed, the interface can be changed to a simple and intuitive design to make it easier to operate. If the user is relaxed, the interface can be changed to display more detailed information and options and be more customizable. If the user is in a hurry, the interface can be changed to make the most important functions and information easily accessible. In this way, the user experience can be improved by dynamically changing the interface according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Interface changes are made based on, for example, color, layout, displayed items, etc.

[0085] The automated year-end tax adjustment application can create year-end tax adjustment sheets that comply with regional tax laws and regulations based on the user's geographical location. For example, if the user lives in a different state or country, the application customizes the year-end tax adjustment sheet based on the tax laws and regulations of that region. If the user is on a business trip, the application processes expenses in accordance with the tax laws and regulations of the destination country. If the user moves, the application updates the year-end tax adjustment sheet based on the new address. This enables accurate year-end tax adjustments by creating year-end tax adjustment sheets that comply with regional tax laws and regulations based on the user's geographical location. Geographical location information is obtained, for example, from GPS data or location services.

[0086] The year-end tax adjustment automatic creation application can analyze a user's social media activity and automatically extract relevant expense information. For example, if a user posts about a business trip on social media, it will automatically extract expense information related to that trip. If a user posts about attending an event, it will extract expense information related to that event. If a user posts about a specific project, it will extract expense information related to that project. This enables efficient expense management by analyzing a user's social media activity and automatically extracting relevant expense information. Social media activity is analyzed based on factors such as the content of posts and follower reactions.

[0087] The year-end tax adjustment automated creation application can estimate the user's emotions and adjust the frequency and content of notifications based on those emotions. For example, if the user is stressed, the frequency of notifications will be reduced and only important notifications will be sent. If the user is relaxed, detailed notifications and reminders will be sent. If the user is in a hurry, the most important notifications will be sent quickly. This reduces the user's burden by adjusting the frequency and content of notifications according to their emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Notification adjustments are made based on, for example, the timing, content, and frequency of notifications.

[0088] The year-end tax adjustment automated creation application can analyze a user's past expense data and predict future expenses. For example, it can predict expenses for the following year and create a budget based on past expense data. It analyzes past expense data and provides advice on how to reduce unnecessary expenses. Based on past expense data, it can understand expense trends over specific periods and use this information to improve future expense management. This enables efficient expense management by analyzing the user's past expense data and predicting future expenses. The analysis of expense data is performed based on methods such as statistical analysis and machine learning models.

[0089] The year-end tax adjustment automatic creation application can estimate the user's emotions and adjust expense categorization based on those emotions. For example, if the user is stressed, expense categorization is simplified, displaying only the main categories. If the user is relaxed, detailed categorization is displayed, allowing for more precise expense management. If the user is in a hurry, only the most important categories are displayed, supporting quick expense management. This allows for efficient expense management by adjusting expense categorization according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Categorization adjustments are made based on, for example, the number of categories, display order, and level of detail.

[0090] The year-end tax adjustment automatic creation application can integrate user expense data with other financial management tools. For example, it can integrate with the user's accounting software to automatically synchronize expense data, integrate with the user's bank account to automatically retrieve expense information, and integrate with the user's credit card to automatically import expense details. This enables efficient expense management by integrating user expense data with other financial management tools. Integration is achieved using methods such as APIs and data import / export functions.

[0091] The year-end tax adjustment automated creation application can estimate the user's emotions and adjust the expense input method based on those emotions. For example, if the user is stressed, it can provide a simple input form and require only the minimum necessary information. If the user is relaxed, it can provide a detailed input form and require more detailed information. If the user is in a hurry, it can use voice input or scanning functions to quickly input expenses. This allows for efficient expense management by adjusting the expense input method according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Adjustments to the input method are made based on, for example, the design of the input form, the number of input fields, and the input method.

[0092] The year-end tax adjustment automated creation application can analyze the user's expense data and provide tax-saving advice. For example, it can suggest the optimal processing method for each expense category to reduce taxes. Based on past expense data, it can advise on how to use expenses to maximize tax savings. It also provides the latest tax-saving information in response to changes in tax laws. By analyzing the user's expense data and providing tax-saving advice, it enables efficient expense management. The advice is provided based on, for example, statistical analysis, machine learning models, and a knowledge base of tax laws.

[0093] The year-end tax adjustment automated creation application can estimate the user's emotions and adjust the expense approval process based on those emotions. For example, if the user is stressed, the approval process is simplified and approval is expedited. If the user is relaxed, a detailed approval process is provided, allowing for thorough verification. If the user is in a hurry, only the most important items are prioritized for approval. This allows for efficient expense management by adjusting the expense approval process according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The adjustment of the approval process is based on, for example, the number of approval steps, the level of detail of the approval items, and the approval method.

[0094] The following briefly describes the processing flow for example form 2.

[0095] Step 1: The scanning unit scans and stores expense and other information necessary for year-end tax adjustments. For example, a user can use their smartphone camera to take pictures of documents such as receipts and invoices and upload them to the app. A user can take a picture of a receipt for a meal at a restaurant and store it in the app. This information is stored in the app's database. Step 2: The review unit reviews the information scanned by the scanning unit. The review unit uses AI to analyze the uploaded documents and extract the information necessary for year-end tax adjustments. For example, it automatically reads information such as the amount, date, and payee of receipts to identify the items required for year-end tax adjustments. Step 3: The creation department creates the year-end tax adjustment sheet based on the information reviewed by the verification department. The creation department automatically organizes the necessary documents and information for year-end tax adjustment and generates a format for submission. For example, it calculates the total amount of expenses and deductions and reflects them in the year-end tax adjustment sheet.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] Each of the multiple elements described above, including the scanning unit, scrutiny unit, and creation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the scanning unit uses the camera 42 of the smart device 14 to photograph documents such as receipts and invoices and uploads them to the app. The scrutiny unit is implemented in the specific processing unit 290 of the data processing unit 12, which uses AI to analyze the uploaded documents and extract the information necessary for year-end tax adjustments. The creation unit is implemented in the specific processing unit 290 of the data processing unit 12, which creates a year-end tax adjustment sheet based on the scrutinized information and generates a format for submission. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0100] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the scanning unit, scrutiny unit, and creation unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the scanning unit uses the camera 42 of the smart glasses 214 to photograph documents such as receipts and invoices and upload them to the app. The scrutiny unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which uses AI to analyze the uploaded documents and extract the information necessary for year-end tax adjustments. The creation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which creates a year-end tax adjustment sheet based on the scrutinized information and generates a format for submission. 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.

[0116] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the scanning unit, scrutiny unit, and creation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the scanning unit uses the camera 42 of the headset terminal 314 to photograph documents such as receipts and invoices and upload them to the application. The scrutiny unit is implemented in the specific processing unit 290 of the data processing unit 12, which uses AI to analyze the uploaded documents and extract the information necessary for year-end tax adjustments. The creation unit is implemented in the specific processing unit 290 of the data processing unit 12, which creates a year-end tax adjustment sheet based on the scrutinized information and generates a format for submission. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0132] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the scanning unit, scrutiny unit, and creation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the scanning unit uses the camera 42 of the robot 414 to photograph documents such as receipts and invoices and upload them to the app. The scrutiny unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which uses AI to analyze the uploaded documents and extract the information necessary for year-end tax adjustments. The creation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which creates a year-end tax adjustment sheet based on the scrutinized information and generates a format for submission. 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] (Note 1) A scanning unit that scans and stores expense information and other information necessary for year-end tax adjustments, A review unit that examines the information scanned by the aforementioned scanning unit, The system includes a creation unit that creates a year-end tax adjustment sheet based on the information examined by the aforementioned examination unit. A system characterized by the following features. (Note 2) The aforementioned inspection unit, AI analyzes uploaded documents and extracts the information necessary for year-end tax adjustments. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned creation unit, Create a year-end tax adjustment sheet based on the carefully reviewed information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned creation unit, Calculate the total amount of expenses and deductions, and reflect them in the year-end tax adjustment sheet. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned creation unit, Generate a format for users to download and submit. The system described in Appendix 1, characterized by the features described herein. (Note 6) The scanning unit is Use your smartphone camera to take pictures of documents such as receipts and invoices, and upload them to the app. The system described in Appendix 1, characterized by the features described herein. (Note 7) The scanning unit is It estimates the user's emotions and adjusts the timing of scans based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The scanning unit is During scanning, the system automatically selects the optimal scan settings based on the type of document. The system described in Appendix 1, characterized by the features described herein. (Note 9) The scanning unit is During scanning, the system detects the document's condition and performs optimal correction processing. The system described in Appendix 1, characterized by the features described herein. (Note 10) The scanning unit is It estimates the user's emotions and prioritizes the documents to scan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The scanning unit is During scanning, the system prioritizes scanning documents that are more relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The scanning unit is During scanning, the system analyzes the user's social media activity and scans relevant documents. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned inspection unit, We estimate the user's emotions and adjust the scrutiny criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned inspection unit, During the review process, different review algorithms are applied based on the content of the documents. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned inspection unit, During the review process, the attribute information of the document submitter will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned inspection unit, It estimates the user's sentiment and adjusts the order in which the analysis results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned inspection unit, During the review process, the geographical distribution of the documents will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned inspection unit, During the review process, refer to relevant documents to improve the accuracy of the review. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned creation unit, The system estimates the user's emotions and adjusts the method of creating the year-end tax adjustment sheet based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned creation unit, When creating the sheet, adjust the level of detail based on the importance of the information that has been scrutinized. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned creation unit, When creating the sheets, prioritize them based on when the reviewed information will be submitted. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned creation unit, When creating the document, adjust the order of the sheets based on the relevance of the information that has been reviewed. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0168] 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 scanning unit that scans and stores expense information and other information necessary for year-end tax adjustments, A review unit that examines the information scanned by the aforementioned scanning unit, The system includes a creation unit that creates a year-end tax adjustment sheet based on the information examined by the aforementioned examination unit. A system characterized by the following features.

2. The aforementioned inspection unit, The AI ​​analyzes uploaded documents and extracts the information necessary for year-end tax adjustments. The system according to feature 1.

3. The aforementioned creation unit, Create a year-end tax adjustment sheet based on the carefully reviewed information. The system according to feature 1.

4. The aforementioned creation unit, Calculate the total amount of expenses and deductions, and reflect them in the year-end tax adjustment sheet. The system according to feature 1.

5. The aforementioned creation unit, Generate a format for users to download and submit. The system according to feature 1.

6. The scanning unit is Use your smartphone camera to take pictures of documents such as receipts and invoices, and upload them to the app. The system according to feature 1.

7. The scanning unit is It estimates the user's emotions and adjusts the timing of scans based on the estimated emotions. The system according to feature 1.

8. The scanning unit is During scanning, the system automatically selects the optimal scan settings based on the type of document. The system according to feature 1.

Images