System

Abstract

An object of a system according to an embodiment is to improve the efficiency of work from bookkeeping to tax return.SOLUTION: A system includes a sorting part, a bill issuing part, a money reception confirming part, and a tax return preparing part. The sorting unit analyzes the transaction data and performs accounting and tax sorting. The bill issuing unit issues a bill based on the data sorted by the sorting unit. The deposit confirming unit confirms the deposit based on the bill issued by the bill issuing unit. The tax return preparing section prepares an tax return based on the payment confirmed by the payment confirming section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, sole proprietors and small companies had the problem that tasks from bookkeeping to tax returns were time-consuming and labor-intensive.

[0005] The system according to the embodiment aims to improve the efficiency of operations from bookkeeping to tax returns. [Means for solving the problem]

[0006] The system according to the embodiment includes a classification unit, an invoice issuing unit, a payment confirmation unit, and a tax return preparation unit. The classification unit analyzes transaction data and performs accounting and tax classification. The invoice issuing unit issues invoices based on the data classified by the classification unit. The payment confirmation unit confirms payments based on the invoices issued by the invoice issuing unit. The tax return preparation unit prepares a tax return based on the payment data confirmed by the payment confirmation unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of operations ranging from bookkeeping to tax returns. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An accounting work efficiency improvement system according to an embodiment of the present invention is a system that streamlines work from daily bookkeeping to tax returns for sole proprietors and small companies. This system uses AI to automatically perform accounting and tax classifications, automatically record invoice issuance and payment confirmation, and prepare tax returns in designated formats based on that bookkeeping data, which can then be submitted to the tax office. As a result, the accounting work efficiency improvement system can streamline accounting and tax return work for sole proprietors and small companies, significantly reducing the time and effort required.

[0029] An accounting efficiency improvement system according to an embodiment includes a classification unit, an invoice issuing unit, a payment confirmation unit, and a tax return preparation unit. The classification unit analyzes transaction data to perform accounting and tax classification. For example, the classification unit analyzes sales data and expense data and classifies them into appropriate account categories. The classification unit can also analyze background information of the transaction data and perform classification based on the purpose of the transaction and related contracts. The classification unit can also learn from past classification history and propose optimal classification for similar transactions. The invoice issuing unit issues invoices based on the data classified by the classification unit. For example, the invoice issuing unit automatically creates invoices based on sales data and sends them to customers. The invoice issuing unit can also analyze customers' payment history and propose optimal payment terms. The payment confirmation unit confirms payments based on invoices issued by the invoice issuing unit. For example, the payment confirmation unit uses a bank's API to obtain payment information in real time and immediately update the ledger. The tax return preparation unit prepares tax returns based on the payment data confirmed by the payment confirmation unit. For example, the tax return preparation unit can learn from past tax return data and propose the optimal tax return method. The tax return preparation unit can also automatically obtain the latest tax law amendment information and reflect it in the tax return content. As a result, the accounting work efficiency system according to the embodiment can improve the efficiency of accounting and tax return work for sole proprietors and small companies, significantly reducing time and effort.

[0030] The classification department can analyze the background information of the transaction data and perform classification based on the purpose of the transaction and related contracts. The classification department, for example, analyzes the background information of the transaction data and performs classification based on the purpose of the transaction and related contracts. For example, if the purpose of the transaction is "capital investment," the classification department classifies it into the appropriate account based on that information. The classification department also analyzes the contents of the contracts and invoices included in the transaction data to understand the details of the transaction. For example, the classification department performs appropriate classification based on the payment terms and delivery date stated in the contract. Furthermore, by analyzing the background information of the transaction, the classification department can understand the purpose of the transaction and related documents, achieving more accurate classification. For example, if the purpose of the transaction is "research and development," the classification department will classify it as research and development expenses based on that information. In this way, by analyzing the background information of the transaction, more accurate classification can be achieved.

[0031] The sorting unit can learn from past sorting history and propose optimal sorting for similar transactions. The sorting unit, for example, learns from past sorting history and proposes optimal sorting for similar transactions. For example, if a similar transaction has occurred in the past, the sorting unit can refer to that sorting method to sort new transactions. The sorting unit can also analyze past sorting data and extract patterns to propose optimal sorting for similar transactions. For example, if a specific transaction pattern is repeated, the sorting unit will perform sorting based on that pattern. The sorting unit can also propose optimal sorting methods based on the content and amount of the transaction based on past sorting history. For example, it selects appropriate account items for transactions with the same client based on past data. In this way, by learning from past sorting history, it can propose optimal sorting for similar transactions.

[0032] The invoice issuing unit can analyze a customer's payment history and propose optimal payment terms. The invoice issuing unit, for example, analyzes a customer's payment history and proposes optimal payment terms. For example, based on past payment history, it proposes an early payment discount to the customer. The invoice issuing unit also proposes optimal payment terms based on the customer's payment history. For example, it proposes advance payment to a customer who frequently makes late payments. The invoice issuing unit also analyzes a customer's payment history and proposes optimal payment terms, thereby smoothing transactions with customers. For example, it proposes an extension of the payment deadline to a customer with a good payment history. In this way, it is possible to propose optimal payment terms by analyzing a customer's payment history.

[0033] The deposit confirmation unit can obtain deposit information in real time using the bank's API and instantly reflect it in the ledger. The deposit confirmation unit, for example, obtains deposit information in real time using the bank's API and instantly reflects it in the ledger. For example, when a deposit is confirmed, the deposit confirmation unit automatically records it in the ledger. Furthermore, by using the bank's API, the deposit confirmation unit obtains deposit information in real time and instantly reflects it in the ledger. For example, when a deposit is confirmed, the deposit confirmation unit automatically clears accounts receivable. Furthermore, the deposit confirmation unit obtains deposit information in real time using the bank's API and instantly reflects it in the ledger, thereby streamlining the deposit confirmation process. For example, when a deposit is confirmed, the deposit confirmation unit automatically sends a deposit notification. This allows deposit information to be obtained in real time and instantly reflected in the ledger.

[0034] The tax return preparation unit can learn from past return data and propose the optimal filing method. The tax return preparation unit, for example, learns from past return data and proposes the optimal filing method. For example, based on past return data, it proposes the filing method that is most effective for tax savings. In addition, the tax return preparation unit analyzes past return data and proposes the optimal filing method. For example, based on past return data, it proposes the most appropriate deduction items. In addition, the tax return preparation unit learns from past return data and proposes the optimal filing method, thereby making filing work more efficient. For example, based on past data, it proposes the most appropriate return format. In this way, it is possible to propose the optimal filing method by learning from past return data.

[0035] The tax return preparation unit can automatically obtain the latest tax law amendment information and reflect it in the tax return content. The tax return preparation unit, for example, automatically obtains the latest tax law amendment information and reflects it in the tax return content. For example, if the tax law is amended, the tax return preparation unit automatically obtains that information and reflects it in the tax return. The tax return preparation unit also automatically updates the tax return content based on the latest tax law amendment information. For example, if the deduction amount is changed, the tax return preparation unit automatically reflects that information and updates the tax return. The tax return preparation unit also automatically obtains the latest tax law amendment information and reflects it in the tax return content, thereby preventing filing errors. For example, if the tax rate is changed, the tax return preparation unit automatically reflects that information and prepares an accurate tax return. In this way, the latest tax law amendment information can be automatically obtained and reflected in the tax return content.

[0036] The tax return preparation unit can manage multiple business entities in a unified manner and realize unified filing. The tax return preparation unit, for example, manages multiple business entities in a unified manner and realizes unified filing. For example, if a company operates both a sole proprietorship and a corporation, the tax return preparation unit prepares tax returns in a unified manner. Furthermore, by managing multiple business entities in a unified manner, the tax return preparation unit realizes unified filing. For example, if a company operates multiple corporations, the tax return preparation unit prepares and submits tax returns in a unified manner. Furthermore, the tax return preparation unit manages multiple business entities in a unified manner and realizes unified filing, thereby streamlining filing work. For example, tax returns for both sole proprietorships and corporations are prepared and submitted in a unified manner. This allows multiple business entities to be managed in a unified manner and realize unified filing.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The accounting work efficiency improvement system can further include a data analysis unit. The data analysis unit can analyze accounting data and make proposals for improving management. For example, it can analyze sales data and identify products and services that are expected to increase sales. It can also analyze expense data and make specific proposals for reducing costs. Furthermore, the data analysis unit can predict future sales and expenses based on past data and use this information to help formulate management strategies. This allows the accounting work efficiency improvement system to not only improve the efficiency of accounting work, but also provide useful information for improving management.

[0039] The accounting work efficiency system can further include a risk management unit. The risk management unit can analyze transaction data and detect potential risks. For example, it can analyze a client's credit information and predict the risk of payment delays or bankruptcy. It can also detect signs of fraudulent transactions based on transaction data and issue early warnings. Furthermore, the risk management unit can calculate the probability of risk occurrence based on past data and propose specific measures to avoid risk. This allows the accounting work efficiency system to support users in terms of risk management as well.

[0040] The accounting work efficiency system can further include an education support unit. The education support unit can provide users with knowledge about accounting and taxation. For example, it can provide online courses that explain basic accounting concepts and tax return procedures. It can also propose customized learning plans based on the user's skill level. Furthermore, the education support unit provides information on the latest tax law revisions and changes in accounting standards, supporting users to always have the latest knowledge. In this way, the accounting work efficiency system can also contribute to improving users' skills.

[0041] The accounting work efficiency system can further include an environmental management unit. The environmental management unit makes proposals for achieving both business efficiency and environmental protection. For example, it can promote paperless operations and encourage the use of electronic data. It can also propose efficient power usage with the aim of reducing energy consumption. Furthermore, the environmental management unit provides specific advice for achieving a balance between business efficiency and environmental protection. In this way, the accounting work efficiency system can support users from the perspective of environmental protection and achieve sustainable business operations.

[0042] The accounting work efficiency improvement system can further include a marketing support section. The marketing support section supports the formulation of marketing strategies based on accounting data. For example, it analyzes sales data to identify the most effective marketing channels. It can also analyze customer data to understand the characteristics of target customers. Furthermore, the marketing support section proposes optimal marketing strategies based on past campaign data. In this way, the accounting work efficiency improvement system can support users in marketing as well, contributing to increased sales.

[0043] The accounting work efficiency improvement system can further include a project management section. The project management section manages the progress of projects based on accounting data and supports efficient project management. For example, it compares the project budget with actual results to detect early risks of budget overruns. It can also analyze project progress data and predict risks of delays. Furthermore, the project management section proposes optimal project management methods based on past project data. This allows the accounting work efficiency improvement system to support users in project management as well, enabling efficient project management.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The classification department analyzes transaction data and performs accounting and tax classification. For example, the classification department analyzes sales data and expense data and classifies them into appropriate account items. It can also analyze the background information of the transaction data and perform classification based on the purpose of the transaction and related contracts. It can also learn from past classification history and propose optimal classification for similar transactions. Step 2: The billing department issues invoices based on the data sorted by the sorting department. For example, it can automatically create invoices based on sales data and send them to customers. It can also analyze customers' payment history and propose optimal payment terms. Step 3: The payment confirmation department confirms the payment based on the invoice issued by the invoice issuing department. For example, it uses a bank's API to obtain payment information in real time and immediately updates the ledger. Step 4: The tax return preparation unit prepares a tax return based on the deposit data confirmed by the deposit confirmation unit. For example, it can learn from past tax return data and suggest the optimal filing method. It can also automatically obtain the latest tax law amendment information and reflect it in the tax return content.

[0046] (Example 2) An accounting work efficiency improvement system according to an embodiment of the present invention is a system that streamlines work from daily bookkeeping to tax returns for sole proprietors and small companies. This system uses AI to automatically perform accounting and tax classifications, automatically record invoice issuance and payment confirmation, and prepare tax returns in designated formats based on that bookkeeping data, which can then be submitted to the tax office. As a result, the accounting work efficiency improvement system can streamline accounting and tax return work for sole proprietors and small companies, significantly reducing the time and effort required.

[0047] An accounting efficiency improvement system according to an embodiment includes a classification unit, an invoice issuing unit, a payment confirmation unit, and a tax return preparation unit. The classification unit analyzes transaction data to perform accounting and tax classification. For example, the classification unit analyzes sales data and expense data and classifies them into appropriate account categories. The classification unit can also analyze background information of the transaction data and perform classification based on the purpose of the transaction and related contracts. The classification unit can also learn from past classification history and propose optimal classification for similar transactions. The invoice issuing unit issues invoices based on the data classified by the classification unit. For example, the invoice issuing unit automatically creates invoices based on sales data and sends them to customers. The invoice issuing unit can also analyze customers' payment history and propose optimal payment terms. The payment confirmation unit confirms payments based on invoices issued by the invoice issuing unit. For example, the payment confirmation unit uses a bank's API to obtain payment information in real time and immediately update the ledger. The tax return preparation unit prepares tax returns based on the payment data confirmed by the payment confirmation unit. For example, the tax return preparation unit can learn from past tax return data and propose the optimal tax return method. The tax return preparation unit can also automatically obtain the latest tax law amendment information and reflect it in the tax return content. As a result, the accounting work efficiency system according to the embodiment can improve the efficiency of accounting and tax return work for sole proprietors and small companies, significantly reducing time and effort.

[0048] The classification department can analyze the background information of the transaction data and perform classification based on the purpose of the transaction and related contracts. The classification department, for example, analyzes the background information of the transaction data and performs classification based on the purpose of the transaction and related contracts. For example, if the purpose of the transaction is "capital investment," the classification department classifies it into the appropriate account based on that information. The classification department also analyzes the contents of the contracts and invoices included in the transaction data to understand the details of the transaction. For example, the classification department performs appropriate classification based on the payment terms and delivery date stated in the contract. Furthermore, by analyzing the background information of the transaction, the classification department can understand the purpose of the transaction and related documents, achieving more accurate classification. For example, if the purpose of the transaction is "research and development," the classification department will classify it as research and development expenses based on that information. In this way, by analyzing the background information of the transaction, more accurate classification can be achieved.

[0049] The sorting unit can learn from past sorting history and propose optimal sorting for similar transactions. The sorting unit, for example, learns from past sorting history and proposes optimal sorting for similar transactions. For example, if a similar transaction has occurred in the past, the sorting unit can refer to that sorting method to sort new transactions. The sorting unit can also analyze past sorting data and extract patterns to propose optimal sorting for similar transactions. For example, if a specific transaction pattern is repeated, the sorting unit will perform sorting based on that pattern. The sorting unit can also propose optimal sorting methods based on the content and amount of the transaction based on past sorting history. For example, it selects appropriate account items for transactions with the same client based on past data. In this way, by learning from past sorting history, it can propose optimal sorting for similar transactions.

[0050] The sorting unit can use the emotion estimation function to analyze the user's emotions and suggest a less stressful sorting method. The sorting unit, for example, uses the emotion estimation function to analyze the user's emotions and suggest a less stressful sorting method. For example, if the user is feeling stressed, the sorting unit suggests a method that allows sorting with simple operations. The sorting unit also suggests a less stressful sorting method based on the user's emotion data. For example, if the user is tired, the sorting unit automatically sorts, reducing the user's burden. The emotion estimation function also analyzes the user's emotional state in real time and suggests a less stressful sorting method. For example, if the user is relaxed, the sorting unit suggests a detailed sorting method. In this way, by analyzing the user's emotions, a less stressful sorting method can be suggested.

[0051] The invoice issuing unit can analyze a customer's payment history and propose optimal payment terms. The invoice issuing unit, for example, analyzes a customer's payment history and proposes optimal payment terms. For example, based on past payment history, it proposes an early payment discount to the customer. The invoice issuing unit also proposes optimal payment terms based on the customer's payment history. For example, it proposes advance payment to a customer who frequently makes late payments. The invoice issuing unit also analyzes a customer's payment history and proposes optimal payment terms, thereby smoothing transactions with customers. For example, it proposes an extension of the payment deadline to a customer with a good payment history. In this way, it is possible to propose optimal payment terms by analyzing a customer's payment history.

[0052] The deposit confirmation unit can obtain deposit information in real time using the bank's API and instantly reflect it in the ledger. The deposit confirmation unit, for example, obtains deposit information in real time using the bank's API and instantly reflects it in the ledger. For example, when a deposit is confirmed, the deposit confirmation unit automatically records it in the ledger. Furthermore, by using the bank's API, the deposit confirmation unit obtains deposit information in real time and instantly reflects it in the ledger. For example, when a deposit is confirmed, the deposit confirmation unit automatically clears accounts receivable. Furthermore, the deposit confirmation unit obtains deposit information in real time using the bank's API and instantly reflects it in the ledger, thereby streamlining the deposit confirmation process. For example, when a deposit is confirmed, the deposit confirmation unit automatically sends a deposit notification. This allows deposit information to be obtained in real time and instantly reflected in the ledger.

[0053] The tax return preparation unit can learn from past return data and propose the optimal filing method. The tax return preparation unit, for example, learns from past return data and proposes the optimal filing method. For example, based on past return data, it proposes the filing method that is most effective for tax savings. In addition, the tax return preparation unit analyzes past return data and proposes the optimal filing method. For example, based on past return data, it proposes the most appropriate deduction items. In addition, the tax return preparation unit learns from past return data and proposes the optimal filing method, thereby making filing work more efficient. For example, based on past data, it proposes the most appropriate return format. In this way, it is possible to propose the optimal filing method by learning from past return data.

[0054] The tax return preparation unit can automatically obtain the latest tax law amendment information and reflect it in the tax return content. The tax return preparation unit, for example, automatically obtains the latest tax law amendment information and reflects it in the tax return content. For example, if the tax law is amended, the tax return preparation unit automatically obtains that information and reflects it in the tax return. The tax return preparation unit also automatically updates the tax return content based on the latest tax law amendment information. For example, if the deduction amount is changed, the tax return preparation unit automatically reflects that information and updates the tax return. The tax return preparation unit also automatically obtains the latest tax law amendment information and reflects it in the tax return content, thereby preventing filing errors. For example, if the tax rate is changed, the tax return preparation unit automatically reflects that information and prepares an accurate tax return. In this way, the latest tax law amendment information can be automatically obtained and reflected in the tax return content.

[0055] The tax return preparation unit can use the emotion estimation function to analyze the user's emotions and suggest a less stressful method of preparing a tax return. The tax return preparation unit, for example, uses the emotion estimation function to analyze the user's emotions and suggest a less stressful method of preparing a tax return. For example, if the user is feeling stressed, the tax return preparation unit suggests a method that allows the user to prepare a tax return with simple operations. Furthermore, based on the user's emotion data, the tax return preparation unit suggests a less stressful method of preparing a tax return. For example, if the user is tired, the tax return preparation unit automatically prepares a tax return, reducing the user's burden. Furthermore, the emotion estimation function can be used to analyze the user's emotional state in real time and suggest a less stressful method of preparing a tax return. For example, if the user is relaxed, the tax return preparation unit suggests a detailed method of preparing a tax return. In this way, by analyzing the user's emotions, a less stressful method of preparing a tax return can be suggested.

[0056] The tax return preparation unit can manage multiple business entities in a unified manner and realize unified filing. The tax return preparation unit, for example, manages multiple business entities in a unified manner and realizes unified filing. For example, if a company operates both a sole proprietorship and a corporation, the tax return preparation unit prepares tax returns in a unified manner. Furthermore, by managing multiple business entities in a unified manner, the tax return preparation unit realizes unified filing. For example, if a company operates multiple corporations, the tax return preparation unit prepares and submits tax returns in a unified manner. Furthermore, the tax return preparation unit manages multiple business entities in a unified manner and realizes unified filing, thereby streamlining filing work. For example, tax returns for both sole proprietorships and corporations are prepared and submitted in a unified manner. This allows multiple business entities to be managed in a unified manner and realize unified filing.

[0057] The tax return preparation unit can use the emotion estimation function to monitor the emotions of a user while preparing a tax return in real time and provide an interface that elicits positive emotions. The tax return preparation unit can, for example, use the emotion estimation function to monitor the emotions of a user while preparing a tax return in real time and provide an interface that elicits positive emotions. For example, if the user is feeling stressed, relaxing music can be played. Also, an interface that elicits positive emotions can be provided based on the user's emotion data. For example, if the user is tired, an encouraging message can be displayed. Also, the emotion estimation function can be used to monitor the emotions of a user while preparing a tax return in real time and provide an interface that elicits positive emotions. For example, if the user is relaxed, a fun animation can be displayed. In this way, it is possible to provide an interface that monitors the emotions of a user in real time and elicits positive emotions.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The accounting work efficiency improvement system can further include a data analysis unit. The data analysis unit can analyze accounting data and make proposals for improving management. For example, it can analyze sales data and identify products and services that are expected to increase sales. It can also analyze expense data and make specific proposals for reducing costs. Furthermore, the data analysis unit can predict future sales and expenses based on past data and use this information to help formulate management strategies. This allows the accounting work efficiency improvement system to not only improve the efficiency of accounting work, but also provide useful information for improving management.

[0060] The accounting work efficiency system can further include a risk management unit. The risk management unit can analyze transaction data and detect potential risks. For example, it can analyze a client's credit information and predict the risk of payment delays or bankruptcy. It can also detect signs of fraudulent transactions based on transaction data and issue early warnings. Furthermore, the risk management unit can calculate the probability of risk occurrence based on past data and propose specific measures to avoid risk. This allows the accounting work efficiency system to support users in terms of risk management as well.

[0061] The accounting work efficiency system can further include an education support unit. The education support unit can provide users with knowledge about accounting and taxation. For example, it can provide online courses that explain basic accounting concepts and tax return procedures. It can also propose customized learning plans based on the user's skill level. Furthermore, the education support unit provides information on the latest tax law revisions and changes in accounting standards, supporting users to always have the latest knowledge. In this way, the accounting work efficiency system can also contribute to improving users' skills.

[0062] The accounting work efficiency system can further include a communication support unit. The communication support unit provides functions to facilitate communication between the user and clients and tax accountants. For example, it can centrally manage email and chat history with clients to ensure important messages are not overlooked. It can also strengthen collaboration with tax accountants and quickly share necessary documents and information. Furthermore, the communication support unit can analyze the user's emotions and send reminders and follow-up messages at appropriate times. In this way, the accounting work efficiency system can support user communication and contribute to smoother work.

[0063] The accounting work efficiency system can further include a health management unit. The health management unit monitors the user's health status and makes suggestions to balance work efficiency with health maintenance. For example, if a user continues working for a long period of time, it can display a reminder to take an appropriate break. It can also analyze the user's emotions and suggest relaxing activities if stress levels are high. Furthermore, the health management unit provides specific advice based on the user's health data to balance work efficiency with health maintenance. In this way, the accounting work efficiency system can support the user's health and achieve work efficiency.

[0064] The accounting work efficiency system can further include an environmental management unit. The environmental management unit makes proposals for achieving both business efficiency and environmental protection. For example, it can promote paperless operations and encourage the use of electronic data. It can also propose efficient power usage with the aim of reducing energy consumption. Furthermore, the environmental management unit provides specific advice for achieving a balance between business efficiency and environmental protection. In this way, the accounting work efficiency system can support users from the perspective of environmental protection and achieve sustainable business operations.

[0065] The accounting work efficiency improvement system can further include a marketing support section. The marketing support section supports the formulation of marketing strategies based on accounting data. For example, it analyzes sales data to identify the most effective marketing channels. It can also analyze customer data to understand the characteristics of target customers. Furthermore, the marketing support section proposes optimal marketing strategies based on past campaign data. In this way, the accounting work efficiency improvement system can support users in marketing as well, contributing to increased sales.

[0066] The accounting work efficiency system can further include a customer satisfaction improvement unit. The customer satisfaction improvement unit analyzes user emotions and makes suggestions to improve customer satisfaction. For example, it can analyze communication history with customers and identify points of satisfaction and dissatisfaction for the customer. It can also suggest specific actions to improve customer satisfaction based on customer emotion data. Furthermore, the customer satisfaction improvement unit monitors customer emotions in real time and sends follow-up messages at appropriate times. In this way, the accounting work efficiency system can contribute to improving customer satisfaction.

[0067] The accounting work efficiency improvement system can further include a project management section. The project management section manages the progress of projects based on accounting data and supports efficient project management. For example, it compares the project budget with actual results to detect early risks of budget overruns. It can also analyze project progress data and predict risks of delays. Furthermore, the project management section proposes optimal project management methods based on past project data. This allows the accounting work efficiency improvement system to support users in project management as well, enabling efficient project management.

[0068] The accounting work efficiency system can further include a user support unit. The user support unit analyzes the user's emotions and provides appropriate support. For example, if the user is experiencing difficulties, it immediately notifies the support team, urging them to respond quickly. It can also suggest specific actions to improve the quality of support based on the user's emotional data. Furthermore, the user support unit monitors the user's emotions in real time and provides support at the appropriate time. This allows the accounting work efficiency system to improve user satisfaction and support smooth business operations.

[0069] The processing flow of the second embodiment will be briefly explained below.

[0070] Step 1: The classification department analyzes transaction data and performs accounting and tax classification. For example, the classification department analyzes sales data and expense data and classifies them into appropriate account items. It can also analyze the background information of the transaction data and perform classification based on the purpose of the transaction and related contracts. It can also learn from past classification history and propose optimal classification for similar transactions. Step 2: The billing department issues invoices based on the data sorted by the sorting department. For example, it can automatically create invoices based on sales data and send them to customers. It can also analyze customers' payment history and propose optimal payment terms. Step 3: The payment confirmation department confirms the payment based on the invoice issued by the invoice issuing department. For example, it uses a bank's API to obtain payment information in real time and immediately updates the ledger. Step 4: The tax return preparation unit prepares a tax return based on the deposit data confirmed by the deposit confirmation unit. For example, it can learn from past tax return data and suggest the optimal filing method. It can also automatically obtain the latest tax law amendment information and reflect it in the tax return content.

[0071] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0072] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0073] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0074] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0075] 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.

[0076] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0077] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0078] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0079] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0080] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0081] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0082] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0083] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0084] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0085] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0086] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0087] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0088] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0089] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0090] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0091] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0092] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0093] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0094] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0095] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0096] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0097] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0098] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0099] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0100] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0101] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0104] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0105] 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.

[0106] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0111] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0112] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0114] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0118] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0121] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0122] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0123] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0124] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0125] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0126] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0127] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0128] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0129] 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.

[0130] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0131] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0132] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0133] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0134] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0135] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0136] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0137] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0138] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A classification department that analyzes transaction data and performs accounting and tax classification; an invoice issuing unit that issues an invoice based on the data sorted by the sorting unit; a payment confirmation unit that confirms payment based on the invoice issued by the invoice issuing unit; a tax return preparation unit that prepares a tax return based on the deposit data confirmed by the deposit confirmation unit; A system characterized by:

2. The sorting unit includes: Analyze the background information of the transaction data and sort it based on the purpose of the transaction and related contracts.

2. The system of claim 1.

3. The sorting unit includes: Learns from past sorting history and suggests optimal sorting for similar transactions 2. The system of claim 1.

4. The sorting unit includes: Analyzing user emotions and proposing a less stressful sorting method 2. The system of claim 1.

5. The bill issuing unit Analyze customer payment history and suggest optimal payment terms 2. The system of claim 1.

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