System

Abstract

The system according to the embodiment aims to automate accounting related data processing and improve efficiency and accuracy.SOLUTION: A system includes a data input part, a journalizing part, a report creation part, and an abnormality detection part. The data input unit inputs accounting related data. The journalizing unit performs journalizing based on the data input by the data input unit. The report creation unit creates a report based on the data journalized by the journalizing unit. The abnormality detection unit analyzes the data processed by the data input unit, the journalizing unit, and the report creation unit, and detects an abnormality.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, accounting-related data processing involves a lot of manual work, and there is room for improvement in efficiency and accuracy.

[0005] The system according to the embodiment aims to automate accounting-related data processing and improve efficiency and accuracy. [Means for solving the problem]

[0006] The system according to the embodiment includes a data input unit, a journalizing unit, a report creation unit, and an anomaly detection unit. The data input unit inputs accounting-related data. The journalizing unit performs journalizing based on the data input by the data input unit. The report creation unit creates a report based on the data journalized by the journalizing unit. The anomaly detection unit analyzes the data processed by the data input unit, journalizing unit, and report creation unit, and detects anomalies. [Effects of the Invention]

[0007] The system according to the embodiment can automate accounting-related data processing, improving efficiency and accuracy. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The accounting-related information automation system according to the embodiment of the present invention is a system in which AI performs processes in accounting work such as data entry, journalization, report creation, anomaly detection, etc. As a result, the accounting-related information automation system can improve the efficiency and accuracy of accounting work.

[0029] An automated system for accounting-related information according to an embodiment includes a data input unit, a journalizing unit, a report creation unit, and an anomaly detection unit. The data input unit inputs accounting-related data. For example, it analyzes scanned data of invoices and receipts, extracts necessary information, and inputs it into a database. The data input unit can also scan handwritten receipts and convert them into text data using OCR technology. The data input unit can also directly read digital invoices. The journalizing unit performs journalizing based on the data input by the data input unit. For example, it analyzes transaction details and journalizes them into appropriate account items. The journalizing unit can also automatically perform journalizing based on the transaction details using a generation AI. The journalizing unit can also learn from past journalizing data to improve the accuracy of journalizing. The report creation unit creates reports based on the data journalized by the journalizing unit. For example, it automatically generates monthly reports and annual reports. The report creation unit can also automatically optimize the content of reports using a generation AI. The report creation unit can also learn from past report data and suggest more effective report formats. The anomaly detection unit analyzes data processed by the data input unit, accounting unit, and report creation unit to detect anomalies. For example, it learns past transaction data and detects abnormal patterns. The anomaly detection unit can also use generative AI to refer to background information about transactions and perform risk assessments. Furthermore, the anomaly detection unit can automatically answer questions that arise when detecting anomalies in the form of FAQs. This allows the accounting-related information automation system according to the embodiment to improve the efficiency and accuracy of accounting work. For example, it can reduce the burden on accounting staff and increase the accuracy of work. It also enables early detection and countermeasures of abnormal transactions.

[0030] The data input unit automatically classifies information extracted from scanned data, enabling uniform management of data in different formats. For example, the generation AI automatically classifies information extracted from scanned data in the data input unit, enabling uniform management of data in different formats. For example, it centralizes invoice and receipt data and stores it in a database. The data input unit also uses the generation AI to analyze scanned data and build a system for uniform management of data in different formats. For example, it extracts text data from PDFs and image files and converts it into a uniform format. The data input unit also uses the generation AI to automatically classify information extracted from scanned data, automating the process of uniformly managing data in different formats. For example, it centrally manages handwritten receipts and digital invoices. This enables uniform management of data in different formats.

[0031] The data input unit can evaluate the reliability of input data and generate an alert to prompt reconfirmation of low-reliability data. In the data input unit, for example, the generation AI evaluates the reliability of input data and generates an alert to prompt reconfirmation of low-reliability data. For example, an alert is displayed if the quality of scanned data is low. The data input unit also builds a system in which the generation AI evaluates the reliability of input data and prompts reconfirmation of low-reliability data. For example, a prompt is displayed if handwritten characters are unclear. In addition, the data input unit also builds a system in which the generation AI evaluates the reliability of input data and automatically generates an alert to prompt reconfirmation of low-reliability data. For example, an alert is displayed if part of the data is missing. This makes it possible to prompt reconfirmation of low-reliability data.

[0032] The data entry department can also be applied to departments other than accounting, improving the efficiency of company-wide data management. For example, the data entry department applies data entry automation to departments other than accounting to improve the efficiency of company-wide data management. For example, it automatically enters employee data from the human resources department and customer data from the sales department. The data entry department also uses generative AI to automate data entry in departments other than accounting, building a system that improves the efficiency of company-wide data management. For example, it automatically enters project management data. The data entry department also applies data entry automation to departments other than accounting to automate processes that improve the efficiency of company-wide data management. For example, it automatically enters inventory management data. This improves the efficiency of company-wide data management.

[0033] The data input unit can extract data from voice input and input data in combination with voice recognition technology. The data input unit, for example, uses generation AI to extract data from voice input and input data in combination with voice recognition technology. For example, it automatically inputs invoice information read aloud. The data input unit also uses voice recognition technology to extract data from voice input and builds a system in which generation AI automatically inputs the data into a database. For example, it automatically generates meeting minutes using voice input. The data input unit also uses generation AI to extract data from voice input and automate the data input process in combination with voice recognition technology. For example, it automatically inputs order information over the phone. This makes it possible to extract data from voice input and input data in combination with voice recognition technology.

[0034] The journal entry unit can learn from past journal entry data and build a feedback loop to make more accurate journal entries. For example, the journal entry unit makes more accurate journal entries by having the generation AI learn from past journal entry data and build a feedback loop. For example, it automatically generates journal entries based on past transaction patterns. The journal entry unit also develops a system in which the generation AI learns from past journal entry data and builds a feedback loop. For example, it automatically detects and corrects journal entry errors. The journal entry unit also automates the process in which the generation AI learns from past journal entry data and builds a feedback loop. For example, it performs continuous learning to improve the accuracy of journal entries. This allows it to learn from past journal entry data and make more accurate journal entries.

[0035] The journal entry unit can refer to the background information of a transaction and perform risk assessment. For example, the generation AI refers to the background information of a transaction when journalizing and performs risk assessment. For example, it identifies high-risk transactions based on the credit information of the trading partner. The journal entry unit also builds a system where the generation AI refers to the background information of a transaction when journalizing and performs risk assessment. For example, it performs risk assessment based on the trading partner's past trading history. The journal entry unit also automates the process where the generation AI refers to the background information of a transaction when journalizing and performs risk assessment. For example, it performs risk assessment based on the trading partner's credit score. This makes it possible to refer to the background information of a transaction and perform risk assessment.

[0036] The journal entry department can accommodate different accounting standards and support international accounting processing. For example, the journal entry department automates journal entries to accommodate different accounting standards and support international accounting processing. For example, it automatically generates journal entries based on IFRS and GAAP. The journal entry department also uses generative AI to automate journal entries that comply with different accounting standards and support international accounting processing. For example, it automatically generates journal entries that comply with the accounting standards of each country. The journal entry department also automates journal entries to comply with different accounting standards and automates processes that support international accounting processing. For example, it centrally manages journal entries that comply with multiple accounting standards. This allows it to accommodate different accounting standards and support international accounting processing.

[0037] The journal entry unit can add a function that automatically answers questions that arise when making journal entries in FAQ format. The journal entry unit, for example, uses generation AI to add a function that automatically answers questions that arise when making journal entries in FAQ format. For example, it automatically answers questions about journal entry rules and account selection. The journal entry unit also uses generation AI to build a system that automatically answers questions that arise when making journal entries in FAQ format. For example, it automatically answers questions about journal entry procedures and points to note. The journal entry unit also uses generation AI to automate the process of automatically answering questions that arise when making journal entries in FAQ format. For example, it automatically answers questions about journal entry errors and unclear points. This makes it possible to automatically answer questions that arise when making journal entries in FAQ format.

[0038] The report creation unit can learn from past report data and propose a more effective report format. For example, the report creation unit allows the generation AI to learn from past report data and propose a more effective report format. For example, it creates a new report based on the format of a past successful report. The report creation unit also builds a system where the generation AI learns from past report data and proposes an effective report format. For example, it automatically optimizes the structure and content of the report. The report creation unit also automates the process where the generation AI learns from past report data and proposes an effective report format. For example, it automatically generates report templates. This allows it to learn from past report data and propose an effective report format.

[0039] The report creation unit can automatically import relevant external data when creating a report, thereby enriching the content of the report. For example, the report creation unit allows the generation AI to automatically import relevant external data when creating a report, thereby enriching the content of the report. For example, market data and competitive information can be reflected in the report. The report creation unit also builds a system where the generation AI automatically imports external data when creating a report, thereby enriching the content of the report. For example, the latest market trends and competitive analysis can be added to the report. The report creation unit also automates the process where the generation AI automatically imports external data when creating a report, thereby enriching the content of the report. For example, the necessary information can be automatically retrieved from an external database. This allows the import of relevant external data to enrich the content of the report.

[0040] The report creation department can also be applied to departments other than accounting, improving the efficiency of company-wide report creation. For example, the report creation department applies automation of report creation to departments other than accounting, improving the efficiency of company-wide report creation. For example, it automatically generates market analysis reports for the marketing department and employee evaluation reports for the human resources department. The report creation department also uses generation AI to automate report creation for departments other than accounting, building a system that realizes the efficiency of company-wide report creation. For example, it automatically generates project management reports. The report creation department also applies automation of report creation to departments other than accounting, automating the process of improving the efficiency of company-wide report creation. For example, it automatically generates inventory management reports. This improves the efficiency of company-wide report creation.

[0041] The report creation unit can automatically generate visual data when creating a report, and create a report that is visually easy to understand. The report creation unit, for example, uses a generation AI to automatically generate visual data when creating a report, and create a report that is visually easy to understand. For example, sales data is displayed in graphs and charts. The report creation unit also uses a generation AI to automatically generate visual data when creating a report, and build a system that creates a report that is visually easy to understand. For example, financial data is visualized. The report creation unit also uses a generation AI to automatically generate visual data when creating a report, and automate the process of creating a report that is visually easy to understand. For example, it provides a data visualization tool. This makes it possible to automatically generate visual data and create a report that is visually easy to understand.

[0042] The anomaly detection unit can learn from past anomaly detection data and build a more accurate anomaly detection algorithm. For example, the generation AI in the anomaly detection unit learns from past anomaly detection data and builds a more accurate anomaly detection algorithm. For example, it detects anomalies based on past fraudulent transaction patterns. The anomaly detection unit also develops a system in which the generation AI learns from past anomaly detection data and builds a more accurate anomaly detection algorithm. For example, it automatically extracts the characteristics of abnormal transactions. The anomaly detection unit also automates the process in which the generation AI learns from past anomaly detection data and builds a more accurate anomaly detection algorithm. For example, it continuously updates the anomaly detection model. This makes it possible to learn from past anomaly detection data and build a more accurate anomaly detection algorithm.

[0043] The anomaly detection unit can refer to the background information of a transaction and perform risk assessment. For example, when the generation AI detects an anomaly, the anomaly detection unit refers to the background information of the transaction and performs risk assessment. For example, it identifies high-risk transactions based on the credit information of the trading partner. The anomaly detection unit also builds a system where the generation AI refers to the background information of the transaction and performs risk assessment when it detects an anomaly. For example, it performs risk assessment based on the trading partner's past transaction history. The anomaly detection unit also automates the process where the generation AI refers to the background information of the transaction and performs risk assessment when it detects an anomaly. For example, it performs risk assessment based on the trading partner's credit score. This makes it possible to refer to the background information of the transaction and perform risk assessment.

[0044] The anomaly detection unit can also be applied to departments other than accounting, improving the efficiency of anomaly detection across the company. For example, the anomaly detection unit applies automated anomaly detection to departments other than accounting, improving the efficiency of anomaly detection across the company. For example, it automates anomaly detection in the supply chain and in customer management. The anomaly detection unit also uses generative AI to automate anomaly detection in departments other than accounting, building a system that improves the efficiency of anomaly detection across the company. For example, it automates anomaly detection in inventory management. The anomaly detection unit also applies automated anomaly detection to departments other than accounting, automating the process of improving the efficiency of anomaly detection across the company. For example, it automates anomaly detection in customer data. This improves the efficiency of anomaly detection across the company.

[0045] The anomaly detection unit can add a function that automatically answers questions that arise during anomaly detection in FAQ format. The anomaly detection unit, for example, uses a generation AI to add a function that automatically answers questions that arise during anomaly detection in FAQ format. For example, it automatically answers questions about anomaly detection rules and procedures. The anomaly detection unit also uses a generation AI to build a system that automatically answers questions that arise during anomaly detection in FAQ format. For example, it automatically answers questions about anomaly detection errors and unclear points. The anomaly detection unit also uses a generation AI to automate the process of automatically answering questions that arise during anomaly detection in FAQ format. For example, it automatically answers questions about anomaly detection procedures and points to note. This makes it possible to automatically answer questions that arise during anomaly detection in FAQ format.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The data input unit can monitor the user's input speed in real time and enhance input assistance if the input speed is slow. For example, an auto-complete function is provided if the input speed is slow. The data input unit also displays an input guide if the input speed is slow, building a system to assist the user in inputting efficiently. Furthermore, the data input unit automates the process of automatically correcting input errors if the input speed is slow. This allows input assistance to be enhanced according to the user's input speed.

[0048] The data input unit can learn the user's input history and automatically suggest frequently used input patterns. For example, it can automatically complete frequently used words and phrases based on past input data. The data input unit also builds a system that learns the user's input history, predicts input patterns, and automatically displays input candidates. Furthermore, the data input unit automates the process of providing customized input assistance to improve input efficiency based on the user's input history. This makes it possible to provide efficient input assistance based on the user's input history.

[0049] The data input unit can extract data from voice input and input data in combination with voice recognition technology. For example, it can automatically input invoice information read aloud. The data input unit can also use voice recognition technology to extract data from voice input and build a system in which a generation AI automatically inputs the data into a database. For example, it can automatically generate meeting minutes using voice input. The data input unit can also use a generation AI to extract data from voice input and automate the data input process in combination with voice recognition technology. For example, it can automatically input order information over the phone. This makes it possible to extract data from voice input and input data in combination with voice recognition technology.

[0050] The data entry unit can increase the user's options for input devices and support input from tablets and smartphones. For example, input from tablets and smartphones can be automatically reflected in a database. The data entry unit also uses generative AI to build a system that supports input from tablets and smartphones. For example, it synchronizes input data from mobile devices in real time. The data entry unit also increases the user's options for input devices and automates the process of supporting input from tablets and smartphones. For example, it automatically corrects input errors from mobile devices. This increases the user's options for input devices and supports input from tablets and smartphones.

[0051] The data input unit can provide input assistance customized according to the user's input style. For example, it can automatically complete words and phrases frequently used by the user. The data input unit also uses generative AI to learn the user's input style and build a system that provides customized input assistance. For example, it can analyze the user's input patterns and suggest efficient input methods. The data input unit also automates the process of providing customized input assistance according to the user's input style. For example, it can automatically correct input errors based on the user's input history. This makes it possible to provide customized input assistance according to the user's input style.

[0052] The data input unit can encrypt user input data and enhance security. For example, it can encrypt input data in real time and store it in a database. The data input unit can also use generative AI to build a system that encrypts input data and enhances security. For example, it can apply encryption protocols when sending and receiving data. The data input unit can also automate the process of encrypting user input data and enhancing security. For example, it can automatically encrypt data when it is saved. This allows the user input data to be encrypted and enhance security.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The data entry section inputs accounting-related data. For example, it analyzes scanned data of invoices and receipts, extracts the necessary information, and enters it into a database. It can also scan handwritten receipts and convert them into text data using OCR technology. It can also directly read digital invoices. Step 2: The journal entry unit performs journal entries based on the data entered by the data entry unit. For example, it analyzes the transaction details and journalizes them into the appropriate account. It can also use generation AI to automatically perform journal entries based on the transaction details. It can also learn from past journal entry data to improve the accuracy of journal entries. Step 3: The report creation department creates reports based on the data journalized by the accounting department. For example, it automatically generates monthly and annual reports. It can also use generation AI to automatically optimize the content of reports. It can also learn from past report data and suggest more effective report formats. Step 4: The anomaly detection unit analyzes the data processed by the data entry unit, accounting unit, and report creation unit to detect anomalies. For example, it can study past transaction data and detect abnormal patterns. It can also use generative AI to refer to background information about transactions and perform risk assessments. It can also automatically answer questions that arise when anomalies are detected in FAQ format.

[0055] (Example 2) The accounting-related information automation system according to the embodiment of the present invention is a system in which AI performs processes in accounting work such as data entry, journalization, report creation, anomaly detection, etc. As a result, the accounting-related information automation system can improve the efficiency and accuracy of accounting work.

[0056] An automated system for accounting-related information according to an embodiment includes a data input unit, a journalizing unit, a report creation unit, and an anomaly detection unit. The data input unit inputs accounting-related data. For example, it analyzes scanned data of invoices and receipts, extracts necessary information, and inputs it into a database. The data input unit can also scan handwritten receipts and convert them into text data using OCR technology. The data input unit can also directly read digital invoices. The journalizing unit performs journalizing based on the data input by the data input unit. For example, it analyzes transaction details and journalizes them into appropriate account items. The journalizing unit can also automatically perform journalizing based on the transaction details using a generation AI. The journalizing unit can also learn from past journalizing data to improve the accuracy of journalizing. The report creation unit creates reports based on the data journalized by the journalizing unit. For example, it automatically generates monthly reports and annual reports. The report creation unit can also automatically optimize the content of reports using a generation AI. The report creation unit can also learn from past report data and suggest more effective report formats. The anomaly detection unit analyzes data processed by the data input unit, accounting unit, and report creation unit to detect anomalies. For example, it learns past transaction data and detects abnormal patterns. The anomaly detection unit can also use generative AI to refer to background information about transactions and perform risk assessments. Furthermore, the anomaly detection unit can automatically answer questions that arise when detecting anomalies in the form of FAQs. This allows the accounting-related information automation system according to the embodiment to improve the efficiency and accuracy of accounting work. For example, it can reduce the burden on accounting staff and increase the accuracy of work. It also enables early detection and countermeasures of abnormal transactions.

[0057] The data input unit automatically classifies information extracted from scanned data, enabling uniform management of data in different formats. For example, the generation AI automatically classifies information extracted from scanned data in the data input unit, enabling uniform management of data in different formats. For example, it centralizes invoice and receipt data and stores it in a database. The data input unit also uses the generation AI to analyze scanned data and build a system for uniform management of data in different formats. For example, it extracts text data from PDFs and image files and converts it into a uniform format. The data input unit also uses the generation AI to automatically classify information extracted from scanned data, automating the process of uniformly managing data in different formats. For example, it centrally manages handwritten receipts and digital invoices. This enables uniform management of data in different formats.

[0058] The data input unit can evaluate the reliability of input data and generate an alert to prompt reconfirmation of low-reliability data. In the data input unit, for example, the generation AI evaluates the reliability of input data and generates an alert to prompt reconfirmation of low-reliability data. For example, an alert is displayed if the quality of scanned data is low. The data input unit also builds a system in which the generation AI evaluates the reliability of input data and prompts reconfirmation of low-reliability data. For example, a prompt is displayed if handwritten characters are unclear. In addition, the data input unit also builds a system in which the generation AI evaluates the reliability of input data and automatically generates an alert to prompt reconfirmation of low-reliability data. For example, an alert is displayed if part of the data is missing. This makes it possible to prompt reconfirmation of low-reliability data.

[0059] The data input unit can use the emotion estimation function to estimate the user's stress level when entering data, and enhance input assistance when the stress level is high. The data input unit, for example, uses the emotion estimation function to estimate the user's stress level when entering data, and enhances input assistance when the stress level is high. For example, it automatically provides an input assistance tool. The data input unit also uses the emotion estimation function to monitor the user's stress level when entering data in real time, and builds a system that enhances input assistance when the stress level is high. For example, it automatically corrects input errors. The data input unit also uses the emotion estimation function to estimate the user's stress level when entering data, and automates the process of enhancing input assistance when the stress level is high. For example, it displays an input guide. This allows input assistance to be enhanced according to the user's stress level.

[0060] The data entry department can also be applied to departments other than accounting, improving the efficiency of company-wide data management. For example, the data entry department applies data entry automation to departments other than accounting to improve the efficiency of company-wide data management. For example, it automatically enters employee data from the human resources department and customer data from the sales department. The data entry department also uses generative AI to automate data entry in departments other than accounting, building a system that improves the efficiency of company-wide data management. For example, it automatically enters project management data. The data entry department also applies data entry automation to departments other than accounting to automate processes that improve the efficiency of company-wide data management. For example, it automatically enters inventory management data. This improves the efficiency of company-wide data management.

[0061] The data input unit can extract data from voice input and input data in combination with voice recognition technology. The data input unit, for example, uses generation AI to extract data from voice input and input data in combination with voice recognition technology. For example, it automatically inputs invoice information read aloud. The data input unit also uses voice recognition technology to extract data from voice input and builds a system in which generation AI automatically inputs the data into a database. For example, it automatically generates meeting minutes using voice input. The data input unit also uses generation AI to extract data from voice input and automate the data input process in combination with voice recognition technology. For example, it automatically inputs order information over the phone. This makes it possible to extract data from voice input and input data in combination with voice recognition technology.

[0062] The data input unit can use the emotion estimation function to monitor the user's emotions during data entry in real time and provide positive feedback. For example, the data input unit uses the emotion estimation function to monitor the user's emotions during data entry in real time and provide positive feedback. For example, a compliment is displayed when the entry is accurate. The data input unit also uses the emotion estimation function to monitor the user's emotions during data entry in real time and build a system to provide positive feedback. For example, an encouraging message is displayed according to the progress of the entry work. The data input unit also uses the emotion estimation function to monitor the user's emotions during data entry in real time and automate the process of providing positive feedback. For example, reward points are awarded when there are few input errors. This makes it possible to provide positive feedback according to the user's emotions.

[0063] The journal entry unit can learn from past journal entry data and build a feedback loop to make more accurate journal entries. For example, the journal entry unit makes more accurate journal entries by having the generation AI learn from past journal entry data and build a feedback loop. For example, it automatically generates journal entries based on past transaction patterns. The journal entry unit also develops a system in which the generation AI learns from past journal entry data and builds a feedback loop. For example, it automatically detects and corrects journal entry errors. The journal entry unit also automates the process in which the generation AI learns from past journal entry data and builds a feedback loop. For example, it performs continuous learning to improve the accuracy of journal entries. This allows it to learn from past journal entry data and make more accurate journal entries.

[0064] The journal entry unit can refer to the background information of a transaction and perform risk assessment. For example, the generation AI refers to the background information of a transaction when journalizing and performs risk assessment. For example, it identifies high-risk transactions based on the credit information of the trading partner. The journal entry unit also builds a system where the generation AI refers to the background information of a transaction when journalizing and performs risk assessment. For example, it performs risk assessment based on the trading partner's past trading history. The journal entry unit also automates the process where the generation AI refers to the background information of a transaction when journalizing and performs risk assessment. For example, it performs risk assessment based on the trading partner's credit score. This makes it possible to refer to the background information of a transaction and perform risk assessment.

[0065] The journal entry unit can use the emotion estimation function to analyze the user's emotions during journal entry work, and strengthen the automation of journal entry when negative emotions are detected. The journal entry unit, for example, uses the emotion estimation function to analyze the user's emotions during journal entry work, and strengthen the automation of journal entry when negative emotions are detected. For example, it increases automatic journal entry when stress is high. The journal entry unit also uses the emotion estimation function to monitor the user's emotions during journal entry work in real time, and builds a system that strengthens the automation of journal entry when negative emotions are detected. For example, it reduces the effort required for journal entry. The journal entry unit also uses the emotion estimation function to analyze the user's emotions during journal entry work, and automates the process of strengthening the automation of journal entry when negative emotions are detected. For example, it adjusts the automation rate of journal entry. This makes it possible to strengthen the automation of journal entry according to the user's emotions.

[0066] The journal entry department can accommodate different accounting standards and support international accounting processing. For example, the journal entry department automates journal entries to accommodate different accounting standards and support international accounting processing. For example, it automatically generates journal entries based on IFRS and GAAP. The journal entry department also uses generative AI to automate journal entries that comply with different accounting standards and support international accounting processing. For example, it automatically generates journal entries that comply with the accounting standards of each country. The journal entry department also automates journal entries to comply with different accounting standards and automates processes that support international accounting processing. For example, it centrally manages journal entries that comply with multiple accounting standards. This allows it to accommodate different accounting standards and support international accounting processing.

[0067] The journal entry unit can add a function that automatically answers questions that arise when making journal entries in FAQ format. The journal entry unit, for example, uses generation AI to add a function that automatically answers questions that arise when making journal entries in FAQ format. For example, it automatically answers questions about journal entry rules and account selection. The journal entry unit also uses generation AI to build a system that automatically answers questions that arise when making journal entries in FAQ format. For example, it automatically answers questions about journal entry procedures and points to note. The journal entry unit also uses generation AI to automate the process of automatically answering questions that arise when making journal entries in FAQ format. For example, it automatically answers questions about journal entry errors and unclear points. This makes it possible to automatically answer questions that arise when making journal entries in FAQ format.

[0068] The classification unit can use the emotion estimation function to monitor the user's emotions in real time while the user is entering entries, and provide an interactive guide to elicit positive emotions. For example, the classification unit can use the emotion estimation function to monitor the user's emotions in real time while the user is entering entries, and provide an interactive guide to elicit positive emotions. For example, it can display encouraging messages according to the progress of the entry. The classification unit can also use the emotion estimation function to monitor the user's emotions in real time while the user is entering entries, and build a system that provides an interactive guide to elicit positive emotions. For example, it can provide easy-to-understand explanations of the entry procedures. The classification unit can also use the emotion estimation function to monitor the user's emotions in real time while the user is entering entries, and automate the process of providing an interactive guide to elicit positive emotions. For example, it can display advice to reduce journal entry errors. This makes it possible to provide an interactive guide to elicit positive emotions according to the user's emotions.

[0069] The report creation unit can learn from past report data and propose a more effective report format. For example, the report creation unit allows the generation AI to learn from past report data and propose a more effective report format. For example, it creates a new report based on the format of a past successful report. The report creation unit also builds a system where the generation AI learns from past report data and proposes an effective report format. For example, it automatically optimizes the structure and content of the report. The report creation unit also automates the process where the generation AI learns from past report data and proposes an effective report format. For example, it automatically generates report templates. This allows it to learn from past report data and propose an effective report format.

[0070] The report creation unit can automatically import relevant external data when creating a report, thereby enriching the content of the report. For example, the report creation unit allows the generation AI to automatically import relevant external data when creating a report, thereby enriching the content of the report. For example, market data and competitive information can be reflected in the report. The report creation unit also builds a system where the generation AI automatically imports external data when creating a report, thereby enriching the content of the report. For example, the latest market trends and competitive analysis can be added to the report. The report creation unit also automates the process where the generation AI automatically imports external data when creating a report, thereby enriching the content of the report. For example, the necessary information can be automatically retrieved from an external database. This allows the import of relevant external data to enrich the content of the report.

[0071] The report creation unit can use the emotion estimation function to analyze the user's emotions while creating a report, and enhance automatic report generation if negative emotions are detected. The report creation unit, for example, uses the emotion estimation function to analyze the user's emotions while creating a report, and enhances automatic report generation if negative emotions are detected. For example, automatic generation is increased when stress is high. The report creation unit also uses the emotion estimation function to monitor the user's emotions while creating a report in real time, and builds a system that enhances automatic report generation if negative emotions are detected. For example, this reduces the effort required for reporting. The report creation unit also uses the emotion estimation function to analyze the user's emotions while creating a report, and automates the process of enhancing automatic report generation if negative emotions are detected. For example, it adjusts the automatic report generation rate. This allows automatic report generation to be enhanced according to the user's emotions.

[0072] The report creation department can also be applied to departments other than accounting, improving the efficiency of company-wide report creation. For example, the report creation department applies automation of report creation to departments other than accounting, improving the efficiency of company-wide report creation. For example, it automatically generates market analysis reports for the marketing department and employee evaluation reports for the human resources department. The report creation department also uses generation AI to automate report creation for departments other than accounting, building a system that realizes the efficiency of company-wide report creation. For example, it automatically generates project management reports. The report creation department also applies automation of report creation to departments other than accounting, automating the process of improving the efficiency of company-wide report creation. For example, it automatically generates inventory management reports. This improves the efficiency of company-wide report creation.

[0073] The report creation unit can automatically generate visual data when creating a report, and create a report that is visually easy to understand. The report creation unit, for example, uses a generation AI to automatically generate visual data when creating a report, and create a report that is visually easy to understand. For example, sales data is displayed in graphs and charts. The report creation unit also uses a generation AI to automatically generate visual data when creating a report, and build a system that creates a report that is visually easy to understand. For example, financial data is visualized. The report creation unit also uses a generation AI to automatically generate visual data when creating a report, and automate the process of creating a report that is visually easy to understand. For example, it provides a data visualization tool. This makes it possible to automatically generate visual data and create a report that is visually easy to understand.

[0074] The report creation unit can use the emotion estimation function to monitor the user's emotions while creating a report in real time and provide an interactive guide to elicit positive emotions. For example, the report creation unit can use the emotion estimation function to monitor the user's emotions while creating a report in real time and provide an interactive guide to elicit positive emotions. For example, it can display encouraging messages according to the progress of the report. The report creation unit can also use the emotion estimation function to monitor the user's emotions while creating a report in real time and build a system that provides an interactive guide to elicit positive emotions. For example, it can provide easy-to-understand explanations of the report procedure. The report creation unit can also use the emotion estimation function to monitor the user's emotions while creating a report in real time and automate the process of providing an interactive guide to elicit positive emotions. For example, it can display advice to reduce errors in the report. This makes it possible to provide an interactive guide to elicit positive emotions according to the user's emotions.

[0075] The anomaly detection unit can learn from past anomaly detection data and build a more accurate anomaly detection algorithm. For example, the generation AI in the anomaly detection unit learns from past anomaly detection data and builds a more accurate anomaly detection algorithm. For example, it detects anomalies based on past fraudulent transaction patterns. The anomaly detection unit also develops a system in which the generation AI learns from past anomaly detection data and builds a more accurate anomaly detection algorithm. For example, it automatically extracts the characteristics of abnormal transactions. The anomaly detection unit also automates the process in which the generation AI learns from past anomaly detection data and builds a more accurate anomaly detection algorithm. For example, it continuously updates the anomaly detection model. This makes it possible to learn from past anomaly detection data and build a more accurate anomaly detection algorithm.

[0076] The anomaly detection unit can refer to the background information of a transaction and perform risk assessment. For example, when the generation AI detects an anomaly, the anomaly detection unit refers to the background information of the transaction and performs risk assessment. For example, it identifies high-risk transactions based on the credit information of the trading partner. The anomaly detection unit also builds a system where the generation AI refers to the background information of the transaction and performs risk assessment when it detects an anomaly. For example, it performs risk assessment based on the trading partner's past transaction history. The anomaly detection unit also automates the process where the generation AI refers to the background information of the transaction and performs risk assessment when it detects an anomaly. For example, it performs risk assessment based on the trading partner's credit score. This makes it possible to refer to the background information of the transaction and perform risk assessment.

[0077] The anomaly detection unit can use the emotion estimation function to analyze the user's emotion during anomaly detection, and strengthen the automation of anomaly detection if a negative emotion is detected. The anomaly detection unit, for example, uses the emotion estimation function to analyze the user's emotion during anomaly detection, and strengthen the automation of anomaly detection if a negative emotion is detected. For example, it increases automatic detection when stress is high. The anomaly detection unit also uses the emotion estimation function to monitor the user's emotion during anomaly detection in real time, and builds a system that strengthens the automation of anomaly detection if a negative emotion is detected. For example, it reduces the effort required for detection. The anomaly detection unit also uses the emotion estimation function to analyze the user's emotion during anomaly detection, and automates the process of strengthening the automation of anomaly detection if a negative emotion is detected. For example, it adjusts the automation rate of detection. This allows the automation of anomaly detection to be strengthened according to the user's emotion.

[0078] The anomaly detection unit can also be applied to departments other than accounting, improving the efficiency of anomaly detection across the company. For example, the anomaly detection unit applies automated anomaly detection to departments other than accounting, improving the efficiency of anomaly detection across the company. For example, it automates anomaly detection in the supply chain and in customer management. The anomaly detection unit also uses generative AI to automate anomaly detection in departments other than accounting, building a system that improves the efficiency of anomaly detection across the company. For example, it automates anomaly detection in inventory management. The anomaly detection unit also applies automated anomaly detection to departments other than accounting, automating the process of improving the efficiency of anomaly detection across the company. For example, it automates anomaly detection in customer data. This improves the efficiency of anomaly detection across the company.

[0079] The anomaly detection unit can add a function that automatically answers questions that arise during anomaly detection in FAQ format. The anomaly detection unit, for example, uses a generation AI to add a function that automatically answers questions that arise during anomaly detection in FAQ format. For example, it automatically answers questions about anomaly detection rules and procedures. The anomaly detection unit also uses a generation AI to build a system that automatically answers questions that arise during anomaly detection in FAQ format. For example, it automatically answers questions about anomaly detection errors and unclear points. The anomaly detection unit also uses a generation AI to automate the process of automatically answering questions that arise during anomaly detection in FAQ format. For example, it automatically answers questions about anomaly detection procedures and points to note. This makes it possible to automatically answer questions that arise during anomaly detection in FAQ format.

[0080] The anomaly detection unit can use the emotion estimation function to monitor the user's emotions during anomaly detection in real time and provide an interactive guide to elicit positive emotions. For example, the anomaly detection unit can use the emotion estimation function to monitor the user's emotions during anomaly detection in real time and provide an interactive guide to elicit positive emotions. For example, an encouraging message can be displayed according to the progress of anomaly detection. Furthermore, the anomaly detection unit can use the emotion estimation function to monitor the user's emotions during anomaly detection in real time and build a system that provides an interactive guide to elicit positive emotions. For example, the anomaly detection procedure can be explained in an easy-to-understand manner. Furthermore, the anomaly detection unit can use the emotion estimation function to monitor the user's emotions during anomaly detection in real time and automate the process of providing an interactive guide to elicit positive emotions. For example, advice can be displayed to reduce anomaly detection errors. This makes it possible to provide an interactive guide to elicit positive emotions according to the user's emotions.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The data input unit can monitor the user's input speed in real time and enhance input assistance if the input speed is slow. For example, an auto-complete function is provided if the input speed is slow. The data input unit also displays an input guide if the input speed is slow, building a system to assist the user in inputting efficiently. Furthermore, the data input unit automates the process of automatically correcting input errors if the input speed is slow. This allows input assistance to be enhanced according to the user's input speed.

[0083] The data input unit can learn the user's input history and automatically suggest frequently used input patterns. For example, it can automatically complete frequently used words and phrases based on past input data. The data input unit also builds a system that learns the user's input history, predicts input patterns, and automatically displays input candidates. Furthermore, the data input unit automates the process of providing customized input assistance to improve input efficiency based on the user's input history. This makes it possible to provide efficient input assistance based on the user's input history.

[0084] The data input unit uses the emotion estimation function to estimate the user's level of concentration when entering data, and can enhance input assistance when the level of concentration is low. For example, an input guide is displayed when the level of concentration is low. The data input unit also uses the emotion estimation function to monitor the user's level of concentration in real time, and builds a system that enhances input assistance when the level of concentration is low. Furthermore, the data input unit uses the emotion estimation function to estimate the user's level of concentration, and automates the process of automatically correcting input errors when the level of concentration is low. This makes it possible to enhance input assistance according to the user's level of concentration.

[0085] The data input unit uses the emotion estimation function to estimate the user's fatigue level when entering data, and can enhance input assistance when the fatigue level is high. For example, input errors are automatically corrected when the fatigue level is high. The data input unit also uses the emotion estimation function to monitor the user's fatigue level in real time, and builds a system that enhances input assistance when the fatigue level is high. Furthermore, the data input unit uses the emotion estimation function to estimate the user's fatigue level, and automates the process of displaying input guidance when the fatigue level is high. This makes it possible to enhance input assistance according to the user's fatigue level.

[0086] The data input unit uses the emotion estimation function to estimate the user's motivation when entering data, and can enhance input support if the user's motivation is low. For example, an encouraging message can be displayed if the user's motivation is low. The data input unit also uses the emotion estimation function to monitor the user's motivation in real time, and builds a system that enhances input support if the user's motivation is low. Furthermore, the data input unit uses the emotion estimation function to estimate the user's motivation, and automates the process of automatically correcting input errors if the user's motivation is low. This allows input support to be enhanced according to the user's motivation.

[0087] The data input unit can extract data from voice input and input data in combination with voice recognition technology. For example, it can automatically input invoice information read aloud. The data input unit can also use voice recognition technology to extract data from voice input and build a system in which a generation AI automatically inputs the data into a database. For example, it can automatically generate meeting minutes using voice input. The data input unit can also use a generation AI to extract data from voice input and automate the data input process in combination with voice recognition technology. For example, it can automatically input order information over the phone. This makes it possible to extract data from voice input and input data in combination with voice recognition technology.

[0088] The data entry unit can increase the user's options for input devices and support input from tablets and smartphones. For example, input from tablets and smartphones can be automatically reflected in a database. The data entry unit also uses generative AI to build a system that supports input from tablets and smartphones. For example, it synchronizes input data from mobile devices in real time. The data entry unit also increases the user's options for input devices and automates the process of supporting input from tablets and smartphones. For example, it automatically corrects input errors from mobile devices. This increases the user's options for input devices and supports input from tablets and smartphones.

[0089] The data input unit can provide input assistance customized according to the user's input style. For example, it can automatically complete words and phrases frequently used by the user. The data input unit also uses generative AI to learn the user's input style and build a system that provides customized input assistance. For example, it can analyze the user's input patterns and suggest efficient input methods. The data input unit also automates the process of providing customized input assistance according to the user's input style. For example, it can automatically correct input errors based on the user's input history. This makes it possible to provide customized input assistance according to the user's input style.

[0090] The data input unit can use the emotion estimation function to monitor the user's emotions during data input in real time and provide positive feedback. For example, a compliment can be displayed when the input is accurate. The data input unit can also use the emotion estimation function to monitor the user's emotions during data input in real time and build a system to provide positive feedback. For example, an encouraging message can be displayed according to the progress of the input work. The data input unit can also use the emotion estimation function to monitor the user's emotions during data input in real time and automate the process of providing positive feedback. For example, reward points can be awarded if there are few input errors. This makes it possible to provide positive feedback according to the user's emotions.

[0091] The data input unit can encrypt user input data and enhance security. For example, it can encrypt input data in real time and store it in a database. The data input unit can also use generative AI to build a system that encrypts input data and enhances security. For example, it can apply encryption protocols when sending and receiving data. The data input unit can also automate the process of encrypting user input data and enhancing security. For example, it can automatically encrypt data when it is saved. This allows the user input data to be encrypted and enhance security.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The data entry section inputs accounting-related data. For example, it analyzes scanned data of invoices and receipts, extracts the necessary information, and enters it into a database. It can also scan handwritten receipts and convert them into text data using OCR technology. It can also directly read digital invoices. Step 2: The journal entry unit performs journal entries based on the data entered by the data entry unit. For example, it analyzes the transaction details and journalizes them into the appropriate account. It can also use generation AI to automatically perform journal entries based on the transaction details. It can also learn from past journal entry data to improve the accuracy of journal entries. Step 3: The report creation department creates reports based on the data journalized by the accounting department. For example, it automatically generates monthly and annual reports. It can also use generation AI to automatically optimize the content of reports. It can also learn from past report data and suggest more effective report formats. Step 4: The anomaly detection unit analyzes the data processed by the data entry unit, accounting unit, and report creation unit to detect anomalies. For example, it can study past transaction data and detect abnormal patterns. It can also use generative AI to refer to background information about transactions and perform risk assessments. It can also automatically answer questions that arise when anomalies are detected in FAQ format.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0138] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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]

[0161] 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 data input section for inputting accounting-related data; a journalizing unit that journalizes the data input by the data input unit; a report creation unit that creates a report based on the data journalized by the journalization unit; an anomaly detection unit that analyzes the data processed by the data input unit, the journalization unit, and the report creation unit and detects anomalies. A system characterized by:

2. The data input unit The stress level of the user when inputting data is estimated, and input support is strengthened if the stress level is high.

2. The system of claim 1.

3. The sorting unit Learn from past journal entry data and build a feedback loop to make more accurate journal entries 2. The system of claim 1.

4. The report creation unit Learn from past report data and suggest more effective report formats 2. The system of claim 1.

5. The abnormality detection unit Learn from past anomaly detection data to build a more accurate anomaly detection algorithm 2. The system of claim 1.

6. The data input unit Monitor user sentiment in real time as they enter data and provide positive feedback 2. The system of claim 1.

7. The sorting unit Analyze the user's emotions during journal entry work, and if negative emotions are detected, strengthen the automation of the journal entry.

2. The system of claim 1.

8. The report creation unit Monitor user sentiment in real time during reporting and provide interactive guidance to elicit positive emotions 2. The system of claim 1.

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