system

Abstract

The system according to this embodiment aims to streamline expense reimbursement operations by utilizing historical data from 2D code payments. [Solution] The system according to the embodiment comprises a collection unit, a classification unit, a detection unit, and a generation unit. The collection unit collects historical data of 2D code payments. The classification unit classifies the data collected by the collection unit. The detection unit detects the data classified by the classification unit. The generation unit generates expense reports and tax returns based on the data detected by the detection unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there are problems that the expense settlement work is complicated, there are manual work burdens and violations of expense regulations, and it is difficult to manage receipts and documents.

[0005] The system according to the embodiment aims to improve the efficiency of expense settlement work by utilizing the historical data of two-dimensional code settlement.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a collection unit, a classification unit, a detection unit, and a generation unit. The collection unit collects historical data of 2D code payments. The classification unit classifies the data collected by the collection unit. The detection unit detects the data classified by the classification unit. The generation unit generates expense reports and tax returns based on the data detected by the detection unit. [Effects of the Invention]

[0007] The system according to this embodiment can streamline expense reimbursement operations by utilizing historical data from 2D code payments. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An expense reimbursement system according to an embodiment of the present invention is a mechanism that links a 2D code payment system (e.g., QR code® payment) with an AI expense reimbursement system in order to solve problems in expense reimbursement. This expense reimbursement system collects 2D code payment history data, which the AI ​​can automatically read. This eliminates the need to scan receipt images, significantly improving the efficiency of expense reimbursement. It reduces input errors that often occur when data is entered manually, improving the accuracy of expense reimbursement data. Furthermore, it eliminates the need to store and manage receipts, promoting a paperless environment. In addition, by classifying expenses using AI, areas where expenses are high can be visualized, improving the transparency of expense management. The AI ​​can also detect abnormal transaction patterns, reducing the risk of fraudulent expenses. Furthermore, the generating AI automatically creates expense reimbursement reports and tax returns based on the 2D code payment history, reducing the workload of the accounting department. For example, the expense reimbursement system collects 2D code payment history data, which the AI ​​can automatically read. For example, the expense reimbursement system eliminates the need to scan receipt images, improving the efficiency of expense reimbursement. For example, expense reimbursement systems reduce input errors that often occur with manual data entry, improving the accuracy of expense reimbursement data. For example, expense reimbursement systems eliminate the need to store and manage receipts, promoting a paperless environment. For example, expense reimbursement systems use AI to classify expenses and visualize areas where costs are high. For example, expense reimbursement systems use AI to detect abnormal transaction patterns, reducing the risk of fraudulent expenses. For example, expense reimbursement systems use generating AI to automatically create expense reimbursement reports and tax returns based on 2D code payment history. This eliminates the complexity of expense reimbursement and reduces the burden of manual work. It also reduces the risk of expense policy violations and fraudulent expenses, improving the transparency of expense management. Furthermore, it promotes a paperless environment, resulting in environmentally friendly expense reimbursement. In summary, expense reimbursement systems can achieve increased efficiency and transparency in expense reimbursement.

[0029] The expense reimbursement system according to this embodiment comprises a collection unit, a classification unit, a detection unit, and a generation unit. The collection unit collects historical data of 2D code payments. The historical data of 2D code payments includes, but is not limited to, transaction date and time, transaction amount, and trading partner information. The collection unit can, for example, automatically collect the historical data of 2D code payments. The collection unit can also manually collect the historical data of 2D code payments. Furthermore, the collection unit can periodically collect the historical data of 2D code payments. For example, the collection unit collects the historical data of 2D code payments daily. The classification unit classifies the data collected by the collection unit. Classification is performed based on, for example, criteria such as transaction type, amount, and date and time, but is not limited to these examples. The classification unit can, for example, classify the data based on the transaction type. The classification unit can also classify the data based on the transaction amount. The classification unit can also classify the data based on the date and time of the transaction. For example, the classification unit classifies the data based on the transaction type. The detection unit detects the data classified by the classification unit. Detection is performed based on criteria such as the detection of abnormal transaction patterns, but is not limited to such examples. The detection unit can, for example, detect abnormal transaction patterns. The detection unit can also detect fraudulent transactions. The detection unit can also detect abnormal transactions. For example, the detection unit can detect abnormal transaction patterns. The generation unit generates expense reports and tax returns based on the data detected by the detection unit. Generation is performed based on criteria such as the format of the report and the content of the tax return, but is not limited to such examples. The generation unit can, for example, generate expense reports. The generation unit can also generate tax returns. The generation unit can also generate expense reports and tax returns simultaneously. For example, the generation unit generates expense reports. As a result, the expense reimbursement system according to the embodiment can achieve increased efficiency and transparency in expense reimbursement.

[0030] The data collection unit collects historical data from 2D code payments. This historical data includes, but is not limited to, transaction date and time, transaction amount, and customer information. The data collection unit can, for example, automatically collect historical data from 2D code payments. Specifically, it works in conjunction with the 2D code payment system to acquire historical data in real time each time a payment is made. The data collection unit can also manually collect historical data from 2D code payments. For example, it can provide a function for users to manually upload their payment history, which the data collection unit then uses to acquire the data. Furthermore, the data collection unit can collect historical data from 2D code payments on a regular basis. For example, the data collection unit can collect historical data from 2D code payments daily. This ensures that daily transaction data is reliably collected and that the latest information is always reflected in the system. The data collection unit centrally manages this data and stores it in a database. The database is equipped with security measures to prevent data tampering and unauthorized access. The data collection unit also has functions to check for data duplication and missing data, and to maintain data integrity. This allows the data collection unit to provide accurate and reliable data, improving the overall performance of the system.

[0031] The classification unit categorizes the data collected by the collection unit. Classification is based on criteria such as transaction type, amount, and date / time, but is not limited to these examples. For instance, the classification unit categorizes data based on transaction type. Specifically, transactions are classified into categories such as meals, transportation, and accommodation. The classification unit can also categorize data based on transaction amount. For example, transactions above a certain amount can be classified as high-value transactions and processed accordingly. Furthermore, the classification unit can categorize data based on the date / time of the transaction. For example, transaction data can be aggregated monthly or quarterly to analyze expense trends. The classification unit offers flexible settings for these classification criteria and can be customized to meet user needs. The classification unit can also perform automated data classification using AI. The AI ​​learns from past transaction data and categorizes new transaction data appropriately. This improves classification accuracy and significantly reduces manual classification work. Additionally, the classification unit includes a function to visualize classification results, allowing users to intuitively understand expense breakdowns through graphs and charts. This allows the classification unit to efficiently and effectively classify data, improving the accuracy and transparency of expense management.

[0032] The detection unit detects data classified by the classification unit. Detection is performed based on criteria such as the detection of abnormal transaction patterns, but is not limited to such examples. For example, the detection unit detects abnormal transaction patterns. Specifically, it identifies transactions that deviate from normal transaction patterns and issues alerts. The detection unit can also detect fraudulent transactions. For example, it detects potentially fraudulent transactions such as when the same transaction is performed multiple times or when the transaction amount is abnormally high. The detection unit can also detect anomalies in transactions. For example, it detects transactions conducted outside of normal business hours or transactions with specific trading partners at an unusually high frequency. The detection unit uses AI to perform these anomaly detections. The AI ​​learns from past transaction data and models normal transaction patterns. If new transaction data deviates from this model, the AI ​​detects it as an anomaly. This allows the detection unit to detect abnormal transactions with high accuracy and respond quickly. Furthermore, the detection unit also has a function to output the detection results as a report and notify administrators. This allows administrators to grasp abnormal transactions early and take appropriate measures. This allows the detection unit to improve the reliability and security of expense management.

[0033] The generation unit generates expense reports and tax returns based on data detected by the detection unit. Generation is performed based on criteria such as the format of the report and the content of the tax return, but is not limited to such examples. For example, the generation unit generates expense reports. Specifically, it aggregates information such as the type, amount, and date of transactions to create a report that clearly shows the breakdown of expenses. The generation unit can also generate tax returns. For example, it automatically aggregates the information necessary for tax filing based on annual expense data and creates a tax return. Furthermore, the generation unit can generate expense reports and tax returns simultaneously. For example, it can generate monthly expense reports and quarterly tax returns at the same time and provide them to the user. The generation unit also has the function to output these reports and tax returns in formats such as PDF and Excel, and users can download and print these documents as needed. In addition, the generation unit has the function to customize the content of reports and tax returns, allowing for flexible responses to user needs. As a result, the generation unit can efficiently and accurately generate expense reports and tax returns, improving the efficiency and transparency of expense management.

[0034] The data collection unit can collect historical data of 2D code payments. The data collection unit can, for example, automatically collect historical data of 2D code payments. For example, the data collection unit can collect historical data of 2D code payments in real time. The data collection unit can also manually collect historical data of 2D code payments. For example, the data collection unit can collect historical data of 2D code payments at a time specified by the user. The data collection unit can also collect historical data of 2D code payments periodically. For example, the data collection unit can collect historical data of 2D code payments at a fixed time every day. This eliminates the need for manual data entry by collecting historical data of 2D code payments. Historical data of 2D code payments includes, but is not limited to, transaction date and time, transaction amount, and trading partner information. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input historical data of 2D code payments into a generating AI, and the generating AI can collect the data.

[0035] The classification unit can classify the collected data using AI. For example, the classification unit classifies the collected data using AI. For example, the classification unit classifies the data using a machine learning algorithm. The classification unit can also classify the data using deep learning. For example, the classification unit inputs the collected data into a deep learning model, and the model classifies the data. The classification unit can also classify the collected data using a clustering algorithm. For example, the classification unit inputs the collected data into a clustering algorithm, and the algorithm classifies the data. As a result, expense classification is automated and more efficient through AI-based data classification. AI includes, but is not limited to, machine learning algorithms, deep learning, and clustering algorithms. Some or all of the above-described processes in the classification unit may be performed using, for example, generative AI, or not using generative AI. For example, the classification unit inputs the collected data into a generative AI, and the generative AI can classify the data.

[0036] The detection unit can detect abnormal transaction patterns by detecting classified data using AI. For example, the detection unit can use a machine learning algorithm to detect abnormal transaction patterns. The detection unit can also use deep learning to detect abnormal transaction patterns. For example, the detection unit inputs classified data into a deep learning model, and the model detects abnormal transaction patterns. The detection unit can also use an anomaly detection algorithm to detect abnormal transaction patterns. For example, the detection unit inputs classified data into an anomaly detection algorithm, and the algorithm detects abnormal transaction patterns. By detecting abnormal transaction patterns, the risk of fraudulent expenses can be reduced. Abnormal transaction patterns include, but are not limited to, abnormal transaction amounts and abnormal transaction frequencies. Some or all of the above processing in the detection unit may be performed using, for example, generative AI, or without generative AI. For example, the detection unit can input classified data into generative AI, and the generative AI can detect abnormal transaction patterns.

[0037] The generation unit can generate expense reports and tax returns based on the detected data. For example, the generation unit can generate an expense report based on the detected data. For example, the generation unit organizes the data according to the format of the expense report and generates the report. The generation unit can also generate a tax return based on the detected data. For example, the generation unit organizes the data according to the content of the tax return and generates the return. The generation unit can also generate expense reports and tax returns simultaneously based on the detected data. For example, the generation unit generates expense reports and tax returns simultaneously. This reduces the workload of the accounting department by automatically generating expense reports and tax returns. Expense reports include, for example, expense summaries by item and attachment of supporting documents. Tax returns include, for example, declaration items and required attachments. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit inputs the detected data into the generation AI, which can then generate expense reports and tax returns.

[0038] The data collection unit can analyze the user's past payment history and select the optimal data collection method. For example, the data collection unit may prioritize collecting data from stores the user frequently uses. For example, the data collection unit may analyze the user's past payment patterns and propose the most efficient data collection method. The data collection unit can also concentrate data collection during specific time periods based on the user's past payment history. For example, the data collection unit may analyze the user's past payment history and concentrate data collection during specific time periods. This allows for efficient data collection by selecting the optimal data collection method through analysis of the user's past payment history. The optimal data collection method includes, but is not limited to, methods based on the results of past data analysis. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's past payment history data into a generative AI, which can then select the optimal data collection method.

[0039] The data collection unit can filter 2D code payment history data based on the user's current purchasing trends and areas of interest. For example, the data collection unit can filter data based on the product categories recently purchased by the user. For example, the data collection unit can prioritize the collection of data from stores related to the user's areas of interest. The data collection unit can also analyze the user's purchasing trends and filter highly relevant data. For example, the data collection unit can analyze the user's purchasing trends and filter highly relevant data. This allows for the efficient collection of highly relevant data by filtering data based on the user's purchasing trends and areas of interest. Purchasing trends include, but are not limited to, past purchase history and frequency. Areas of interest include, but are not limited to, the user's search history and browsing history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input user purchasing trend data into a generative AI, which can then filter the data.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information when collecting 2D code payment history data. For example, the data collection unit can prioritize the collection of store data in the area where the user is currently located. For example, the data collection unit can collect highly relevant regional data based on the user's past travel history. The data collection unit can also update the user's current location information in real time and collect the most relevant data. For example, the data collection unit updates the user's current location information in real time and collects the most relevant data. This allows for the efficient collection of highly relevant data by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location information data into a generative AI, which can then prioritize the collection of highly relevant data.

[0041] The data collection unit can analyze the user's social media activity and collect relevant data when collecting 2D code payment history data. For example, the data collection unit can prioritize collecting data on stores mentioned by the user on social media. For example, the data collection unit can collect data on product categories of interest from the user's social media activity. The data collection unit can also analyze the content of the user's social media posts and collect highly relevant data. For example, the data collection unit can analyze the content of the user's social media posts and collect highly relevant data. This allows for the efficient collection of highly relevant data by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Some or all of the processing described above in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input the user's social media activity data into a generative AI, which can then collect relevant data.

[0042] The classification unit can adjust the level of detail in the classification based on the importance of the transactions when classifying data. For example, the classification unit can classify high-importance transactions in detail and low-importance transactions in a simplified manner. For example, the classification unit can adjust the level of detail in the classification based on the transaction amount. The classification unit can also adjust the level of detail in the classification based on the frequency of transactions. For example, the classification unit can adjust the level of detail in the classification based on the frequency of transactions. This allows for efficient data classification by adjusting the level of detail in the classification based on the importance of the transactions. Transaction importance includes, but is not limited to, the transaction amount and the reliability of the trading partner. Some or all of the above processing in the classification unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the classification unit can input transaction importance data into a generative AI, and the generative AI can adjust the level of detail in the classification.

[0043] The classification unit can apply different classification algorithms depending on the transaction category when classifying data. For example, the classification unit can apply different classification algorithms to food and beverage expenses and transportation expenses. For example, the classification unit can select the optimal classification algorithm for each transaction category. The classification unit can also dynamically change the classification algorithm depending on the transaction category. For example, the classification unit dynamically changes the classification algorithm depending on the transaction category. This enables efficient data classification by applying different classification algorithms depending on the transaction category. Transaction categories include, but are not limited to, product categories and service categories. Some or all of the above processing in the classification unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the classification unit can input transaction category data into a generative AI, and the generative AI can apply different classification algorithms.

[0044] The classification unit can determine classification priorities based on the timing of transactions when classifying data. For example, the classification unit may prioritize recent transactions and postpone past transactions. For example, the classification unit can dynamically change classification priorities based on the timing of transactions. The classification unit can also adjust the level of detail of classifications according to the timing of transactions. For example, the classification unit adjusts the level of detail of classifications according to the timing of transactions. This enables efficient data classification by determining classification priorities based on the timing of transactions. The timing of transactions includes, but is not limited to, monthly or quarterly transactions. Some or all of the above processing in the classification unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the classification unit can input transaction timing data into a generative AI, which can then determine the classification priorities.

[0045] The classification unit can adjust the order of classification based on the relevance of transactions during data classification. For example, the classification unit may prioritize highly relevant transactions and postpone less relevant transactions. For example, the classification unit can dynamically change the order of classification based on the relevance of transactions. The classification unit can also adjust the level of detail of classification according to the relevance of transactions. For example, the classification unit adjusts the level of detail of classification according to the relevance of transactions. This allows for efficient data classification by adjusting the order of classification based on the relevance of transactions. Transaction relevance includes, but is not limited to, the relevance of trading partners and the relevance of transaction content. Some or all of the above processing in the classification unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the classification unit can input transaction relevance data into a generative AI, and the generative AI can adjust the order of classification.

[0046] The detection unit can improve the accuracy of detection by considering the interrelationships of transactions at the time of detection. For example, the detection unit analyzes the interrelationships of transactions to detect abnormal transactions. For example, the detection unit adjusts the detection algorithm based on the interrelationships of transactions. The detection unit can also improve the accuracy of detection by considering the interrelationships of transactions. For example, the detection unit improves the accuracy of detection by considering the interrelationships of transactions. This makes it possible to improve the accuracy of detecting abnormal transactions by considering the interrelationships of transactions. Interrelationships of transactions include, but are not limited to, transaction chains and the impact of transactions. Some or all of the above processing in the detection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the detection unit can input transaction interrelationship data into a generative AI, which can then improve the accuracy of detection.

[0047] The detection unit can perform detection while considering the attribute information of the transaction originator. For example, the detection unit can detect abnormal transactions based on the attribute information of the transaction originator. For example, the detection unit can adjust the detection algorithm based on the attribute information of the transaction originator. The detection unit can also improve the accuracy of detection by considering the attribute information of the transaction originator. For example, the detection unit can improve the accuracy of detection by considering the attribute information of the transaction originator. This makes it possible to improve the accuracy of detecting abnormal transactions by considering the attribute information of the transaction originator. The attribute information of the transaction originator includes, but is not limited to, age, occupation, and purchase history. Some or all of the above processing in the detection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the detection unit can input the attribute information data of the transaction originator into a generating AI, and the generating AI can perform detection.

[0048] The detection unit can perform detection while considering the geographical distribution of transactions. For example, the detection unit analyzes the geographical distribution of transactions and detects abnormal transactions. For example, the detection unit adjusts the detection algorithm based on the geographical distribution of transactions. The detection unit can also improve the accuracy of detection by considering the geographical distribution of transactions. For example, the detection unit improves the accuracy of detection by considering the geographical distribution of transactions. This improves the accuracy of detecting abnormal transactions by considering the geographical distribution of transactions. The geographical distribution of transactions includes, but is not limited to, examples of transaction frequency by region and transaction amount by region. Some or all of the above processing in the detection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the detection unit can input geographical distribution data of transactions into a generative AI, and the generative AI can perform detection.

[0049] The detection unit can improve the accuracy of detection by referring to relevant transaction literature when detecting a transaction. For example, the detection unit can detect an abnormal transaction by referring to relevant transaction literature. For example, the detection unit can adjust the detection algorithm based on the relevant transaction literature. The detection unit can also improve the accuracy of detection by considering relevant transaction literature. For example, the detection unit can improve the accuracy of detection by considering relevant transaction literature. This makes it possible to improve the accuracy of detecting abnormal transactions by referring to relevant transaction literature. Relevant transaction literature includes, but is not limited to, historical transaction data and industry reports. Some or all of the above processing in the detection unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the detection unit can input relevant transaction literature data into a generating AI, and the generating AI can perform detection.

[0050] The generation unit can adjust the level of detail in reports and declarations based on the importance of the transactions. For example, it can describe high-importance transactions in detail and low-importance transactions in a simplified manner. For example, it can adjust the level of detail based on the transaction amount. It can also adjust the level of detail based on the frequency of the transactions. For example, it can adjust the level of detail based on the frequency of the transactions. This allows for the efficient generation of reports and declarations by adjusting the level of detail based on the importance of the transactions. Transaction importance includes, but is not limited to, the transaction amount and the reliability of the trading partner. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input transaction importance data into a generation AI, which can then adjust the level of detail.

[0051] The generation unit can apply different generation algorithms depending on the transaction category when generating reports and declarations. For example, the generation unit can apply different generation algorithms for food and beverage expenses and transportation expenses. For example, the generation unit can select the optimal generation algorithm for each transaction category. The generation unit can also dynamically change the generation algorithm depending on the transaction category. For example, the generation unit dynamically changes the generation algorithm depending on the transaction category. This enables efficient generation of reports and declarations by applying different generation algorithms depending on the transaction category. Transaction categories include, but are not limited to, product categories and service categories. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input transaction category data into a generation AI, and the generation AI can apply different generation algorithms.

[0052] The generation unit can determine the generation priority based on the timing of transactions when generating reports and declarations. For example, the generation unit may prioritize recent transactions in reports and postpone past transactions. For example, the generation unit can dynamically change the generation priority based on the timing of transactions. The generation unit can also adjust the level of detail of the generation depending on the timing of transactions. For example, the generation unit adjusts the level of detail of the generation depending on the timing of transactions. This enables efficient generation of reports and declarations by determining the generation priority based on the timing of transactions. The timing of transactions includes, but is not limited to, monthly, quarterly, etc. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input transaction timing data into a generation AI, which can then determine the priority.

[0053] The generation unit can adjust the order of generation based on the relevance of transactions when generating reports and declarations. For example, the generation unit can prioritize highly relevant transactions in reports and postpone less relevant transactions. For example, the generation unit can dynamically change the generation order based on the relevance of transactions. The generation unit can also adjust the level of detail of generation according to the relevance of transactions. For example, the generation unit adjusts the level of detail of generation according to the relevance of transactions. This allows for efficient generation of reports and declarations by adjusting the generation order based on the relevance of transactions. Transaction relevance includes, but is not limited to, the relevance of trading partners and the relevance of transaction content. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input transaction relevance data into a generation AI, and the generation AI can adjust the order.

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

[0055] The data collection unit can analyze a user's past expense report history and propose the optimal collection timing. For example, it can analyze the time periods when a user previously submitted expense reports and collect data accordingly. It can also consider the user's work schedule and collect data in between tasks. Furthermore, it can analyze the frequency of a user's past expense reports and propose the optimal collection frequency. This enables efficient data collection based on the user's past expense report history.

[0056] The detection unit can detect abnormal transactions by considering the seasonality of transactions. For example, it can learn transaction patterns that occur frequently during specific seasons and detect transactions that deviate from those patterns as abnormal. The detection unit can also detect abnormal transactions by considering seasonal fluctuations in transaction volume. Furthermore, the detection unit can detect abnormal transactions by considering seasonal fluctuations in trading partners. In this way, the accuracy of abnormal transaction detection can be improved by considering the seasonality of transactions.

[0057] The data collection unit can propose the optimal data collection method, taking into account the user's geographical location. For example, if the user is on a business trip, it can prioritize collecting data from stores in their destination city. Similarly, if the user frequently visits a particular region, it can prioritize collecting data from stores in that region. Furthermore, it can suggest the optimal data collection timing based on the user's current location. This enables efficient data collection by considering the user's geographical location.

[0058] The detection unit can detect abnormal transactions by considering the attribute information of the transaction initiator. For example, if the transaction initiator is a new employee, it will detect transactions that differ from normal transaction patterns as abnormal. Furthermore, if the transaction initiator belongs to a specific department, it can also detect abnormal transactions based on the transaction patterns of that department. In addition, it can detect abnormal transactions based on the job title of the transaction initiator. In this way, the accuracy of abnormal transaction detection can be improved by considering the attribute information of the transaction initiator.

[0059] The data collection unit can analyze users' social media activity and collect relevant data. For example, the data collection unit prioritizes collecting data on stores mentioned by users on social media. For example, the data collection unit collects data on product categories of interest from users' social media activity. The data collection unit can also analyze the content of users' social media posts and collect highly relevant data. This allows for the efficient collection of highly relevant data by analyzing users' social media activity.

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

[0061] Step 1: The collection unit collects historical data for 2D code payments. This historical data includes transaction date and time, transaction amount, and customer information. The collection unit can collect this historical data automatically, manually, or periodically. For example, it can be collected daily. Step 2: The classification unit classifies the data collected by the collection unit. Classification is performed based on criteria such as transaction type, amount, and date / time. For example, data can be classified based on transaction type, amount, and date / time. Step 3: The detection unit detects the data classified by the classification unit. Detection is performed based on criteria such as the detection of abnormal trading patterns. For example, it can detect abnormal trading patterns, fraudulent trading, and abnormal trading. Step 4: The generation unit generates expense reports and tax returns based on the data detected by the detection unit. Generation is performed based on criteria such as the format of the report and the content of the tax return. For example, expense reports and tax returns can be generated.

[0062] (Example of form 2) The expense reimbursement system according to an embodiment of the present invention is a mechanism that links a 2D code payment system (e.g., QR code payment) with an AI expense reimbursement system in order to solve problems in expense reimbursement. This expense reimbursement system collects 2D code payment history data, which the AI ​​can automatically read. This eliminates the need to scan receipt images, significantly improving the efficiency of expense reimbursement. It reduces input errors that often occur when data is entered manually, improving the accuracy of expense reimbursement data. Furthermore, it eliminates the need to store and manage receipts, promoting a paperless environment. In addition, by classifying expenses using AI, areas where expenses are high can be visualized, improving the transparency of expense management. The AI ​​can also detect abnormal transaction patterns, reducing the risk of fraudulent expenses. Furthermore, the generating AI automatically creates expense reimbursement reports and tax returns based on the 2D code payment history, reducing the workload of the accounting department. For example, the expense reimbursement system collects 2D code payment history data, which the AI ​​reads automatically. For example, the expense reimbursement system eliminates the need to scan receipt images, improving the efficiency of expense reimbursement. For example, expense reimbursement systems reduce input errors that often occur with manual data entry, improving the accuracy of expense reimbursement data. For example, expense reimbursement systems eliminate the need to store and manage receipts, promoting a paperless environment. For example, expense reimbursement systems use AI to classify expenses and visualize areas where costs are high. For example, expense reimbursement systems use AI to detect abnormal transaction patterns, reducing the risk of fraudulent expenses. For example, expense reimbursement systems use generating AI to automatically create expense reimbursement reports and tax returns based on 2D code payment history. This eliminates the complexity of expense reimbursement and reduces the burden of manual work. It also reduces the risk of expense policy violations and fraudulent expenses, improving the transparency of expense management. Furthermore, it promotes a paperless environment, resulting in environmentally friendly expense reimbursement. In summary, expense reimbursement systems can achieve increased efficiency and transparency in expense reimbursement.

[0063] The expense reimbursement system according to this embodiment comprises a collection unit, a classification unit, a detection unit, and a generation unit. The collection unit collects historical data of 2D code payments. The historical data of 2D code payments includes, but is not limited to, transaction date and time, transaction amount, and trading partner information. The collection unit can, for example, automatically collect the historical data of 2D code payments. The collection unit can also manually collect the historical data of 2D code payments. Furthermore, the collection unit can periodically collect the historical data of 2D code payments. For example, the collection unit collects the historical data of 2D code payments daily. The classification unit classifies the data collected by the collection unit. Classification is performed based on, for example, criteria such as transaction type, amount, and date and time, but is not limited to these examples. The classification unit can, for example, classify the data based on the transaction type. The classification unit can also classify the data based on the transaction amount. The classification unit can also classify the data based on the date and time of the transaction. For example, the classification unit classifies the data based on the transaction type. The detection unit detects the data classified by the classification unit. Detection is performed based on criteria such as the detection of abnormal transaction patterns, but is not limited to such examples. The detection unit can, for example, detect abnormal transaction patterns. The detection unit can also detect fraudulent transactions. The detection unit can also detect abnormal transactions. For example, the detection unit can detect abnormal transaction patterns. The generation unit generates expense reports and tax returns based on the data detected by the detection unit. Generation is performed based on criteria such as the format of the report and the content of the tax return, but is not limited to such examples. The generation unit can, for example, generate expense reports. The generation unit can also generate tax returns. The generation unit can also generate expense reports and tax returns simultaneously. For example, the generation unit generates expense reports. As a result, the expense reimbursement system according to the embodiment can achieve increased efficiency and transparency in expense reimbursement.

[0064] The data collection unit collects historical data from 2D code payments. This historical data includes, but is not limited to, transaction date and time, transaction amount, and customer information. The data collection unit can, for example, automatically collect historical data from 2D code payments. Specifically, it works in conjunction with the 2D code payment system to acquire historical data in real time each time a payment is made. The data collection unit can also manually collect historical data from 2D code payments. For example, it can provide a function for users to manually upload their payment history, which the data collection unit then uses to acquire the data. Furthermore, the data collection unit can collect historical data from 2D code payments on a regular basis. For example, the data collection unit can collect historical data from 2D code payments daily. This ensures that daily transaction data is reliably collected and that the latest information is always reflected in the system. The data collection unit centrally manages this data and stores it in a database. The database is equipped with security measures to prevent data tampering and unauthorized access. The data collection unit also has functions to check for data duplication and missing data, and to maintain data integrity. This allows the data collection unit to provide accurate and reliable data, improving the overall performance of the system.

[0065] The classification unit categorizes the data collected by the collection unit. Classification is based on criteria such as transaction type, amount, and date / time, but is not limited to these examples. For instance, the classification unit categorizes data based on transaction type. Specifically, transactions are classified into categories such as meals, transportation, and accommodation. The classification unit can also categorize data based on transaction amount. For example, transactions above a certain amount can be classified as high-value transactions and processed accordingly. Furthermore, the classification unit can categorize data based on the date / time of the transaction. For example, transaction data can be aggregated monthly or quarterly to analyze expense trends. The classification unit offers flexible settings for these classification criteria and can be customized to meet user needs. The classification unit can also perform automated data classification using AI. The AI ​​learns from past transaction data and categorizes new transaction data appropriately. This improves classification accuracy and significantly reduces manual classification work. Additionally, the classification unit includes a function to visualize classification results, allowing users to intuitively understand expense breakdowns through graphs and charts. This allows the classification unit to efficiently and effectively classify data, improving the accuracy and transparency of expense management.

[0066] The detection unit detects data classified by the classification unit. Detection is performed based on criteria such as the detection of abnormal transaction patterns, but is not limited to such examples. For example, the detection unit detects abnormal transaction patterns. Specifically, it identifies transactions that deviate from normal transaction patterns and issues alerts. The detection unit can also detect fraudulent transactions. For example, it detects potentially fraudulent transactions such as when the same transaction is performed multiple times or when the transaction amount is abnormally high. The detection unit can also detect anomalies in transactions. For example, it detects transactions conducted outside of normal business hours or transactions with specific trading partners at an unusually high frequency. The detection unit uses AI to perform these anomaly detections. The AI ​​learns from past transaction data and models normal transaction patterns. If new transaction data deviates from this model, the AI ​​detects it as an anomaly. This allows the detection unit to detect abnormal transactions with high accuracy and respond quickly. Furthermore, the detection unit also has a function to output the detection results as a report and notify administrators. This allows administrators to grasp abnormal transactions early and take appropriate measures. This allows the detection unit to improve the reliability and security of expense management.

[0067] The generation unit generates expense reports and tax returns based on data detected by the detection unit. Generation is performed based on criteria such as the format of the report and the content of the tax return, but is not limited to such examples. For example, the generation unit generates expense reports. Specifically, it aggregates information such as the type, amount, and date of transactions to create a report that clearly shows the breakdown of expenses. The generation unit can also generate tax returns. For example, it automatically aggregates the information necessary for tax filing based on annual expense data and creates a tax return. Furthermore, the generation unit can generate expense reports and tax returns simultaneously. For example, it can generate monthly expense reports and quarterly tax returns at the same time and provide them to the user. The generation unit also has the function to output these reports and tax returns in formats such as PDF and Excel, and users can download and print these documents as needed. In addition, the generation unit has the function to customize the content of reports and tax returns, allowing for flexible responses to user needs. As a result, the generation unit can efficiently and accurately generate expense reports and tax returns, improving the efficiency and transparency of expense management.

[0068] The data collection unit can collect historical data of 2D code payments. The data collection unit can, for example, automatically collect historical data of 2D code payments. For example, the data collection unit can collect historical data of 2D code payments in real time. The data collection unit can also manually collect historical data of 2D code payments. For example, the data collection unit can collect historical data of 2D code payments at a time specified by the user. The data collection unit can also collect historical data of 2D code payments periodically. For example, the data collection unit can collect historical data of 2D code payments at a fixed time every day. This eliminates the need for manual data entry by collecting historical data of 2D code payments. Historical data of 2D code payments includes, but is not limited to, transaction date and time, transaction amount, and trading partner information. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input historical data of 2D code payments into a generating AI, and the generating AI can collect the data.

[0069] The classification unit can classify the collected data using AI. For example, the classification unit classifies the collected data using AI. For example, the classification unit classifies the data using a machine learning algorithm. The classification unit can also classify the data using deep learning. For example, the classification unit inputs the collected data into a deep learning model, and the model classifies the data. The classification unit can also classify the collected data using a clustering algorithm. For example, the classification unit inputs the collected data into a clustering algorithm, and the algorithm classifies the data. As a result, expense classification is automated and more efficient through AI-based data classification. AI includes, but is not limited to, machine learning algorithms, deep learning, and clustering algorithms. Some or all of the above-described processes in the classification unit may be performed using, for example, generative AI, or not using generative AI. For example, the classification unit inputs the collected data into a generative AI, and the generative AI can classify the data.

[0070] The detection unit can detect abnormal transaction patterns by detecting classified data using AI. For example, the detection unit can use a machine learning algorithm to detect abnormal transaction patterns. The detection unit can also use deep learning to detect abnormal transaction patterns. For example, the detection unit inputs classified data into a deep learning model, and the model detects abnormal transaction patterns. The detection unit can also use an anomaly detection algorithm to detect abnormal transaction patterns. For example, the detection unit inputs classified data into an anomaly detection algorithm, and the algorithm detects abnormal transaction patterns. By detecting abnormal transaction patterns, the risk of fraudulent expenses can be reduced. Abnormal transaction patterns include, but are not limited to, abnormal transaction amounts and abnormal transaction frequencies. Some or all of the above processing in the detection unit may be performed using, for example, generative AI, or without generative AI. For example, the detection unit can input classified data into generative AI, and the generative AI can detect abnormal transaction patterns.

[0071] The generation unit can generate expense reports and tax returns based on the detected data. For example, the generation unit can generate an expense report based on the detected data. For example, the generation unit organizes the data according to the format of the expense report and generates the report. The generation unit can also generate a tax return based on the detected data. For example, the generation unit organizes the data according to the content of the tax return and generates the return. The generation unit can also generate expense reports and tax returns simultaneously based on the detected data. For example, the generation unit generates expense reports and tax returns simultaneously. This reduces the workload of the accounting department by automatically generating expense reports and tax returns. Expense reports include, for example, expense summaries by item and attachment of supporting documents. Tax returns include, for example, declaration items and required attachments. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit inputs the detected data into the generation AI, which can then generate expense reports and tax returns.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to reduce the user's burden. For example, if the user is relaxed, the data collection unit can immediately collect the data and process it quickly. The data collection unit can also advance the collection timing to quickly acquire data if the user is in a hurry. In this way, the user's burden can be reduced by adjusting the collection timing according to the user's emotions. The user's emotions are estimated using technologies such as facial recognition and voice analysis. Some or all of the processing described above in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's facial expression data into a generative AI, which can estimate the emotions and adjust the collection timing.

[0073] The data collection unit can analyze the user's past payment history and select the optimal data collection method. For example, the data collection unit may prioritize collecting data from stores the user frequently uses. For example, the data collection unit may analyze the user's past payment patterns and propose the most efficient data collection method. The data collection unit can also concentrate data collection during specific time periods based on the user's past payment history. For example, the data collection unit may analyze the user's past payment history and concentrate data collection during specific time periods. This allows for efficient data collection by selecting the optimal data collection method through analysis of the user's past payment history. The optimal data collection method includes, but is not limited to, methods based on the results of past data analysis. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's past payment history data into a generative AI, which can then select the optimal data collection method.

[0074] The data collection unit can filter 2D code payment history data based on the user's current purchasing trends and areas of interest. For example, the data collection unit can filter data based on the product categories recently purchased by the user. For example, the data collection unit can prioritize the collection of data from stores related to the user's areas of interest. The data collection unit can also analyze the user's purchasing trends and filter highly relevant data. For example, the data collection unit can analyze the user's purchasing trends and filter highly relevant data. This allows for the efficient collection of highly relevant data by filtering data based on the user's purchasing trends and areas of interest. Purchasing trends include, but are not limited to, past purchase history and frequency. Areas of interest include, but are not limited to, the user's search history and browsing history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input user purchasing trend data into a generative AI, which can then filter the data.

[0075] The data collection unit can estimate the user's emotions and determine the priority of the payment history data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone collecting less important data. For example, if the user is relaxed, the data collection unit will collect all data equally. Also, if the user is in a hurry, the data collection unit can prioritize collecting more important data. This reduces the user's burden by prioritizing data according to their emotions. The user's emotions are estimated using technologies such as facial recognition and voice analysis. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's facial expression data into a generative AI, which can estimate emotions and determine the priority of the data.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information when collecting 2D code payment history data. For example, the data collection unit can prioritize the collection of store data in the area where the user is currently located. For example, the data collection unit can collect highly relevant regional data based on the user's past travel history. The data collection unit can also update the user's current location information in real time and collect the most relevant data. For example, the data collection unit updates the user's current location information in real time and collects the most relevant data. This allows for the efficient collection of highly relevant data by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location information data into a generative AI, which can then prioritize the collection of highly relevant data.

[0077] The data collection unit can analyze the user's social media activity and collect relevant data when collecting 2D code payment history data. For example, the data collection unit can prioritize collecting data on stores mentioned by the user on social media. For example, the data collection unit can collect data on product categories of interest from the user's social media activity. The data collection unit can also analyze the content of the user's social media posts and collect highly relevant data. For example, the data collection unit can analyze the content of the user's social media posts and collect highly relevant data. This allows for the efficient collection of highly relevant data by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Some or all of the processing described above in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input the user's social media activity data into a generative AI, which can then collect relevant data.

[0078] The classification unit can estimate the user's emotions and adjust the data classification method based on the estimated user emotions. For example, if the user is stressed, the classification unit may employ a simple classification method. For example, if the user is relaxed, the classification unit may employ a more detailed classification method. The classification unit may also employ a method that allows for rapid classification if the user is in a hurry. By adjusting the classification method according to the user's emotions, the burden on the user can be reduced. The user's emotions are estimated using technologies such as facial recognition and voice analysis. The classification method includes, but is not limited to, adjustment methods that respond to changes in emotions. Some or all of the above processing in the classification unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the classification unit can input the user's facial expression data into a generative AI, which can estimate emotions and adjust the classification method.

[0079] The classification unit can adjust the level of detail in the classification based on the importance of the transactions when classifying data. For example, the classification unit can classify high-importance transactions in detail and low-importance transactions in a simplified manner. For example, the classification unit can adjust the level of detail in the classification based on the transaction amount. The classification unit can also adjust the level of detail in the classification based on the frequency of transactions. For example, the classification unit can adjust the level of detail in the classification based on the frequency of transactions. This allows for efficient data classification by adjusting the level of detail in the classification based on the importance of the transactions. Transaction importance includes, but is not limited to, the transaction amount and the reliability of the trading partner. Some or all of the above processing in the classification unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the classification unit can input transaction importance data into a generative AI, and the generative AI can adjust the level of detail in the classification.

[0080] The classification unit can apply different classification algorithms depending on the transaction category when classifying data. For example, the classification unit can apply different classification algorithms to food and beverage expenses and transportation expenses. For example, the classification unit can select the optimal classification algorithm for each transaction category. The classification unit can also dynamically change the classification algorithm depending on the transaction category. For example, the classification unit dynamically changes the classification algorithm depending on the transaction category. This enables efficient data classification by applying different classification algorithms depending on the transaction category. Transaction categories include, but are not limited to, product categories and service categories. Some or all of the above processing in the classification unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the classification unit can input transaction category data into a generative AI, and the generative AI can apply different classification algorithms.

[0081] The classification unit can estimate the user's emotions and determine classification priorities based on those estimated emotions. For example, if the user is stressed, the classification unit will prioritize less important data. If the user is relaxed, the classification unit will classify all data equally. If the user is in a hurry, the classification unit can also prioritize classifying more important data. This reduces the user's burden by determining classification priorities according to their emotions. The user's emotions are estimated using technologies such as facial recognition and voice analysis. Classification priorities include, but are not limited to, the intensity and importance of emotions. Some or all of the processing described above in the classification unit may be performed using, for example, generative AI, or without generative AI. For example, the classification unit can input user facial data into a generative AI, which can then estimate emotions and determine classification priorities.

[0082] The classification unit can determine classification priorities based on the timing of transactions when classifying data. For example, the classification unit may prioritize recent transactions and postpone past transactions. For example, the classification unit can dynamically change classification priorities based on the timing of transactions. The classification unit can also adjust the level of detail of classifications according to the timing of transactions. For example, the classification unit adjusts the level of detail of classifications according to the timing of transactions. This enables efficient data classification by determining classification priorities based on the timing of transactions. The timing of transactions includes, but is not limited to, monthly or quarterly transactions. Some or all of the above processing in the classification unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the classification unit can input transaction timing data into a generative AI, which can then determine the classification priorities.

[0083] The classification unit can adjust the order of classification based on the relevance of transactions during data classification. For example, the classification unit may prioritize highly relevant transactions and postpone less relevant transactions. For example, the classification unit can dynamically change the order of classification based on the relevance of transactions. The classification unit can also adjust the level of detail of classification according to the relevance of transactions. For example, the classification unit adjusts the level of detail of classification according to the relevance of transactions. This allows for efficient data classification by adjusting the order of classification based on the relevance of transactions. Transaction relevance includes, but is not limited to, the relevance of trading partners and the relevance of transaction content. Some or all of the above processing in the classification unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the classification unit can input transaction relevance data into a generative AI, and the generative AI can adjust the order of classification.

[0084] The detection unit can estimate the user's emotions and adjust the detection criteria for abnormal transactions based on the estimated user emotions. For example, if the user is stressed, the detection unit can apply strict detection criteria. For example, if the user is relaxed, the detection unit can apply flexible detection criteria. The detection unit can also apply criteria that allow for quick detection if the user is in a hurry. In this way, the accuracy of abnormal transaction detection can be improved by adjusting the detection criteria according to the user's emotions. The user's emotions are estimated using technologies such as facial recognition and voice analysis. Abnormal transaction detection criteria include, but are not limited to, adjusting criteria in response to changes in emotions. Some or all of the above processing in the detection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the detection unit can input the user's facial expression data into a generative AI, which can estimate emotions and adjust the detection criteria.

[0085] The detection unit can improve the accuracy of detection by considering the interrelationships of transactions at the time of detection. For example, the detection unit analyzes the interrelationships of transactions to detect abnormal transactions. For example, the detection unit adjusts the detection algorithm based on the interrelationships of transactions. The detection unit can also improve the accuracy of detection by considering the interrelationships of transactions. For example, the detection unit improves the accuracy of detection by considering the interrelationships of transactions. This makes it possible to improve the accuracy of detecting abnormal transactions by considering the interrelationships of transactions. Interrelationships of transactions include, but are not limited to, transaction chains and the impact of transactions. Some or all of the above processing in the detection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the detection unit can input transaction interrelationship data into a generative AI, which can then improve the accuracy of detection.

[0086] The detection unit can perform detection while considering the attribute information of the transaction originator. For example, the detection unit can detect abnormal transactions based on the attribute information of the transaction originator. For example, the detection unit can adjust the detection algorithm based on the attribute information of the transaction originator. The detection unit can also improve the accuracy of detection by considering the attribute information of the transaction originator. For example, the detection unit can improve the accuracy of detection by considering the attribute information of the transaction originator. This makes it possible to improve the accuracy of detecting abnormal transactions by considering the attribute information of the transaction originator. The attribute information of the transaction originator includes, but is not limited to, age, occupation, and purchase history. Some or all of the above processing in the detection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the detection unit can input the attribute information data of the transaction originator into a generating AI, and the generating AI can perform detection.

[0087] The detection unit can estimate the user's emotions and adjust the display method of the detection results based on the estimated user emotions. For example, if the user is stressed, the detection unit provides a simple and highly visible display method. For example, if the user is relaxed, the detection unit provides a display method that includes detailed information. The detection unit can also provide a concise display method if the user is in a hurry. By adjusting the display method according to the user's emotions, the burden on the user can be reduced. The user's emotions are estimated using technologies such as facial recognition and voice analysis. The display method includes, but is not limited to, adjusting the display method in response to changes in emotions. Some or all of the above processing in the detection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the detection unit can input the user's facial expression data into a generative AI, which can estimate emotions and adjust the display method.

[0088] The detection unit can perform detection while considering the geographical distribution of transactions. For example, the detection unit analyzes the geographical distribution of transactions and detects abnormal transactions. For example, the detection unit adjusts the detection algorithm based on the geographical distribution of transactions. The detection unit can also improve the accuracy of detection by considering the geographical distribution of transactions. For example, the detection unit improves the accuracy of detection by considering the geographical distribution of transactions. This improves the accuracy of detecting abnormal transactions by considering the geographical distribution of transactions. The geographical distribution of transactions includes, but is not limited to, examples of transaction frequency by region and transaction amount by region. Some or all of the above processing in the detection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the detection unit can input geographical distribution data of transactions into a generative AI, and the generative AI can perform detection.

[0089] The detection unit can improve the accuracy of detection by referring to relevant transaction literature when detecting a transaction. For example, the detection unit can detect an abnormal transaction by referring to relevant transaction literature. For example, the detection unit can adjust the detection algorithm based on the relevant transaction literature. The detection unit can also improve the accuracy of detection by considering relevant transaction literature. For example, the detection unit can improve the accuracy of detection by considering relevant transaction literature. This makes it possible to improve the accuracy of detecting abnormal transactions by referring to relevant transaction literature. Relevant transaction literature includes, but is not limited to, historical transaction data and industry reports. Some or all of the above processing in the detection unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the detection unit can input relevant transaction literature data into a generating AI, and the generating AI can perform detection.

[0090] The generation unit can estimate the user's emotions and adjust the presentation of reports and declarations based on the estimated emotions. For example, if the user is stressed, the generation unit can generate a simple and easy-to-read report. For example, if the user is relaxed, the generation unit can generate a report with detailed information. The generation unit can also generate a concise report if the user is in a hurry. This reduces the user's burden by adjusting the presentation according to their emotions. The user's emotions are estimated using technologies such as facial recognition and voice analysis. Presentation methods include, but are not limited to, adjusting the presentation according to changes in emotions. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can input the user's facial expression data into the generation AI, which can estimate emotions and adjust the presentation.

[0091] The generation unit can adjust the level of detail in reports and declarations based on the importance of the transactions. For example, it can describe high-importance transactions in detail and low-importance transactions in a simplified manner. For example, it can adjust the level of detail based on the transaction amount. It can also adjust the level of detail based on the frequency of the transactions. For example, it can adjust the level of detail based on the frequency of the transactions. This allows for the efficient generation of reports and declarations by adjusting the level of detail based on the importance of the transactions. Transaction importance includes, but is not limited to, the transaction amount and the reliability of the trading partner. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input transaction importance data into a generation AI, which can then adjust the level of detail.

[0092] The generation unit can apply different generation algorithms depending on the transaction category when generating reports and declarations. For example, the generation unit can apply different generation algorithms for food and beverage expenses and transportation expenses. For example, the generation unit can select the optimal generation algorithm for each transaction category. The generation unit can also dynamically change the generation algorithm depending on the transaction category. For example, the generation unit dynamically changes the generation algorithm depending on the transaction category. This enables efficient generation of reports and declarations by applying different generation algorithms depending on the transaction category. Transaction categories include, but are not limited to, product categories and service categories. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input transaction category data into a generation AI, and the generation AI can apply different generation algorithms.

[0093] The generation unit can estimate the user's emotions and determine the priority of reports and declarations to be generated based on the estimated user emotions. For example, if the user is stressed, the generation unit will postpone less important reports. For example, if the user is relaxed, the generation unit will generate all reports equally. Also, if the user is in a hurry, the generation unit can prioritize the generation of high-priority reports. This reduces the user's burden by determining priorities according to the user's emotions. The user's emotions are estimated using technologies such as facial recognition and voice analysis. Prioritization includes, but is not limited to, the intensity and importance of emotions. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can input the user's facial expression data into a generation AI, which can estimate emotions and determine priorities.

[0094] The generation unit can determine the generation priority based on the timing of transactions when generating reports and declarations. For example, the generation unit may prioritize recent transactions in reports and postpone past transactions. For example, the generation unit can dynamically change the generation priority based on the timing of transactions. The generation unit can also adjust the level of detail of the generation depending on the timing of transactions. For example, the generation unit adjusts the level of detail of the generation depending on the timing of transactions. This enables efficient generation of reports and declarations by determining the generation priority based on the timing of transactions. The timing of transactions includes, but is not limited to, monthly, quarterly, etc. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input transaction timing data into a generation AI, which can then determine the priority.

[0095] The generation unit can adjust the order of generation based on the relevance of transactions when generating reports and declarations. For example, the generation unit can prioritize highly relevant transactions in reports and postpone less relevant transactions. For example, the generation unit can dynamically change the generation order based on the relevance of transactions. The generation unit can also adjust the level of detail of generation according to the relevance of transactions. For example, the generation unit adjusts the level of detail of generation according to the relevance of transactions. This allows for efficient generation of reports and declarations by adjusting the generation order based on the relevance of transactions. Transaction relevance includes, but is not limited to, the relevance of trading partners and the relevance of transaction content. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input transaction relevance data into a generation AI, and the generation AI can adjust the order.

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

[0097] The data collection unit can analyze a user's past expense report history and propose the optimal collection timing. For example, it can analyze the time periods when a user previously submitted expense reports and collect data accordingly. It can also consider the user's work schedule and collect data in between tasks. Furthermore, it can analyze the frequency of a user's past expense reports and propose the optimal collection frequency. This enables efficient data collection based on the user's past expense report history.

[0098] The classification unit can estimate the user's emotions and adjust the data classification criteria based on those emotions. For example, if the user is stressed, a simplified classification criterion can be applied to reduce the user's burden. Conversely, if the user is relaxed, a more detailed classification criterion can be applied to perform more accurate data classification. Furthermore, if the user is in a hurry, a criterion that allows for quick classification can be applied. In this way, the user's burden can be reduced by adjusting the classification criteria according to their emotions.

[0099] The detection unit can detect abnormal transactions by considering the seasonality of transactions. For example, it can learn transaction patterns that occur frequently during specific seasons and detect transactions that deviate from those patterns as abnormal. The detection unit can also detect abnormal transactions by considering seasonal fluctuations in transaction volume. Furthermore, the detection unit can detect abnormal transactions by considering seasonal fluctuations in trading partners. In this way, the accuracy of abnormal transaction detection can be improved by considering the seasonality of transactions.

[0100] The generation unit can estimate the user's emotions and adjust the content of the generated report based on those emotions. For example, if the user is stressed, it can generate a concise report summarizing only the important points. If the user is relaxed, it can generate a report containing detailed information. Furthermore, if the user is in a hurry, it can generate a report that is concise and easy to understand in a short amount of time. In this way, by adjusting the report content according to the user's emotions, the burden on the user can be reduced.

[0101] The data collection unit can propose the optimal data collection method, taking into account the user's geographical location. For example, if the user is on a business trip, it can prioritize collecting data from stores in their destination city. Similarly, if the user frequently visits a particular region, it can prioritize collecting data from stores in that region. Furthermore, it can suggest the optimal data collection timing based on the user's current location. This enables efficient data collection by considering the user's geographical location.

[0102] The classification unit can estimate the user's emotions and adjust the data classification method based on the estimated emotions. For example, if the user is stressed, a simple classification method is adopted. For example, if the user is relaxed, the classification unit adopts a more detailed classification method. Furthermore, if the user is in a hurry, the classification unit can adopt a method that allows for rapid classification. This reduces the user's burden by adjusting the classification method according to their emotions.

[0103] The detection unit can detect abnormal transactions by considering the attribute information of the transaction initiator. For example, if the transaction initiator is a new employee, it will detect transactions that differ from normal transaction patterns as abnormal. Furthermore, if the transaction initiator belongs to a specific department, it can also detect abnormal transactions based on the transaction patterns of that department. In addition, it can detect abnormal transactions based on the job title of the transaction initiator. In this way, the accuracy of abnormal transaction detection can be improved by considering the attribute information of the transaction initiator.

[0104] The generation unit can estimate the user's emotions and adjust the presentation of reports and declarations based on those estimated emotions. For example, if the user is stressed, the generation unit will generate a simple and easy-to-read report. For example, if the user is relaxed, the generation unit will generate a report with detailed information. Furthermore, if the user is in a hurry, the generation unit can generate a concise report. This reduces the user's burden by adjusting the presentation style according to their emotions.

[0105] The data collection unit can analyze users' social media activity and collect relevant data. For example, the data collection unit prioritizes collecting data on stores mentioned by users on social media. For example, the data collection unit collects data on product categories of interest from users' social media activity. The data collection unit can also analyze the content of users' social media posts and collect highly relevant data. This allows for the efficient collection of highly relevant data by analyzing users' social media activity.

[0106] The detection unit can estimate the user's emotions and adjust the detection criteria for abnormal transactions based on the estimated emotions. For example, if the user is stressed, the detection unit will apply strict detection criteria. For example, if the user is relaxed, the detection unit will apply flexible detection criteria. The detection unit can also apply criteria that allow for quick detection if the user is in a hurry. In this way, the accuracy of detecting abnormal transactions can be improved by adjusting the detection criteria according to the user's emotions.

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

[0108] Step 1: The collection unit collects historical data for 2D code payments. This historical data includes transaction date and time, transaction amount, and customer information. The collection unit can collect this historical data automatically, manually, or periodically. For example, it can be collected daily. Step 2: The classification unit classifies the data collected by the collection unit. Classification is performed based on criteria such as transaction type, amount, and date / time. For example, data can be classified based on transaction type, amount, and date / time. Step 3: The detection unit detects the data classified by the classification unit. Detection is performed based on criteria such as the detection of abnormal trading patterns. For example, it can detect abnormal trading patterns, fraudulent trading, and abnormal trading. Step 4: The generation unit generates expense reports and tax returns based on the data detected by the detection unit. Generation is performed based on criteria such as the format of the report and the content of the tax return. For example, expense reports and tax returns can be generated.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the collection unit, classification unit, detection unit, and generation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects 2D code payment history data via the communication I / F 44 of the smart device 14. The classification unit is implemented in the identification processing unit 290 of the data processing unit 12 and classifies the collected data based on the type and amount of the transaction. The detection unit is implemented in the identification processing unit 290 of the data processing unit 12 and detects abnormal transaction patterns. The generation unit is implemented in the identification processing unit 290 of the data processing unit 12 and generates expense reports and tax returns. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the collection unit, classification unit, detection unit, and generation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects 2D code payment history data via the communication I / F 44 of the smart glasses 214. The classification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and classifies the collected data based on the type and amount of the transaction. The detection unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and detects abnormal transaction patterns. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and generates expense reports and tax returns. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the collection unit, classification unit, detection unit, and generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects 2D code payment history data via the communication I / F 44 of the headset terminal 314. The classification unit is implemented in the identification processing unit 290 of the data processing unit 12 and classifies the collected data based on the type and amount of the transaction. The detection unit is implemented in the identification processing unit 290 of the data processing unit 12 and detects abnormal transaction patterns. The generation unit is implemented in the identification processing unit 290 of the data processing unit 12 and generates expense reports and tax returns. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

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

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the collection unit, classification unit, detection unit, and generation unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects historical data of 2D code payments via the communication I / F 44 of the robot 414. The classification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and classifies the collected data based on the type and amount of the transaction. The detection unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and detects abnormal transaction patterns. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and generates expense reports and tax returns. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) A collection unit that collects historical data of 2D code payments, A classification unit that classifies the data collected by the aforementioned collection unit, A detection unit that detects data classified by the classification unit, A generation unit generates expense reimbursement reports and tax returns based on the data detected by the aforementioned detection unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect historical data from 2D code payments. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned classification unit is The collected data is classified using AI. The system described in Appendix 1, characterized by the features described herein. (Note 4) The detection unit, The classified data is detected by AI to identify abnormal trading patterns. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Based on the detected data, expense reports and tax returns are generated. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of QR code payment history data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past payment history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting 2D code payment history data, filtering is performed based on the user's current purchasing trends and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates the user's emotions and prioritizes the payment history data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting 2D code payment history data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting 2D code payment history data, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned classification unit is We estimate user sentiment and adjust the data classification method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned classification unit is When classifying data, adjust the level of detail in the classification based on the importance of the transactions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned classification unit is When classifying data, different classification algorithms are applied depending on the transaction category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned classification unit is It estimates the user's emotions and determines the classification priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned classification unit is When classifying data, the priority of classification is determined based on when the transaction occurred. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned classification unit is When classifying data, adjust the order of classification based on the relevance of the transactions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The detection unit, The system estimates user sentiment and adjusts the detection criteria for abnormal transactions based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The detection unit, When detecting transactions, the accuracy of the detection is improved by considering the interrelationships between them. The system described in Appendix 1, characterized by the features described herein. (Note 20) The detection unit, When detecting a transaction, the system takes into account the attribute information of the parties involved. The system described in Appendix 1, characterized by the features described herein. (Note 21) The detection unit, It estimates the user's emotions and adjusts how the detection results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The detection unit, When detecting a transaction, the geographical distribution of the transaction is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The detection unit, During detection, the accuracy of the detection is improved by referring to relevant literature related to the transaction. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is We estimate user sentiment and adjust the way reports and declarations are presented based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is When generating reports and declarations, adjust the level of detail based on the importance of the transactions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is When generating reports and declarations, different generation algorithms are applied depending on the transaction category. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is It estimates user sentiment and determines the priority of reports and declarations generated based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is When generating reports and declarations, the generation priority is determined based on when the transactions occurred. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is When generating reports and declarations, adjust the generation order based on the relevance of transactions. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

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

Claims

1. A collection unit that collects historical data of 2D code payments, A classification unit that classifies the data collected by the aforementioned collection unit, A detection unit that detects data classified by the classification unit, A generation unit generates expense reimbursement reports and tax returns based on the data detected by the aforementioned detection unit, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is Collect historical data from 2D code payments. The system according to feature 1.

3. The aforementioned classification unit is The collected data is classified using AI. The system according to feature 1.

4. The detection unit, The system uses AI to detect classified data and identify abnormal trading patterns. The system according to feature 1.

5. The generating unit is Based on the detected data, expense reports and tax returns are generated. The system according to feature 1.

6. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of data collection for 2D code payment history based on the estimated emotions. The system according to feature 1.

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

8. The aforementioned collection unit is When collecting 2D code payment history data, filtering is performed based on the user's current purchasing trends and areas of interest. The system according to feature 1.

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