system

Abstract

The system according to this embodiment aims to automatically calculate and withhold taxes on profits from the sale of goods and financial transactions. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a calculation unit, and a withholding tax unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The calculation unit calculates the cost based on the data analyzed by the analysis unit. The withholding tax unit withholds tax on the profit margin between the cost and the selling price calculated by the calculation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that the calculation and withholding at source of taxes on profits in merchandise sales and financial transactions are complicated and final tax returns are required.

[0005] The system according to the embodiment aims to automatically calculate and withhold at source taxes on profits in merchandise sales and financial transactions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a calculation unit, and a withholding tax unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The calculation unit calculates the cost based on the data analyzed by the analysis unit. The withholding tax unit withholds tax on the profit margin between the cost and the selling price calculated by the calculation unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically calculate and withhold taxes on profits from the sale of goods and financial transactions. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system that withholds taxes on profits, including those from the sale of goods, by linking with a cloud POS register utilizing My Number, a financial account, and a trading application such as a flea market app. This system collects data on sales of goods conducted through the cloud POS register, flea market app, etc. The collected data includes the location information of the product, the date and time, the purchase price of the product, the exchange rate, the cost of production of the product, and the cost ratio. This data is collected and analyzed by a generating AI to calculate the cost of the product. Next, profit and loss offsetting is performed in the financial account. Based on the financial account data, the generating AI calculates the cost associated with the provision of goods or services in transactions where there is no "buy" data or in personal transactions. Withholding tax is applied to the profit difference between this calculated cost and the selling price. Furthermore, income tax is withheld on all financial transactions related to the individual. This eliminates the need for filing a tax return and simplifies the tax processing for individuals. For example, when selling an item on a flea market app, data such as the item's location, date and time, purchase price, exchange rate, production cost, and cost ratio are collected and analyzed by a generating AI to calculate the item's cost. Next, profits and losses are offset based on data from financial accounts, and withholding tax is applied to the profit difference between the sale price and the cost. In cases where there is no "buy" data in the financial institution account, or when the cost associated with the provision of goods or services in a personal transaction is collected from the market by the generating AI, a simplified cost is calculated. Withholding tax is applied to the profit difference between this calculated cost and the selling price. This system simplifies individual tax processing and eliminates the need for filing a tax return. Furthermore, by creating accounting records and calculating costs precisely before filing a tax return, it is possible to receive a refund of overpaid taxes. In summary, the system simplifies individual tax processing and eliminates the need for filing a tax return.

[0029] The system according to this embodiment comprises a data collection unit, an analysis unit, a calculation unit, and a withholding tax unit. The data collection unit collects data. For example, the data collection unit collects data such as the location information of the product, the date and time, the purchase price of the product, the exchange rate, the cost of production of the product, and the cost ratio. The data collection unit can automatically collect this data using a generation AI. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the cost of the product based on the collected data. The analysis unit can perform data analysis using a generation AI. The calculation unit calculates the cost based on the data analyzed by the analysis unit. For example, the calculation unit calculates the cost of the product based on the analysis results. The calculation unit can perform cost calculation using a generation AI. The withholding tax unit withholds tax on the profit margin between the cost and the selling price calculated by the calculation unit. For example, the withholding tax unit withholds tax based on the profit margin between the calculated cost and the selling price. The withholding tax unit can perform withholding tax using a generation AI. As a result, the system according to this embodiment can efficiently perform data collection, analysis, cost calculation, and withholding tax.

[0030] The data collection unit collects data such as product location, date and time, product purchase price, exchange rate, product manufacturing cost, and cost ratio. Specifically, product location information is acquired in real time using GPS devices, and date and time information is recorded as a timestamp. Product purchase prices are obtained from online marketplaces and exchanges via APIs, and exchange rates are collected using data feeds from financial institutions and foreign exchange exchanges. Product manufacturing costs include detailed cost data such as material costs, labor costs, and equipment costs used in the manufacturing process. The cost ratio is calculated based on this cost data, and the data collection unit centrally manages this data. The data collection unit can automatically collect this data using generative AI. Generative AI automates the data collection process from various data sources and performs filtering and preprocessing to maintain data integrity and consistency. For example, generative AI uses algorithms to unify data in different formats and fill in missing data. Generative AI also optimizes the frequency and timing of data collection, enabling real-time data updates. This allows the data collection unit to efficiently and accurately collect diverse data, thereby strengthening the data infrastructure of the entire system.

[0031] The analysis department analyzes the data collected by the data collection department. For example, the analysis department analyzes the cost of goods based on the collected data. Specifically, it calculates logistics costs and storage costs based on the location and date / time information of the goods, and evaluates the overall cost by considering the purchase price and exchange rate of the goods. The analysis department can perform data analysis using generative AI. Generative AI uses machine learning algorithms to extract patterns and trends from the collected data and identify factors that affect the cost of goods. For example, generative AI builds a cost forecasting model that takes into account seasonal fluctuations and the balance of supply and demand in the market, based on historical data. In addition, generative AI uses anomaly detection algorithms to detect outliers and inconsistencies hidden in the data, improving the accuracy of the analysis results. Furthermore, generative AI uses data visualization tools to display the analysis results as graphs and charts, making them intuitively understandable to the user. This allows the analysis department to analyze the collected data from multiple perspectives and provide detailed insights into the cost of goods.

[0032] The calculation unit calculates costs based on data analyzed by the analysis unit. For example, the calculation unit calculates the cost of a product based on the analysis results. Specifically, it integrates data such as logistics costs, storage costs, procurement prices, and exchange rates provided by the analysis unit to calculate the overall cost of the product. The calculation unit can perform cost calculations using generative AI. Generative AI uses algorithms that integrate multiple data points and perform complex calculations quickly and accurately. For example, generative AI weights different cost elements and applies the optimal cost calculation model. Furthermore, generative AI can handle real-time data updates and perform cost calculations that reflect the latest market conditions and exchange rate fluctuations. In addition, generative AI can perform simulations based on historical data to predict future cost fluctuations. As a result, the calculation unit can achieve accurate and reliable cost calculations, contributing to a company's cost management and pricing.

[0033] The Withholding Department withholds taxes on the profit margin between the cost price and the selling price calculated by the Calculation Department. Specifically, it withholds taxes based on the calculated profit margin between the cost price and the selling price. The Withholding Department can perform withholding using Generative AI. Generative AI uses algorithms that automate withholding calculations based on tax laws and regulations and calculate accurate tax amounts. For example, Generative AI takes into account the tax laws and regulations of each country and automatically calculates applicable tax rates and deductions. In addition, Generative AI can integrate sales data and expense data to calculate the overall tax amount. Furthermore, Generative AI automates the creation and submission of tax returns, supporting corporate tax compliance. This allows the Withholding Department to perform withholding efficiently and accurately, reducing the tax burden on companies. Moreover, the Withholding Department can also use Generative AI to assess tax risks and propose optimal tax strategies. This allows the Withholding Department to comprehensively support corporate tax management, minimizing tax risks and maximizing tax efficiency.

[0034] The data collection unit can collect data including the location information of a product, date and time, purchase price of the product, exchange rate, production cost of the product, and cost ratio. For example, the data collection unit can collect the location information of a product as GPS data. For example, the data collection unit can collect the date and time of a product as a timestamp. For example, the data collection unit can collect the purchase price of a product as market price. For example, the data collection unit can collect exchange rates as real-time data. For example, the data collection unit can collect the production cost of a product as material costs and labor costs. For example, the data collection unit can collect the cost ratio as the ratio of the cost to the selling price. This improves the accuracy of cost calculation by collecting detailed data on the product. Some or all of the above processing in the data collection unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the data collection unit can input GPS data into a generating AI and have the generating AI perform the collection of location information.

[0035] The collection unit can collect data from financial institution accounts and perform profit and loss offsetting. For example, the collection unit can collect data from bank accounts. For example, the collection unit can collect data from securities accounts. For example, the collection unit can collect profit and loss data in order to perform profit and loss offsetting. This makes it possible to accurately withhold taxes by collecting data from financial institution accounts and performing profit and loss offsetting. Some or all of the above processing in the collection unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the collection unit can input bank account data into a generating AI and have the generating AI perform data collection for profit and loss offsetting.

[0036] The data collection unit can change the types of data it collects based on the user's past purchase history. For example, the data collection unit can prioritize the collection of relevant data based on the categories of products the user has previously purchased. For example, the data collection unit can collect data related to products purchased in a particular season from the user's purchase history. For example, the data collection unit can analyze the user's purchase history and collect necessary data based on future purchasing trends. This allows for the collection of more relevant data by dynamically changing the types of data collected based on the user's past purchase history. Some or all of the above-described processes in the data collection unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the data collection unit can input the user's purchase history data into a generative AI and have the generative AI perform the change in the types of data to collect.

[0037] The data collection unit can change the level of detail of the data collected according to the product's lifecycle stage. For example, when a product is in the introduction phase, the data collection unit can collect detailed market research data. For example, when a product is in the growth phase, the data collection unit can focus on collecting sales data and customer feedback. For example, when a product is in the maturity phase, the data collection unit can collect competitor activity and price fluctuation data. By adjusting the level of detail of the data collected according to the product's lifecycle stage, appropriate data can be collected. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input product lifecycle stage data into a generative AI and have the generative AI adjust the level of detail of the data collection.

[0038] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit can prioritize the collection of market data related to that region. For example, if the user is traveling, the data collection unit can collect economic data and exchange rate information for the travel destination. For example, if the user is participating in a specific event, the data collection unit can collect data related to that event. By prioritizing the collection of highly relevant data while considering the user's geographical location information, more useful data can be collected. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI and have the generative AI perform the collection of highly relevant data.

[0039] The data collection unit can analyze a user's social media activity during data collection and collect data related to that activity. For example, if a user mentions a specific product on social media, the data collection unit can collect data related to that product. For example, the data collection unit can collect influential data based on the number of followers and engagement rate of a user's social media accounts. For example, the data collection unit can analyze trends in online communities that a user participates in and collect relevant data. By analyzing a user's social media activity and collecting relevant data, more accurate data can be collected. Some or all of the above processing in the data collection unit may be performed using generative AI, or not. For example, the data collection unit can input the user's social media data into a generative AI and have the generative AI collect the relevant data.

[0040] The analysis unit can evaluate the reliability of the collected data during analysis and prioritize the analysis of highly reliable data. For example, the analysis unit can verify the source of the data and prioritize the use of highly reliable data. For example, the analysis unit can evaluate the consistency of the data and prioritize the analysis of consistent data. For example, the analysis unit can verify the timeliness of the data and prioritize the use of the most recent data. By evaluating the reliability of the collected data and prioritizing the analysis of highly reliable data, the analysis unit can provide highly accurate analysis results. Some or all of the above processes in the analysis unit may be performed using generative AI, or they may be performed without generative AI. For example, the analysis unit can have generative AI perform the data reliability evaluation.

[0041] The analysis unit can apply different analysis algorithms depending on the product category during analysis. For example, in the case of electronic products, the analysis unit can use an analysis algorithm that takes into account technical specifications and market trends. For example, in the case of food products, the analysis unit can use an analysis algorithm that takes into account expiration dates and seasonality. For example, in the case of fashion items, the analysis unit can use an analysis algorithm that takes into account fashion trends and design trends. By applying different analysis algorithms depending on the product category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input product category data into a generative AI and have the generative AI execute the application of the analysis algorithm.

[0042] The analysis department can prioritize analyses based on the data submission timing. For example, the analysis department can prioritize analyzing the most recent data to provide real-time information. For example, the analysis department can prioritize analyzing regularly collected data to grasp trends. For example, the analysis department can prioritize analyzing data related to a specific event to evaluate the impact of the event. This allows for the provision of real-time information by prioritizing analyses based on the data submission timing. Some or all of the above processes in the analysis department may be performed using or without generative AI. For example, the analysis department can input the data submission timing into the generative AI and have the generative AI determine the analysis priorities.

[0043] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to provide highly accurate results. For example, the analysis unit can evaluate the correlation between data and prioritize the analysis of highly relevant data. For example, the analysis unit can evaluate the importance of data and prioritize the analysis of important data. By adjusting the order of analysis based on the relevance of the data, highly accurate analysis results can be provided. Some or all of the above processes in the analysis unit may be performed using generative AI, or they may be performed without generative AI. For example, the analysis unit can input the relevance of the data into the generative AI and have the generative AI perform the adjustment of the order of analysis.

[0044] The calculation unit can optimize its calculation algorithm by referring to past cost data during the calculation process. For example, the calculation unit can use a calculation algorithm that is tailored to current market conditions based on past cost data. For example, the calculation unit can use an algorithm that analyzes trends in past cost data and predicts future costs. For example, the calculation unit can use a calculation algorithm that minimizes risk by considering fluctuations in past cost data. By optimizing the calculation algorithm by referring to past cost data, it is possible to provide highly accurate cost calculation results. Some or all of the above-described processes in the calculation unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the calculation unit can input past cost data into a generating AI and have the generating AI perform the optimization of the calculation algorithm.

[0045] The calculation unit can adjust the level of detail in cost calculation according to the product's lifecycle stage. For example, if a product is in the introduction phase, the calculation unit can calculate the cost based on detailed market research data. For example, if a product is in the growth phase, the calculation unit can calculate the cost based on sales data and customer feedback. For example, if a product is in the maturity phase, the calculation unit can calculate the cost based on competitor trends and price fluctuation data. By adjusting the level of detail in cost calculation according to the product's lifecycle stage, the calculation unit can provide appropriate cost calculation results. Some or all of the above processing in the calculation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the calculation unit can input product lifecycle stage data into a generation AI and have the generation AI perform the adjustment of the level of detail in cost calculation.

[0046] The calculation unit can calculate the cost while considering the geographical distribution of the product. For example, if the product is popular in a particular region, the calculation unit can calculate the cost based on market data for that region. For example, if the product is sold in multiple regions, the calculation unit can calculate the cost while considering market data for each region. For example, if the product is imported or exported, the calculation unit can calculate the cost while considering customs duties and transportation costs. By calculating the cost while considering the geographical distribution of the product, it is possible to provide a cost that is appropriate to the market conditions of each region. Some or all of the above processing in the calculation unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the calculation unit can input geographical distribution data of the product into a generating AI and have the generating AI perform the cost calculation.

[0047] The calculation unit can improve the accuracy of cost calculation by referring to relevant literature on the product during the calculation process. For example, the calculation unit can improve the accuracy of cost calculation by referring to literature on the product's manufacturing process. For example, the calculation unit can improve the accuracy of cost calculation by referring to literature on the product's market trends. For example, the calculation unit can improve the accuracy of cost calculation by referring to literature on the product's technical specifications. By improving the accuracy of cost calculation by referring to relevant literature on the product, a more accurate cost can be provided. Some or all of the above processing in the calculation unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the calculation unit can input relevant literature data on the product into a generating AI and have the generating AI perform the cost calculation accuracy improvement.

[0048] The withholding tax department can optimize its withholding algorithm by referring to past withholding tax data during the withholding process. For example, the withholding tax department can use a withholding algorithm that is adapted to current market conditions based on past withholding tax data. For example, the withholding tax department can use an algorithm that analyzes trends in past withholding tax data and predicts future withholding amounts. For example, the withholding tax department can use a withholding algorithm that minimizes risk by considering fluctuations in past withholding tax data. By optimizing the withholding algorithm by referring to past withholding tax data, it is possible to provide highly accurate withholding. Some or all of the above processes in the withholding tax department may be performed using a generative AI, or they may not be performed using a generative AI. For example, the withholding tax department can input past withholding tax data into a generative AI and have the generative AI perform the optimization of the withholding algorithm.

[0049] The withholding tax department can adjust the level of detail of withholding tax according to the product's lifecycle stage. For example, if a product is in the introduction phase, the withholding tax department can calculate the amount to be withheld based on detailed market research data. If a product is in the growth phase, the withholding tax department can calculate the amount to be withheld based on sales data and customer feedback. If a product is in the maturity phase, the withholding tax department can calculate the amount to be withheld based on competitor trends and price fluctuation data. By adjusting the level of detail of withholding tax according to the product's lifecycle stage, appropriate withholding tax can be provided. Some or all of the above processing in the withholding tax department may be performed using a generating AI, or not. For example, the withholding tax department can input product lifecycle stage data into a generating AI and have the generating AI perform the adjustment of the level of detail of withholding tax.

[0050] The withholding tax department can collect taxes while considering the geographical distribution of the goods. For example, if a product is popular in a particular region, the withholding tax department can calculate the amount to be collected based on market data for that region. For example, if a product is sold in multiple regions, the withholding tax department can calculate the amount to be collected while considering market data for each region. For example, if a product is imported or exported, the withholding tax department can calculate the amount to be collected while considering customs duties and transportation costs. By collecting taxes while considering the geographical distribution of the goods, the withholding tax department can provide withholding taxes that are tailored to the market conditions of each region. Some or all of the above processing in the withholding tax department may be performed using a generating AI, or it may be performed without using a generating AI. For example, the withholding tax department can input geographical distribution data of the goods into a generating AI and have the generating AI perform the collection.

[0051] The withholding tax department can improve the accuracy of withholding by referring to relevant literature on the product. For example, the withholding tax department can improve the accuracy of withholding by referring to literature on the product's manufacturing process. For example, the withholding tax department can improve the accuracy of withholding by referring to literature on the product's market trends. For example, the withholding tax department can improve the accuracy of withholding by referring to literature on the product's technical specifications. By improving the accuracy of withholding by referring to relevant literature on the product, more accurate withholding can be provided. Some or all of the above processing in the withholding tax department may be performed using a generating AI, or not. For example, the withholding tax department can input relevant literature data on the product into a generating AI and have the generating AI perform the task of improving the accuracy of withholding.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The data collection unit can predict future purchasing trends based on the user's purchase history and determine the priority of data collection based on the predicted purchasing trends. For example, the data collection unit can analyze the categories of products the user has purchased in the past and prioritize the collection of relevant data. It can also collect data related to products purchased during specific seasons from the user's purchase history. Furthermore, the data collection unit can analyze the user's purchase history and collect necessary data based on future purchasing trends. This allows for the collection of more relevant data by dynamically changing the types of data collected based on the user's purchase history. Some or all of the above-described processes in the data collection unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the data collection unit can input the user's purchase history data into a generative AI and have the generative AI change the types of data to be collected.

[0054] The analysis unit can evaluate the reliability of the collected data and prioritize the analysis of reliable data. For example, the analysis unit can verify the source of the data and prioritize the use of reliable data. Furthermore, the analysis unit can evaluate the consistency of the data and prioritize the analysis of consistent data. In addition, the analysis unit can verify the timeliness of the data and prioritize the use of the most recent data. This allows for the provision of highly accurate analysis results by evaluating the reliability of the collected data and prioritizing the analysis of reliable data. Some or all of the above processes in the analysis unit may be performed using generative AI, or they may be performed without generative AI. For example, the analysis unit can have generative AI perform the data reliability evaluation.

[0055] The calculation unit can adjust the level of detail in cost calculation according to the product's lifecycle stage. For example, when a product is in the introduction phase, it can calculate the cost based on detailed market research data. When a product is in the growth phase, it can calculate the cost based on sales data and customer feedback. Furthermore, when a product is in the maturity phase, it can calculate the cost based on competitor trends and price fluctuation data. By adjusting the level of detail in cost calculation according to the product's lifecycle stage, appropriate cost calculation results can be provided. Some or all of the above processing in the calculation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the calculation unit can input product lifecycle stage data into a generation AI and have the generation AI perform the adjustment of the level of detail in cost calculation.

[0056] The withholding tax department can collect taxes while considering the geographical distribution of the goods. For example, if a product is popular in a particular region, the amount to be collected can be calculated based on market data from that region. Also, if a product is sold in multiple regions, the amount to be collected can be calculated while considering market data from each region. Furthermore, if the goods are imported or exported, the amount to be collected can be calculated while considering customs duties and transportation costs. In this way, by collecting taxes while considering the geographical distribution of the goods, it is possible to provide withholding taxes that are tailored to the market conditions of each region. Some or all of the above processing in the withholding tax department may be performed using a generating AI, or it may be performed without using a generating AI. For example, the withholding tax department can input geographical distribution data of the goods into a generating AI and have the generating AI perform the collection.

[0057] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is in a specific region, it can prioritize the collection of market data related to that region. Also, if the user is traveling, it can collect economic data and exchange rate information for the travel destination. Furthermore, if the user is participating in a specific event, it can collect data related to that event. In this way, more useful data can be collected by prioritizing the collection of highly relevant data while considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI and have the generative AI perform the collection of highly relevant data.

[0058] The analysis unit can apply different analysis algorithms depending on the product category during analysis. For example, for electronic products, an analysis algorithm that takes into account technical specifications and market trends can be used. For food products, an analysis algorithm that takes into account expiration dates and seasonality can be used. Furthermore, for fashion items, an analysis algorithm that takes into account fashion trends and design trends can be used. By applying different analysis algorithms depending on the product category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input product category data into a generative AI and have the generative AI execute the application of the analysis algorithm.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The data collection unit collects data. For example, the data collection unit collects data such as the location of the product, the date and time, the purchase price of the product, the exchange rate, the cost of production of the product, and the cost ratio. The data collection unit can automatically collect this data using generation AI. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the cost of goods based on the collected data. The analysis unit can perform data analysis using generative AI. Step 3: The calculation unit calculates the cost based on the data analyzed by the analysis unit. For example, the calculation unit calculates the cost of a product based on the analysis results. The calculation unit can use generating AI to calculate the cost. Step 4: The withholding tax unit withholds tax on the profit margin between the cost price and the selling price calculated by the calculation unit. The withholding tax unit withholds tax based, for example, on the profit margin between the calculated cost price and the selling price. The withholding tax unit can perform withholding tax using a generating AI.

[0061] (Example of form 2) The system according to an embodiment of the present invention is a system that withholds taxes on profits, including those from the sale of goods, by linking with a cloud POS register utilizing My Number, a financial account, and a trading application such as a flea market app. This system collects data on sales of goods conducted through the cloud POS register, flea market app, etc. The collected data includes the location information of the product, the date and time, the purchase price of the product, the exchange rate, the cost of production of the product, and the cost ratio. This data is collected and analyzed by a generating AI to calculate the cost of the product. Next, profit and loss offsetting is performed in the financial account. Based on the financial account data, the generating AI calculates the cost associated with the provision of goods or services in transactions where there is no "buy" data or in personal transactions. Withholding tax is applied to the profit difference between this calculated cost and the selling price. Furthermore, income tax is withheld on all financial transactions related to the individual. This eliminates the need for filing a tax return and simplifies the tax processing for individuals. For example, when selling an item on a flea market app, data such as the item's location, date and time, purchase price, exchange rate, production cost, and cost ratio are collected and analyzed by a generating AI to calculate the item's cost. Next, profits and losses are offset based on data from financial accounts, and withholding tax is applied to the profit difference between the sale price and the cost. In cases where there is no "buy" data in the financial institution account, or when the cost associated with the provision of goods or services in a personal transaction is collected from the market by the generating AI, a simplified cost is calculated. Withholding tax is applied to the profit difference between this calculated cost and the selling price. This system simplifies individual tax processing and eliminates the need for filing a tax return. Furthermore, by creating accounting records and calculating costs precisely before filing a tax return, it is possible to receive a refund of overpaid taxes. In summary, the system simplifies individual tax processing and eliminates the need for filing a tax return.

[0062] The system according to this embodiment comprises a data collection unit, an analysis unit, a calculation unit, and a withholding tax unit. The data collection unit collects data. For example, the data collection unit collects data such as the location information of the product, the date and time, the purchase price of the product, the exchange rate, the cost of production of the product, and the cost ratio. The data collection unit can automatically collect this data using a generation AI. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the cost of the product based on the collected data. The analysis unit can perform data analysis using a generation AI. The calculation unit calculates the cost based on the data analyzed by the analysis unit. For example, the calculation unit calculates the cost of the product based on the analysis results. The calculation unit can perform cost calculation using a generation AI. The withholding tax unit withholds tax on the profit margin between the cost and the selling price calculated by the calculation unit. For example, the withholding tax unit withholds tax based on the profit margin between the calculated cost and the selling price. The withholding tax unit can perform withholding tax using a generation AI. As a result, the system according to this embodiment can efficiently perform data collection, analysis, cost calculation, and withholding tax.

[0063] The data collection unit collects data such as product location, date and time, product purchase price, exchange rate, product manufacturing cost, and cost ratio. Specifically, product location information is acquired in real time using GPS devices, and date and time information is recorded as a timestamp. Product purchase prices are obtained from online marketplaces and exchanges via APIs, and exchange rates are collected using data feeds from financial institutions and foreign exchange exchanges. Product manufacturing costs include detailed cost data such as material costs, labor costs, and equipment costs used in the manufacturing process. The cost ratio is calculated based on this cost data, and the data collection unit centrally manages this data. The data collection unit can automatically collect this data using generative AI. Generative AI automates the data collection process from various data sources and performs filtering and preprocessing to maintain data integrity and consistency. For example, generative AI uses algorithms to unify data in different formats and fill in missing data. Generative AI also optimizes the frequency and timing of data collection, enabling real-time data updates. This allows the data collection unit to efficiently and accurately collect diverse data, thereby strengthening the data infrastructure of the entire system.

[0064] The analysis department analyzes the data collected by the data collection department. For example, the analysis department analyzes the cost of goods based on the collected data. Specifically, it calculates logistics costs and storage costs based on the location and date / time information of the goods, and evaluates the overall cost by considering the purchase price and exchange rate of the goods. The analysis department can perform data analysis using generative AI. Generative AI uses machine learning algorithms to extract patterns and trends from the collected data and identify factors that affect the cost of goods. For example, generative AI builds a cost forecasting model that takes into account seasonal fluctuations and the balance of supply and demand in the market, based on historical data. In addition, generative AI uses anomaly detection algorithms to detect outliers and inconsistencies hidden in the data, improving the accuracy of the analysis results. Furthermore, generative AI uses data visualization tools to display the analysis results as graphs and charts, making them intuitively understandable to the user. This allows the analysis department to analyze the collected data from multiple perspectives and provide detailed insights into the cost of goods.

[0065] The calculation unit calculates costs based on data analyzed by the analysis unit. For example, the calculation unit calculates the cost of a product based on the analysis results. Specifically, it integrates data such as logistics costs, storage costs, procurement prices, and exchange rates provided by the analysis unit to calculate the overall cost of the product. The calculation unit can perform cost calculations using generative AI. Generative AI uses algorithms that integrate multiple data points and perform complex calculations quickly and accurately. For example, generative AI weights different cost elements and applies the optimal cost calculation model. Furthermore, generative AI can handle real-time data updates and perform cost calculations that reflect the latest market conditions and exchange rate fluctuations. In addition, generative AI can perform simulations based on historical data to predict future cost fluctuations. As a result, the calculation unit can achieve accurate and reliable cost calculations, contributing to a company's cost management and pricing.

[0066] The Withholding Department withholds taxes on the profit margin between the cost price and the selling price calculated by the Calculation Department. Specifically, it withholds taxes based on the calculated profit margin between the cost price and the selling price. The Withholding Department can perform withholding using Generative AI. Generative AI uses algorithms that automate withholding calculations based on tax laws and regulations and calculate accurate tax amounts. For example, Generative AI takes into account the tax laws and regulations of each country and automatically calculates applicable tax rates and deductions. In addition, Generative AI can integrate sales data and expense data to calculate the overall tax amount. Furthermore, Generative AI automates the creation and submission of tax returns, supporting corporate tax compliance. This allows the Withholding Department to perform withholding efficiently and accurately, reducing the tax burden on companies. Moreover, the Withholding Department can also use Generative AI to assess tax risks and propose optimal tax strategies. This allows the Withholding Department to comprehensively support corporate tax management, minimizing tax risks and maximizing tax efficiency.

[0067] The data collection unit can collect data including the location information of a product, date and time, purchase price of the product, exchange rate, production cost of the product, and cost ratio. For example, the data collection unit can collect the location information of a product as GPS data. For example, the data collection unit can collect the date and time of a product as a timestamp. For example, the data collection unit can collect the purchase price of a product as market price. For example, the data collection unit can collect exchange rates as real-time data. For example, the data collection unit can collect the production cost of a product as material costs and labor costs. For example, the data collection unit can collect the cost ratio as the ratio of the cost to the selling price. This improves the accuracy of cost calculation by collecting detailed data on the product. Some or all of the above processing in the data collection unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the data collection unit can input GPS data into a generating AI and have the generating AI perform the collection of location information.

[0068] The collection unit can collect data from financial institution accounts and perform profit and loss offsetting. For example, the collection unit can collect data from bank accounts. For example, the collection unit can collect data from securities accounts. For example, the collection unit can collect profit and loss data in order to perform profit and loss offsetting. This makes it possible to accurately withhold taxes by collecting data from financial institution accounts and performing profit and loss offsetting. Some or all of the above processing in the collection unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the collection unit can input bank account data into a generating AI and have the generating AI perform data collection for profit and loss offsetting.

[0069] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to lessen the user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of collection to collect more detailed data. For example, if the user is in a hurry, the data collection unit can quickly collect only the minimum necessary data. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using or without a generative AI. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0070] The data collection unit can change the types of data it collects based on the user's past purchase history. For example, the data collection unit can prioritize the collection of relevant data based on the categories of products the user has previously purchased. For example, the data collection unit can collect data related to products purchased in a particular season from the user's purchase history. For example, the data collection unit can analyze the user's purchase history and collect necessary data based on future purchasing trends. This allows for the collection of more relevant data by dynamically changing the types of data collected based on the user's past purchase history. Some or all of the above-described processes in the data collection unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the data collection unit can input the user's purchase history data into a generative AI and have the generative AI perform the change in the types of data to collect.

[0071] The data collection unit can change the level of detail of the data collected according to the product's lifecycle stage. For example, when a product is in the introduction phase, the data collection unit can collect detailed market research data. For example, when a product is in the growth phase, the data collection unit can focus on collecting sales data and customer feedback. For example, when a product is in the maturity phase, the data collection unit can collect competitor activity and price fluctuation data. By adjusting the level of detail of the data collected according to the product's lifecycle stage, appropriate data can be collected. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input product lifecycle stage data into a generative AI and have the generative AI adjust the level of detail of the data collection.

[0072] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit can prioritize collecting only high-priority data. For example, if the user is relaxed, the data collection unit can prioritize collecting detailed data. For example, if the user is in a hurry, the data collection unit can prioritize data that can be collected quickly. In this way, important data can be collected preferentially by determining the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using or without a generative AI. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0073] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit can prioritize the collection of market data related to that region. For example, if the user is traveling, the data collection unit can collect economic data and exchange rate information for the travel destination. For example, if the user is participating in a specific event, the data collection unit can collect data related to that event. By prioritizing the collection of highly relevant data while considering the user's geographical location information, more useful data can be collected. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI and have the generative AI perform the collection of highly relevant data.

[0074] The data collection unit can analyze a user's social media activity during data collection and collect data related to that activity. For example, if a user mentions a specific product on social media, the data collection unit can collect data related to that product. For example, the data collection unit can collect influential data based on the number of followers and engagement rate of a user's social media accounts. For example, the data collection unit can analyze trends in online communities that a user participates in and collect relevant data. By analyzing a user's social media activity and collecting relevant data, more accurate data can be collected. Some or all of the above processing in the data collection unit may be performed using generative AI, or not. For example, the data collection unit can input the user's social media data into a generative AI and have the generative AI collect the relevant data.

[0075] The analysis unit can estimate the user's emotions and adjust the analysis method based on those emotions. For example, if the user is stressed, the analysis unit can provide a concise and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit can use a simplified analysis method to produce results quickly. This allows the analysis unit to provide the user with the most optimal analysis result by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without generative AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0076] The analysis unit can evaluate the reliability of the collected data during analysis and prioritize the analysis of highly reliable data. For example, the analysis unit can verify the source of the data and prioritize the use of highly reliable data. For example, the analysis unit can evaluate the consistency of the data and prioritize the analysis of consistent data. For example, the analysis unit can verify the timeliness of the data and prioritize the use of the most recent data. By evaluating the reliability of the collected data and prioritizing the analysis of highly reliable data, the analysis unit can provide highly accurate analysis results. Some or all of the above processes in the analysis unit may be performed using generative AI, or they may be performed without generative AI. For example, the analysis unit can have generative AI perform the data reliability evaluation.

[0077] The analysis unit can apply different analysis algorithms depending on the product category during analysis. For example, in the case of electronic products, the analysis unit can use an analysis algorithm that takes into account technical specifications and market trends. For example, in the case of food products, the analysis unit can use an analysis algorithm that takes into account expiration dates and seasonality. For example, in the case of fashion items, the analysis unit can use an analysis algorithm that takes into account fashion trends and design trends. By applying different analysis algorithms depending on the product category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input product category data into a generative AI and have the generative AI execute the application of the analysis algorithm.

[0078] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on those emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results according to the user's emotions, the optimal display method can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0079] The analysis department can prioritize analyses based on the data submission timing. For example, the analysis department can prioritize analyzing the most recent data to provide real-time information. For example, the analysis department can prioritize analyzing regularly collected data to grasp trends. For example, the analysis department can prioritize analyzing data related to a specific event to evaluate the impact of the event. This allows for the provision of real-time information by prioritizing analyses based on the data submission timing. Some or all of the above processes in the analysis department may be performed using or without generative AI. For example, the analysis department can input the data submission timing into the generative AI and have the generative AI determine the analysis priorities.

[0080] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to provide highly accurate results. For example, the analysis unit can evaluate the correlation between data and prioritize the analysis of highly relevant data. For example, the analysis unit can evaluate the importance of data and prioritize the analysis of important data. By adjusting the order of analysis based on the relevance of the data, highly accurate analysis results can be provided. Some or all of the above processes in the analysis unit may be performed using generative AI, or they may be performed without generative AI. For example, the analysis unit can input the relevance of the data into the generative AI and have the generative AI perform the adjustment of the order of analysis.

[0081] The calculation unit can estimate the user's emotions and adjust the cost calculation method based on those emotions. For example, if the user is stressed, the calculation unit can use a simple and easy-to-understand cost calculation method. For example, if the user is relaxed, the calculation unit can use a detailed cost calculation method. For example, if the user is in a hurry, the calculation unit can use a simplified cost calculation method to produce results quickly. By adjusting the cost calculation method according to the user's emotions, the calculation unit can provide the user with the optimal cost calculation result. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the calculation unit may be performed using or without a generative AI. For example, the calculation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0082] The calculation unit can optimize its calculation algorithm by referring to past cost data during the calculation process. For example, the calculation unit can use a calculation algorithm that is tailored to current market conditions based on past cost data. For example, the calculation unit can use an algorithm that analyzes trends in past cost data and predicts future costs. For example, the calculation unit can use a calculation algorithm that minimizes risk by considering fluctuations in past cost data. By optimizing the calculation algorithm by referring to past cost data, it is possible to provide highly accurate cost calculation results. Some or all of the above-described processes in the calculation unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the calculation unit can input past cost data into a generating AI and have the generating AI perform the optimization of the calculation algorithm.

[0083] The calculation unit can adjust the level of detail in cost calculation according to the product's lifecycle stage. For example, if a product is in the introduction phase, the calculation unit can calculate the cost based on detailed market research data. For example, if a product is in the growth phase, the calculation unit can calculate the cost based on sales data and customer feedback. For example, if a product is in the maturity phase, the calculation unit can calculate the cost based on competitor trends and price fluctuation data. By adjusting the level of detail in cost calculation according to the product's lifecycle stage, the calculation unit can provide appropriate cost calculation results. Some or all of the above processing in the calculation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the calculation unit can input product lifecycle stage data into a generation AI and have the generation AI perform the adjustment of the level of detail in cost calculation.

[0084] The calculation unit can estimate the user's emotions and determine the priority of cost calculation based on those emotions. For example, if the user is stressed, the calculation unit can prioritize calculating the cost of high-priority items. For example, if the user is relaxed, the calculation unit can perform a detailed cost calculation. For example, if the user is in a hurry, the calculation unit can prioritize calculating the cost of items that can be calculated quickly. In this way, by determining the priority of cost calculation according to the user's emotions, the cost of important items can be calculated preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the calculation unit may be performed using or without a generative AI. For example, the calculation unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0085] The calculation unit can calculate the cost while considering the geographical distribution of the product. For example, if the product is popular in a particular region, the calculation unit can calculate the cost based on market data for that region. For example, if the product is sold in multiple regions, the calculation unit can calculate the cost while considering market data for each region. For example, if the product is imported or exported, the calculation unit can calculate the cost while considering customs duties and transportation costs. By calculating the cost while considering the geographical distribution of the product, it is possible to provide a cost that is appropriate to the market conditions of each region. Some or all of the above processing in the calculation unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the calculation unit can input geographical distribution data of the product into a generating AI and have the generating AI perform the cost calculation.

[0086] The calculation unit can improve the accuracy of cost calculation by referring to relevant literature on the product during the calculation process. For example, the calculation unit can improve the accuracy of cost calculation by referring to literature on the product's manufacturing process. For example, the calculation unit can improve the accuracy of cost calculation by referring to literature on the product's market trends. For example, the calculation unit can improve the accuracy of cost calculation by referring to literature on the product's technical specifications. By improving the accuracy of cost calculation by referring to relevant literature on the product, a more accurate cost can be provided. Some or all of the above processing in the calculation unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the calculation unit can input relevant literature data on the product into a generating AI and have the generating AI perform the cost calculation accuracy improvement.

[0087] The withholding unit can estimate the user's emotions and adjust the withholding method based on those emotions. For example, if the user is stressed, the withholding unit can use a simple and easy-to-understand withholding method. For example, if the user is relaxed, the withholding unit can use a detailed withholding method. For example, if the user is in a hurry, the withholding unit can use a simplified withholding method to produce results quickly. This allows the withholding unit to provide the user with the optimal withholding method by adjusting the withholding method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the withholding unit may be performed using or without a generative AI. For example, the withholding unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0088] The withholding tax department can optimize its withholding algorithm by referring to past withholding tax data during the withholding process. For example, the withholding tax department can use a withholding algorithm that is adapted to current market conditions based on past withholding tax data. For example, the withholding tax department can use an algorithm that analyzes trends in past withholding tax data and predicts future withholding amounts. For example, the withholding tax department can use a withholding algorithm that minimizes risk by considering fluctuations in past withholding tax data. By optimizing the withholding algorithm by referring to past withholding tax data, it is possible to provide highly accurate withholding. Some or all of the above processes in the withholding tax department may be performed using a generative AI, or they may not be performed using a generative AI. For example, the withholding tax department can input past withholding tax data into a generative AI and have the generative AI perform the optimization of the withholding algorithm.

[0089] The withholding tax department can adjust the level of detail of withholding tax according to the product's lifecycle stage. For example, if a product is in the introduction phase, the withholding tax department can calculate the amount to be withheld based on detailed market research data. If a product is in the growth phase, the withholding tax department can calculate the amount to be withheld based on sales data and customer feedback. If a product is in the maturity phase, the withholding tax department can calculate the amount to be withheld based on competitor trends and price fluctuation data. By adjusting the level of detail of withholding tax according to the product's lifecycle stage, appropriate withholding tax can be provided. Some or all of the above processing in the withholding tax department may be performed using a generating AI, or not. For example, the withholding tax department can input product lifecycle stage data into a generating AI and have the generating AI perform the adjustment of the level of detail of withholding tax.

[0090] The withholding unit can estimate the user's emotions and determine the priority of withholding based on those emotions. For example, if the user is stressed, the withholding unit can prioritize withholding high-priority items. For example, if the user is relaxed, the withholding unit can perform detailed withholding. For example, if the user is in a hurry, the withholding unit can prioritize withholding items that can be withheld quickly. In this way, by determining the priority of withholding according to the user's emotions, the withholding of important items can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the withholding unit may be performed using or without a generative AI. For example, the withholding unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0091] The withholding tax department can collect taxes while considering the geographical distribution of the goods. For example, if a product is popular in a particular region, the withholding tax department can calculate the amount to be collected based on market data for that region. For example, if a product is sold in multiple regions, the withholding tax department can calculate the amount to be collected while considering market data for each region. For example, if a product is imported or exported, the withholding tax department can calculate the amount to be collected while considering customs duties and transportation costs. By collecting taxes while considering the geographical distribution of the goods, the withholding tax department can provide withholding taxes that are tailored to the market conditions of each region. Some or all of the above processing in the withholding tax department may be performed using a generating AI, or it may be performed without using a generating AI. For example, the withholding tax department can input geographical distribution data of the goods into a generating AI and have the generating AI perform the collection.

[0092] The withholding tax department can improve the accuracy of withholding by referring to relevant literature on the product. For example, the withholding tax department can improve the accuracy of withholding by referring to literature on the product's manufacturing process. For example, the withholding tax department can improve the accuracy of withholding by referring to literature on the product's market trends. For example, the withholding tax department can improve the accuracy of withholding by referring to literature on the product's technical specifications. By improving the accuracy of withholding by referring to relevant literature on the product, more accurate withholding can be provided. Some or all of the above processing in the withholding tax department may be performed using a generating AI, or not. For example, the withholding tax department can input relevant literature data on the product into a generating AI and have the generating AI perform the task of improving the accuracy of withholding.

[0093] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0094] The data collection unit can predict future purchasing trends based on the user's purchase history and determine the priority of data collection based on the predicted purchasing trends. For example, the data collection unit can analyze the categories of products the user has purchased in the past and prioritize the collection of relevant data. It can also collect data related to products purchased during specific seasons from the user's purchase history. Furthermore, the data collection unit can analyze the user's purchase history and collect necessary data based on future purchasing trends. This allows for the collection of more relevant data by dynamically changing the types of data collected based on the user's purchase history. Some or all of the above-described processes in the data collection unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the data collection unit can input the user's purchase history data into a generative AI and have the generative AI change the types of data to be collected.

[0095] The analysis unit can evaluate the reliability of the collected data and prioritize the analysis of reliable data. For example, the analysis unit can verify the source of the data and prioritize the use of reliable data. Furthermore, the analysis unit can evaluate the consistency of the data and prioritize the analysis of consistent data. In addition, the analysis unit can verify the timeliness of the data and prioritize the use of the most recent data. This allows for the provision of highly accurate analysis results by evaluating the reliability of the collected data and prioritizing the analysis of reliable data. Some or all of the above processes in the analysis unit may be performed using generative AI, or they may be performed without generative AI. For example, the analysis unit can have generative AI perform the data reliability evaluation.

[0096] The calculation unit can adjust the level of detail in cost calculation according to the product's lifecycle stage. For example, when a product is in the introduction phase, it can calculate the cost based on detailed market research data. When a product is in the growth phase, it can calculate the cost based on sales data and customer feedback. Furthermore, when a product is in the maturity phase, it can calculate the cost based on competitor trends and price fluctuation data. By adjusting the level of detail in cost calculation according to the product's lifecycle stage, appropriate cost calculation results can be provided. Some or all of the above processing in the calculation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the calculation unit can input product lifecycle stage data into a generation AI and have the generation AI perform the adjustment of the level of detail in cost calculation.

[0097] The withholding tax department can collect taxes while considering the geographical distribution of the goods. For example, if a product is popular in a particular region, the amount to be collected can be calculated based on market data from that region. Also, if a product is sold in multiple regions, the amount to be collected can be calculated while considering market data from each region. Furthermore, if the goods are imported or exported, the amount to be collected can be calculated while considering customs duties and transportation costs. In this way, by collecting taxes while considering the geographical distribution of the goods, it is possible to provide withholding taxes that are tailored to the market conditions of each region. Some or all of the above processing in the withholding tax department may be performed using a generating AI, or it may be performed without using a generating AI. For example, the withholding tax department can input geographical distribution data of the goods into a generating AI and have the generating AI perform the collection.

[0098] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is stressed, the frequency of data collection can be reduced to lessen the user's burden. Conversely, if the user is relaxed, the frequency of collection can be increased to collect more detailed data. Furthermore, if the user is in a hurry, only the minimum necessary data can be quickly collected. In this way, the user's burden can be reduced by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using or without a generative AI. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0099] The analysis unit can estimate the user's emotions and adjust the analysis method based on those emotions. For example, if the user is stressed, it can provide a concise and easy-to-understand analysis result. If the user is relaxed, it can provide a detailed analysis result. Furthermore, if the user is in a hurry, a simplified analysis method can be used to quickly produce results. In this way, by adjusting the analysis method according to the user's emotions, the optimal analysis result can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using generative AI or not. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0100] The calculation unit can estimate the user's emotions and adjust the cost calculation method based on those emotions. For example, if the user is stressed, a simple and easy-to-understand cost calculation method can be used. If the user is relaxed, a detailed cost calculation method can be used. Furthermore, if the user is in a hurry, a simplified cost calculation method can be used to quickly produce results. In this way, by adjusting the cost calculation method according to the user's emotions, the optimal cost calculation result can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the calculation unit may be performed using or without a generative AI. For example, the calculation unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0101] The withholding unit can estimate the user's emotions and adjust the withholding method based on those emotions. For example, if the user is stressed, a simple and easy-to-understand withholding method can be used. If the user is relaxed, a detailed withholding method can be used. Furthermore, if the user is in a hurry, a simplified withholding method can be used to quickly produce results. In this way, by adjusting the withholding method according to the user's emotions, the optimal withholding method can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the withholding unit may be performed using or without a generative AI. For example, the withholding unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0102] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is in a specific region, it can prioritize the collection of market data related to that region. Also, if the user is traveling, it can collect economic data and exchange rate information for the travel destination. Furthermore, if the user is participating in a specific event, it can collect data related to that event. In this way, more useful data can be collected by prioritizing the collection of highly relevant data while considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI and have the generative AI perform the collection of highly relevant data.

[0103] The analysis unit can apply different analysis algorithms depending on the product category during analysis. For example, for electronic products, an analysis algorithm that takes into account technical specifications and market trends can be used. For food products, an analysis algorithm that takes into account expiration dates and seasonality can be used. Furthermore, for fashion items, an analysis algorithm that takes into account fashion trends and design trends can be used. By applying different analysis algorithms depending on the product category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input product category data into a generative AI and have the generative AI execute the application of the analysis algorithm.

[0104] The following briefly describes the processing flow for example form 2.

[0105] Step 1: The data collection unit collects data. For example, the data collection unit collects data such as the location of the product, the date and time, the purchase price of the product, the exchange rate, the cost of production of the product, and the cost ratio. The data collection unit can automatically collect this data using generation AI. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the cost of goods based on the collected data. The analysis unit can perform data analysis using generative AI. Step 3: The calculation unit calculates the cost based on the data analyzed by the analysis unit. For example, the calculation unit calculates the cost of a product based on the analysis results. The calculation unit can use generating AI to calculate the cost. Step 4: The withholding tax unit withholds tax on the profit margin between the cost price and the selling price calculated by the calculation unit. The withholding tax unit withholds tax based, for example, on the profit margin between the calculated cost price and the selling price. The withholding tax unit can perform withholding tax using a generating AI.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] For example, the data collection unit can collect data such as the location information and date / time of the product using the camera 42 and communication I / F 44 of the smart device 14. The data collection unit can also be implemented by the specific processing unit 290 of the data processing device 12, which automatically collects data using generating AI. The analysis unit analyzes the data collected by the control unit 46A of the smart device 14 and calculates the cost of the product. The analysis unit can also be implemented by the specific processing unit 290 of the data processing device 12, which performs data analysis using generating AI. The calculation unit calculates the cost of the product based on the analysis results by the specific processing unit 290 of the data processing device 12. The withholding tax unit withholds taxes based on the profit margin between the cost and the selling price calculated by the specific processing unit 290 of the data processing device 12. The withholding tax unit can perform withholding using generating AI. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0110] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] For example, the data collection unit can collect data such as the location information and date / time of the product using the camera 42 and communication I / F 44 of the smart glasses 214. The data collection unit can also be implemented by the specific processing unit 290 of the data processing device 12, which automatically collects data using generating AI. The analysis unit can analyze the data collected by the control unit 46A of the smart glasses 214 and calculate the cost of the product. The analysis unit can also be implemented by the specific processing unit 290 of the data processing device 12, which performs data analysis using generating AI. The calculation unit can calculate the cost of the product based on the analysis results by the specific processing unit 290 of the data processing device 12. The withholding tax unit can withhold taxes based on the profit margin between the cost and the selling price calculated by the specific processing unit 290 of the data processing device 12. The withholding tax unit can perform withholding tax using generating AI. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0126] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] For example, the data collection unit can collect data such as the location information and date / time of products using the camera 42 and communication I / F 44 of the headset terminal 314. The data collection unit can also be implemented by the specific processing unit 290 of the data processing device 12, which automatically collects data using generation AI. The analysis unit analyzes the data collected by the control unit 46A of the headset terminal 314 and calculates the cost of the products. The analysis unit can also be implemented by the specific processing unit 290 of the data processing device 12, which performs data analysis using generation AI. The calculation unit calculates the cost of the products based on the analysis results by the specific processing unit 290 of the data processing device 12. The withholding tax unit withholds taxes based on the profit margin between the cost and the selling price calculated by the specific processing unit 290 of the data processing device 12. The withholding tax unit can perform withholding tax using generation AI. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0142] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] For example, the data collection unit can collect data such as the location information and date / time of products using the camera 42 and communication I / F 44 of the robot 414. The data collection unit can also be implemented by the specific processing unit 290 of the data processing device 12, which automatically collects data using generating AI. The analysis unit can, for example, analyze the data collected by the control unit 46A of the robot 414 and calculate the cost of the products. The analysis unit can also be implemented by the specific processing unit 290 of the data processing device 12, which performs data analysis using generating AI. The calculation unit can, for example, calculate the cost of the products based on the analysis results using the specific processing unit 290 of the data processing device 12. The withholding tax unit can, for example, withhold taxes based on the profit margin between the cost and the selling price calculated by the specific processing unit 290 of the data processing device 12. The withholding tax unit can perform withholding tax using generating AI. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A calculation unit that calculates the cost based on the data analyzed by the aforementioned analysis unit, The system includes a withholding tax unit that withholds tax from the profit margin between the cost and the selling price calculated by the calculation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system collects data including the location of the product, the date and time, the product's purchase price, the exchange rate, the product's manufacturing cost, and the cost ratio. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is We collect data from financial institution accounts and perform profit and loss offsetting. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Change the types of data collected based on the user's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is During data collection, the level of detail of the data collected can be changed according to the product's lifecycle stage. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, the system prioritizes collecting data that is highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects data related to that activity. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis method based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is During analysis, the reliability of the collected data is evaluated, and the most reliable data is prioritized for analysis. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The calculation unit described above, We estimate the user's emotions and adjust the cost calculation method based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The calculation unit described above, During calculation, the calculation algorithm is optimized by referring to past cost data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The calculation unit described above, When calculating costs, adjust the level of detail in the cost calculation according to the product's lifecycle stage. The system described in Appendix 1, characterized by the features described herein. (Note 19) The calculation unit described above, The system estimates user emotions and determines cost calculation priorities based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The calculation unit described above, When calculating the cost, the geographical distribution of the product is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The calculation unit described above, When calculating costs, we refer to relevant literature on the product to improve the accuracy of cost calculation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned withholding tax department, The system estimates the user's emotions and adjusts the withholding tax method based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned withholding tax department, When withholding tax, the collection algorithm is optimized by referring to past withholding tax data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned withholding tax department, When withholding tax, adjust the level of detail of the withholding tax according to the product's lifecycle stage. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned withholding tax department, The system estimates the user's emotions and determines the priority of withholding tax based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned withholding tax department, When withholding tax, the geographical distribution of the goods should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned withholding tax department, When withholding taxes, refer to relevant literature on the product to improve the accuracy of the collection. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0178] 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 data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A calculation unit that calculates the cost based on the data analyzed by the aforementioned analysis unit, The system includes a withholding tax unit that withholds tax from the profit margin between the cost and the selling price calculated by the calculation unit. A system characterized by the following features.

2. The aforementioned collection unit is The system collects data including the location of the product, the date and time, the product's purchase price, the exchange rate, the product's manufacturing cost, and the cost ratio. The system according to feature 1.

3. The aforementioned collection unit is We collect data from financial institution accounts and perform profit and loss offsetting. The system according to feature 1.

4. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those emotions. The system according to feature 1.

5. The aforementioned collection unit is Change the types of data collected based on the user's past purchase history. The system according to feature 1.

6. The aforementioned collection unit is During data collection, the level of detail of the data collected can be changed according to the product's lifecycle stage. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is During data collection, the system prioritizes collecting data that is highly relevant based on the user's geographical location. The system according to feature 1.

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