system

Abstract

The system according to this embodiment aims to efficiently collect and organize negotiation materials based on past transaction records. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a conversion unit. The collection unit collects past transaction records. The analysis unit analyzes the transaction records collected by the collection unit. The conversion unit converts the transaction records analyzed by the analysis unit into current prices.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is time-consuming to collect and organize negotiation materials based on past transaction records, and it is difficult to conduct efficient price negotiations.

[0005] The system according to the embodiment aims to efficiently collect and organize negotiation materials based on past transaction records.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a conversion unit. The collection unit collects past transaction records. The analysis unit analyzes the transaction records collected by the collection unit. The conversion unit converts the transaction records analyzed by the analysis unit into current prices.

Effects of the Invention

[0007] The system according to this embodiment can efficiently collect and organize negotiation materials based on past transaction records. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An agent AI system according to an embodiment of the present invention is a system for collecting negotiation materials for procurement negotiations conducted daily in the purchasing department. This agent AI system reads electronic files (e.g., PDF files) of past purchase orders and quotations and finds transactions that are the same as or similar to the goods being traded in the current transaction. The agent AI system collects and organizes the prices and conditions of the transactions it finds. Furthermore, the agent AI system compares the prices and exchange rates at the time and converts them to current prices to derive a fair price. This mechanism allows for the collection and organization of negotiation materials for all items, ensuring that the strongest negotiation materials are available for all price negotiations, regardless of the quantity or amount. This improves the negotiating power of all buyers and can achieve an average cost reduction of 3-5% per year. For example, the agent AI system picks out similar transaction records from a vast number of purchase orders and quotations from the past several years. For example, even if the item, model number, or transaction conditions are not registered, the agent AI system can read the amounts from multiple purchase orders and quotations and pick out only what is necessary. The agent AI system collects and organizes the prices and conditions of the transactions it finds. For example, even if a single transaction involves dozens of items, the agent AI system can gather all the necessary data. This allows buyers to quickly and easily gather bargaining chips. The agent AI system compares prices and exchange rates at the time and converts them to current prices. For example, it can derive the optimal current transaction price based on past transaction data. This enables buyers to conduct transactions at fair prices. As a result, bargaining chips can be collected and organized for all items, providing the strongest bargaining chips for all price negotiations, regardless of quantity or value. This improves the bargaining power of all buyers and can result in an average annual cost reduction of 3-5%. For example, companies with a large volume of transactions can expect even greater effects. In this way, the agent AI system can provide the strongest bargaining chips for daily procurement negotiations in the purchasing department.

[0029] The agent AI system according to this embodiment comprises a collection unit, an analysis unit, and a conversion unit. The collection unit collects past transaction records. For example, the collection unit reads electronic files (e.g., PDF files) of past purchase orders and quotations and finds transactions that are the same as or similar to the goods subject to the current transaction. The collection unit can also extract text data from electronic files (e.g., PDF files) using OCR technology. For example, the collection unit picks out similar transaction records from a vast number of purchase orders and quotations from the past several years. Even if the items, model numbers, and transaction conditions are not registered, the collection unit can read the amounts of multiple purchase orders and quotations and pick out only the necessary ones. The analysis unit analyzes the transaction records collected by the collection unit. For example, the analysis unit stores the data collected by the collection unit in a database. The analysis unit organizes the collected data and collects the transaction prices and conditions. Based on the collected data, the analysis unit compares the transaction prices and conditions and derives the optimal transaction conditions. The conversion unit converts the transaction data analyzed by the analysis unit into current prices. The conversion unit, for example, refers to past price and exchange rate data and converts it to current prices. Based on past transaction data, the conversion unit derives the optimal current transaction price. The conversion unit derives a fair price by comparing past prices and exchange rates and converting them to current prices. As a result, the agent AI system according to this embodiment can provide the strongest bargaining chip in daily procurement negotiations in the purchasing department.

[0030] The data collection unit collects past transaction records. For example, the unit reads electronic files (e.g., PDF files) of past purchase orders and quotations to find transactions that are the same as or similar to the goods being traded in the current transaction. Specifically, the data collection unit scans past purchase orders and quotations stored in the company's database or file server and extracts text data from the electronic files (e.g., PDF files) using OCR (Optical Character Recognition) technology. OCR technology recognizes characters in an image and converts them into digital text, allowing for efficient data collection from paper documents and scanned documents. The data collection unit picks out similar transaction records from a vast amount of purchase orders and quotations spanning the past several years. For example, even if a specific item, model number, or transaction conditions are not registered, the data collection unit can read the amounts from multiple purchase orders and quotations and pick out only what is necessary. The data collection unit uses AI to analyze the text data and extract information such as the name, model number, quantity, price, and transaction conditions of the goods being traded. This allows the data collection unit to efficiently collect past transaction records and store them in the database. Furthermore, the data collection unit can centrally manage the collected data and collaborate with other systems and departments as needed. For example, the collected data can be stored on a cloud server and accessed by the analysis and conversion units. Additionally, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes transaction data collected by the data collection unit. For example, the analysis unit stores the data collected by the data collection unit in a database. The analysis unit organizes the collected data and extracts transaction prices and conditions. Specifically, based on the collected data, the analysis unit compares transaction prices and conditions to derive optimal transaction conditions. The analysis unit uses AI to analyze the data and identify patterns in transaction prices and conditions. For example, based on past transaction data, it can derive optimal price ranges and transaction conditions for specific items or model numbers. The analysis unit organizes the collected data and extracts transaction prices and conditions. Based on the collected data, the analysis unit compares transaction prices and conditions to derive optimal transaction conditions. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past transaction data, it can predict price fluctuations for specific items or model numbers and formulate future transaction conditions. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The conversion unit converts transaction data analyzed by the analysis unit into current prices. For example, the conversion unit references past price and exchange rate data to convert to current prices. Specifically, the conversion unit derives the optimal current transaction price based on past transaction data. The conversion unit derives a fair price by comparing past prices and exchange rates and converting to current prices. For example, it can derive the optimal price range and transaction conditions for a specific item or model number based on past transaction data. The conversion unit uses AI to analyze data and identify patterns in transaction prices and conditions. This allows the conversion unit to derive the optimal current transaction price based on past transaction data. Furthermore, the conversion unit can continuously revise conversion results based on real-time updated data to respond to the latest situations. For example, if prices or exchange rates change rapidly, the conversion unit immediately incorporates new data and updates the conversion results. The conversion unit can also perform more accurate conversions by considering regional characteristics and past transaction history. This allows the conversion unit to always provide highly accurate conversion results based on the latest information, supporting quick and appropriate responses.

[0033] The data collection unit can extract text data from electronic files (e.g., PDF files) using OCR technology. The data collection unit uses OCR technology to extract text data from electronic files (e.g., PDF files). The data collection unit uses OCR technology to recognize text within electronic files (e.g., PDF files) and extracts it as digital data. The data collection unit uses OCR software to scan text within electronic files (e.g., PDF files) and saves it as text data. The data collection unit can use OCR technology to recognize text within electronic files (e.g., PDF files) with high accuracy and extract it as digital data. This streamlines the collection of transaction records by extracting text data from electronic files (e.g., PDF files). Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input an electronic file (e.g., PDF file) into a generating AI and have the generating AI perform the text data extraction.

[0034] The data collection unit can pick out similar transaction records from purchase orders and quotations from the past several years. For example, the data collection unit reads purchase orders and quotations from the past several years and picks out similar transaction records. The data collection unit finds transactions that are the same as or similar to the goods subject to the current transaction from among the purchase orders and quotations from the past several years. For example, even if the item, model number, or transaction terms are not registered, the data collection unit can read the amounts of multiple purchase orders and quotations and pick out only the necessary ones. The data collection unit picks out the most relevant transaction records from the purchase orders and quotations from the past several years. This allows for the efficient collection of negotiation materials by picking out similar transaction records from purchase orders and quotations from the past several years. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input purchase orders and quotations from the past several years into a generating AI and have the generating AI pick out similar transaction records.

[0035] The analysis unit can store the data collected by the collection unit in a database. For example, the analysis unit stores the data collected by the collection unit in a database. The analysis unit organizes the collected data and saves it in a database. By storing the collected data in a database, the analysis unit makes data management and analysis easier. The analysis unit stores the data in an appropriate database, such as a relational database or a NoSQL database. By storing the collected data in a database, the analysis unit enables faster data retrieval and analysis. This makes data management and analysis easier by storing the collected data in a database. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the storage of the data in the database.

[0036] The conversion unit can refer to past price and exchange rate data and convert it to current prices. For example, the conversion unit refers to past price and exchange rate data and converts it to current prices. The conversion unit converts it to current prices based on past price and exchange rate data. The conversion unit derives the current optimal transaction price based on past transaction records. The conversion unit derives a fair price by comparing past prices and exchange rates and converting them to current prices. In this way, a fair price can be derived by referring to past price and exchange rate data and converting it to current prices. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without using AI. For example, the conversion unit can input past price and exchange rate data into a generating AI and have the generating AI perform the conversion to current prices.

[0037] The data collection unit can evaluate the reliability of transaction records during collection and prioritize the collection of highly reliable data. For example, the data collection unit can verify the source of transaction records and prioritize the collection of data from reliable companies. The data collection unit can cross-check the content of transaction records and prioritize the collection of consistent data. The data collection unit can evaluate the update frequency of transaction records and prioritize the collection of the latest data. In this way, by evaluating the reliability of transaction records, highly reliable data can be prioritized. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, in order to evaluate the reliability of transaction records, the data collection unit can use generative AI to verify the source and consistency of the data and prioritize the collection of highly reliable data.

[0038] The data collection unit can evaluate the relevance of transaction records during collection and prioritize the collection of highly relevant data. For example, the data collection unit can evaluate relevance based on the item or model number of the transaction records and prioritize the collection of highly relevant data. The data collection unit can evaluate relevance based on the transaction conditions of the transaction records and prioritize the collection of highly relevant data. The data collection unit can evaluate relevance based on the trading partners of the transaction records and prioritize the collection of highly relevant data. In this way, by evaluating the relevance of transaction records, highly relevant data can be collected preferentially. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, in order to evaluate the relevance of transaction records, the data collection unit can use generative AI to analyze items, model numbers, transaction conditions, etc., and prioritize the collection of highly relevant data.

[0039] The data collection unit can collect data while considering the geographical distribution of transaction data. For example, the data collection unit can collect data from each region in a balanced manner based on the geographical distribution of transaction data. The data collection unit can prioritize the collection of transaction data from a specific region and provide region-specific information. The data collection unit collects data from a wide range of areas to prevent bias in transaction data, taking geographical distribution into consideration. This allows for the collection of data from each region in a balanced manner by considering the geographical distribution of transaction data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical distribution of transaction data into a generating AI and have the generating AI perform the collection of data for each region.

[0040] The data collection unit can collect data while considering the attribute information of the submitters of transaction records. For example, the data collection unit can consider the size of the companies submitting the transaction records and prioritize the collection of data from large companies. The data collection unit can consider the industry of the submitters of transaction records and prioritize the collection of data from related industries. The data collection unit can evaluate the reliability of the submitters of transaction records and prioritize the collection of data from reliable submitters. In this way, reliable data can be collected preferentially by considering the attribute information of the submitters of transaction records. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input the attribute information of the submitters of transaction records into a generating AI and have the generating AI perform the collection of reliable data.

[0041] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of transaction records during the analysis process. For example, the analysis unit can perform a highly accurate analysis by considering the interrelationships of items and model numbers in the transaction records. The analysis unit can perform a highly accurate analysis by considering the interrelationships of transaction conditions in the transaction records. The analysis unit can perform a highly accurate analysis by considering the interrelationships of trading partners in the transaction records. As a result, a highly accurate analysis becomes possible by considering the interrelationships of transaction records. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the interrelationships of transaction records into a generating AI and have the generating AI perform a highly accurate analysis.

[0042] The analysis unit can apply different analysis algorithms to each category of transaction data during analysis. For example, the analysis unit can apply the optimal analysis algorithm to each item category of transaction data. The analysis unit can apply the optimal analysis algorithm to each transaction condition category of transaction data. The analysis unit can apply the optimal analysis algorithm to each trading partner category of transaction data. By applying different analysis algorithms to each category of transaction data, highly accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input different analysis algorithms for each category of transaction data into a generating AI and have the generating AI perform the optimal analysis.

[0043] The analysis unit can determine the priority of analysis based on the submission timing of transaction records during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent transaction records. The analysis unit may also prioritize the analysis of transaction records submitted in a concentrated period. Furthermore, the analysis unit may prioritize the analysis of data from important periods within past transaction records. This allows for the prioritization of analysis based on the submission timing of transaction records, thereby ensuring that the most recent data is analyzed first. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input the submission timing of transaction records into a generating AI and have the generating AI determine the analysis priority.

[0044] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on transaction history during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to academic papers related to transaction history. The analysis unit can improve the accuracy of its analysis by referring to industry reports related to transaction history. The analysis unit can improve the accuracy of its analysis by referring to market research data related to transaction history. Thus, the accuracy of the analysis is improved by referring to relevant literature on transaction history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input relevant literature on transaction history into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0045] The conversion unit can evaluate the reliability of historical price and exchange rate data during conversion and prioritize the use of highly reliable data. For example, the conversion unit can verify the source of historical price data and prioritize the use of highly reliable data. The conversion unit can evaluate the consistency of historical exchange rate data and prioritize the use of highly reliable data. The conversion unit can evaluate the update frequency of historical price and exchange rate data and prioritize the use of the latest data. This allows conversion to be performed using highly reliable data by evaluating the reliability of historical price and exchange rate data. Some or all of the above processes in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input the reliability of historical price and exchange rate data into a generating AI and have the generating AI perform the task of using highly reliable data.

[0046] The conversion unit can apply different conversion algorithms to each category of transaction data during conversion. For example, the conversion unit can apply the optimal conversion algorithm for each item category of transaction data. The conversion unit can apply the optimal conversion algorithm for each transaction condition category of transaction data. The conversion unit can apply the optimal conversion algorithm for each trading partner category of transaction data. By applying different conversion algorithms to each category of transaction data, highly accurate conversions become possible. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input different conversion algorithms for each category of transaction data into a generating AI and have the generating AI perform the optimal conversion.

[0047] The conversion unit can perform conversions while considering the geographical distribution of transaction data. For example, the conversion unit can perform conversions while considering regional prices based on the geographical distribution of transaction data. The conversion unit can prioritize the consideration of prices in a specific region and provide region-specific information. The conversion unit uses a wide range of data to perform conversions while considering the geographical distribution and preventing bias in transaction data. This makes it possible to perform conversions that take regional prices into account by considering the geographical distribution of transaction data. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without using AI. For example, the conversion unit can input the geographical distribution of transaction data into a generating AI and have the generating AI perform conversions that take regional prices into account.

[0048] The conversion unit can improve the accuracy of the conversion by referring to relevant literature on transaction history during the conversion process. For example, the conversion unit can improve the accuracy of the conversion by referring to academic papers related to transaction history. The conversion unit can improve the accuracy of the conversion by referring to industry reports related to transaction history. The conversion unit can improve the accuracy of the conversion by referring to market research data related to transaction history. Thus, the accuracy of the conversion is improved by referring to relevant literature on transaction history. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input relevant literature on transaction history into a generating AI and have the generating AI perform the conversion accuracy improvement.

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

[0050] The data collection unit can refer to the user's purchase history and prioritize the collection of transaction data based on past purchasing trends. For example, the data collection unit can prioritize the collection of items that the user has frequently purchased in the past. The data collection unit can prioritize the collection of items that the user has purchased at a high price in the past. The data collection unit can prioritize the collection of items that the user has purchased from specific suppliers in the past. By prioritizing the collection of transaction data based on the user's purchase history, the system can efficiently collect data that meets the user's needs.

[0051] The conversion unit can perform conversions while taking into account the seasonality of transaction data. For example, for items whose prices fluctuate in a particular season, the conversion unit can perform conversions by referring to seasonal price data. The conversion unit can perform conversions while taking into account seasonal fluctuations in supply and demand. The conversion unit can perform conversions by referring to seasonal price indices. This allows for more accurate conversions by taking into account the seasonality of transaction data.

[0052] The analysis unit can evaluate the reliability of transaction data and prioritize the analysis of highly reliable data. For example, the analysis unit can verify the source of transaction data and prioritize the analysis of data from reliable companies. The analysis unit can cross-check the content of transaction data and prioritize the analysis of consistent data. The analysis unit can evaluate the update frequency of transaction data and prioritize the analysis of the latest data. In this way, by evaluating the reliability of transaction data, analysis can be performed using highly reliable data.

[0053] The data collection unit can collect data while considering the attribute information of those who submit transaction records. For example, the data collection unit can consider the size of the companies that submit transaction records and prioritize collecting data from large companies. The data collection unit can consider the industry of those that submit transaction records and prioritize collecting data from related industries. The data collection unit can evaluate the reliability of those who submit transaction records and prioritize collecting data from highly reliable submitters. In this way, by considering the attribute information of those who submit transaction records, it is possible to prioritize the collection of highly reliable data.

[0054] The conversion unit can determine the conversion priority based on when the transaction data was submitted. For example, the conversion unit can prioritize the conversion of the most recent transaction data. The conversion unit can also prioritize the conversion of transaction data submitted in a concentrated period. The conversion unit can also prioritize the conversion of data from important periods within past transaction data. This allows for the prioritization of the most recent data by determining the conversion priority based on when the transaction data was submitted.

[0055] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on transaction performance. For example, the analysis unit can improve the accuracy of its analysis by referring to academic papers related to transaction performance. The analysis unit can improve the accuracy of its analysis by referring to industry reports related to transaction performance. The analysis unit can improve the accuracy of its analysis by referring to market research data related to transaction performance. In this way, the accuracy of the analysis is improved by referring to relevant literature on transaction performance.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The collection unit collects past transaction records. For example, it reads electronic files (e.g., PDF files) of past purchase orders and quotations to find transactions that are the same as or similar to the items being traded in the current transaction. The collection unit can also extract text data from electronic files (e.g., PDF files) using OCR technology. It picks out similar transaction records from a vast amount of purchase orders and quotations from the past several years, and even if the item, model number, or terms of trade are not registered, it reads the amounts from multiple purchase orders and quotations and picks out only the necessary ones. Step 2: The analysis unit analyzes the transaction data collected by the collection unit. For example, it stores the data collected by the collection unit in a database, organizes it, and collects the transaction prices and conditions. Based on the collected data, it compares the transaction prices and conditions to derive the optimal transaction conditions. Step 3: The conversion unit converts the transaction data analyzed by the analysis unit into current prices. For example, it refers to past price and exchange rate data and converts it to current prices. Based on past transaction data, it derives the optimal current transaction price. By comparing past prices and exchange rates and converting them to current prices, it derives a fair price.

[0058] (Example of form 2) An agent AI system according to an embodiment of the present invention is a system for collecting negotiation materials for procurement negotiations conducted daily in the purchasing department. This agent AI system reads electronic files (e.g., PDF files) of past purchase orders and quotations and finds transactions that are the same as or similar to the goods being traded in the current transaction. The agent AI system collects and organizes the prices and conditions of the transactions it finds. Furthermore, the agent AI system compares the prices and exchange rates at the time and converts them to current prices to derive a fair price. This mechanism allows for the collection and organization of negotiation materials for all items, ensuring that the strongest negotiation materials are available for all price negotiations, regardless of the quantity or amount. This improves the negotiating power of all buyers and can achieve an average cost reduction of 3-5% per year. For example, the agent AI system picks out similar transaction records from a vast number of purchase orders and quotations from the past several years. For example, even if the item, model number, or transaction conditions are not registered, the agent AI system can read the amounts from multiple purchase orders and quotations and pick out only what is necessary. The agent AI system collects and organizes the prices and conditions of the transactions it finds. For example, even if a single transaction involves dozens of items, the agent AI system can gather all the necessary data. This allows buyers to quickly and easily gather bargaining chips. The agent AI system compares prices and exchange rates at the time and converts them to current prices. For example, it can derive the optimal current transaction price based on past transaction data. This enables buyers to conduct transactions at fair prices. As a result, bargaining chips can be collected and organized for all items, providing the strongest bargaining chips for all price negotiations, regardless of quantity or value. This improves the bargaining power of all buyers and can result in an average annual cost reduction of 3-5%. For example, companies with a large volume of transactions can expect even greater effects. In this way, the agent AI system can provide the strongest bargaining chips for daily procurement negotiations in the purchasing department.

[0059] The agent AI system according to this embodiment comprises a collection unit, an analysis unit, and a conversion unit. The collection unit collects past transaction records. For example, the collection unit reads electronic files (e.g., PDF files) of past purchase orders and quotations and finds transactions that are the same as or similar to the goods subject to the current transaction. The collection unit can also extract text data from electronic files (e.g., PDF files) using OCR technology. For example, the collection unit picks out similar transaction records from a vast number of purchase orders and quotations from the past several years. Even if the items, model numbers, and transaction conditions are not registered, the collection unit can read the amounts of multiple purchase orders and quotations and pick out only the necessary ones. The analysis unit analyzes the transaction records collected by the collection unit. For example, the analysis unit stores the data collected by the collection unit in a database. The analysis unit organizes the collected data and collects the transaction prices and conditions. Based on the collected data, the analysis unit compares the transaction prices and conditions and derives the optimal transaction conditions. The conversion unit converts the transaction data analyzed by the analysis unit into current prices. The conversion unit, for example, refers to past price and exchange rate data and converts it to current prices. Based on past transaction data, the conversion unit derives the optimal current transaction price. The conversion unit derives a fair price by comparing past prices and exchange rates and converting them to current prices. As a result, the agent AI system according to this embodiment can provide the strongest bargaining chip in daily procurement negotiations in the purchasing department.

[0060] The data collection unit collects past transaction records. For example, the unit reads electronic files (e.g., PDF files) of past purchase orders and quotations to find transactions that are the same as or similar to the goods being traded in the current transaction. Specifically, the data collection unit scans past purchase orders and quotations stored in the company's database or file server and extracts text data from the electronic files (e.g., PDF files) using OCR (Optical Character Recognition) technology. OCR technology recognizes characters in an image and converts them into digital text, allowing for efficient data collection from paper documents and scanned documents. The data collection unit picks out similar transaction records from a vast amount of purchase orders and quotations spanning the past several years. For example, even if a specific item, model number, or transaction conditions are not registered, the data collection unit can read the amounts from multiple purchase orders and quotations and pick out only what is necessary. The data collection unit uses AI to analyze the text data and extract information such as the name, model number, quantity, price, and transaction conditions of the goods being traded. This allows the data collection unit to efficiently collect past transaction records and store them in the database. Furthermore, the data collection unit can centrally manage the collected data and collaborate with other systems and departments as needed. For example, the collected data can be stored on a cloud server and accessed by the analysis and conversion units. Additionally, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0061] The analysis unit analyzes transaction data collected by the data collection unit. For example, the analysis unit stores the data collected by the data collection unit in a database. The analysis unit organizes the collected data and extracts transaction prices and conditions. Specifically, based on the collected data, the analysis unit compares transaction prices and conditions to derive optimal transaction conditions. The analysis unit uses AI to analyze the data and identify patterns in transaction prices and conditions. For example, based on past transaction data, it can derive optimal price ranges and transaction conditions for specific items or model numbers. The analysis unit organizes the collected data and extracts transaction prices and conditions. Based on the collected data, the analysis unit compares transaction prices and conditions to derive optimal transaction conditions. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past transaction data, it can predict price fluctuations for specific items or model numbers and formulate future transaction conditions. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0062] The conversion unit converts transaction data analyzed by the analysis unit into current prices. For example, the conversion unit references past price and exchange rate data to convert to current prices. Specifically, the conversion unit derives the optimal current transaction price based on past transaction data. The conversion unit derives a fair price by comparing past prices and exchange rates and converting to current prices. For example, it can derive the optimal price range and transaction conditions for a specific item or model number based on past transaction data. The conversion unit uses AI to analyze data and identify patterns in transaction prices and conditions. This allows the conversion unit to derive the optimal current transaction price based on past transaction data. Furthermore, the conversion unit can continuously revise conversion results based on real-time updated data to respond to the latest situations. For example, if prices or exchange rates change rapidly, the conversion unit immediately incorporates new data and updates the conversion results. The conversion unit can also perform more accurate conversions by considering regional characteristics and past transaction history. This allows the conversion unit to always provide highly accurate conversion results based on the latest information, supporting quick and appropriate responses.

[0063] The data collection unit can extract text data from electronic files (e.g., PDF files) using OCR technology. The data collection unit uses OCR technology to extract text data from electronic files (e.g., PDF files). The data collection unit uses OCR technology to recognize text within electronic files (e.g., PDF files) and extracts it as digital data. The data collection unit uses OCR software to scan text within electronic files (e.g., PDF files) and saves it as text data. The data collection unit can use OCR technology to recognize text within electronic files (e.g., PDF files) with high accuracy and extract it as digital data. This streamlines the collection of transaction records by extracting text data from electronic files (e.g., PDF files). Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input an electronic file (e.g., PDF file) into a generating AI and have the generating AI perform the text data extraction.

[0064] The data collection unit can pick out similar transaction records from purchase orders and quotations from the past several years. For example, the data collection unit reads purchase orders and quotations from the past several years and picks out similar transaction records. The data collection unit finds transactions that are the same as or similar to the goods subject to the current transaction from among the purchase orders and quotations from the past several years. For example, even if the item, model number, or transaction terms are not registered, the data collection unit can read the amounts of multiple purchase orders and quotations and pick out only the necessary ones. The data collection unit picks out the most relevant transaction records from the purchase orders and quotations from the past several years. This allows for the efficient collection of negotiation materials by picking out similar transaction records from purchase orders and quotations from the past several years. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input purchase orders and quotations from the past several years into a generating AI and have the generating AI pick out similar transaction records.

[0065] The analysis unit can store the data collected by the collection unit in a database. For example, the analysis unit stores the data collected by the collection unit in a database. The analysis unit organizes the collected data and saves it in a database. By storing the collected data in a database, the analysis unit makes data management and analysis easier. The analysis unit stores the data in an appropriate database, such as a relational database or a NoSQL database. By storing the collected data in a database, the analysis unit enables faster data retrieval and analysis. This makes data management and analysis easier by storing the collected data in a database. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the storage of the data in the database.

[0066] The conversion unit can refer to past price and exchange rate data and convert it to current prices. For example, the conversion unit refers to past price and exchange rate data and converts it to current prices. The conversion unit converts it to current prices based on past price and exchange rate data. The conversion unit derives the current optimal transaction price based on past transaction records. The conversion unit derives a fair price by comparing past prices and exchange rates and converting them to current prices. In this way, a fair price can be derived by referring to past price and exchange rate data and converting it to current prices. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without using AI. For example, the conversion unit can input past price and exchange rate data into a generating AI and have the generating AI perform the conversion to current prices.

[0067] The data collection unit can estimate the user's emotions and determine the priority of transaction history to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will quickly prioritize collecting important transaction history. If the user is relaxed, the data collection unit can collect a wide range of detailed transaction history. If the user is in a hurry, the data collection unit will prioritize collecting the most relevant past transaction history. This allows for situation-appropriate data collection by prioritizing transaction history based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of transaction history.

[0068] The data collection unit can evaluate the reliability of transaction records during collection and prioritize the collection of highly reliable data. For example, the data collection unit can verify the source of transaction records and prioritize the collection of data from reliable companies. The data collection unit can cross-check the content of transaction records and prioritize the collection of consistent data. The data collection unit can evaluate the update frequency of transaction records and prioritize the collection of the latest data. In this way, by evaluating the reliability of transaction records, highly reliable data can be prioritized. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, in order to evaluate the reliability of transaction records, the data collection unit can use generative AI to verify the source and consistency of the data and prioritize the collection of highly reliable data.

[0069] The data collection unit can evaluate the relevance of transaction records during collection and prioritize the collection of highly relevant data. For example, the data collection unit can evaluate relevance based on the item or model number of the transaction records and prioritize the collection of highly relevant data. The data collection unit can evaluate relevance based on the transaction conditions of the transaction records and prioritize the collection of highly relevant data. The data collection unit can evaluate relevance based on the trading partners of the transaction records and prioritize the collection of highly relevant data. In this way, by evaluating the relevance of transaction records, highly relevant data can be collected preferentially. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, in order to evaluate the relevance of transaction records, the data collection unit can use generative AI to analyze items, model numbers, transaction conditions, etc., and prioritize the collection of highly relevant data.

[0070] The data collection unit can estimate the user's emotions and adjust the display method of collected transaction history based on the estimated user emotions. For example, if the user is stressed, the data collection unit can provide a simple and highly visible display method. If the user is relaxed, the data collection unit can provide a display method that includes detailed information. If the user is in a hurry, the data collection unit can provide a concise display method. By adjusting the display method based on the user's emotions, it becomes possible to provide a display that is easy for the user to understand. 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 AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the display method of transaction history.

[0071] The data collection unit can collect data while considering the geographical distribution of transaction data. For example, the data collection unit can collect data from each region in a balanced manner based on the geographical distribution of transaction data. The data collection unit can prioritize the collection of transaction data from a specific region and provide region-specific information. The data collection unit collects data from a wide range of areas to prevent bias in transaction data, taking geographical distribution into consideration. This allows for the collection of data from each region in a balanced manner by considering the geographical distribution of transaction data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical distribution of transaction data into a generating AI and have the generating AI perform the collection of data for each region.

[0072] The data collection unit can collect data while considering the attribute information of the submitters of transaction records. For example, the data collection unit can consider the size of the companies submitting the transaction records and prioritize the collection of data from large companies. The data collection unit can consider the industry of the submitters of transaction records and prioritize the collection of data from related industries. The data collection unit can evaluate the reliability of the submitters of transaction records and prioritize the collection of data from reliable submitters. In this way, reliable data can be collected preferentially by considering the attribute information of the submitters of transaction records. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input the attribute information of the submitters of transaction records into a generating AI and have the generating AI perform the collection of reliable data.

[0073] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize the analysis of important transaction history. If the user is relaxed, the analysis unit can analyze a wide range of detailed transaction history. If the user is in a hurry, the analysis unit will prioritize the analysis of the most relevant past transaction history. This allows for analysis tailored to the user's situation by determining the priority of analysis based on 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 AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of analysis.

[0074] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of transaction records during the analysis process. For example, the analysis unit can perform a highly accurate analysis by considering the interrelationships of items and model numbers in the transaction records. The analysis unit can perform a highly accurate analysis by considering the interrelationships of transaction conditions in the transaction records. The analysis unit can perform a highly accurate analysis by considering the interrelationships of trading partners in the transaction records. As a result, a highly accurate analysis becomes possible by considering the interrelationships of transaction records. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the interrelationships of transaction records into a generating AI and have the generating AI perform a highly accurate analysis.

[0075] The analysis unit can apply different analysis algorithms to each category of transaction data during analysis. For example, the analysis unit can apply the optimal analysis algorithm to each item category of transaction data. The analysis unit can apply the optimal analysis algorithm to each transaction condition category of transaction data. The analysis unit can apply the optimal analysis algorithm to each trading partner category of transaction data. By applying different analysis algorithms to each category of transaction data, highly accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input different analysis algorithms for each category of transaction data into a generating AI and have the generating AI perform the optimal analysis.

[0076] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method based on the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the analysis results.

[0077] The analysis unit can determine the priority of analysis based on the submission timing of transaction records during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent transaction records. The analysis unit may also prioritize the analysis of transaction records submitted in a concentrated period. Furthermore, the analysis unit may prioritize the analysis of data from important periods within past transaction records. This allows for the prioritization of analysis based on the submission timing of transaction records, thereby ensuring that the most recent data is analyzed first. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input the submission timing of transaction records into a generating AI and have the generating AI determine the analysis priority.

[0078] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on transaction history during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to academic papers related to transaction history. The analysis unit can improve the accuracy of its analysis by referring to industry reports related to transaction history. The analysis unit can improve the accuracy of its analysis by referring to market research data related to transaction history. Thus, the accuracy of the analysis is improved by referring to relevant literature on transaction history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input relevant literature on transaction history into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0079] The conversion unit can estimate the user's emotions and determine conversion priorities based on the estimated emotions. For example, if the user is stressed, the conversion unit will quickly prioritize and convert important transaction data. If the user is relaxed, the conversion unit can convert a wide range of detailed transaction data. If the user is in a hurry, the conversion unit will prioritize and convert the most relevant past transaction data. This allows for conversions tailored to the user's situation by determining conversion priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the conversion unit may be performed using AI or not. For example, the conversion unit can input user emotion data into a generative AI and have the generative AI determine the conversion priorities.

[0080] The conversion unit can evaluate the reliability of historical price and exchange rate data during conversion and prioritize the use of highly reliable data. For example, the conversion unit can verify the source of historical price data and prioritize the use of highly reliable data. The conversion unit can evaluate the consistency of historical exchange rate data and prioritize the use of highly reliable data. The conversion unit can evaluate the update frequency of historical price and exchange rate data and prioritize the use of the latest data. This allows conversion to be performed using highly reliable data by evaluating the reliability of historical price and exchange rate data. Some or all of the above processes in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input the reliability of historical price and exchange rate data into a generating AI and have the generating AI perform the task of using highly reliable data.

[0081] The conversion unit can apply different conversion algorithms to each category of transaction data during conversion. For example, the conversion unit can apply the optimal conversion algorithm for each item category of transaction data. The conversion unit can apply the optimal conversion algorithm for each transaction condition category of transaction data. The conversion unit can apply the optimal conversion algorithm for each trading partner category of transaction data. By applying different conversion algorithms to each category of transaction data, highly accurate conversions become possible. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input different conversion algorithms for each category of transaction data into a generating AI and have the generating AI perform the optimal conversion.

[0082] The conversion unit can estimate the user's emotions and adjust the display method of the conversion results based on the estimated emotions. For example, if the user is stressed, the conversion unit can provide a simple and highly visible display method. If the user is relaxed, the conversion unit can provide a display method that includes detailed information. If the user is in a hurry, the conversion unit can provide a concise display method. By adjusting the display method based on the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the conversion unit may be performed using AI, for example, or not using AI. For example, the conversion unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the conversion results.

[0083] The conversion unit can perform conversions while considering the geographical distribution of transaction data. For example, the conversion unit can perform conversions while considering regional prices based on the geographical distribution of transaction data. The conversion unit can prioritize the consideration of prices in a specific region and provide region-specific information. The conversion unit uses a wide range of data to perform conversions while considering the geographical distribution and preventing bias in transaction data. This makes it possible to perform conversions that take regional prices into account by considering the geographical distribution of transaction data. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without using AI. For example, the conversion unit can input the geographical distribution of transaction data into a generating AI and have the generating AI perform conversions that take regional prices into account.

[0084] The conversion unit can improve the accuracy of the conversion by referring to relevant literature on transaction history during the conversion process. For example, the conversion unit can improve the accuracy of the conversion by referring to academic papers related to transaction history. The conversion unit can improve the accuracy of the conversion by referring to industry reports related to transaction history. The conversion unit can improve the accuracy of the conversion by referring to market research data related to transaction history. Thus, the accuracy of the conversion is improved by referring to relevant literature on transaction history. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input relevant literature on transaction history into a generating AI and have the generating AI perform the conversion accuracy improvement.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The data collection unit can refer to the user's purchase history and prioritize the collection of transaction data based on past purchasing trends. For example, the data collection unit can prioritize the collection of items that the user has frequently purchased in the past. The data collection unit can prioritize the collection of items that the user has purchased at a high price in the past. The data collection unit can prioritize the collection of items that the user has purchased from specific suppliers in the past. By prioritizing the collection of transaction data based on the user's purchase history, the system can efficiently collect data that meets the user's needs.

[0087] The analysis unit can estimate the user's emotions and provide feedback based on the estimated emotions. For example, if the user is stressed, the analysis unit provides concise and to-the-point feedback. If the user is relaxed, the analysis unit can provide feedback including detailed analysis results. If the user is in a hurry, the analysis unit provides quick feedback on the important points. In this way, by providing feedback based on the user's emotions, useful information can be appropriately provided to the user.

[0088] The conversion unit can perform conversions while taking into account the seasonality of transaction data. For example, for items whose prices fluctuate in a particular season, the conversion unit can perform conversions by referring to seasonal price data. The conversion unit can perform conversions while taking into account seasonal fluctuations in supply and demand. The conversion unit can perform conversions by referring to seasonal price indices. This allows for more accurate conversions by taking into account the seasonality of transaction data.

[0089] The data collection unit can estimate the user's emotions and adjust the level of detail in the data it collects based on those emotions. For example, if the user is stressed, the unit will collect concise, to-the-point data. If the user is relaxed, the unit can collect a wide range of detailed data. If the user is in a hurry, the unit will prioritize collecting important data. This allows the system to provide the user with the most relevant information by adjusting the level of detail based on their emotions.

[0090] The analysis unit can evaluate the reliability of transaction data and prioritize the analysis of highly reliable data. For example, the analysis unit can verify the source of transaction data and prioritize the analysis of data from reliable companies. The analysis unit can cross-check the content of transaction data and prioritize the analysis of consistent data. The analysis unit can evaluate the update frequency of transaction data and prioritize the analysis of the latest data. In this way, by evaluating the reliability of transaction data, analysis can be performed using highly reliable data.

[0091] The conversion unit can estimate the user's emotions and adjust the notification method of the conversion result based on the estimated emotions. For example, if the user is stressed, the conversion unit can provide a simple and highly visible notification method. If the user is relaxed, the conversion unit can provide a notification method that includes detailed information. If the user is in a hurry, the conversion unit can provide a notification method that gets straight to the point. In this way, by adjusting the notification method based on the user's emotions, it becomes possible to provide notifications that are easy for the user to see.

[0092] The data collection unit can collect data while considering the attribute information of those who submit transaction records. For example, the data collection unit can consider the size of the companies that submit transaction records and prioritize collecting data from large companies. The data collection unit can consider the industry of those that submit transaction records and prioritize collecting data from related industries. The data collection unit can evaluate the reliability of those who submit transaction records and prioritize collecting data from highly reliable submitters. In this way, by considering the attribute information of those who submit transaction records, it is possible to prioritize the collection of highly reliable data.

[0093] The analysis unit can estimate the user's emotions and adjust the feedback method of the analysis based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide concise and to-the-point feedback. If the user is relaxed, the analysis unit can provide feedback that includes detailed analysis results. If the user is in a hurry, the analysis unit can provide quick feedback on the important points. In this way, by adjusting the feedback method of the analysis based on the user's emotions, useful information can be appropriately provided to the user.

[0094] The conversion unit can determine the conversion priority based on when the transaction data was submitted. For example, the conversion unit can prioritize the conversion of the most recent transaction data. The conversion unit can also prioritize the conversion of transaction data submitted in a concentrated period. The conversion unit can also prioritize the conversion of data from important periods within past transaction data. This allows for the prioritization of the most recent data by determining the conversion priority based on when the transaction data was submitted.

[0095] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on transaction performance. For example, the analysis unit can improve the accuracy of its analysis by referring to academic papers related to transaction performance. The analysis unit can improve the accuracy of its analysis by referring to industry reports related to transaction performance. The analysis unit can improve the accuracy of its analysis by referring to market research data related to transaction performance. In this way, the accuracy of the analysis is improved by referring to relevant literature on transaction performance.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The collection unit collects past transaction records. For example, it reads electronic files (e.g., PDF files) of past purchase orders and quotations to find transactions that are the same as or similar to the items being traded in the current transaction. The collection unit can also extract text data from electronic files (e.g., PDF files) using OCR technology. It picks out similar transaction records from a vast amount of purchase orders and quotations from the past several years, and even if the item, model number, or terms of trade are not registered, it reads the amounts from multiple purchase orders and quotations and picks out only the necessary ones. Step 2: The analysis unit analyzes the transaction data collected by the collection unit. For example, it stores the data collected by the collection unit in a database, organizes it, and collects the transaction prices and conditions. Based on the collected data, it compares the transaction prices and conditions to derive the optimal transaction conditions. Step 3: The conversion unit converts the transaction data analyzed by the analysis unit into current prices. For example, it refers to past price and exchange rate data and converts it to current prices. Based on past transaction data, it derives the optimal current transaction price. By comparing past prices and exchange rates and converting them to current prices, it derives a fair price.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] Each of the multiple elements described above, including the data collection unit, analysis unit, and conversion unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit uses the camera 42 and communication I / F 44 of the smart device 14 to read electronic files (e.g., PDF files) of past purchase orders and quotations, and extracts text data using OCR technology. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to store the collected data in the database 24 and organize the transaction prices and conditions. The conversion unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to convert prices to current prices by referring to past price and exchange rate data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the data collection unit, analysis unit, and conversion unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit uses the camera 42 and communication I / F 44 of the smart glasses 214 to read electronic files (e.g., PDF files) of past purchase orders and quotations, and extracts text data using OCR technology. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which stores the collected data in the database 24 and organizes the prices and conditions of the transactions. The conversion unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which converts to current prices by referring to past price and exchange rate data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the data collection unit, analysis unit, and conversion unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit reads electronic files (e.g., PDF files) of past purchase orders and quotations using the camera 42 and communication I / F 44 of the headset terminal 314 and extracts text data using OCR technology. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and stores the collected data in the database 24 and organizes the prices and conditions of the transactions. The conversion unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and converts prices to current prices by referring to past price and exchange rate data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the data collection unit, analysis unit, and conversion unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit uses the camera 42 and communication I / F 44 of the robot 414 to read electronic files (e.g., PDF files) of past purchase orders and quotations, and extracts text data using OCR technology. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to store the collected data in the database 24 and organize the transaction prices and conditions. The conversion unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to convert prices to current prices by referring to past price and exchange rate data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] (Note 1) The collection department collects past transaction records, An analysis unit analyzes the transaction records collected by the aforementioned collection unit, A conversion unit converts the transaction data analyzed by the aforementioned analysis unit into current prices, Equipped with A system characterized by the following features. (Note 2) The aforementioned collection unit is Extract text data from electronic files using OCR technology. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Select similar transaction records from purchase orders and quotations from the past few years. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, The data collected by the aforementioned collection unit is stored in a database. The system described in Appendix 1, characterized by the features described herein. (Note 5) The conversion unit is, Referencing past price and exchange rate data, convert to current prices. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates user sentiment and determines the priority of transaction history to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is During data collection, the reliability of transaction history is evaluated, and reliable data is prioritized for collection. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, the relevance of transaction history is evaluated, and highly relevant data is prioritized for collection. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We estimate user sentiment and adjust how transaction history is displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, the geographical distribution of transaction records should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the attribute information of the person submitting the transaction history will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, we improve the accuracy of the analysis by considering the interrelationships of transaction history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied to each category of transaction history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on the timing of submission of transaction records. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, we refer to relevant literature on transaction history to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The conversion unit is, The system estimates the user's emotions and determines the conversion priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The conversion unit is, During conversion, the reliability of historical price and exchange rate data is evaluated, and reliable data is used preferentially. The system described in Appendix 1, characterized by the features described herein. (Note 20) The conversion unit is, When converting, different conversion algorithms are applied to each category of transaction history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The conversion unit is, The system estimates the user's emotions and adjusts how the conversion results are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The conversion unit is, When performing conversions, the geographical distribution of transaction data is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The conversion unit is, During conversion, we improve the accuracy of the conversion by referring to relevant literature on transaction history. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0170] 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. The collection department collects past transaction records, An analysis unit analyzes the transaction records collected by the aforementioned collection unit, A conversion unit converts the transaction data analyzed by the aforementioned analysis unit into current prices, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is Extract text data from electronic files using OCR technology. The system according to feature 1.

3. The aforementioned collection unit is Select similar transaction records from purchase orders and quotations from the past few years. The system according to feature 1.

4. The aforementioned analysis unit, The data collected by the aforementioned collection unit is stored in a database. The system according to feature 1.

5. The conversion unit is, Referencing past price and exchange rate data, convert to current prices. The system according to feature 1.

6. The aforementioned collection unit is It estimates user sentiment and determines the priority of transaction history to collect based on the estimated user sentiment. The system according to feature 1.

7. The aforementioned collection unit is During data collection, the reliability of transaction history is evaluated, and reliable data is prioritized for collection. The system according to feature 1.

8. The aforementioned collection unit is During data collection, the relevance of transaction history is evaluated, and highly relevant data is prioritized for collection. The system according to feature 1.

9. The aforementioned collection unit is We estimate user sentiment and adjust how transaction history is displayed based on that estimated sentiment. The system according to feature 1.

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