system

Abstract

Provide a system. A terminal acquires an image of a banknote and reads its serial number; a means for a user to input transaction information; means for the terminal to transmit the banknote serial number and transaction information; A means for the server to collect and store the serial numbers and transaction information of the banknotes in a database; A means for the server to detect abnormalities in banknotes using an AI model based on the collected data; a means for the server to transmit the anomaly detection result to the terminal and notify the user; A way for users to view notifications and take necessary actions. A banknote management system including:

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Counterfeiting and fraudulent transactions of banknotes are serious problems in modern society. However, conventional methods for managing banknotes lack the means to track banknote movements in real time and automatically detect anomalies. As a result, it has been difficult to detect counterfeit banknotes and improve transaction transparency. To solve these problems, the present invention aims to provide a system that utilizes AI to completely manage banknote movements and prevent fraudulent transactions and the circulation of counterfeit banknotes. [Means for solving the problem]

[0005] The present invention solves this problem with a banknote management system that includes a terminal that acquires an image of a banknote and reads its serial number, a user input of transaction information, a terminal that transmits the banknote serial number and transaction information, a server that collects the banknote serial number and transaction information and stores them in a database, a server that uses an AI model to detect banknote anomalies based on the collected data, a server that transmits the anomaly detection results to the terminal and notifies the user, and a user that confirms the notification and takes necessary action. This system provides even higher security and reliability through encrypted transmission of the serial number and transaction information and updating of the AI ​​model.

[0006] "Terminal" refers to a device that scans an image of a banknote and reads its serial number.

[0007] "Banknote image" means visual information containing the banknote's serial number and other identifying information.

[0008] "Serial number" refers to a number unique to each banknote that is used to identify and track the banknote.

[0009] "Transaction Information" refers to information related to the use of a banknote, such as user ID, transaction ID, date, and location.

[0010] "Server" refers to the computer system that collects, stores, and analyzes serial numbers and transaction information.

[0011] "Database" means a structured data storage system for storing collected serial number and transaction information.

[0012] An "AI model" refers to a program that analyzes data using algorithms and machine learning techniques, and has the ability to detect abnormalities in banknotes.

[0013] "Anomaly detection" refers to the process of analyzing banknote transaction information and identifying suspected fraudulent or counterfeit transactions.

[0014] "Notification" refers to communication from the server to the user to convey analysis results and anomaly detection results.

[0015] "Encryption" refers to the process of transforming plain text using a specific algorithm to protect the data. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] System Overview

[0038] The present invention provides a banknote management system in which a terminal, a server, and a user work together. The terminal captures an image of the banknote and reads its serial number. The user inputs transaction information, and the terminal sends this information to the server. The server collects this data and stores it in a database. The server then analyzes the collected data using an AI model to detect abnormalities in the banknote. The detected abnormalities are sent from the server to the terminal and notified to the user.

[0039] Device behavior

[0040] The terminal captures an image of the bill and reads its serial number. For example, when a user purchases an item at a convenience store, they pass the bill through the terminal's scanner. The terminal analyzes the image of the bill and extracts the serial number. Along with this serial number, the terminal also inputs the transaction information entered by the user (user ID, transaction ID, date, location). This information is then sent to the server.

[0041] Server Operation

[0042] The server receives the serial number and transaction information sent from the terminal. The received data is first stored in a database. The server then analyzes this data using an AI model. The AI ​​model has an algorithm that detects anomalies based on the banknote transaction information. For example, if a particular serial number is used in a large number of transactions in a short period of time, it is marked as an anomaly. If an anomaly is detected, the result is sent to the terminal.

[0043] User Actions

[0044] When making a transaction, the user scans the banknote using a terminal and inputs the transaction information into the terminal. After the transaction, the user checks for a notification from the server. For example, if the server notifies the user that "this banknote has an abnormal transaction history," the user can stop using the banknote or report it to the relevant authorities.

[0045] Specific examples

[0046] For example, when User A purchases a 1,000 yen item at a convenience store, the following process can be considered: User A passes a 1,000 yen bill through the terminal's scanner to obtain its serial number. User A follows the instructions on the screen and enters their user ID, transaction ID, and the date and location of the transaction. The terminal then sends the entered information and the bill's serial number to the server. The server stores this information in a database and analyzes it using an AI model. If the analysis determines that the bill has a normal transaction history, no particular notification is sent to the user. However, if an abnormality is detected, the server sends the analysis results to the terminal, which then notifies User A. For example, the terminal may display a message saying, "This bill has an abnormal transaction history." User A confirms this notification and either discontinues use of the bill or reports it to the appropriate authorities.

[0047] The above is a specific embodiment of the present invention. The present invention not only effectively prevents fraudulent use and counterfeiting of banknotes, but also improves the transparency of transactions.

[0048] The processing flow will be explained below.

[0049] Step 1:

[0050] The terminal captures an image of the bill and reads its serial number, and when the user swipes the bill through the scanner, the terminal uses image processing techniques to extract the serial number.

[0051] Step 2:

[0052] The user enters transaction information into the terminal, specifically, the user ID, transaction ID, date and location of the transaction.

[0053] Step 3:

[0054] The terminal encrypts the acquired serial number and entered transaction information and sends it to the server, thereby protecting the data.

[0055] Step 4:

[0056] The server receives the transmitted serial number and transaction information and stores this information in a database.

[0057] Step 5:

[0058] The server uses an AI model to analyze the transaction information received from the database, with the goal of detecting any abnormalities in the banknotes.

[0059] Step 6:

[0060] If the server detects an abnormality based on the analysis results, it will send the result to the terminal. For example, a serial number that has been traded many times in a short period of time will be deemed abnormal.

[0061] Step 7:

[0062] The device notifies the user of the analysis results received from the server, and the notification message includes details of the anomaly.

[0063] Step 8:

[0064] Users can check the notification and take necessary action, such as stopping the use of the banknotes on which an abnormality has been detected or reporting it to the relevant authorities.

[0065] The above is a specific processing flow of this system.

[0066] Example 1

[0067] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0068] Conventional banknote management systems have difficulty effectively preventing fraudulent use and counterfeiting of banknotes, and lack the means to ensure transaction transparency. Furthermore, technology for efficiently managing banknote serial numbers and detecting anomalies has not been fully established. This has made it difficult to track banknotes and detect fraud early, leading to problems such as a decline in the credibility of financial institutions and transactions.

[0069] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0070] In this invention, the server includes a means for collecting banknote serial numbers and transaction information and storing them in a database, a means for detecting abnormalities in banknotes using a generative AI model based on the collected data, and a means for transmitting the abnormality detection results to a terminal and notifying the user, thereby enabling early detection of fraudulent use and counterfeit banknotes and improving transaction transparency.

[0071] A "terminal" is a device that includes hardware and software that allows a user to capture an image of a banknote and read its serial number.

[0072] A "user" is a person or organization that uses the banknote management system to input transaction information and manage banknotes.

[0073] "Transaction Information" refers to information related to a transaction, such as user ID, transaction ID, transaction date, and location.

[0074] A "serial number" is a unique identification number printed on a banknote.

[0075] The "server" is a central center that collects banknote serial numbers and transaction information sent from terminals, stores them in a database, and analyzes them.

[0076] "Database" refers to a system for storing and managing banknote serial numbers and transaction information collected by the server.

[0077] A "generative AI model" is an artificial intelligence model used to detect anomalies in banknotes based on collected data.

[0078] An "anomaly" is a condition that exhibits an unusual or unexpected pattern or behavior within a series of transactions.

[0079] A "notification" is a message containing an abnormality detection result sent from the server to the user.

[0080] MODE FOR CARRYING OUT THE INVENTION

[0081] The present invention provides a banknote management system in which a terminal, a server, and a user work in cooperation with each other. The operation of this system and details of its hardware and software will be described in the following specific embodiment.

[0082] System Overview

[0083] In this system, the terminal captures an image of the banknote and reads its serial number. The user enters transaction information, and the terminal sends this information to the server. The server collects this data and stores it in a database. The server then analyzes the collected data using a generative AI model to detect abnormalities in the banknote. The server then sends the detected abnormalities to the terminal and notifies the user.

[0084] Hardware and Software

[0085] Terminal: A terminal equipped with a high-resolution CCD scanner and a touch panel. OpenCV is used as image processing software.

[0086] Server: MySQL (registered trademark) is used as the database, and a generative AI model is used for data analysis. Tensorflow (registered trademark) and PyTorch are used to build the generative AI model.

[0087] Device behavior

[0088] The terminal performs the following operations:

[0089] 1. The user swipes the bill through the terminal's scanner.

[0090] 2. The device's scanner captures an image of the banknote and performs image analysis using OpenCV.

[0091] 3. Read the banknote serial number from the analyzed image.

[0092] 4. The user follows the instructions displayed on the screen and enters the necessary transaction information, such as user ID, transaction ID, transaction date and location, via the touch panel.

[0093] 5. The terminal encrypts the serial number and transaction information it has obtained and sends them to the server.

[0094] Examples:

[0095] A user passes a 1,000 yen bill through a scanner at a convenience store, obtaining the serial number "AB12345678." The user then enters transaction information, such as "User ID: user123," "Transaction ID: TX001," "Date: 2023-10-01," and "Location: Convenience Store A." The terminal then encrypts this information and sends it to the server.

[0096] Server Operation

[0097] The server performs the following actions:

[0098] 1. Receive the serial number and transaction information sent from the terminal.

[0099] 2. Save the received data in the database.

[0100] 3. The saved data is input into the generative AI model for analysis.

[0101] 4. The generative AI model has an algorithm that detects anomalies based on transaction information on banknotes. For example, if a particular serial number is used in a large number of transactions in a short period of time, it will mark this as an anomaly.

[0102] 5. If an abnormality is detected, the result is sent to the terminal.

[0103] Examples:

[0104] The server receives the user's transaction information and serial number, stores them in a database, and then analyzes them using an anomaly detection model using TensorFlow. If an anomaly is detected, the server sends a notification to the terminal stating, "This banknote has an abnormal transaction history."

[0105] User Actions

[0106] The user performs the following actions:

[0107] 1. When making a transaction, use the terminal to scan the bill.

[0108] 2. Enter the transaction information into the terminal.

[0109] 3. After the transaction, check the notification from the server. If an abnormality is detected, take action according to the notification.

[0110] Examples:

[0111] A user purchases a 1000 yen item at a convenience store, scans the bill, and enters the transaction information. If the server notifies the user that "this bill has an abnormal transaction history," the user should stop using the bill and report it to the relevant authorities.

[0112] Prompt Sentence Examples

[0113] An example of a prompt to input to a generative AI model is:

[0114] 1. "If a note with this serial number has been used in many transactions in a short period of time, how can I mark it as abnormal?"

[0115] 2. "Describe how you can improve an algorithm for extracting banknote serial numbers from images."

[0116] The above is a specific embodiment of the present invention, which makes it possible to effectively prevent fraudulent use and counterfeiting of banknotes and improve the transparency of transactions.

[0117] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0118] The flow of this system's program processing

[0119] Step 1:

[0120] The terminal captures an image of the bill and reads the serial number.

[0121] Input: The user swipes a bill through the terminal's scanner.

[0122] How it works: The device's high-resolution CCD scanner captures an image of the bill. OpenCV is used to analyze the image and extract the serial number.

[0123] Output: The extracted serial number (e.g. "AB12345678").

[0124] Step 2:

[0125] The user enters the transaction information into the terminal.

[0126] Input: Information such as user ID, transaction ID, transaction date, and transaction location.

[0127] Specific operation: The user uses the device's touch panel to enter transaction information according to the instructions on the screen.

[0128] Output: The entered transaction information (e.g., "User ID: user123", "Transaction ID: TX001", "Date: 2023-10-01", "Location: Convenience Store A").

[0129] Step 3:

[0130] The terminal encrypts the banknote's serial number and transaction information and sends it to the server.

[0131] Input: Serial number, transaction information.

[0132] Specific operation: The terminal encrypts the serial number and transaction information using the HTTPS protocol and sends it to the server.

[0133] Output: The encrypted serial number and transaction information are sent to the server.

[0134] Step 4:

[0135] The server receives the banknote serial number and transaction information and stores it in a database.

[0136] Input: Encrypted serial number and transaction information.

[0137] Specific operation: The server receives the encrypted data, decrypts it, and stores it in a database (e.g., MySQL).

[0138] Output: Transaction information and serial number stored in the database.

[0139] Step 5:

[0140] The server analyzes the collected data using a generative AI model.

[0141] Input: Transaction information and serial number stored in the database.

[0142] How it works: The server inputs the stored data into a generative AI model (e.g., TensorFlow) and analyzes the data. The analysis algorithm analyzes the banknote transaction history and detects anomalies.

[0143] Output: If an anomaly is detected, it is flagged and detailed information.

[0144] Step 6:

[0145] If an abnormality is detected, the server transmits the result to the terminal.

[0146] Input: Analysis results (abnormality flags and detailed information).

[0147] Specific operation: The server creates a message to notify the user based on the analysis results and sends it to the terminal using the HTTPS protocol.

[0148] Output: A message of the anomaly detection result is sent to the terminal.

[0149] Step 7:

[0150] The terminal displays an abnormality notification from the server to the user.

[0151] Input: The anomaly detection message sent by the server.

[0152] Specific operation: The terminal receives a message from the server and displays it on the screen to notify the user of an abnormality.

[0153] Output: The user confirms the abnormality notification.

[0154] Step 8:

[0155] The user checks the notification and takes the necessary action.

[0156] Input: Abnormal notification displayed on the terminal.

[0157] Specific action: The user checks the notification and either stops using the note or reports it to the appropriate authorities.

[0158] Output: Prevents fraud and counterfeiting by requiring the user to take appropriate action.

[0159] The above are the specific processing steps of the program of this system.

[0160] (Application example 1)

[0161] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0162] Conventional banknote management systems have had the problem of making it difficult to quickly detect fraudulent or counterfeit banknotes. In particular, they have been unable to ensure transaction transparency or detect abnormal banknote transactions in real time, limiting their ability to prevent fraud.

[0163] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0164] In this invention, the server includes a means for a terminal to collect banknote serial numbers and transaction information and store them in a database, a means for detecting abnormalities in banknotes using an AI model based on the collected data, a means for transmitting the abnormality detection results to the terminal and notifying the user, a means for capturing images of banknotes using a smartphone camera and extracting serial numbers using image processing technology, and a means for notifying the user in real time when an abnormal transaction is detected. This makes it possible to detect fraudulent use and counterfeit banknotes in real time and increase transaction transparency.

[0165] A "terminal" is a device used by a user to capture images of banknotes and read their serial numbers. It also has the function of inputting transaction information and sending it to a server.

[0166] "Capturing an image of a banknote" refers to the act of capturing image data of the banknote using a smartphone camera or the like.

[0167] "Reading the serial number" refers to the act of extracting the serial number from an image of a banknote using optical character recognition (OCR) technology.

[0168] "Transaction Information" refers to detailed information related to a transaction, such as user ID, transaction ID, transaction date, and location.

[0169] The "server" is a computer system that receives the banknote serial numbers and transaction information sent from the terminal, and stores and analyzes this data in a database.

[0170] A "database" is a memory space for storing and managing banknote serial numbers and transaction information collected by the server.

[0171] "AI model" refers to artificial intelligence (AI) that has algorithms to analyze collected data and detect abnormalities in banknotes.

[0172] "Anomaly detection" refers to the act of using AI models to analyze a banknote's transaction history and identify unusual transaction patterns.

[0173] "Notifying the user" refers to the act of the server sending the abnormality detection result to the terminal so that the user can check it.

[0174] "Image processing technology" refers to the general technology used to analyze images of banknotes and extract serial numbers. Libraries such as OpenCV are mainly used.

[0175] "Real-time notification" refers to the act of sending information about an abnormality to the user almost immediately upon detection.

[0176] System Overview

[0177] The present invention is a banknote management system in which a terminal, a server, and a user work together. The terminal acquires an image of the banknote and reads its serial number. The user inputs transaction information, and the terminal sends this information to the server. The server collects this data and stores it in a database. The server then analyzes the collected data using an AI model to detect abnormalities in the banknote. The detected abnormalities are sent from the server to the terminal and notified to the user.

[0178] Device behavior

[0179] The terminal uses a smartphone. First, an image of the banknote is captured using the smartphone's camera. Next, the captured image is processed using the OpenCV library, and the serial number is extracted using OCR technology. The user enters transaction information (user ID, transaction ID, date, location) into the application. This data is sent from the terminal to the server.

[0180] Server Operation

[0181] The server receives the serial number and transaction information sent from the terminal. The received data is first stored in a database. The server then uses TensorFlow to build an AI model and analyze this data. The AI ​​model has an algorithm that detects anomalies based on the banknote transaction information. For example, if a particular serial number is used in a large number of transactions in a short period of time, it is marked as an anomaly. If an anomaly is detected, the result is sent to the terminal.

[0182] User Actions

[0183] When making a transaction, the user scans the banknote using their smartphone and enters the transaction information into the terminal. After the transaction, the user checks the notification from the server and takes the necessary action. For example, if the server notifies the user that "this banknote has an abnormal transaction history," the user can stop using the banknote or report it to the relevant authorities.

[0184] Specific examples

[0185] For example, when User A purchases a 1,000 yen item at a store, the process can be as follows: User A holds a 1,000 yen bill over their smartphone camera and captures an image. The application follows the instructions on the screen and enters the user ID, transaction ID, and transaction date and location. The device sends the entered information and the bill's serial number to the server. The server stores this information in a database and uses TensorFlow to analyze it with an AI model. If the analysis results indicate that the bill has a normal transaction history, no notification is sent to the user. However, if an abnormality is detected, the server sends the analysis results to the device, which notifies User A. For example, the device may display a message saying, "This bill has an abnormal transaction history." User A confirms this notification and either discontinues use of the bill or reports it to the appropriate authorities.

[0186] Prompt Sentence Examples

[0187] "We will analyze all transaction history based on this transaction information and the banknote serial number to determine whether there is any suspicion of fraud."

[0188] As explained above, by using the system of the present invention, it is possible to detect fraudulent use and counterfeiting of banknotes in real time and increase the transparency of transactions.

[0189] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0190] Step 1:

[0191] The terminal (smartphone) acquires an image of a banknote through user operation. The image of the banknote is taken using the smartphone's camera. The input is the actual banknote, which is then acquired as a digital image. The output is a digital image of the acquired banknote.

[0192] Step 2:

[0193] The image of the banknote acquired by the terminal is preprocessed using OpenCV to extract the serial number. Specifically, the image is converted to grayscale, subjected to adaptive binarization processing, and then the serial number is extracted as text data using OCR (Optical Character Recognition) technology. The input is the digital image of the banknote acquired in Step 1, and the output is the banknote serial number.

[0194] Step 3:

[0195] The user inputs transaction information (user ID, transaction ID, date, location) into the terminal. The input is manual input by the user, and the output is digital data of the transaction information entered by the user.

[0196] Step 4:

[0197] The terminal extracts the serial number and sends the transaction information entered by the user to the server. The input is digital data of the serial number and transaction information, and the output is a process of encrypting this data and sending it to the server via a communication network.

[0198] Step 5:

[0199] The server stores the received serial number and transaction information in a database. The input is the encrypted data of the serial number and transaction information received over the network, and the output is this data stored in the database.

[0200] Step 6:

[0201] The server uses a generative AI model (TensorFlow) based on data collected from the database to detect anomalies in banknotes. The input is the digital data of the serial number and transaction information stored in the database, and the prompt for the AI ​​model is "Analyze all transaction history based on this transaction information and the banknote serial number and determine whether there is any suspicion of fraud." The output is the analysis result indicating whether an anomaly was detected.

[0202] Step 7:

[0203] If the server detects an abnormality in the serial number based on the analysis results, it generates a notification message and sends it to the terminal. The input is the analysis result obtained in step 6, and the output is the message to be notified to the user.

[0204] Step 8:

[0205] The terminal receives a notification message from the server and displays it to the user. The input is the notification message sent from the server, and the output is the notification message displayed to the user. For example, a warning message such as "This banknote has an abnormal transaction history" is displayed.

[0206] Step 9:

[0207] The user checks the notification message from the server and, if necessary, stops using the bill or reports it to the relevant authorities. The input is the displayed notification message, and the output is the specific action the user takes.

[0208] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0209] System Overview

[0210] The present invention provides a banknote management system in which a terminal, a server, and a user work together. In particular, the present invention is equipped with a function that recognizes a user's emotions and provides adaptive notifications based on them by combining an emotion engine. The terminal acquires an image of the banknote and reads its serial number. The user enters transaction information, and the terminal sends this information to the server. The server collects this data and stores it in a database. The server then analyzes the collected data using an AI model to detect abnormalities in the banknote. The detected abnormalities are sent from the server to the terminal, and adaptive notifications are provided to the user using the emotion engine.

[0211] Device behavior

[0212] The terminal captures an image of the bill and reads its serial number. For example, when a user purchases an item at a convenience store, they pass the bill through the terminal's scanner. The terminal analyzes the image of the bill and extracts the serial number. Along with this serial number, the terminal also inputs the transaction information entered by the user (user ID, transaction ID, date, location). This information is then sent to the server.

[0213] The device is equipped with an emotion engine that recognizes the user's emotions, using the camera, microphone, and other sensors to analyze the user's facial expressions and tone of voice. For example, if the user has an anxious expression, the emotion engine will recognize this and adapt the tone of the warning message as needed.

[0214] Server Operation

[0215] The server receives the serial number and transaction information sent from the terminal. The received data is first stored in a database. The server then analyzes this data using an AI model. The AI ​​model has an algorithm that detects anomalies based on the banknote transaction information. For example, if a particular serial number is used in a large number of transactions in a short period of time, it is marked as an anomaly. If an anomaly is detected, the result is sent to the terminal.

[0216] User Actions

[0217] When making a transaction, the user scans the banknote using a terminal and inputs the transaction information into the terminal. After the transaction, the user checks for a notification from the server. For example, if the server notifies the user that "this banknote has an abnormal transaction history," the user can stop using the banknote or report it to the relevant authorities.

[0218] The emotion engine recognizes the user's emotions and provides adaptive notifications. For example, if the user has a surprised expression, the emotion engine softens the notification message and displays something like, "Do you want more information about this banknote?"

[0219] Specific examples

[0220] For example, if User B purchases a 500 yen item at a cafe, the following process can be considered: User B passes the 500 yen bill through the terminal's scanner to obtain its serial number. User B follows the instructions on the screen and enters their user ID, transaction ID, and the date and location of the transaction. The terminal then sends the entered information and the bill's serial number to the server. The server stores this information in a database and analyzes it using an AI model. If the analysis determines that the bill has a normal transaction history, no special notification is given to the user. However, if an abnormality is detected, the server sends the analysis results to the terminal, and the terminal notifies User B using an emotion engine. For example, if User B looks confused, the terminal may display the message, "This bill has an abnormal transaction history. Don't worry, please check the details."

[0221] The above is a specific embodiment of the present invention, which not only effectively prevents fraudulent use and counterfeiting of banknotes, but also improves the transparency of transactions and the sense of security of users.

[0222] The processing flow will be explained below.

[0223] Step 1:

[0224] The user scans the bill into the terminal, which uses the terminal's camera or scanner to capture an image of the bill.

[0225] Step 2:

[0226] The terminal analyzes the image of the banknote and reads its serial number, extracting it using image processing technology.

[0227] Step 3:

[0228] The user enters transaction information into the terminal, including the user ID, transaction ID, transaction date, and location.

[0229] Step 4:

[0230] The terminal encrypts the acquired serial number and entered transaction information and sends it to the server. Encryption ensures data security.

[0231] Step 5:

[0232] The server receives the serial number and transaction information and stores it in a database.

[0233] Step 6:

[0234] The server analyzes the transaction information in the database using an AI model, which has an algorithm to detect anomalies in the banknote transaction information.

[0235] Step 7:

[0236] If the server detects an anomaly as a result of the analysis, it will send the result to the terminal. If no anomaly is found, it will not notify the terminal.

[0237] Step 8:

[0238] The terminal displays the analysis results received from the server, for example, displaying a warning message if an abnormality is detected.

[0239] Step 9:

[0240] To recognize the user's emotions, the device uses a camera, microphone, and other sensors to analyze the user's facial expressions and voice, determining whether the user is feeling surprise, anxiety, confusion, etc.

[0241] Step 10:

[0242] The device's emotion engine adapts the tone and content of notification messages based on the recognized emotion, for example, softening the notification message if the user looks anxious.

[0243] Step 11:

[0244] The user checks the notification from the terminal and takes necessary action if an abnormality is detected, such as stopping the use of the banknote or reporting it to the relevant authorities.

[0245] The above is a specific processing flow of the present invention.

[0246] Example 2

[0247] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0248] Conventional banknote management systems have difficulty in ensuring sufficient transaction transparency and user security, making it difficult to detect fraudulent or counterfeit banknotes. Furthermore, because they do not provide adaptive notifications that take user emotions into consideration, anomaly detection results are often not accepted smoothly.

[0249] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting banknote serial numbers and transaction information and storing them in a database, means for detecting abnormalities in banknotes using a generative AI model based on the collected data, and means for transmitting the abnormality detection results to the terminal and notifying the user. This enables early detection of fraudulent use and counterfeit banknotes. Furthermore, the terminal uses an emotion engine to recognize the user's emotions and provides adaptive notifications, making it easier for the user to accept notifications and improving the transparency and security of transactions.

[0250] A "terminal" is a device that captures an image of a banknote and reads its serial number, and is a device through which a user inputs transaction information and communicates with a server.

[0251] The "server" is a central processing unit that collects and stores the serial numbers of banknotes and transaction information sent from the terminal and analyzes this data.

[0252] A "serial number" is a unique identification number assigned to each banknote, and is used to track transaction history and detect anomalies.

[0253] "Transaction information" is detailed data about the use of the banknote, such as user ID, transaction ID, date and location of the transaction.

[0254] "Collected data" is a data set containing banknote serial numbers and transaction information sent from the terminal.

[0255] A "database" is a system for structuring and storing data collected by a server.

[0256] A "generative AI model" is an artificial intelligence model that has algorithms for analyzing large amounts of data and detecting specific patterns and anomalies.

[0257] An "anomaly" is an unusual transaction pattern detected based on the banknote's serial number and transaction information, which may indicate fraud or counterfeiting.

[0258] An "emotion engine" is a system that uses sensors such as cameras and microphones to analyze a user's facial expressions and tone of voice and recognize the user's emotions.

[0259] An "adaptive notification" is a notification message whose content and tone are adjusted to take into account the user's emotions.

[0260] "Encryption" is a technology that converts data into a format that cannot be read by third parties, ensuring the security of communications.

[0261] "Update" is the process of introducing new data and algorithms to improve the performance of a generative AI model.

[0262] This invention is a banknote management system in which terminals, servers, and users work together. In particular, by combining an emotion engine, it has the function of recognizing users' emotions and providing adaptive notifications based on those emotions. This system aims to effectively prevent fraudulent use and counterfeiting of banknotes, improve transaction transparency, and increase user peace of mind.

[0263] Device behavior

[0264] The terminal captures an image of the bill the user passes through the scanner and reads its serial number. For example, when a user passes a 500 yen bill through the scanner to purchase an item at a convenience store, the terminal's camera takes an image of the bill and uses OCR (Optical Character Recognition) software to extract the serial number. The terminal then prompts the user to enter transaction information (user ID, transaction ID, date, and location). Once the user enters this information, the terminal sends it to the server. HTTPS is used as the communication protocol to ensure data security.

[0265] Server Operation

[0266] The server receives the serial number and transaction information sent from the device and stores this data in a database management system (e.g., MySQL or PostgreSQL). The stored data is analyzed using a generative AI model (e.g., TensorFlow or PyTorch). The analysis includes algorithms that detect anomalies, such as when the same serial number is used in multiple different places within a short period of time. If an anomaly is detected, the results are sent to the device.

[0267] User operations

[0268] When making a transaction, the user scans the banknote using the terminal and enters the transaction information. After the transaction, the user checks the notification from the server. For example, if the user receives a notification that "this banknote has an abnormal transaction history," the user is required to stop using the banknote or report it to the relevant authorities.

[0269] Emotion Engine Functions

[0270] The emotion engine uses the device's camera, microphone, and other sensors to analyze the user's facial expressions and tone of voice. If the user has a confused expression, the emotion engine will recognize this and display an adaptive notification. For example, a soft-spoken message might say, "This bill has an unusual transaction history. Don't worry, please check the details."

[0271] Specific examples

[0272] For example, if User B purchases a 500 yen item at a cafe, the following process can be considered: User B passes the 500 yen bill through the terminal's scanner to obtain the serial number. Next, User B follows the instructions on the screen to enter their user ID, transaction ID, and the date and location of the transaction. The terminal sends the entered information and the bill's serial number to the server. The server stores this information in a database and analyzes it using a generative AI model. If the analysis results indicate that the bill has a normal transaction history, no notification is sent to the user. However, if an abnormality is detected, the server sends the analysis results to the terminal, and the terminal notifies User B using an emotion engine. For example, if User B looks confused, the terminal may display a message saying, "This bill has an abnormal transaction history. Don't worry, please check the details."

[0273] Example of input prompt for generative AI model

[0274] "How can I detect anomalies based on banknote serial numbers and transaction information?"

[0275] The above is a specific embodiment of the present invention, which not only effectively prevents fraudulent use and counterfeiting of banknotes, but also improves the transparency of transactions and the sense of security of users.

[0276] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0277] Step 1:

[0278] The terminal captures an image of the bill and reads its serial number.

[0279] Specifically, when a user swipes a banknote through the terminal's scanner, the terminal's camera takes an image of the banknote. This image is analyzed using OCR software to extract the serial number. The input is the image of the banknote, and the output is the banknote's serial number.

[0280] Step 2:

[0281] The terminal allows the user to enter transaction information.

[0282] For example, a user follows instructions on the terminal to enter a user ID, transaction ID, transaction date, and location. These input fields are displayed on the terminal screen, and the user enters the information using a keyboard or touch panel. The input is the user's transaction information, and the output is a dataset of the entered transaction information.

[0283] Step 3:

[0284] The terminal sends the banknote serial number and transaction information to the server.

[0285] This causes the terminal to send the serial number and transaction information to the server using the HTTPS protocol. The input is a dataset of the serial number and transaction information, and the output is the secure data transfer to the server.

[0286] Step 4:

[0287] The server stores the received data in a database.

[0288] The server receives data sent from the devices and stores it in a database. This data is stored in a database management system (e.g., MySQL, PostgreSQL). The input is the data received from the devices, and the output is the data records stored in the database.

[0289] Step 5:

[0290] The server analyzes the data using a generative AI model to detect anomalies.

[0291] The server retrieves the stored data from the database and analyzes it using a generative AI model (e.g., TensorFlow, PyTorch). For example, if the same serial number is used in multiple places within a short period of time, this can be detected as an anomaly. The input is the transaction information stored in the database, and the output is the result of the anomaly detection.

[0292] Step 6:

[0293] The server sends the abnormality detection result to the terminal.

[0294] If an anomaly is detected, the server sends the result to the terminal. The server composes the analysis result in JSON format and sends it as an HTTP response. The input is the anomaly detection result, and the output is the data transfer to the terminal.

[0295] Step 7:

[0296] The terminal notifies the user using an emotion engine.

[0297] The device applies an emotion engine based on the anomaly detection results received from the server to recognize the user's emotions. It uses a camera and microphone to analyze the user's facial expressions and tone of voice and displays an adaptive notification. For example, if the user has a confused expression, it displays a message saying, "This banknote has an abnormal transaction history. Don't worry, please check the details." The input is the anomaly detection results from the server and the user's emotional data, and the output is a notification message.

[0298] Step 8:

[0299] The user acknowledges the notification and takes the necessary action.

[0300] The user checks the notification message displayed on the terminal and, if necessary, stops using the banknote or reports it to the relevant authorities. The input is the notification message, and the output is the user's action.

[0301] The above are the specific processing steps of this system.

[0302] (Application example 2)

[0303] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0304] Conventional banknote management systems only detect abnormalities in banknotes and do not provide notifications based on the user's emotions, which means that the user's sense of security and transaction transparency are not sufficiently ensured. Furthermore, notifying only the user of detected banknote abnormalities can cause excessive anxiety and confusion. In response to this, the present invention aims to improve the user's sense of security and transaction transparency by combining a user emotion analysis function with a banknote management system to provide adaptive notifications based on the user's emotions.

[0305] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a terminal to acquire an image of a banknote and read its serial number, means for a user to input transaction information, means for the terminal to transmit the banknote serial number and transaction information, means for the server to collect the banknote serial number and transaction information and store them in a database, means for the server to detect anomalies in the banknote using an AI model based on the collected data, means for the terminal to analyze the user's emotions and adaptively issue a notification based on the emotions, means for the server to transmit the anomaly detection result to the terminal and notify the user, and means for the user to check the notification and take necessary action. This enables adaptive notifications that take the user's emotions into consideration, avoiding excessive anxiety and confusion and improving transaction transparency and user peace of mind.

[0306] A "terminal" is an electronic device in the system that captures images of banknotes and reads their serial numbers.

[0307] "Emotion analysis" is the process of analyzing a user's facial expressions and tone of voice to identify their emotional state.

[0308] "Transaction information" refers to information entered by a user, including a user ID, transaction ID, date, location, and the like.

[0309] A "serial number" is a unique identification code printed on a banknote that is used to track and verify the banknote.

[0310] "Transmission means" refers to a technology for electronically transmitting the serial number of the banknote and transaction information from the terminal to the server.

[0311] The "collection means" is the process by which the server receives the serial number of the banknote and the transaction information sent from the terminal and stores them in a database.

[0312] A "database" is a collection of banknote serial numbers and transaction information managed within the system.

[0313] An "AI model" is an artificial intelligence algorithm used by the server to detect anomalies based on banknote transaction information.

[0314] "Anomaly detection" is the process by which the server uses AI models to identify abnormal transaction patterns on banknotes.

[0315] "Adaptive notifications" is a technology in which a terminal notifies a user with an appropriate tone based on the user's emotional state.

[0316] The "notification means" is a method for transmitting information from the terminal to the user when an abnormal banknote transaction is detected.

[0317] "Means of action" refers to specific actions or instructions that a user should take when receiving a notification.

[0318] The present invention relates to a banknote management system that detects abnormalities in banknotes and provides adaptive notification based on the user's emotions. Next, the operation and embodiments of this system will be described in detail.

[0319] 1. System Overview

[0320] The system consists of the following main components:

[0321] Terminal

[0322] server

[0323] Database

[0324] Sentiment Analysis Engine

[0325] AI model

[0326] 2. Device operation

[0327] The device uses the following hardware and software:

[0328] Hardware: Smartphone camera

[0329] Software: OpenCV (image analysis), Pytesseract (character recognition)

[0330] The terminal captures an image of the bill when the user scans it with a camera. From this image, it uses Pytesseract to extract the serial number. The terminal also provides an interface for the user to input transaction information (user ID, transaction ID, date, and location). Furthermore, it utilizes an emotion analysis engine to analyze the user's facial expressions and tone of voice to identify the user's emotional state.

[0331] 3. Server operation

[0332] The server uses the following software:

[0333] Database: MySQL, PostgreSQL, etc.

[0334] AI model: Anomaly detection algorithm using TensorFlow and PyTorch

[0335] The server receives the serial numbers and transaction information sent from the terminal and stores them in a database. Based on the stored data, an AI model is used to detect anomalies in the banknotes. Specifically, it identifies serial numbers that have been frequently used in transactions over a short period of time and other abnormal patterns.

[0336] 4.Means of notification

[0337] When the server detects an anomaly, it sends the result to the device, which then displays an adaptive notification based on the user's emotional state. For example, if the user has a surprised expression, the notification message is softened.

[0338] 5. User operations

[0339] The user checks the notification and takes necessary action, such as canceling transactions of the banknotes in which the abnormality was detected or reporting it to the relevant authorities.

[0340] Specific examples

[0341] For example, when a user uses a 500 yen bill at a cafe, they scan the bill with their smartphone camera to obtain the serial number. Then, they enter the user ID, transaction ID, date, and location into the terminal. If the system detects an anomaly and the user looks confused, the emotion analysis engine will display a message saying, "There is an anomaly in the transaction history of this bill. Don't worry, please check the details."

[0342] Prompt Sentence Examples

[0343] "Scan the serial number of a 500 yen bill with your smartphone camera and enter your transaction information. We then analyze the captured user's facial expressions and provide adaptive notifications based on their emotions."

[0344] In this way, the banknote management system of the present invention can prevent fraudulent use and counterfeiting of banknotes, and improve the transparency of transactions and the sense of security of users.

[0345] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0346] Step 1:

[0347] The terminal uses the smartphone camera to capture an image of the banknote. The user holds the banknote in front of the camera and presses the capture button to capture the image. The input is the image of the banknote that was captured, and the output is the captured image data.

[0348] Step 2:

[0349] To extract the serial number from the captured image of the banknote, the terminal preprocesses the image using OpenCV and performs optical character recognition using Pytesseract. The input is the preprocessed image, and the output is text data containing the banknote serial number.

[0350] Step 3:

[0351] The user uses the terminal interface to input transaction information (user ID, transaction ID, date, location). The input is the data manually entered by the user, and the output is the transaction information data stored in the terminal.

[0352] Step 4:

[0353] The device uses an emotion analysis engine to analyze the user's facial expressions and tone of voice. The camera captures the user's facial expressions, and the microphone records any audio input. The input is the captured facial image and audio data, and the output is data indicating the user's emotional state.

[0354] Step 5:

[0355] The terminal sends the serial number, transaction information, and emotional state data to the server. The transmission is via HTTPS protocol and the data is encrypted. The input is the serial number, transaction information, and emotional state data, and the output is the transmission completion status to the server.

[0356] Step 6:

[0357] The server receives the data sent from the terminal and stores it in a database. The stored data includes the serial number, transaction information, and the user's emotional state. The input is the sent data, and the output is the record stored in the database.

[0358] Step 7:

[0359] The server uses the stored data to detect anomalies in banknotes using an AI model, matching the serial number with transaction history and identifying abnormal patterns. The input is transaction information and serial number retrieved from the database, and the output is the anomaly detection result.

[0360] Step 8:

[0361] The server sends the anomaly detection results to the terminal. The HTTPS protocol is used for transmission, and the data is encrypted. The input is the anomaly detection result data, and the output is the transmission completion status to the terminal.

[0362] Step 9:

[0363] The device analyzes the anomaly detection results received from the server and displays an adaptive notification according to the user's emotional state. The input is the anomaly detection result data and the emotional state data, and the output is a notification message displayed to the user.

[0364] Step 10:

[0365] The user checks the notification from the terminal and takes necessary action. For example, if an abnormal banknote is detected, the user may stop using the banknote or report it to the relevant authorities. The input is the notification message from the terminal, and the output is the specific action taken by the user.

[0366] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0367] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0368] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0369] [Second embodiment]

[0370] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0371] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0372] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0373] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0374] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0375] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0376] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0377] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0378] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0379] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0380] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0381] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0382] System Overview

[0383] The present invention provides a banknote management system in which a terminal, a server, and a user work together. The terminal captures an image of the banknote and reads its serial number. The user inputs transaction information, and the terminal sends this information to the server. The server collects this data and stores it in a database. The server then analyzes the collected data using an AI model to detect abnormalities in the banknote. The detected abnormalities are sent from the server to the terminal and notified to the user.

[0384] Device behavior

[0385] The terminal captures an image of the bill and reads its serial number. For example, when a user purchases an item at a convenience store, they pass the bill through the terminal's scanner. The terminal analyzes the image of the bill and extracts the serial number. Along with this serial number, the terminal also inputs the transaction information entered by the user (user ID, transaction ID, date, location). This information is then sent to the server.

[0386] Server Operation

[0387] The server receives the serial number and transaction information sent from the terminal. The received data is first stored in a database. The server then analyzes this data using an AI model. The AI ​​model has an algorithm that detects anomalies based on the banknote transaction information. For example, if a particular serial number is used in a large number of transactions in a short period of time, it is marked as an anomaly. If an anomaly is detected, the result is sent to the terminal.

[0388] User Actions

[0389] When making a transaction, the user scans the banknote using a terminal and inputs the transaction information into the terminal. After the transaction, the user checks for a notification from the server. For example, if the server notifies the user that "this banknote has an abnormal transaction history," the user can stop using the banknote or report it to the relevant authorities.

[0390] Specific examples

[0391] For example, when User A purchases a 1,000 yen item at a convenience store, the following process can be considered: User A passes a 1,000 yen bill through the terminal's scanner to obtain its serial number. User A follows the instructions on the screen and enters their user ID, transaction ID, and the date and location of the transaction. The terminal then sends the entered information and the bill's serial number to the server. The server stores this information in a database and analyzes it using an AI model. If the analysis determines that the bill has a normal transaction history, no particular notification is sent to the user. However, if an abnormality is detected, the server sends the analysis results to the terminal, which then notifies User A. For example, the terminal may display a message saying, "This bill has an abnormal transaction history." User A confirms this notification and either discontinues use of the bill or reports it to the appropriate authorities.

[0392] The above is a specific embodiment of the present invention. The present invention not only effectively prevents fraudulent use and counterfeiting of banknotes, but also improves the transparency of transactions.

[0393] The processing flow will be explained below.

[0394] Step 1:

[0395] The terminal captures an image of the bill and reads its serial number, and when the user swipes the bill through the scanner, the terminal uses image processing techniques to extract the serial number.

[0396] Step 2:

[0397] The user enters transaction information into the terminal, specifically, the user ID, transaction ID, date and location of the transaction.

[0398] Step 3:

[0399] The terminal encrypts the acquired serial number and entered transaction information and sends it to the server, thereby protecting the data.

[0400] Step 4:

[0401] The server receives the transmitted serial number and transaction information and stores this information in a database.

[0402] Step 5:

[0403] The server uses an AI model to analyze the transaction information received from the database, with the goal of detecting any abnormalities in the banknotes.

[0404] Step 6:

[0405] If the server detects an abnormality based on the analysis results, it will send the result to the terminal. For example, a serial number that has been traded many times in a short period of time will be deemed abnormal.

[0406] Step 7:

[0407] The device notifies the user of the analysis results received from the server, and the notification message includes details of the anomaly.

[0408] Step 8:

[0409] Users can check the notification and take necessary action, such as stopping the use of the banknotes on which an abnormality has been detected or reporting it to the relevant authorities.

[0410] The above is a specific processing flow of this system.

[0411] Example 1

[0412] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0413] Conventional banknote management systems have difficulty effectively preventing fraudulent use and counterfeiting of banknotes, and lack the means to ensure transaction transparency. Furthermore, technology for efficiently managing banknote serial numbers and detecting anomalies has not been fully established. This has made it difficult to track banknotes and detect fraud early, leading to problems such as a decline in the credibility of financial institutions and transactions.

[0414] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0415] In this invention, the server includes a means for collecting banknote serial numbers and transaction information and storing them in a database, a means for detecting abnormalities in banknotes using a generative AI model based on the collected data, and a means for transmitting the abnormality detection results to a terminal and notifying the user, thereby enabling early detection of fraudulent use and counterfeit banknotes and improving transaction transparency.

[0416] A "terminal" is a device that includes hardware and software that allows a user to capture an image of a banknote and read its serial number.

[0417] A "user" is a person or organization that uses the banknote management system to input transaction information and manage banknotes.

[0418] "Transaction Information" refers to information related to a transaction, such as user ID, transaction ID, transaction date, and location.

[0419] A "serial number" is a unique identification number printed on a banknote.

[0420] The "server" is a central center that collects banknote serial numbers and transaction information sent from terminals, stores them in a database, and analyzes them.

[0421] "Database" refers to a system for storing and managing banknote serial numbers and transaction information collected by the server.

[0422] A "generative AI model" is an artificial intelligence model used to detect anomalies in banknotes based on collected data.

[0423] An "anomaly" is a condition that exhibits an unusual or unexpected pattern or behavior within a series of transactions.

[0424] A "notification" is a message containing an abnormality detection result sent from the server to the user.

[0425] MODE FOR CARRYING OUT THE INVENTION

[0426] The present invention provides a banknote management system in which a terminal, a server, and a user work in cooperation with each other. The operation of this system and details of its hardware and software will be described in the following specific embodiment.

[0427] System Overview

[0428] In this system, the terminal captures an image of the banknote and reads its serial number. The user enters transaction information, and the terminal sends this information to the server. The server collects this data and stores it in a database. The server then analyzes the collected data using a generative AI model to detect abnormalities in the banknote. The server then sends the detected abnormalities to the terminal and notifies the user.

[0429] Hardware and Software

[0430] Terminal: A terminal equipped with a high-resolution CCD scanner and a touch panel. OpenCV is used as image processing software.

[0431] Server: MySQL is used as the database, and generative AI models are used for data analysis. TensorFlow and PyTorch are used to build the generative AI models.

[0432] Device behavior

[0433] The terminal performs the following operations:

[0434] 1. The user swipes the bill through the terminal's scanner.

[0435] 2. The device's scanner captures an image of the banknote and performs image analysis using OpenCV.

[0436] 3. Read the banknote serial number from the analyzed image.

[0437] 4. The user follows the instructions displayed on the screen and enters the necessary transaction information, such as user ID, transaction ID, transaction date and location, via the touch panel.

[0438] 5. The terminal encrypts the serial number and transaction information it has obtained and sends them to the server.

[0439] Examples:

[0440] A user passes a 1,000 yen bill through a scanner at a convenience store, obtaining the serial number "AB12345678." The user then enters transaction information, such as "User ID: user123," "Transaction ID: TX001," "Date: 2023-10-01," and "Location: Convenience Store A." The terminal then encrypts this information and sends it to the server.

[0441] Server Operation

[0442] The server performs the following actions:

[0443] 1. Receive the serial number and transaction information sent from the terminal.

[0444] 2. Save the received data in the database.

[0445] 3. The saved data is input into the generative AI model for analysis.

[0446] 4. The generative AI model has an algorithm that detects anomalies based on transaction information on banknotes. For example, if a particular serial number is used in a large number of transactions in a short period of time, it will mark this as an anomaly.

[0447] 5. If an abnormality is detected, the result is sent to the terminal.

[0448] Examples:

[0449] The server receives the user's transaction information and serial number, stores them in a database, and then analyzes them using an anomaly detection model using TensorFlow. If an anomaly is detected, the server sends a notification to the terminal stating, "This banknote has an abnormal transaction history."

[0450] User Actions

[0451] The user performs the following actions:

[0452] 1. When making a transaction, use the terminal to scan the bill.

[0453] 2. Enter the transaction information into the terminal.

[0454] 3. After the transaction, check the notification from the server. If an abnormality is detected, take action according to the notification.

[0455] Examples:

[0456] A user purchases a 1000 yen item at a convenience store, scans the bill, and enters the transaction information. If the server notifies the user that "this bill has an abnormal transaction history," the user should stop using the bill and report it to the relevant authorities.

[0457] Prompt Sentence Examples

[0458] An example of a prompt to input to a generative AI model is:

[0459] 1. "If a note with this serial number has been used in many transactions in a short period of time, how can I mark it as abnormal?"

[0460] 2. "Describe how you can improve an algorithm for extracting banknote serial numbers from images."

[0461] The above is a specific embodiment of the present invention, which makes it possible to effectively prevent fraudulent use and counterfeiting of banknotes and improve the transparency of transactions.

[0462] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0463] The flow of this system's program processing

[0464] Step 1:

[0465] The terminal captures an image of the bill and reads the serial number.

[0466] Input: The user swipes a bill through the terminal's scanner.

[0467] How it works: The device's high-resolution CCD scanner captures an image of the bill. OpenCV is used to analyze the image and extract the serial number.

[0468] Output: The extracted serial number (e.g. "AB12345678").

[0469] Step 2:

[0470] The user enters the transaction information into the terminal.

[0471] Input: Information such as user ID, transaction ID, transaction date, and transaction location.

[0472] Specific operation: The user uses the device's touch panel to enter transaction information according to the instructions on the screen.

[0473] Output: The entered transaction information (e.g., "User ID: user123", "Transaction ID: TX001", "Date: 2023-10-01", "Location: Convenience Store A").

[0474] Step 3:

[0475] The terminal encrypts the banknote's serial number and transaction information and sends it to the server.

[0476] Input: Serial number, transaction information.

[0477] Specific operation: The terminal encrypts the serial number and transaction information using the HTTPS protocol and sends it to the server.

[0478] Output: The encrypted serial number and transaction information are sent to the server.

[0479] Step 4:

[0480] The server receives the banknote serial number and transaction information and stores it in a database.

[0481] Input: Encrypted serial number and transaction information.

[0482] Specific operation: The server receives the encrypted data, decrypts it, and stores it in a database (e.g., MySQL).

[0483] Output: Transaction information and serial number stored in the database.

[0484] Step 5:

[0485] The server analyzes the collected data using a generative AI model.

[0486] Input: Transaction information and serial number stored in the database.

[0487] How it works: The server inputs the stored data into a generative AI model (e.g., TensorFlow) and analyzes the data. The analysis algorithm analyzes the banknote transaction history and detects anomalies.

[0488] Output: If an anomaly is detected, it is flagged and detailed information.

[0489] Step 6:

[0490] If an abnormality is detected, the server transmits the result to the terminal.

[0491] Input: Analysis results (abnormality flags and detailed information).

[0492] Specific operation: The server creates a message to notify the user based on the analysis results and sends it to the terminal using the HTTPS protocol.

[0493] Output: A message of the anomaly detection result is sent to the terminal.

[0494] Step 7:

[0495] The terminal displays an abnormality notification from the server to the user.

[0496] Input: The anomaly detection message sent by the server.

[0497] Specific operation: The terminal receives a message from the server and displays it on the screen to notify the user of an abnormality.

[0498] Output: The user confirms the abnormality notification.

[0499] Step 8:

[0500] The user checks the notification and takes the necessary action.

[0501] Input: Abnormal notification displayed on the terminal.

[0502] Specific action: The user checks the notification and either stops using the note or reports it to the appropriate authorities.

[0503] Output: Prevents fraud and counterfeiting by requiring the user to take appropriate action.

[0504] The above are the specific processing steps of the program of this system.

[0505] (Application example 1)

[0506] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0507] Conventional banknote management systems have had the problem of making it difficult to quickly detect fraudulent or counterfeit banknotes. In particular, they have been unable to ensure transaction transparency or detect abnormal banknote transactions in real time, limiting their ability to prevent fraud.

[0508] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0509] In this invention, the server includes a means for a terminal to collect banknote serial numbers and transaction information and store them in a database, a means for detecting abnormalities in banknotes using an AI model based on the collected data, a means for transmitting the abnormality detection results to the terminal and notifying the user, a means for capturing images of banknotes using a smartphone camera and extracting serial numbers using image processing technology, and a means for notifying the user in real time when an abnormal transaction is detected. This makes it possible to detect fraudulent use and counterfeit banknotes in real time and increase transaction transparency.

[0510] A "terminal" is a device used by a user to capture images of banknotes and read their serial numbers. It also has the function of inputting transaction information and sending it to a server.

[0511] "Capturing an image of a banknote" refers to the act of capturing image data of the banknote using a smartphone camera or the like.

[0512] "Reading the serial number" refers to the act of extracting the serial number from an image of a banknote using optical character recognition (OCR) technology.

[0513] "Transaction Information" refers to detailed information related to a transaction, such as user ID, transaction ID, transaction date, and location.

[0514] The "server" is a computer system that receives the banknote serial numbers and transaction information sent from the terminal, and stores and analyzes this data in a database.

[0515] A "database" is a memory space for storing and managing banknote serial numbers and transaction information collected by the server.

[0516] "AI model" refers to artificial intelligence (AI) that has algorithms to analyze collected data and detect abnormalities in banknotes.

[0517] "Anomaly detection" refers to the act of using AI models to analyze a banknote's transaction history and identify unusual transaction patterns.

[0518] "Notifying the user" refers to the act of the server sending the abnormality detection result to the terminal so that the user can check it.

[0519] "Image processing technology" refers to the general technology used to analyze images of banknotes and extract serial numbers. Libraries such as OpenCV are mainly used.

[0520] "Real-time notification" refers to the act of sending information about an abnormality to the user almost immediately upon detection.

[0521] System Overview

[0522] The present invention is a banknote management system in which a terminal, a server, and a user work together. The terminal acquires an image of the banknote and reads its serial number. The user inputs transaction information, and the terminal sends this information to the server. The server collects this data and stores it in a database. The server then analyzes the collected data using an AI model to detect abnormalities in the banknote. The detected abnormalities are sent from the server to the terminal and notified to the user.

[0523] Device behavior

[0524] The terminal uses a smartphone. First, an image of the banknote is captured using the smartphone's camera. Next, the captured image is processed using the OpenCV library, and the serial number is extracted using OCR technology. The user enters transaction information (user ID, transaction ID, date, location) into the application. This data is sent from the terminal to the server.

[0525] Server Operation

[0526] The server receives the serial number and transaction information sent from the terminal. The received data is first stored in a database. The server then uses TensorFlow to build an AI model and analyze this data. The AI ​​model has an algorithm that detects anomalies based on the banknote transaction information. For example, if a particular serial number is used in a large number of transactions in a short period of time, it is marked as an anomaly. If an anomaly is detected, the result is sent to the terminal.

[0527] User Actions

[0528] When making a transaction, the user scans the banknote using their smartphone and enters the transaction information into the terminal. After the transaction, the user checks the notification from the server and takes the necessary action. For example, if the server notifies the user that "this banknote has an abnormal transaction history," the user can stop using the banknote or report it to the relevant authorities.

[0529] Specific examples

[0530] For example, when User A purchases a 1,000 yen item at a store, the process can be as follows: User A holds a 1,000 yen bill over their smartphone camera and captures an image. The application follows the instructions on the screen and enters the user ID, transaction ID, and transaction date and location. The device sends the entered information and the bill's serial number to the server. The server stores this information in a database and uses TensorFlow to analyze it with an AI model. If the analysis results indicate that the bill has a normal transaction history, no notification is sent to the user. However, if an abnormality is detected, the server sends the analysis results to the device, which notifies User A. For example, the device may display a message saying, "This bill has an abnormal transaction history." User A confirms this notification and either discontinues use of the bill or reports it to the appropriate authorities.

[0531] Prompt Sentence Examples

[0532] "We will analyze all transaction history based on this transaction information and the banknote serial number to determine whether there is any suspicion of fraud."

[0533] As explained above, by using the system of the present invention, it is possible to detect fraudulent use and counterfeiting of banknotes in real time and increase the transparency of transactions.

[0534] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0535] Step 1:

[0536] The terminal (smartphone) acquires an image of a banknote through user operation. The image of the banknote is taken using the smartphone's camera. The input is the actual banknote, which is then acquired as a digital image. The output is a digital image of the acquired banknote.

[0537] Step 2:

[0538] The image of the banknote acquired by the terminal is preprocessed using OpenCV to extract the serial number. Specifically, the image is converted to grayscale, subjected to adaptive binarization processing, and then the serial number is extracted as text data using OCR (Optical Character Recognition) technology. The input is the digital image of the banknote acquired in Step 1, and the output is the banknote serial number.

[0539] Step 3:

[0540] The user inputs transaction information (user ID, transaction ID, date, location) into the terminal. The input is manual input by the user, and the output is digital data of the transaction information entered by the user.

[0541] Step 4:

[0542] The terminal extracts the serial number and sends the transaction information entered by the user to the server. The input is digital data of the serial number and transaction information, and the output is a process of encrypting this data and sending it to the server via a communication network.

[0543] Step 5:

[0544] The server stores the received serial number and transaction information in a database. The input is the encrypted data of the serial number and transaction information received over the network, and the output is this data stored in the database.

[0545] Step 6:

[0546] The server uses a generative AI model (TensorFlow) based on data collected from the database to detect anomalies in banknotes. The input is the digital data of the serial number and transaction information stored in the database, and the prompt for the AI ​​model is "Analyze all transaction history based on this transaction information and the banknote serial number and determine whether there is any suspicion of fraud." The output is the analysis result indicating whether an anomaly was detected.

[0547] Step 7:

[0548] If the server detects an abnormality in the serial number based on the analysis results, it generates a notification message and sends it to the terminal. The input is the analysis result obtained in step 6, and the output is the message to be notified to the user.

[0549] Step 8:

[0550] The terminal receives a notification message from the server and displays it to the user. The input is the notification message sent from the server, and the output is the notification message displayed to the user. For example, a warning message such as "This banknote has an abnormal transaction history" is displayed.

[0551] Step 9:

[0552] The user checks the notification message from the server and, if necessary, stops using the bill or reports it to the relevant authorities. The input is the displayed notification message, and the output is the specific action the user takes.

[0553] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0554] System Overview

[0555] The present invention provides a banknote management system in which a terminal, a server, and a user work together. In particular, the present invention is equipped with a function that recognizes a user's emotions and provides adaptive notifications based on them by combining an emotion engine. The terminal acquires an image of the banknote and reads its serial number. The user enters transaction information, and the terminal sends this information to the server. The server collects this data and stores it in a database. The server then analyzes the collected data using an AI model to detect abnormalities in the banknote. The detected abnormalities are sent from the server to the terminal, and adaptive notifications are provided to the user using the emotion engine.

[0556] Device behavior

[0557] The terminal captures an image of the bill and reads its serial number. For example, when a user purchases an item at a convenience store, they pass the bill through the terminal's scanner. The terminal analyzes the image of the bill and extracts the serial number. Along with this serial number, the terminal also inputs the transaction information entered by the user (user ID, transaction ID, date, location). This information is then sent to the server.

[0558] The device is equipped with an emotion engine that recognizes the user's emotions, using the camera, microphone, and other sensors to analyze the user's facial expressions and tone of voice. For example, if the user has an anxious expression, the emotion engine will recognize this and adapt the tone of the warning message as needed.

[0559] Server Operation

[0560] The server receives the serial number and transaction information sent from the terminal. The received data is first stored in a database. The server then analyzes this data using an AI model. The AI ​​model has an algorithm that detects anomalies based on the banknote transaction information. For example, if a particular serial number is used in a large number of transactions in a short period of time, it is marked as an anomaly. If an anomaly is detected, the result is sent to the terminal.

[0561] User Actions

[0562] When making a transaction, the user scans the banknote using a terminal and inputs the transaction information into the terminal. After the transaction, the user checks for a notification from the server. For example, if the server notifies the user that "this banknote has an abnormal transaction history," the user can stop using the banknote or report it to the relevant authorities.

[0563] The emotion engine recognizes the user's emotions and provides adaptive notifications. For example, if the user has a surprised expression, the emotion engine softens the notification message and displays something like, "Do you want more information about this banknote?"

[0564] Specific examples

[0565] For example, if User B purchases a 500 yen item at a cafe, the following process can be considered: User B passes the 500 yen bill through the terminal's scanner to obtain its serial number. User B follows the instructions on the screen and enters their user ID, transaction ID, and the date and location of the transaction. The terminal then sends the entered information and the bill's serial number to the server. The server stores this information in a database and analyzes it using an AI model. If the analysis determines that the bill has a normal transaction history, no special notification is given to the user. However, if an abnormality is detected, the server sends the analysis results to the terminal, and the terminal notifies User B using an emotion engine. For example, if User B looks confused, the terminal may display the message, "This bill has an abnormal transaction history. Don't worry, please check the details."

[0566] The above is a specific embodiment of the present invention, which not only effectively prevents fraudulent use and counterfeiting of banknotes, but also improves the transparency of transactions and the sense of security of users.

[0567] The processing flow will be explained below.

[0568] Step 1:

[0569] The user scans the bill into the terminal, which uses the terminal's camera or scanner to capture an image of the bill.

[0570] Step 2:

[0571] The terminal analyzes the image of the banknote and reads its serial number, extracting it using image processing technology.

[0572] Step 3:

[0573] The user enters transaction information into the terminal, including the user ID, transaction ID, transaction date, and location.

[0574] Step 4:

[0575] The terminal encrypts the acquired serial number and entered transaction information and sends it to the server. Encryption ensures data security.

[0576] Step 5:

[0577] The server receives the serial number and transaction information and stores it in a database.

[0578] Step 6:

[0579] The server analyzes the transaction information in the database using an AI model, which has an algorithm to detect anomalies in the banknote transaction information.

[0580] Step 7:

[0581] If the server detects an anomaly as a result of the analysis, it will send the result to the terminal. If no anomaly is found, it will not notify the terminal.

[0582] Step 8:

[0583] The terminal displays the analysis results received from the server, for example, displaying a warning message if an abnormality is detected.

[0584] Step 9:

[0585] To recognize the user's emotions, the device uses a camera, microphone, and other sensors to analyze the user's facial expressions and voice, determining whether the user is feeling surprise, anxiety, confusion, etc.

[0586] Step 10:

[0587] The device's emotion engine adapts the tone and content of notification messages based on the recognized emotion, for example, softening the notification message if the user looks anxious.

[0588] Step 11:

[0589] The user checks the notification from the terminal and takes necessary action if an abnormality is detected, such as stopping the use of the banknote or reporting it to the relevant authorities.

[0590] The above is a specific processing flow of the present invention.

[0591] Example 2

[0592] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0593] Conventional banknote management systems have difficulty in ensuring sufficient transaction transparency and user security, making it difficult to detect fraudulent or counterfeit banknotes. Furthermore, because they do not provide adaptive notifications that take user emotions into consideration, anomaly detection results are often not accepted smoothly.

[0594] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting banknote serial numbers and transaction information and storing them in a database, means for detecting abnormalities in banknotes using a generative AI model based on the collected data, and means for transmitting the abnormality detection results to the terminal and notifying the user. This enables early detection of fraudulent use and counterfeit banknotes. Furthermore, the terminal uses an emotion engine to recognize the user's emotions and provides adaptive notifications, making it easier for the user to accept notifications and improving the transparency and security of transactions.

[0595] A "terminal" is a device that captures an image of a banknote and reads its serial number, and is a device through which a user inputs transaction information and communicates with a server.

[0596] The "server" is a central processing unit that collects and stores the serial numbers of banknotes and transaction information sent from the terminal and analyzes this data.

[0597] A "serial number" is a unique identification number assigned to each banknote, and is used to track transaction history and detect anomalies.

[0598] "Transaction information" is detailed data about the use of the banknote, such as user ID, transaction ID, date and location of the transaction.

[0599] "Collected data" is a data set containing banknote serial numbers and transaction information sent from the terminal.

[0600] A "database" is a system for structuring and storing data collected by a server.

[0601] A "generative AI model" is an artificial intelligence model that has algorithms for analyzing large amounts of data and detecting specific patterns and anomalies.

[0602] An "anomaly" is an unusual transaction pattern detected based on the banknote's serial number and transaction information, which may indicate fraud or counterfeiting.

[0603] An "emotion engine" is a system that uses sensors such as cameras and microphones to analyze a user's facial expressions and tone of voice and recognize the user's emotions.

[0604] An "adaptive notification" is a notification message whose content and tone are adjusted to take into account the user's emotions.

[0605] "Encryption" is a technology that converts data into a format that cannot be read by third parties, ensuring the security of communications.

[0606] "Update" is the process of introducing new data and algorithms to improve the performance of a generative AI model.

[0607] This invention is a banknote management system in which terminals, servers, and users work together. In particular, by combining an emotion engine, it has the function of recognizing users' emotions and providing adaptive notifications based on those emotions. This system aims to effectively prevent fraudulent use and counterfeiting of banknotes, improve transaction transparency, and increase user peace of mind.

[0608] Device behavior

[0609] The terminal captures an image of the bill the user passes through the scanner and reads its serial number. For example, when a user passes a 500 yen bill through the scanner to purchase an item at a convenience store, the terminal's camera takes an image of the bill and uses OCR (Optical Character Recognition) software to extract the serial number. The terminal then prompts the user to enter transaction information (user ID, transaction ID, date, and location). Once the user enters this information, the terminal sends it to the server. HTTPS is used as the communication protocol to ensure data security.

[0610] Server Operation

[0611] The server receives the serial number and transaction information sent from the device and stores this data in a database management system (e.g., MySQL or PostgreSQL). The stored data is analyzed using a generative AI model (e.g., TensorFlow or PyTorch). The analysis includes algorithms that detect anomalies, such as when the same serial number is used in multiple different places within a short period of time. If an anomaly is detected, the results are sent to the device.

[0612] User operations

[0613] When making a transaction, the user scans the banknote using the terminal and enters the transaction information. After the transaction, the user checks the notification from the server. For example, if the user receives a notification that "this banknote has an abnormal transaction history," the user is required to stop using the banknote or report it to the relevant authorities.

[0614] Emotion Engine Functions

[0615] The emotion engine uses the device's camera, microphone, and other sensors to analyze the user's facial expressions and tone of voice. If the user has a confused expression, the emotion engine will recognize this and display an adaptive notification. For example, a soft-spoken message might say, "This bill has an unusual transaction history. Don't worry, please check the details."

[0616] Specific examples

[0617] For example, if User B purchases a 500 yen item at a cafe, the following process can be considered: User B passes the 500 yen bill through the terminal's scanner to obtain the serial number. Next, User B follows the instructions on the screen to enter their user ID, transaction ID, and the date and location of the transaction. The terminal sends the entered information and the bill's serial number to the server. The server stores this information in a database and analyzes it using a generative AI model. If the analysis results indicate that the bill has a normal transaction history, no notification is sent to the user. However, if an abnormality is detected, the server sends the analysis results to the terminal, and the terminal notifies User B using an emotion engine. For example, if User B looks confused, the terminal may display a message saying, "This bill has an abnormal transaction history. Don't worry, please check the details."

[0618] Example of input prompt for generative AI model

[0619] "How can I detect anomalies based on banknote serial numbers and transaction information?"

[0620] The above is a specific embodiment of the present invention, which not only effectively prevents fraudulent use and counterfeiting of banknotes, but also improves the transparency of transactions and the sense of security of users.

[0621] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0622] Step 1:

[0623] The terminal captures an image of the bill and reads its serial number.

[0624] Specifically, when a user swipes a banknote through the terminal's scanner, the terminal's camera takes an image of the banknote. This image is analyzed using OCR software to extract the serial number. The input is the image of the banknote, and the output is the banknote's serial number.

[0625] Step 2:

[0626] The terminal allows the user to enter transaction information.

[0627] For example, a user follows instructions on the terminal to enter a user ID, transaction ID, transaction date, and location. These input fields are displayed on the terminal screen, and the user enters the information using a keyboard or touch panel. The input is the user's transaction information, and the output is a dataset of the entered transaction information.

[0628] Step 3:

[0629] The terminal sends the banknote serial number and transaction information to the server.

[0630] This causes the terminal to send the serial number and transaction information to the server using the HTTPS protocol. The input is a dataset of the serial number and transaction information, and the output is the secure data transfer to the server.

[0631] Step 4:

[0632] The server stores the received data in a database.

[0633] The server receives data sent from the devices and stores it in a database. This data is stored in a database management system (e.g., MySQL, PostgreSQL). The input is the data received from the devices, and the output is the data records stored in the database.

[0634] Step 5:

[0635] The server analyzes the data using a generative AI model to detect anomalies.

[0636] The server retrieves the stored data from the database and analyzes it using a generative AI model (e.g., TensorFlow, PyTorch). For example, if the same serial number is used in multiple places within a short period of time, this can be detected as an anomaly. The input is the transaction information stored in the database, and the output is the result of the anomaly detection.

[0637] Step 6:

[0638] The server sends the abnormality detection result to the terminal.

[0639] If an anomaly is detected, the server sends the result to the terminal. The server composes the analysis result in JSON format and sends it as an HTTP response. The input is the anomaly detection result, and the output is the data transfer to the terminal.

[0640] Step 7:

[0641] The terminal notifies the user using an emotion engine.

[0642] The device applies an emotion engine based on the anomaly detection results received from the server to recognize the user's emotions. It uses a camera and microphone to analyze the user's facial expressions and tone of voice and displays an adaptive notification. For example, if the user has a confused expression, it displays a message saying, "This banknote has an abnormal transaction history. Don't worry, please check the details." The input is the anomaly detection results from the server and the user's emotional data, and the output is a notification message.

[0643] Step 8:

[0644] The user acknowledges the notification and takes the necessary action.

[0645] The user checks the notification message displayed on the terminal and, if necessary, stops using the banknote or reports it to the relevant authorities. The input is the notification message, and the output is the user's action.

[0646] The above are the specific processing steps of this system.

[0647] (Application example 2)

[0648] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0649] Conventional banknote management systems only detect abnormalities in banknotes and do not provide notifications based on the user's emotions, which means that the user's sense of security and transaction transparency are not sufficiently ensured. Furthermore, notifying only the user of detected banknote abnormalities can cause excessive anxiety and confusion. In response to this, the present invention aims to improve the user's sense of security and transaction transparency by combining a user emotion analysis function with a banknote management system to provide adaptive notifications based on the user's emotions.

[0650] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a terminal to acquire an image of a banknote and read its serial number, means for a user to input transaction information, means for the terminal to transmit the banknote serial number and transaction information, means for the server to collect the banknote serial number and transaction information and store them in a database, means for the server to detect anomalies in the banknote using an AI model based on the collected data, means for the terminal to analyze the user's emotions and adaptively issue a notification based on the emotions, means for the server to transmit the anomaly detection result to the terminal and notify the user, and means for the user to check the notification and take necessary action. This enables adaptive notifications that take the user's emotions into consideration, avoiding excessive anxiety and confusion and improving transaction transparency and user peace of mind.

[0651] A "terminal" is an electronic device in the system that captures images of banknotes and reads their serial numbers.

[0652] "Emotion analysis" is the process of analyzing a user's facial expressions and tone of voice to identify their emotional state.

[0653] "Transaction information" refers to information entered by a user, including a user ID, transaction ID, date, location, and the like.

[0654] A "serial number" is a unique identification code printed on a banknote that is used to track and verify the banknote.

[0655] "Transmission means" refers to a technology for electronically transmitting the serial number of the banknote and transaction information from the terminal to the server.

[0656] The "collection means" is the process by which the server receives the serial number of the banknote and the transaction information sent from the terminal and stores them in a database.

[0657] A "database" is a collection of banknote serial numbers and transaction information managed within the system.

[0658] An "AI model" is an artificial intelligence algorithm used by the server to detect anomalies based on banknote transaction information.

[0659] "Anomaly detection" is the process by which the server uses AI models to identify abnormal transaction patterns on banknotes.

[0660] "Adaptive notifications" is a technology in which a terminal notifies a user with an appropriate tone based on the user's emotional state.

[0661] The "notification means" is a method for transmitting information from the terminal to the user when an abnormal banknote transaction is detected.

[0662] "Means of action" refers to specific actions or instructions that a user should take when receiving a notification.

[0663] The present invention relates to a banknote management system that detects abnormalities in banknotes and provides adaptive notification based on the user's emotions. Next, the operation and embodiments of this system will be described in detail.

[0664] 1. System Overview

[0665] The system consists of the following main components:

[0666] Terminal

[0667] server

[0668] Database

[0669] Sentiment Analysis Engine

[0670] AI model

[0671] 2. Device operation

[0672] The device uses the following hardware and software:

[0673] Hardware: Smartphone camera

[0674] Software: OpenCV (image analysis), Pytesseract (character recognition)

[0675] The terminal captures an image of the bill when the user scans it with a camera. From this image, it uses Pytesseract to extract the serial number. The terminal also provides an interface for the user to input transaction information (user ID, transaction ID, date, and location). Furthermore, it utilizes an emotion analysis engine to analyze the user's facial expressions and tone of voice to identify the user's emotional state.

[0676] 3. Server operation

[0677] The server uses the following software:

[0678] Database: MySQL, PostgreSQL, etc.

[0679] AI model: Anomaly detection algorithm using TensorFlow and PyTorch

[0680] The server receives the serial numbers and transaction information sent from the terminal and stores them in a database. Based on the stored data, an AI model is used to detect anomalies in the banknotes. Specifically, it identifies serial numbers that have been frequently used in transactions over a short period of time and other abnormal patterns.

[0681] 4.Means of notification

[0682] When the server detects an anomaly, it sends the result to the device, which then displays an adaptive notification based on the user's emotional state. For example, if the user has a surprised expression, the notification message is softened.

[0683] 5. User operations

[0684] The user checks the notification and takes necessary action, such as canceling transactions of the banknotes in which the abnormality was detected or reporting it to the relevant authorities.

[0685] Specific examples

[0686] For example, when a user uses a 500 yen bill at a cafe, they scan the bill with their smartphone camera to obtain the serial number. Then, they enter the user ID, transaction ID, date, and location into the terminal. If the system detects an anomaly and the user looks confused, the emotion analysis engine will display a message saying, "There is an anomaly in the transaction history of this bill. Don't worry, please check the details."

[0687] Prompt Sentence Examples

[0688] "Scan the serial number of a 500 yen bill with your smartphone camera and enter your transaction information. We then analyze the captured user's facial expressions and provide adaptive notifications based on their emotions."

[0689] In this way, the banknote management system of the present invention can prevent fraudulent use and counterfeiting of banknotes, and improve the transparency of transactions and the sense of security of users.

[0690] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0691] Step 1:

[0692] The terminal uses the smartphone camera to capture an image of the banknote. The user holds the banknote in front of the camera and presses the capture button to capture the image. The input is the image of the banknote that was captured, and the output is the captured image data.

[0693] Step 2:

[0694] To extract the serial number from the captured image of the banknote, the terminal preprocesses the image using OpenCV and performs optical character recognition using Pytesseract. The input is the preprocessed image, and the output is text data containing the banknote serial number.

[0695] Step 3:

[0696] The user uses the terminal interface to input transaction information (user ID, transaction ID, date, location). The input is the data manually entered by the user, and the output is the transaction information data stored in the terminal.

[0697] Step 4:

[0698] The device uses an emotion analysis engine to analyze the user's facial expressions and tone of voice. The camera captures the user's facial expressions, and the microphone records any audio input. The input is the captured facial image and audio data, and the output is data indicating the user's emotional state.

[0699] Step 5:

[0700] The terminal sends the serial number, transaction information, and emotional state data to the server. The transmission is via HTTPS protocol and the data is encrypted. The input is the serial number, transaction information, and emotional state data, and the output is the transmission completion status to the server.

[0701] Step 6:

[0702] The server receives the data sent from the terminal and stores it in a database. The stored data includes the serial number, transaction information, and the user's emotional state. The input is the sent data, and the output is the record stored in the database.

[0703] Step 7:

[0704] The server uses the stored data to detect anomalies in banknotes using an AI model, matching the serial number with transaction history and identifying abnormal patterns. The input is transaction information and serial number retrieved from the database, and the output is the anomaly detection result.

[0705] Step 8:

[0706] The server sends the anomaly detection results to the terminal. The HTTPS protocol is used for transmission, and the data is encrypted. The input is the anomaly detection result data, and the output is the transmission completion status to the terminal.

[0707] Step 9:

[0708] The device analyzes the anomaly detection results received from the server and displays an adaptive notification according to the user's emotional state. The input is the anomaly detection result data and the emotional state data, and the output is a notification message displayed to the user.

[0709] Step 10:

[0710] The user checks the notification from the terminal and takes necessary action. For example, if an abnormal banknote is detected, the user may stop using the banknote or report it to the relevant authorities. The input is the notification message from the terminal, and the output is the specific action taken by the user.

[0711] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0712] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0713] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0714] [Third embodiment]

[0715] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0716] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0717] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0718] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0719] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0720] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0721] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0722] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0723] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0724] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0725] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0726] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0727] System Overview

[0728] The present invention provides a banknote management system in which a terminal, a server, and a user work together. The terminal captures an image of the banknote and reads its serial number. The user inputs transaction information, and the terminal sends this information to the server. The server collects this data and stores it in a database. The server then analyzes the collected data using an AI model to detect abnormalities in the banknote. The detected abnormalities are sent from the server to the terminal and notified to the user.

[0729] Device behavior

[0730] The terminal captures an image of the bill and reads its serial number. For example, when a user purchases an item at a convenience store, they pass the bill through the terminal's scanner. The terminal analyzes the image of the bill and extracts the serial number. Along with this serial number, the terminal also inputs the transaction information entered by the user (user ID, transaction ID, date, location). This information is then sent to the server.

[0731] Server Operation

[0732] The server receives the serial number and transaction information sent from the terminal. The received data is first stored in a database. The server then analyzes this data using an AI model. The AI ​​model has an algorithm that detects anomalies based on the banknote transaction information. For example, if a particular serial number is used in a large number of transactions in a short period of time, it is marked as an anomaly. If an anomaly is detected, the result is sent to the terminal.

[0733] User Actions

[0734] When making a transaction, the user scans the banknote using a terminal and inputs the transaction information into the terminal. After the transaction, the user checks for a notification from the server. For example, if the server notifies the user that "this banknote has an abnormal transaction history," the user can stop using the banknote or report it to the relevant authorities.

[0735] Specific examples

[0736] For example, when User A purchases a 1,000 yen item at a convenience store, the following process can be considered: User A passes a 1,000 yen bill through the terminal's scanner to obtain its serial number. User A follows the instructions on the screen and enters their user ID, transaction ID, and the date and location of the transaction. The terminal then sends the entered information and the bill's serial number to the server. The server stores this information in a database and analyzes it using an AI model. If the analysis determines that the bill has a normal transaction history, no particular notification is sent to the user. However, if an abnormality is detected, the server sends the analysis results to the terminal, which then notifies User A. For example, the terminal may display a message saying, "This bill has an abnormal transaction history." User A confirms this notification and either discontinues use of the bill or reports it to the appropriate authorities.

[0737] The above is a specific embodiment of the present invention. The present invention not only effectively prevents fraudulent use and counterfeiting of banknotes, but also improves the transparency of transactions.

[0738] The processing flow will be explained below.

[0739] Step 1:

[0740] The terminal captures an image of the bill and reads its serial number, and when the user swipes the bill through the scanner, the terminal uses image processing techniques to extract the serial number.

[0741] Step 2:

[0742] The user enters transaction information into the terminal, specifically, the user ID, transaction ID, date and location of the transaction.

[0743] Step 3:

[0744] The terminal encrypts the acquired serial number and entered transaction information and sends it to the server, thereby protecting the data.

[0745] Step 4:

[0746] The server receives the transmitted serial number and transaction information and stores this information in a database.

[0747] Step 5:

[0748] The server uses an AI model to analyze the transaction information received from the database, with the goal of detecting any abnormalities in the banknotes.

[0749] Step 6:

[0750] If the server detects an abnormality based on the analysis results, it will send the result to the terminal. For example, a serial number that has been traded many times in a short period of time will be deemed abnormal.

[0751] Step 7:

[0752] The device notifies the user of the analysis results received from the server, and the notification message includes details of the anomaly.

[0753] Step 8:

[0754] Users can check the notification and take necessary action, such as stopping the use of the banknotes on which an abnormality has been detected or reporting it to the relevant authorities.

[0755] The above is a specific processing flow of this system.

[0756] Example 1

[0757] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0758] Conventional banknote management systems have difficulty effectively preventing fraudulent use and counterfeiting of banknotes, and lack the means to ensure transaction transparency. Furthermore, technology for efficiently managing banknote serial numbers and detecting anomalies has not been fully established. This has made it difficult to track banknotes and detect fraud early, leading to problems such as a decline in the credibility of financial institutions and transactions.

[0759] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0760] In this invention, the server includes a means for collecting banknote serial numbers and transaction information and storing them in a database, a means for detecting abnormalities in banknotes using a generative AI model based on the collected data, and a means for transmitting the abnormality detection results to a terminal and notifying the user, thereby enabling early detection of fraudulent use and counterfeit banknotes and improving transaction transparency.

[0761] A "terminal" is a device that includes hardware and software that allows a user to capture an image of a banknote and read its serial number.

[0762] A "user" is a person or organization that uses the banknote management system to input transaction information and manage banknotes.

[0763] "Transaction Information" refers to information related to a transaction, such as user ID, transaction ID, transaction date, and location.

[0764] A "serial number" is a unique identification number printed on a banknote.

[0765] The "server" is a central center that collects banknote serial numbers and transaction information sent from terminals, stores them in a database, and analyzes them.

[0766] "Database" refers to a system for storing and managing banknote serial numbers and transaction information collected by the server.

[0767] A "generative AI model" is an artificial intelligence model used to detect anomalies in banknotes based on collected data.

[0768] An "anomaly" is a condition that exhibits an unusual or unexpected pattern or behavior within a series of transactions.

[0769] A "notification" is a message containing an abnormality detection result sent from the server to the user.

[0770] MODE FOR CARRYING OUT THE INVENTION

[0771] The present invention provides a banknote management system in which a terminal, a server, and a user work in cooperation with each other. The operation of this system and details of its hardware and software will be described in the following specific embodiment.

[0772] System Overview

[0773] In this system, the terminal captures an image of the banknote and reads its serial number. The user enters transaction information, and the terminal sends this information to the server. The server collects this data and stores it in a database. The server then analyzes the collected data using a generative AI model to detect abnormalities in the banknote. The server then sends the detected abnormalities to the terminal and notifies the user.

[0774] Hardware and Software

[0775] Terminal: A terminal equipped with a high-resolution CCD scanner and a touch panel. OpenCV is used as image processing software.

[0776] Server: MySQL is used as the database, and generative AI models are used for data analysis. TensorFlow and PyTorch are used to build the generative AI models.

[0777] Device behavior

[0778] The terminal performs the following operations:

[0779] 1. The user swipes the bill through the terminal's scanner.

[0780] 2. The device's scanner captures an image of the banknote and performs image analysis using OpenCV.

[0781] 3. Read the banknote serial number from the analyzed image.

[0782] 4. The user follows the instructions displayed on the screen and enters the necessary transaction information, such as user ID, transaction ID, transaction date and location, via the touch panel.

[0783] 5. The terminal encrypts the serial number and transaction information it has obtained and sends them to the server.

[0784] Examples:

[0785] A user passes a 1,000 yen bill through a scanner at a convenience store, obtaining the serial number "AB12345678." The user then enters transaction information, such as "User ID: user123," "Transaction ID: TX001," "Date: 2023-10-01," and "Location: Convenience Store A." The terminal then encrypts this information and sends it to the server.

[0786] Server Operation

[0787] The server performs the following actions:

[0788] 1. Receive the serial number and transaction information sent from the terminal.

[0789] 2. Save the received data in the database.

[0790] 3. The saved data is input into the generative AI model for analysis.

[0791] 4. The generative AI model has an algorithm that detects anomalies based on transaction information on banknotes. For example, if a particular serial number is used in a large number of transactions in a short period of time, it will mark this as an anomaly.

[0792] 5. If an abnormality is detected, the result is sent to the terminal.

[0793] Examples:

[0794] The server receives the user's transaction information and serial number, stores them in a database, and then analyzes them using an anomaly detection model using TensorFlow. If an anomaly is detected, the server sends a notification to the terminal stating, "This banknote has an abnormal transaction history."

[0795] User Actions

[0796] The user performs the following actions:

[0797] 1. When making a transaction, use the terminal to scan the bill.

[0798] 2. Enter the transaction information into the terminal.

[0799] 3. After the transaction, check the notification from the server. If an abnormality is detected, take action according to the notification.

[0800] Examples:

[0801] A user purchases a 1000 yen item at a convenience store, scans the bill, and enters the transaction information. If the server notifies the user that "this bill has an abnormal transaction history," the user should stop using the bill and report it to the relevant authorities.

[0802] Prompt Sentence Examples

[0803] An example of a prompt to input to a generative AI model is:

[0804] 1. "If a note with this serial number has been used in many transactions in a short period of time, how can I mark it as abnormal?"

[0805] 2. "Describe how you can improve an algorithm for extracting banknote serial numbers from images."

[0806] The above is a specific embodiment of the present invention, which makes it possible to effectively prevent fraudulent use and counterfeiting of banknotes and improve the transparency of transactions.

[0807] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0808] The flow of this system's program processing

[0809] Step 1:

[0810] The terminal captures an image of the bill and reads the serial number.

[0811] Input: The user swipes a bill through the terminal's scanner.

[0812] How it works: The device's high-resolution CCD scanner captures an image of the bill. OpenCV is used to analyze the image and extract the serial number.

[0813] Output: The extracted serial number (e.g. "AB12345678").

[0814] Step 2:

[0815] The user enters the transaction information into the terminal.

[0816] Input: Information such as user ID, transaction ID, transaction date, and transaction location.

[0817] Specific operation: The user uses the device's touch panel to enter transaction information according to the instructions on the screen.

[0818] Output: The entered transaction information (e.g., "User ID: user123", "Transaction ID: TX001", "Date: 2023-10-01", "Location: Convenience Store A").

[0819] Step 3:

[0820] The terminal encrypts the banknote's serial number and transaction information and sends it to the server.

[0821] Input: Serial number, transaction information.

[0822] Specific operation: The terminal encrypts the serial number and transaction information using the HTTPS protocol and sends it to the server.

[0823] Output: The encrypted serial number and transaction information are sent to the server.

[0824] Step 4:

[0825] The server receives the banknote serial number and transaction information and stores it in a database.

[0826] Input: Encrypted serial number and transaction information.

[0827] Specific operation: The server receives the encrypted data, decrypts it, and stores it in a database (e.g., MySQL).

[0828] Output: Transaction information and serial number stored in the database.

[0829] Step 5:

[0830] The server analyzes the collected data using a generative AI model.

[0831] Input: Transaction information and serial number stored in the database.

[0832] How it works: The server inputs the stored data into a generative AI model (e.g., TensorFlow) and analyzes the data. The analysis algorithm analyzes the banknote transaction history and detects anomalies.

[0833] Output: If an anomaly is detected, it is flagged and detailed information.

[0834] Step 6:

[0835] If an abnormality is detected, the server transmits the result to the terminal.

[0836] Input: Analysis results (abnormality flags and detailed information).

[0837] Specific operation: The server creates a message to notify the user based on the analysis results and sends it to the terminal using the HTTPS protocol.

[0838] Output: A message of the anomaly detection result is sent to the terminal.

[0839] Step 7:

[0840] The terminal displays an abnormality notification from the server to the user.

[0841] Input: The anomaly detection message sent by the server.

[0842] Specific operation: The terminal receives a message from the server and displays it on the screen to notify the user of an abnormality.

[0843] Output: The user confirms the abnormality notification.

[0844] Step 8:

[0845] The user checks the notification and takes the necessary action.

[0846] Input: Abnormal notification displayed on the terminal.

[0847] Specific action: The user checks the notification and either stops using the note or reports it to the appropriate authorities.

[0848] Output: Prevents fraud and counterfeiting by requiring the user to take appropriate action.

[0849] The above are the specific processing steps of the program of this system.

[0850] (Application example 1)

[0851] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0852] Conventional banknote management systems have had the problem of making it difficult to quickly detect fraudulent or counterfeit banknotes. In particular, they have been unable to ensure transaction transparency or detect abnormal banknote transactions in real time, limiting their ability to prevent fraud.

[0853] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0854] In this invention, the server includes a means for a terminal to collect banknote serial numbers and transaction information and store them in a database, a means for detecting abnormalities in banknotes using an AI model based on the collected data, a means for transmitting the abnormality detection results to the terminal and notifying the user, a means for capturing images of banknotes using a smartphone camera and extracting serial numbers using image processing technology, and a means for notifying the user in real time when an abnormal transaction is detected. This makes it possible to detect fraudulent use and counterfeit banknotes in real time and increase transaction transparency.

[0855] A "terminal" is a device used by a user to capture images of banknotes and read their serial numbers. It also has the function of inputting transaction information and sending it to a server.

[0856] "Capturing an image of a banknote" refers to the act of capturing image data of the banknote using a smartphone camera or the like.

[0857] "Reading the serial number" refers to the act of extracting the serial number from an image of a banknote using optical character recognition (OCR) technology.

[0858] "Transaction Information" refers to detailed information related to a transaction, such as user ID, transaction ID, transaction date, and location.

[0859] The "server" is a computer system that receives the banknote serial numbers and transaction information sent from the terminal, and stores and analyzes this data in a database.

[0860] A "database" is a memory space for storing and managing banknote serial numbers and transaction information collected by the server.

[0861] "AI model" refers to artificial intelligence (AI) that has algorithms to analyze collected data and detect abnormalities in banknotes.

[0862] "Anomaly detection" refers to the act of using AI models to analyze a banknote's transaction history and identify unusual transaction patterns.

[0863] "Notifying the user" refers to the act of the server sending the abnormality detection result to the terminal so that the user can check it.

[0864] "Image processing technology" refers to the general technology used to analyze images of banknotes and extract serial numbers. Libraries such as OpenCV are mainly used.

[0865] "Real-time notification" refers to the act of sending information about an abnormality to the user almost immediately upon detection.

[0866] System Overview

[0867] The present invention is a banknote management system in which a terminal, a server, and a user work together. The terminal acquires an image of the banknote and reads its serial number. The user inputs transaction information, and the terminal sends this information to the server. The server collects this data and stores it in a database. The server then analyzes the collected data using an AI model to detect abnormalities in the banknote. The detected abnormalities are sent from the server to the terminal and notified to the user.

[0868] Device behavior

[0869] The terminal uses a smartphone. First, an image of the banknote is captured using the smartphone's camera. Next, the captured image is processed using the OpenCV library, and the serial number is extracted using OCR technology. The user enters transaction information (user ID, transaction ID, date, location) into the application. This data is sent from the terminal to the server.

[0870] Server Operation

[0871] The server receives the serial number and transaction information sent from the terminal. The received data is first stored in a database. The server then uses TensorFlow to build an AI model and analyze this data. The AI ​​model has an algorithm that detects anomalies based on the banknote transaction information. For example, if a particular serial number is used in a large number of transactions in a short period of time, it is marked as an anomaly. If an anomaly is detected, the result is sent to the terminal.

[0872] User Actions

[0873] When making a transaction, the user scans the banknote using their smartphone and enters the transaction information into the terminal. After the transaction, the user checks the notification from the server and takes the necessary action. For example, if the server notifies the user that "this banknote has an abnormal transaction history," the user can stop using the banknote or report it to the relevant authorities.

[0874] Specific examples

[0875] For example, when User A purchases a 1,000 yen item at a store, the process can be as follows: User A holds a 1,000 yen bill over their smartphone camera and captures an image. The application follows the instructions on the screen and enters the user ID, transaction ID, and transaction date and location. The device sends the entered information and the bill's serial number to the server. The server stores this information in a database and uses TensorFlow to analyze it with an AI model. If the analysis results indicate that the bill has a normal transaction history, no notification is sent to the user. However, if an abnormality is detected, the server sends the analysis results to the device, which notifies User A. For example, the device may display a message saying, "This bill has an abnormal transaction history." User A confirms this notification and either discontinues use of the bill or reports it to the appropriate authorities.

[0876] Prompt Sentence Examples

[0877] "We will analyze all transaction history based on this transaction information and the banknote serial number to determine whether there is any suspicion of fraud."

[0878] As explained above, by using the system of the present invention, it is possible to detect fraudulent use and counterfeiting of banknotes in real time and increase the transparency of transactions.

[0879] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0880] Step 1:

[0881] The terminal (smartphone) acquires an image of a banknote through user operation. The image of the banknote is taken using the smartphone's camera. The input is the actual banknote, which is then acquired as a digital image. The output is a digital image of the acquired banknote.

[0882] Step 2:

[0883] The image of the banknote acquired by the terminal is preprocessed using OpenCV to extract the serial number. Specifically, the image is converted to grayscale, subjected to adaptive binarization processing, and then the serial number is extracted as text data using OCR (Optical Character Recognition) technology. The input is the digital image of the banknote acquired in Step 1, and the output is the banknote serial number.

[0884] Step 3:

[0885] The user inputs transaction information (user ID, transaction ID, date, location) into the terminal. The input is manual input by the user, and the output is digital data of the transaction information entered by the user.

[0886] Step 4:

[0887] The terminal extracts the serial number and sends the transaction information entered by the user to the server. The input is digital data of the serial number and transaction information, and the output is a process of encrypting this data and sending it to the server via a communication network.

[0888] Step 5:

[0889] The server stores the received serial number and transaction information in a database. The input is the encrypted data of the serial number and transaction information received over the network, and the output is this data stored in the database.

[0890] Step 6:

[0891] The server uses a generative AI model (TensorFlow) based on data collected from the database to detect anomalies in banknotes. The input is the digital data of the serial number and transaction information stored in the database, and the prompt for the AI ​​model is "Analyze all transaction history based on this transaction information and the banknote serial number and determine whether there is any suspicion of fraud." The output is the analysis result indicating whether an anomaly was detected.

[0892] Step 7:

[0893] If the server detects an abnormality in the serial number based on the analysis results, it generates a notification message and sends it to the terminal. The input is the analysis result obtained in step 6, and the output is the message to be notified to the user.

[0894] Step 8:

[0895] The terminal receives a notification message from the server and displays it to the user. The input is the notification message sent from the server, and the output is the notification message displayed to the user. For example, a warning message such as "This banknote has an abnormal transaction history" is displayed.

[0896] Step 9:

[0897] The user checks the notification message from the server and, if necessary, stops using the bill or reports it to the relevant authorities. The input is the displayed notification message, and the output is the specific action the user takes.

[0898] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0899] System Overview

[0900] The present invention provides a banknote management system in which a terminal, a server, and a user work together. In particular, the present invention is equipped with a function that recognizes a user's emotions and provides adaptive notifications based on them by combining an emotion engine. The terminal acquires an image of the banknote and reads its serial number. The user enters transaction information, and the terminal sends this information to the server. The server collects this data and stores it in a database. The server then analyzes the collected data using an AI model to detect abnormalities in the banknote. The detected abnormalities are sent from the server to the terminal, and adaptive notifications are provided to the user using the emotion engine.

[0901] Device behavior

[0902] The terminal captures an image of the bill and reads its serial number. For example, when a user purchases an item at a convenience store, they pass the bill through the terminal's scanner. The terminal analyzes the image of the bill and extracts the serial number. Along with this serial number, the terminal also inputs the transaction information entered by the user (user ID, transaction ID, date, location). This information is then sent to the server.

[0903] The device is equipped with an emotion engine that recognizes the user's emotions, using the camera, microphone, and other sensors to analyze the user's facial expressions and tone of voice. For example, if the user has an anxious expression, the emotion engine will recognize this and adapt the tone of the warning message as needed.

[0904] Server Operation

[0905] The server receives the serial number and transaction information sent from the terminal. The received data is first stored in a database. The server then analyzes this data using an AI model. The AI ​​model has an algorithm that detects anomalies based on the banknote transaction information. For example, if a particular serial number is used in a large number of transactions in a short period of time, it is marked as an anomaly. If an anomaly is detected, the result is sent to the terminal.

[0906] User Actions

[0907] When making a transaction, the user scans the banknote using a terminal and inputs the transaction information into the terminal. After the transaction, the user checks for a notification from the server. For example, if the server notifies the user that "this banknote has an abnormal transaction history," the user can stop using the banknote or report it to the relevant authorities.

[0908] The emotion engine recognizes the user's emotions and provides adaptive notifications. For example, if the user has a surprised expression, the emotion engine softens the notification message and displays something like, "Do you want more information about this banknote?"

[0909] Specific examples

[0910] For example, if User B purchases a 500 yen item at a cafe, the following process can be considered: User B passes the 500 yen bill through the terminal's scanner to obtain its serial number. User B follows the instructions on the screen and enters their user ID, transaction ID, and the date and location of the transaction. The terminal then sends the entered information and the bill's serial number to the server. The server stores this information in a database and analyzes it using an AI model. If the analysis determines that the bill has a normal transaction history, no special notification is given to the user. However, if an abnormality is detected, the server sends the analysis results to the terminal, and the terminal notifies User B using an emotion engine. For example, if User B looks confused, the terminal may display the message, "This bill has an abnormal transaction history. Don't worry, please check the details."

[0911] The above is a specific embodiment of the present invention, which not only effectively prevents fraudulent use and counterfeiting of banknotes, but also improves the transparency of transactions and the sense of security of users.

[0912] The processing flow will be explained below.

[0913] Step 1:

[0914] The user scans the bill into the terminal, which uses the terminal's camera or scanner to capture an image of the bill.

[0915] Step 2:

[0916] The terminal analyzes the image of the banknote and reads its serial number, extracting it using image processing technology.

[0917] Step 3:

[0918] The user enters transaction information into the terminal, including the user ID, transaction ID, transaction date, and location.

[0919] Step 4:

[0920] The terminal encrypts the acquired serial number and entered transaction information and sends it to the server. Encryption ensures data security.

[0921] Step 5:

[0922] The server receives the serial number and transaction information and stores it in a database.

[0923] Step 6:

[0924] The server analyzes the transaction information in the database using an AI model, which has an algorithm to detect anomalies in the banknote transaction information.

[0925] Step 7:

[0926] If the server detects an anomaly as a result of the analysis, it will send the result to the terminal. If no anomaly is found, it will not notify the terminal.

[0927] Step 8:

[0928] The terminal displays the analysis results received from the server, for example, displaying a warning message if an abnormality is detected.

[0929] Step 9:

[0930] To recognize the user's emotions, the device uses a camera, microphone, and other sensors to analyze the user's facial expressions and voice, determining whether the user is feeling surprise, anxiety, confusion, etc.

[0931] Step 10:

[0932] The device's emotion engine adapts the tone and content of notification messages based on the recognized emotion, for example, softening the notification message if the user looks anxious.

[0933] Step 11:

[0934] The user checks the notification from the terminal and takes necessary action if an abnormality is detected, such as stopping the use of the banknote or reporting it to the relevant authorities.

[0935] The above is a specific processing flow of the present invention.

[0936] Example 2

[0937] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0938] Conventional banknote management systems have difficulty in ensuring sufficient transaction transparency and user security, making it difficult to detect fraudulent or counterfeit banknotes. Furthermore, because they do not provide adaptive notifications that take user emotions into consideration, anomaly detection results are often not accepted smoothly.

[0939] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting banknote serial numbers and transaction information and storing them in a database, means for detecting abnormalities in banknotes using a generative AI model based on the collected data, and means for transmitting the abnormality detection results to the terminal and notifying the user. This enables early detection of fraudulent use and counterfeit banknotes. Furthermore, the terminal uses an emotion engine to recognize the user's emotions and provides adaptive notifications, making it easier for the user to accept notifications and improving the transparency and security of transactions.

[0940] A "terminal" is a device that captures an image of a banknote and reads its serial number, and is a device through which a user inputs transaction information and communicates with a server.

[0941] The "server" is a central processing unit that collects and stores the serial numbers of banknotes and transaction information sent from the terminal and analyzes this data.

[0942] A "serial number" is a unique identification number assigned to each banknote, and is used to track transaction history and detect anomalies.

[0943] "Transaction information" is detailed data about the use of the banknote, such as user ID, transaction ID, date and location of the transaction.

[0944] "Collected data" is a data set containing banknote serial numbers and transaction information sent from the terminal.

[0945] A "database" is a system for structuring and storing data collected by a server.

[0946] A "generative AI model" is an artificial intelligence model that has algorithms for analyzing large amounts of data and detecting specific patterns and anomalies.

[0947] An "anomaly" is an unusual transaction pattern detected based on the banknote's serial number and transaction information, which may indicate fraud or counterfeiting.

[0948] An "emotion engine" is a system that uses sensors such as cameras and microphones to analyze a user's facial expressions and tone of voice and recognize the user's emotions.

[0949] An "adaptive notification" is a notification message whose content and tone are adjusted to take into account the user's emotions.

[0950] "Encryption" is a technology that converts data into a format that cannot be read by third parties, ensuring the security of communications.

[0951] "Update" is the process of introducing new data and algorithms to improve the performance of a generative AI model.

[0952] This invention is a banknote management system in which terminals, servers, and users work together. In particular, by combining an emotion engine, it has the function of recognizing users' emotions and providing adaptive notifications based on those emotions. This system aims to effectively prevent fraudulent use and counterfeiting of banknotes, improve transaction transparency, and increase user peace of mind.

[0953] Device behavior

[0954] The terminal captures an image of the bill the user passes through the scanner and reads its serial number. For example, when a user passes a 500 yen bill through the scanner to purchase an item at a convenience store, the terminal's camera takes an image of the bill and uses OCR (Optical Character Recognition) software to extract the serial number. The terminal then prompts the user to enter transaction information (user ID, transaction ID, date, and location). Once the user enters this information, the terminal sends it to the server. HTTPS is used as the communication protocol to ensure data security.

[0955] Server Operation

[0956] The server receives the serial number and transaction information sent from the device and stores this data in a database management system (e.g., MySQL or PostgreSQL). The stored data is analyzed using a generative AI model (e.g., TensorFlow or PyTorch). The analysis includes algorithms that detect anomalies, such as when the same serial number is used in multiple different places within a short period of time. If an anomaly is detected, the results are sent to the device.

[0957] User operations

[0958] When making a transaction, the user scans the banknote using the terminal and enters the transaction information. After the transaction, the user checks the notification from the server. For example, if the user receives a notification that "this banknote has an abnormal transaction history," the user is required to stop using the banknote or report it to the relevant authorities.

[0959] Emotion Engine Functions

[0960] The emotion engine uses the device's camera, microphone, and other sensors to analyze the user's facial expressions and tone of voice. If the user has a confused expression, the emotion engine will recognize this and display an adaptive notification. For example, a soft-spoken message might say, "This bill has an unusual transaction history. Don't worry, please check the details."

[0961] Specific examples

[0962] For example, if User B purchases a 500 yen item at a cafe, the following process can be considered: User B passes the 500 yen bill through the terminal's scanner to obtain the serial number. Next, User B follows the instructions on the screen to enter their user ID, transaction ID, and the date and location of the transaction. The terminal sends the entered information and the bill's serial number to the server. The server stores this information in a database and analyzes it using a generative AI model. If the analysis results indicate that the bill has a normal transaction history, no notification is sent to the user. However, if an abnormality is detected, the server sends the analysis results to the terminal, and the terminal notifies User B using an emotion engine. For example, if User B looks confused, the terminal may display a message saying, "This bill has an abnormal transaction history. Don't worry, please check the details."

[0963] Example of input prompt for generative AI model

[0964] "How can I detect anomalies based on banknote serial numbers and transaction information?"

[0965] The above is a specific embodiment of the present invention, which not only effectively prevents fraudulent use and counterfeiting of banknotes, but also improves the transparency of transactions and the sense of security of users.

[0966] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0967] Step 1:

[0968] The terminal captures an image of the bill and reads its serial number.

[0969] Specifically, when a user swipes a banknote through the terminal's scanner, the terminal's camera takes an image of the banknote. This image is analyzed using OCR software to extract the serial number. The input is the image of the banknote, and the output is the banknote's serial number.

[0970] Step 2:

[0971] The terminal allows the user to enter transaction information.

[0972] For example, a user follows instructions on the terminal to enter a user ID, transaction ID, transaction date, and location. These input fields are displayed on the terminal screen, and the user enters the information using a keyboard or touch panel. The input is the user's transaction information, and the output is a dataset of the entered transaction information.

[0973] Step 3:

[0974] The terminal sends the banknote serial number and transaction information to the server.

[0975] This causes the terminal to send the serial number and transaction information to the server using the HTTPS protocol. The input is a dataset of the serial number and transaction information, and the output is the secure data transfer to the server.

[0976] Step 4:

[0977] The server stores the received data in a database.

[0978] The server receives data sent from the devices and stores it in a database. This data is stored in a database management system (e.g., MySQL, PostgreSQL). The input is the data received from the devices, and the output is the data records stored in the database.

[0979] Step 5:

[0980] The server analyzes the data using a generative AI model to detect anomalies.

[0981] The server retrieves the stored data from the database and analyzes it using a generative AI model (e.g., TensorFlow, PyTorch). For example, if the same serial number is used in multiple places within a short period of time, this can be detected as an anomaly. The input is the transaction information stored in the database, and the output is the result of the anomaly detection.

[0982] Step 6:

[0983] The server sends the abnormality detection result to the terminal.

[0984] If an anomaly is detected, the server sends the result to the terminal. The server composes the analysis result in JSON format and sends it as an HTTP response. The input is the anomaly detection result, and the output is the data transfer to the terminal.

[0985] Step 7:

[0986] The terminal notifies the user using an emotion engine.

[0987] The device applies an emotion engine based on the anomaly detection results received from the server to recognize the user's emotions. It uses a camera and microphone to analyze the user's facial expressions and tone of voice and displays an adaptive notification. For example, if the user has a confused expression, it displays a message saying, "This banknote has an abnormal transaction history. Don't worry, please check the details." The input is the anomaly detection results from the server and the user's emotional data, and the output is a notification message.

[0988] Step 8:

[0989] The user acknowledges the notification and takes the necessary action.

[0990] The user checks the notification message displayed on the terminal and, if necessary, stops using the banknote or reports it to the relevant authorities. The input is the notification message, and the output is the user's action.

[0991] The above are the specific processing steps of this system.

[0992] (Application example 2)

[0993] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0994] Conventional banknote management systems only detect abnormalities in banknotes and do not provide notifications based on the user's emotions, which means that the user's sense of security and transaction transparency are not sufficiently ensured. Furthermore, notifying only the user of detected banknote abnormalities can cause excessive anxiety and confusion. In response to this, the present invention aims to improve the user's sense of security and transaction transparency by combining a user emotion analysis function with a banknote management system to provide adaptive notifications based on the user's emotions.

[0995] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a terminal to acquire an image of a banknote and read its serial number, means for a user to input transaction information, means for the terminal to transmit the banknote serial number and transaction information, means for the server to collect the banknote serial number and transaction information and store them in a database, means for the server to detect anomalies in the banknote using an AI model based on the collected data, means for the terminal to analyze the user's emotions and adaptively issue a notification based on the emotions, means for the server to transmit the anomaly detection result to the terminal and notify the user, and means for the user to check the notification and take necessary action. This enables adaptive notifications that take the user's emotions into consideration, avoiding excessive anxiety and confusion and improving transaction transparency and user peace of mind.

[0996] A "terminal" is an electronic device in the system that captures images of banknotes and reads their serial numbers.

[0997] "Emotion analysis" is the process of analyzing a user's facial expressions and tone of voice to identify their emotional state.

[0998] "Transaction information" refers to information entered by a user, including a user ID, transaction ID, date, location, and the like.

[0999] A "serial number" is a unique identification code printed on a banknote that is used to track and verify the banknote.

[1000] "Transmission means" refers to a technology for electronically transmitting the serial number of the banknote and transaction information from the terminal to the server.

[1001] The "collection means" is the process by which the server receives the serial number of the banknote and the transaction information sent from the terminal and stores them in a database.

[1002] A "database" is a collection of banknote serial numbers and transaction information managed within the system.

[1003] An "AI model" is an artificial intelligence algorithm used by the server to detect anomalies based on banknote transaction information.

[1004] "Anomaly detection" is the process by which the server uses AI models to identify abnormal transaction patterns on banknotes.

[1005] "Adaptive notifications" is a technology in which a terminal notifies a user with an appropriate tone based on the user's emotional state.

[1006] The "notification means" is a method for transmitting information from the terminal to the user when an abnormal banknote transaction is detected.

[1007] "Means of action" refers to specific actions or instructions that a user should take when receiving a notification.

[1008] The present invention relates to a banknote management system that detects abnormalities in banknotes and provides adaptive notification based on the user's emotions. Next, the operation and embodiments of this system will be described in detail.

[1009] 1. System Overview

[1010] The system consists of the following main components:

[1011] Terminal

[1012] server

[1013] Database

[1014] Sentiment Analysis Engine

[1015] AI model

[1016] 2. Device operation

[1017] The device uses the following hardware and software:

[1018] Hardware: Smartphone camera

[1019] Software: OpenCV (image analysis), Pytesseract (character recognition)

[1020] The terminal captures an image of the bill when the user scans it with a camera. From this image, it uses Pytesseract to extract the serial number. The terminal also provides an interface for the user to input transaction information (user ID, transaction ID, date, and location). Furthermore, it utilizes an emotion analysis engine to analyze the user's facial expressions and tone of voice to identify the user's emotional state.

[1021] 3. Server operation

[1022] The server uses the following software:

[1023] Database: MySQL, PostgreSQL, etc.

[1024] AI model: Anomaly detection algorithm using TensorFlow and PyTorch

[1025] The server receives the serial numbers and transaction information sent from the terminal and stores them in a database. Based on the stored data, an AI model is used to detect anomalies in the banknotes. Specifically, it identifies serial numbers that have been frequently used in transactions over a short period of time and other abnormal patterns.

[1026] 4.Means of notification

[1027] When the server detects an anomaly, it sends the result to the device, which then displays an adaptive notification based on the user's emotional state. For example, if the user has a surprised expression, the notification message is softened.

[1028] 5. User operations

[1029] The user checks the notification and takes necessary action, such as canceling transactions of the banknotes in which the abnormality was detected or reporting it to the relevant authorities.

[1030] Specific examples

[1031] For example, when a user uses a 500 yen bill at a cafe, they scan the bill with their smartphone camera to obtain the serial number. Then, they enter the user ID, transaction ID, date, and location into the terminal. If the system detects an anomaly and the user looks confused, the emotion analysis engine will display a message saying, "There is an anomaly in the transaction history of this bill. Don't worry, please check the details."

[1032] Prompt Sentence Examples

[1033] "Scan the serial number of a 500 yen bill with your smartphone camera and enter your transaction information. We then analyze the captured user's facial expressions and provide adaptive notifications based on their emotions."

[1034] In this way, the banknote management system of the present invention can prevent fraudulent use and counterfeiting of banknotes, and improve the transparency of transactions and the sense of security of users.

[1035] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1036] Step 1:

[1037] The terminal uses the smartphone camera to capture an image of the banknote. The user holds the banknote in front of the camera and presses the capture button to capture the image. The input is the image of the banknote that was captured, and the output is the captured image data.

[1038] Step 2:

[1039] To extract the serial number from the captured image of the banknote, the terminal preprocesses the image using OpenCV and performs optical character recognition using Pytesseract. The input is the preprocessed image, and the output is text data containing the banknote serial number.

[1040] Step 3:

[1041] The user uses the terminal interface to input transaction information (user ID, transaction ID, date, location). The input is the data manually entered by the user, and the output is the transaction information data stored in the terminal.

[1042] Step 4:

[1043] The device uses an emotion analysis engine to analyze the user's facial expressions and tone of voice. The camera captures the user's facial expressions, and the microphone records any audio input. The input is the captured facial image and audio data, and the output is data indicating the user's emotional state.

[1044] Step 5:

[1045] The terminal sends the serial number, transaction information, and emotional state data to the server. The transmission is via HTTPS protocol and the data is encrypted. The input is the serial number, transaction information, and emotional state data, and the output is the transmission completion status to the server.

[1046] Step 6:

[1047] The server receives the data sent from the terminal and stores it in a database. The stored data includes the serial number, transaction information, and the user's emotional state. The input is the sent data, and the output is the record stored in the database.

[1048] Step 7:

[1049] The server uses the stored data to detect anomalies in banknotes using an AI model, matching the serial number with transaction history and identifying abnormal patterns. The input is transaction information and serial number retrieved from the database, and the output is the anomaly detection result.

[1050] Step 8:

[1051] The server sends the anomaly detection results to the terminal. The HTTPS protocol is used for transmission, and the data is encrypted. The input is the anomaly detection result data, and the output is the transmission completion status to the terminal.

[1052] Step 9:

[1053] The device analyzes the anomaly detection results received from the server and displays an adaptive notification according to the user's emotional state. The input is the anomaly detection result data and the emotional state data, and the output is a notification message displayed to the user.

[1054] Step 10:

[1055] The user checks the notification from the terminal and takes necessary action. For example, if an abnormal banknote is detected, the user may stop using the banknote or report it to the relevant authorities. The input is the notification message from the terminal, and the output is the specific action taken by the user.

[1056] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1057] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1058] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1059] [Fourth embodiment]

[1060] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1061] 7, a 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.

[1062] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1063] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1064] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1065] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1066] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1067] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1068] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1069] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1070] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1071] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1072] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1073] System Overview

[1074] The present invention provides a banknote management system in which a terminal, a server, and a user work together. The terminal captures an image of the banknote and reads its serial number. The user inputs transaction information, and the terminal sends this information to the server. The server collects this data and stores it in a database. The server then analyzes the collected data using an AI model to detect abnormalities in the banknote. The detected abnormalities are sent from the server to the terminal and notified to the user.

[1075] Device behavior

[1076] The terminal captures an image of the bill and reads its serial number. For example, when a user purchases an item at a convenience store, they pass the bill through the terminal's scanner. The terminal analyzes the image of the bill and extracts the serial number. Along with this serial number, the terminal also inputs the transaction information entered by the user (user ID, transaction ID, date, location). This information is then sent to the server.

[1077] Server Operation

[1078] The server receives the serial number and transaction information sent from the terminal. The received data is first stored in a database. The server then analyzes this data using an AI model. The AI ​​model has an algorithm that detects anomalies based on the banknote transaction information. For example, if a particular serial number is used in a large number of transactions in a short period of time, it is marked as an anomaly. If an anomaly is detected, the result is sent to the terminal.

[1079] User Actions

[1080] When making a transaction, the user scans the banknote using a terminal and inputs the transaction information into the terminal. After the transaction, the user checks for a notification from the server. For example, if the server notifies the user that "this banknote has an abnormal transaction history," the user can stop using the banknote or report it to the relevant authorities.

[1081] Specific examples

[1082] For example, when User A purchases a 1,000 yen item at a convenience store, the following process can be considered: User A passes a 1,000 yen bill through the terminal's scanner to obtain its serial number. User A follows the instructions on the screen and enters their user ID, transaction ID, and the date and location of the transaction. The terminal then sends the entered information and the bill's serial number to the server. The server stores this information in a database and analyzes it using an AI model. If the analysis determines that the bill has a normal transaction history, no particular notification is sent to the user. However, if an abnormality is detected, the server sends the analysis results to the terminal, which then notifies User A. For example, the terminal may display a message saying, "This bill has an abnormal transaction history." User A confirms this notification and either discontinues use of the bill or reports it to the appropriate authorities.

[1083] The above is a specific embodiment of the present invention. The present invention not only effectively prevents fraudulent use and counterfeiting of banknotes, but also improves the transparency of transactions.

[1084] The processing flow will be explained below.

[1085] Step 1:

[1086] The terminal captures an image of the bill and reads its serial number, and when the user swipes the bill through the scanner, the terminal uses image processing techniques to extract the serial number.

[1087] Step 2:

[1088] The user enters transaction information into the terminal, specifically, the user ID, transaction ID, date and location of the transaction.

[1089] Step 3:

[1090] The terminal encrypts the acquired serial number and entered transaction information and sends it to the server, thereby protecting the data.

[1091] Step 4:

[1092] The server receives the transmitted serial number and transaction information and stores this information in a database.

[1093] Step 5:

[1094] The server uses an AI model to analyze the transaction information received from the database, with the goal of detecting any abnormalities in the banknotes.

[1095] Step 6:

[1096] If the server detects an abnormality based on the analysis results, it will send the result to the terminal. For example, a serial number that has been traded many times in a short period of time will be deemed abnormal.

[1097] Step 7:

[1098] The device notifies the user of the analysis results received from the server, and the notification message includes details of the anomaly.

[1099] Step 8:

[1100] Users can check the notification and take necessary action, such as stopping the use of the banknotes on which an abnormality has been detected or reporting it to the relevant authorities.

[1101] The above is a specific processing flow of this system.

[1102] Example 1

[1103] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1104] Conventional banknote management systems have difficulty effectively preventing fraudulent use and counterfeiting of banknotes, and lack the means to ensure transaction transparency. Furthermore, technology for efficiently managing banknote serial numbers and detecting anomalies has not been fully established. This has made it difficult to track banknotes and detect fraud early, leading to problems such as a decline in the credibility of financial institutions and transactions.

[1105] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1106] In this invention, the server includes a means for collecting banknote serial numbers and transaction information and storing them in a database, a means for detecting abnormalities in banknotes using a generative AI model based on the collected data, and a means for transmitting the abnormality detection results to a terminal and notifying the user, thereby enabling early detection of fraudulent use and counterfeit banknotes and improving transaction transparency.

[1107] A "terminal" is a device that includes hardware and software that allows a user to capture an image of a banknote and read its serial number.

[1108] A "user" is a person or organization that uses the banknote management system to input transaction information and manage banknotes.

[1109] "Transaction Information" refers to information related to a transaction, such as user ID, transaction ID, transaction date, and location.

[1110] A "serial number" is a unique identification number printed on a banknote.

[1111] The "server" is a central center that collects banknote serial numbers and transaction information sent from terminals, stores them in a database, and analyzes them.

[1112] "Database" refers to a system for storing and managing banknote serial numbers and transaction information collected by the server.

[1113] A "generative AI model" is an artificial intelligence model used to detect anomalies in banknotes based on collected data.

[1114] An "anomaly" is a condition that exhibits an unusual or unexpected pattern or behavior within a series of transactions.

[1115] A "notification" is a message containing an abnormality detection result sent from the server to the user.

[1116] MODE FOR CARRYING OUT THE INVENTION

[1117] The present invention provides a banknote management system in which a terminal, a server, and a user work in cooperation with each other. The operation of this system and details of its hardware and software will be described in the following specific embodiment.

[1118] System Overview

[1119] In this system, the terminal captures an image of the banknote and reads its serial number. The user enters transaction information, and the terminal sends this information to the server. The server collects this data and stores it in a database. The server then analyzes the collected data using a generative AI model to detect abnormalities in the banknote. The server then sends the detected abnormalities to the terminal and notifies the user.

[1120] Hardware and Software

[1121] Terminal: A terminal equipped with a high-resolution CCD scanner and a touch panel. OpenCV is used as image processing software.

[1122] Server: MySQL is used as the database, and generative AI models are used for data analysis. TensorFlow and PyTorch are used to build the generative AI models.

[1123] Device behavior

[1124] The terminal performs the following operations:

[1125] 1. The user swipes the bill through the terminal's scanner.

[1126] 2. The device's scanner captures an image of the banknote and performs image analysis using OpenCV.

[1127] 3. Read the banknote serial number from the analyzed image.

[1128] 4. The user follows the instructions displayed on the screen and enters the necessary transaction information, such as user ID, transaction ID, transaction date and location, via the touch panel.

[1129] 5. The terminal encrypts the serial number and transaction information it has obtained and sends them to the server.

[1130] Examples:

[1131] A user passes a 1,000 yen bill through a scanner at a convenience store, obtaining the serial number "AB12345678." The user then enters transaction information, such as "User ID: user123," "Transaction ID: TX001," "Date: 2023-10-01," and "Location: Convenience Store A." The terminal then encrypts this information and sends it to the server.

[1132] Server Operation

[1133] The server performs the following actions:

[1134] 1. Receive the serial number and transaction information sent from the terminal.

[1135] 2. Save the received data in the database.

[1136] 3. The saved data is input into the generative AI model for analysis.

[1137] 4. The generative AI model has an algorithm that detects anomalies based on transaction information on banknotes. For example, if a particular serial number is used in a large number of transactions in a short period of time, it will mark this as an anomaly.

[1138] 5. If an abnormality is detected, the result is sent to the terminal.

[1139] Examples:

[1140] The server receives the user's transaction information and serial number, stores them in a database, and then analyzes them using an anomaly detection model using TensorFlow. If an anomaly is detected, the server sends a notification to the terminal stating, "This banknote has an abnormal transaction history."

[1141] User Actions

[1142] The user performs the following actions:

[1143] 1. When making a transaction, use the terminal to scan the bill.

[1144] 2. Enter the transaction information into the terminal.

[1145] 3. After the transaction, check the notification from the server. If an abnormality is detected, take action according to the notification.

[1146] Examples:

[1147] A user purchases a 1000 yen item at a convenience store, scans the bill, and enters the transaction information. If the server notifies the user that "this bill has an abnormal transaction history," the user should stop using the bill and report it to the relevant authorities.

[1148] Prompt Sentence Examples

[1149] An example of a prompt to input to a generative AI model is:

[1150] 1. "If a note with this serial number has been used in many transactions in a short period of time, how can I mark it as abnormal?"

[1151] 2. "Describe how you can improve an algorithm for extracting banknote serial numbers from images."

[1152] The above is a specific embodiment of the present invention, which makes it possible to effectively prevent fraudulent use and counterfeiting of banknotes and improve the transparency of transactions.

[1153] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1154] The flow of this system's program processing

[1155] Step 1:

[1156] The terminal captures an image of the bill and reads the serial number.

[1157] Input: The user swipes a bill through the terminal's scanner.

[1158] How it works: The device's high-resolution CCD scanner captures an image of the bill. OpenCV is used to analyze the image and extract the serial number.

[1159] Output: The extracted serial number (e.g. "AB12345678").

[1160] Step 2:

[1161] The user enters the transaction information into the terminal.

[1162] Input: Information such as user ID, transaction ID, transaction date, and transaction location.

[1163] Specific operation: The user uses the device's touch panel to enter transaction information according to the instructions on the screen.

[1164] Output: The entered transaction information (e.g., "User ID: user123", "Transaction ID: TX001", "Date: 2023-10-01", "Location: Convenience Store A").

[1165] Step 3:

[1166] The terminal encrypts the banknote's serial number and transaction information and sends it to the server.

[1167] Input: Serial number, transaction information.

[1168] Specific operation: The terminal encrypts the serial number and transaction information using the HTTPS protocol and sends it to the server.

[1169] Output: The encrypted serial number and transaction information are sent to the server.

[1170] Step 4:

[1171] The server receives the banknote serial number and transaction information and stores it in a database.

[1172] Input: Encrypted serial number and transaction information.

[1173] Specific operation: The server receives the encrypted data, decrypts it, and stores it in a database (e.g., MySQL).

[1174] Output: Transaction information and serial number stored in the database.

[1175] Step 5:

[1176] The server analyzes the collected data using a generative AI model.

[1177] Input: Transaction information and serial number stored in the database.

[1178] How it works: The server inputs the stored data into a generative AI model (e.g., TensorFlow) and analyzes the data. The analysis algorithm analyzes the banknote transaction history and detects anomalies.

[1179] Output: If an anomaly is detected, it is flagged and detailed information.

[1180] Step 6:

[1181] If an abnormality is detected, the server transmits the result to the terminal.

[1182] Input: Analysis results (abnormality flags and detailed information).

[1183] Specific operation: The server creates a message to notify the user based on the analysis results and sends it to the terminal using the HTTPS protocol.

[1184] Output: A message of the anomaly detection result is sent to the terminal.

[1185] Step 7:

[1186] The terminal displays an abnormality notification from the server to the user.

[1187] Input: The anomaly detection message sent by the server.

[1188] Specific operation: The terminal receives a message from the server and displays it on the screen to notify the user of an abnormality.

[1189] Output: The user confirms the abnormality notification.

[1190] Step 8:

[1191] The user checks the notification and takes the necessary action.

[1192] Input: Abnormal notification displayed on the terminal.

[1193] Specific action: The user checks the notification and either stops using the note or reports it to the appropriate authorities.

[1194] Output: Prevents fraud and counterfeiting by requiring the user to take appropriate action.

[1195] The above are the specific processing steps of the program of this system.

[1196] (Application example 1)

[1197] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1198] Conventional banknote management systems have had the problem of making it difficult to quickly detect fraudulent or counterfeit banknotes. In particular, they have been unable to ensure transaction transparency or detect abnormal banknote transactions in real time, limiting their ability to prevent fraud.

[1199] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1200] In this invention, the server includes a means for a terminal to collect banknote serial numbers and transaction information and store them in a database, a means for detecting abnormalities in banknotes using an AI model based on the collected data, a means for transmitting the abnormality detection results to the terminal and notifying the user, a means for capturing images of banknotes using a smartphone camera and extracting serial numbers using image processing technology, and a means for notifying the user in real time when an abnormal transaction is detected. This makes it possible to detect fraudulent use and counterfeit banknotes in real time and increase transaction transparency.

[1201] A "terminal" is a device used by a user to capture images of banknotes and read their serial numbers. It also has the function of inputting transaction information and sending it to a server.

[1202] "Capturing an image of a banknote" refers to the act of capturing image data of the banknote using a smartphone camera or the like.

[1203] "Reading the serial number" refers to the act of extracting the serial number from an image of a banknote using optical character recognition (OCR) technology.

[1204] "Transaction Information" refers to detailed information related to a transaction, such as user ID, transaction ID, transaction date, and location.

[1205] The "server" is a computer system that receives the banknote serial numbers and transaction information sent from the terminal, and stores and analyzes this data in a database.

[1206] A "database" is a memory space for storing and managing banknote serial numbers and transaction information collected by the server.

[1207] "AI model" refers to artificial intelligence (AI) that has algorithms to analyze collected data and detect abnormalities in banknotes.

[1208] "Anomaly detection" refers to the act of using AI models to analyze a banknote's transaction history and identify unusual transaction patterns.

[1209] "Notifying the user" refers to the act of the server sending the abnormality detection result to the terminal so that the user can check it.

[1210] "Image processing technology" refers to the general technology used to analyze images of banknotes and extract serial numbers. Libraries such as OpenCV are mainly used.

[1211] "Real-time notification" refers to the act of sending information about an abnormality to the user almost immediately upon detection.

[1212] System Overview

[1213] The present invention is a banknote management system in which a terminal, a server, and a user work together. The terminal acquires an image of the banknote and reads its serial number. The user inputs transaction information, and the terminal sends this information to the server. The server collects this data and stores it in a database. The server then analyzes the collected data using an AI model to detect abnormalities in the banknote. The detected abnormalities are sent from the server to the terminal and notified to the user.

[1214] Device behavior

[1215] The terminal uses a smartphone. First, an image of the banknote is captured using the smartphone's camera. Next, the captured image is processed using the OpenCV library, and the serial number is extracted using OCR technology. The user enters transaction information (user ID, transaction ID, date, location) into the application. This data is sent from the terminal to the server.

[1216] Server Operation

[1217] The server receives the serial number and transaction information sent from the terminal. The received data is first stored in a database. The server then uses TensorFlow to build an AI model and analyze this data. The AI ​​model has an algorithm that detects anomalies based on the banknote transaction information. For example, if a particular serial number is used in a large number of transactions in a short period of time, it is marked as an anomaly. If an anomaly is detected, the result is sent to the terminal.

[1218] User Actions

[1219] When making a transaction, the user scans the banknote using their smartphone and enters the transaction information into the terminal. After the transaction, the user checks the notification from the server and takes the necessary action. For example, if the server notifies the user that "this banknote has an abnormal transaction history," the user can stop using the banknote or report it to the relevant authorities.

[1220] Specific examples

[1221] For example, when User A purchases a 1,000 yen item at a store, the process can be as follows: User A holds a 1,000 yen bill over their smartphone camera and captures an image. The application follows the instructions on the screen and enters the user ID, transaction ID, and transaction date and location. The device sends the entered information and the bill's serial number to the server. The server stores this information in a database and uses TensorFlow to analyze it with an AI model. If the analysis results indicate that the bill has a normal transaction history, no notification is sent to the user. However, if an abnormality is detected, the server sends the analysis results to the device, which notifies User A. For example, the device may display a message saying, "This bill has an abnormal transaction history." User A confirms this notification and either discontinues use of the bill or reports it to the appropriate authorities.

[1222] Prompt Sentence Examples

[1223] "We will analyze all transaction history based on this transaction information and the banknote serial number to determine whether there is any suspicion of fraud."

[1224] As explained above, by using the system of the present invention, it is possible to detect fraudulent use and counterfeiting of banknotes in real time and increase the transparency of transactions.

[1225] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1226] Step 1:

[1227] The terminal (smartphone) acquires an image of a banknote through user operation. The image of the banknote is taken using the smartphone's camera. The input is the actual banknote, which is then acquired as a digital image. The output is a digital image of the acquired banknote.

[1228] Step 2:

[1229] The image of the banknote acquired by the terminal is preprocessed using OpenCV to extract the serial number. Specifically, the image is converted to grayscale, subjected to adaptive binarization processing, and then the serial number is extracted as text data using OCR (Optical Character Recognition) technology. The input is the digital image of the banknote acquired in Step 1, and the output is the banknote serial number.

[1230] Step 3:

[1231] The user inputs transaction information (user ID, transaction ID, date, location) into the terminal. The input is manual input by the user, and the output is digital data of the transaction information entered by the user.

[1232] Step 4:

[1233] The terminal extracts the serial number and sends the transaction information entered by the user to the server. The input is digital data of the serial number and transaction information, and the output is a process of encrypting this data and sending it to the server via a communication network.

[1234] Step 5:

[1235] The server stores the received serial number and transaction information in a database. The input is the encrypted data of the serial number and transaction information received over the network, and the output is this data stored in the database.

[1236] Step 6:

[1237] The server uses a generative AI model (TensorFlow) based on data collected from the database to detect anomalies in banknotes. The input is the digital data of the serial number and transaction information stored in the database, and the prompt for the AI ​​model is "Analyze all transaction history based on this transaction information and the banknote serial number and determine whether there is any suspicion of fraud." The output is the analysis result indicating whether an anomaly was detected.

[1238] Step 7:

[1239] If the server detects an abnormality in the serial number based on the analysis results, it generates a notification message and sends it to the terminal. The input is the analysis result obtained in step 6, and the output is the message to be notified to the user.

[1240] Step 8:

[1241] The terminal receives a notification message from the server and displays it to the user. The input is the notification message sent from the server, and the output is the notification message displayed to the user. For example, a warning message such as "This banknote has an abnormal transaction history" is displayed.

[1242] Step 9:

[1243] The user checks the notification message from the server and, if necessary, stops using the bill or reports it to the relevant authorities. The input is the displayed notification message, and the output is the specific action the user takes.

[1244] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1245] System Overview

[1246] The present invention provides a banknote management system in which a terminal, a server, and a user work together. In particular, the present invention is equipped with a function that recognizes a user's emotions and provides adaptive notifications based on them by combining an emotion engine. The terminal acquires an image of the banknote and reads its serial number. The user enters transaction information, and the terminal sends this information to the server. The server collects this data and stores it in a database. The server then analyzes the collected data using an AI model to detect abnormalities in the banknote. The detected abnormalities are sent from the server to the terminal, and adaptive notifications are provided to the user using the emotion engine.

[1247] Device behavior

[1248] The terminal captures an image of the bill and reads its serial number. For example, when a user purchases an item at a convenience store, they pass the bill through the terminal's scanner. The terminal analyzes the image of the bill and extracts the serial number. Along with this serial number, the terminal also inputs the transaction information entered by the user (user ID, transaction ID, date, location). This information is then sent to the server.

[1249] The device is equipped with an emotion engine that recognizes the user's emotions, using the camera, microphone, and other sensors to analyze the user's facial expressions and tone of voice. For example, if the user has an anxious expression, the emotion engine will recognize this and adapt the tone of the warning message as needed.

[1250] Server Operation

[1251] The server receives the serial number and transaction information sent from the terminal. The received data is first stored in a database. The server then analyzes this data using an AI model. The AI ​​model has an algorithm that detects anomalies based on the banknote transaction information. For example, if a particular serial number is used in a large number of transactions in a short period of time, it is marked as an anomaly. If an anomaly is detected, the result is sent to the terminal.

[1252] User Actions

[1253] When making a transaction, the user scans the banknote using a terminal and inputs the transaction information into the terminal. After the transaction, the user checks for a notification from the server. For example, if the server notifies the user that "this banknote has an abnormal transaction history," the user can stop using the banknote or report it to the relevant authorities.

[1254] The emotion engine recognizes the user's emotions and provides adaptive notifications. For example, if the user has a surprised expression, the emotion engine softens the notification message and displays something like, "Do you want more information about this banknote?"

[1255] Specific examples

[1256] For example, if User B purchases a 500 yen item at a cafe, the following process can be considered: User B passes the 500 yen bill through the terminal's scanner to obtain its serial number. User B follows the instructions on the screen and enters their user ID, transaction ID, and the date and location of the transaction. The terminal then sends the entered information and the bill's serial number to the server. The server stores this information in a database and analyzes it using an AI model. If the analysis determines that the bill has a normal transaction history, no special notification is given to the user. However, if an abnormality is detected, the server sends the analysis results to the terminal, and the terminal notifies User B using an emotion engine. For example, if User B looks confused, the terminal may display the message, "This bill has an abnormal transaction history. Don't worry, please check the details."

[1257] The above is a specific embodiment of the present invention, which not only effectively prevents fraudulent use and counterfeiting of banknotes, but also improves the transparency of transactions and the sense of security of users.

[1258] The processing flow will be explained below.

[1259] Step 1:

[1260] The user scans the bill into the terminal, which uses the terminal's camera or scanner to capture an image of the bill.

[1261] Step 2:

[1262] The terminal analyzes the image of the banknote and reads its serial number, extracting it using image processing technology.

[1263] Step 3:

[1264] The user enters transaction information into the terminal, including the user ID, transaction ID, transaction date, and location.

[1265] Step 4:

[1266] The terminal encrypts the acquired serial number and entered transaction information and sends it to the server. Encryption ensures data security.

[1267] Step 5:

[1268] The server receives the serial number and transaction information and stores it in a database.

[1269] Step 6:

[1270] The server analyzes the transaction information in the database using an AI model, which has an algorithm to detect anomalies in the banknote transaction information.

[1271] Step 7:

[1272] If the server detects an anomaly as a result of the analysis, it will send the result to the terminal. If no anomaly is found, it will not notify the terminal.

[1273] Step 8:

[1274] The terminal displays the analysis results received from the server, for example, displaying a warning message if an abnormality is detected.

[1275] Step 9:

[1276] To recognize the user's emotions, the device uses a camera, microphone, and other sensors to analyze the user's facial expressions and voice, determining whether the user is feeling surprise, anxiety, confusion, etc.

[1277] Step 10:

[1278] The device's emotion engine adapts the tone and content of notification messages based on the recognized emotion, for example, softening the notification message if the user looks anxious.

[1279] Step 11:

[1280] The user checks the notification from the terminal and takes necessary action if an abnormality is detected, such as stopping the use of the banknote or reporting it to the relevant authorities.

[1281] The above is a specific processing flow of the present invention.

[1282] Example 2

[1283] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1284] Conventional banknote management systems have difficulty in ensuring sufficient transaction transparency and user security, making it difficult to detect fraudulent or counterfeit banknotes. Furthermore, because they do not provide adaptive notifications that take user emotions into consideration, anomaly detection results are often not accepted smoothly.

[1285] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting banknote serial numbers and transaction information and storing them in a database, means for detecting abnormalities in banknotes using a generative AI model based on the collected data, and means for transmitting the abnormality detection results to the terminal and notifying the user. This enables early detection of fraudulent use and counterfeit banknotes. Furthermore, the terminal uses an emotion engine to recognize the user's emotions and provides adaptive notifications, making it easier for the user to accept notifications and improving the transparency and security of transactions.

[1286] A "terminal" is a device that captures an image of a banknote and reads its serial number, and is a device through which a user inputs transaction information and communicates with a server.

[1287] The "server" is a central processing unit that collects and stores the serial numbers of banknotes and transaction information sent from the terminal and analyzes this data.

[1288] A "serial number" is a unique identification number assigned to each banknote, and is used to track transaction history and detect anomalies.

[1289] "Transaction information" is detailed data about the use of the banknote, such as user ID, transaction ID, date and location of the transaction.

[1290] "Collected data" is a data set containing banknote serial numbers and transaction information sent from the terminal.

[1291] A "database" is a system for structuring and storing data collected by a server.

[1292] A "generative AI model" is an artificial intelligence model that has algorithms for analyzing large amounts of data and detecting specific patterns and anomalies.

[1293] An "anomaly" is an unusual transaction pattern detected based on the banknote's serial number and transaction information, which may indicate fraud or counterfeiting.

[1294] An "emotion engine" is a system that uses sensors such as cameras and microphones to analyze a user's facial expressions and tone of voice and recognize the user's emotions.

[1295] An "adaptive notification" is a notification message whose content and tone are adjusted to take into account the user's emotions.

[1296] "Encryption" is a technology that converts data into a format that cannot be read by third parties, ensuring the security of communications.

[1297] "Update" is the process of introducing new data and algorithms to improve the performance of a generative AI model.

[1298] This invention is a banknote management system in which terminals, servers, and users work together. In particular, by combining an emotion engine, it has the function of recognizing users' emotions and providing adaptive notifications based on those emotions. This system aims to effectively prevent fraudulent use and counterfeiting of banknotes, improve transaction transparency, and increase user peace of mind.

[1299] Device behavior

[1300] The terminal captures an image of the bill the user passes through the scanner and reads its serial number. For example, when a user passes a 500 yen bill through the scanner to purchase an item at a convenience store, the terminal's camera takes an image of the bill and uses OCR (Optical Character Recognition) software to extract the serial number. The terminal then prompts the user to enter transaction information (user ID, transaction ID, date, and location). Once the user enters this information, the terminal sends it to the server. HTTPS is used as the communication protocol to ensure data security.

[1301] Server Operation

[1302] The server receives the serial number and transaction information sent from the device and stores this data in a database management system (e.g., MySQL or PostgreSQL). The stored data is analyzed using a generative AI model (e.g., TensorFlow or PyTorch). The analysis includes algorithms that detect anomalies, such as when the same serial number is used in multiple different places within a short period of time. If an anomaly is detected, the results are sent to the device.

[1303] User operations

[1304] When making a transaction, the user scans the banknote using the terminal and enters the transaction information. After the transaction, the user checks the notification from the server. For example, if the user receives a notification that "this banknote has an abnormal transaction history," the user is required to stop using the banknote or report it to the relevant authorities.

[1305] Emotion Engine Functions

[1306] The emotion engine uses the device's camera, microphone, and other sensors to analyze the user's facial expressions and tone of voice. If the user has a confused expression, the emotion engine will recognize this and display an adaptive notification. For example, a soft-spoken message might say, "This bill has an unusual transaction history. Don't worry, please check the details."

[1307] Specific examples

[1308] For example, if User B purchases a 500 yen item at a cafe, the following process can be considered: User B passes the 500 yen bill through the terminal's scanner to obtain the serial number. Next, User B follows the instructions on the screen to enter their user ID, transaction ID, and the date and location of the transaction. The terminal sends the entered information and the bill's serial number to the server. The server stores this information in a database and analyzes it using a generative AI model. If the analysis results indicate that the bill has a normal transaction history, no notification is sent to the user. However, if an abnormality is detected, the server sends the analysis results to the terminal, and the terminal notifies User B using an emotion engine. For example, if User B looks confused, the terminal may display a message saying, "This bill has an abnormal transaction history. Don't worry, please check the details."

[1309] Example of input prompt for generative AI model

[1310] "How can I detect anomalies based on banknote serial numbers and transaction information?"

[1311] The above is a specific embodiment of the present invention, which not only effectively prevents fraudulent use and counterfeiting of banknotes, but also improves the transparency of transactions and the sense of security of users.

[1312] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1313] Step 1:

[1314] The terminal captures an image of the bill and reads its serial number.

[1315] Specifically, when a user swipes a banknote through the terminal's scanner, the terminal's camera takes an image of the banknote. This image is analyzed using OCR software to extract the serial number. The input is the image of the banknote, and the output is the banknote's serial number.

[1316] Step 2:

[1317] The terminal allows the user to enter transaction information.

[1318] For example, a user follows instructions on the terminal to enter a user ID, transaction ID, transaction date, and location. These input fields are displayed on the terminal screen, and the user enters the information using a keyboard or touch panel. The input is the user's transaction information, and the output is a dataset of the entered transaction information.

[1319] Step 3:

[1320] The terminal sends the banknote serial number and transaction information to the server.

[1321] This causes the terminal to send the serial number and transaction information to the server using the HTTPS protocol. The input is a dataset of the serial number and transaction information, and the output is the secure data transfer to the server.

[1322] Step 4:

[1323] The server stores the received data in a database.

[1324] The server receives data sent from the devices and stores it in a database. This data is stored in a database management system (e.g., MySQL, PostgreSQL). The input is the data received from the devices, and the output is the data records stored in the database.

[1325] Step 5:

[1326] The server analyzes the data using a generative AI model to detect anomalies.

[1327] The server retrieves the stored data from the database and analyzes it using a generative AI model (e.g., TensorFlow, PyTorch). For example, if the same serial number is used in multiple places within a short period of time, this can be detected as an anomaly. The input is the transaction information stored in the database, and the output is the result of the anomaly detection.

[1328] Step 6:

[1329] The server sends the abnormality detection result to the terminal.

[1330] If an anomaly is detected, the server sends the result to the terminal. The server composes the analysis result in JSON format and sends it as an HTTP response. The input is the anomaly detection result, and the output is the data transfer to the terminal.

[1331] Step 7:

[1332] The terminal notifies the user using an emotion engine.

[1333] The device applies an emotion engine based on the anomaly detection results received from the server to recognize the user's emotions. It uses a camera and microphone to analyze the user's facial expressions and tone of voice and displays an adaptive notification. For example, if the user has a confused expression, it displays a message saying, "This banknote has an abnormal transaction history. Don't worry, please check the details." The input is the anomaly detection results from the server and the user's emotional data, and the output is a notification message.

[1334] Step 8:

[1335] The user acknowledges the notification and takes the necessary action.

[1336] The user checks the notification message displayed on the terminal and, if necessary, stops using the banknote or reports it to the relevant authorities. The input is the notification message, and the output is the user's action.

[1337] The above are the specific processing steps of this system.

[1338] (Application example 2)

[1339] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1340] Conventional banknote management systems only detect abnormalities in banknotes and do not provide notifications based on the user's emotions, which means that the user's sense of security and transaction transparency are not sufficiently ensured. Furthermore, notifying only the user of detected banknote abnormalities can cause excessive anxiety and confusion. In response to this, the present invention aims to improve the user's sense of security and transaction transparency by combining a user emotion analysis function with a banknote management system to provide adaptive notifications based on the user's emotions.

[1341] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a terminal to acquire an image of a banknote and read its serial number, means for a user to input transaction information, means for the terminal to transmit the banknote serial number and transaction information, means for the server to collect the banknote serial number and transaction information and store them in a database, means for the server to detect anomalies in the banknote using an AI model based on the collected data, means for the terminal to analyze the user's emotions and adaptively issue a notification based on the emotions, means for the server to transmit the anomaly detection result to the terminal and notify the user, and means for the user to check the notification and take necessary action. This enables adaptive notifications that take the user's emotions into consideration, avoiding excessive anxiety and confusion and improving transaction transparency and user peace of mind.

[1342] A "terminal" is an electronic device in the system that captures images of banknotes and reads their serial numbers.

[1343] "Emotion analysis" is the process of analyzing a user's facial expressions and tone of voice to identify their emotional state.

[1344] "Transaction information" refers to information entered by a user, including a user ID, transaction ID, date, location, and the like.

[1345] A "serial number" is a unique identification code printed on a banknote that is used to track and verify the banknote.

[1346] "Transmission means" refers to a technology for electronically transmitting the serial number of the banknote and transaction information from the terminal to the server.

[1347] The "collection means" is the process by which the server receives the serial number of the banknote and the transaction information sent from the terminal and stores them in a database.

[1348] A "database" is a collection of banknote serial numbers and transaction information managed within the system.

[1349] An "AI model" is an artificial intelligence algorithm used by the server to detect anomalies based on banknote transaction information.

[1350] "Anomaly detection" is the process by which the server uses AI models to identify abnormal transaction patterns on banknotes.

[1351] "Adaptive notifications" is a technology in which a terminal notifies a user with an appropriate tone based on the user's emotional state.

[1352] The "notification means" is a method for transmitting information from the terminal to the user when an abnormal banknote transaction is detected.

[1353] "Means of action" refers to specific actions or instructions that a user should take when receiving a notification.

[1354] The present invention relates to a banknote management system that detects abnormalities in banknotes and provides adaptive notification based on the user's emotions. Next, the operation and embodiments of this system will be described in detail.

[1355] 1. System Overview

[1356] The system consists of the following main components:

[1357] Terminal

[1358] server

[1359] Database

[1360] Sentiment Analysis Engine

[1361] AI model

[1362] 2. Device operation

[1363] The device uses the following hardware and software:

[1364] Hardware: Smartphone camera

[1365] Software: OpenCV (image analysis), Pytesseract (character recognition)

[1366] The terminal captures an image of the bill when the user scans it with a camera. From this image, it uses Pytesseract to extract the serial number. The terminal also provides an interface for the user to input transaction information (user ID, transaction ID, date, and location). Furthermore, it utilizes an emotion analysis engine to analyze the user's facial expressions and tone of voice to identify the user's emotional state.

[1367] 3. Server operation

[1368] The server uses the following software:

[1369] Database: MySQL, PostgreSQL, etc.

[1370] AI model: Anomaly detection algorithm using TensorFlow and PyTorch

[1371] The server receives the serial numbers and transaction information sent from the terminal and stores them in a database. Based on the stored data, an AI model is used to detect anomalies in the banknotes. Specifically, it identifies serial numbers that have been frequently used in transactions over a short period of time and other abnormal patterns.

[1372] 4.Means of notification

[1373] When the server detects an anomaly, it sends the result to the device, which then displays an adaptive notification based on the user's emotional state. For example, if the user has a surprised expression, the notification message is softened.

[1374] 5. User operations

[1375] The user checks the notification and takes necessary action, such as canceling transactions of the banknotes in which the abnormality was detected or reporting it to the relevant authorities.

[1376] Specific examples

[1377] For example, when a user uses a 500 yen bill at a cafe, they scan the bill with their smartphone camera to obtain the serial number. Then, they enter the user ID, transaction ID, date, and location into the terminal. If the system detects an anomaly and the user looks confused, the emotion analysis engine will display a message saying, "There is an anomaly in the transaction history of this bill. Don't worry, please check the details."

[1378] Prompt Sentence Examples

[1379] "Scan the serial number of a 500 yen bill with your smartphone camera and enter your transaction information. We then analyze the captured user's facial expressions and provide adaptive notifications based on their emotions."

[1380] In this way, the banknote management system of the present invention can prevent fraudulent use and counterfeiting of banknotes, and improve the transparency of transactions and the sense of security of users.

[1381] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1382] Step 1:

[1383] The terminal uses the smartphone camera to capture an image of the banknote. The user holds the banknote in front of the camera and presses the capture button to capture the image. The input is the image of the banknote that was captured, and the output is the captured image data.

[1384] Step 2:

[1385] To extract the serial number from the captured image of the banknote, the terminal preprocesses the image using OpenCV and performs optical character recognition using Pytesseract. The input is the preprocessed image, and the output is text data containing the banknote serial number.

[1386] Step 3:

[1387] The user uses the terminal interface to input transaction information (user ID, transaction ID, date, location). The input is the data manually entered by the user, and the output is the transaction information data stored in the terminal.

[1388] Step 4:

[1389] The device uses an emotion analysis engine to analyze the user's facial expressions and tone of voice. The camera captures the user's facial expressions, and the microphone records any audio input. The input is the captured facial image and audio data, and the output is data indicating the user's emotional state.

[1390] Step 5:

[1391] The terminal sends the serial number, transaction information, and emotional state data to the server. The transmission is via HTTPS protocol and the data is encrypted. The input is the serial number, transaction information, and emotional state data, and the output is the transmission completion status to the server.

[1392] Step 6:

[1393] The server receives the data sent from the terminal and stores it in a database. The stored data includes the serial number, transaction information, and the user's emotional state. The input is the sent data, and the output is the record stored in the database.

[1394] Step 7:

[1395] The server uses the stored data to detect anomalies in banknotes using an AI model, matching the serial number with transaction history and identifying abnormal patterns. The input is transaction information and serial number retrieved from the database, and the output is the anomaly detection result.

[1396] Step 8:

[1397] The server sends the anomaly detection results to the terminal. The HTTPS protocol is used for transmission, and the data is encrypted. The input is the anomaly detection result data, and the output is the transmission completion status to the terminal.

[1398] Step 9:

[1399] The device analyzes the anomaly detection results received from the server and displays an adaptive notification according to the user's emotional state. The input is the anomaly detection result data and the emotional state data, and the output is a notification message displayed to the user.

[1400] Step 10:

[1401] The user checks the notification from the terminal and takes necessary action. For example, if an abnormal banknote is detected, the user may stop using the banknote or report it to the relevant authorities. The input is the notification message from the terminal, and the output is the specific action taken by the user.

[1402] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1403] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1404] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1405] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1406] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1407] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1408] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1409] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1410] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1411] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1412] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1413] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1414] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1415] 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.

[1416] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1417] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1418] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1419] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1420] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1421] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1422] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1423] The following is further disclosed regarding the above embodiment.

[1424] (Claim 1)

[1425] a means for the terminal to capture an image of the banknote and read its serial number;

[1426] a means for a user to input transaction information;

[1427] means for the terminal to transmit the banknote serial number and transaction information;

[1428] A means for the server to collect and store the serial numbers and transaction information of the banknotes in a database;

[1429] A means for the server to detect abnormalities in banknotes using an AI model based on the collected data;

[1430] a means for the server to transmit the anomaly detection result to the terminal and notify the user;

[1431] A way for users to view notifications and take necessary actions.

[1432] A banknote management system including:

[1433] (Claim 2)

[1434] 2. The banknote management system according to claim 1, wherein the terminal encrypts and transmits the acquired serial number and transaction information.

[1435] (Claim 3)

[1436] 2. The banknote management system according to claim 1, wherein the server further comprises means for updating and improving the AI ​​model for anomaly detection.

[1437] "Example 1"

[1438] (Claim 1)

[1439] a means for the terminal to capture an image of the banknote and read its serial number;

[1440] a means for a user to input transaction information;

[1441] A means for the terminal to encrypt and transmit the serial number of the banknote and transaction information;

[1442] A means for the server to collect and store the serial numbers and transaction information of the banknotes in a database;

[1443] A means for detecting anomalies in banknotes using a generative AI model based on the collected data by the server;

[1444] a means for the server to transmit the anomaly detection result to the terminal and notify the user;

[1445] A way for users to view notifications and take necessary actions.

[1446] A system including:

[1447] (Claim 2)

[1448] 2. The system according to claim 1, wherein the terminal encrypts and transmits the acquired serial number and transaction information.

[1449] (Claim 3)

[1450] 10. The system of claim 1, wherein the server further comprises means for updating and improving the generative AI model for anomaly detection.

[1451] "Application Example 1"

[1452] (Claim 1)

[1453] a means for the terminal to capture an image of the banknote and read its serial number;

[1454] a means for a user to input transaction information;

[1455] means for the terminal to transmit the banknote serial number and transaction information;

[1456] A means for the server to collect and store the serial numbers and transaction information of the banknotes in a database;

[1457] A means for the server to detect abnormalities in banknotes using an AI model based on the collected data;

[1458] a means for the server to transmit the anomaly detection result to the terminal and notify the user;

[1459] A way for users to view notifications and take necessary actions;

[1460] A method to capture an image of the banknote using a smartphone camera and extract the serial number using image processing technology.

[1461] A means to notify users in real time when abnormal transactions are detected.

[1462] A system including:

[1463] (Claim 2)

[1464] 2. The system according to claim 1, wherein the terminal encrypts and transmits the acquired serial number and transaction information.

[1465] (Claim 3)

[1466] 10. The system of claim 1, wherein the server further comprises means for updating and improving the AI ​​model for anomaly detection.

[1467] "Example 2: Combining Emotion Engines"

[1468] (Claim 1)

[1469] a means for the terminal to capture an image of the banknote and read its serial number;

[1470] a means for a user to input transaction information;

[1471] means for the terminal to transmit the banknote serial number and transaction information;

[1472] A means for the server to collect and store the serial numbers and transaction information of the banknotes in a database;

[1473] A means for detecting anomalies in banknotes using a generative AI model based on the collected data by the server;

[1474] a means for the server to transmit the anomaly detection result to the terminal and notify the user;

[1475] a means for the terminal to recognize the user's emotion using an emotion engine and provide an adaptive notification;

[1476] A way for users to view notifications and take necessary actions.

[1477] A system including:

[1478] (Claim 2)

[1479] 2. The system according to claim 1, wherein the terminal encrypts and transmits the acquired serial number and transaction information.

[1480] (Claim 3)

[1481] 10. The system of claim 1, wherein the server further comprises means for updating and improving the generative AI model for anomaly detection.

[1482] "Application example 2 when combining emotion engines"

[1483] (Claim 1)

[1484] a means for the terminal to capture an image of the banknote and read its serial number;

[1485] a means for a user to input transaction information;

[1486] means for the terminal to transmit the banknote serial number and transaction information;

[1487] A means for the server to collect and store the serial numbers and transaction information of the banknotes in a database;

[1488] A means for the server to detect abnormalities in banknotes using an AI model based on the collected data;

[1489] A device that analyzes the user's emotions and adaptively sends notifications based on the emotions;

[1490] a means for the server to transmit the anomaly detection result to the terminal and notify the user;

[1491] A way for users to view notifications and take necessary actions.

[1492] A system including:

[1493] (Claim 2)

[1494] 2. The system according to claim 1, wherein the terminal encrypts and transmits the acquired serial number and transaction information.

[1495] (Claim 3)

[1496] 10. The system of claim 1, wherein the server further comprises means for updating and improving the AI ​​model for anomaly detection. [Explanation of symbols]

[1497] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for the terminal to capture an image of the banknote and read its serial number; a means for a user to input transaction information; means for the terminal to transmit the banknote serial number and transaction information; A means for the server to collect and store the serial numbers and transaction information of the banknotes in a database; A means for the server to detect abnormalities in banknotes using an AI model based on the collected data; a means for the server to transmit the anomaly detection result to the terminal and notify the user; A way for users to view notifications and take necessary actions. A banknote management system including:

2. 2. The banknote management system according to claim 1, wherein the serial number and transaction information acquired by the terminal are encrypted and then transmitted.

3. The banknote management system according to claim 1, wherein the server further comprises means for updating and improving the AI ​​model for anomaly detection.

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