system

Abstract

The system according to this embodiment aims to reduce errors in calculating change and improve the efficiency of cash handling. [Solution] The system according to the embodiment comprises an identification unit, a verification unit, a calculation unit, and a supply unit. The identification unit identifies the type and amount of cash. The verification unit verifies the total weight of the cash identified by the identification unit. The calculation unit calculates the change based on the data obtained by the identification unit and the verification unit. The supply unit provides the change calculated by the calculation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there are calculation errors in change and insufficient efficiency in cash handling, which may lead to a decrease in customer satisfaction.

[0005] The system according to the embodiment aims to reduce calculation errors in change and improve the efficiency of cash handling.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an identification unit, a verification unit, a calculation unit, and a supply unit. The identification unit identifies the type and amount of cash. The verification unit verifies the total weight of the cash identified by the identification unit. The calculation unit calculates the change based on the data obtained by the identification unit and the verification unit. The supply unit provides the change calculated by the calculation unit. [Effects of the Invention]

[0007] The system according to this embodiment can reduce errors in calculating change and improve the efficiency of cash handling. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network). <​​The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The Smart Cash AI Agent according to an embodiment of the present invention is a system for solving conventional problems such as errors in calculating change, inefficiency in cash handling, and decreased customer satisfaction in customer service. This Smart Cash AI Agent automatically identifies the type and amount of cash, checks the total weight of the cash and corrects any errors. Furthermore, the AI ​​calculates the change required for each transaction and cross-checks sensor and image recognition data to ensure accuracy. For example, the Smart Cash AI Agent uses a visual recognition library to identify banknotes and coins. Next, the Smart Cash AI Agent attaches a commercially available pressure sensor to the POS system, checks the total weight of the cash and corrects any errors. This improves the accuracy of identifying the type and amount of cash. Furthermore, the Smart Cash AI Agent's AI calculates the change required for each transaction and cross-checks sensor and image recognition data to ensure accuracy. This prevents errors in calculating change and improves the efficiency of cash handling. It can also improve the customer experience by reducing customer waiting times and alleviating the workload of store employees. Moreover, by integrating existing technologies and tools, new capital investment can be minimized. For example, by combining image recognition technology and weight sensor technology, it is possible to prevent errors in giving change and ensure the accuracy of transactions. In this way, the Smart Cash AI Agent can provide a future checkout experience, offering customers a new checkout experience through AI-powered automated payments, the speed and convenience of cashless payments, and advanced security features. As a result, the Smart Cash AI Agent can prevent errors in calculating change and improve the efficiency of cash handling.

[0029] The smart cash AI agent according to this embodiment comprises an identification unit, a verification unit, a calculation unit, and a provision unit. The identification unit identifies the type and amount of cash. The identification unit identifies the type and amount of cash using, for example, a visual recognition library. The identification unit can use a visual recognition library to identify banknotes and coins. The identification unit identifies the type of banknotes and coins using, for example, a visual recognition library. The identification unit can also identify the amount of cash using a visual recognition library. The identification unit identifies the amount of banknotes and coins using, for example, a visual recognition library. The verification unit verifies the total weight of the cash identified by the identification unit. The verification unit verifies the total weight of the cash using, for example, a commercially available pressure sensor. The verification unit can use a commercially available pressure sensor to verify the total weight of the cash. The verification unit verifies the total weight of the cash using, for example, a commercially available pressure sensor. The verification unit can also verify the total weight of the cash using a commercially available pressure sensor. The verification unit verifies the total weight of the cash using, for example, a commercially available pressure sensor. The calculation unit calculates the change based on the data obtained by the identification unit and the verification unit. The calculation unit can, for example, cross-check the data obtained by the identification unit and the verification unit to calculate the change. The calculation unit can, for example, cross-check the data obtained by the identification unit and the verification unit to calculate the change. The calculation unit can also, for example, cross-check the data obtained by the identification unit and the verification unit to calculate the change. The calculation unit can, for example, cross-check the data obtained by the identification unit and the verification unit to calculate the change. The supply unit provides the change calculated by the calculation unit. The supply unit can, for example, provide the change calculated by the calculation unit. The supply unit can provide the change calculated by the calculation unit. The supply unit can, for example, provide the change calculated by the calculation unit. The supply unit can also, for example, provide the change calculated by the calculation unit.As a result, the smart cash AI agent according to the embodiment can accurately identify the type and amount of cash, and efficiently calculate and provide change.

[0030] The identification unit identifies the type and amount of cash. For example, it uses a visual recognition library to identify the type and amount of cash. Specifically, the identification unit uses a high-resolution camera to acquire images of banknotes and coins and analyzes these images through the visual recognition library. Based on a pre-trained database, the visual recognition library extracts features such as the design, color, size, and pattern of banknotes and coins, and identifies the type of cash by matching these features. For example, in identifying banknotes, it recognizes security features such as portraits, serial numbers, and holograms printed on the banknotes, and determines the type and amount of the banknote based on this information. In identifying coins, it analyzes physical features such as the diameter, thickness, edge shape, and markings of the coin to determine the type and amount of the coin. By utilizing these visual recognition technologies, the identification unit can quickly and accurately identify the type and amount of cash. Furthermore, the identification unit records the identification results in a database in real time and provides them to the subsequent processing department. This allows the identification unit to accurately identify the type and amount of cash, improving the overall efficiency and reliability of the system.

[0031] The verification unit verifies the total weight of the cash identified by the identification unit. The verification unit verifies the total weight of the cash using, for example, a commercially available pressure sensor. Specifically, the verification unit places a high-precision pressure sensor in the cash tray and measures the weight of the cash when it is placed there. The pressure sensor converts the weight of the cash into an electrical signal and processes that signal as digital data. The verification unit analyzes this digital data and calculates the total weight of the cash. Based on the type and amount of cash identified by the identification unit, the verification unit calculates the expected total weight and compares it to the actual weight. For example, if the identification unit identifies one 1000 yen bill and two 500 yen coins, the verification unit calculates their total weight and compares it with the weight measured by the pressure sensor. This comparison verifies the accuracy of the identification result and issues a warning if there is an error. This allows the verification unit to ensure consistency between cash identification and weight, improving the reliability of the entire system. Furthermore, the verification unit periodically calibrates the pressure sensor to maintain measurement accuracy. This allows the verification unit to accurately confirm the total weight of the cash, improving the overall accuracy and reliability of the system.

[0032] The calculation unit calculates the change based on the data obtained by the identification and verification units. For example, the calculation unit cross-checks the data obtained by the identification and verification units to calculate the change. Specifically, the calculation unit receives data on the type and amount of cash provided by the identification unit and data on the total weight of the cash provided by the verification unit, and compares this data. The calculation unit calculates the expected total amount based on the data from the identification unit and verifies the actual total weight based on the data from the verification unit. This allows the calculation unit to verify the consistency between the cash identification result and the weight, and to ensure there are no errors. Next, the calculation unit compares the amount paid by the user with the purchase amount and calculates the amount of change. For example, if a user purchases an item for 1500 yen and pays 2000 yen, the calculation unit calculates 500 yen in change. Based on the amount of change, the calculation unit calculates the optimal combination of change. For example, to provide 500 yen in change, it selects one 500 yen coin. This allows the calculation unit to calculate change quickly and accurately and provide the user with the appropriate change. Furthermore, the calculation unit records the calculation results and provides them to the subsequent supply unit. This allows the calculation unit to efficiently calculate change and improve the overall system performance.

[0033] The supply unit provides the change calculated by the calculation unit. Specifically, the supply unit controls the mechanism for providing change to the user based on the change data provided by the calculation unit. The supply unit provides the calculated change to the user, for example, using a change dispenser. The change dispenser selects the appropriate coins and bills according to instructions from the calculation unit and provides them to the user. For example, when providing 500 yen in change, the supply unit dispenses one 500 yen coin from the dispenser. The supply unit monitors the change provision process in real time and issues a warning if an abnormality occurs. For example, if the dispenser is jammed or there are insufficient coins, the supply unit issues a warning and takes appropriate measures. This allows the supply unit to provide change quickly and accurately, improving user convenience. Furthermore, the supply unit records the history of change provision and uses it for subsequent analysis and audits. This allows the cashier to efficiently manage the change distribution process and improve the overall reliability and security of the system.

[0034] The identification unit can identify the type and amount of cash using a visual recognition library. The identification unit can, for example, use a visual recognition library to identify the type and amount of cash. The identification unit can, for example, use a visual recognition library to identify the type of banknotes and coins. Furthermore, the identification unit can also identify the amount of cash using a visual recognition library. The identification unit can, for example, use a visual recognition library to identify the amount of banknotes and coins. This allows for accurate identification of the type and amount of cash by utilizing a visual recognition library. The visual recognition library needs to clearly define specific types and implementation methods, such as the software and recognition algorithms used. For example, a visual recognition library uses specific software to identify the type and amount of cash. Furthermore, a visual recognition library can also identify the type and amount of cash using specific recognition algorithms. The visual recognition library uses specific software and recognition algorithms to identify the type and amount of cash.

[0035] The verification unit can verify the total weight of cash using a commercially available pressure sensor. The verification unit can, for example, verify the total weight of cash using a commercially available pressure sensor. The verification unit can verify the total weight of cash using a commercially available pressure sensor. The verification unit can, for example, verify the total weight of cash using a commercially available pressure sensor. This allows for accurate verification of the total weight of cash using a commercially available pressure sensor. The specific type and performance of the commercially available pressure sensor, such as the manufacturer, measurement range, and accuracy, must be clearly defined. For example, a commercially available pressure sensor can be used to verify the total weight of cash using a sensor from a specific manufacturer. Furthermore, a commercially available pressure sensor can also be used to verify the total weight of cash using a sensor with a specific measurement range and accuracy.

[0036] The calculation unit can calculate the change by cross-checking the data obtained by the identification unit and the verification unit. The calculation unit can calculate the change by cross-checking the data obtained by the identification unit and the verification unit. The calculation unit can calculate the change by cross-checking the data obtained by the identification unit and the verification unit. The calculation unit can calculate the change by cross-checking the data obtained by the identification unit and the verification unit. This improves the accuracy of the change calculation by cross-checking the data from the identification unit and the verification unit. The specific methods and criteria for mutual verification need to be clearly defined, for example, the method for confirming data consistency and the procedure for cross-checking. For example, the calculation unit uses a specific algorithm to confirm the consistency of the data obtained by the identification unit and the verification unit. The calculation unit can also confirm data consistency by clearly defining the procedure for cross-checking. For example, the calculation unit uses a specific algorithm or procedure to mutually confirm the data obtained by the identification unit and the verification unit.

[0037] The supply unit can provide the change calculated by the calculation unit. The supply unit can, for example, provide the change calculated by the calculation unit. The supply unit can provide the change calculated by the calculation unit. The supply unit can, for example, provide the change calculated by the calculation unit. This ensures that the change calculated by the calculation unit is provided accurately. The specific method and criteria of provision need to be clarified, for example, the means of provision and the timing of provision. For example, the supply unit can provide the change calculated by the calculation unit using specific means. The supply unit can also provide the change calculated by the calculation unit at a specific timing. The supply unit can, for example, provide the change calculated by the calculation unit using specific means and timing.

[0038] The identification unit can detect dirt and damage to banknotes and coins during cash identification, thereby improving identification accuracy. For example, the identification unit can detect dirty banknotes and perform additional image processing to improve identification accuracy. The identification unit can also detect damaged coins and introduce additional verification processes to improve identification accuracy. The identification unit can adjust its identification accuracy according to the degree of dirt and damage. This improves identification accuracy by detecting dirt and damage to banknotes and coins. Specific detection methods and criteria for dirt and damage need to be clearly defined, such as detection algorithms and acceptable ranges. For example, the identification unit can detect dirty banknotes using a specific algorithm. The identification unit can also detect damaged coins based on specific criteria. For example, the identification unit can detect dirt and damage to banknotes and coins using specific algorithms and criteria.

[0039] The identification unit can automatically identify and match different currencies when identifying cash. For example, the identification unit can refer to an additional database to identify and match banknotes of different currencies. The identification unit can perform additional image processing to identify and match coins of different currencies. The identification unit can periodically update its database to improve the accuracy of different currency identification. This improves the accuracy of identification by automatically identifying different currencies. The specific types of different currencies and how they are matched need to be clearly defined, for example, by specifying a list of corresponding currencies or identification methods. For example, the identification unit can identify different currencies using a specific list of currencies or identification methods.

[0040] The identification unit can improve identification accuracy by referring to the user's past transaction history when identifying cash. For example, the identification unit can prioritize the identification of frequently used banknotes and coins based on the user's past transaction history. The identification unit can predict banknotes and coins used during specific time periods based on the user's past transaction history, thereby improving identification accuracy. The identification unit can analyze the user's past transaction history and find patterns to improve identification accuracy. As a result, identification accuracy is improved by referring to the user's past transaction history. The specific content and method of referencing past transaction history need to be clearly defined, for example, the retention period and the type of database. For example, the identification unit can refer to the user's past transaction history using a database with a specific retention period. The identification unit can also refer to the user's past transaction history using a specific type of database. For example, the identification unit can refer to the user's past transaction history using a specific retention period and type of database.

[0041] The identification unit can link identification results with other systems in real time when identifying cash. For example, the identification unit can link identification results with a POS system in real time and automatically update transaction data. The identification unit can link identification results with an inventory management system and update cash inventory status in real time. The identification unit can link identification results with an accounting system and automatically record transaction data. This streamlines the updating of transaction data by linking identification results with other systems in real time. The specific methods and criteria for linking with other systems in real time need to be clarified, for example, communication protocols and data synchronization methods. For example, the identification unit can link identification results with other systems using a specific communication protocol. The identification unit can also link identification results with other systems using a specific data synchronization method. For example, the identification unit can link identification results with other systems in real time using a specific communication protocol or data synchronization method.

[0042] The verification unit can automatically correct the weight of each type of cash during weight verification. For example, the verification unit can automatically correct the weight of banknotes and coins to ensure accurate weight verification. The verification unit can automatically correct the weight of different currencies to ensure accurate weight verification. The verification unit can automatically correct for weight fluctuations due to dirt or damage to ensure accurate weight verification. This improves the accuracy of weight verification by automatically correcting the weight of each type of cash. The specific methods and criteria for correcting the weight of each type of cash need to be clearly defined, for example, by correction algorithms and standard weights. For example, the verification unit can correct the weight of banknotes and coins using a specific algorithm. The verification unit can also correct the weight of different currencies based on specific criteria. For example, the verification unit can correct the weight of each type of cash using specific algorithms and criteria.

[0043] The verification unit can improve the accuracy of weight verification by coordinating with other sensor information. For example, the verification unit can improve the accuracy of weight verification by coordinating a pressure sensor and a temperature sensor. The verification unit can improve the accuracy of weight verification by coordinating a pressure sensor and a humidity sensor. The verification unit can improve the accuracy of weight verification by coordinating a pressure sensor and a vibration sensor. In this way, the accuracy of weight verification is improved by coordinating with other sensor information. The specific types of other sensor information and the methods of coordination need to be clarified, for example, the types of sensors used and the data integration methods. For example, the verification unit can improve the accuracy of weight verification using a specific type of sensor. The verification unit can also coordinate with other sensor information using a specific data integration method. For example, the verification unit coordinates with other sensor information during weight verification using a specific type of sensor and data integration method.

[0044] The calculation unit can calculate the optimal amount of change by referring to past transaction data when calculating change. For example, the calculation unit calculates the optimal amount of change based on past transaction data. The calculation unit can prioritize the calculation of frequently used banknotes and coins from past transaction data. The calculation unit can analyze past transaction data and calculate the most efficient amount of change. In this way, the optimal amount of change can be calculated by referring to past transaction data. The specific method and criteria for calculating the optimal amount of change need to be clearly defined, for example, the calculation algorithm and optimization criteria. For example, the calculation unit uses a specific algorithm to calculate the optimal amount of change based on past transaction data. The calculation unit can also calculate the optimal amount of change from past transaction data based on specific criteria. For example, the calculation unit uses a specific algorithm or criteria to refer to past transaction data and calculate the optimal amount of change.

[0045] The calculation unit can automatically calculate change in different currencies when calculating change. For example, the calculation unit can automatically calculate change based on banknotes and coins of different currencies. The calculation unit can automatically calculate change considering exchange rates between different currencies. The calculation unit can periodically update its database to improve the accuracy of calculating change in different currencies. This improves the accuracy of calculations by automatically calculating change in different currencies. The specific calculation methods and criteria for different currencies need to be clearly defined, such as currency conversion rates and calculation algorithms. For example, the calculation unit can calculate change in different currencies using a specific conversion rate. The calculation unit can also calculate change in different currencies using a specific algorithm. For example, the calculation unit can calculate change in different currencies using a specific conversion rate and algorithm.

[0046] The calculation unit can share calculation results in real time by coordinating with other systems when calculating change. For example, the calculation unit can synchronize the change calculation results with a POS system in real time and automatically update transaction data. The calculation unit can synchronize the change calculation results with an inventory management system and update the cash inventory status in real time. The calculation unit can synchronize the change calculation results with an accounting system and automatically record transaction data. This makes sharing calculation results more efficient by coordinating with other systems. The specific methods and criteria for coordinating with other systems need to be clearly defined, for example, communication protocols and data synchronization methods. For example, the calculation unit can use a specific communication protocol to synchronize the change calculation results with other systems. The calculation unit can also use a specific data synchronization method to synchronize the change calculation results with other systems, for example, by using a specific communication protocol and data synchronization method when calculating change and sharing the calculation results in real time.

[0047] The calculation unit can automatically record the calculation results when calculating change, making them available for later reference. For example, the calculation unit can automatically record the change calculation results for later reference. The calculation unit can periodically back up the change calculation results to ensure data security. The calculation unit can analyze the change calculation results and provide data to be used for future transactions. This ensures data security by automatically recording the calculation results for later reference. Specific methods and criteria for automatically recording calculation results need to be clearly defined, such as the recording format and retention period. For example, the calculation unit can record the change calculation results using a specific format. The calculation unit can also set a specific retention period for recording the change calculation results. For example, the calculation unit can automatically record the calculation results when calculating change using a specific format and retention period, making them available for later reference.

[0048] The change provider can select the optimal method of providing change by referring to the user's past transaction history. For example, the change provider can select the optimal method of providing change based on the user's past transaction history. The change provider can prioritize providing frequently used banknotes and coins based on the user's past transaction history. The change provider can analyze the user's past transaction history and select the most efficient method of providing change. This allows the change provider to select the optimal method of providing change by referring to the user's past transaction history. The specific selection criteria and means for the optimal method of providing change need to be clearly defined, such as the means of provision and the timing of provision. For example, the change provider can use specific means to select the optimal method of providing change based on the user's past transaction history. The change provider can also select the optimal method of providing change based on the user's past transaction history at a specific time. For example, the change provider can use specific means and timing to refer to the user's past transaction history and select the optimal method of providing change.

[0049] The change-giving unit can share the results of change distribution in real time by coordinating with other systems. For example, the change-giving unit can link the change-giving results with the POS system in real time and automatically update transaction data. The change-giving unit can link the change-giving results with the inventory management system and update the cash inventory status in real time. The change-giving unit can link the change-giving results with the accounting system and automatically record transaction data. This makes sharing the results of change distribution more efficient by coordinating with other systems. The specific methods and criteria for sharing the results of change distribution in real time need to be clarified, for example, communication protocols and data synchronization methods. For example, the change-giving unit can use a specific communication protocol to link the change-giving results with other systems. The change-giving unit can also use a specific data synchronization method to link the change-giving results with other systems in real time when distributing change, for example, by coordinating with other systems using a specific communication protocol and data synchronization method.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The identification unit can improve identification accuracy by detecting dirt and damage to banknotes and coins when identifying the type and amount of cash. For example, it can detect dirty banknotes and improve identification accuracy by performing additional image processing. It can also detect damaged coins and introduce an additional verification process. Furthermore, it can adjust the identification accuracy according to the degree of dirt and damage. In this way, the identification accuracy is improved by detecting dirt and damage to banknotes and coins.

[0052] The calculation unit can calculate the optimal amount of change by referring to past transaction data. For example, it can prioritize the calculation of frequently used banknotes and coins based on past transaction data. It can also predict which banknotes and coins will be used during specific time periods based on past transaction data, thereby improving identification accuracy. Furthermore, it can analyze past transaction data to calculate the most efficient amount of change. In this way, the optimal amount of change can be calculated by referring to past transaction data.

[0053] The identification unit can automatically identify and match different currencies when identifying the type and amount of cash. For example, it can refer to an additional database to identify and match banknotes of different currencies. It can also perform additional image processing to identify and match coins of different currencies. Furthermore, the database can be updated periodically to improve the accuracy of different currency identification. This improves the accuracy of identification by automatically identifying different currencies.

[0054] The verification unit can improve the accuracy of the verification when confirming the total weight of cash by coordinating with other sensor information. For example, the accuracy of weight verification can be improved by coordinating the pressure sensor and the temperature sensor. Furthermore, the accuracy of weight verification can be improved by coordinating the pressure sensor and the humidity sensor. In addition, the accuracy of weight verification can be improved by coordinating the pressure sensor and the vibration sensor. In this way, the accuracy of weight verification is improved by coordinating with other sensor information.

[0055] The change-giving unit can select the optimal method of providing change by referring to the user's past transaction history. For example, it can select the optimal method of providing change based on the user's past transaction history. It can also prioritize providing frequently used banknotes and coins based on the user's past transaction history. Furthermore, it can analyze the user's past transaction history to select the most efficient method of providing change. In this way, the optimal method of providing change can be selected by referring to the user's past transaction history.

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

[0057] Step 1: The identification unit identifies the type and amount of cash. The identification unit can identify the type and amount of banknotes and coins using a visual recognition library. Step 2: The verification unit verifies the total weight of the cash identified by the identification unit. The verification unit can verify the total weight of the cash using a commercially available pressure sensor. Step 3: The calculation unit calculates the change based on the data obtained by the identification unit and the verification unit. The calculation unit can calculate the change by cross-checking the data obtained by the identification unit and the verification unit. Step 4: The dispensing unit provides the change calculated by the calculation unit. The dispensing unit can provide the change calculated by the calculation unit.

[0058] (Example of form 2) The Smart Cash AI Agent according to an embodiment of the present invention is a system for solving conventional problems such as errors in calculating change, inefficiency in cash handling, and decreased customer satisfaction in customer service. This Smart Cash AI Agent automatically identifies the type and amount of cash, checks the total weight of the cash and corrects any errors. Furthermore, the AI ​​calculates the change required for each transaction and cross-checks sensor and image recognition data to ensure accuracy. For example, the Smart Cash AI Agent uses a visual recognition library to identify banknotes and coins. Next, the Smart Cash AI Agent attaches a commercially available pressure sensor to the POS system, checks the total weight of the cash and corrects any errors. This improves the accuracy of identifying the type and amount of cash. Furthermore, the Smart Cash AI Agent's AI calculates the change required for each transaction and cross-checks sensor and image recognition data to ensure accuracy. This prevents errors in calculating change and improves the efficiency of cash handling. It can also improve the customer experience by reducing customer waiting times and alleviating the workload of store employees. Moreover, by integrating existing technologies and tools, new capital investment can be minimized. For example, by combining image recognition technology and weight sensor technology, it is possible to prevent errors in giving change and ensure the accuracy of transactions. In this way, the Smart Cash AI Agent can provide a future checkout experience, offering customers a new checkout experience through AI-powered automated payments, the speed and convenience of cashless payments, and advanced security features. As a result, the Smart Cash AI Agent can prevent errors in calculating change and improve the efficiency of cash handling.

[0059] The smart cash AI agent according to this embodiment comprises an identification unit, a verification unit, a calculation unit, and a provision unit. The identification unit identifies the type and amount of cash. The identification unit identifies the type and amount of cash using, for example, a visual recognition library. The identification unit can use a visual recognition library to identify banknotes and coins. The identification unit identifies the type of banknotes and coins using, for example, a visual recognition library. The identification unit can also identify the amount of cash using a visual recognition library. The identification unit identifies the amount of banknotes and coins using, for example, a visual recognition library. The verification unit verifies the total weight of the cash identified by the identification unit. The verification unit verifies the total weight of the cash using, for example, a commercially available pressure sensor. The verification unit can use a commercially available pressure sensor to verify the total weight of the cash. The verification unit verifies the total weight of the cash using, for example, a commercially available pressure sensor. The verification unit can also verify the total weight of the cash using a commercially available pressure sensor. The verification unit verifies the total weight of the cash using, for example, a commercially available pressure sensor. The calculation unit calculates the change based on the data obtained by the identification unit and the verification unit. The calculation unit can, for example, cross-check the data obtained by the identification unit and the verification unit to calculate the change. The calculation unit can, for example, cross-check the data obtained by the identification unit and the verification unit to calculate the change. The calculation unit can also, for example, cross-check the data obtained by the identification unit and the verification unit to calculate the change. The calculation unit can, for example, cross-check the data obtained by the identification unit and the verification unit to calculate the change. The supply unit provides the change calculated by the calculation unit. The supply unit can, for example, provide the change calculated by the calculation unit. The supply unit can provide the change calculated by the calculation unit. The supply unit can, for example, provide the change calculated by the calculation unit. The supply unit can also, for example, provide the change calculated by the calculation unit.As a result, the smart cash AI agent according to the embodiment can accurately identify the type and amount of cash, and efficiently calculate and provide change.

[0060] The identification unit identifies the type and amount of cash. For example, it uses a visual recognition library to identify the type and amount of cash. Specifically, the identification unit uses a high-resolution camera to acquire images of banknotes and coins and analyzes these images through the visual recognition library. Based on a pre-trained database, the visual recognition library extracts features such as the design, color, size, and pattern of banknotes and coins, and identifies the type of cash by matching these features. For example, in identifying banknotes, it recognizes security features such as portraits, serial numbers, and holograms printed on the banknotes, and determines the type and amount of the banknote based on this information. In identifying coins, it analyzes physical features such as the diameter, thickness, edge shape, and markings of the coin to determine the type and amount of the coin. By utilizing these visual recognition technologies, the identification unit can quickly and accurately identify the type and amount of cash. Furthermore, the identification unit records the identification results in a database in real time and provides them to the subsequent processing department. This allows the identification unit to accurately identify the type and amount of cash, improving the overall efficiency and reliability of the system.

[0061] The verification unit verifies the total weight of the cash identified by the identification unit. The verification unit verifies the total weight of the cash using, for example, a commercially available pressure sensor. Specifically, the verification unit places a high-precision pressure sensor in the cash tray and measures the weight of the cash when it is placed there. The pressure sensor converts the weight of the cash into an electrical signal and processes that signal as digital data. The verification unit analyzes this digital data and calculates the total weight of the cash. Based on the type and amount of cash identified by the identification unit, the verification unit calculates the expected total weight and compares it to the actual weight. For example, if the identification unit identifies one 1000 yen bill and two 500 yen coins, the verification unit calculates their total weight and compares it with the weight measured by the pressure sensor. This comparison verifies the accuracy of the identification result and issues a warning if there is an error. This allows the verification unit to ensure consistency between cash identification and weight, improving the reliability of the entire system. Furthermore, the verification unit periodically calibrates the pressure sensor to maintain measurement accuracy. This allows the verification unit to accurately confirm the total weight of the cash, improving the overall accuracy and reliability of the system.

[0062] The calculation unit calculates the change based on the data obtained by the identification and verification units. For example, the calculation unit cross-checks the data obtained by the identification and verification units to calculate the change. Specifically, the calculation unit receives data on the type and amount of cash provided by the identification unit and data on the total weight of the cash provided by the verification unit, and compares this data. The calculation unit calculates the expected total amount based on the data from the identification unit and verifies the actual total weight based on the data from the verification unit. This allows the calculation unit to verify the consistency between the cash identification result and the weight, and to ensure there are no errors. Next, the calculation unit compares the amount paid by the user with the purchase amount and calculates the amount of change. For example, if a user purchases an item for 1500 yen and pays 2000 yen, the calculation unit calculates 500 yen in change. Based on the amount of change, the calculation unit calculates the optimal combination of change. For example, to provide 500 yen in change, it selects one 500 yen coin. This allows the calculation unit to calculate change quickly and accurately and provide the user with the appropriate change. Furthermore, the calculation unit records the calculation results and provides them to the subsequent supply unit. This allows the calculation unit to efficiently calculate change and improve the overall system performance.

[0063] The supply unit provides the change calculated by the calculation unit. Specifically, the supply unit controls the mechanism for providing change to the user based on the change data provided by the calculation unit. The supply unit provides the calculated change to the user, for example, using a change dispenser. The change dispenser selects the appropriate coins and bills according to instructions from the calculation unit and provides them to the user. For example, when providing 500 yen in change, the supply unit dispenses one 500 yen coin from the dispenser. The supply unit monitors the change provision process in real time and issues a warning if an abnormality occurs. For example, if the dispenser is jammed or there are insufficient coins, the supply unit issues a warning and takes appropriate measures. This allows the supply unit to provide change quickly and accurately, improving user convenience. Furthermore, the supply unit records the history of change provision and uses it for subsequent analysis and audits. This allows the cashier to efficiently manage the change distribution process and improve the overall reliability and security of the system.

[0064] The identification unit can identify the type and amount of cash using a visual recognition library. The identification unit can, for example, use a visual recognition library to identify the type and amount of cash. The identification unit can, for example, use a visual recognition library to identify the type of banknotes and coins. Furthermore, the identification unit can also identify the amount of cash using a visual recognition library. The identification unit can, for example, use a visual recognition library to identify the amount of banknotes and coins. This allows for accurate identification of the type and amount of cash by utilizing a visual recognition library. The visual recognition library needs to clearly define specific types and implementation methods, such as the software and recognition algorithms used. For example, a visual recognition library uses specific software to identify the type and amount of cash. Furthermore, a visual recognition library can also identify the type and amount of cash using specific recognition algorithms. The visual recognition library uses specific software and recognition algorithms to identify the type and amount of cash.

[0065] The verification unit can verify the total weight of cash using a commercially available pressure sensor. The verification unit can, for example, verify the total weight of cash using a commercially available pressure sensor. The verification unit can verify the total weight of cash using a commercially available pressure sensor. The verification unit can, for example, verify the total weight of cash using a commercially available pressure sensor. This allows for accurate verification of the total weight of cash using a commercially available pressure sensor. The specific type and performance of the commercially available pressure sensor, such as the manufacturer, measurement range, and accuracy, must be clearly defined. For example, a commercially available pressure sensor can be used to verify the total weight of cash using a sensor from a specific manufacturer. Furthermore, a commercially available pressure sensor can also be used to verify the total weight of cash using a sensor with a specific measurement range and accuracy.

[0066] The calculation unit can calculate the change by cross-checking the data obtained by the identification unit and the verification unit. The calculation unit can calculate the change by cross-checking the data obtained by the identification unit and the verification unit. The calculation unit can calculate the change by cross-checking the data obtained by the identification unit and the verification unit. The calculation unit can calculate the change by cross-checking the data obtained by the identification unit and the verification unit. This improves the accuracy of the change calculation by cross-checking the data from the identification unit and the verification unit. The specific methods and criteria for mutual verification need to be clearly defined, for example, the method for confirming data consistency and the procedure for cross-checking. For example, the calculation unit uses a specific algorithm to confirm the consistency of the data obtained by the identification unit and the verification unit. The calculation unit can also confirm data consistency by clearly defining the procedure for cross-checking. For example, the calculation unit uses a specific algorithm or procedure to mutually confirm the data obtained by the identification unit and the verification unit.

[0067] The supply unit can provide the change calculated by the calculation unit. The supply unit can, for example, provide the change calculated by the calculation unit. The supply unit can provide the change calculated by the calculation unit. The supply unit can, for example, provide the change calculated by the calculation unit. This ensures that the change calculated by the calculation unit is provided accurately. The specific method and criteria of provision need to be clarified, for example, the means of provision and the timing of provision. For example, the supply unit can provide the change calculated by the calculation unit using specific means. The supply unit can also provide the change calculated by the calculation unit at a specific timing. The supply unit can, for example, provide the change calculated by the calculation unit using specific means and timing.

[0068] The identification unit can estimate the user's emotions and adjust the accuracy of cash identification based on the estimated emotions. For example, if the user is stressed, the identification unit can introduce an additional verification process to improve identification accuracy. If the user is relaxed, the identification unit can process with normal identification accuracy. If the user is in a hurry, the identification unit can slightly reduce the identification accuracy and process quickly. This improves the accuracy of identification by adjusting the accuracy of cash identification according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI or not using AI. For example, the identification unit can input user emotion data into a generative AI and have the generative AI adjust the accuracy of cash identification.

[0069] The identification unit can detect dirt and damage to banknotes and coins during cash identification, thereby improving identification accuracy. For example, the identification unit can detect dirty banknotes and perform additional image processing to improve identification accuracy. The identification unit can also detect damaged coins and introduce additional verification processes to improve identification accuracy. The identification unit can adjust its identification accuracy according to the degree of dirt and damage. This improves identification accuracy by detecting dirt and damage to banknotes and coins. Specific detection methods and criteria for dirt and damage need to be clearly defined, such as detection algorithms and acceptable ranges. For example, the identification unit can detect dirty banknotes using a specific algorithm. The identification unit can also detect damaged coins based on specific criteria. For example, the identification unit can detect dirt and damage to banknotes and coins using specific algorithms and criteria.

[0070] The identification unit can automatically identify and match different currencies when identifying cash. For example, the identification unit can refer to an additional database to identify and match banknotes of different currencies. The identification unit can perform additional image processing to identify and match coins of different currencies. The identification unit can periodically update its database to improve the accuracy of different currency identification. This improves the accuracy of identification by automatically identifying different currencies. The specific types of different currencies and how they are matched need to be clearly defined, for example, by specifying a list of corresponding currencies or identification methods. For example, the identification unit can identify different currencies using a specific list of currencies or identification methods.

[0071] The identification unit can estimate the user's emotions and adjust the display method of the identification results based on the estimated user emotions. For example, if the user is nervous, the identification unit can provide a simple and highly visible display method. If the user is relaxed, the identification unit can provide a display method that includes detailed information. If the user is in a hurry, the identification unit can provide a display method that gets straight to the point. This improves user convenience by adjusting the display method of the identification results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the identification results.

[0072] The identification unit can improve identification accuracy by referring to the user's past transaction history when identifying cash. For example, the identification unit can prioritize the identification of frequently used banknotes and coins based on the user's past transaction history. The identification unit can predict banknotes and coins used during specific time periods based on the user's past transaction history, thereby improving identification accuracy. The identification unit can analyze the user's past transaction history and find patterns to improve identification accuracy. As a result, identification accuracy is improved by referring to the user's past transaction history. The specific content and method of referencing past transaction history need to be clearly defined, for example, the retention period and the type of database. For example, the identification unit can refer to the user's past transaction history using a database with a specific retention period. The identification unit can also refer to the user's past transaction history using a specific type of database. For example, the identification unit can refer to the user's past transaction history using a specific retention period and type of database.

[0073] The identification unit can link identification results with other systems in real time when identifying cash. For example, the identification unit can link identification results with a POS system in real time and automatically update transaction data. The identification unit can link identification results with an inventory management system and update cash inventory status in real time. The identification unit can link identification results with an accounting system and automatically record transaction data. This streamlines the updating of transaction data by linking identification results with other systems in real time. The specific methods and criteria for linking with other systems in real time need to be clarified, for example, communication protocols and data synchronization methods. For example, the identification unit can link identification results with other systems using a specific communication protocol. The identification unit can also link identification results with other systems using a specific data synchronization method. For example, the identification unit can link identification results with other systems in real time using a specific communication protocol or data synchronization method.

[0074] The verification unit can estimate the user's emotions and adjust the accuracy of weight verification based on the estimated emotions. For example, if the user is stressed, the verification unit can introduce an additional verification process to improve the accuracy of weight verification. If the user is relaxed, the verification unit can process with normal weight verification accuracy. If the user is in a hurry, the verification unit can slightly loosen the weight verification accuracy and process quickly. This improves the accuracy of verification by adjusting the accuracy of weight verification according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the verification unit may be performed using AI or not using AI. For example, the verification unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of weight verification accuracy.

[0075] The verification unit can automatically correct the weight of each type of cash during weight verification. For example, the verification unit can automatically correct the weight of banknotes and coins to ensure accurate weight verification. The verification unit can automatically correct the weight of different currencies to ensure accurate weight verification. The verification unit can automatically correct for weight fluctuations due to dirt or damage to ensure accurate weight verification. This improves the accuracy of weight verification by automatically correcting the weight of each type of cash. The specific methods and criteria for correcting the weight of each type of cash need to be clearly defined, for example, by correction algorithms and standard weights. For example, the verification unit can correct the weight of banknotes and coins using a specific algorithm. The verification unit can also correct the weight of different currencies based on specific criteria. For example, the verification unit can correct the weight of each type of cash using specific algorithms and criteria.

[0076] The verification unit can estimate the user's emotions and adjust the display method of the weight verification result based on the estimated user emotions. For example, if the user is nervous, the verification unit can provide a simple and highly visible display method. If the user is relaxed, the verification unit can provide a display method that includes detailed information. If the user is in a hurry, the verification unit can provide a display method that gets straight to the point. This improves user convenience by adjusting the display method of the weight verification result according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the verification unit may be performed using AI, for example, or not using AI. For example, the verification unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the weight verification result.

[0077] The verification unit can improve the accuracy of weight verification by coordinating with other sensor information. For example, the verification unit can improve the accuracy of weight verification by coordinating a pressure sensor and a temperature sensor. The verification unit can improve the accuracy of weight verification by coordinating a pressure sensor and a humidity sensor. The verification unit can improve the accuracy of weight verification by coordinating a pressure sensor and a vibration sensor. In this way, the accuracy of weight verification is improved by coordinating with other sensor information. The specific types of other sensor information and the methods of coordination need to be clarified, for example, the types of sensors used and the data integration methods. For example, the verification unit can improve the accuracy of weight verification using a specific type of sensor. The verification unit can also coordinate with other sensor information using a specific data integration method. For example, the verification unit coordinates with other sensor information during weight verification using a specific type of sensor and data integration method.

[0078] The calculation unit can estimate the user's emotions and adjust the change calculation algorithm based on the estimated emotions. For example, if the user is stressed, the calculation unit can adjust the change calculation algorithm with high accuracy. If the user is relaxed, the calculation unit can process using the normal change calculation algorithm. If the user is in a hurry, the calculation unit can quickly adjust the change calculation algorithm. This improves the accuracy of the calculation by adjusting the change calculation algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the calculation unit may be performed using AI, or not using AI. For example, the calculation unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the change calculation algorithm.

[0079] The calculation unit can calculate the optimal amount of change by referring to past transaction data when calculating change. For example, the calculation unit calculates the optimal amount of change based on past transaction data. The calculation unit can prioritize the calculation of frequently used banknotes and coins from past transaction data. The calculation unit can analyze past transaction data and calculate the most efficient amount of change. In this way, the optimal amount of change can be calculated by referring to past transaction data. The specific method and criteria for calculating the optimal amount of change need to be clearly defined, for example, the calculation algorithm and optimization criteria. For example, the calculation unit uses a specific algorithm to calculate the optimal amount of change based on past transaction data. The calculation unit can also calculate the optimal amount of change from past transaction data based on specific criteria. For example, the calculation unit uses a specific algorithm or criteria to refer to past transaction data and calculate the optimal amount of change.

[0080] The calculation unit can automatically calculate change in different currencies when calculating change. For example, the calculation unit can automatically calculate change based on banknotes and coins of different currencies. The calculation unit can automatically calculate change considering exchange rates between different currencies. The calculation unit can periodically update its database to improve the accuracy of calculating change in different currencies. This improves the accuracy of calculations by automatically calculating change in different currencies. The specific calculation methods and criteria for different currencies need to be clearly defined, such as currency conversion rates and calculation algorithms. For example, the calculation unit can calculate change in different currencies using a specific conversion rate. The calculation unit can also calculate change in different currencies using a specific algorithm. For example, the calculation unit can calculate change in different currencies using a specific conversion rate and algorithm.

[0081] The calculation unit can estimate the user's emotions and adjust the display method of the change calculation result based on the estimated user emotions. For example, if the user is nervous, the calculation unit can provide a simple and highly visible display method. If the user is relaxed, the calculation unit can provide a display method that includes detailed information. If the user is in a hurry, the calculation unit can provide a display method that gets straight to the point. This improves user convenience by adjusting the display method of the change calculation result according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the calculation unit may be performed using AI, for example, or not using AI. For example, the calculation unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the change calculation result.

[0082] The calculation unit can share calculation results in real time by coordinating with other systems when calculating change. For example, the calculation unit can synchronize the change calculation results with a POS system in real time and automatically update transaction data. The calculation unit can synchronize the change calculation results with an inventory management system and update the cash inventory status in real time. The calculation unit can synchronize the change calculation results with an accounting system and automatically record transaction data. This makes sharing calculation results more efficient by coordinating with other systems. The specific methods and criteria for coordinating with other systems need to be clearly defined, for example, communication protocols and data synchronization methods. For example, the calculation unit can use a specific communication protocol to synchronize the change calculation results with other systems. The calculation unit can also use a specific data synchronization method to synchronize the change calculation results with other systems, for example, by using a specific communication protocol and data synchronization method when calculating change and sharing the calculation results in real time.

[0083] The calculation unit can automatically record the calculation results when calculating change, making them available for later reference. For example, the calculation unit can automatically record the change calculation results for later reference. The calculation unit can periodically back up the change calculation results to ensure data security. The calculation unit can analyze the change calculation results and provide data to be used for future transactions. This ensures data security by automatically recording the calculation results for later reference. Specific methods and criteria for automatically recording calculation results need to be clearly defined, such as the recording format and retention period. For example, the calculation unit can record the change calculation results using a specific format. The calculation unit can also set a specific retention period for recording the change calculation results. For example, the calculation unit can automatically record the calculation results when calculating change using a specific format and retention period, making them available for later reference.

[0084] The service provider can estimate the user's emotions and adjust the method of providing change based on the estimated emotions. For example, if the user is stressed, the service provider will provide change quickly and accurately. If the user is relaxed, the service provider can process the change using the normal method. If the user is in a hurry, the service provider can quickly adjust the method of providing change. This improves the accuracy of the service by adjusting the method of providing change according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the method of providing change.

[0085] The change provider can select the optimal method of providing change by referring to the user's past transaction history. For example, the change provider can select the optimal method of providing change based on the user's past transaction history. The change provider can prioritize providing frequently used banknotes and coins based on the user's past transaction history. The change provider can analyze the user's past transaction history and select the most efficient method of providing change. This allows the change provider to select the optimal method of providing change by referring to the user's past transaction history. The specific selection criteria and means for the optimal method of providing change need to be clearly defined, such as the means of provision and the timing of provision. For example, the change provider can use specific means to select the optimal method of providing change based on the user's past transaction history. The change provider can also select the optimal method of providing change based on the user's past transaction history at a specific time. For example, the change provider can use specific means and timing to refer to the user's past transaction history and select the optimal method of providing change.

[0086] The service provider can estimate the user's emotions and determine the priority of change provision based on the estimated emotions. For example, if the user is nervous, the service provider may set a higher priority for providing change. If the user is relaxed, the service provider may provide change with the normal priority. If the user is in a hurry, the service provider may set a higher priority for providing change. This improves the efficiency of service by determining the priority of change provision according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI determine the priority of change provision.

[0087] The change-giving unit can share the results of change distribution in real time by coordinating with other systems. For example, the change-giving unit can link the change-giving results with the POS system in real time and automatically update transaction data. The change-giving unit can link the change-giving results with the inventory management system and update the cash inventory status in real time. The change-giving unit can link the change-giving results with the accounting system and automatically record transaction data. This makes sharing the results of change distribution more efficient by coordinating with other systems. The specific methods and criteria for sharing the results of change distribution in real time need to be clarified, for example, communication protocols and data synchronization methods. For example, the change-giving unit can use a specific communication protocol to link the change-giving results with other systems. The change-giving unit can also use a specific data synchronization method to link the change-giving results with other systems in real time when distributing change, for example, by coordinating with other systems using a specific communication protocol and data synchronization method.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The identification unit not only identifies the type and amount of cash, but can also estimate the user's emotions and adjust the identification accuracy based on those emotions. For example, if the user is stressed, the identification unit can introduce an additional verification process to improve identification accuracy. If the user is relaxed, it can process with normal identification accuracy. Furthermore, if the user is in a hurry, it can slightly reduce the identification accuracy to process quickly. In this way, the accuracy of identification is improved by adjusting the cash identification accuracy according to the user's emotions.

[0090] The identification unit can improve identification accuracy by detecting dirt and damage to banknotes and coins when identifying the type and amount of cash. For example, it can detect dirty banknotes and improve identification accuracy by performing additional image processing. It can also detect damaged coins and introduce an additional verification process. Furthermore, it can adjust the identification accuracy according to the degree of dirt and damage. In this way, the identification accuracy is improved by detecting dirt and damage to banknotes and coins.

[0091] The verification unit can estimate the user's emotions when checking the total weight of cash and adjust the accuracy of the weight check based on the estimated emotions. For example, if the user is stressed, an additional verification process can be introduced to improve the accuracy of the weight check. If the user is relaxed, the process can be handled with normal weight check accuracy. Furthermore, if the user is in a hurry, the weight check accuracy can be slightly reduced to speed up the process. In this way, the accuracy of the verification is improved by adjusting the accuracy of the weight check according to the user's emotions.

[0092] The calculation unit can calculate the optimal amount of change by referring to past transaction data. For example, it can prioritize the calculation of frequently used banknotes and coins based on past transaction data. It can also predict which banknotes and coins will be used during specific time periods based on past transaction data, thereby improving identification accuracy. Furthermore, it can analyze past transaction data to calculate the most efficient amount of change. In this way, the optimal amount of change can be calculated by referring to past transaction data.

[0093] The change-giving unit can estimate the user's emotions when providing change and adjust the method of providing change based on those emotions. For example, if the user is stressed, the change can be provided quickly and accurately. If the user is relaxed, the change can be processed using the normal method. Furthermore, if the user is in a hurry, the method of providing change can be quickly adjusted. By adjusting the method of providing change according to the user's emotions, the accuracy of the change provision is improved.

[0094] The identification unit can automatically identify and match different currencies when identifying the type and amount of cash. For example, it can refer to an additional database to identify and match banknotes of different currencies. It can also perform additional image processing to identify and match coins of different currencies. Furthermore, the database can be updated periodically to improve the accuracy of different currency identification. This improves the accuracy of identification by automatically identifying different currencies.

[0095] The verification unit can improve the accuracy of the verification when confirming the total weight of cash by coordinating with other sensor information. For example, the accuracy of weight verification can be improved by coordinating the pressure sensor and the temperature sensor. Furthermore, the accuracy of weight verification can be improved by coordinating the pressure sensor and the humidity sensor. In addition, the accuracy of weight verification can be improved by coordinating the pressure sensor and the vibration sensor. In this way, the accuracy of weight verification is improved by coordinating with other sensor information.

[0096] The calculation unit can estimate the user's emotions when calculating change and adjust the change calculation algorithm based on the estimated emotions. For example, if the user is stressed, the change calculation algorithm can be adjusted with high accuracy. If the user is relaxed, the normal change calculation algorithm can be used. Furthermore, if the user is in a hurry, the change calculation algorithm can be quickly adjusted. In this way, the accuracy of the calculation is improved by adjusting the change calculation algorithm according to the user's emotions.

[0097] The change-giving unit can select the optimal method of providing change by referring to the user's past transaction history. For example, it can select the optimal method of providing change based on the user's past transaction history. It can also prioritize providing frequently used banknotes and coins based on the user's past transaction history. Furthermore, it can analyze the user's past transaction history to select the most efficient method of providing change. In this way, the optimal method of providing change can be selected by referring to the user's past transaction history.

[0098] The change-giving unit can estimate the user's emotions when providing change and determine the priority of change provision based on those emotions. For example, if the user is nervous, the priority of providing change can be set higher. If the user is relaxed, change can be provided with the normal priority. Furthermore, if the user is in a hurry, the priority of providing change can be set higher. This improves the efficiency of change provision by determining the priority of change provision according to the user's emotions.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The identification unit identifies the type and amount of cash. The identification unit can identify the type and amount of banknotes and coins using a visual recognition library. Step 2: The verification unit verifies the total weight of the cash identified by the identification unit. The verification unit can verify the total weight of the cash using a commercially available pressure sensor. Step 3: The calculation unit calculates the change based on the data obtained by the identification unit and the verification unit. The calculation unit can calculate the change by cross-checking the data obtained by the identification unit and the verification unit. Step 4: The dispensing unit provides the change calculated by the calculation unit. The dispensing unit can provide the change calculated by the calculation unit.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0103] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] Each of the multiple elements described above, including the identification unit, verification unit, calculation unit, and providing unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the identification unit identifies the type and amount of cash using the camera 42 of the smart device 14. The verification unit verifies the total weight of the cash using a commercially available pressure sensor attached to the smart device 14. The calculation unit calculates the change by cross-checking the data obtained from the identification unit and the verification unit using the identification processing unit 290 of the data processing unit 12. The providing unit provides the change calculated by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0111] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0112] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0113] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0114] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0115] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the identification unit, verification unit, calculation unit, and providing unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the identification unit identifies the type and amount of cash using the camera 42 of the smart glasses 214. The verification unit verifies the total weight of the cash using, for example, a commercially available pressure sensor attached to the smart glasses 214. The calculation unit calculates the change by cross-checking the data obtained from the identification unit and the verification unit using, for example, the identification processing unit 290 of the data processing unit 12. The providing unit provides the change calculated by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the identification unit, verification unit, calculation unit, and providing unit, is implemented in, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the identification unit identifies the type and amount of cash using the camera 42 of the headset terminal 314. The verification unit verifies the total weight of the cash using, for example, a commercially available pressure sensor attached to the headset terminal 314. The calculation unit calculates the change by cross-checking the data obtained from the identification unit and the verification unit using, for example, the identification processing unit 290 of the data processing unit 12. The providing unit provides the change calculated by, for example, the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0145] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] Each of the multiple elements described above, including the identification unit, verification unit, calculation unit, and supply unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the identification unit identifies the type and amount of cash using the camera 42 of the robot 414. The verification unit verifies the total weight of the cash using, for example, a commercially available pressure sensor attached to the robot 414. The calculation unit calculates the change by cross-checking the data obtained from the identification unit and the verification unit using, for example, the identification processing unit 290 of the data processing unit 12. The supply unit provides the change calculated by, for example, the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0154] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0156] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0157] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0158] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0162] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0163] 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.

[0164] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0165] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0166] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0167] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0169] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0170] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0171] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0172] (Note 1) An identification unit that identifies the type and amount of cash, A verification unit that verifies the total weight of the cash identified by the aforementioned identification unit, A calculation unit that calculates the change based on the data obtained by the identification unit and the verification unit, The system includes a supply unit that provides the change calculated by the calculation unit. A system characterized by the following features. (Note 2) The aforementioned identification unit is Identify the type and amount of cash using a visual recognition library. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned verification unit is Use a commercially available pressure sensor to determine the total weight of the cash. The system described in Appendix 1, characterized by the features described herein. (Note 4) The calculation unit, The data obtained by the identification unit and the verification unit are mutually verified to calculate the change. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provides the change calculated by the calculation unit. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned identification unit is It estimates the user's emotions and adjusts the accuracy of cash recognition based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned identification unit is When identifying cash, it detects dirt and damage to banknotes and coins, improving identification accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned identification unit is When identifying cash, it automatically identifies different currencies and the corresponding The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned identification unit is It estimates the user's emotions and adjusts how the identification results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned identification unit is When identifying cash, improve identification accuracy based on the user's past transaction history. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned identification unit is When identifying cash, the identification results are linked in real time with other systems. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned verification unit is The system estimates the user's emotions and adjusts the accuracy of weight verification based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned verification unit is The system automatically adjusts the weight of each type of cash during weight verification. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned verification unit is The system estimates the user's emotions and adjusts how the weight confirmation results are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned verification unit is When verifying weight, the system works in conjunction with other sensor information to improve verification accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 16) The calculation unit, The system estimates the user's emotions and adjusts the change calculation algorithm based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The calculation unit, When calculating change, the system calculates the optimal amount of change based on past transaction data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The calculation unit, When calculating change, automatically calculate the change amount for different currencies. The system described in Appendix 1, characterized by the features described herein. (Note 19) The calculation unit, The system estimates the user's emotions and adjusts how the change calculation result is displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The calculation unit, When calculating change, the system integrates with other systems to share calculation results in real time. The system described in Appendix 1, characterized by the features described herein. (Note 21) The calculation unit, When calculating change, the calculation result is automatically recorded so that it can be referenced later. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the method of providing change based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing change, the system selects the optimal method of provision based on the user's past transaction history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, The system estimates the user's emotions and determines the priority of change provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing change, the system integrates with other systems to share the results in real time. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An identification unit for identifying the type and amount of cash, A verification unit that verifies the total weight of the cash identified by the aforementioned identification unit, A calculation unit that calculates the change based on the data obtained by the identification unit and the verification unit, The system includes a supply unit that provides the change calculated by the calculation unit. A system characterized by the following features.

2. The aforementioned identification unit is Identify the type and amount of cash using a visual recognition library. The system according to feature 1.

3. The aforementioned verification unit is Use a commercially available pressure sensor to determine the total weight of the cash. The system according to feature 1.

4. The calculation unit, The data obtained by the identification unit and the verification unit are mutually verified to calculate the change. The system according to feature 1.

5. The aforementioned supply unit is, The calculation unit provides the change calculated by the calculation unit. The system according to feature 1.

6. The aforementioned identification unit is It estimates the user's emotions and adjusts the accuracy of cash recognition based on the estimated user emotions. The system according to feature 1.

7. The aforementioned identification unit is When identifying cash, it detects dirt and damage to banknotes and coins, improving identification accuracy. The system according to feature 1.

8. The aforementioned identification unit is When identifying cash, it automatically identifies different currencies and the corresponding The system according to feature 1.

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