system

Abstract

The system according to the embodiment aims to detect operational errors and payment errors at self-checkout registers and to improve the efficiency of store inventory management and sales data analysis. [Solution] A system according to an embodiment includes a guide unit, a detection unit, and an analysis unit. The guide unit analyzes a user's operations and provides guidance. The detection unit detects product scanning errors or payment errors based on the operations analyzed by the guide unit. The analysis unit performs store inventory management or analyzes sales data based on the errors detected by the detection unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology is prone to self-checkout operation errors and payment errors, which can affect store inventory management and sales data analysis.

[0005] The system according to the embodiment aims to detect operational errors and payment errors at self-checkout registers and to improve the efficiency of store inventory management and sales data analysis. [Means for solving the problem]

[0006] The system according to the embodiment includes a guide unit, a detection unit, and an analysis unit. The guide unit analyzes user operations and provides guidance. The detection unit detects product scanning errors or payment errors based on the operations analyzed by the guide unit. The analysis unit performs store inventory management or analyzes sales data based on the errors detected by the detection unit. [Effects of the Invention]

[0007] The system according to the embodiment can detect operational errors and payment errors at self-checkout registers, and improve the efficiency of store inventory management and sales data analysis. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The self-checkout system according to an embodiment of the present invention utilizes a generative AI to eliminate the disadvantages of self-checkout systems and achieve user-friendly operation. In this self-checkout system, the generative AI provides real-time operational guidance when a user uses the self-checkout system. The generative AI automatically detects product scanning errors and payment errors and prompts the user to take appropriate action. Furthermore, the generative AI analyzes store inventory management and sales data to support efficient store operations. For example, when a user uses a self-checkout system, the generative AI provides real-time operational guidance. The generative AI learns the user's operation history and behavioral patterns and provides optimal guidance for each individual user. Next, the generative AI automatically detects product scanning errors and payment errors and prompts the user to take appropriate action. For example, if a product is not scanned correctly or if the payment is not completed, the generative AI immediately notifies the user and guides them to rescan or complete the payment procedure. This minimizes problems that occur during the use of self-checkout systems. Furthermore, the generative AI analyzes store inventory management and sales data to support efficient store operations. For example, the generative AI identifies best-selling and low-stock items in real time and appropriately orders and replenishes inventory, improving store operations efficiency. In addition, analyzing sales data can help understand consumer purchasing trends and develop marketing strategies. This system eliminates the disadvantages of self-checkouts and enables user-friendly operation, promoting efficient checkout operations, compensating for labor shortages in stores, and increasing labor productivity per employee. Japan's retail industry is undergoing major transformation, and it is expected to provide a new consumer experience. As a result, the self-checkout system can eliminate the disadvantages of self-checkouts and enable user-friendly operation.

[0029] The self-checkout system according to the embodiment includes a guide unit, a detection unit, and an analysis unit. The guide unit analyzes user operations and provides guidance. For example, when a user uses a self-checkout, the guide unit uses a generation AI to provide real-time operation guidance. The generation AI learns the user's operation history and behavioral patterns and provides guidance optimized for each individual user. For example, the generation AI guides the user through how to scan items and the payment process, allowing even first-time users to operate the self-checkout smoothly. The detection unit detects item scanning errors and payment errors based on the operations analyzed by the guide unit. For example, if an item is not scanned correctly or the payment is not completed, the detection unit immediately notifies the user using the generation AI and guides them to rescan or complete the payment process. This minimizes problems that occur during the use of the self-checkout. The analysis unit performs store inventory management and analyzes sales data based on errors detected by the detection unit. For example, the analysis unit identifies best-selling items and items with low inventory in real time and improves store operational efficiency by appropriately ordering and replenishing inventory. Furthermore, by analyzing sales data, the analysis unit can grasp consumer purchasing trends and use the data to develop marketing strategies. As a result, the self-checkout system according to the embodiment can eliminate the disadvantages of self-checkouts and achieve user-friendly operation. Some or all of the above-described processing in the guide unit, detection unit, and analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the guide unit can input a user's operation history into the generation AI and cause the generation AI to execute optimal guidance. The detection unit can cause the generation AI to detect product scanning errors and payment errors. The analysis unit can cause the generation AI to analyze sales data.

[0030] The guide unit can learn the user's operation history and provide guidance to each individual user. For example, the guide unit learns the user's operation history and provides the optimal guide for each individual user. The guide unit uses a generation AI to learn the user's operation history and behavior patterns and provide the optimal guide for each individual user. For example, the generation AI predicts the next operation to be performed based on the user's past operations and provides appropriate guidance. This allows the user to use the self-checkout smoothly. Some or all of the above-mentioned processing in the guide unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the guide unit can input the user's operation history into the generation AI and have the generation AI execute the optimal guidance. This improves operation efficiency by providing the user with the optimal guide.

[0031] The detection unit can automatically detect product scanning errors and payment errors. For example, the detection unit automatically detects product scanning errors and payment errors. The detection unit automatically detects product scanning errors and payment errors using the generation AI. For example, if the product is not scanned correctly or the payment is not completed, the generation AI immediately notifies the user and guides them to rescan or complete the payment procedure. This makes it possible to minimize problems that occur when using the self-checkout. Some or all of the above-mentioned processing in the detection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the detection unit can have the generation AI detect product scanning errors and payment errors. This makes it possible to minimize problems by automatically detecting scanning errors and payment errors.

[0032] The analysis unit can identify best-selling items or items with low inventory in real time. For example, the analysis unit identifies best-selling items or items with low inventory in real time. The analysis unit uses a generation AI to identify best-selling items or items with low inventory in real time. For example, the generation AI analyzes sales data and identifies best-selling items or items with low inventory. This allows the store to place appropriate orders or replenish inventory. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input sales data into the generation AI and have the generation AI identify best-selling items or items with low inventory. This allows the inventory status to be identified in real time, enabling appropriate orders or replenishment of inventory.

[0033] The analysis unit can analyze sales data and analyze consumer purchasing trends. For example, the analysis unit analyzes sales data and analyzes consumer purchasing trends. The analysis unit uses a generation AI to analyze sales data and understand consumer purchasing trends. For example, the generation AI identifies consumer purchasing trends based on sales data. This can be useful for stores in formulating marketing strategies. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input sales data into the generation AI and have the generation AI identify consumer purchasing trends. This can be useful for formulating marketing strategies by understanding consumer purchasing trends.

[0034] The guide unit can analyze the user's past operation history and determine the timing of the guidance. For example, the guide unit analyzes the user's past operation history and determines the timing of the guidance. The guide unit uses a generation AI to analyze the user's past operation history and determine the optimal timing of the guidance. For example, the generation AI can identify times when the user frequently made operation errors in the past and provide guidance at those times. The generation AI can also refer to times when the user was able to operate smoothly in the past and provide guidance at those times. Furthermore, if the generation AI determines from the user's operation history that a specific operation takes time, it can provide guidance before that operation. This can reduce operation errors by providing guidance at the optimal timing based on the past operation history. Some or all of the above-mentioned processing in the guide unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the guide unit can input the user's operation history data into the generation AI and have the generation AI determine the timing of the guidance.

[0035] The guide unit can update the guide content in real time based on the user's current operation status when providing a guide. For example, the guide unit updates the guide content in real time based on the user's current operation status when providing a guide. The guide unit uses the generation AI to monitor the user's current operation status and update the guide content in real time. For example, when the user scans an item, the generation AI checks in real time whether the scan was successful and, if successful, guides the user on the next step. Furthermore, when the user is unsure which payment method to select when making a payment, the generation AI can suggest an appropriate payment method in real time. Furthermore, if an error occurs during operation, the generation AI can guide the user on the cause of the error and how to resolve it in real time. This allows the user's operation to proceed smoothly by updating the guide content in real time. Some or all of the above-described processing in the guide unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the guide unit may input user operation status data into the generation AI and have the generation AI update the guide content.

[0036] When providing a guide, the guide unit can provide a guide tailored to the region by taking into account the user's geographical location information. For example, when providing a guide, the guide unit can provide a region-specific guide by taking into account the user's geographical location information. The guide unit uses a generation AI to acquire the user's geographical location information and provide a region-specific guide based on that information. For example, if the user is in a specific region, the generation AI can guide the user on payment methods and product scanning methods specific to that region. Also, if the user is in a tourist destination, the generation AI can guide the user on product scanning methods and payment methods specific to that tourist destination. Furthermore, if the user is in a specific store, the generation AI can guide the user on operation methods specific to that store. This can improve user convenience by providing a region-specific guide. Some or all of the above-described processing in the guide unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the guide unit can input the user's geographical location information into the generation AI and cause the generation AI to provide a region-specific guide.

[0037] The guide unit can analyze the user's social media activity and provide a relevant guide when providing a guide. For example, the guide unit can analyze the user's social media activity and provide a relevant guide when providing a guide. The guide unit can use a generation AI to analyze the user's social media activity and provide a relevant guide based on that information. For example, if the user mentions a specific product on social media, the generation AI can provide a guide related to that product. Also, if the user mentions a specific payment method on social media, the generation AI can provide a guide related to that payment method. Furthermore, if the user mentions a specific store on social media, the generation AI can provide a guide related to that store. This enables guidance tailored to the user's needs by providing a relevant guide based on social media activity. Some or all of the above-described processing in the guide unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the guide unit can input the user's social media activity data into the generation AI and cause the generation AI to provide a relevant guide.

[0038] The detection unit can adjust the error detection algorithm by referring to past error data during detection. For example, the detection unit can adjust the error detection algorithm by referring to past error data during detection. The detection unit uses the generation AI to refer to past error data and optimize the error detection algorithm. For example, the generation AI builds an algorithm that prioritizes detecting frequently occurring errors based on past error data. The generation AI can also analyze past error data, identify error occurrence patterns, and optimize the algorithm. Furthermore, the generation AI can refer to past error data to identify the cause of the error and improve the algorithm. In this way, the error detection algorithm is optimized based on past error data, thereby improving the accuracy of error detection. Some or all of the above-mentioned processing in the detection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the detection unit can input past error data to the generation AI and have the generation AI adjust the error detection algorithm.

[0039] The detection unit can apply different error detection methods depending on the product category during detection. For example, the detection unit applies different error detection methods depending on the product category during detection. The detection unit uses the generation AI to apply different error detection methods depending on the product category. For example, the generation AI can prioritize detecting errors related to expiration dates and freshness for products in the food category. Furthermore, the generation AI can prioritize detecting errors related to operation checks and warranties for products in the electronics category. Furthermore, the generation AI can prioritize detecting errors related to size and color for products in the clothing category. Thus, by applying an error detection method depending on the product category, the accuracy of error detection is improved. Some or all of the above-mentioned processing in the detection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the detection unit can input product category data to the generation AI and cause the generation AI to apply an error detection method.

[0040] The detection unit can set the priority of errors during detection, taking into account the user's geographical location information. For example, the detection unit can set the priority of errors during detection, taking into account the user's geographical location information. The detection unit uses the generation AI to obtain the user's geographical location information and set the priority of errors based on that information. For example, if the user is in a specific area, the generation AI can prioritize detecting errors specific to that area. Also, if the user is in a tourist destination, the generation AI can prioritize detecting errors specific to that tourist destination. Furthermore, if the user is in a specific store, the generation AI can prioritize detecting errors specific to that store. This allows for quick response to region-specific errors by determining the priority of errors based on the geographical location information. Some or all of the above-described processing in the detection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the detection unit can input the user's geographical location information to the generation AI and have the generation AI set the priority of errors.

[0041] The detection unit can analyze the user's social media activity and prioritize detecting related errors during detection. For example, the detection unit can analyze the user's social media activity and prioritize detecting related errors during detection. The detection unit uses a generation AI to analyze the user's social media activity and prioritize detecting related errors based on the information. For example, if the user mentions a specific product on social media, the generation AI can prioritize detecting errors related to that product. Furthermore, if the user mentions a specific payment method on social media, the generation AI can prioritize detecting errors related to that payment method. Furthermore, if the user mentions a specific store on social media, the generation AI can prioritize detecting errors related to that store. This enables error handling tailored to the user's needs by prioritizing the detection of related errors based on social media activity. Some or all of the above-described processing in the detection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the detection unit can input the user's social media activity data into the generation AI and cause the generation AI to detect related errors.

[0042] The analysis unit can adjust the analysis algorithm by referring to past sales data during analysis. For example, the analysis unit adjusts the analysis algorithm by referring to past sales data during analysis. The analysis unit uses the generation AI to optimize the analysis algorithm by referring to past sales data. For example, the generation AI identifies best-selling products based on past sales data and optimizes the analysis algorithm. The generation AI can also analyze past sales data, identify seasonal sales trends, and optimize the algorithm. Furthermore, the generation AI can improve the algorithm by referring to past sales data and analyzing the effectiveness of specific campaigns and promotions. This improves the accuracy of the analysis by optimizing the analysis algorithm based on past sales data. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input past sales data into the generation AI and have the generation AI adjust the analysis algorithm.

[0043] The analysis unit can apply different analysis methods to each product category. For example, the analysis unit applies different analysis methods to each product category. The analysis unit uses the generation AI to apply different analysis methods to each product category. For example, the generation AI applies analysis methods related to expiration dates and freshness to products in the food category. The generation AI can also apply analysis methods related to operation checks and warranties to products in the electronics category. The generation AI can also apply analysis methods related to size and color to products in the clothing category. This improves the accuracy of the analysis by applying an analysis method according to the product category. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input product category data into the generation AI and have the generation AI apply the analysis method.

[0044] The analysis unit can display the analysis results taking into account the user's geographical location information during analysis. For example, the analysis unit can display the analysis results taking into account the user's geographical location information during analysis. The analysis unit uses the generation AI to acquire the user's geographical location information and display the analysis results based on that information. For example, if the user is in a specific area, the generation AI can analyze sales data specific to that area and provide the results. Also, if the user is in a tourist destination, the generation AI can analyze sales data specific to that tourist destination and provide the results. Furthermore, if the user is in a specific store, the generation AI can analyze sales data specific to that store and provide the results. This enables analysis that reflects region-specific information by providing analysis results based on geographical location information. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's geographical location information into the generation AI and have the generation AI display the analysis results.

[0045] The analysis unit can analyze the user's social media activity during analysis and provide related analysis results. For example, the analysis unit can analyze the user's social media activity during analysis and provide related analysis results. The analysis unit can use a generation AI to analyze the user's social media activity and provide related analysis results based on the information. For example, if the user mentions a specific product on social media, the generation AI can provide analysis results related to the product. Furthermore, if the user mentions a specific payment method on social media, the generation AI can provide analysis results related to the payment method. Furthermore, if the user mentions a specific store on social media, the generation AI can provide analysis results related to the store. This allows for providing information tailored to the user's needs by providing related analysis results based on social media activity. Some or all of the above-described processing in the analysis unit can be performed using or without the generation AI. For example, the analysis unit can input the user's social media activity data into the generation AI and have the generation AI provide related analysis results.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The guide unit can prioritize displaying functions that the user has frequently used in the past based on the user's operation history. For example, if the user has frequently used a specific payment method in the past, that payment method can be prioritized. Also, if the user frequently purchases a specific product category, products in that category can be prioritized. Furthermore, if the user has used the self-checkout during a specific time period in the past, special offers and discount information related to that time period can be displayed. This improves operation efficiency by providing optimal guidance based on the user's operation history.

[0048] The detection unit can monitor the user's operation speed and adjust the error detection sensitivity according to the operation speed. For example, if the user is operating quickly, the error detection sensitivity can be increased to quickly detect errors. On the other hand, if the user is operating slowly, the error detection sensitivity can be decreased to prevent false detection. Furthermore, if the user is operating at a constant speed, errors can be detected with normal sensitivity. This makes it possible to reduce operation stress by detecting errors according to the user's operation speed.

[0049] The analysis unit can recommend products that the user is likely to be interested in based on the user's purchasing history. For example, it can recommend products similar to products the user has previously purchased. It can also recommend products that complement products that the user has previously purchased. Furthermore, it can recommend products that other users have given high ratings to based on reviews and ratings of products the user has previously purchased. This can improve the purchasing experience by recommending optimal products based on the user's purchasing history.

[0050] The guide unit can provide real-time answers to any doubts or questions that arise during user operations. For example, if a user has a question about a specific operation method, an answer can be provided on the spot. Also, if a user asks about a specific product, detailed information about that product can be provided. Furthermore, if a user asks about payment methods, the advantages and procedures of each payment method can be explained. This allows for a quick response to the user's doubts and questions, improving the smoothness of operation.

[0051] The detection unit can predict timings when specific operational errors are likely to occur based on the user's operation history and increase the error detection sensitivity at those timings. For example, if a user has frequently made mistakes in a specific operational procedure in the past, the error detection sensitivity can be increased during that procedure. Also, if a user is likely to make operational errors during a specific time period, the error detection sensitivity can be increased during that time period. Furthermore, if a user is likely to make mistakes when scanning a specific product, the error detection sensitivity can be increased when scanning that product. In this way, by adjusting the error detection sensitivity at times when operational errors are likely to occur, the occurrence of errors can be minimized.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The guide unit analyzes the user's operations and provides guidance. Specifically, when a user uses a self-checkout, it uses a generation AI to provide real-time operation guidance. The generation AI learns the user's operation history and behavioral patterns and provides guidance that is optimal for each individual user. For example, by guiding the user through how to scan an item and the payment procedure, even first-time users can operate the self-checkout smoothly. Step 2: The detection unit detects product scanning errors and payment errors based on the operations analyzed by the guide unit. Specifically, if a product is not scanned correctly or the payment is not completed, the generation AI immediately notifies the user and guides them to scan again or complete the payment procedure. This minimizes problems that occur when using self-checkouts. Step 3: The analysis unit analyzes store inventory management and sales data based on the errors detected by the detection unit. Specifically, it identifies best-selling items and items with low stock in real time, and improves store operational efficiency by placing appropriate orders and replenishing inventory. In addition, analyzing sales data can help understand consumer purchasing trends and develop marketing strategies.

[0054] (Example 2) The self-checkout system according to an embodiment of the present invention utilizes a generative AI to eliminate the disadvantages of self-checkout systems and achieve user-friendly operation. In this self-checkout system, the generative AI provides real-time operational guidance when a user uses the self-checkout system. The generative AI automatically detects product scanning errors and payment errors and prompts the user to take appropriate action. Furthermore, the generative AI analyzes store inventory management and sales data to support efficient store operations. For example, when a user uses a self-checkout system, the generative AI provides real-time operational guidance. The generative AI learns the user's operation history and behavioral patterns and provides optimal guidance for each individual user. Next, the generative AI automatically detects product scanning errors and payment errors and prompts the user to take appropriate action. For example, if a product is not scanned correctly or if the payment is not completed, the generative AI immediately notifies the user and guides them to rescan or complete the payment procedure. This minimizes problems that occur during the use of self-checkout systems. Furthermore, the generative AI analyzes store inventory management and sales data to support efficient store operations. For example, the generative AI identifies best-selling and low-stock items in real time and appropriately orders and replenishes inventory, improving store operations efficiency. In addition, analyzing sales data can help understand consumer purchasing trends and develop marketing strategies. This system eliminates the disadvantages of self-checkouts and enables user-friendly operation, promoting efficient checkout operations, compensating for labor shortages in stores, and increasing labor productivity per employee. Japan's retail industry is undergoing major transformation, and it is expected to provide a new consumer experience. As a result, the self-checkout system can eliminate the disadvantages of self-checkouts and enable user-friendly operation.

[0055] The self-checkout system according to the embodiment includes a guide unit, a detection unit, and an analysis unit. The guide unit analyzes user operations and provides guidance. For example, when a user uses a self-checkout, the guide unit uses a generation AI to provide real-time operation guidance. The generation AI learns the user's operation history and behavioral patterns and provides guidance optimized for each individual user. For example, the generation AI guides the user through how to scan items and the payment process, allowing even first-time users to operate the self-checkout smoothly. The detection unit detects item scanning errors and payment errors based on the operations analyzed by the guide unit. For example, if an item is not scanned correctly or the payment is not completed, the detection unit immediately notifies the user using the generation AI and guides them to rescan or complete the payment process. This minimizes problems that occur during the use of the self-checkout. The analysis unit performs store inventory management and analyzes sales data based on errors detected by the detection unit. For example, the analysis unit identifies best-selling items and items with low inventory in real time and improves store operational efficiency by appropriately ordering and replenishing inventory. Furthermore, by analyzing sales data, the analysis unit can grasp consumer purchasing trends and use the data to develop marketing strategies. As a result, the self-checkout system according to the embodiment can eliminate the disadvantages of self-checkouts and achieve user-friendly operation. Some or all of the above-described processing in the guide unit, detection unit, and analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the guide unit can input a user's operation history into the generation AI and cause the generation AI to execute optimal guidance. The detection unit can cause the generation AI to detect product scanning errors and payment errors. The analysis unit can cause the generation AI to analyze sales data.

[0056] The guide unit can learn the user's operation history and provide guidance to each individual user. For example, the guide unit learns the user's operation history and provides the optimal guide for each individual user. The guide unit uses a generation AI to learn the user's operation history and behavior patterns and provide the optimal guide for each individual user. For example, the generation AI predicts the next operation to be performed based on the user's past operations and provides appropriate guidance. This allows the user to use the self-checkout smoothly. Some or all of the above-mentioned processing in the guide unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the guide unit can input the user's operation history into the generation AI and have the generation AI execute the optimal guidance. This improves operation efficiency by providing the user with the optimal guide.

[0057] The detection unit can automatically detect product scanning errors and payment errors. For example, the detection unit automatically detects product scanning errors and payment errors. The detection unit automatically detects product scanning errors and payment errors using the generation AI. For example, if the product is not scanned correctly or the payment is not completed, the generation AI immediately notifies the user and guides them to rescan or complete the payment procedure. This makes it possible to minimize problems that occur when using the self-checkout. Some or all of the above-mentioned processing in the detection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the detection unit can have the generation AI detect product scanning errors and payment errors. This makes it possible to minimize problems by automatically detecting scanning errors and payment errors.

[0058] The analysis unit can identify best-selling items or items with low inventory in real time. For example, the analysis unit identifies best-selling items or items with low inventory in real time. The analysis unit uses a generation AI to identify best-selling items or items with low inventory in real time. For example, the generation AI analyzes sales data and identifies best-selling items or items with low inventory. This allows the store to place appropriate orders or replenish inventory. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input sales data into the generation AI and have the generation AI identify best-selling items or items with low inventory. This allows the inventory status to be identified in real time, enabling appropriate orders or replenishment of inventory.

[0059] The analysis unit can analyze sales data and analyze consumer purchasing trends. For example, the analysis unit analyzes sales data and analyzes consumer purchasing trends. The analysis unit uses a generation AI to analyze sales data and understand consumer purchasing trends. For example, the generation AI identifies consumer purchasing trends based on sales data. This can be useful for stores in formulating marketing strategies. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input sales data into the generation AI and have the generation AI identify consumer purchasing trends. This can be useful for formulating marketing strategies by understanding consumer purchasing trends.

[0060] The guide unit can estimate the user's emotions and adjust the way the guide is presented based on the estimated user's emotions. For example, the guide unit estimates the user's emotions and adjusts the way the guide is presented based on the estimated user's emotions. The guide unit uses a generation AI to estimate the user's emotions and adjust the way the guide is presented based on the estimated user's emotions. For example, if the user is feeling stressed, the generation AI can provide simple and intuitive guidance and minimize the number of operation steps. Furthermore, if the user is relaxed, the generation AI can provide guidance with detailed explanations and also explain background information about the operation. Furthermore, if the user is in a hurry, the generation AI can prioritize audio guidance to enable the user to complete the operation quickly. This reduces the stress of operation by providing guidance that corresponds to the user's emotions. Some or all of the above-mentioned processing in the guide unit may be performed using or without the generation AI. For example, the guide unit can input user emotion data into the generation AI and have the generation AI adjust the way the guide is presented.

[0061] The guide unit can analyze the user's past operation history and determine the timing of the guidance. For example, the guide unit analyzes the user's past operation history and determines the timing of the guidance. The guide unit uses a generation AI to analyze the user's past operation history and determine the optimal timing of the guidance. For example, the generation AI can identify times when the user frequently made operation errors in the past and provide guidance at those times. The generation AI can also refer to times when the user was able to operate smoothly in the past and provide guidance at those times. Furthermore, if the generation AI determines from the user's operation history that a specific operation takes time, it can provide guidance before that operation. This can reduce operation errors by providing guidance at the optimal timing based on the past operation history. Some or all of the above-mentioned processing in the guide unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the guide unit can input the user's operation history data into the generation AI and have the generation AI determine the timing of the guidance.

[0062] The guide unit can update the guide content in real time based on the user's current operation status when providing a guide. For example, the guide unit updates the guide content in real time based on the user's current operation status when providing a guide. The guide unit uses the generation AI to monitor the user's current operation status and update the guide content in real time. For example, when the user scans an item, the generation AI checks in real time whether the scan was successful and, if successful, guides the user on the next step. Furthermore, when the user is unsure which payment method to select when making a payment, the generation AI can suggest an appropriate payment method in real time. Furthermore, if an error occurs during operation, the generation AI can guide the user on the cause of the error and how to resolve it in real time. This allows the user's operation to proceed smoothly by updating the guide content in real time. Some or all of the above-described processing in the guide unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the guide unit may input user operation status data into the generation AI and have the generation AI update the guide content.

[0063] The guide unit can estimate the user's emotions and adjust the level of detail of the guide based on the estimated user's emotions. For example, the guide unit can estimate the user's emotions and adjust the level of detail of the guide based on the estimated user's emotions. The guide unit can use a generation AI to estimate the user's emotions and adjust the level of detail of the guide based on the estimated user's emotions. For example, if the user is nervous, the generation AI can provide a concise and to-the-point guide. Furthermore, if the user is relaxed, the generation AI can provide a guide including detailed explanations. Furthermore, if the user is in a hurry, the generation AI can provide a guide showing the shortest route. This allows for improved understanding of the operation by providing a guide with a level of detail according to the user's emotions. Some or all of the above-described processing in the guide unit may be performed using or without the generation AI. For example, the guide unit can input user emotion data into the generation AI and have the generation AI adjust the level of detail of the guide.

[0064] When providing a guide, the guide unit can provide a guide tailored to the region by taking into account the user's geographical location information. For example, when providing a guide, the guide unit can provide a region-specific guide by taking into account the user's geographical location information. The guide unit uses a generation AI to acquire the user's geographical location information and provide a region-specific guide based on that information. For example, if the user is in a specific region, the generation AI can guide the user on payment methods and product scanning methods specific to that region. Also, if the user is in a tourist destination, the generation AI can guide the user on product scanning methods and payment methods specific to that tourist destination. Furthermore, if the user is in a specific store, the generation AI can guide the user on operation methods specific to that store. This can improve user convenience by providing a region-specific guide. Some or all of the above-described processing in the guide unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the guide unit can input the user's geographical location information into the generation AI and cause the generation AI to provide a region-specific guide.

[0065] The guide unit can analyze the user's social media activity and provide a relevant guide when providing a guide. For example, the guide unit can analyze the user's social media activity and provide a relevant guide when providing a guide. The guide unit can use a generation AI to analyze the user's social media activity and provide a relevant guide based on that information. For example, if the user mentions a specific product on social media, the generation AI can provide a guide related to that product. Also, if the user mentions a specific payment method on social media, the generation AI can provide a guide related to that payment method. Furthermore, if the user mentions a specific store on social media, the generation AI can provide a guide related to that store. This enables guidance tailored to the user's needs by providing a relevant guide based on social media activity. Some or all of the above-described processing in the guide unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the guide unit can input the user's social media activity data into the generation AI and cause the generation AI to provide a relevant guide.

[0066] The detection unit can estimate the user's emotions and adjust the error notification method based on the estimated user emotions. For example, the detection unit can estimate the user's emotions and adjust the error notification method based on the estimated user emotions. The detection unit can use the generation AI to estimate the user's emotions and adjust the error notification method based on the estimated user emotions. For example, the generation AI can provide a simple and intuitive error notification when the user is stressed. The generation AI can also provide a detailed error explanation when the user is relaxed. Furthermore, the generation AI can provide a quick and concise error notification when the user is in a hurry. This reduces the stress of dealing with errors by providing an error notification that corresponds to the user's emotions. Some or all of the above-described processing in the detection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the detection unit can input user emotion data into the generation AI and have the generation AI adjust the error notification method.

[0067] The detection unit can adjust the error detection algorithm by referring to past error data during detection. For example, the detection unit can adjust the error detection algorithm by referring to past error data during detection. The detection unit uses the generation AI to refer to past error data and optimize the error detection algorithm. For example, the generation AI builds an algorithm that prioritizes detecting frequently occurring errors based on past error data. The generation AI can also analyze past error data, identify error occurrence patterns, and optimize the algorithm. Furthermore, the generation AI can refer to past error data to identify the cause of the error and improve the algorithm. In this way, the error detection algorithm is optimized based on past error data, thereby improving the accuracy of error detection. Some or all of the above-mentioned processing in the detection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the detection unit can input past error data to the generation AI and have the generation AI adjust the error detection algorithm.

[0068] The detection unit can apply different error detection methods depending on the product category during detection. For example, the detection unit applies different error detection methods depending on the product category during detection. The detection unit uses the generation AI to apply different error detection methods depending on the product category. For example, the generation AI can prioritize detecting errors related to expiration dates and freshness for products in the food category. Furthermore, the generation AI can prioritize detecting errors related to operation checks and warranties for products in the electronics category. Furthermore, the generation AI can prioritize detecting errors related to size and color for products in the clothing category. Thus, by applying an error detection method depending on the product category, the accuracy of error detection is improved. Some or all of the above-mentioned processing in the detection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the detection unit can input product category data to the generation AI and cause the generation AI to apply an error detection method.

[0069] The detection unit can estimate the user's emotions and adjust the timing of the error notification based on the estimated user emotions. For example, the detection unit estimates the user's emotions and adjusts the timing of the error notification based on the estimated user emotions. The detection unit uses the generation AI to estimate the user's emotions and adjusts the timing of the error notification based on the estimated user emotions. For example, if the user is nervous, the generation AI can notify the user immediately after the error occurs. Alternatively, if the user is relaxed, the generation AI can notify the user a short time after the error occurs. Furthermore, if the user is in a hurry, the generation AI can notify the user immediately the moment the error occurs. This reduces the stress of dealing with errors by providing error notification timing that corresponds to the user's emotions. Some or all of the above-described processing in the detection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the detection unit can input user emotion data into the generation AI and have the generation AI adjust the timing of the error notification.

[0070] The detection unit can set the priority of errors during detection, taking into account the user's geographical location information. For example, the detection unit can set the priority of errors during detection, taking into account the user's geographical location information. The detection unit uses the generation AI to obtain the user's geographical location information and set the priority of errors based on that information. For example, if the user is in a specific area, the generation AI can prioritize detecting errors specific to that area. Also, if the user is in a tourist destination, the generation AI can prioritize detecting errors specific to that tourist destination. Furthermore, if the user is in a specific store, the generation AI can prioritize detecting errors specific to that store. This allows for quick response to region-specific errors by determining the priority of errors based on the geographical location information. Some or all of the above-described processing in the detection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the detection unit can input the user's geographical location information to the generation AI and have the generation AI set the priority of errors.

[0071] The detection unit can analyze the user's social media activity and prioritize detecting related errors during detection. For example, the detection unit can analyze the user's social media activity and prioritize detecting related errors during detection. The detection unit uses a generation AI to analyze the user's social media activity and prioritize detecting related errors based on the information. For example, if the user mentions a specific product on social media, the generation AI can prioritize detecting errors related to that product. Furthermore, if the user mentions a specific payment method on social media, the generation AI can prioritize detecting errors related to that payment method. Furthermore, if the user mentions a specific store on social media, the generation AI can prioritize detecting errors related to that store. This enables error handling tailored to the user's needs by prioritizing the detection of related errors based on social media activity. Some or all of the above-described processing in the detection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the detection unit can input the user's social media activity data into the generation AI and cause the generation AI to detect related errors.

[0072] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit can use a generation AI to estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the generation AI can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the generation AI can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the generation AI can provide a display method that focuses on the main points. This improves the understandability of the analysis results by providing a display method that corresponds to the user's emotions. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input user emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0073] The analysis unit can adjust the analysis algorithm by referring to past sales data during analysis. For example, the analysis unit adjusts the analysis algorithm by referring to past sales data during analysis. The analysis unit uses the generation AI to optimize the analysis algorithm by referring to past sales data. For example, the generation AI identifies best-selling products based on past sales data and optimizes the analysis algorithm. The generation AI can also analyze past sales data, identify seasonal sales trends, and optimize the algorithm. Furthermore, the generation AI can improve the algorithm by referring to past sales data and analyzing the effectiveness of specific campaigns and promotions. This improves the accuracy of the analysis by optimizing the analysis algorithm based on past sales data. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input past sales data into the generation AI and have the generation AI adjust the analysis algorithm.

[0074] The analysis unit can apply different analysis methods to each product category. For example, the analysis unit applies different analysis methods to each product category. The analysis unit uses the generation AI to apply different analysis methods to each product category. For example, the generation AI applies analysis methods related to expiration dates and freshness to products in the food category. The generation AI can also apply analysis methods related to operation checks and warranties to products in the electronics category. The generation AI can also apply analysis methods related to size and color to products in the clothing category. This improves the accuracy of the analysis by applying an analysis method according to the product category. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input product category data into the generation AI and have the generation AI apply the analysis method.

[0075] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. The analysis unit can use a generation AI to estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. For example, the generation AI can prioritize displaying important analysis results when the user is nervous. Furthermore, the generation AI can display detailed analysis results when the user is relaxed. Furthermore, the generation AI can prioritize displaying analysis results that focus on the main points when the user is in a hurry. This allows the user to quickly grasp important information by providing analysis results prioritized according to the user's emotions. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input user emotion data into the generation AI and have the generation AI determine the priority of the analysis results.

[0076] The analysis unit can display the analysis results taking into account the user's geographical location information during analysis. For example, the analysis unit can display the analysis results taking into account the user's geographical location information during analysis. The analysis unit uses the generation AI to acquire the user's geographical location information and display the analysis results based on that information. For example, if the user is in a specific area, the generation AI can analyze sales data specific to that area and provide the results. Also, if the user is in a tourist destination, the generation AI can analyze sales data specific to that tourist destination and provide the results. Furthermore, if the user is in a specific store, the generation AI can analyze sales data specific to that store and provide the results. This enables analysis that reflects region-specific information by providing analysis results based on geographical location information. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's geographical location information into the generation AI and have the generation AI display the analysis results.

[0077] The analysis unit can analyze the user's social media activity during analysis and provide related analysis results. For example, the analysis unit can analyze the user's social media activity during analysis and provide related analysis results. The analysis unit can use a generation AI to analyze the user's social media activity and provide related analysis results based on the information. For example, if the user mentions a specific product on social media, the generation AI can provide analysis results related to the product. Furthermore, if the user mentions a specific payment method on social media, the generation AI can provide analysis results related to the payment method. Furthermore, if the user mentions a specific store on social media, the generation AI can provide analysis results related to the store. This allows for providing information tailored to the user's needs by providing related analysis results based on social media activity. Some or all of the above-described processing in the analysis unit can be performed using or without the generation AI. For example, the analysis unit can input the user's social media activity data into the generation AI and have the generation AI provide related analysis results. === Hard Collateral 1-1 === Each of the multiple elements including the guide unit, detection unit, and analysis unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the guide unit is realized by the control unit 46A of the smart device 14, analyzes the user's operation, and provides operation guidance in real time. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, detects product scanning errors and payment errors, and notifies the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs store inventory management and sales data analysis. === Hard Collateral 1-2 === Each of the multiple elements, including the guide unit, detection unit, and analysis unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the guide unit is realized by the control unit 46A of the smart glasses 214, analyzes the user's operation, and provides operation guidance in real time. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, detects product scanning errors and payment errors, and notifies the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs store inventory management and sales data analysis. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned guide unit, detection unit, and analysis unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the guide unit is realized by the control unit 46A of the headset type terminal 314, analyzes the user's operation, and provides operation guidance in real time. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, detects product scanning errors and payment errors, and notifies the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs store inventory management and analyzes sales data. === Hard Collateral 1-4 === Each of the multiple elements including the guide unit, detection unit, and analysis unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the guide unit is realized by the control unit 46A of the robot 414, analyzes the user's operation, and provides operation guidance in real time. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, detects product scanning errors and payment errors, and notifies the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs store inventory management and sales data analysis.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The guide unit can prioritize displaying functions that the user has frequently used in the past based on the user's operation history. For example, if the user has frequently used a specific payment method in the past, that payment method can be prioritized. Also, if the user frequently purchases a specific product category, products in that category can be prioritized. Furthermore, if the user has used the self-checkout during a specific time period in the past, special offers and discount information related to that time period can be displayed. This improves operation efficiency by providing optimal guidance based on the user's operation history.

[0080] The detection unit can monitor the user's operation speed and adjust the error detection sensitivity according to the operation speed. For example, if the user is operating quickly, the error detection sensitivity can be increased to quickly detect errors. On the other hand, if the user is operating slowly, the error detection sensitivity can be decreased to prevent false detection. Furthermore, if the user is operating at a constant speed, errors can be detected with normal sensitivity. This makes it possible to reduce operation stress by detecting errors according to the user's operation speed.

[0081] The analysis unit can recommend products that the user is likely to be interested in based on the user's purchasing history. For example, it can recommend products similar to products the user has previously purchased. It can also recommend products that complement products that the user has previously purchased. Furthermore, it can recommend products that other users have given high ratings to based on reviews and ratings of products the user has previously purchased. This can improve the purchasing experience by recommending optimal products based on the user's purchasing history.

[0082] The guide unit can provide real-time answers to any doubts or questions that arise during user operations. For example, if a user has a question about a specific operation method, an answer can be provided on the spot. Also, if a user asks about a specific product, detailed information about that product can be provided. Furthermore, if a user asks about payment methods, the advantages and procedures of each payment method can be explained. This allows for a quick response to the user's doubts and questions, improving the smoothness of operation.

[0083] The detection unit can predict timings when specific operational errors are likely to occur based on the user's operation history and increase the error detection sensitivity at those timings. For example, if a user has frequently made mistakes in a specific operational procedure in the past, the error detection sensitivity can be increased during that procedure. Also, if a user is likely to make operational errors during a specific time period, the error detection sensitivity can be increased during that time period. Furthermore, if a user is likely to make mistakes when scanning a specific product, the error detection sensitivity can be increased when scanning that product. In this way, by adjusting the error detection sensitivity at times when operational errors are likely to occur, the occurrence of errors can be minimized.

[0084] The guide unit can estimate the user's emotions and adjust the tone of the guidance based on the estimated user's emotions. For example, if the user is feeling stressed, the guidance unit can provide guidance in a calm tone. If the user is relaxed, the guidance unit can provide guidance in a friendly tone. Furthermore, if the user is in a hurry, the guidance unit can provide guidance in a quick and concise tone. Thus, by providing guidance in a tone that corresponds to the user's emotions, it is possible to reduce the stress of operation.

[0085] The detection unit can estimate the user's emotion and adjust the content of the error notification based on the estimated user emotion. For example, if the user is stressed, a concise and intuitive error notification can be provided. If the user is relaxed, a detailed error explanation can be provided. If the user is in a hurry, a quick and concise error notification can be provided. In this way, by providing an error notification that corresponds to the user's emotion, the stress of dealing with errors can be reduced.

[0086] The analysis unit can estimate the user's emotions and adjust the display format of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display format can be provided. If the user is relaxed, a display format including detailed information can be provided. Furthermore, if the user is in a hurry, a display format that focuses on the main points can be provided. In this way, by providing a display format that corresponds to the user's emotions, the understandability of the analysis results can be improved.

[0087] The guide unit can estimate the user's emotions and adjust the level of detail of the guide based on the estimated user's emotions. For example, if the user is nervous, a concise guide that focuses on the main points can be provided. If the user is relaxed, a guide that includes detailed explanations can be provided. Furthermore, if the user is in a hurry, a guide showing the shortest route can be provided. In this way, by providing a guide with a level of detail according to the user's emotions, it is possible to improve the user's understanding of the operation.

[0088] The detection unit can estimate the user's emotions and adjust the timing of the error notification based on the estimated user emotions. For example, if the user is nervous, the notification can be sent immediately after the error occurs. If the user is relaxed, the notification can be sent a short time after the error occurs. Furthermore, if the user is in a hurry, the notification can be sent immediately at the moment the error occurs. This makes it possible to reduce the stress of dealing with errors by providing error notification timing that corresponds to the user's emotions.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The guide unit analyzes the user's operations and provides guidance. Specifically, when a user uses a self-checkout, it uses a generation AI to provide real-time operation guidance. The generation AI learns the user's operation history and behavioral patterns and provides guidance that is optimal for each individual user. For example, by guiding the user through how to scan an item and the payment procedure, even first-time users can operate the self-checkout smoothly. Step 2: The detection unit detects product scanning errors and payment errors based on the operations analyzed by the guide unit. Specifically, if a product is not scanned correctly or the payment is not completed, the generation AI immediately notifies the user and guides them to scan again or complete the payment procedure. This minimizes problems that occur when using self-checkouts. Step 3: The analysis unit analyzes store inventory management and sales data based on the errors detected by the detection unit. Specifically, it identifies best-selling items and items with low stock in real time, and improves store operational efficiency by placing appropriate orders and replenishing inventory. In addition, analyzing sales data can help understand consumer purchasing trends and develop marketing strategies.

[0091] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0092] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0093] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0094] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0098] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0099] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0100] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0101] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0102] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0103] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0104] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0105] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0106] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0109] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0110] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0113] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0122] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0125] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0129] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0135] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0139] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0141] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0142] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0145] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0146] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0147] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0148] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0149] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0150] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0151] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0152] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0153] 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.

[0154] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0155] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0156] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0157] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0158] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0159] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0160] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0161] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0162] [Explanation of symbols]

[0163] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a guide unit that analyzes a user's operation and provides a guide; a detection unit that detects a product scanning error or a payment error based on the operation analyzed by the guide unit; an analysis unit that analyzes inventory management or sales data of the store based on the error detected by the detection unit. A system characterized by:

2. The guide portion is Learn user operation history and provide guidance to individual users 2. The system of claim 1.

3. The detection unit Automatically detect mis-scanned items and payment errors 2. The system of claim 1.

4. The analysis unit Know which items are selling well or low in stock in real time 2. The system of claim 1.

5. The analysis unit Analyze sales data and analyze consumer purchasing trends 2. The system of claim 1.

6. The guide portion is Estimate the user's emotions and adjust the guide's presentation based on the estimated user emotions.

2. The system of claim 1.

7. The guide portion is Analyze the user's past operation history to determine the timing of guidance 2. The system of claim 1.

8. The guide portion is When providing guidance, the content of the guidance is updated in real time based on the user's current operating status.

2. The system of claim 1.

Images