System

Abstract

An object of a system according to an embodiment is to detect a sign of shoplifting in real time and take appropriate measures.SOLUTION: A system according to an embodiment includes an in-store announcement unit, a staff alert unit, a resale monitoring unit, and a theft prevention unit. The in-store announcement unit analyzes the monitoring camera image in the store, and makes an announcement in a case where an action indicating a sign of shoplifting is detected. The staff warning unit warns the staff based on the behavior with the sign of shoplifting detected by the in-store announcement unit. The resale monitoring unit monitors the online platform and detects resale of the shoplifted item. The theft prevention unit analyzes the monitoring camera image in the store in real time, and detects an action having a sign of shoplifting.SELECTED DRAWING: Figure 1

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 has the problem that it is difficult to detect signs of shoplifting in real time and take appropriate measures.

[0005] The system according to the embodiment aims to detect signs of shoplifting in real time and take appropriate measures. [Means for solving the problem]

[0006] The system according to the embodiment includes an in-store announcement unit, a staff attention alert unit, a resale monitoring unit, and a theft prevention unit. The in-store announcement unit analyzes surveillance camera footage within the store and makes an announcement if it detects behavior that indicates shoplifting. The staff attention alert unit alerts staff based on behavior that indicates shoplifting detected by the in-store announcement unit. The resale monitoring unit monitors online platforms and detects resale of shoplifted items. The theft prevention unit analyzes surveillance camera footage within the store in real time and detects behavior that indicates shoplifting. [Effects of the Invention]

[0007] The system according to the embodiment can detect signs of shoplifting in real time and take appropriate measures. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 shoplifting prevention system according to the embodiment of the present invention is a system that prevents shoplifting both offline in stores and online, thereby preventing the resale of shoplifted goods. As a result, the shoplifting prevention system can prevent shoplifting damage both offline and online, thereby reducing losses for retailers.

[0029] The shoplifting prevention system according to the embodiment includes an in-store announcement unit, a staff alert unit, a resale monitoring unit, and a theft prevention unit. The in-store announcement unit analyzes surveillance camera footage in the store and makes an announcement if it detects behavior indicative of shoplifting. For example, if the generation AI analyzes surveillance camera footage in the store and detects behavior indicative of shoplifting, it makes an announcement such as, "Customers, please feel free to use the store. However, please be sure to pay for your items at the register." The staff alert unit alerts staff based on the behavior indicative of shoplifting detected by the in-store announcement unit. For example, if the generation AI detects suspicious activity in a specific area, it makes an alert such as, "Suspicious activity has been detected in a specific area. Please check." The resale monitoring unit monitors online platforms to detect resale of shoplifted items. For example, the generation AI monitors online platforms such as auction sites and flea market apps and issues an alert if a specific item is listed in large quantities or sold at an abnormally low price. The theft prevention unit analyzes in-store surveillance camera footage in real time to detect behavior that may indicate shoplifting. For example, if the generation AI detects someone hiding an item or behaving unnaturally, it immediately issues an alert and notifies staff. This allows the shoplifting prevention system of the embodiment to prevent shoplifting from both offline and online perspectives, reducing losses for retailers.

[0030] The in-store announcement unit can analyze the audio environment and make announcements when specific sounds are detected. For example, the in-store announcement unit uses a generation AI to constantly monitor the audio environment in the store and make announcements when specific sounds (such as the sound of shelves or footsteps) are detected. For example, if the sound of shelves is abnormally loud, an announcement such as "Customers, please handle products with care" can be made. By making announcements when specific sounds are detected, shoplifting can be prevented before it happens.

[0031] The in-store announcement unit analyzes environmental data such as temperature and humidity, and can issue warnings if there are any abnormal changes. For example, the in-store announcement unit uses a generation AI to constantly monitor the temperature and humidity in the store and issue warnings if there are any abnormal changes. For example, if a sudden rise in temperature is detected, an announcement such as "Customers, the temperature in the store is rising. Please take care of your health." This allows the safety of the in-store environment to be ensured by detecting abnormal changes in temperature and humidity and issuing warnings.

[0032] The in-store announcement unit can make in-store announcements in multiple languages ​​so that it can also accommodate foreign tourists. The in-store announcement unit can, for example, make in-store announcements in multiple languages ​​so that it can also accommodate foreign tourists. For example, announcements can be made in multiple languages ​​such as Japanese, English, Chinese, and Korean. This makes it possible to make announcements in multiple languages ​​so that it can also accommodate foreign tourists.

[0033] The in-store announcement unit can control lighting and music to automatically create an environment that is effective in preventing shoplifting. For example, the in-store announcement unit uses a generative AI to control the lighting in the store, automatically creating an environment that is effective in preventing shoplifting. For example, brightening the lights in specific areas can deter shoplifting. This makes it possible to automatically create an environment that is effective in preventing shoplifting by controlling the lighting and music.

[0034] The resale monitoring unit can perform image recognition of products, automatically detect similar products, and evaluate the possibility of resale. For example, the resale monitoring unit uses a generation AI to perform image recognition of products, automatically detect similar products, and evaluate the possibility of resale. For example, it issues a warning if the same product is being listed by multiple sellers. This makes it possible to effectively monitor the resale of shoplifted products by performing image recognition of products, automatically detecting similar products, and evaluating the possibility of resale.

[0035] The resale monitoring unit can analyze a seller's transaction history, detect abnormal patterns, and issue a warning. For example, the resale monitoring unit uses a generation AI to analyze a seller's past transaction history, detect abnormal patterns, and issue a warning. For example, a warning can be issued to a seller who is listing a large number of items in a short period of time. In this way, by analyzing a seller's transaction history, detecting abnormal patterns, and issuing a warning, it is possible to effectively monitor the resale of shoplifted items.

[0036] The resale monitoring unit can monitor social media and collect information on the resale of shoplifted items. For example, the resale monitoring unit uses a generation AI to monitor social media and collect information on the resale of shoplifted items. For example, it monitors specific hashtags and keywords and detects related posts. This makes it possible to effectively monitor the resale of shoplifted items by monitoring social media and collecting information on the resale of shoplifted items.

[0037] The resale monitoring unit can link data between different online platforms and monitor signs of resale over a wider area. For example, the resale monitoring unit can link data between online platforms with different generation AIs and monitor signs of resale over a wider area. For example, data from auction sites and flea market apps can be integrated and monitored. This allows data linking between different online platforms and monitoring signs of resale over a wider area, making it possible to effectively monitor the resale of shoplifted items.

[0038] The theft prevention unit can analyze the flow of people in the store and identify areas where shoplifting is likely to occur for intensive monitoring. For example, the theft prevention unit uses a generation AI to analyze the flow of people in the store and identify areas where shoplifting is likely to occur for intensive monitoring. For example, areas where shoplifting is likely to occur can be identified based on the placement of products and the height of shelves. This allows the unit to analyze the flow of people in the store and identify areas where shoplifting is likely to occur for intensive monitoring, thereby preventing shoplifting acts from occurring.

[0039] The theft prevention unit can analyze product placement and shelf height, and propose the optimal layout to prevent shoplifting. For example, the generative AI can analyze product placement and shelf height, and propose the optimal layout to prevent shoplifting. For example, it can adjust shelf height so that the view is not obstructed. In this way, by analyzing product placement and shelf height, and proposing the optimal layout to prevent shoplifting, it is possible to prevent shoplifting acts before they occur.

[0040] The anti-theft unit can control lighting and music to automatically create an environment that is effective in preventing shoplifting. For example, the anti-theft unit uses generative AI to control the lighting in a store, automatically creating an environment that is effective in preventing shoplifting. For example, brightening the lights in specific areas can deter shoplifting. This makes it possible to automatically create an environment that is effective in preventing shoplifting by controlling lighting and music.

[0041] The theft prevention unit analyzes environmental data such as temperature and humidity, and can issue warnings if there are any abnormal changes. For example, the theft prevention unit's generating AI constantly monitors the temperature and humidity in the store, and issues warnings if there are any abnormal changes. For example, if a sudden rise in temperature is detected, an announcement such as "Customers, the temperature in the store is rising. Please take care of your health." This allows the system to detect abnormal changes in temperature and humidity and issue warnings, ensuring the safety of the store environment.

[0042] The system can manage inventory in unmanned stores in real time and automate product replenishment and ordering. For example, generative AI can manage inventory in unmanned stores in real time and automate product replenishment and ordering. For example, an order can be placed automatically when inventory falls below a certain level. This allows for real-time inventory management in unmanned stores and automates product replenishment and ordering, improving the operational efficiency of unmanned stores.

[0043] The system can analyze customer behavior in unmanned stores and propose optimal product placement and promotions. For example, the system uses generative AI to analyze customer behavior in unmanned stores and propose optimal product placement and promotions. For example, it analyzes customer movement paths and optimizes the placement of popular products. This allows the system to analyze customer behavior in unmanned stores and propose optimal product placement and promotions, thereby improving the operational efficiency of unmanned stores.

[0044] The system can analyze security camera footage from unmanned stores, detect abnormal behavior, and issue a warning. For example, the system uses generative AI to analyze security camera footage from unmanned stores, detect abnormal behavior, and issue a warning. For example, it will issue a warning if it detects an attempt to hide a product or unnatural movement. This allows the system to analyze security camera footage from unmanned stores, detect abnormal behavior, and issue a warning, thereby improving the safety of unmanned stores.

[0045] The system can optimize energy consumption in unmanned stores and reduce the environmental impact. For example, generative AI can reduce energy consumption by optimizing the use of lighting and air conditioning. This allows unmanned stores to achieve sustainable operations by optimizing energy consumption and reducing the environmental impact.

[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 in-store announcement section can analyze a customer's purchase history and run promotions for specific products. For example, the generation AI can analyze a customer's past purchase history and run promotions for specific products. For example, to a customer who has previously purchased a specific brand of product, it can announce, "Customer, products from brand X are on sale today only." This allows sales to be increased by analyzing a customer's purchase history and running promotions for specific products.

[0048] The in-store announcement section can make different announcements depending on the specific time of day. For example, the generation AI can make different announcements depending on the specific time of day. For example, in the morning it can make an announcement such as "Good morning. Thank you for visiting us today," and in the evening it can make an announcement such as "Thank you for visiting us today. We look forward to seeing you again." This allows the quality of service to customers to be improved by making different announcements depending on the specific time of day.

[0049] The in-store announcement section can make announcements to coincide with specific events or campaigns. For example, the generation AI can make announcements to coincide with specific events or campaigns. For example, during the Christmas season, it can make an announcement such as "Merry Christmas! We're having a Christmas sale today," and on Valentine's Day, it can make an announcement such as "Happy Valentine's Day! We're having a Valentine's Day limited edition product on sale today." In this way, by making announcements to coincide with specific events or campaigns, it is possible to increase customer purchasing motivation.

[0050] The in-store announcement section can make announcements depending on the stock status of specific products. For example, the generation AI can monitor the stock status of specific products and make an announcement when stock is low. For example, it can make an announcement such as, "Customer, there are only a few items left of product X. Please purchase as soon as possible." This makes it possible to make announcements depending on the stock status of specific products, thereby streamlining inventory management and increasing sales.

[0051] The in-store announcement unit can make announcements explaining how to use and the features of specific products. For example, the generation AI can make announcements explaining how to use and the features of specific products. For example, it can make an announcement such as, "Customer, this product has XX function and is easy to use." This can increase customer purchasing motivation by making announcements explaining how to use and the features of specific products.

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

[0053] Step 1: The in-store announcement unit analyzes the surveillance camera footage inside the store and makes an announcement if it detects any behavior that could be indicative of shoplifting. For example, if the generation AI analyzes the surveillance camera footage inside the store and detects any behavior that could be indicative of shoplifting, it will make an announcement such as, "Customers, please feel free to use the store as you like. However, please be sure to pay for your items at the register." Step 2: The staff alerting unit alerts staff based on the shoplifting-indicating behavior detected by the in-store announcement unit. For example, if the generation AI detects suspicious activity in a specific area, it will issue an alert such as, "Suspicious activity has been detected in a specific area. Please check." Step 3: The resale monitoring unit monitors online platforms to detect the resale of shoplifted items. For example, the generative AI monitors online platforms such as auction sites and flea market apps, and issues a warning if a specific item is listed in large quantities or sold at an abnormally low price. Step 4: The anti-theft department analyzes the in-store surveillance camera footage in real time to detect behavior that may indicate shoplifting. For example, if the generative AI detects someone hiding an item or behaving unnaturally, it will immediately issue an alert and notify staff.

[0054] (Example 2) The shoplifting prevention system according to the embodiment of the present invention is a system that prevents shoplifting both offline in stores and online, thereby preventing the resale of shoplifted goods. As a result, the shoplifting prevention system can prevent shoplifting damage both offline and online, thereby reducing losses for retailers.

[0055] The shoplifting prevention system according to the embodiment includes an in-store announcement unit, a staff alert unit, a resale monitoring unit, and a theft prevention unit. The in-store announcement unit analyzes surveillance camera footage in the store and makes an announcement if it detects behavior indicative of shoplifting. For example, if the generation AI analyzes surveillance camera footage in the store and detects behavior indicative of shoplifting, it makes an announcement such as, "Customers, please feel free to use the store. However, please be sure to pay for your items at the register." The staff alert unit alerts staff based on the behavior indicative of shoplifting detected by the in-store announcement unit. For example, if the generation AI detects suspicious activity in a specific area, it makes an alert such as, "Suspicious activity has been detected in a specific area. Please check." The resale monitoring unit monitors online platforms to detect resale of shoplifted items. For example, the generation AI monitors online platforms such as auction sites and flea market apps and issues an alert if a specific item is listed in large quantities or sold at an abnormally low price. The theft prevention unit analyzes in-store surveillance camera footage in real time to detect behavior that may indicate shoplifting. For example, if the generation AI detects someone hiding an item or behaving unnaturally, it immediately issues an alert and notifies staff. This allows the shoplifting prevention system of the embodiment to prevent shoplifting from both offline and online perspectives, reducing losses for retailers.

[0056] The in-store announcement unit can analyze the audio environment and make announcements when specific sounds are detected. For example, the in-store announcement unit uses a generation AI to constantly monitor the audio environment in the store and make announcements when specific sounds (such as the sound of shelves or footsteps) are detected. For example, if the sound of shelves is abnormally loud, an announcement such as "Customers, please handle products with care" can be made. By making announcements when specific sounds are detected, shoplifting can be prevented before it happens.

[0057] The in-store announcement unit analyzes environmental data such as temperature and humidity, and can issue warnings if there are any abnormal changes. For example, the in-store announcement unit uses a generation AI to constantly monitor the temperature and humidity in the store and issue warnings if there are any abnormal changes. For example, if a sudden rise in temperature is detected, an announcement such as "Customers, the temperature in the store is rising. Please take care of your health." This allows the safety of the in-store environment to be ensured by detecting abnormal changes in temperature and humidity and issuing warnings.

[0058] The in-store announcement unit can use the emotion estimation function to estimate the emotion of a customer from their facial expressions and movements, and make an announcement if suspicious behavior is observed. For example, the in-store announcement unit can use the emotion estimation function to estimate the emotion of a customer from their facial expressions and movements, and make an announcement if suspicious behavior is observed. For example, if a nervous expression or unnatural movements are detected, the announcement can be made such as, "Customer, please feel free to use the store." In this way, by estimating the emotion of a customer and making an announcement if suspicious behavior is observed, shoplifting can be prevented before it happens.

[0059] The in-store announcement unit can make in-store announcements in multiple languages ​​so that it can also accommodate foreign tourists. The in-store announcement unit can, for example, make in-store announcements in multiple languages ​​so that it can also accommodate foreign tourists. For example, announcements can be made in multiple languages ​​such as Japanese, English, Chinese, and Korean. This makes it possible to make announcements in multiple languages ​​so that it can also accommodate foreign tourists.

[0060] The in-store announcement unit can control lighting and music to automatically create an environment that is effective in preventing shoplifting. For example, the in-store announcement unit uses a generative AI to control the lighting in the store, automatically creating an environment that is effective in preventing shoplifting. For example, brightening the lights in specific areas can deter shoplifting. This makes it possible to automatically create an environment that is effective in preventing shoplifting by controlling the lighting and music.

[0061] The in-store announcement unit can use the emotion estimation function to monitor staff stress levels and encourage them to take breaks at appropriate times. For example, the in-store announcement unit can use the emotion estimation function to monitor stress levels from staff facial expressions and movements and encourage them to take breaks at appropriate times. For example, if tired facial expressions or movements are detected, an announcement such as "Staff, please take a break" can be made. In this way, by monitoring staff stress levels and encouraging them to take breaks at appropriate times, it is possible to improve staff health and work efficiency.

[0062] The resale monitoring unit can perform image recognition of products, automatically detect similar products, and evaluate the possibility of resale. For example, the resale monitoring unit uses a generation AI to perform image recognition of products, automatically detect similar products, and evaluate the possibility of resale. For example, it issues a warning if the same product is being listed by multiple sellers. This makes it possible to effectively monitor the resale of shoplifted products by performing image recognition of products, automatically detecting similar products, and evaluating the possibility of resale.

[0063] The resale monitoring unit can analyze a seller's transaction history, detect abnormal patterns, and issue a warning. For example, the resale monitoring unit uses a generation AI to analyze a seller's past transaction history, detect abnormal patterns, and issue a warning. For example, a warning can be issued to a seller who is listing a large number of items in a short period of time. In this way, by analyzing a seller's transaction history, detecting abnormal patterns, and issuing a warning, it is possible to effectively monitor the resale of shoplifted items.

[0064] The resale monitoring unit can use the emotion estimation function to estimate emotions from sellers' profile pictures and comments and detect suspicious behavior. The resale monitoring unit, for example, uses the emotion estimation function to estimate emotions from sellers' profile pictures and comments and detect suspicious behavior. For example, it issues a warning if the profile picture has been unnaturally edited. This makes it possible to effectively monitor the resale of shoplifted items by estimating emotions from sellers' profile pictures and comments and detecting suspicious behavior.

[0065] The resale monitoring unit can monitor social media and collect information on the resale of shoplifted items. For example, the resale monitoring unit uses a generation AI to monitor social media and collect information on the resale of shoplifted items. For example, it monitors specific hashtags and keywords and detects related posts. This makes it possible to effectively monitor the resale of shoplifted items by monitoring social media and collecting information on the resale of shoplifted items.

[0066] The resale monitoring unit can link data between different online platforms and monitor signs of resale over a wider area. For example, the resale monitoring unit can link data between online platforms with different generation AIs and monitor signs of resale over a wider area. For example, data from auction sites and flea market apps can be integrated and monitored. This allows data linking between different online platforms and monitoring signs of resale over a wider area, making it possible to effectively monitor the resale of shoplifted items.

[0067] The resale monitoring unit can use the emotion estimation function to estimate emotions from buyer reviews and comments and evaluate the possibility of reselling the product. The resale monitoring unit, for example, uses the emotion estimation function to estimate emotions from buyer reviews and comments and evaluate the possibility of reselling the product. For example, it issues a warning if the review is unnaturally positive. In this way, by estimating emotions from buyer reviews and comments and evaluating the possibility of reselling the product, it is possible to effectively monitor the resale of shoplifted products.

[0068] The theft prevention unit can analyze the flow of people in the store and identify areas where shoplifting is likely to occur for intensive monitoring. For example, the theft prevention unit uses a generation AI to analyze the flow of people in the store and identify areas where shoplifting is likely to occur for intensive monitoring. For example, areas where shoplifting is likely to occur can be identified based on the placement of products and the height of shelves. This allows the unit to analyze the flow of people in the store and identify areas where shoplifting is likely to occur for intensive monitoring, thereby preventing shoplifting acts from occurring.

[0069] The theft prevention unit can analyze product placement and shelf height, and propose the optimal layout to prevent shoplifting. For example, the generative AI can analyze product placement and shelf height, and propose the optimal layout to prevent shoplifting. For example, it can adjust shelf height so that the view is not obstructed. In this way, by analyzing product placement and shelf height, and proposing the optimal layout to prevent shoplifting, it is possible to prevent shoplifting acts before they occur.

[0070] The theft prevention unit can use the emotion estimation function to infer emotions from the customer's movements and facial expressions and detect suspicious behavior. The theft prevention unit, for example, uses the emotion estimation function to infer emotions from the customer's movements and facial expressions and detect suspicious behavior. For example, if it detects a nervous expression or unnatural movements, it issues a warning. In this way, by inferring emotions from the customer's movements and facial expressions and detecting suspicious behavior, it is possible to prevent shoplifting before it happens.

[0071] The anti-theft unit can control lighting and music to automatically create an environment that is effective in preventing shoplifting. For example, the anti-theft unit uses generative AI to control the lighting in a store, automatically creating an environment that is effective in preventing shoplifting. For example, brightening the lights in specific areas can deter shoplifting. This makes it possible to automatically create an environment that is effective in preventing shoplifting by controlling lighting and music.

[0072] The theft prevention unit analyzes environmental data such as temperature and humidity, and can issue warnings if there are any abnormal changes. For example, the theft prevention unit's generating AI constantly monitors the temperature and humidity in the store, and issues warnings if there are any abnormal changes. For example, if a sudden rise in temperature is detected, an announcement such as "Customers, the temperature in the store is rising. Please take care of your health." This allows the system to detect abnormal changes in temperature and humidity and issue warnings, ensuring the safety of the store environment.

[0073] The anti-theft department can use the emotion estimation function to monitor staff stress levels and encourage them to take breaks at appropriate times. For example, the anti-theft department can use the emotion estimation function to monitor stress levels from staff facial expressions and movements and encourage them to take breaks at appropriate times. For example, if tired facial expressions or movements are detected, an announcement such as "Staff, please take a break" can be made. In this way, by monitoring staff stress levels and encouraging them to take breaks at appropriate times, it is possible to improve staff health and work efficiency.

[0074] The system can manage inventory in unmanned stores in real time and automate product replenishment and ordering. For example, generative AI can manage inventory in unmanned stores in real time and automate product replenishment and ordering. For example, an order can be placed automatically when inventory falls below a certain level. This allows for real-time inventory management in unmanned stores and automates product replenishment and ordering, improving the operational efficiency of unmanned stores.

[0075] The system can analyze customer behavior in unmanned stores and propose optimal product placement and promotions. For example, the system uses generative AI to analyze customer behavior in unmanned stores and propose optimal product placement and promotions. For example, it analyzes customer movement paths and optimizes the placement of popular products. This allows the system to analyze customer behavior in unmanned stores and propose optimal product placement and promotions, thereby improving the operational efficiency of unmanned stores.

[0076] The system can use the emotion estimation function to infer emotions from the facial expressions and movements of customers in unmanned stores and provide appropriate announcements and services. For example, the system can use the emotion estimation function to infer emotions from the facial expressions and movements of customers in unmanned stores and provide appropriate announcements and services. For example, if a customer looks troubled, the system can make an announcement such as, "If you need any help, please let a staff member know." In this way, customer satisfaction can be improved by inferring emotions from the facial expressions and movements of customers in unmanned stores and providing appropriate announcements and services.

[0077] The system can analyze security camera footage from unmanned stores, detect abnormal behavior, and issue a warning. For example, the system uses generative AI to analyze security camera footage from unmanned stores, detect abnormal behavior, and issue a warning. For example, it will issue a warning if it detects an attempt to hide a product or unnatural movement. This allows the system to analyze security camera footage from unmanned stores, detect abnormal behavior, and issue a warning, thereby improving the safety of unmanned stores.

[0078] The system can optimize energy consumption in unmanned stores and reduce the environmental impact. For example, generative AI can reduce energy consumption by optimizing the use of lighting and air conditioning. This allows unmanned stores to achieve sustainable operations by optimizing energy consumption and reducing the environmental impact.

[0079] The system can use the emotion estimation function to monitor the stress levels of customers in unmanned stores and provide a relaxing environment. For example, the system can use the emotion estimation function to monitor the stress levels of customers in unmanned stores and provide a relaxing environment. For example, if a customer is feeling stressed, the system can play relaxing music. In this way, by monitoring the stress levels of customers in unmanned stores and providing a relaxing environment, customer satisfaction can be improved.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The in-store announcement section can analyze a customer's purchase history and run promotions for specific products. For example, the generation AI can analyze a customer's past purchase history and run promotions for specific products. For example, to a customer who has previously purchased a specific brand of product, it can announce, "Customer, products from brand X are on sale today only." This allows sales to be increased by analyzing a customer's purchase history and running promotions for specific products.

[0082] The in-store announcement unit can use the emotion estimation function to monitor the customer's stress level and play relaxing music. For example, the emotion estimation function can be used to monitor the customer's stress level from their facial expressions and movements, and then play relaxing music. For example, if a customer is feeling stressed, relaxing classical music can be played. In this way, customer satisfaction can be improved by monitoring the customer's stress level and playing relaxing music.

[0083] The in-store announcement section can make different announcements depending on the specific time of day. For example, the generation AI can make different announcements depending on the specific time of day. For example, in the morning it can make an announcement such as "Good morning. Thank you for visiting us today," and in the evening it can make an announcement such as "Thank you for visiting us today. We look forward to seeing you again." This allows the quality of service to customers to be improved by making different announcements depending on the specific time of day.

[0084] The in-store announcement unit can use the emotion estimation function to estimate a customer's purchasing intent and recommend specific products. For example, the emotion estimation function can be used to estimate a customer's purchasing intent from their facial expressions and movements, and then recommend specific products. For example, when a customer shows interest in a product, the announcement can be made such as, "Customer, this product is on discount for today only." This makes it possible to estimate a customer's purchasing intent and recommend specific products, thereby increasing sales.

[0085] The in-store announcement section can make announcements to coincide with specific events or campaigns. For example, the generation AI can make announcements to coincide with specific events or campaigns. For example, during the Christmas season, it can make an announcement such as "Merry Christmas! We're having a Christmas sale today," and on Valentine's Day, it can make an announcement such as "Happy Valentine's Day! We're having a Valentine's Day limited edition product on sale today." In this way, by making announcements to coincide with specific events or campaigns, it is possible to increase customer purchasing motivation.

[0086] The in-store announcement unit can use the emotion estimation function to monitor customer satisfaction and ask for feedback at the appropriate time. For example, using the emotion estimation function, the satisfaction level can be monitored from the customer's facial expressions and movements, and feedback can be asked at the appropriate time. For example, if a customer has a satisfied expression, an announcement can be made such as, "Customer, how was your experience today? Please give us your feedback." This makes it possible to monitor customer satisfaction and ask for feedback at the appropriate time, thereby improving the quality of service.

[0087] The in-store announcement section can make announcements depending on the stock status of specific products. For example, the generation AI can monitor the stock status of specific products and make an announcement when stock is low. For example, it can make an announcement such as, "Customer, there are only a few items left of product X. Please purchase as soon as possible." This makes it possible to make announcements depending on the stock status of specific products, thereby streamlining inventory management and increasing sales.

[0088] The in-store announcement unit can use the emotion estimation function to make announcements that reduce customer anxiety. For example, the emotion estimation function can be used to estimate anxiety from a customer's facial expressions and movements, and an announcement that reduces anxiety can be made. For example, if a customer looks anxious, an announcement such as "Customer, if you have any questions, please speak to a member of staff" can be made. By making announcements that reduce customer anxiety, customer satisfaction can be improved.

[0089] The in-store announcement unit can make announcements explaining how to use and the features of specific products. For example, the generation AI can make announcements explaining how to use and the features of specific products. For example, it can make an announcement such as, "Customer, this product has XX function and is easy to use." This can increase customer purchasing motivation by making announcements explaining how to use and the features of specific products.

[0090] The in-store announcement unit can use the emotion estimation function to estimate a customer's purchasing intent and recommend specific products. For example, the emotion estimation function can be used to estimate a customer's purchasing intent from their facial expressions and movements, and then recommend specific products. For example, when a customer shows interest in a product, the announcement can be made such as, "Customer, this product is on discount for today only." This makes it possible to estimate a customer's purchasing intent and recommend specific products, thereby increasing sales.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The in-store announcement unit analyzes the surveillance camera footage inside the store and makes an announcement if it detects any behavior that could be indicative of shoplifting. For example, if the generation AI analyzes the surveillance camera footage inside the store and detects any behavior that could be indicative of shoplifting, it will make an announcement such as, "Customers, please feel free to use the store as you like. However, please be sure to pay for your items at the register." Step 2: The staff alerting unit alerts staff based on the shoplifting-indicating behavior detected by the in-store announcement unit. For example, if the generation AI detects suspicious activity in a specific area, it will issue an alert such as, "Suspicious activity has been detected in a specific area. Please check." Step 3: The resale monitoring unit monitors online platforms to detect the resale of shoplifted items. For example, the generative AI monitors online platforms such as auction sites and flea market apps, and issues a warning if a specific item is listed in large quantities or sold at an abnormally low price. Step 4: The anti-theft department analyzes the in-store surveillance camera footage in real time to detect behavior that may indicate shoplifting. For example, if the generative AI detects someone hiding an item or behaving unnaturally, it will immediately issue an alert and notify staff.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] 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.

[0108] 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.

[0109] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] 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.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] 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.

[0139] 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.

[0140] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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. [Explanation of symbols]

[0160] 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. An in-store announcement unit that analyzes surveillance camera footage in the store and makes an announcement if it detects any behavior that indicates shoplifting; a staff warning unit that warns staff based on the behavior indicating shoplifting detected by the in-store announcement unit; a resale monitoring unit that monitors the online platform to detect resale of shoplifted items; Theft prevention unit analyzes in-store surveillance camera footage in real time and detects behavior that indicates shoplifting. A system characterized by:

2. The in-store announcement unit The system estimates the customer's emotions from their facial expressions and movements, and makes the announcement if any suspicious behavior is observed.

2. The system of claim 1.

3. The resale monitoring unit Perform image recognition on the product, automatically detect similar products, and evaluate the possibility of resale.

2. The system of claim 1.

4. The theft prevention unit includes: The flow of shoplifting within the store is analyzed, and areas where shoplifting is likely to occur are identified and monitored intensively.

2. The system of claim 1.

5. The system comprises: Inventory management for unmanned stores is performed in real time, and replenishment and ordering of products is automated.

2. The system of claim 1.

6. The in-store announcement unit Monitor staff stress levels and encourage them to take breaks at appropriate times 2. The system of claim 1.

7. The resale monitoring unit Detect suspicious behavior by estimating emotions from sellers' profile photos and comments 2. The system of claim 1.

8. The theft prevention unit includes: Estimate emotions from customer movements and facial expressions to detect suspicious behavior 2. The system of claim 1.

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