system

Abstract

The system according to this embodiment aims to reduce the burden on consumers through inventory management and automated ordering of consumables. [Solution] The system according to the embodiment comprises an inventory tracking unit, an inventory checking unit, an order unit, and a security unit. The inventory tracking unit tracks inventory. The inventory checking unit periodically checks inventory based on the inventory status tracked by the inventory tracking unit. The order unit automatically places orders based on the inventory status checked by the inventory checking unit. The security unit enhances privacy protection by preventing data from being transmitted externally.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that inventory management of consumables and purchasing when they are insufficient are time-consuming.

[0005] The system according to the embodiment aims to save consumers' time through inventory management of consumables and automatic ordering.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an inventory tracking unit, an inventory checking unit, an order unit, and a security unit. The inventory tracking unit tracks inventory. The inventory checking unit periodically checks inventory based on the inventory status tracked by the inventory tracking unit. The order unit automatically places orders based on the inventory status checked by the inventory checking unit. The security unit enhances privacy protection by preventing data from being transmitted externally. [Effects of the Invention]

[0007] The system according to this embodiment can reduce the burden on consumers through inventory management and automated ordering of consumables. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The inventory management system according to an embodiment of the present invention is a system that saves consumers the trouble of managing and automatically replenishing consumables inventory using AI. This inventory management system works by having consumers take photos of their home inventory with their smartphones, and the smartphone's edge AI analyzes the images to determine the types of products and quantities in stock. Furthermore, by performing periodic analyses, the system understands the consumption pace and keeps the inventory status constantly up-to-date. This eliminates the need for consumers to send images of their homes to a server, thus protecting their privacy. Next, the smartphone's AI periodically checks the inventory status and automatically places orders as needed. Specifically, the AI ​​collaborates with e-commerce sites, taking into account delivery dates, purchase amounts, and shipping costs to place orders. In this process, there is no need to separately transmit sensitive information such as addresses or credit card numbers, ensuring the same level of security as regular orders. This system frees consumers from the hassle of keeping track of inventory and purchasing when supplies run low, allowing them to live a stress-free life. Additionally, the use of edge AI enhances privacy protection and security, lowering the psychological barrier to use. For example, consumers can take photos of their toilet paper or mineral water inventory with their smartphones, and edge AI analyzes the data to determine the stock levels. The system regularly checks inventory levels and automatically places orders on e-commerce sites as needed. This allows consumers to always have essential supplies on hand without worrying about running out of stock. In this way, AI handles everything from inventory tracking to ordering, saving consumers time and enabling a stress-free lifestyle. As a result, inventory management systems can efficiently manage and automatically replenish inventory while saving consumers time and effort.

[0029] The inventory management system according to this embodiment comprises an inventory tracking unit, an inventory checking unit, an order unit, and a security unit. The inventory tracking unit is the unit for consumers to keep track of their home inventory. The inventory tracking unit can, for example, analyze images taken with a smartphone to determine the type and quantity of products in stock. For example, the inventory tracking unit can use an image recognition algorithm to identify each product in the image and count its quantity. The inventory tracking unit can also use a machine learning model to automatically classify the types of products. Furthermore, the inventory tracking unit can supplement inventory information manually entered by consumers. For example, consumers can enter inventory information through a smartphone app. The inventory checking unit is the unit that periodically checks inventory based on the inventory status grasped by the inventory tracking unit. The inventory checking unit can check the inventory status at frequencies such as daily, weekly, or monthly. For example, the inventory checking unit checks the inventory status at a fixed time each day and issues an alert as needed. The inventory checking unit can also learn the consumer's consumption pace and automatically set the optimal checking frequency. For example, the inventory checking department analyzes consumer consumption patterns based on past consumption data and optimizes the timing of inventory checks. The ordering department automatically places orders based on the inventory status checked by the inventory checking department. The ordering department can, for example, collaborate with e-commerce sites and place orders considering delivery dates, purchase amounts, and shipping costs comprehensively. For example, the ordering department selects the best conditions from multiple e-commerce sites and places an order. The ordering department can also suggest the best products based on the consumer's past purchase history. For example, the ordering department prioritizes ordering products that consumers frequently purchase. The security department is responsible for ensuring that data is not transmitted externally and for enhancing privacy protection. The security department can enhance privacy protection using methods such as data encryption, access control, and data anonymization. For example, the security department encrypts consumers' personal information to prevent unauthorized access from external sources. The security department can also anonymize consumer data to protect privacy.As a result, the inventory management system according to this embodiment can efficiently manage and automatically replenish inventory while saving consumers time and effort.

[0030] The inventory tracking department is responsible for helping consumers keep track of their home inventory. For example, it can analyze images taken with a smartphone to determine product types and quantities. Specifically, users can use their smartphone camera to photograph the inside of their refrigerator or pantry and upload the images to a dedicated application. The application uses image recognition algorithms to identify each product in the image and count its quantity. For instance, by reading barcodes or QR codes (registered trademarks) in the image, it can obtain detailed information such as product name, manufacturer, and expiration date. The inventory tracking department can also automatically classify product types using machine learning models. For example, it can train deep learning-based image classification models to accurately identify different product categories (beverages, food, daily necessities, etc.). Furthermore, the inventory tracking department can supplement inventory information manually entered by consumers. For example, consumers can input inventory information through a smartphone app. The application supports voice and text input, allowing consumers to easily add and modify inventory information. This enables the inventory tracking department to provide accurate inventory information while minimizing the effort required from consumers. Furthermore, the inventory management department can store the collected inventory data on a cloud server and share it with other departments. This improves the efficiency and accuracy of the entire inventory management system.

[0031] The Inventory Check Department is responsible for regularly checking inventory based on the inventory status tracked by the Inventory Management Department. The Inventory Check Department can check inventory status at various frequencies, such as daily, weekly, or monthly. Specifically, it checks inventory status at a set time each day and issues alerts as needed. For example, if inventory falls below a certain threshold, it sends a notification to consumers informing them of the need for replenishment. The Inventory Check Department can also learn consumer consumption patterns and automatically set the optimal check frequency. For instance, it analyzes consumer consumption patterns based on past consumption data to optimize the timing of inventory checks, ensuring that consumers can replenish inventory at the right time. Furthermore, the Inventory Check Department can use AI to analyze inventory data and detect abnormal consumption patterns or sudden inventory drops. For example, if a sudden inventory decrease differing from the normal consumption pace is detected, it issues an alert to consumers, prompting them to investigate the cause. This allows the Inventory Check Department to support consumers' inventory management and prevent wasteful consumption and shortages. Furthermore, the inventory checking unit will integrate with multiple devices and platforms, allowing consumers to check inventory status wherever they are. For example, it will provide real-time inventory information through smartphone apps, web browsers, smart speakers, and other means. This will enable the inventory checking unit to improve consumer convenience and achieve efficient inventory management.

[0032] The Order Department is responsible for automatically placing orders based on inventory status checked by the Inventory Check Department. For example, the Order Department can collaborate with e-commerce sites to place orders, taking into account delivery dates, purchase amounts, and shipping costs. Specifically, the Order Department selects the most suitable conditions from multiple e-commerce sites and places orders. For instance, if inventory falls below a certain threshold, the Order Department can suggest the most suitable products based on the consumer's past purchase history and current inventory status. For example, it might prioritize ordering products that consumers frequently purchase. The Order Department can also select the most suitable products based on the consumer's preferences and budget. For example, if a consumer prefers a specific brand or price range, it will suggest the most suitable products based on that information. Furthermore, the Order Department optimizes delivery schedules to ensure consumers receive their products at their desired date and time. For example, it can place orders according to the date and time specified by the consumer and coordinate with delivery companies to ensure smooth delivery. The Order Department also notifies consumers of the order status in real time, allowing them to check the progress of their orders. This allows the Order Department to replenish inventory efficiently and quickly, saving consumers time and effort. Furthermore, the ordering department can collect consumer feedback and use it to improve the ordering process. For example, by having consumers provide ratings and comments on their orders, the ordering department can improve the quality of its service. This allows the ordering department to increase consumer satisfaction and provide a reliable inventory management system.

[0033] The Security Department is responsible for ensuring that data is not transmitted externally and for enhancing privacy protection. The Security Department can enhance privacy protection through methods such as data encryption, access control, and data anonymization. Specifically, the Security Department encrypts consumer personal information to prevent unauthorized external access. For example, it encrypts data using strong encryption algorithms such as AES (Advanced Encryption Standard). The Security Department also implements access control, restricting users and devices that can access data. For example, it implements two-factor authentication (2FA) or biometric authentication to ensure that only authenticated users can access the data. Furthermore, the Security Department can anonymize consumer data to protect privacy. For example, it removes personally identifiable information and uses the data as statistical data, maintaining data usefulness while protecting privacy. The Security Department also conducts regular security audits to detect system vulnerabilities and take countermeasures. For example, it conducts penetration testing to evaluate the system's security level and makes necessary improvements. This allows the Security Department to securely protect consumer data and provide a reliable inventory management system. Furthermore, the security department can raise security awareness by providing consumers with security education and information. For example, they can provide security guidelines and best practices to ensure consumers can use the system safely. This allows the security department to create an inventory management system that protects consumer privacy and can be used with confidence.

[0034] The inventory tracking unit can analyze images taken with a smartphone to determine the type and quantity of products in stock. For example, the inventory tracking unit can use an image recognition algorithm to identify each product in the image and count its quantity. For example, the inventory tracking unit can also use a machine learning model to automatically classify the type of product. Furthermore, the inventory tracking unit can supplement inventory information manually entered by consumers. For example, consumers can enter inventory information through a smartphone app. This allows for accurate determination of product types and quantities by analyzing images taken with a smartphone. Some or all of the above-described processes in the inventory tracking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the inventory tracking unit can input image data taken with a smartphone into a generative AI and have the generative AI determine the type and quantity of products from the image data.

[0035] The inventory checking unit can periodically check inventory based on the inventory status captured by the inventory tracking unit. The inventory checking unit can check inventory status at frequencies such as daily, weekly, or monthly. For example, the inventory checking unit can check inventory status at a fixed time each day and issue alerts as needed. The inventory checking unit can also learn consumer consumption patterns and automatically set the optimal check frequency. For example, the inventory checking unit can analyze consumer consumption patterns based on past consumption data and optimize the timing of inventory checks. This prevents stockouts by regularly checking inventory status. Some or all of the above processes in the inventory checking unit may be performed using AI, or not. For example, the inventory checking unit can input inventory status data captured by the inventory tracking unit into the AI ​​and have the AI ​​perform inventory checks at the appropriate frequency.

[0036] The ordering department can collaborate with e-commerce sites to place orders, taking into account delivery dates, purchase amounts, and shipping costs. For example, the ordering department can select the optimal conditions from multiple e-commerce sites and place an order. For example, the ordering department can suggest the most suitable products based on the consumer's past purchase history. For example, the ordering department can prioritize ordering products that consumers frequently purchase. In this way, by collaborating with e-commerce sites, orders can be placed under optimal conditions. Some or all of the above processes in the ordering department may be performed using, for example, a generative AI, or not. For example, the ordering department can input data from e-commerce sites into a generative AI and have the generative AI select the optimal order conditions.

[0037] The security department can enhance privacy protection by preventing data from being transmitted externally. The security department can enhance privacy protection by methods such as data encryption, access control, and data anonymization. For example, the security department can encrypt consumers' personal information to prevent unauthorized external access. The security department can also anonymize consumer data to protect privacy. This enhances privacy protection by preventing data from being transmitted externally. Some or all of the above processes in the security department may be performed using AI, for example, or not. For example, the security department can input a data encryption algorithm into an AI and let the AI ​​perform the encryption.

[0038] The inventory tracking unit can predict inventory levels by considering the frequency of product use. For example, if a product is used frequently, the inventory tracking unit will predict a higher inventory level to prevent stockouts. For example, if a product is used infrequently, the inventory tracking unit will predict a lower inventory level to reduce unnecessary stock. Furthermore, if the frequency of product use fluctuates, the inventory tracking unit can dynamically predict inventory levels based on past usage data. This improves the accuracy of inventory predictions by considering the frequency of product use. Some or all of the above processes in the inventory tracking unit may be performed using AI, for example, or without AI. For example, the inventory tracking unit can input product usage frequency data into AI and have the AI ​​perform inventory predictions.

[0039] The inventory management unit can adjust inventory levels considering product expiration dates. For example, the inventory management unit can prioritize products with approaching expiration dates in its inventory levels to reduce waste. For example, the inventory management unit can postpone the processing of products with longer expiration dates and adjust inventory levels accordingly. The inventory management unit can also manage multiple products with different expiration dates simultaneously and calculate the optimal inventory level. This reduces unnecessary inventory by considering product expiration dates. Some or all of the above processes in the inventory management unit may be performed using AI, for example, or without AI. For example, the inventory management unit can input product expiration date data into AI and have the AI ​​perform inventory level adjustments.

[0040] The inventory tracking unit can analyze the user's lifestyle patterns and set the optimal inventory tracking timing. For example, if the user is busy during the day, the inventory tracking unit will perform inventory tracking at night. For example, if the user has free time on weekends, the inventory tracking unit will perform inventory tracking on weekends. Furthermore, if the user's lifestyle patterns fluctuate, the inventory tracking unit can dynamically set the optimal timing. This optimizes the timing of inventory tracking according to the user's lifestyle patterns, enabling efficient inventory management. Some or all of the above processes in the inventory tracking unit may be performed using AI, for example, or without AI. For example, the inventory tracking unit can input user lifestyle pattern data into AI and have the AI ​​set the optimal inventory tracking timing.

[0041] The inventory tracking unit can predict inventory levels by referring to the user's purchase history. For example, the inventory tracking unit predicts inventory levels based on products that the user has frequently purchased in the past. For example, the inventory tracking unit dynamically predicts the inventory level of a specific product from the user's purchase history. The inventory tracking unit can also analyze the user's purchase patterns and calculate the optimal inventory level. This improves the accuracy of inventory level predictions by referring to the user's purchase history. Some or all of the above processes in the inventory tracking unit may be performed using AI, for example, or without AI. For example, the inventory tracking unit can input user purchase history data into AI and have the AI ​​perform inventory level predictions.

[0042] The inventory checking unit can optimize the timing of checks by considering the rate of product consumption. For example, if the rate of product consumption is fast, the inventory checking unit will increase the frequency of inventory checks. For example, if the rate of product consumption is slow, the inventory checking unit will decrease the frequency of inventory checks. Furthermore, if the rate of product consumption fluctuates, the inventory checking unit can dynamically optimize the timing of checks. This enables efficient inventory management by optimizing the timing of checks according to the rate of product consumption. Some or all of the above processes in the inventory checking unit may be performed using AI, for example, or without AI. For example, the inventory checking unit can input product consumption rate data into AI and have the AI ​​perform the optimization of the timing of checks.

[0043] The inventory check unit can set inventory priorities based on the importance of each product. For example, the inventory check unit can prioritize inventory checks of important products to prevent stockouts. For example, the inventory check unit can postpone inventory checks of less important products to manage inventory efficiently. The inventory check unit can also dynamically set inventory priorities if the importance of each product changes. This enables efficient inventory management by setting inventory priorities according to the importance of each product. Some or all of the above processes in the inventory check unit may be performed using AI, for example, or not. For example, the inventory check unit can input product importance data into the AI ​​and have the AI ​​set the inventory priorities.

[0044] The inventory check unit can analyze the user's daily routine and set the optimal check timing. For example, if the user is busy during the day, the inventory check unit will perform the check at night. For example, if the user has free time on the weekend, the inventory check unit will perform the check on the weekend. The inventory check unit can also dynamically set the optimal timing if the user's daily routine fluctuates. This optimizes the check timing according to the user's daily routine, enabling efficient inventory management. Some or all of the above processes in the inventory check unit may be performed using AI, or not. For example, the inventory check unit can input the user's daily routine data into the AI ​​and have the AI ​​set the optimal check timing.

[0045] The inventory checking unit can improve the accuracy of its checks by referring to the user's purchase history. For example, the inventory checking unit performs inventory checks based on products that the user has frequently purchased in the past. For example, the inventory checking unit can focus on checking the inventory of specific products based on the user's purchase history. The inventory checking unit can also analyze the user's purchase patterns and suggest the optimal inventory checking method. This improves the accuracy of the checks by referring to the user's purchase history. Some or all of the above processes in the inventory checking unit may be performed using AI, for example, or not using AI. For example, the inventory checking unit can input user purchase history data into AI and have the AI ​​perform the check accuracy improvement.

[0046] The order department can select the optimal timing for placing orders by considering price fluctuations of the products. For example, the order department can place orders when the price of a product is low to reduce costs. For example, the order department can avoid placing orders when the price of a product is high to reduce costs. The order department can also predict price fluctuations of the products and place orders at the optimal time. In this way, costs can be reduced by considering price fluctuations of the products. Some or all of the above processes in the order department may be performed using AI, for example, or not using AI. For example, the order department can input product price data into AI and have the AI ​​perform price fluctuation prediction and select the optimal timing for placing orders.

[0047] The ordering department can check the inventory status of products in real time and determine the optimal order quantity. For example, if the inventory of a product is low, the ordering department can place a larger order to prevent stockouts. For example, if the inventory of a product is high, the ordering department can place a smaller order to reduce unnecessary inventory. Furthermore, if the inventory status of a product fluctuates, the ordering department can check the inventory in real time and determine the optimal order quantity. This allows the optimal order quantity to be determined by checking the inventory status in real time. Some or all of the above processes in the ordering department may be performed using AI, for example, or not. For example, the ordering department can input product inventory data into AI and have the AI ​​perform inventory status checks and determine the optimal order quantity.

[0048] The ordering department can suggest the most suitable products by referring to the user's purchase history. For example, the ordering department can suggest the most suitable products based on the products the user has frequently purchased in the past. For example, the ordering department can focus on suggesting orders for specific products based on the user's purchase history. The ordering department can also analyze the user's purchasing patterns and suggest the most suitable products. In this way, the ordering department can suggest the most suitable products by referring to the user's purchase history. Some or all of the above processes in the ordering department may be performed using AI, for example, or not using AI. For example, the ordering department can input the user's purchase history data into AI and have the AI ​​perform the task of suggesting the most suitable products.

[0049] The order processing unit can analyze the user's daily routine and set the optimal delivery timing. For example, if the user is busy during the day, the order processing unit will deliver at night. For example, if the user has free time on the weekend, the order processing unit will deliver on the weekend. The order processing unit can also dynamically set the optimal delivery timing if the user's daily routine changes. This enables efficient delivery by setting the optimal delivery timing according to the user's daily routine. Some or all of the above processing in the order processing unit may be performed using AI, for example, or not. For example, the order processing unit can input the user's daily routine data into the AI ​​and have the AI ​​set the optimal delivery timing.

[0050] The security department can enhance security by dynamically changing the data encryption method. For example, the security department can change the encryption algorithm according to the user's access status to enhance security. For example, the security department can adjust the encryption strength according to the importance of the data to provide optimal security. The security department can also assess security risks in real time and select an appropriate encryption method. This allows for enhanced security by dynamically changing the data encryption method. Some or all of the above processes in the security department may be performed using AI, for example, or without AI. For example, the security department can input the selection of an encryption algorithm into the AI ​​and have the AI ​​perform the dynamic change of the encryption method.

[0051] The security department can monitor access logs in real time and immediately detect unauthorized access. For example, the security department can analyze access logs in real time and detect signs of unauthorized access. For example, if unauthorized access is detected, the security department can immediately issue an alert and take countermeasures. The security department can also periodically monitor access logs and detect abnormal access patterns. This allows for immediate detection of unauthorized access by monitoring access logs in real time. Some or all of the above processes in the security department may be performed using AI, for example, or not using AI. For example, the security department can input access log data into AI and have the AI ​​perform the detection of unauthorized access.

[0052] The security department can analyze the user's device information and propose optimal security settings. For example, the security department can propose optimal security settings according to the type of device the user has. For example, the security department can analyze the user's device usage and propose appropriate security measures. The security department can also evaluate the security risks of the user's device and propose optimal settings. In this way, by analyzing the user's device information, it is possible to propose optimal security settings. Some or all of the above processes in the security department may be performed using AI, for example, or not using AI. For example, the security department can input the user's device information into AI and have the AI ​​propose optimal security settings.

[0053] The security department can monitor the user's network environment and issue alerts if an anomaly is detected. For example, the security department can monitor the user's network environment in real time and detect abnormal traffic. For example, if an anomaly is detected, the security department can immediately issue an alert and notify the user. The security department can also monitor changes in the network environment and assess security risks. This allows for immediate response if an anomaly is detected by monitoring the user's network environment. Some or all of the above processes in the security department may be performed using AI, or not. For example, the security department can input network traffic data into the AI ​​and have the AI ​​perform anomaly detection and alert generation.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The inventory management system can also include an energy consumption management unit. This unit can monitor and optimize the energy consumption of each element of the inventory management system. For example, it can monitor the energy consumption of the image analysis processing in the inventory tracking unit and adjust the frequency and timing of the processing as needed. It can also optimize the energy consumption of the periodic check processing in the inventory checking unit, improving the overall energy efficiency of the system. Furthermore, it can monitor the energy consumption of the order processing unit's integration with e-commerce sites and reduce energy consumption by placing orders at the optimal time. In this way, the inventory management system can optimize energy consumption and reduce its environmental impact.

[0056] The inventory management system can also include a health management unit that monitors the user's health status. This unit can collect user health data and incorporate it into inventory management. For example, it can analyze the user's dietary records and prioritize the inventory of products containing necessary nutrients. It can also predict the consumption rate of consumables based on the user's exercise data and adjust inventory levels accordingly. Furthermore, it can refer to the user's sleep data to optimize inventory checks and ordering timing. This allows the inventory management system to manage inventory according to the user's health status and support a healthy lifestyle.

[0057] The inventory management system can further predict inventory levels by referencing the user's purchase history. For example, it can predict inventory levels based on products that the user has frequently purchased in the past, thereby preventing stockouts. It can also dynamically predict the inventory level of specific products based on the user's purchase history. Furthermore, it can analyze the user's purchasing patterns and calculate the optimal inventory level. As a result, by referring to the user's purchase history, the accuracy of inventory predictions is improved, enabling efficient inventory management. Some or all of the above-described processes in the inventory tracking unit may be performed using AI, for example, or without AI.

[0058] The inventory management system can further analyze the user's daily routine and set the optimal inventory check timing. For example, if the user is busy during the day, the inventory check can be performed at night. Similarly, if the user has free time on weekends, the inventory check can be performed on weekends. Furthermore, if the user's daily routine fluctuates, the optimal timing can be dynamically set. This allows for efficient inventory management by optimizing the timing of inventory checks according to the user's daily routine. Some or all of the above-described processes in the inventory check section may be performed using AI, for example, or without AI.

[0059] The inventory management system can further suggest optimal products by referencing the user's purchase history. For example, it can suggest the best products based on items the user has frequently purchased in the past, preventing stockouts. It can also prioritize suggesting orders for specific products based on the user's purchase history. Furthermore, it can analyze the user's purchasing patterns and suggest the best products. This enables efficient inventory management by suggesting optimal products based on the user's purchase history. Some or all of the above processes in the order department may be performed using AI, for example, or not.

[0060] The inventory management system can also monitor the user's network environment and issue alerts if an anomaly is detected. For example, it can monitor the user's network environment in real time and detect abnormal traffic. If an anomaly is detected, it can immediately issue an alert and notify the user. Furthermore, it can monitor changes in the network environment and assess security risks. This allows for immediate response to detected anomalies by monitoring the user's network environment, enabling efficient inventory management. Some or all of the above processes in the security department may be performed using AI, for example, or without AI.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The inventory tracking department is responsible for helping consumers keep track of their home inventory. For example, the inventory tracking department can analyze images taken with a smartphone to determine product types and quantities. It uses image recognition algorithms to identify each product in the image and count their quantities. It can also automatically classify product types using machine learning models. Furthermore, it can supplement inventory information manually entered by consumers. For example, consumers can enter inventory information through a smartphone app. Step 2: The Inventory Check Department is responsible for regularly checking inventory based on the inventory status tracked by the Inventory Management Department. The Inventory Check Department can check inventory status at various frequencies, such as daily, weekly, or monthly. It checks inventory status at a fixed time each day and issues alerts as needed. It can also learn consumer spending patterns and automatically set the optimal check frequency. Based on past consumption data, it analyzes consumer spending patterns and optimizes the timing of inventory checks. Step 3: The Order Department is responsible for automatically placing orders based on the inventory status checked by the Inventory Check Department. The Order Department can, for example, integrate with e-commerce sites and place orders considering delivery dates, purchase amounts, and shipping costs. It can select the best conditions from multiple e-commerce sites and place orders. It can also suggest optimal products based on the consumer's past purchase history. It prioritizes ordering products that consumers frequently purchase. Step 4: The Security Department is responsible for ensuring that data is not transmitted externally and for enhancing privacy protection. The Security Department can enhance privacy protection using methods such as data encryption, access control, and data anonymization. Consumer personal information can be encrypted to prevent unauthorized access from external sources. It can also anonymize consumer data to protect privacy.

[0063] (Example of form 2) The inventory management system according to an embodiment of the present invention is a system that saves consumers the trouble of managing and automatically replenishing consumables inventory using AI. This inventory management system works by having consumers take photos of their home inventory with their smartphones, and the smartphone's edge AI analyzes the images to determine the types of products and quantities in stock. Furthermore, by performing periodic analyses, the system understands the consumption pace and keeps the inventory status constantly up-to-date. This eliminates the need for consumers to send images of their homes to a server, thus protecting their privacy. Next, the smartphone's AI periodically checks the inventory status and automatically places orders as needed. Specifically, the AI ​​collaborates with e-commerce sites, taking into account delivery dates, purchase amounts, and shipping costs to place orders. In this process, there is no need to separately transmit sensitive information such as addresses or credit card numbers, ensuring the same level of security as regular orders. This system frees consumers from the hassle of keeping track of inventory and purchasing when supplies run low, allowing them to live a stress-free life. Additionally, the use of edge AI enhances privacy protection and security, lowering the psychological barrier to use. For example, consumers can take photos of their toilet paper or mineral water inventory with their smartphones, and edge AI analyzes the data to determine the stock levels. The system regularly checks inventory levels and automatically places orders on e-commerce sites as needed. This allows consumers to always have essential supplies on hand without worrying about running out of stock. In this way, AI handles everything from inventory tracking to ordering, saving consumers time and enabling a stress-free lifestyle. As a result, inventory management systems can efficiently manage and automatically replenish inventory while saving consumers time and effort.

[0064] The inventory management system according to this embodiment comprises an inventory tracking unit, an inventory checking unit, an order unit, and a security unit. The inventory tracking unit is the unit for consumers to keep track of their home inventory. The inventory tracking unit can, for example, analyze images taken with a smartphone to determine the type and quantity of products in stock. For example, the inventory tracking unit can use an image recognition algorithm to identify each product in the image and count its quantity. The inventory tracking unit can also use a machine learning model to automatically classify the types of products. Furthermore, the inventory tracking unit can supplement inventory information manually entered by consumers. For example, consumers can enter inventory information through a smartphone app. The inventory checking unit is the unit that periodically checks inventory based on the inventory status grasped by the inventory tracking unit. The inventory checking unit can check the inventory status at frequencies such as daily, weekly, or monthly. For example, the inventory checking unit checks the inventory status at a fixed time each day and issues an alert as needed. The inventory checking unit can also learn the consumer's consumption pace and automatically set the optimal checking frequency. For example, the inventory checking department analyzes consumer consumption patterns based on past consumption data and optimizes the timing of inventory checks. The ordering department automatically places orders based on the inventory status checked by the inventory checking department. The ordering department can, for example, collaborate with e-commerce sites and place orders considering delivery dates, purchase amounts, and shipping costs comprehensively. For example, the ordering department selects the best conditions from multiple e-commerce sites and places an order. The ordering department can also suggest the best products based on the consumer's past purchase history. For example, the ordering department prioritizes ordering products that consumers frequently purchase. The security department is responsible for ensuring that data is not transmitted externally and for enhancing privacy protection. The security department can enhance privacy protection using methods such as data encryption, access control, and data anonymization. For example, the security department encrypts consumers' personal information to prevent unauthorized access from external sources. The security department can also anonymize consumer data to protect privacy.As a result, the inventory management system according to this embodiment can efficiently manage and automatically replenish inventory while saving consumers time and effort.

[0065] The inventory tracking department is responsible for helping consumers keep track of their home inventory. For example, it can analyze images taken with a smartphone to determine product types and quantities. Specifically, users can use their smartphone camera to photograph the inside of their refrigerator or pantry and upload the images to a dedicated application. The application uses image recognition algorithms to identify each product in the image and count its quantity. For instance, it can read barcodes or QR codes in the image to obtain detailed information such as product name, manufacturer, and expiration date. The inventory tracking department can also automatically classify product types using machine learning models. For example, it can train deep learning-based image classification models to accurately identify different product categories (beverages, food, daily necessities, etc.). Furthermore, the inventory tracking department can supplement inventory information manually entered by consumers. For example, consumers can input inventory information through a smartphone app. The application supports voice and text input, allowing consumers to easily add and modify inventory information. This enables the inventory tracking department to provide accurate inventory information while minimizing the effort required from consumers. Furthermore, the inventory management department can store the collected inventory data on a cloud server and share it with other departments. This improves the efficiency and accuracy of the entire inventory management system.

[0066] The Inventory Check Department is responsible for regularly checking inventory based on the inventory status tracked by the Inventory Management Department. The Inventory Check Department can check inventory status at various frequencies, such as daily, weekly, or monthly. Specifically, it checks inventory status at a set time each day and issues alerts as needed. For example, if inventory falls below a certain threshold, it sends a notification to consumers informing them of the need for replenishment. The Inventory Check Department can also learn consumer consumption patterns and automatically set the optimal check frequency. For instance, it analyzes consumer consumption patterns based on past consumption data to optimize the timing of inventory checks, ensuring that consumers can replenish inventory at the right time. Furthermore, the Inventory Check Department can use AI to analyze inventory data and detect abnormal consumption patterns or sudden inventory drops. For example, if a sudden inventory decrease differing from the normal consumption pace is detected, it issues an alert to consumers, prompting them to investigate the cause. This allows the Inventory Check Department to support consumers' inventory management and prevent wasteful consumption and shortages. Furthermore, the inventory checking unit will integrate with multiple devices and platforms, allowing consumers to check inventory status wherever they are. For example, it will provide real-time inventory information through smartphone apps, web browsers, smart speakers, and other means. This will enable the inventory checking unit to improve consumer convenience and achieve efficient inventory management.

[0067] The Order Department is responsible for automatically placing orders based on inventory status checked by the Inventory Check Department. For example, the Order Department can collaborate with e-commerce sites to place orders, taking into account delivery dates, purchase amounts, and shipping costs. Specifically, the Order Department selects the most suitable conditions from multiple e-commerce sites and places orders. For instance, if inventory falls below a certain threshold, the Order Department can suggest the most suitable products based on the consumer's past purchase history and current inventory status. For example, it might prioritize ordering products that consumers frequently purchase. The Order Department can also select the most suitable products based on the consumer's preferences and budget. For example, if a consumer prefers a specific brand or price range, it will suggest the most suitable products based on that information. Furthermore, the Order Department optimizes delivery schedules to ensure consumers receive their products at their desired date and time. For example, it can place orders according to the date and time specified by the consumer and coordinate with delivery companies to ensure smooth delivery. The Order Department also notifies consumers of the order status in real time, allowing them to check the progress of their orders. This allows the Order Department to replenish inventory efficiently and quickly, saving consumers time and effort. Furthermore, the ordering department can collect consumer feedback and use it to improve the ordering process. For example, by having consumers provide ratings and comments on their orders, the ordering department can improve the quality of its service. This allows the ordering department to increase consumer satisfaction and provide a reliable inventory management system.

[0068] The Security Department is responsible for ensuring that data is not transmitted externally and for enhancing privacy protection. The Security Department can enhance privacy protection through methods such as data encryption, access control, and data anonymization. Specifically, the Security Department encrypts consumer personal information to prevent unauthorized external access. For example, it encrypts data using strong encryption algorithms such as AES (Advanced Encryption Standard). The Security Department also implements access control, restricting users and devices that can access data. For example, it implements two-factor authentication (2FA) or biometric authentication to ensure that only authenticated users can access the data. Furthermore, the Security Department can anonymize consumer data to protect privacy. For example, it removes personally identifiable information and uses the data as statistical data, maintaining data usefulness while protecting privacy. The Security Department also conducts regular security audits to detect system vulnerabilities and take countermeasures. For example, it conducts penetration testing to evaluate the system's security level and makes necessary improvements. This allows the Security Department to securely protect consumer data and provide a reliable inventory management system. Furthermore, the security department can raise security awareness by providing consumers with security education and information. For example, they can provide security guidelines and best practices to ensure consumers can use the system safely. This allows the security department to create an inventory management system that protects consumer privacy and can be used with confidence.

[0069] The inventory tracking unit can analyze images taken with a smartphone to determine the type and quantity of products in stock. For example, the inventory tracking unit can use an image recognition algorithm to identify each product in the image and count its quantity. For example, the inventory tracking unit can also use a machine learning model to automatically classify the type of product. Furthermore, the inventory tracking unit can supplement inventory information manually entered by consumers. For example, consumers can enter inventory information through a smartphone app. This allows for accurate determination of product types and quantities by analyzing images taken with a smartphone. Some or all of the above-described processes in the inventory tracking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the inventory tracking unit can input image data taken with a smartphone into a generative AI and have the generative AI determine the type and quantity of products from the image data.

[0070] The inventory checking unit can periodically check inventory based on the inventory status captured by the inventory tracking unit. The inventory checking unit can check inventory status at frequencies such as daily, weekly, or monthly. For example, the inventory checking unit can check inventory status at a fixed time each day and issue alerts as needed. The inventory checking unit can also learn consumer consumption patterns and automatically set the optimal check frequency. For example, the inventory checking unit can analyze consumer consumption patterns based on past consumption data and optimize the timing of inventory checks. This prevents stockouts by regularly checking inventory status. Some or all of the above processes in the inventory checking unit may be performed using AI, or not. For example, the inventory checking unit can input inventory status data captured by the inventory tracking unit into the AI ​​and have the AI ​​perform inventory checks at the appropriate frequency.

[0071] The ordering department can collaborate with e-commerce sites to place orders, taking into account delivery dates, purchase amounts, and shipping costs. For example, the ordering department can select the optimal conditions from multiple e-commerce sites and place an order. For example, the ordering department can suggest the most suitable products based on the consumer's past purchase history. For example, the ordering department can prioritize ordering products that consumers frequently purchase. In this way, by collaborating with e-commerce sites, orders can be placed under optimal conditions. Some or all of the above processes in the ordering department may be performed using, for example, a generative AI, or not. For example, the ordering department can input data from e-commerce sites into a generative AI and have the generative AI select the optimal order conditions.

[0072] The security department can enhance privacy protection by preventing data from being transmitted externally. The security department can enhance privacy protection by methods such as data encryption, access control, and data anonymization. For example, the security department can encrypt consumers' personal information to prevent unauthorized external access. The security department can also anonymize consumer data to protect privacy. This enhances privacy protection by preventing data from being transmitted externally. Some or all of the above processes in the security department may be performed using AI, for example, or not. For example, the security department can input a data encryption algorithm into an AI and let the AI ​​perform the encryption.

[0073] The inventory tracking unit can estimate the user's emotions and adjust the frequency of inventory checks based on the estimated emotions. For example, if the user is stressed, the inventory tracking unit can reduce the frequency of inventory checks to alleviate the user's burden. For example, if the user is relaxed, the inventory tracking unit can increase the frequency of inventory checks and provide detailed inventory information. Also, if the user is in a hurry, the inventory tracking unit can minimize the frequency of inventory checks to quickly obtain inventory information. In this way, the user's burden can be reduced by adjusting the frequency of inventory checks according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the inventory tracking unit may be performed using AI, or not using AI. For example, the inventory tracking unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of inventory check frequency.

[0074] The inventory tracking unit can predict inventory levels by considering the frequency of product use. For example, if a product is used frequently, the inventory tracking unit will predict a higher inventory level to prevent stockouts. For example, if a product is used infrequently, the inventory tracking unit will predict a lower inventory level to reduce unnecessary stock. Furthermore, if the frequency of product use fluctuates, the inventory tracking unit can dynamically predict inventory levels based on past usage data. This improves the accuracy of inventory predictions by considering the frequency of product use. Some or all of the above processes in the inventory tracking unit may be performed using AI, for example, or without AI. For example, the inventory tracking unit can input product usage frequency data into AI and have the AI ​​perform inventory predictions.

[0075] The inventory management unit can adjust inventory levels considering product expiration dates. For example, the inventory management unit can prioritize products with approaching expiration dates in its inventory levels to reduce waste. For example, the inventory management unit can postpone the processing of products with longer expiration dates and adjust inventory levels accordingly. The inventory management unit can also manage multiple products with different expiration dates simultaneously and calculate the optimal inventory level. This reduces unnecessary inventory by considering product expiration dates. Some or all of the above processes in the inventory management unit may be performed using AI, for example, or without AI. For example, the inventory management unit can input product expiration date data into AI and have the AI ​​perform inventory level adjustments.

[0076] The inventory management unit can estimate the user's emotions and determine inventory management priorities based on those emotions. For example, if the user is stressed, the inventory management unit will prioritize inventory management of important items. For example, if the user is relaxed, the inventory management unit will manage inventory of all items equally. Also, if the user is in a hurry, the inventory management unit can prioritize inventory management of only the essential items. In this way, by determining inventory management priorities according to the user's emotions, inventory management of important items can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the inventory management unit may be performed using AI, or not using AI. For example, the inventory management unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and inventory management priority determination.

[0077] The inventory tracking unit can analyze the user's lifestyle patterns and set the optimal inventory tracking timing. For example, if the user is busy during the day, the inventory tracking unit will perform inventory tracking at night. For example, if the user has free time on weekends, the inventory tracking unit will perform inventory tracking on weekends. Furthermore, if the user's lifestyle patterns fluctuate, the inventory tracking unit can dynamically set the optimal timing. This optimizes the timing of inventory tracking according to the user's lifestyle patterns, enabling efficient inventory management. Some or all of the above processes in the inventory tracking unit may be performed using AI, for example, or without AI. For example, the inventory tracking unit can input user lifestyle pattern data into AI and have the AI ​​set the optimal inventory tracking timing.

[0078] The inventory tracking unit can predict inventory levels by referring to the user's purchase history. For example, the inventory tracking unit predicts inventory levels based on products that the user has frequently purchased in the past. For example, the inventory tracking unit dynamically predicts the inventory level of a specific product from the user's purchase history. The inventory tracking unit can also analyze the user's purchase patterns and calculate the optimal inventory level. This improves the accuracy of inventory level predictions by referring to the user's purchase history. Some or all of the above processes in the inventory tracking unit may be performed using AI, for example, or without AI. For example, the inventory tracking unit can input user purchase history data into AI and have the AI ​​perform inventory level predictions.

[0079] The inventory check unit can estimate the user's emotions and adjust the frequency of inventory checks based on the estimated emotions. For example, if the user is stressed, the inventory check unit can reduce the frequency of inventory checks to alleviate the user's burden. For example, if the user is relaxed, the inventory check unit can increase the frequency of inventory checks to provide more detailed inventory information. Also, if the user is in a hurry, the inventory check unit can minimize the frequency of inventory checks to quickly obtain inventory information. In this way, the user's burden can be reduced by adjusting the frequency of inventory checks according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the inventory check unit may be performed using AI, for example, or not using AI. For example, the inventory check unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of inventory check frequency.

[0080] The inventory checking unit can optimize the timing of checks by considering the rate of product consumption. For example, if the rate of product consumption is fast, the inventory checking unit will increase the frequency of inventory checks. For example, if the rate of product consumption is slow, the inventory checking unit will decrease the frequency of inventory checks. Furthermore, if the rate of product consumption fluctuates, the inventory checking unit can dynamically optimize the timing of checks. This enables efficient inventory management by optimizing the timing of checks according to the rate of product consumption. Some or all of the above processes in the inventory checking unit may be performed using AI, for example, or without AI. For example, the inventory checking unit can input product consumption rate data into AI and have the AI ​​perform the optimization of the timing of checks.

[0081] The inventory check unit can set inventory priorities based on the importance of each product. For example, the inventory check unit can prioritize inventory checks of important products to prevent stockouts. For example, the inventory check unit can postpone inventory checks of less important products to manage inventory efficiently. The inventory check unit can also dynamically set inventory priorities if the importance of each product changes. This enables efficient inventory management by setting inventory priorities according to the importance of each product. Some or all of the above processes in the inventory check unit may be performed using AI, for example, or not. For example, the inventory check unit can input product importance data into the AI ​​and have the AI ​​set the inventory priorities.

[0082] The inventory checking unit can estimate the user's emotions and adjust the inventory checking method based on the estimated emotions. For example, if the user is stressed, the inventory checking unit can provide a simple checking method to reduce the burden. For example, if the user is relaxed, the inventory checking unit can provide a detailed checking method to obtain accurate inventory information. Also, if the user is in a hurry, the inventory checking unit can provide a quick checking method to efficiently obtain inventory information. In this way, the burden on the user can be reduced by adjusting the inventory checking method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the inventory checking unit may be performed using AI, for example, or not using AI. For example, the inventory checking unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the inventory checking method.

[0083] The inventory check unit can analyze the user's daily routine and set the optimal check timing. For example, if the user is busy during the day, the inventory check unit will perform the check at night. For example, if the user has free time on the weekend, the inventory check unit will perform the check on the weekend. The inventory check unit can also dynamically set the optimal timing if the user's daily routine fluctuates. This optimizes the check timing according to the user's daily routine, enabling efficient inventory management. Some or all of the above processes in the inventory check unit may be performed using AI, or not. For example, the inventory check unit can input the user's daily routine data into the AI ​​and have the AI ​​set the optimal check timing.

[0084] The inventory checking unit can improve the accuracy of its checks by referring to the user's purchase history. For example, the inventory checking unit performs inventory checks based on products that the user has frequently purchased in the past. For example, the inventory checking unit can focus on checking the inventory of specific products based on the user's purchase history. The inventory checking unit can also analyze the user's purchase patterns and suggest the optimal inventory checking method. This improves the accuracy of the checks by referring to the user's purchase history. Some or all of the above processes in the inventory checking unit may be performed using AI, for example, or not using AI. For example, the inventory checking unit can input user purchase history data into AI and have the AI ​​perform the check accuracy improvement.

[0085] The ordering system can estimate the user's emotions and adjust the timing of the order based on those emotions. For example, if the user is stressed, the ordering system can delay the order to reduce the user's burden. For example, if the user is relaxed, the ordering system can expedite the order and provide the goods quickly. The ordering system can also place an order immediately and provide the goods quickly if the user is in a hurry. In this way, the user's burden can be reduced by adjusting the timing of the order according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the ordering system may be performed using AI, or not using AI. For example, the ordering system can input user emotion data into a generative AI and have the generative AI perform emotion estimation and order timing adjustment.

[0086] The order department can select the optimal timing for placing orders by considering price fluctuations of the products. For example, the order department can place orders when the price of a product is low to reduce costs. For example, the order department can avoid placing orders when the price of a product is high to reduce costs. The order department can also predict price fluctuations of the products and place orders at the optimal time. In this way, costs can be reduced by considering price fluctuations of the products. Some or all of the above processes in the order department may be performed using AI, for example, or not using AI. For example, the order department can input product price data into AI and have the AI ​​perform price fluctuation prediction and select the optimal timing for placing orders.

[0087] The ordering department can check the inventory status of products in real time and determine the optimal order quantity. For example, if the inventory of a product is low, the ordering department can place a larger order to prevent stockouts. For example, if the inventory of a product is high, the ordering department can place a smaller order to reduce unnecessary inventory. Furthermore, if the inventory status of a product fluctuates, the ordering department can check the inventory in real time and determine the optimal order quantity. This allows the optimal order quantity to be determined by checking the inventory status in real time. Some or all of the above processes in the ordering department may be performed using AI, for example, or not. For example, the ordering department can input product inventory data into AI and have the AI ​​perform inventory status checks and determine the optimal order quantity.

[0088] The ordering system can estimate the user's emotions and prioritize orders based on those emotions. For example, if the user is stressed, the ordering system will prioritize orders for important items. If the user is relaxed, the ordering system will distribute all items evenly. If the user is in a hurry, the ordering system can also prioritize orders for essential items. This allows for prioritizing orders based on the user's emotions, thereby ensuring that important items are prioritized. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the ordering system may be performed using AI or not. For example, the ordering system can input user emotion data into a generative AI and have the generative AI perform emotion estimation and order priority determination.

[0089] The ordering department can suggest the most suitable products by referring to the user's purchase history. For example, the ordering department can suggest the most suitable products based on the products the user has frequently purchased in the past. For example, the ordering department can focus on suggesting orders for specific products based on the user's purchase history. The ordering department can also analyze the user's purchasing patterns and suggest the most suitable products. In this way, the ordering department can suggest the most suitable products by referring to the user's purchase history. Some or all of the above processes in the ordering department may be performed using AI, for example, or not using AI. For example, the ordering department can input the user's purchase history data into AI and have the AI ​​perform the task of suggesting the most suitable products.

[0090] The order processing unit can analyze the user's daily routine and set the optimal delivery timing. For example, if the user is busy during the day, the order processing unit will deliver at night. For example, if the user has free time on the weekend, the order processing unit will deliver on the weekend. The order processing unit can also dynamically set the optimal delivery timing if the user's daily routine changes. This enables efficient delivery by setting the optimal delivery timing according to the user's daily routine. Some or all of the above processing in the order processing unit may be performed using AI, for example, or not. For example, the order processing unit can input the user's daily routine data into the AI ​​and have the AI ​​set the optimal delivery timing.

[0091] The security unit can estimate the user's emotions and adjust security settings based on those emotions. For example, if the user is stressed, the security unit can loosen security settings to reduce the user's burden. For example, if the user is relaxed, the security unit can strengthen security settings to provide a sense of security. The security unit can also quickly adjust security settings to respond efficiently if the user is in a hurry. In this way, the user's burden can be reduced by adjusting security settings according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the security unit may be performed using AI, or not using AI. For example, the security unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and security setting adjustments.

[0092] The security department can enhance security by dynamically changing the data encryption method. For example, the security department can change the encryption algorithm according to the user's access status to enhance security. For example, the security department can adjust the encryption strength according to the importance of the data to provide optimal security. The security department can also assess security risks in real time and select an appropriate encryption method. This allows for enhanced security by dynamically changing the data encryption method. Some or all of the above processes in the security department may be performed using AI, for example, or without AI. For example, the security department can input the selection of an encryption algorithm into the AI ​​and have the AI ​​perform the dynamic change of the encryption method.

[0093] The security department can monitor access logs in real time and immediately detect unauthorized access. For example, the security department can analyze access logs in real time and detect signs of unauthorized access. For example, if unauthorized access is detected, the security department can immediately issue an alert and take countermeasures. The security department can also periodically monitor access logs and detect abnormal access patterns. This allows for immediate detection of unauthorized access by monitoring access logs in real time. Some or all of the above processes in the security department may be performed using AI, for example, or not using AI. For example, the security department can input access log data into AI and have the AI ​​perform the detection of unauthorized access.

[0094] The security unit can estimate the user's emotions and adjust the frequency of security notifications based on the estimated emotions. For example, if the user is stressed, the security unit can reduce the frequency of security notifications to lessen the user's burden. For example, if the user is relaxed, the security unit can increase the frequency of security notifications and provide more detailed information. The security unit can also prioritize only important notifications if the user is in a hurry. In this way, the user's burden can be reduced by adjusting the frequency of security notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the security unit may be performed using AI or not using AI. For example, the security unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of security notification frequency.

[0095] The security department can analyze the user's device information and propose optimal security settings. For example, the security department can propose optimal security settings according to the type of device the user has. For example, the security department can analyze the user's device usage and propose appropriate security measures. The security department can also evaluate the security risks of the user's device and propose optimal settings. In this way, by analyzing the user's device information, it is possible to propose optimal security settings. Some or all of the above processes in the security department may be performed using AI, for example, or not using AI. For example, the security department can input the user's device information into AI and have the AI ​​propose optimal security settings.

[0096] The security department can monitor the user's network environment and issue alerts if an anomaly is detected. For example, the security department can monitor the user's network environment in real time and detect abnormal traffic. For example, if an anomaly is detected, the security department can immediately issue an alert and notify the user. The security department can also monitor changes in the network environment and assess security risks. This allows for immediate response if an anomaly is detected by monitoring the user's network environment. Some or all of the above processes in the security department may be performed using AI, or not. For example, the security department can input network traffic data into the AI ​​and have the AI ​​perform anomaly detection and alert generation.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The inventory management system can also include an energy consumption management unit. This unit can monitor and optimize the energy consumption of each element of the inventory management system. For example, it can monitor the energy consumption of the image analysis processing in the inventory tracking unit and adjust the frequency and timing of the processing as needed. It can also optimize the energy consumption of the periodic check processing in the inventory checking unit, improving the overall energy efficiency of the system. Furthermore, it can monitor the energy consumption of the order processing unit's integration with e-commerce sites and reduce energy consumption by placing orders at the optimal time. In this way, the inventory management system can optimize energy consumption and reduce its environmental impact.

[0099] The inventory management system can also include a health management unit that monitors the user's health status. This unit can collect user health data and incorporate it into inventory management. For example, it can analyze the user's dietary records and prioritize the inventory of products containing necessary nutrients. It can also predict the consumption rate of consumables based on the user's exercise data and adjust inventory levels accordingly. Furthermore, it can refer to the user's sleep data to optimize inventory checks and ordering timing. This allows the inventory management system to manage inventory according to the user's health status and support a healthy lifestyle.

[0100] The inventory management system can further estimate the user's emotions and adjust inventory management priorities based on those emotions. For example, if the user is stressed, the system can prioritize inventory management of important items to reduce the user's burden. If the user is relaxed, inventory management of all items can be distributed evenly. Furthermore, if the user is in a hurry, the system can prioritize inventory management of only the essential items. By adjusting inventory management priorities according to the user's emotions, the system can reduce the user's burden and achieve efficient inventory management. Emotion estimation can be achieved using, for example, an emotion engine or generative AI.

[0101] The inventory management system can further predict inventory levels by referencing the user's purchase history. For example, it can predict inventory levels based on products that the user has frequently purchased in the past, thereby preventing stockouts. It can also dynamically predict the inventory level of specific products based on the user's purchase history. Furthermore, it can analyze the user's purchasing patterns and calculate the optimal inventory level. As a result, by referring to the user's purchase history, the accuracy of inventory predictions is improved, enabling efficient inventory management. Some or all of the above-described processes in the inventory tracking unit may be performed using AI, for example, or without AI.

[0102] The inventory management system can further estimate the user's emotions and adjust the frequency of inventory checks based on those emotions. For example, if the user is stressed, the frequency of inventory checks can be reduced to lessen the user's burden. Conversely, if the user is relaxed, the frequency of inventory checks can be increased to provide more detailed inventory information. Furthermore, if the user is in a hurry, the frequency of inventory checks can be minimized to quickly obtain inventory information. In this way, by adjusting the frequency of inventory checks according to the user's emotions, the user's burden can be reduced and efficient inventory management can be achieved. Emotion estimation can be achieved, for example, using an emotion engine or generative AI.

[0103] The inventory management system can further analyze the user's daily routine and set the optimal inventory check timing. For example, if the user is busy during the day, the inventory check can be performed at night. Similarly, if the user has free time on weekends, the inventory check can be performed on weekends. Furthermore, if the user's daily routine fluctuates, the optimal timing can be dynamically set. This allows for efficient inventory management by optimizing the timing of inventory checks according to the user's daily routine. Some or all of the above-described processes in the inventory check section may be performed using AI, for example, or without AI.

[0104] The inventory management system can further estimate the user's emotions and adjust the timing of orders based on those emotions. For example, if a user is stressed, the system can delay the order to reduce the user's burden. Conversely, if a user is relaxed, the system can expedite the order to provide goods quickly. Furthermore, if a user is in a hurry, the system can place an order immediately for quick delivery. By adjusting the timing of orders according to the user's emotions, the system can reduce the user's burden and achieve efficient inventory management. Emotion estimation can be achieved using, for example, an emotion engine or generative AI.

[0105] The inventory management system can further suggest optimal products by referencing the user's purchase history. For example, it can suggest the best products based on items the user has frequently purchased in the past, preventing stockouts. It can also prioritize suggesting orders for specific products based on the user's purchase history. Furthermore, it can analyze the user's purchasing patterns and suggest the best products. This enables efficient inventory management by suggesting optimal products based on the user's purchase history. Some or all of the above processes in the order department may be performed using AI, for example, or not.

[0106] The inventory management system can further estimate the user's emotions and adjust security settings based on those emotions. For example, if the user is stressed, security settings can be relaxed to reduce the user's burden. Conversely, if the user is relaxed, security settings can be strengthened to provide a sense of security. Furthermore, if the user is in a hurry, security settings can be quickly adjusted to respond efficiently. In this way, by adjusting security settings according to the user's emotions, the burden on the user can be reduced and inventory management can be made more efficient. Emotion estimation can be achieved using, for example, an emotion engine or generative AI.

[0107] The inventory management system can also monitor the user's network environment and issue alerts if an anomaly is detected. For example, it can monitor the user's network environment in real time and detect abnormal traffic. If an anomaly is detected, it can immediately issue an alert and notify the user. Furthermore, it can monitor changes in the network environment and assess security risks. This allows for immediate response to detected anomalies by monitoring the user's network environment, enabling efficient inventory management. Some or all of the above processes in the security department may be performed using AI, for example, or without AI.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The inventory tracking department is responsible for helping consumers keep track of their home inventory. For example, the inventory tracking department can analyze images taken with a smartphone to determine product types and quantities. It uses image recognition algorithms to identify each product in the image and count their quantities. It can also automatically classify product types using machine learning models. Furthermore, it can supplement inventory information manually entered by consumers. For example, consumers can enter inventory information through a smartphone app. Step 2: The Inventory Check Department is responsible for regularly checking inventory based on the inventory status tracked by the Inventory Management Department. The Inventory Check Department can check inventory status at various frequencies, such as daily, weekly, or monthly. It checks inventory status at a fixed time each day and issues alerts as needed. It can also learn consumer spending patterns and automatically set the optimal check frequency. Based on past consumption data, it analyzes consumer spending patterns and optimizes the timing of inventory checks. Step 3: The Order Department is responsible for automatically placing orders based on the inventory status checked by the Inventory Check Department. The Order Department can, for example, integrate with e-commerce sites and place orders considering delivery dates, purchase amounts, and shipping costs. It can select the best conditions from multiple e-commerce sites and place orders. It can also suggest optimal products based on the consumer's past purchase history. It prioritizes ordering products that consumers frequently purchase. Step 4: The Security Department is responsible for ensuring that data is not transmitted externally and for enhancing privacy protection. The Security Department can enhance privacy protection using methods such as data encryption, access control, and data anonymization. Consumer personal information can be encrypted to prevent unauthorized access from external sources. It can also anonymize consumer data to protect privacy.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements described above, including the inventory tracking unit, inventory checking unit, order unit, and security unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the inventory tracking unit uses the camera 42 of the smart device 14 to photograph the consumer's home inventory, and the control unit 46A of the smart device 14 analyzes the image to determine the type and quantity of goods in stock. The inventory checking unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which periodically checks the inventory status and issues alerts as needed. The order unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically places orders in cooperation with e-commerce sites. The security unit is implemented by, for example, the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing unit 12, which performs data encryption and access control. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the inventory tracking unit, inventory checking unit, order unit, and security unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the inventory tracking unit uses the camera 42 of the smart glasses 214 to photograph the consumer's home inventory, and the control unit 46A of the smart glasses 214 analyzes the image to determine the type of product and the quantity in stock. The inventory checking unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which periodically checks the inventory status and issues alerts as needed. The order unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which automatically places orders in cooperation with e-commerce sites. The security unit is implemented, for example, by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing unit 12, which performs data encryption and access control. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the inventory tracking unit, inventory checking unit, order unit, and security unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the inventory tracking unit uses the camera 42 of the headset terminal 314 to photograph the consumer's home inventory, and the control unit 46A of the headset terminal 314 analyzes the image to determine the type of product and the quantity in stock. The inventory checking unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which periodically checks the inventory status and issues alerts as needed. The order unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically places orders in cooperation with e-commerce sites. The security unit is implemented by, for example, the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12, which performs data encryption and access control. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the inventory tracking unit, inventory checking unit, order unit, and security unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the inventory tracking unit uses the camera 42 of the robot 414 to photograph the consumer's home inventory, and the control unit 46A of the robot 414 analyzes the image to determine the type of product and the quantity in stock. The inventory checking unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which periodically checks the inventory status and issues alerts as needed. The order unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically places orders in cooperation with e-commerce sites. The security unit is implemented by, for example, the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing unit 12, which performs data encryption and access control. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0163] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] 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.

[0173] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) The inventory management department is responsible for keeping track of inventory, Based on the inventory status grasped by the aforementioned inventory tracking unit, an inventory checking unit periodically checks the inventory, An ordering unit that automatically places orders based on the inventory status checked by the aforementioned inventory checking unit, It includes a security unit that prevents data from being transmitted externally and enhances privacy protection. A system characterized by the following features. (Note 2) The aforementioned inventory tracking unit is By analyzing images taken with a smartphone, the product type and inventory level can be determined. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned inventory checking unit is Based on the inventory status tracked by the inventory management department, inventory is checked regularly. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned ordering section is, We collaborate with e-commerce sites to process orders by comprehensively considering delivery date, purchase amount, and shipping costs. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned security unit is This ensures that data is not transmitted externally and enhances privacy protection. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned inventory tracking unit is The system estimates user sentiment and adjusts the frequency of inventory checks based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned inventory tracking unit is Inventory levels are predicted by considering the frequency of product use. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned inventory tracking unit is Adjust inventory levels considering product expiration dates. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned inventory tracking unit is The system estimates user sentiment and determines inventory priorities based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned inventory tracking unit is Analyze users' lifestyle patterns and set the optimal timing for inventory management. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned inventory tracking unit is Predict inventory levels by referencing user purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned inventory checking unit is The system estimates the user's emotions and adjusts the frequency of inventory checks based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned inventory checking unit is Optimize the timing of checks by considering the rate at which products are consumed. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned inventory checking unit is Prioritize checks based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned inventory checking unit is The system estimates user sentiment and adjusts inventory check methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned inventory checking unit is Analyze the user's daily routine and set the optimal check-in time. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned inventory checking unit is Improve the accuracy of checks by referencing the user's purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned ordering section is, It estimates the user's emotions and adjusts the timing of orders based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned ordering section is, We select the optimal timing for ordering by taking into account fluctuations in product prices. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned ordering section is, Check product availability in real time and determine the optimal order quantity. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned ordering section is, It estimates the user's emotions and determines order priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned ordering section is, We suggest the most suitable products by referring to the user's purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned ordering section is, Analyze the user's daily routine and set the optimal delivery time. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned security unit is It estimates the user's emotions and adjusts security settings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned security unit is Enhance security by dynamically changing the data encryption method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned security unit is It monitors access logs in real time and detects unauthorized access immediately. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned security unit is It estimates the user's sentiment and adjusts the frequency of security notifications based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned security unit is It analyzes the user's device information and suggests optimal security settings. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned security unit is It monitors the user's network environment and issues alerts if an anomaly is detected. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The inventory management department is responsible for keeping track of inventory, Based on the inventory status grasped by the aforementioned inventory tracking unit, an inventory checking unit periodically checks the inventory, An ordering unit that automatically places orders based on the inventory status checked by the aforementioned inventory checking unit, It includes a security unit that prevents data from being transmitted externally and enhances privacy protection. A system characterized by the following features.

2. The aforementioned inventory tracking unit is By analyzing images taken with a smartphone, the product type and inventory level can be determined. The system according to feature 1.

3. The aforementioned inventory checking unit is Based on the inventory status as determined by the aforementioned inventory tracking unit, the inventory is checked periodically. The system according to feature 1.

4. The aforementioned ordering section is, We collaborate with e-commerce sites to process orders by comprehensively considering delivery date, purchase amount, and shipping costs. The system according to feature 1.

5. The aforementioned security unit is This ensures that data is not transmitted externally and enhances privacy protection. The system according to feature 1.

6. The aforementioned inventory tracking unit is The system estimates user sentiment and adjusts the frequency of inventory checks based on the estimated sentiment. The system according to feature 1.

7. The aforementioned inventory tracking unit is Inventory levels are predicted by considering the frequency of product use. The system according to feature 1.

8. The aforementioned inventory tracking unit is Adjust inventory levels considering product expiration dates. The system according to feature 1.

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