system

Abstract

We provide the system. [Solution] A means for monitoring inventory in a warehouse in real time using a detection device and updating the information in an information storage unit, A method for forecasting supply and demand using AI technology based on past sales and inventory information, A means for receiving order information from users and optimizing the order of item collection, A means for collecting items according to an optimized item collection sequence using an autonomous flight device, A means of calculating the optimal transport route and sending instructions to an autonomous flight device, Means for monitoring the progress of transportation, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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 warehouse operations, there may be delays and inaccuracies in inventory management, resulting in problems such as deterioration of picking efficiency and delays in delivery. Furthermore, there is an issue that the logistics cost increases due to urgent orders and shortages or surpluses of inventory. In addition, in the current system, due to the limitations of human resources and the risk of work mistakes, a more efficient and reliable automated system is required.

Means for Solving the Problems

[0005] This invention proposes a system that provides an AI algorithm for real-time monitoring of warehouse inventory and supply and demand forecasting based on past sales data. This aims to improve the efficiency and accuracy of inventory management. Furthermore, it provides a means to instantly process user order information and realize optimized picking and delivery using autonomous unmanned aerial vehicles (UAVs). The autonomous UAVs transport goods to designated delivery points via the optimal route, enabling rapid logistics and achieving improved work efficiency and cost reduction. In addition, by using generative AI to integrate and manage each process, overall log management and situation monitoring are performed, improving the fault tolerance and flexibility of the system.

[0006] A "warehouse" is a facility used in logistics operations for storing, managing, and handling goods.

[0007] "Inventory" refers to the quantity and types of goods stored in a warehouse.

[0008] A "sensor" is a device that can detect physical phenomena (such as temperature, humidity, and motion) and output them as digital signals.

[0009] A "database" is a system that organizes and stores digital information so that it can be efficiently searched and used later.

[0010] An "algorithm" is a set of steps or calculation methods used to solve a specific problem.

[0011] "AI (Artificial Intelligence)" is a type of computer system that imitates human intellectual behavior and performs learning and reasoning.

[0012] "Order information" refers to information such as the name, quantity, and delivery address of the products that the customer wishes to purchase.

[0013] "Picking" refers to the process of taking goods out of a warehouse and preparing them for sale.

[0014] An "autonomous unmanned aircraft" is an unmanned aerial vehicle that can automatically fly based on a pre-programmed route and has the ability to perform specific tasks.

[0015] A "delivery route" refers to the selected sequence of roads for transporting goods to the destination.

[0016] "Generative AI" is a type of artificial intelligence that mimics large-scale datasets to generate new data or make independent judgments in a virtual environment.

Brief Explanation of Drawings

[0017] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Embodiments for Carrying out the Invention

[0018] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the language used in the following description will be explained.

[0020] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one 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), etc.

[0021] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] 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.

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] As shown in Figure 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.

[0028] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0030] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0032] 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.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] The 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.

[0036] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0037] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0038] This invention presents an embodiment of a picking and delivery system using autonomous unmanned aerial vehicles to automate warehouse inventory management and achieve an efficient logistics process. The main components of the system and their respective roles are described below.

[0039] First, the server acquires inventory information from sensors installed in the warehouse. This makes it possible to constantly know in real time which shelf each product is on and how much of it is available. The sensor data is aggregated on the server and stored in a database. This database also includes information such as past order history and seasonal fluctuations, and an AI algorithm predicts supply and demand based on this data.

[0040] The terminal is responsible for receiving order information from users and transferring it to the server. For example, when a user orders products from an online store, the order information is sent to the server via the terminal. This information includes the type and quantity of products ordered, the delivery address, and other details.

[0041] Upon receiving the order information, the server compares it with inventory data and calculates which items should be picked and in what order. An optimization algorithm is used in the calculation, enabling the autonomous drone to pick items along an efficient route. The server then transmits instructions for the picking order to the autonomous drone, controlling its movements.

[0042] The autonomous drone (UAV) flies autonomously through the warehouse based on instructions from the server, picking the specified items. After picking is complete, the server calculates the optimal delivery route and issues instructions to the autonomous drone. The autonomous drone then follows this route, quickly and safely transporting the items to the delivery point. After delivery is complete, the autonomous drone either takes on its next mission or returns to the charging station if necessary.

[0043] As a concrete example, when a user orders a specific product, the server already knows the product's location in the warehouse. Based on the product's location data, the server assembles instructions for an autonomous drone and sends a command to retrieve the product via the shortest route. In this way, logistics are accelerated and made more efficient. Furthermore, the server uses continuously generated AI to comprehensively manage each process and optimize the overall operation of the warehouse.

[0044] Thus, the embodiments of the present invention make it possible to perform warehouse inventory management and logistics processes with high efficiency.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] The server acquires inventory information in real time from sensors installed within the warehouse. The acquired data is stored in a database, keeping inventory quantities and product locations constantly up-to-date.

[0048] Step 2:

[0049] The server uses an AI algorithm to predict future supply and demand based on past order history and seasonal fluctuation data stored in the database. This analysis allows for inventory replenishment planning and improved picking efficiency.

[0050] Step 3:

[0051] Users order products through the online store. Order information includes product name, quantity, and shipping address, and is sent to the server by the device.

[0052] Step 4:

[0053] The server compares the order information received from the terminal with the inventory data in the database. Based on this, it calculates the optimal order for picking the ordered items and sends instructions to the autonomous unmanned aerial vehicle.

[0054] Step 5:

[0055] The autonomous unmanned aerial vehicle (UAV) flies through the warehouse following instructions from a server and picks the specified items. Each item is detected by sensors, and the UAV automatically grasps and moves the items.

[0056] Step 6:

[0057] The server calculates the optimal delivery route based on delivery address information and current traffic conditions. The calculated delivery route is then transmitted to an autonomous unmanned aerial vehicle (UAV).

[0058] Step 7:

[0059] The autonomous unmanned aerial vehicle (UAV) transports goods to designated delivery locations based on delivery routes received from a server. After delivery is complete, it waits for instructions to pick new orders or returns to a charging station if necessary.

[0060] (Example 1)

[0061] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0062] In today's environment, where efficient inventory management and logistics processes are crucial, obtaining accurate real-time inventory information and planning optimal picking and delivery is challenging. This leads to wasted costs and time due to excess inventory and stockouts. Furthermore, inaccurate demand forecasts and inability to respond quickly can result in decreased customer satisfaction.

[0063] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0064] In this invention, the server includes means for monitoring items in a warehouse in real time using a detection device and updating information in a data storage device, means for performing supply and demand forecasts using a computational model based on historical data and item information, and means for receiving request information from users and optimizing the order of item collection. This enables efficient inventory management and a rapid logistics process based on real-time monitoring and accurate demand forecasting.

[0065] A "detection device" is a device that recognizes the location and quantity of items in real time and transmits the information to a server.

[0066] A "data storage device" is a device used by a server to store inventory information, order data, and supply and demand forecast data.

[0067] "Historical data" refers to data such as past order history and sales data that is used to forecast supply and demand.

[0068] A "computational model" is a mathematical model that uses machine learning algorithms to predict supply and demand from historical data and inventory information.

[0069] "User" refers to anyone who uses the system to order goods from the warehouse.

[0070] "Request information" refers to information including the details and conditions of an order sent from the user to the server.

[0071] The "item collection order" is a sequence designed to efficiently retrieve items, and is determined by an optimization algorithm.

[0072] An "autonomous aircraft" is an unmanned aircraft that flies autonomously within a warehouse based on instructions from a server to collect or transport goods.

[0073] A "transport route" is the optimal route for an autonomous aircraft to transport goods, and it is derived through calculation.

[0074] "Generative artificial intelligence" is a technology that enables efficient system management by comprehensively analyzing inventory data and conditions within a warehouse in order to optimize warehouse operations.

[0075] This invention is a system using autonomous aircraft to streamline inventory management and logistics processes within warehouses. The following describes embodiments of this system.

[0076] First, the server collects information in real time from detection devices installed within the warehouse. These detection devices use RFID tags and barcode scanners to identify the location and quantity of items. The server stores this information in a data storage device, ensuring that it always maintains the most up-to-date inventory information.

[0077] Next, the server uses a computational model to forecast supply and demand based on historical data and item information stored in the data storage device. This computational model utilizes machine learning algorithms to predict future demand. This makes it possible to mitigate the risk of inventory shortages or surpluses.

[0078] The terminal is responsible for receiving request information from users. For example, when a user orders goods online, the terminal sends the details of the order, quantity, and delivery address to the server. Based on this information, the server performs calculations to optimize the order in which the goods are collected. The collection order is determined by an optimization algorithm, which supports the efficient operation of autonomous aircraft.

[0079] The optimized collection order and transport route are instructed from the server to the autonomous aircraft. Based on these instructions, the aircraft autonomously moves within the warehouse and collects the specified items. After collection is complete, the aircraft transports the items to their destination according to the optimal transport route.

[0080] As a concrete example, when a user orders a new product, the server already knows where that product is located. The server then transmits the exact location and collection order of the items to an autonomous aircraft, instructing it to collect them via the shortest route. This allows for faster and more efficient logistics.

[0081] The generative artificial intelligence manages the entire logistics process in an integrated manner, optimizing each step. A specific example of a prompt in this system would be: "Retrieve all inventory data in the warehouse and calculate which item should be picked next."

[0082] This invention significantly improves the efficiency of inventory management and logistics processes in warehouses.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The server acquires inventory information in real time from detection devices within the warehouse. The input is sensor data representing the location and quantity of each item. The server analyzes this data, performs data processing to identify the location and quantity of each item, and records the results in a data storage device. This allows the server to constantly monitor the inventory status in the warehouse.

[0086] Step 2:

[0087] The server uses historical data stored in a data storage device and current inventory data as input to perform supply and demand forecasting using a generative AI model. The AI ​​model learns from this data and performs data calculations to predict future demand. The output is the demand forecast result, which the server uses to plan the next steps.

[0088] Step 3:

[0089] The terminal receives order information from the user. The input is order data including the type of product, quantity, and delivery address. The terminal then transfers this information to the server. Based on the received order information, the server calculates the priority for collecting the items and re-evaluates the inventory status as needed.

[0090] Step 4:

[0091] The server matches order information with inventory data and uses an optimization algorithm to calculate the order in which to collect items. The input is order information and inventory data, and the output is the efficient collection order. The server generates this order and prepares it as instructions for autonomous aircraft.

[0092] Step 5:

[0093] The server transmits an optimized collection order to the autonomous aircraft. Based on the instructions from the server, the aircraft autonomously flies through the warehouse and begins collecting the specified items. The aircraft's sensors are used to send feedback data back to the server to verify picking accuracy.

[0094] Step 6:

[0095] After data collection is complete, the server calculates the most efficient transport route. Traffic and weather data are also considered as input, and the optimal route is output. The server transmits this route to the autonomous aircraft, which immediately begins transport operations.

[0096] Step 7:

[0097] Autonomous aircraft follow designated transport routes and deliver goods to their destinations. Once transport is complete, the aircraft awaits further instructions from the server and returns to a charging station if necessary. This process ensures that logistics are consistently fast, safe, and efficient.

[0098] (Application Example 1)

[0099] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0100] Managing warehouse inventory and streamlining logistics processes has presented various challenges with traditional methods. These include delays and errors due to manual inventory updates, insufficient accuracy in supply and demand forecasts, and increased time and costs due to manual picking and delivery. There is a need to solve these problems and accelerate and streamline logistics.

[0101] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0102] In this invention, the server includes means for monitoring warehouse inventory in real time using a detection device and updating information in an information storage unit, means for performing supply and demand forecasting using AI technology based on past sales information and inventory information, and means for receiving order information from users and optimizing the order of item collection. This enables the automation and efficiency of inventory management and logistics processes in a logistics center.

[0103] A "detection device" is a device used to measure the inventory status in a warehouse in real time and collect information.

[0104] The "information storage unit" is a storage device used to hold and manage acquired inventory data.

[0105] "AI technology" refers to technology that uses artificial intelligence to analyze data and support decision-making for supply and demand forecasting and efficient business operations.

[0106] An "autonomous flying device" is a flying device that has the ability to pick and deliver items in a specified order without external intervention.

[0107] A "transportation route" is the planned path for transporting picked goods to their destination.

[0108] "Transportation progress status" refers to information indicating the position of goods along the transportation route and how far they have traveled towards their destination.

[0109] The system for implementing this invention is a system that automates inventory management and logistics processes within a warehouse. First, a server uses a detection device to monitor the inventory in the warehouse in real time and updates this information in the information storage unit. As a result, detailed information such as the location and quantity of goods is always accurate.

[0110] The server utilizes AI technology to forecast supply and demand based on past sales data and current inventory information. This forecast clearly identifies the types and quantities of goods that will be needed next. Based on this information, the server determines the optimal order for collecting items.

[0111] When a customer places an order, the server receives the order information and optimizes the order in which items are collected. The autonomous flight system follows instructions from the server, flying through the warehouse along an efficient route to pick the goods. The server also calculates the optimal transport route and sends instructions to the autonomous flight system.

[0112] The progress of shipments is constantly monitored by servers to ensure that goods are delivered quickly and safely to their destination. Generative AI is used to comprehensively manage warehouse conditions and inventory awareness, thereby improving the efficiency of the entire business process.

[0113] For example, when a user orders a specific electronic product, the server determines the location of the product in the warehouse and uses an autonomous flight device to direct the user to the shortest route. Another example of a prompt used for the generated AI is, "Predict the amount of inventory needed for the next two weeks and display it along with the current inventory status."

[0114] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0115] Step 1:

[0116] The server uses detection devices to acquire inventory data from the warehouse in real time. This provides input information such as the location and quantity of each product. The server updates its information storage unit with this data to gain an overall understanding of the warehouse's inventory status.

[0117] Step 2:

[0118] The server receives historical sales information and current inventory information as input and uses AI technology to perform supply and demand forecasting. It processes the data through time series analysis and trend analysis to predict future demand and records the results as output.

[0119] Step 3:

[0120] The terminal receives order information from the user and transmits it to the server. Based on this order information, the server uses AI technology to calculate the optimal order for collecting items. This clearly instructs the user which items should be collected and in what order.

[0121] Step 4:

[0122] The server transmits the calculated item collection order to the autonomous flight device. The flight device receives instructions from the server and picks items while flying along an efficient route. Here, the optimization of the flight path is performed as a data calculation.

[0123] Step 5:

[0124] Once picking is complete, the server calculates the optimal transport route for the autonomous flight device and issues instructions. This calculation selects the delivery route and instructs the device to ensure safe and rapid delivery.

[0125] Step 6:

[0126] The server constantly monitors the progress of the transport and verifies that the aircraft is following the designated route precisely. Once the transport is complete, it records the information as output and prepares for the next mission.

[0127] Step 7:

[0128] Using generation AI, the server analyzes warehouse conditions and inventory information, and manages the entire process in an integrated manner. This allows for adjustments to improve overall operational efficiency. By using prompts such as, "Predict the inventory needed for the next two weeks and display it along with the current inventory status," continuous improvement becomes possible.

[0129] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0130] This invention presents an embodiment of a system that, in addition to automating warehouse inventory management and logistics processes, recognizes user emotions and provides a personalized experience. The main components of the system and their respective functions are described below.

[0131] The server acquires real-time inventory information from sensors installed in the warehouse and stores it in a database. This ensures that the quantity and location information of products are always up-to-date. Furthermore, based on past sales data and current inventory data, an AI algorithm is used to forecast supply and demand, thereby improving the efficiency of product supply.

[0132] The terminal receives order information sent by the user through the online store and forwards it to the server. In addition to the product name, quantity, and shipping address, the order information is analyzed by an emotion engine based on the user's input. The emotion engine analyzes the user's voice tone, input speed, selected words, etc., and evaluates their emotional state.

[0133] Based on the analysis results, the server dynamically adjusts the ordering process. For example, if it determines that the user is experiencing dissatisfaction or stress, the server adjusts the priority of autonomous drones and optimizes thermal processing to quickly process the user's order. Additionally, an emotion engine analyzes the user's preferences and provides personalized product suggestions.

[0134] As a benefit of the system, users can receive more personalized product recommendations and appropriate support. For example, special offers and new product information are presented in an emotionally resonant way. This improves the user experience.

[0135] Autonomous unmanned aerial vehicles (UAVs) operate based on instructions from a server during the picking and delivery process. They pick goods according to optimized routes and efficiently transport them to delivery locations.

[0136] As a concrete example, when a user selects products online, the emotion engine suggests related products that the user might be interested in. Once the order is complete, the server comprehensively manages the logistics flow, including referencing the results of the emotion engine in addition to the normal process and performing high-priority processing.

[0137] This system can improve not only the accuracy of inventory management and the efficiency of logistics, but also the user experience.

[0138] The following describes the processing flow.

[0139] Step 1:

[0140] The server retrieves inventory information in real time from sensors within the warehouse. The retrieved data is stored in a database, keeping the quantity and location information of products constantly up-to-date.

[0141] Step 2:

[0142] The server uses an AI algorithm to forecast supply and demand based on historical sales data stored in the database and current inventory data. This forecast makes it possible to create an appropriate inventory replenishment plan.

[0143] Step 3:

[0144] Users select products and enter order information through the online store. During this process, the emotion engine analyzes the user's input speed and word choices to evaluate their emotional state.

[0145] Step 4:

[0146] The terminal receives order information from the user and sends it to the server along with emotion engine data. The server then optimizes the order process based on this information.

[0147] Step 5:

[0148] The server takes the user's emotional state into consideration and adjusts order priorities accordingly. For example, if a user expresses dissatisfaction, it instructs the autonomous drone to prioritize processing that order.

[0149] Step 6:

[0150] The server calculates the optimal picking order and sends instructions to the autonomous drone. The autonomous drone flies around the warehouse according to the instructions and picks the specified items.

[0151] Step 7:

[0152] The server calculates the optimal delivery route and transmits it to the autonomous drone. The autonomous drone follows the route and quickly delivers the goods to the user.

[0153] Step 8:

[0154] Once the autonomous drone completes its delivery, it awaits further instructions or returns to the station for recharging if necessary. The server records the entire process and uses it to plan future deliveries.

[0155] (Example 2)

[0156] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0157] Modern logistics systems demand improved accuracy in inventory management, more efficient supply and demand forecasting, greater flexibility in order processing, and a better user experience. In particular, real-time inventory tracking, dynamic order processing, and personalized experiences based on sentiment data are key challenges. Addressing these issues comprehensively is crucial for optimizing logistics and enhancing the user experience.

[0158] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0159] In this invention, the server includes means for monitoring warehouse inventory in real time using a measuring device and updating information in an information recording device; means for performing supply and demand forecasting using a machine learning algorithm based on past transaction data and inventory data; means for receiving order information from users and optimizing the picking order; means for analyzing the emotional state of users; means for dynamically adjusting order processing based on the analysis results; and means for providing personalized product suggestions. This enables improved accuracy in inventory management, increased efficiency in logistics processes, and an enhanced user experience.

[0160] A "measuring device" is a device that detects goods and environmental conditions in a warehouse in real time and provides that data.

[0161] An "information recording device" is a device that stores data acquired from measuring devices and organizes it for later analysis and use.

[0162] "Transaction data" is a collection of information about past sales and purchases, and is fundamental information used for supply and demand forecasting.

[0163] A "machine learning algorithm" is a mathematical model used by computer systems to learn patterns from past data and predict future supply and demand.

[0164] "Users" refers to customers or users who order products and receive services through the system.

[0165] "Picking sequence" refers to the efficient order or route for collecting goods within a warehouse.

[0166] An "autonomous transport device" is a device that automatically travels along a designated route under programmatic control, picking and delivering goods.

[0167] "Emotional state" refers to the user's current psychological state, which the system analyzes from the user's input and behavior.

[0168] "Personalized product recommendations" is a method of presenting products and services that are best suited to a particular user based on their interests and preferences.

[0169] This system integrates multiple advanced technologies to optimize warehouse inventory management and logistics.

[0170] The system primarily involves servers, terminals, and users, and its overall efficiency is achieved through the coordinated actions of each component.

[0171] The server acquires inventory data in real time from measuring devices placed within the warehouse and stores it in an information recording device. Various sensors are used to monitor details such as the number of items, shelf location, and storage conditions. This data is recorded in a database and forms the basis for subsequent supply and demand forecasts. When using machine learning algorithms, libraries such as TENSORFLOW® and PyTorch are used to perform supply and demand forecasts based on transaction data. This is to create an appropriate supply plan for goods and prevent stockouts and surpluses.

[0172] The terminal receives order information sent by users via the online store. This order information includes details such as product name, quantity, and shipping address. The terminal also uses an emotion analysis engine to analyze user input data. This analysis includes voice data and input speed, and utilizes natural language processing technologies such as Hugging Face's Transformers. The analysis results are sent to a server and used to adjust dynamic order processing.

[0173] As a concrete example, the server optimizes the picking order based on data analysis results and issues instructions to the autonomous transport system. This allows the autonomous transport system to follow the optimized route and pick items quickly and accurately. This entire process significantly improves logistics efficiency.

[0174] Furthermore, users receive personalized product recommendations based on sentiment analysis results. For example, if a user shows interest in products in a specific category, related products are automatically recommended. This allows users to have a better purchasing experience.

[0175] An example of a prompt used to control a generative AI model is, "Tell me how to improve the user experience using sentiment analysis in online store orders." Using this prompt, the system will generate specific strategies to enhance the user experience.

[0176] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0177] Step 1:

[0178] The server acquires inventory data in real time from measuring devices placed within the warehouse. Its inputs include receiving sensor signals from each measuring device to obtain product counts, location information, and environmental data. Specifically, the server integrates these signals and writes the data to an information recording device. The output is stored in a database as the latest inventory data.

[0179] Step 2:

[0180] The server uses machine learning algorithms to perform supply and demand forecasting based on inventory and transaction data stored in the database. This historical data is used as input. The server utilizes TensorFlow and PyTorch libraries to apply the algorithms and predict future supply and demand. The output generates supply and demand forecast data, which is used to optimize supply plans.

[0181] Step 3:

[0182] The terminal receives order information submitted by the user from the online store. The input includes the user's specified product name, quantity, and shipping address. The received order information is then forwarded to the server. Specifically, the terminal sends this information to the server as input and begins order processing. The output is used by the server as accurate order information.

[0183] Step 4:

[0184] The device analyzes user input using an emotion analysis engine. This input includes user voice data, text input speed, and selected words. The device processes this data using the emotion analysis engine to analyze the user's emotional state. Specifically, the device generates analysis results and outputs them to the server as emotion data.

[0185] Step 5:

[0186] The server dynamically adjusts order processing using sentiment analysis results. Input includes sentiment analysis results and user order information. Based on this data, the server instructs the automated guided vehicles (AGVs) to adjust the picking order. Specifically, it generates picking instructions in order of priority and sends them to the AGVs. The output is an optimized picking plan.

[0187] Step 6:

[0188] The server generates personalized product recommendations based on the user's preference data. Input includes the user's past purchase history and sentiment analysis results. The server analyzes this data and prepares relevant product information. Specifically, it creates and outputs suggestions for similar or recommended products to the user.

[0189] Step 7:

[0190] The autonomous transport system retrieves goods according to an optimized picking sequence and moves them to the delivery point. Inputs include picking and delivery instructions from a server. The transport system automatically follows a designated route, efficiently picking goods. Specifically, it follows a path from the collection point to the destination, delivering the goods to the final delivery point as output.

[0191] (Application Example 2)

[0192] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0193] In recent years, there has been a growing demand for streamlining warehouse inventory management and logistics processes. However, consistently managing inventory in real time, forecasting supply and demand, and calculating optimal delivery routes remains challenging. Furthermore, improving the user experience requires analyzing customer emotions and providing personalized services, but few systems can achieve this efficiently and effectively. To address these challenges, there is a need for comprehensive inventory management and logistics systems that include dynamic adjustments to support content based on emotional states.

[0194] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0195] In this invention, the server includes means for monitoring warehouse inventory in real time using a detection device and updating information in a database; means for forecasting supply and demand using an artificial intelligence algorithm based on past sales data and inventory data; means for receiving customer order information and optimizing the order of product selection; means for calculating the optimal delivery route and sending instructions to an autonomous aircraft; and means for analyzing the customer's emotional state and dynamically adjusting the support content based on that emotional state. This makes it possible to improve not only the efficiency of inventory management and logistics processes but also the user experience.

[0196] A "detection device" is a device used to monitor the inventory status in a warehouse in real time.

[0197] A "database" is an information management system that stores inventory information acquired from detection devices and performs searches and updates as needed.

[0198] An "artificial intelligence algorithm" is a computational method that analyzes past sales and inventory data to predict supply and demand.

[0199] "Selection order" refers to the sequence setting used to optimize the order in which products are picked up based on customer order information.

[0200] An "autonomous flying machine" is an unmanned aerial vehicle that sorts goods according to an optimized sorting sequence and delivers those goods using the most suitable delivery route.

[0201] "Customer emotional state" refers to information that forms the basis for providing individualized service by analyzing and evaluating the customer's emotions at the time of ordering.

[0202] "Support content" refers to the methods and content of service and product delivery that are dynamically adjusted according to the customer's emotional state.

[0203] The server monitors inventory information in real time using detection devices installed within the warehouse and updates this information in a database. Specifically, the detection devices include various types of sensors, which are typically used to determine the location and quantity of inventory. For database management, SQL or NoSQL databases capable of real-time updates are commonly used.

[0204] Based on this information, the server analyzes past sales data and uses artificial intelligence algorithms to predict supply and demand. Machine learning libraries such as TensorFlow and PyTorch may be used in the software. This is expected to optimize inventory and improve the accuracy of sales forecasts.

[0205] The terminal processes order information received from customers and optimizes the sorting order of products. This process considers the product name, quantity, and delivery address information, and the sorting order is instructed from the server to the autonomous flying machine. The flying machine is equipped with GPS and an IMU (Inertial Measurement Unit) to automatically calculate and navigate the optimal delivery route.

[0206] The user's emotional state is determined by the device analyzing input information (such as voice commands and touch speed). The server then uses this information to dynamically adjust the support provided. This allows for immediate support or the suggestion of special services if the user is feeling anxious.

[0207] As a concrete example, the server queries the generation AI model through a prompt message, sending a request such as, "Please tell me the best response to reduce user stress." As a result, a customized response that matches the user's emotional state is proposed, and appropriate notifications or coupons can be issued if there is a delay in product delivery.

[0208] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0209] Step 1:

[0210] The server retrieves inventory information from detection devices in the warehouse and updates the database. The input is real-time data from the sensors, and the output is the latest database state, including inventory quantity and location information. The data is processed using SQL queries and written to the database.

[0211] Step 2:

[0212] The server uses historical sales data and current inventory data to run an artificial intelligence algorithm and predict supply and demand. The input is sales history and inventory data, which are analyzed using machine learning tools such as TensorFlow. The output is predicted future supply and demand data. Based on this prediction, inventory optimization becomes possible.

[0213] Step 3:

[0214] The terminal transfers order information received from the user to the server and uses this information to optimize the product sorting order. The input is order information (product name, quantity, delivery address), and the output is a list of the optimized sorting order. In this process, an algorithm calculates priorities to streamline the selection process.

[0215] Step 4:

[0216] The server transmits an optimized sorting sequence as instructions to the autonomous flying machine. The input is sorting sequence data, and the output is a movement command to the flying machine. In this process, a route plan based on GPS coordinates is created and instructions are sent to the flying machine to ensure accurate picking and delivery of goods.

[0217] Step 5:

[0218] The terminal processes user input information (voice tone, input speed) using an emotion analysis engine to determine the customer's emotional state. The input is user interaction data, and the output is the analyzed emotional state. This allows the system to begin providing services tailored to the user's emotions.

[0219] Step 6:

[0220] The server sends prompt messages to a generating AI model based on the analyzed emotional state, and determines dynamic support content. The input is emotional state data, and the output is user support measures based on the prompt messages. Specific actions include determining immediate responses for users experiencing stress and presenting special offers.

[0221] 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.

[0222] Data generation model 58 is a type 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0223] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0224] [Second Embodiment]

[0225] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0226] 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.

[0227] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0228] 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.

[0229] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0230] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0231] 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.

[0232] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0233] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0234] The 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.

[0235] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0236] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0237] This invention presents an embodiment of a picking and delivery system using autonomous unmanned aerial vehicles to automate warehouse inventory management and achieve an efficient logistics process. The main components of the system and their respective roles are described below.

[0238] First, the server acquires inventory information from sensors installed in the warehouse. This makes it possible to constantly know in real time which shelf each product is on and how much of it is available. The sensor data is aggregated on the server and stored in a database. This database also includes information such as past order history and seasonal fluctuations, and an AI algorithm predicts supply and demand based on this data.

[0239] The terminal is responsible for receiving order information from users and transferring it to the server. For example, when a user orders products from an online store, the order information is sent to the server via the terminal. This information includes the type and quantity of products ordered, the delivery address, and other details.

[0240] Upon receiving the order information, the server compares it with inventory data and calculates which items should be picked and in what order. An optimization algorithm is used in the calculation, enabling the autonomous drone to pick items along an efficient route. The server then transmits instructions for the picking order to the autonomous drone, controlling its movements.

[0241] The autonomous drone (UAV) flies autonomously through the warehouse based on instructions from the server, picking the specified items. After picking is complete, the server calculates the optimal delivery route and issues instructions to the autonomous drone. The autonomous drone then follows this route, quickly and safely transporting the items to the delivery point. After delivery is complete, the autonomous drone either takes on its next mission or returns to the charging station if necessary.

[0242] As a concrete example, when a user orders a specific product, the server already knows the product's location in the warehouse. Based on the product's location data, the server assembles instructions for an autonomous drone and sends a command to retrieve the product via the shortest route. In this way, logistics are accelerated and made more efficient. Furthermore, the server uses continuously generated AI to comprehensively manage each process and optimize the overall operation of the warehouse.

[0243] Thus, the embodiments of the present invention make it possible to perform warehouse inventory management and logistics processes with high efficiency.

[0244] The following describes the processing flow.

[0245] Step 1:

[0246] The server acquires inventory information in real time from sensors installed within the warehouse. The acquired data is stored in a database, keeping inventory quantities and product locations constantly up-to-date.

[0247] Step 2:

[0248] The server uses an AI algorithm to predict future supply and demand based on past order history and seasonal fluctuation data stored in the database. This analysis allows for inventory replenishment planning and improved picking efficiency.

[0249] Step 3:

[0250] Users order products through the online store. Order information includes product name, quantity, and shipping address, and is sent to the server by the device.

[0251] Step 4:

[0252] The server compares the order information received from the terminal with the inventory data in the database. Based on this, it calculates the optimal order for picking the ordered items and sends instructions to the autonomous unmanned aerial vehicle.

[0253] Step 5:

[0254] The autonomous unmanned aerial vehicle (UAV) flies through the warehouse following instructions from a server and picks the specified items. Each item is detected by sensors, and the UAV automatically grasps and moves the items.

[0255] Step 6:

[0256] The server calculates the optimal delivery route based on delivery address information and current traffic conditions. The calculated delivery route is then transmitted to an autonomous unmanned aerial vehicle (UAV).

[0257] Step 7:

[0258] The autonomous unmanned aerial vehicle (UAV) transports goods to designated delivery locations based on delivery routes received from a server. After delivery is complete, it waits for instructions to pick new orders or returns to a charging station if necessary.

[0259] (Example 1)

[0260] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0261] In today's environment, where efficient inventory management and logistics processes are crucial, obtaining accurate real-time inventory information and planning optimal picking and delivery is challenging. This leads to wasted costs and time due to excess inventory and stockouts. Furthermore, inaccurate demand forecasts and inability to respond quickly can result in decreased customer satisfaction.

[0262] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0263] In this invention, the server includes means for monitoring items in a warehouse in real time using a detection device and updating information in a data storage device, means for performing supply and demand forecasts using a computational model based on historical data and item information, and means for receiving request information from users and optimizing the order of item collection. This enables efficient inventory management and a rapid logistics process based on real-time monitoring and accurate demand forecasting.

[0264] A "detection device" is a device that recognizes the location and quantity of items in real time and transmits the information to a server.

[0265] A "data storage device" is a device used by a server to store inventory information, order data, and supply and demand forecast data.

[0266] "Historical data" refers to data such as past order history and sales data that is used to forecast supply and demand.

[0267] A "computational model" is a mathematical model that uses machine learning algorithms to predict supply and demand from historical data and inventory information.

[0268] "User" refers to anyone who uses the system to order goods from the warehouse.

[0269] "Request information" refers to information including the details and conditions of an order sent from the user to the server.

[0270] The "item collection order" is a sequence designed to efficiently retrieve items, and is determined by an optimization algorithm.

[0271] An "autonomous aircraft" is an unmanned aircraft that flies autonomously within a warehouse based on instructions from a server to collect or transport goods.

[0272] A "transport route" is the optimal route for an autonomous aircraft to transport goods, and it is derived through calculation.

[0273] "Generative artificial intelligence" is a technology that enables efficient system management by comprehensively analyzing inventory data and conditions within a warehouse in order to optimize warehouse operations.

[0274] This invention is a system using autonomous aircraft to streamline inventory management and logistics processes within warehouses. The following describes embodiments of this system.

[0275] First, the server collects information in real time from detection devices installed within the warehouse. These detection devices use RFID tags and barcode scanners to identify the location and quantity of items. The server stores this information in a data storage device, ensuring that it always maintains the most up-to-date inventory information.

[0276] Next, the server uses a computational model to forecast supply and demand based on historical data and item information stored in the data storage device. This computational model utilizes machine learning algorithms to predict future demand. This makes it possible to mitigate the risk of inventory shortages or surpluses.

[0277] The terminal is responsible for receiving request information from users. For example, when a user orders goods online, the terminal sends the details of the order, quantity, and delivery address to the server. Based on this information, the server performs calculations to optimize the order in which the goods are collected. The collection order is determined by an optimization algorithm, which supports the efficient operation of autonomous aircraft.

[0278] The optimized collection order and transport route are instructed from the server to the autonomous aircraft. Based on these instructions, the aircraft autonomously moves within the warehouse and collects the specified items. After collection is complete, the aircraft transports the items to their destination according to the optimal transport route.

[0279] As a concrete example, when a user orders a new product, the server already knows where that product is located. The server then transmits the exact location and collection order of the items to an autonomous aircraft, instructing it to collect them via the shortest route. This allows for faster and more efficient logistics.

[0280] The generative artificial intelligence manages the entire logistics process in an integrated manner, optimizing each step. A specific example of a prompt in this system would be: "Retrieve all inventory data in the warehouse and calculate which item should be picked next."

[0281] According to this invention, the inventory management in the warehouse and the efficiency of the logistics process are significantly improved.

[0282] The flow of the specific process in Example 1 will be described using FIG. 11.

[0283] Step 1:

[0284] The server acquires inventory information from the detection devices in the warehouse in real time. The input is sensor data representing the position and quantity of each item. The server analyzes this data, performs data processing to identify the position and quantity for each item, and records the results in the data storage device. Thereby, the server can always grasp the inventory situation in the warehouse.

[0285] Step 2:

[0286] The server uses the generated AI model to perform supply and demand prediction with the past historical data and current inventory data stored in the data storage device as inputs. The AI model learns based on these data and performs data calculations to predict future demand. As an output, a demand prediction result is obtained, which is utilized by the server for the next-step planning.

[0287] Step 3:

[0288] The terminal receives order information from the user. The input is order data including the type, quantity, and delivery destination of the product. The terminal transfers this information to the server. Based on the received order information, the server calculates the priority of item collection and re-evaluates the inventory situation if necessary.

[0289] Step 4:

[0290] The server matches the order information with the inventory data and calculates the order of item collection using an optimization algorithm. The inputs are the order information and the inventory data, and the output is an efficient collection order. The server generates this order and prepares it as an instruction to the autonomous aircraft.

[0291] Step 5:

[0292] The server transmits an optimized collection order to the autonomous aircraft. Based on the instructions from the server, the aircraft autonomously flies through the warehouse and begins collecting the specified items. The aircraft's sensors are used to send feedback data back to the server to verify picking accuracy.

[0293] Step 6:

[0294] After data collection is complete, the server calculates the most efficient transport route. Traffic and weather data are also considered as input, and the optimal route is output. The server transmits this route to the autonomous aircraft, which immediately begins transport operations.

[0295] Step 7:

[0296] Autonomous aircraft follow designated transport routes and deliver goods to their destinations. Once transport is complete, the aircraft awaits further instructions from the server and returns to a charging station if necessary. This process ensures that logistics are consistently fast, safe, and efficient.

[0297] (Application Example 1)

[0298] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0299] Managing warehouse inventory and streamlining logistics processes has presented various challenges with traditional methods. These include delays and errors due to manual inventory updates, insufficient accuracy in supply and demand forecasts, and increased time and costs due to manual picking and delivery. There is a need to solve these problems and accelerate and streamline logistics.

[0300] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0301] In this invention, the server includes means for monitoring the inventory in the warehouse in real time using a detection device and updating the information in the information storage unit, means for performing supply and demand forecasting using AI technology based on past sales information and inventory information, and means for receiving order information from users and optimizing the order of item collection. As a result, inventory management and automation and efficiency improvement of the logistics process are possible in the logistics center.

[0302] The "detection device" is a device for measuring the inventory status in the warehouse in real time and collecting information.

[0303] The "information storage unit" is a storage device for holding and managing the acquired inventory data.

[0304] "AI technology" is a technology for analyzing data using artificial intelligence and supporting decision-making for supply and demand forecasting and efficient business operation.

[0305] The "autonomous flying device" is a flying device having the ability to perform picking and delivery in the order specified without external operation.

[0306] The "transportation route" is a route planned to transport the picked items to the destination.

[0307] The "transportation progress status" is information indicating the position of the item on the transportation route and how far it has moved towards the destination.

[0308] The system for implementing this invention is a system for automating inventory management and logistics processes in the warehouse. First, the server monitors the inventory in the warehouse in real time using a detection device and updates this information in the information storage unit. As a result, detailed information such as the position and quantity of the goods is always accurate.

[0309] The server utilizes AI technology to forecast supply and demand based on past sales data and current inventory information. This forecast clearly identifies the types and quantities of goods that will be needed next. Based on this information, the server determines the optimal order for collecting items.

[0310] When a customer places an order, the server receives the order information and optimizes the order in which items are collected. The autonomous flight system follows instructions from the server, flying through the warehouse along an efficient route to pick the goods. The server also calculates the optimal transport route and sends instructions to the autonomous flight system.

[0311] The progress of shipments is constantly monitored by servers to ensure that goods are delivered quickly and safely to their destination. Generative AI is used to comprehensively manage warehouse conditions and inventory awareness, thereby improving the efficiency of the entire business process.

[0312] For example, when a user orders a specific electronic product, the server determines the location of the product in the warehouse and uses an autonomous flight device to direct the user to the shortest route. Another example of a prompt used for the generated AI is, "Predict the amount of inventory needed for the next two weeks and display it along with the current inventory status."

[0313] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0314] Step 1:

[0315] The server uses detection devices to acquire inventory data from the warehouse in real time. This provides input information such as the location and quantity of each product. The server updates its information storage unit with this data to gain an overall understanding of the warehouse's inventory status.

[0316] Step 2:

[0317] The server receives historical sales information and current inventory information as input and uses AI technology to perform supply and demand forecasting. It processes the data through time series analysis and trend analysis to predict future demand and records the results as output.

[0318] Step 3:

[0319] The terminal receives order information from the user and transmits it to the server. Based on this order information, the server uses AI technology to calculate the optimal order for collecting items. This clearly instructs the user which items should be collected and in what order.

[0320] Step 4:

[0321] The server transmits the calculated item collection order to the autonomous flight device. The flight device receives instructions from the server and picks items while flying along an efficient route. Here, the optimization of the flight path is performed as a data calculation.

[0322] Step 5:

[0323] Once picking is complete, the server calculates the optimal transport route for the autonomous flight device and issues instructions. This calculation selects the delivery route and instructs the device to ensure safe and rapid delivery.

[0324] Step 6:

[0325] The server constantly monitors the progress of the transport and verifies that the aircraft is following the designated route precisely. Once the transport is complete, it records the information as output and prepares for the next mission.

[0326] Step 7:

[0327] Using generation AI, the server analyzes warehouse conditions and inventory information, and manages the entire process in an integrated manner. This allows for adjustments to improve overall operational efficiency. By using prompts such as, "Predict the inventory needed for the next two weeks and display it along with the current inventory status," continuous improvement becomes possible.

[0328] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0329] This invention presents an embodiment of a system that, in addition to automating warehouse inventory management and logistics processes, recognizes user emotions and provides a personalized experience. The main components of the system and their respective functions are described below.

[0330] The server acquires real-time inventory information from sensors installed in the warehouse and stores it in a database. This ensures that the quantity and location information of products are always up-to-date. Furthermore, based on past sales data and current inventory data, an AI algorithm is used to forecast supply and demand, thereby improving the efficiency of product supply.

[0331] The terminal receives order information sent by the user through the online store and forwards it to the server. In addition to the product name, quantity, and shipping address, the order information is analyzed by an emotion engine based on the user's input. The emotion engine analyzes the user's voice tone, input speed, selected words, etc., and evaluates their emotional state.

[0332] Based on the analysis results, the server dynamically adjusts the ordering process. For example, if it determines that the user is experiencing dissatisfaction or stress, the server adjusts the priority of autonomous drones and optimizes thermal processing to quickly process the user's order. Additionally, an emotion engine analyzes the user's preferences and provides personalized product suggestions.

[0333] As a benefit of the system, users can receive more personalized product recommendations and appropriate support. For example, special offers and new product information are presented in an emotionally resonant way. This improves the user experience.

[0334] Autonomous unmanned aerial vehicles (UAVs) operate based on instructions from a server during the picking and delivery process. They pick goods according to optimized routes and efficiently transport them to delivery locations.

[0335] As a concrete example, when a user selects products online, the emotion engine suggests related products that the user might be interested in. Once the order is complete, the server comprehensively manages the logistics flow, including referencing the results of the emotion engine in addition to the normal process and performing high-priority processing.

[0336] This system can improve not only the accuracy of inventory management and the efficiency of logistics, but also the user experience.

[0337] The following describes the processing flow.

[0338] Step 1:

[0339] The server retrieves inventory information in real time from sensors within the warehouse. The retrieved data is stored in a database, keeping the quantity and location information of products constantly up-to-date.

[0340] Step 2:

[0341] The server uses an AI algorithm to forecast supply and demand based on historical sales data stored in the database and current inventory data. This forecast makes it possible to create an appropriate inventory replenishment plan.

[0342] Step 3:

[0343] Users select products and enter order information through the online store. During this process, the emotion engine analyzes the user's input speed and word choices to evaluate their emotional state.

[0344] Step 4:

[0345] The terminal receives order information from the user and sends it to the server along with emotion engine data. The server then optimizes the order process based on this information.

[0346] Step 5:

[0347] The server takes the user's emotional state into consideration and adjusts order priorities accordingly. For example, if a user expresses dissatisfaction, it instructs the autonomous drone to prioritize processing that order.

[0348] Step 6:

[0349] The server calculates the optimal picking order and sends instructions to the autonomous drone. The autonomous drone flies around the warehouse according to the instructions and picks the specified items.

[0350] Step 7:

[0351] The server calculates the optimal delivery route and transmits it to the autonomous drone. The autonomous drone follows the route and quickly delivers the goods to the user.

[0352] Step 8:

[0353] Once the autonomous drone completes its delivery, it awaits further instructions or returns to the station for recharging if necessary. The server records the entire process and uses it to plan future deliveries.

[0354] (Example 2)

[0355] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0356] Modern logistics systems demand improved accuracy in inventory management, more efficient supply and demand forecasting, greater flexibility in order processing, and a better user experience. In particular, real-time inventory tracking, dynamic order processing, and personalized experiences based on sentiment data are key challenges. Addressing these issues comprehensively is crucial for optimizing logistics and enhancing the user experience.

[0357] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0358] In this invention, the server includes means for monitoring warehouse inventory in real time using a measuring device and updating information in an information recording device; means for performing supply and demand forecasting using a machine learning algorithm based on past transaction data and inventory data; means for receiving order information from users and optimizing the picking order; means for analyzing the emotional state of users; means for dynamically adjusting order processing based on the analysis results; and means for providing personalized product suggestions. This enables improved accuracy in inventory management, increased efficiency in logistics processes, and an enhanced user experience.

[0359] A "measuring device" is a device that detects goods and environmental conditions in a warehouse in real time and provides that data.

[0360] An "information recording device" is a device that stores data acquired from measuring devices and organizes it for later analysis and use.

[0361] "Transaction data" is a collection of information about past sales and purchases, and is fundamental information used for supply and demand forecasting.

[0362] A "machine learning algorithm" is a mathematical model used by computer systems to learn patterns from past data and predict future supply and demand.

[0363] "Users" refers to customers or users who order products and receive services through the system.

[0364] "Picking sequence" refers to the efficient order or route for collecting goods within a warehouse.

[0365] An "autonomous transport device" is a device that automatically travels along a designated route under programmatic control, picking and delivering goods.

[0366] "Emotional state" refers to the user's current psychological state, which the system analyzes from the user's input and behavior.

[0367] "Personalized product recommendations" is a method of presenting products and services that are best suited to a particular user based on their interests and preferences.

[0368] This system integrates multiple advanced technologies to optimize warehouse inventory management and logistics.

[0369] The system primarily involves servers, terminals, and users, and its overall efficiency is achieved through the coordinated actions of each component.

[0370] The server acquires inventory data in real time from measuring devices placed within the warehouse and stores it in an information recording device. Various sensors are used to monitor details such as the number of items, shelf location, and storage conditions. This data is recorded in a database and forms the basis for subsequent supply and demand forecasting. When using machine learning algorithms, libraries such as TensorFlow and PyTorch are used to perform supply and demand forecasting based on transaction data. This is to create an appropriate supply plan for goods and prevent stockouts and surpluses.

[0371] The terminal receives order information sent by users via the online store. This order information includes details such as product name, quantity, and shipping address. The terminal also uses an emotion analysis engine to analyze user input data. This analysis includes voice data and input speed, and utilizes natural language processing technologies such as Hugging Face's Transformers. The analysis results are sent to a server and used to adjust dynamic order processing.

[0372] As a concrete example, the server optimizes the picking order based on data analysis results and issues instructions to the autonomous transport system. This allows the autonomous transport system to follow the optimized route and pick items quickly and accurately. This entire process significantly improves logistics efficiency.

[0373] Furthermore, users receive personalized product recommendations based on sentiment analysis results. For example, if a user shows interest in products in a specific category, related products are automatically recommended. This allows users to have a better purchasing experience.

[0374] An example of a prompt used to control a generative AI model is, "Tell me how to improve the user experience using sentiment analysis in online store orders." Using this prompt, the system will generate specific strategies to enhance the user experience.

[0375] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0376] Step 1:

[0377] The server acquires inventory data in real time from measuring devices placed within the warehouse. Its inputs include receiving sensor signals from each measuring device to obtain product counts, location information, and environmental data. Specifically, the server integrates these signals and writes the data to an information recording device. The output is stored in a database as the latest inventory data.

[0378] Step 2:

[0379] The server uses machine learning algorithms to perform supply and demand forecasting based on inventory and transaction data stored in the database. This historical data is used as input. The server utilizes TensorFlow and PyTorch libraries to apply the algorithms and predict future supply and demand. The output generates supply and demand forecast data, which is used to optimize supply plans.

[0380] Step 3:

[0381] The terminal receives order information submitted by the user from the online store. The input includes the user's specified product name, quantity, and shipping address. The received order information is then forwarded to the server. Specifically, the terminal sends this information to the server as input and begins order processing. The output is used by the server as accurate order information.

[0382] Step 4:

[0383] The device analyzes user input using an emotion analysis engine. This input includes user voice data, text input speed, and selected words. The device processes this data using the emotion analysis engine to analyze the user's emotional state. Specifically, the device generates analysis results and outputs them to the server as emotion data.

[0384] Step 5:

[0385] The server dynamically adjusts order processing using sentiment analysis results. Input includes sentiment analysis results and user order information. Based on this data, the server instructs the automated guided vehicles (AGVs) to adjust the picking order. Specifically, it generates picking instructions in order of priority and sends them to the AGVs. The output is an optimized picking plan.

[0386] Step 6:

[0387] The server generates personalized product recommendations based on the user's preference data. Input includes the user's past purchase history and sentiment analysis results. The server analyzes this data and prepares relevant product information. Specifically, it creates and outputs suggestions for similar or recommended products to the user.

[0388] Step 7:

[0389] The autonomous transport system retrieves goods according to an optimized picking sequence and moves them to the delivery point. Inputs include picking and delivery instructions from a server. The transport system automatically follows a designated route, efficiently picking goods. Specifically, it follows a path from the collection point to the destination, delivering the goods to the final delivery point as output.

[0390] (Application Example 2)

[0391] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0392] In recent years, there has been a growing demand for streamlining warehouse inventory management and logistics processes. However, consistently managing inventory in real time, forecasting supply and demand, and calculating optimal delivery routes remains challenging. Furthermore, improving the user experience requires analyzing customer emotions and providing personalized services, but few systems can achieve this efficiently and effectively. To address these challenges, there is a need for comprehensive inventory management and logistics systems that include dynamic adjustments to support content based on emotional states.

[0393] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0394] In this invention, the server includes means for monitoring warehouse inventory in real time using a detection device and updating information in a database; means for forecasting supply and demand using an artificial intelligence algorithm based on past sales data and inventory data; means for receiving customer order information and optimizing the order of product selection; means for calculating the optimal delivery route and sending instructions to an autonomous aircraft; and means for analyzing the customer's emotional state and dynamically adjusting the support content based on that emotional state. This makes it possible to improve not only the efficiency of inventory management and logistics processes but also the user experience.

[0395] A "detection device" is a device used to monitor the inventory status in a warehouse in real time.

[0396] A "database" is an information management system that stores inventory information acquired from detection devices and performs searches and updates as needed.

[0397] An "artificial intelligence algorithm" is a computational method that analyzes past sales and inventory data to predict supply and demand.

[0398] "Selection order" refers to the sequence setting used to optimize the order in which products are picked up based on customer order information.

[0399] An "autonomous flying machine" is an unmanned aerial vehicle that sorts goods according to an optimized sorting sequence and delivers those goods using the most suitable delivery route.

[0400] "Customer emotional state" refers to information that forms the basis for providing individualized service by analyzing and evaluating the customer's emotions at the time of ordering.

[0401] "Support content" refers to the methods and content of service and product delivery that are dynamically adjusted according to the customer's emotional state.

[0402] The server monitors inventory information in real time using detection devices installed within the warehouse and updates this information in a database. Specifically, the detection devices include various types of sensors, which are typically used to determine the location and quantity of inventory. For database management, SQL or NoSQL databases capable of real-time updates are commonly used.

[0403] Based on this information, the server analyzes past sales data and uses artificial intelligence algorithms to predict supply and demand. Machine learning libraries such as TensorFlow and PyTorch may be used in the software. This is expected to optimize inventory and improve the accuracy of sales forecasts.

[0404] The terminal processes order information received from customers and optimizes the sorting order of products. This process considers the product name, quantity, and delivery address information, and the sorting order is instructed from the server to the autonomous flying machine. The flying machine is equipped with GPS and an IMU (Inertial Measurement Unit) to automatically calculate and navigate the optimal delivery route.

[0405] The user's emotional state is determined by the device analyzing input information (such as voice commands and touch speed). The server then uses this information to dynamically adjust the support provided. This allows for immediate support or the suggestion of special services if the user is feeling anxious.

[0406] As a concrete example, the server queries the generation AI model through a prompt message, sending a request such as, "Please tell me the best response to reduce user stress." As a result, a customized response that matches the user's emotional state is proposed, and appropriate notifications or coupons can be issued if there is a delay in product delivery.

[0407] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0408] Step 1:

[0409] The server retrieves inventory information from detection devices in the warehouse and updates the database. The input is real-time data from the sensors, and the output is the latest database state, including inventory quantity and location information. The data is processed using SQL queries and written to the database.

[0410] Step 2:

[0411] The server uses historical sales data and current inventory data to run an artificial intelligence algorithm and predict supply and demand. The input is sales history and inventory data, which are analyzed using machine learning tools such as TensorFlow. The output is predicted future supply and demand data. Based on this prediction, inventory optimization becomes possible.

[0412] Step 3:

[0413] The terminal transfers order information received from the user to the server and uses this information to optimize the product sorting order. The input is order information (product name, quantity, delivery address), and the output is a list of the optimized sorting order. In this process, an algorithm calculates priorities to streamline the selection process.

[0414] Step 4:

[0415] The server transmits an optimized sorting sequence as instructions to the autonomous flying machine. The input is sorting sequence data, and the output is a movement command to the flying machine. In this process, a route plan based on GPS coordinates is created and instructions are sent to the flying machine to ensure accurate picking and delivery of goods.

[0416] Step 5:

[0417] The terminal processes user input information (voice tone, input speed) using an emotion analysis engine to determine the customer's emotional state. The input is user interaction data, and the output is the analyzed emotional state. This allows the system to begin providing services tailored to the user's emotions.

[0418] Step 6:

[0419] The server sends prompt messages to a generating AI model based on the analyzed emotional state, and determines dynamic support content. The input is emotional state data, and the output is user support measures based on the prompt messages. Specific actions include determining immediate responses for users experiencing stress and presenting special offers.

[0420] 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.

[0421] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0422] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0423] [Third Embodiment]

[0424] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0425] 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.

[0426] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0427] 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.

[0428] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0429] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0430] 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.

[0431] 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.

[0432] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0433] The 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.

[0434] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0435] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0436] This invention presents an embodiment of a picking and delivery system using autonomous unmanned aerial vehicles to automate warehouse inventory management and achieve an efficient logistics process. The main components of the system and their respective roles are described below.

[0437] First, the server acquires inventory information from sensors installed in the warehouse. This makes it possible to constantly know in real time which shelf each product is on and how much of it is available. The sensor data is aggregated on the server and stored in a database. This database also includes information such as past order history and seasonal fluctuations, and an AI algorithm predicts supply and demand based on this data.

[0438] The terminal is responsible for receiving order information from users and transferring it to the server. For example, when a user orders products from an online store, the order information is sent to the server via the terminal. This information includes the type and quantity of products ordered, the delivery address, and other details.

[0439] Upon receiving the order information, the server compares it with inventory data and calculates which items should be picked and in what order. An optimization algorithm is used in the calculation, enabling the autonomous drone to pick items along an efficient route. The server then transmits instructions for the picking order to the autonomous drone, controlling its movements.

[0440] The autonomous drone (UAV) flies autonomously through the warehouse based on instructions from the server, picking the specified items. After picking is complete, the server calculates the optimal delivery route and issues instructions to the autonomous drone. The autonomous drone then follows this route, quickly and safely transporting the items to the delivery point. After delivery is complete, the autonomous drone either takes on its next mission or returns to the charging station if necessary.

[0441] As a concrete example, when a user orders a specific product, the server already knows the product's location in the warehouse. Based on the product's location data, the server assembles instructions for an autonomous drone and sends a command to retrieve the product via the shortest route. In this way, logistics are accelerated and made more efficient. Furthermore, the server uses continuously generated AI to comprehensively manage each process and optimize the overall operation of the warehouse.

[0442] Thus, the embodiments of the present invention make it possible to perform warehouse inventory management and logistics processes with high efficiency.

[0443] The following describes the processing flow.

[0444] Step 1:

[0445] The server acquires inventory information in real time from sensors installed within the warehouse. The acquired data is stored in a database, keeping inventory quantities and product locations constantly up-to-date.

[0446] Step 2:

[0447] The server uses an AI algorithm to predict future supply and demand based on past order history and seasonal fluctuation data stored in the database. This analysis allows for inventory replenishment planning and improved picking efficiency.

[0448] Step 3:

[0449] Users order products through the online store. Order information includes product name, quantity, and shipping address, and is sent to the server by the device.

[0450] Step 4:

[0451] The server compares the order information received from the terminal with the inventory data in the database. Based on this, it calculates the optimal order for picking the ordered items and sends instructions to the autonomous unmanned aerial vehicle.

[0452] Step 5:

[0453] The autonomous unmanned aerial vehicle (UAV) flies through the warehouse following instructions from a server and picks the specified items. Each item is detected by sensors, and the UAV automatically grasps and moves the items.

[0454] Step 6:

[0455] The server calculates the optimal delivery route based on delivery address information and current traffic conditions. The calculated delivery route is then transmitted to an autonomous unmanned aerial vehicle (UAV).

[0456] Step 7:

[0457] The autonomous unmanned aerial vehicle (UAV) transports goods to designated delivery locations based on delivery routes received from a server. After delivery is complete, it waits for instructions to pick new orders or returns to a charging station if necessary.

[0458] (Example 1)

[0459] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0460] In today's environment, where efficient inventory management and logistics processes are crucial, obtaining accurate real-time inventory information and planning optimal picking and delivery is challenging. This leads to wasted costs and time due to excess inventory and stockouts. Furthermore, inaccurate demand forecasts and inability to respond quickly can result in decreased customer satisfaction.

[0461] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0462] In this invention, the server includes means for monitoring items in a warehouse in real time using a detection device and updating information in a data storage device, means for performing supply and demand forecasts using a computational model based on historical data and item information, and means for receiving request information from users and optimizing the order of item collection. This enables efficient inventory management and a rapid logistics process based on real-time monitoring and accurate demand forecasting.

[0463] A "detection device" is a device that recognizes the location and quantity of items in real time and transmits the information to a server.

[0464] A "data storage device" is a device used by a server to store inventory information, order data, and supply and demand forecast data.

[0465] "Historical data" refers to data such as past order history and sales data that is used to forecast supply and demand.

[0466] A "computational model" is a mathematical model that uses machine learning algorithms to predict supply and demand from historical data and inventory information.

[0467] "User" refers to anyone who uses the system to order goods from the warehouse.

[0468] "Request information" refers to information including the details and conditions of an order sent from the user to the server.

[0469] The "item collection order" is a sequence designed to efficiently retrieve items, and is determined by an optimization algorithm.

[0470] An "autonomous aircraft" is an unmanned aircraft that flies autonomously within a warehouse based on instructions from a server to collect or transport goods.

[0471] A "transport route" is the optimal route for an autonomous aircraft to transport goods, and it is derived through calculation.

[0472] "Generative artificial intelligence" is a technology that enables efficient system management by comprehensively analyzing inventory data and conditions within a warehouse in order to optimize warehouse operations.

[0473] This invention is a system using autonomous aircraft to streamline inventory management and logistics processes within warehouses. The following describes embodiments of this system.

[0474] First, the server collects information in real time from detection devices installed within the warehouse. These detection devices use RFID tags and barcode scanners to identify the location and quantity of items. The server stores this information in a data storage device, ensuring that it always maintains the most up-to-date inventory information.

[0475] Next, the server uses a computational model to forecast supply and demand based on historical data and item information stored in the data storage device. This computational model utilizes machine learning algorithms to predict future demand. This makes it possible to mitigate the risk of inventory shortages or surpluses.

[0476] The terminal is responsible for receiving request information from users. For example, when a user orders goods online, the terminal sends the details of the order, quantity, and delivery address to the server. Based on this information, the server performs calculations to optimize the order in which the goods are collected. The collection order is determined by an optimization algorithm, which supports the efficient operation of autonomous aircraft.

[0477] The optimized collection order and transport route are instructed from the server to the autonomous aircraft. Based on these instructions, the aircraft autonomously moves within the warehouse and collects the specified items. After collection is complete, the aircraft transports the items to their destination according to the optimal transport route.

[0478] As a concrete example, when a user orders a new product, the server already knows where that product is located. The server then transmits the exact location and collection order of the items to an autonomous aircraft, instructing it to collect them via the shortest route. This allows for faster and more efficient logistics.

[0479] The generative artificial intelligence manages the entire logistics process in an integrated manner, optimizing each step. A specific example of a prompt in this system would be: "Retrieve all inventory data in the warehouse and calculate which item should be picked next."

[0480] This invention significantly improves the efficiency of inventory management and logistics processes in warehouses.

[0481] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0482] Step 1:

[0483] The server acquires inventory information in real time from detection devices within the warehouse. The input is sensor data representing the location and quantity of each item. The server analyzes this data, performs data processing to identify the location and quantity of each item, and records the results in a data storage device. This allows the server to constantly monitor the inventory status in the warehouse.

[0484] Step 2:

[0485] The server uses historical data stored in a data storage device and current inventory data as input to perform supply and demand forecasting using a generative AI model. The AI ​​model learns from this data and performs data calculations to predict future demand. The output is the demand forecast result, which the server uses to plan the next steps.

[0486] Step 3:

[0487] The terminal receives order information from the user. The input is order data including the type of product, quantity, and delivery address. The terminal then transfers this information to the server. Based on the received order information, the server calculates the priority for collecting the items and re-evaluates the inventory status as needed.

[0488] Step 4:

[0489] The server matches order information with inventory data and uses an optimization algorithm to calculate the order in which to collect items. The input is order information and inventory data, and the output is the efficient collection order. The server generates this order and prepares it as instructions for autonomous aircraft.

[0490] Step 5:

[0491] The server transmits an optimized collection order to the autonomous aircraft. Based on the instructions from the server, the aircraft autonomously flies through the warehouse and begins collecting the specified items. The aircraft's sensors are used to send feedback data back to the server to verify picking accuracy.

[0492] Step 6:

[0493] After data collection is complete, the server calculates the most efficient transport route. Traffic and weather data are also considered as input, and the optimal route is output. The server transmits this route to the autonomous aircraft, which immediately begins transport operations.

[0494] Step 7:

[0495] Autonomous aircraft follow designated transport routes and deliver goods to their destinations. Once transport is complete, the aircraft awaits further instructions from the server and returns to a charging station if necessary. This process ensures that logistics are consistently fast, safe, and efficient.

[0496] (Application Example 1)

[0497] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0498] Managing warehouse inventory and streamlining logistics processes has presented various challenges with traditional methods. These include delays and errors due to manual inventory updates, insufficient accuracy in supply and demand forecasts, and increased time and costs due to manual picking and delivery. There is a need to solve these problems and accelerate and streamline logistics.

[0499] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0500] In this invention, the server includes means for monitoring warehouse inventory in real time using a detection device and updating information in an information storage unit, means for performing supply and demand forecasting using AI technology based on past sales information and inventory information, and means for receiving order information from users and optimizing the order of item collection. This enables the automation and efficiency of inventory management and logistics processes in a logistics center.

[0501] A "detection device" is a device used to measure the inventory status in a warehouse in real time and collect information.

[0502] The "information storage unit" is a storage device used to hold and manage acquired inventory data.

[0503] "AI technology" refers to technology that uses artificial intelligence to analyze data and support decision-making for supply and demand forecasting and efficient business operations.

[0504] An "autonomous flying device" is a flying device that has the ability to pick and deliver items in a specified order without external intervention.

[0505] A "transportation route" is the planned path for transporting picked goods to their destination.

[0506] "Transportation progress status" refers to information indicating the position of goods along the transportation route and how far they have traveled towards their destination.

[0507] The system for implementing this invention is a system that automates inventory management and logistics processes within a warehouse. First, a server uses a detection device to monitor the inventory in the warehouse in real time and updates this information in the information storage unit. As a result, detailed information such as the location and quantity of goods is always accurate.

[0508] The server utilizes AI technology to forecast supply and demand based on past sales data and current inventory information. This forecast clearly identifies the types and quantities of goods that will be needed next. Based on this information, the server determines the optimal order for collecting items.

[0509] When a customer places an order, the server receives the order information and optimizes the order in which items are collected. The autonomous flight system follows instructions from the server, flying through the warehouse along an efficient route to pick the goods. The server also calculates the optimal transport route and sends instructions to the autonomous flight system.

[0510] The progress of shipments is constantly monitored by servers to ensure that goods are delivered quickly and safely to their destination. Generative AI is used to comprehensively manage warehouse conditions and inventory awareness, thereby improving the efficiency of the entire business process.

[0511] For example, when a user orders a specific electronic product, the server determines the location of the product in the warehouse and uses an autonomous flight device to direct the user to the shortest route. Another example of a prompt used for the generated AI is, "Predict the amount of inventory needed for the next two weeks and display it along with the current inventory status."

[0512] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0513] Step 1:

[0514] The server uses detection devices to acquire inventory data from the warehouse in real time. This provides input information such as the location and quantity of each product. The server updates its information storage unit with this data to gain an overall understanding of the warehouse's inventory status.

[0515] Step 2:

[0516] The server receives historical sales information and current inventory information as input and uses AI technology to perform supply and demand forecasting. It processes the data through time series analysis and trend analysis to predict future demand and records the results as output.

[0517] Step 3:

[0518] The terminal receives order information from the user and transmits it to the server. Based on this order information, the server uses AI technology to calculate the optimal order for collecting items. This clearly instructs the user which items should be collected and in what order.

[0519] Step 4:

[0520] The server transmits the calculated item collection order to the autonomous flight device. The flight device receives instructions from the server and picks items while flying along an efficient route. Here, the optimization of the flight path is performed as a data calculation.

[0521] Step 5:

[0522] Once picking is complete, the server calculates the optimal transport route for the autonomous flight device and issues instructions. This calculation selects the delivery route and instructs the device to ensure safe and rapid delivery.

[0523] Step 6:

[0524] The server constantly monitors the progress of the transport and verifies that the aircraft is following the designated route precisely. Once the transport is complete, it records the information as output and prepares for the next mission.

[0525] Step 7:

[0526] Using generation AI, the server analyzes warehouse conditions and inventory information, and manages the entire process in an integrated manner. This allows for adjustments to improve overall operational efficiency. By using prompts such as, "Predict the inventory needed for the next two weeks and display it along with the current inventory status," continuous improvement becomes possible.

[0527] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0528] This invention presents an embodiment of a system that, in addition to automating warehouse inventory management and logistics processes, recognizes user emotions and provides a personalized experience. The main components of the system and their respective functions are described below.

[0529] The server acquires real-time inventory information from sensors installed in the warehouse and stores it in a database. This ensures that the quantity and location information of products are always up-to-date. Furthermore, based on past sales data and current inventory data, an AI algorithm is used to forecast supply and demand, thereby improving the efficiency of product supply.

[0530] The terminal receives order information sent by the user through the online store and forwards it to the server. In addition to the product name, quantity, and shipping address, the order information is analyzed by an emotion engine based on the user's input. The emotion engine analyzes the user's voice tone, input speed, selected words, etc., and evaluates their emotional state.

[0531] Based on the analysis results, the server dynamically adjusts the ordering process. For example, if it determines that the user is experiencing dissatisfaction or stress, the server adjusts the priority of autonomous drones and optimizes thermal processing to quickly process the user's order. Additionally, an emotion engine analyzes the user's preferences and provides personalized product suggestions.

[0532] As a benefit of the system, users can receive more personalized product recommendations and appropriate support. For example, special offers and new product information are presented in an emotionally resonant way. This improves the user experience.

[0533] Autonomous unmanned aerial vehicles (UAVs) operate based on instructions from a server during the picking and delivery process. They pick goods according to optimized routes and efficiently transport them to delivery locations.

[0534] As a concrete example, when a user selects products online, the emotion engine suggests related products that the user might be interested in. Once the order is complete, the server comprehensively manages the logistics flow, including referencing the results of the emotion engine in addition to the normal process and performing high-priority processing.

[0535] This system can improve not only the accuracy of inventory management and the efficiency of logistics, but also the user experience.

[0536] The following describes the processing flow.

[0537] Step 1:

[0538] The server retrieves inventory information in real time from sensors within the warehouse. The retrieved data is stored in a database, keeping the quantity and location information of products constantly up-to-date.

[0539] Step 2:

[0540] The server uses an AI algorithm to forecast supply and demand based on historical sales data stored in the database and current inventory data. This forecast makes it possible to create an appropriate inventory replenishment plan.

[0541] Step 3:

[0542] Users select products and enter order information through the online store. During this process, the emotion engine analyzes the user's input speed and word choices to evaluate their emotional state.

[0543] Step 4:

[0544] The terminal receives order information from the user and sends it to the server along with emotion engine data. The server then optimizes the order process based on this information.

[0545] Step 5:

[0546] The server takes the user's emotional state into consideration and adjusts order priorities accordingly. For example, if a user expresses dissatisfaction, it instructs the autonomous drone to prioritize processing that order.

[0547] Step 6:

[0548] The server calculates the optimal picking order and sends instructions to the autonomous drone. The autonomous drone flies around the warehouse according to the instructions and picks the specified items.

[0549] Step 7:

[0550] The server calculates the optimal delivery route and transmits it to the autonomous drone. The autonomous drone follows the route and quickly delivers the goods to the user.

[0551] Step 8:

[0552] Once the autonomous drone completes its delivery, it awaits further instructions or returns to the station for recharging if necessary. The server records the entire process and uses it to plan future deliveries.

[0553] (Example 2)

[0554] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0555] Modern logistics systems demand improved accuracy in inventory management, more efficient supply and demand forecasting, greater flexibility in order processing, and a better user experience. In particular, real-time inventory tracking, dynamic order processing, and personalized experiences based on sentiment data are key challenges. Addressing these issues comprehensively is crucial for optimizing logistics and enhancing the user experience.

[0556] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0557] In this invention, the server includes means for monitoring warehouse inventory in real time using a measuring device and updating information in an information recording device; means for performing supply and demand forecasting using a machine learning algorithm based on past transaction data and inventory data; means for receiving order information from users and optimizing the picking order; means for analyzing the emotional state of users; means for dynamically adjusting order processing based on the analysis results; and means for providing personalized product suggestions. This enables improved accuracy in inventory management, increased efficiency in logistics processes, and an enhanced user experience.

[0558] A "measuring device" is a device that detects goods and environmental conditions in a warehouse in real time and provides that data.

[0559] An "information recording device" is a device that stores data acquired from measuring devices and organizes it for later analysis and use.

[0560] "Transaction data" is a collection of information about past sales and purchases, and is fundamental information used for supply and demand forecasting.

[0561] A "machine learning algorithm" is a mathematical model used by computer systems to learn patterns from past data and predict future supply and demand.

[0562] "Users" refers to customers or users who order products and receive services through the system.

[0563] "Picking sequence" refers to the efficient order or route for collecting goods within a warehouse.

[0564] An "autonomous transport device" is a device that automatically travels along a designated route under programmatic control, picking and delivering goods.

[0565] "Emotional state" refers to the user's current psychological state, which the system analyzes from the user's input and behavior.

[0566] "Personalized product recommendations" is a method of presenting products and services that are best suited to a particular user based on their interests and preferences.

[0567] This system integrates multiple advanced technologies to optimize warehouse inventory management and logistics.

[0568] The system primarily involves servers, terminals, and users, and its overall efficiency is achieved through the coordinated actions of each component.

[0569] The server acquires inventory data in real time from measuring devices placed within the warehouse and stores it in an information recording device. Various sensors are used to monitor details such as the number of items, shelf location, and storage conditions. This data is recorded in a database and forms the basis for subsequent supply and demand forecasting. When using machine learning algorithms, libraries such as TensorFlow and PyTorch are used to perform supply and demand forecasting based on transaction data. This is to create an appropriate supply plan for goods and prevent stockouts and surpluses.

[0570] The terminal receives order information sent by users via the online store. This order information includes details such as product name, quantity, and shipping address. The terminal also uses an emotion analysis engine to analyze user input data. This analysis includes voice data and input speed, and utilizes natural language processing technologies such as Hugging Face's Transformers. The analysis results are sent to a server and used to adjust dynamic order processing.

[0571] As a concrete example, the server optimizes the picking order based on data analysis results and issues instructions to the autonomous transport system. This allows the autonomous transport system to follow the optimized route and pick items quickly and accurately. This entire process significantly improves logistics efficiency.

[0572] Furthermore, users receive personalized product recommendations based on sentiment analysis results. For example, if a user shows interest in products in a specific category, related products are automatically recommended. This allows users to have a better purchasing experience.

[0573] An example of a prompt used to control a generative AI model is, "Tell me how to improve the user experience using sentiment analysis in online store orders." Using this prompt, the system will generate specific strategies to enhance the user experience.

[0574] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0575] Step 1:

[0576] The server acquires inventory data in real time from measuring devices placed within the warehouse. Its inputs include receiving sensor signals from each measuring device to obtain product counts, location information, and environmental data. Specifically, the server integrates these signals and writes the data to an information recording device. The output is stored in a database as the latest inventory data.

[0577] Step 2:

[0578] The server uses machine learning algorithms to perform supply and demand forecasting based on inventory and transaction data stored in the database. This historical data is used as input. The server utilizes TensorFlow and PyTorch libraries to apply the algorithms and predict future supply and demand. The output generates supply and demand forecast data, which is used to optimize supply plans.

[0579] Step 3:

[0580] The terminal receives order information submitted by the user from the online store. The input includes the user's specified product name, quantity, and shipping address. The received order information is then forwarded to the server. Specifically, the terminal sends this information to the server as input and begins order processing. The output is used by the server as accurate order information.

[0581] Step 4:

[0582] The device analyzes user input using an emotion analysis engine. This input includes user voice data, text input speed, and selected words. The device processes this data using the emotion analysis engine to analyze the user's emotional state. Specifically, the device generates analysis results and outputs them to the server as emotion data.

[0583] Step 5:

[0584] The server dynamically adjusts order processing using sentiment analysis results. Input includes sentiment analysis results and user order information. Based on this data, the server instructs the automated guided vehicles (AGVs) to adjust the picking order. Specifically, it generates picking instructions in order of priority and sends them to the AGVs. The output is an optimized picking plan.

[0585] Step 6:

[0586] The server generates personalized product recommendations based on the user's preference data. Input includes the user's past purchase history and sentiment analysis results. The server analyzes this data and prepares relevant product information. Specifically, it creates and outputs suggestions for similar or recommended products to the user.

[0587] Step 7:

[0588] The autonomous transport system retrieves goods according to an optimized picking sequence and moves them to the delivery point. Inputs include picking and delivery instructions from a server. The transport system automatically follows a designated route, efficiently picking goods. Specifically, it follows a path from the collection point to the destination, delivering the goods to the final delivery point as output.

[0589] (Application Example 2)

[0590] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0591] In recent years, there has been a growing demand for streamlining warehouse inventory management and logistics processes. However, consistently managing inventory in real time, forecasting supply and demand, and calculating optimal delivery routes remains challenging. Furthermore, improving the user experience requires analyzing customer emotions and providing personalized services, but few systems can achieve this efficiently and effectively. To address these challenges, there is a need for comprehensive inventory management and logistics systems that include dynamic adjustments to support content based on emotional states.

[0592] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0593] In this invention, the server includes means for monitoring warehouse inventory in real time using a detection device and updating information in a database; means for forecasting supply and demand using an artificial intelligence algorithm based on past sales data and inventory data; means for receiving customer order information and optimizing the order of product selection; means for calculating the optimal delivery route and sending instructions to an autonomous aircraft; and means for analyzing the customer's emotional state and dynamically adjusting the support content based on that emotional state. This makes it possible to improve not only the efficiency of inventory management and logistics processes but also the user experience.

[0594] A "detection device" is a device used to monitor the inventory status in a warehouse in real time.

[0595] A "database" is an information management system that stores inventory information acquired from detection devices and performs searches and updates as needed.

[0596] An "artificial intelligence algorithm" is a computational method that analyzes past sales and inventory data to predict supply and demand.

[0597] "Selection order" refers to the sequence setting used to optimize the order in which products are picked up based on customer order information.

[0598] An "autonomous flying machine" is an unmanned aerial vehicle that sorts goods according to an optimized sorting sequence and delivers those goods using the most suitable delivery route.

[0599] "Customer emotional state" refers to information that forms the basis for providing individualized service by analyzing and evaluating the customer's emotions at the time of ordering.

[0600] "Support content" refers to the methods and content of service and product delivery that are dynamically adjusted according to the customer's emotional state.

[0601] The server monitors inventory information in real time using detection devices installed within the warehouse and updates this information in a database. Specifically, the detection devices include various types of sensors, which are typically used to determine the location and quantity of inventory. For database management, SQL or NoSQL databases capable of real-time updates are commonly used.

[0602] Based on this information, the server analyzes past sales data and uses artificial intelligence algorithms to predict supply and demand. Machine learning libraries such as TensorFlow and PyTorch may be used in the software. This is expected to optimize inventory and improve the accuracy of sales forecasts.

[0603] The terminal processes order information received from customers and optimizes the sorting order of products. This process considers the product name, quantity, and delivery address information, and the sorting order is instructed from the server to the autonomous flying machine. The flying machine is equipped with GPS and an IMU (Inertial Measurement Unit) to automatically calculate and navigate the optimal delivery route.

[0604] The user's emotional state is determined by the device analyzing input information (such as voice commands and touch speed). The server then uses this information to dynamically adjust the support provided. This allows for immediate support or the suggestion of special services if the user is feeling anxious.

[0605] As a concrete example, the server queries the generation AI model through a prompt message, sending a request such as, "Please tell me the best response to reduce user stress." As a result, a customized response that matches the user's emotional state is proposed, and appropriate notifications or coupons can be issued if there is a delay in product delivery.

[0606] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0607] Step 1:

[0608] The server retrieves inventory information from detection devices in the warehouse and updates the database. The input is real-time data from the sensors, and the output is the latest database state, including inventory quantity and location information. The data is processed using SQL queries and written to the database.

[0609] Step 2:

[0610] The server uses historical sales data and current inventory data to run an artificial intelligence algorithm and predict supply and demand. The input is sales history and inventory data, which are analyzed using machine learning tools such as TensorFlow. The output is predicted future supply and demand data. Based on this prediction, inventory optimization becomes possible.

[0611] Step 3:

[0612] The terminal transfers order information received from the user to the server and uses this information to optimize the product sorting order. The input is order information (product name, quantity, delivery address), and the output is a list of the optimized sorting order. In this process, an algorithm calculates priorities to streamline the selection process.

[0613] Step 4:

[0614] The server transmits an optimized sorting sequence as instructions to the autonomous flying machine. The input is sorting sequence data, and the output is a movement command to the flying machine. In this process, a route plan based on GPS coordinates is created and instructions are sent to the flying machine to ensure accurate picking and delivery of goods.

[0615] Step 5:

[0616] The terminal processes user input information (voice tone, input speed) using an emotion analysis engine to determine the customer's emotional state. The input is user interaction data, and the output is the analyzed emotional state. This allows the system to begin providing services tailored to the user's emotions.

[0617] Step 6:

[0618] The server sends prompt messages to a generating AI model based on the analyzed emotional state, and determines dynamic support content. The input is emotional state data, and the output is user support measures based on the prompt messages. Specific actions include determining immediate responses for users experiencing stress and presenting special offers.

[0619] 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.

[0620] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0621] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0622] [Fourth Embodiment]

[0623] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0624] 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.

[0625] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0626] 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.

[0627] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0628] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0629] 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.

[0630] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0631] 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.

[0632] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0633] The 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.

[0634] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0635] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0636] This invention presents an embodiment of a picking and delivery system using autonomous unmanned aerial vehicles to automate warehouse inventory management and achieve an efficient logistics process. The main components of the system and their respective roles are described below.

[0637] First, the server acquires inventory information from sensors installed in the warehouse. This makes it possible to constantly know in real time which shelf each product is on and how much of it is available. The sensor data is aggregated on the server and stored in a database. This database also includes information such as past order history and seasonal fluctuations, and an AI algorithm predicts supply and demand based on this data.

[0638] The terminal is responsible for receiving order information from users and transferring it to the server. For example, when a user orders products from an online store, the order information is sent to the server via the terminal. This information includes the type and quantity of products ordered, the delivery address, and other details.

[0639] Upon receiving the order information, the server compares it with inventory data and calculates which items should be picked and in what order. An optimization algorithm is used in the calculation, enabling the autonomous drone to pick items along an efficient route. The server then transmits instructions for the picking order to the autonomous drone, controlling its movements.

[0640] The autonomous drone (UAV) flies autonomously through the warehouse based on instructions from the server, picking the specified items. After picking is complete, the server calculates the optimal delivery route and issues instructions to the autonomous drone. The autonomous drone then follows this route, quickly and safely transporting the items to the delivery point. After delivery is complete, the autonomous drone either takes on its next mission or returns to the charging station if necessary.

[0641] As a concrete example, when a user orders a specific product, the server already knows the product's location in the warehouse. Based on the product's location data, the server assembles instructions for an autonomous drone and sends a command to retrieve the product via the shortest route. In this way, logistics are accelerated and made more efficient. Furthermore, the server uses continuously generated AI to comprehensively manage each process and optimize the overall operation of the warehouse.

[0642] Thus, the embodiments of the present invention make it possible to perform warehouse inventory management and logistics processes with high efficiency.

[0643] The following describes the processing flow.

[0644] Step 1:

[0645] The server acquires inventory information in real time from sensors installed within the warehouse. The acquired data is stored in a database, keeping inventory quantities and product locations constantly up-to-date.

[0646] Step 2:

[0647] The server uses an AI algorithm to predict future supply and demand based on past order history and seasonal fluctuation data stored in the database. This analysis allows for inventory replenishment planning and improved picking efficiency.

[0648] Step 3:

[0649] Users order products through the online store. Order information includes product name, quantity, and shipping address, and is sent to the server by the device.

[0650] Step 4:

[0651] The server compares the order information received from the terminal with the inventory data in the database. Based on this, it calculates the optimal order for picking the ordered items and sends instructions to the autonomous unmanned aerial vehicle.

[0652] Step 5:

[0653] The autonomous unmanned aerial vehicle (UAV) flies through the warehouse following instructions from a server and picks the specified items. Each item is detected by sensors, and the UAV automatically grasps and moves the items.

[0654] Step 6:

[0655] The server calculates the optimal delivery route based on delivery address information and current traffic conditions. The calculated delivery route is then transmitted to an autonomous unmanned aerial vehicle (UAV).

[0656] Step 7:

[0657] The autonomous unmanned aerial vehicle (UAV) transports goods to designated delivery locations based on delivery routes received from a server. After delivery is complete, it waits for instructions to pick new orders or returns to a charging station if necessary.

[0658] (Example 1)

[0659] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0660] In today's environment, where efficient inventory management and logistics processes are crucial, obtaining accurate real-time inventory information and planning optimal picking and delivery is challenging. This leads to wasted costs and time due to excess inventory and stockouts. Furthermore, inaccurate demand forecasts and inability to respond quickly can result in decreased customer satisfaction.

[0661] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0662] In this invention, the server includes means for monitoring items in a warehouse in real time using a detection device and updating information in a data storage device, means for performing supply and demand forecasts using a computational model based on historical data and item information, and means for receiving request information from users and optimizing the order of item collection. This enables efficient inventory management and a rapid logistics process based on real-time monitoring and accurate demand forecasting.

[0663] A "detection device" is a device that recognizes the location and quantity of items in real time and transmits the information to a server.

[0664] A "data storage device" is a device used by a server to store inventory information, order data, and supply and demand forecast data.

[0665] "Historical data" refers to data such as past order history and sales data that is used to forecast supply and demand.

[0666] A "computational model" is a mathematical model that uses machine learning algorithms to predict supply and demand from historical data and inventory information.

[0667] "User" refers to anyone who uses the system to order goods from the warehouse.

[0668] "Request information" refers to information including the details and conditions of an order sent from the user to the server.

[0669] The "item collection order" is a sequence designed to efficiently retrieve items, and is determined by an optimization algorithm.

[0670] An "autonomous aircraft" is an unmanned aircraft that flies autonomously within a warehouse based on instructions from a server to collect or transport goods.

[0671] A "transport route" is the optimal route for an autonomous aircraft to transport goods, and it is derived through calculation.

[0672] "Generative artificial intelligence" is a technology that enables efficient system management by comprehensively analyzing inventory data and conditions within a warehouse in order to optimize warehouse operations.

[0673] This invention is a system using autonomous aircraft to streamline inventory management and logistics processes within warehouses. The following describes embodiments of this system.

[0674] First, the server collects information in real time from detection devices installed within the warehouse. These detection devices use RFID tags and barcode scanners to identify the location and quantity of items. The server stores this information in a data storage device, ensuring that it always maintains the most up-to-date inventory information.

[0675] Next, the server uses a computational model to forecast supply and demand based on historical data and item information stored in the data storage device. This computational model utilizes machine learning algorithms to predict future demand. This makes it possible to mitigate the risk of inventory shortages or surpluses.

[0676] The terminal is responsible for receiving request information from users. For example, when a user orders goods online, the terminal sends the details of the order, quantity, and delivery address to the server. Based on this information, the server performs calculations to optimize the order in which the goods are collected. The collection order is determined by an optimization algorithm, which supports the efficient operation of autonomous aircraft.

[0677] The optimized collection order and transport route are instructed from the server to the autonomous aircraft. Based on these instructions, the aircraft autonomously moves within the warehouse and collects the specified items. After collection is complete, the aircraft transports the items to their destination according to the optimal transport route.

[0678] As a concrete example, when a user orders a new product, the server already knows where that product is located. The server then transmits the exact location and collection order of the items to an autonomous aircraft, instructing it to collect them via the shortest route. This allows for faster and more efficient logistics.

[0679] The generative artificial intelligence manages the entire logistics process in an integrated manner, optimizing each step. A specific example of a prompt in this system would be: "Retrieve all inventory data in the warehouse and calculate which item should be picked next."

[0680] This invention significantly improves the efficiency of inventory management and logistics processes in warehouses.

[0681] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0682] Step 1:

[0683] The server acquires inventory information in real time from detection devices within the warehouse. The input is sensor data representing the location and quantity of each item. The server analyzes this data, performs data processing to identify the location and quantity of each item, and records the results in a data storage device. This allows the server to constantly monitor the inventory status in the warehouse.

[0684] Step 2:

[0685] The server uses historical data stored in a data storage device and current inventory data as input to perform supply and demand forecasting using a generative AI model. The AI ​​model learns from this data and performs data calculations to predict future demand. The output is the demand forecast result, which the server uses to plan the next steps.

[0686] Step 3:

[0687] The terminal receives order information from the user. The input is order data including the type of product, quantity, and delivery address. The terminal then transfers this information to the server. Based on the received order information, the server calculates the priority for collecting the items and re-evaluates the inventory status as needed.

[0688] Step 4:

[0689] The server matches order information with inventory data and uses an optimization algorithm to calculate the order in which to collect items. The input is order information and inventory data, and the output is the efficient collection order. The server generates this order and prepares it as instructions for autonomous aircraft.

[0690] Step 5:

[0691] The server transmits an optimized collection order to the autonomous aircraft. Based on the instructions from the server, the aircraft autonomously flies through the warehouse and begins collecting the specified items. The aircraft's sensors are used to send feedback data back to the server to verify picking accuracy.

[0692] Step 6:

[0693] After data collection is complete, the server calculates the most efficient transport route. Traffic and weather data are also considered as input, and the optimal route is output. The server transmits this route to the autonomous aircraft, which immediately begins transport operations.

[0694] Step 7:

[0695] Autonomous aircraft follow designated transport routes and deliver goods to their destinations. Once transport is complete, the aircraft awaits further instructions from the server and returns to a charging station if necessary. This process ensures that logistics are consistently fast, safe, and efficient.

[0696] (Application Example 1)

[0697] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0698] Managing warehouse inventory and streamlining logistics processes has presented various challenges with traditional methods. These include delays and errors due to manual inventory updates, insufficient accuracy in supply and demand forecasts, and increased time and costs due to manual picking and delivery. There is a need to solve these problems and accelerate and streamline logistics.

[0699] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0700] In this invention, the server includes means for monitoring warehouse inventory in real time using a detection device and updating information in an information storage unit, means for performing supply and demand forecasting using AI technology based on past sales information and inventory information, and means for receiving order information from users and optimizing the order of item collection. This enables the automation and efficiency of inventory management and logistics processes in a logistics center.

[0701] A "detection device" is a device used to measure the inventory status in a warehouse in real time and collect information.

[0702] The "information storage unit" is a storage device used to hold and manage acquired inventory data.

[0703] "AI technology" refers to technology that uses artificial intelligence to analyze data and support decision-making for supply and demand forecasting and efficient business operations.

[0704] An "autonomous flying device" is a flying device that has the ability to pick and deliver items in a specified order without external intervention.

[0705] A "transportation route" is the planned path for transporting picked goods to their destination.

[0706] "Transportation progress status" refers to information indicating the position of goods along the transportation route and how far they have traveled towards their destination.

[0707] The system for implementing this invention is a system that automates inventory management and logistics processes within a warehouse. First, a server uses a detection device to monitor the inventory in the warehouse in real time and updates this information in the information storage unit. As a result, detailed information such as the location and quantity of goods is always accurate.

[0708] The server utilizes AI technology to forecast supply and demand based on past sales data and current inventory information. This forecast clearly identifies the types and quantities of goods that will be needed next. Based on this information, the server determines the optimal order for collecting items.

[0709] When a customer places an order, the server receives the order information and optimizes the order in which items are collected. The autonomous flight system follows instructions from the server, flying through the warehouse along an efficient route to pick the goods. The server also calculates the optimal transport route and sends instructions to the autonomous flight system.

[0710] The progress of shipments is constantly monitored by servers to ensure that goods are delivered quickly and safely to their destination. Generative AI is used to comprehensively manage warehouse conditions and inventory awareness, thereby improving the efficiency of the entire business process.

[0711] For example, when a user orders a specific electronic product, the server determines the location of the product in the warehouse and uses an autonomous flight device to direct the user to the shortest route. Another example of a prompt used for the generated AI is, "Predict the amount of inventory needed for the next two weeks and display it along with the current inventory status."

[0712] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0713] Step 1:

[0714] The server uses detection devices to acquire inventory data from the warehouse in real time. This provides input information such as the location and quantity of each product. The server updates its information storage unit with this data to gain an overall understanding of the warehouse's inventory status.

[0715] Step 2:

[0716] The server receives historical sales information and current inventory information as input and uses AI technology to perform supply and demand forecasting. It processes the data through time series analysis and trend analysis to predict future demand and records the results as output.

[0717] Step 3:

[0718] The terminal receives order information from the user and transmits it to the server. Based on this order information, the server uses AI technology to calculate the optimal order for collecting items. This clearly instructs the user which items should be collected and in what order.

[0719] Step 4:

[0720] The server transmits the calculated item collection order to the autonomous flight device. The flight device receives instructions from the server and picks items while flying along an efficient route. Here, the optimization of the flight path is performed as a data calculation.

[0721] Step 5:

[0722] Once picking is complete, the server calculates the optimal transport route for the autonomous flight device and issues instructions. This calculation selects the delivery route and instructs the device to ensure safe and rapid delivery.

[0723] Step 6:

[0724] The server constantly monitors the progress of the transport and verifies that the aircraft is following the designated route precisely. Once the transport is complete, it records the information as output and prepares for the next mission.

[0725] Step 7:

[0726] Using generation AI, the server analyzes warehouse conditions and inventory information, and manages the entire process in an integrated manner. This allows for adjustments to improve overall operational efficiency. By using prompts such as, "Predict the inventory needed for the next two weeks and display it along with the current inventory status," continuous improvement becomes possible.

[0727] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0728] This invention presents an embodiment of a system that, in addition to automating warehouse inventory management and logistics processes, recognizes user emotions and provides a personalized experience. The main components of the system and their respective functions are described below.

[0729] The server acquires real-time inventory information from sensors installed in the warehouse and stores it in a database. This ensures that the quantity and location information of products are always up-to-date. Furthermore, based on past sales data and current inventory data, an AI algorithm is used to forecast supply and demand, thereby improving the efficiency of product supply.

[0730] The terminal receives order information sent by the user through the online store and forwards it to the server. In addition to the product name, quantity, and shipping address, the order information is analyzed by an emotion engine based on the user's input. The emotion engine analyzes the user's voice tone, input speed, selected words, etc., and evaluates their emotional state.

[0731] Based on the analysis results, the server dynamically adjusts the ordering process. For example, if it determines that the user is experiencing dissatisfaction or stress, the server adjusts the priority of autonomous drones and optimizes thermal processing to quickly process the user's order. Additionally, an emotion engine analyzes the user's preferences and provides personalized product suggestions.

[0732] As a benefit of the system, users can receive more personalized product recommendations and appropriate support. For example, special offers and new product information are presented in an emotionally resonant way. This improves the user experience.

[0733] Autonomous unmanned aerial vehicles (UAVs) operate based on instructions from a server during the picking and delivery process. They pick goods according to optimized routes and efficiently transport them to delivery locations.

[0734] As a concrete example, when a user selects products online, the emotion engine suggests related products that the user might be interested in. Once the order is complete, the server comprehensively manages the logistics flow, including referencing the results of the emotion engine in addition to the normal process and performing high-priority processing.

[0735] This system can improve not only the accuracy of inventory management and the efficiency of logistics, but also the user experience.

[0736] The following describes the processing flow.

[0737] Step 1:

[0738] The server retrieves inventory information in real time from sensors within the warehouse. The retrieved data is stored in a database, keeping the quantity and location information of products constantly up-to-date.

[0739] Step 2:

[0740] The server uses an AI algorithm to forecast supply and demand based on historical sales data stored in the database and current inventory data. This forecast makes it possible to create an appropriate inventory replenishment plan.

[0741] Step 3:

[0742] Users select products and enter order information through the online store. During this process, the emotion engine analyzes the user's input speed and word choices to evaluate their emotional state.

[0743] Step 4:

[0744] The terminal receives order information from the user and sends it to the server along with emotion engine data. The server then optimizes the order process based on this information.

[0745] Step 5:

[0746] The server takes the user's emotional state into consideration and adjusts order priorities accordingly. For example, if a user expresses dissatisfaction, it instructs the autonomous drone to prioritize processing that order.

[0747] Step 6:

[0748] The server calculates the optimal picking order and sends instructions to the autonomous drone. The autonomous drone flies around the warehouse according to the instructions and picks the specified items.

[0749] Step 7:

[0750] The server calculates the optimal delivery route and transmits it to the autonomous drone. The autonomous drone follows the route and quickly delivers the goods to the user.

[0751] Step 8:

[0752] Once the autonomous drone completes its delivery, it awaits further instructions or returns to the station for recharging if necessary. The server records the entire process and uses it to plan future deliveries.

[0753] (Example 2)

[0754] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0755] Modern logistics systems demand improved accuracy in inventory management, more efficient supply and demand forecasting, greater flexibility in order processing, and a better user experience. In particular, real-time inventory tracking, dynamic order processing, and personalized experiences based on sentiment data are key challenges. Addressing these issues comprehensively is crucial for optimizing logistics and enhancing the user experience.

[0756] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0757] In this invention, the server includes means for monitoring warehouse inventory in real time using a measuring device and updating information in an information recording device; means for performing supply and demand forecasting using a machine learning algorithm based on past transaction data and inventory data; means for receiving order information from users and optimizing the picking order; means for analyzing the emotional state of users; means for dynamically adjusting order processing based on the analysis results; and means for providing personalized product suggestions. This enables improved accuracy in inventory management, increased efficiency in logistics processes, and an enhanced user experience.

[0758] A "measuring device" is a device that detects goods and environmental conditions in a warehouse in real time and provides that data.

[0759] An "information recording device" is a device that stores data acquired from measuring devices and organizes it for later analysis and use.

[0760] "Transaction data" is a collection of information about past sales and purchases, and is fundamental information used for supply and demand forecasting.

[0761] A "machine learning algorithm" is a mathematical model used by computer systems to learn patterns from past data and predict future supply and demand.

[0762] "Users" refers to customers or users who order products and receive services through the system.

[0763] "Picking sequence" refers to the efficient order or route for collecting goods within a warehouse.

[0764] An "autonomous transport device" is a device that automatically travels along a designated route under programmatic control, picking and delivering goods.

[0765] "Emotional state" refers to the user's current psychological state, which the system analyzes from the user's input and behavior.

[0766] "Personalized product recommendations" is a method of presenting products and services that are best suited to a particular user based on their interests and preferences.

[0767] This system integrates multiple advanced technologies to optimize warehouse inventory management and logistics.

[0768] The system primarily involves servers, terminals, and users, and its overall efficiency is achieved through the coordinated actions of each component.

[0769] The server acquires inventory data in real time from measuring devices placed within the warehouse and stores it in an information recording device. Various sensors are used to monitor details such as the number of items, shelf location, and storage conditions. This data is recorded in a database and forms the basis for subsequent supply and demand forecasting. When using machine learning algorithms, libraries such as TensorFlow and PyTorch are used to perform supply and demand forecasting based on transaction data. This is to create an appropriate supply plan for goods and prevent stockouts and surpluses.

[0770] The terminal receives order information sent by users via the online store. This order information includes details such as product name, quantity, and shipping address. The terminal also uses an emotion analysis engine to analyze user input data. This analysis includes voice data and input speed, and utilizes natural language processing technologies such as Hugging Face's Transformers. The analysis results are sent to a server and used to adjust dynamic order processing.

[0771] As a concrete example, the server optimizes the picking order based on data analysis results and issues instructions to the autonomous transport system. This allows the autonomous transport system to follow the optimized route and pick items quickly and accurately. This entire process significantly improves logistics efficiency.

[0772] Furthermore, users receive personalized product recommendations based on sentiment analysis results. For example, if a user shows interest in products in a specific category, related products are automatically recommended. This allows users to have a better purchasing experience.

[0773] An example of a prompt used to control a generative AI model is, "Tell me how to improve the user experience using sentiment analysis in online store orders." Using this prompt, the system will generate specific strategies to enhance the user experience.

[0774] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0775] Step 1:

[0776] The server acquires inventory data in real time from measuring devices placed within the warehouse. Its inputs include receiving sensor signals from each measuring device to obtain product counts, location information, and environmental data. Specifically, the server integrates these signals and writes the data to an information recording device. The output is stored in a database as the latest inventory data.

[0777] Step 2:

[0778] The server uses machine learning algorithms to perform supply and demand forecasting based on inventory and transaction data stored in the database. This historical data is used as input. The server utilizes TensorFlow and PyTorch libraries to apply the algorithms and predict future supply and demand. The output generates supply and demand forecast data, which is used to optimize supply plans.

[0779] Step 3:

[0780] The terminal receives order information submitted by the user from the online store. The input includes the user's specified product name, quantity, and shipping address. The received order information is then forwarded to the server. Specifically, the terminal sends this information to the server as input and begins order processing. The output is used by the server as accurate order information.

[0781] Step 4:

[0782] The device analyzes user input using an emotion analysis engine. This input includes user voice data, text input speed, and selected words. The device processes this data using the emotion analysis engine to analyze the user's emotional state. Specifically, the device generates analysis results and outputs them to the server as emotion data.

[0783] Step 5:

[0784] The server dynamically adjusts order processing using sentiment analysis results. Input includes sentiment analysis results and user order information. Based on this data, the server instructs the automated guided vehicles (AGVs) to adjust the picking order. Specifically, it generates picking instructions in order of priority and sends them to the AGVs. The output is an optimized picking plan.

[0785] Step 6:

[0786] The server generates personalized product recommendations based on the user's preference data. Input includes the user's past purchase history and sentiment analysis results. The server analyzes this data and prepares relevant product information. Specifically, it creates and outputs suggestions for similar or recommended products to the user.

[0787] Step 7:

[0788] The autonomous transport system retrieves goods according to an optimized picking sequence and moves them to the delivery point. Inputs include picking and delivery instructions from a server. The transport system automatically follows a designated route, efficiently picking goods. Specifically, it follows a path from the collection point to the destination, delivering the goods to the final delivery point as output.

[0789] (Application Example 2)

[0790] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0791] In recent years, there has been a growing demand for streamlining warehouse inventory management and logistics processes. However, consistently managing inventory in real time, forecasting supply and demand, and calculating optimal delivery routes remains challenging. Furthermore, improving the user experience requires analyzing customer emotions and providing personalized services, but few systems can achieve this efficiently and effectively. To address these challenges, there is a need for comprehensive inventory management and logistics systems that include dynamic adjustments to support content based on emotional states.

[0792] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0793] In this invention, the server includes means for monitoring warehouse inventory in real time using a detection device and updating information in a database; means for forecasting supply and demand using an artificial intelligence algorithm based on past sales data and inventory data; means for receiving customer order information and optimizing the order of product selection; means for calculating the optimal delivery route and sending instructions to an autonomous aircraft; and means for analyzing the customer's emotional state and dynamically adjusting the support content based on that emotional state. This makes it possible to improve not only the efficiency of inventory management and logistics processes but also the user experience.

[0794] A "detection device" is a device used to monitor the inventory status in a warehouse in real time.

[0795] A "database" is an information management system that stores inventory information acquired from detection devices and performs searches and updates as needed.

[0796] An "artificial intelligence algorithm" is a computational method that analyzes past sales and inventory data to predict supply and demand.

[0797] "Selection order" refers to the sequence setting used to optimize the order in which products are picked up based on customer order information.

[0798] An "autonomous flying machine" is an unmanned aerial vehicle that sorts goods according to an optimized sorting sequence and delivers those goods using the most suitable delivery route.

[0799] "Customer emotional state" refers to information that forms the basis for providing individualized service by analyzing and evaluating the customer's emotions at the time of ordering.

[0800] "Support content" refers to the methods and content of service and product delivery that are dynamically adjusted according to the customer's emotional state.

[0801] The server monitors inventory information in real time using detection devices installed within the warehouse and updates this information in a database. Specifically, the detection devices include various types of sensors, which are typically used to determine the location and quantity of inventory. For database management, SQL or NoSQL databases capable of real-time updates are commonly used.

[0802] Based on this information, the server analyzes past sales data and uses artificial intelligence algorithms to predict supply and demand. Machine learning libraries such as TensorFlow and PyTorch may be used in the software. This is expected to optimize inventory and improve the accuracy of sales forecasts.

[0803] The terminal processes order information received from customers and optimizes the sorting order of products. This process considers the product name, quantity, and delivery address information, and the sorting order is instructed from the server to the autonomous flying machine. The flying machine is equipped with GPS and an IMU (Inertial Measurement Unit) to automatically calculate and navigate the optimal delivery route.

[0804] The user's emotional state is determined by the device analyzing input information (such as voice commands and touch speed). The server then uses this information to dynamically adjust the support provided. This allows for immediate support or the suggestion of special services if the user is feeling anxious.

[0805] As a concrete example, the server queries the generation AI model through a prompt message, sending a request such as, "Please tell me the best response to reduce user stress." As a result, a customized response that matches the user's emotional state is proposed, and appropriate notifications or coupons can be issued if there is a delay in product delivery.

[0806] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0807] Step 1:

[0808] The server retrieves inventory information from detection devices in the warehouse and updates the database. The input is real-time data from the sensors, and the output is the latest database state, including inventory quantity and location information. The data is processed using SQL queries and written to the database.

[0809] Step 2:

[0810] The server uses historical sales data and current inventory data to run an artificial intelligence algorithm and predict supply and demand. The input is sales history and inventory data, which are analyzed using machine learning tools such as TensorFlow. The output is predicted future supply and demand data. Based on this prediction, inventory optimization becomes possible.

[0811] Step 3:

[0812] The terminal transfers order information received from the user to the server and uses this information to optimize the product sorting order. The input is order information (product name, quantity, delivery address), and the output is a list of the optimized sorting order. In this process, an algorithm calculates priorities to streamline the selection process.

[0813] Step 4:

[0814] The server transmits an optimized sorting sequence as instructions to the autonomous flying machine. The input is sorting sequence data, and the output is a movement command to the flying machine. In this process, a route plan based on GPS coordinates is created and instructions are sent to the flying machine to ensure accurate picking and delivery of goods.

[0815] Step 5:

[0816] The terminal processes user input information (voice tone, input speed) using an emotion analysis engine to determine the customer's emotional state. The input is user interaction data, and the output is the analyzed emotional state. This allows the system to begin providing services tailored to the user's emotions.

[0817] Step 6:

[0818] The server sends prompt messages to a generating AI model based on the analyzed emotional state, and determines dynamic support content. The input is emotional state data, and the output is user support measures based on the prompt messages. Specific actions include determining immediate responses for users experiencing stress and presenting special offers.

[0819] 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.

[0820] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0821] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0822] 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.

[0823] Figure 9 shows an 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.

[0824] 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.

[0825] 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.

[0826] 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, motorcycles, etc., 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, for example, based 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.

[0827] 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."

[0828] 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.

[0829] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0830] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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 the like 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.

[0839] 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.

[0840] The following is further disclosed regarding the embodiments described above.

[0841] (Claim 1)

[0842] A method for monitoring warehouse inventory in real time using sensors and updating the information in a database,

[0843] A method for forecasting supply and demand using an AI algorithm based on past sales data and inventory data,

[0844] A means for receiving order information from users and optimizing the picking order,

[0845] A means of picking goods according to an optimized picking sequence using an autonomous unmanned aerial vehicle,

[0846] A means of calculating the optimal delivery route and sending instructions to an autonomous unmanned aerial vehicle,

[0847] A system that includes this.

[0848] (Claim 2)

[0849] The system according to claim 1, further comprising means for the autonomous unmanned aircraft to autonomously fly to a delivery location after picking the goods and deliver the goods.

[0850] (Claim 3)

[0851] The system according to claim 1, further comprising means for analyzing warehouse conditions and inventory data using generating AI and for integrating the above means.

[0852] "Example 1"

[0853] (Claim 1)

[0854] A means for monitoring items in a warehouse in real time using a detection device and updating the information in a data storage device,

[0855] A means of performing supply and demand forecasting using a calculation model based on historical data and product information,

[0856] A means for receiving request information from users and optimizing the order of item collection,

[0857] A means of collecting items according to an optimized collection sequence using an autonomous aircraft,

[0858] A means of calculating the optimal transport route and sending instructions to an autonomous aircraft,

[0859] A system that includes this.

[0860] (Claim 2)

[0861] The system according to claim 1, further comprising means for the autonomous aircraft to autonomously move to a transport point after collecting the goods and to transport the goods.

[0862] (Claim 3)

[0863] The system according to claim 1, further comprising means for analyzing the state and item information within a warehouse using generating artificial intelligence, and for comprehensively managing each of the above means.

[0864] "Application Example 1"

[0865] (Claim 1)

[0866] A means for monitoring inventory in a warehouse in real time using a detection device and updating the information in an information storage unit,

[0867] A method for forecasting supply and demand using AI technology based on past sales and inventory information,

[0868] A means for receiving order information from users and optimizing the order of item collection,

[0869] A means for collecting items according to an optimized item collection sequence using an autonomous flight device,

[0870] A means of calculating the optimal transport route and sending instructions to an autonomous flight device,

[0871] Means for monitoring the progress of transportation,

[0872] A system that includes this.

[0873] (Claim 2)

[0874] The system according to claim 1, further comprising means for the autonomous flying device to autonomously fly to a transport location after collecting an item and transporting the item.

[0875] (Claim 3)

[0876] The system according to claim 1, further comprising means for analyzing warehouse conditions and inventory information using generating AI and for integrating and managing each of the above means.

[0877] "Example 2 of combining an emotion engine"

[0878] (Claim 1)

[0879] A means for monitoring warehouse inventory in real time using a measuring device and updating the information in an information recording device,

[0880] A method for forecasting supply and demand using machine learning algorithms based on past transaction data and inventory data,

[0881] A means for receiving order information from users and optimizing the picking order,

[0882] A means for acquiring goods according to an optimized picking sequence using an autonomous transport device,

[0883] A means for calculating the optimal delivery route and sending instructions to an autonomous transport device,

[0884] A means of analyzing the emotional state of users,

[0885] A means for dynamically adjusting order processing based on the analysis results,

[0886] Means of providing personalized product recommendations,

[0887] A system that includes this.

[0888] (Claim 2)

[0889] The system according to claim 1, further comprising means for the autonomous transport device to autonomously move to a delivery point after acquiring goods and deliver the goods.

[0890] (Claim 3)

[0891] The system according to claim 1, further comprising means for analyzing warehouse conditions and inventory data using generating AI and for integrating the above means.

[0892] "Application example 2 when combining with an emotional engine"

[0893] (Claim 1)

[0894] A means of monitoring warehouse inventory in real time using detection devices and updating the information in a database,

[0895] A method for forecasting supply and demand using artificial intelligence algorithms based on past sales and inventory data,

[0896] A means of receiving order information from customers and optimizing the order in which products are selected,

[0897] A means of sorting goods according to an optimized sorting sequence using an autonomous flying machine,

[0898] A means of calculating the optimal delivery route and sending instructions to an autonomous flying machine,

[0899] A means of analyzing the customer's emotional state and dynamically adjusting the support provided based on that emotional state,

[0900] A system that includes this.

[0901] (Claim 2)

[0902] The system according to claim 1, further comprising means for the autonomous flying machine to autonomously move to a delivery location after sorting the goods and deliver the goods.

[0903] (Claim 3)

[0904] The system according to claim 1, further comprising means for analyzing warehouse conditions and inventory data using generating artificial intelligence and for integrating the above means. [Explanation of Symbols]

[0905] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for monitoring inventory in a warehouse in real time using a detection device and updating the information in an information storage unit, A method for forecasting supply and demand using AI technology based on past sales and inventory information, A means for receiving order information from users and optimizing the order of item collection, A means for collecting items according to an optimized item collection sequence using an autonomous flight device, A means of calculating the optimal transport route and sending instructions to an autonomous flight device, Means for monitoring the progress of transportation, A system that includes this.

2. The system according to claim 1, further comprising means for the autonomous flying device to autonomously fly to a transport location after collecting an item and transporting the item.

3. The system according to claim 1, further comprising means for analyzing warehouse conditions and inventory information using generating AI and for integrating and managing each of the above means.

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