system

Abstract

We provide the system. [Solution] Multiple detectors for collecting product inventory data in real time, A computing device that analyzes inventory data collected by the detector and predicts supply and demand, A control device that controls the flight device to optimize a predetermined product picking route, A means for automatically sorting and transporting goods using the aforementioned flying device, 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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the logistics industry and e-commerce, the management of in-warehouse inventory is required to be efficient due to labor shortages and high picking costs. In conventional inventory management systems, it is difficult to quickly respond to urgent orders, and inventory data is often not available in real time. Furthermore, in systems that utilize automation technologies such as drones, there are issues such as optimization of flight routes and compliance with regulations.

Means for Solving the Problems

[0005] This invention enables efficient monitoring of warehouse inventory levels by using multiple detectors to collect product inventory data in real time. Furthermore, by analyzing the collected data with a computing device and performing supply and demand forecasts, it allows for preparation for future orders. A management device that controls the aircraft optimizes picking routes, and the aircraft automatically sorts and transports goods. This management device stores and considers local legal regulations and adjusts schedules to ensure safe and legal operation.

[0006] "Product inventory data" refers to information regarding the quantity and location of products stored within a warehouse.

[0007] A "detector" refers to a sensor or device installed in a warehouse to detect the quantity and location of goods.

[0008] A "computational device" refers to a computer system used to process collected data and perform necessary analyses and predictions.

[0009] "Supply and demand forecasting" refers to the process of predicting the future balance of supply and demand based on historical data and current trends.

[0010] "Flying devices" refer to drones or similar mechanical devices that perform autonomous flight within warehouses to pick and transport goods.

[0011] A "management device" is a system for controlling the flight equipment, and it has functions such as optimizing picking routes and managing schedules.

[0012] "Legal and regulatory information" refers to data on laws and regulations related to the operation of aircraft, and is used to ensure that the flight plan complies with them.

[0013] "Sorting" refers to the act of identifying and appropriately retrieving necessary items during the picking process.

[0014] "Transportation" refers to the movement of sorted goods to their destination using aircraft. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] One embodiment of the present invention provides a system for automating product inventory management and streamlining logistics processes. This system includes multiple detectors, computing devices, management devices, and flight devices located within a warehouse.

[0037] First, the server collects inventory data from detectors installed within the warehouse. These detectors consist of RFID sensors and video recognition cameras, and have the capability to detect product quantities in real time.

[0038] Next, the terminal uses an AI algorithm to forecast supply and demand for each product based on inventory data sent from the server. This process also takes into account past transaction data and market trend information. As a result, it predicts the supply and demand balance of the product and calculates the required inventory quantity.

[0039] Subsequently, the server receives supply and demand forecast results from the computing device and optimizes the picking routes for the drones. The drones are drones that fly autonomously within the warehouse and pick specified items. The management device controls these drones to ensure efficient picking.

[0040] Furthermore, users can monitor these processes in real time and intervene manually as needed. For example, if abnormal inventory fluctuations or sensor malfunctions are detected, users can take appropriate action.

[0041] Furthermore, the server can appropriately adjust the drone's flight schedule in accordance with laws and regulations. This ensures safe operation in compliance with regulations set by local governments and related organizations.

[0042] As a concrete example, if demand for product A suddenly increases in a warehouse, the server immediately checks the inventory data, and a terminal uses that information to create a new supply and demand forecast. Based on this data, the server quickly adjusts the picking route, and the flying device prioritizes the selection and transport of product A. This entire process is automated, minimizing human intervention.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The server collects product inventory data in real time from multiple detectors installed within the warehouse. Each detector detects the quantity and location of products and transmits this information to the server. This data is stored in the inventory management system on the server.

[0046] Step 2:

[0047] The terminal uses an AI algorithm to perform supply and demand forecasting based on inventory data obtained from the server. The terminal analyzes past sales history and market trend data to predict future demand for each product. The forecast results are used for future inventory replenishment and picking plans.

[0048] Step 3:

[0049] The server uses supply and demand forecasts to create a priority list of items to be picked. A management system calculates the optimal picking route for the drones and issues instructions. This route is designed based on a map of the warehouse and information on the placement of goods.

[0050] Step 4:

[0051] The user monitors the product picking process performed by the autonomous flight of the aircraft. The aircraft follows a route instructed by the server and performs the picking task. If an anomaly occurs, the user intervenes manually to resolve the issue.

[0052] Step 5:

[0053] The server packs the picked items based on order information and optimizes the delivery route. Factors such as distance to the destination, priority, and traffic information are considered when determining the delivery route.

[0054] Step 6:

[0055] The terminal checks the flight plan for the aircraft in accordance with local laws and regulations and adjusts the plan as needed. If modifications are required based on the legal information, it notifies the server of the update.

[0056] (Example 1)

[0057] 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."

[0058] To streamline and automate logistics processes, accurate inventory management and planned delivery are essential. However, current systems struggle with accurate inventory data collection and supply and demand forecasting, resulting in frequent manual intervention and inefficiency. Furthermore, in automated picking using aircraft, real-time route optimization and compliance with legal flight restrictions are critical challenges.

[0059] 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.

[0060] In this invention, the server includes means using multiple sensors for collecting product inventory information in real time, means using a computing device for processing the collected inventory information and making supply and demand forecasts, and means using a control device for controlling aircraft to optimize the item sorting route. This enables improved inventory accuracy, automation of logistics, efficient route optimization, and implementation of safe operation plans in compliance with laws and regulations.

[0061] "Product inventory information" refers to information such as the type, quantity, and storage location of products in storage locations such as warehouses and stores.

[0062] A "sensor" is a device used to detect a specific object or state, and in this invention, it refers to RFID sensors, video recognition cameras, and the like.

[0063] A "processing unit" is a device used to process data and perform calculations and analyses, and includes processors and computers used for supply and demand forecasting and data analysis.

[0064] An "airplane" is a device that flies autonomously within a warehouse for the purpose of sorting and transporting goods, and in this invention, it refers to a drone.

[0065] A "control device" refers to a device used to manage and instruct the operation of specific equipment or systems.

[0066] A "generative AI model" refers to a model that uses artificial intelligence to generate and analyze data, and in this invention, it is used for supply and demand forecasting and route optimization.

[0067] "Real-time monitoring" means observing and managing the operation of systems and processes immediately and without delay.

[0068] "Legal information" refers to laws and regulations related to operations, including standards and rules that should be considered when planning the operation of aircraft.

[0069] This invention comprises a system combining multiple sensors, computing devices, control devices, aircraft, and generative AI models to streamline inventory management and logistics processes within a warehouse.

[0070] First, the server uses sensors, including RFID sensors and video recognition cameras, installed within the warehouse to collect product inventory information in real time. This makes it possible to instantly determine the current quantity and location of items in stock.

[0071] Next, the terminal uses an AI algorithm to perform supply and demand forecasting based on inventory information received from the server. In doing so, the terminal also refers to past sales data and market trend information, and performs data analysis using its computing device. Based on the supply and demand forecasting results, it predicts future inventory shortages or surpluses and calculates the required inventory levels.

[0072] Subsequently, the server uses the supply and demand forecast results obtained from the computing unit to manage the drones, which are aircraft, via a control device. The server optimizes the picking routes for goods while referring to warehouse map data. This uses advanced algorithms to achieve efficient and rapid handling of goods.

[0073] Users monitor this entire process in real time through an interface. In the event of unusual inventory fluctuations or sensor malfunctions, users can manually adjust the system to resolve the problem.

[0074] As a concrete example, if demand for a particular product surges, the server immediately checks inventory information, and the terminal uses an AI model to create a new supply and demand forecast. Based on this information, the server instantly recalculates the picking route, and the aircraft prioritizes the selection and transportation of the designated products. This entire process is automated, minimizing human intervention.

[0075] An example of a prompt for a generated AI model would be an instruction such as, "Use real-time warehouse inventory data to predict supply and demand using AI and optimize the route." This allows the AI ​​model to properly perform supply and demand forecasting and route calculations.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The server collects product inventory information in real time using RFID sensors and video recognition cameras installed within the warehouse. Inputs include product ID, quantity, and location information detected by each sensor. The server analyzes this data and outputs it as the current inventory status. Specifically, the server communicates with each sensor and updates the collected data at regular intervals.

[0079] Step 2:

[0080] The terminal uses an AI algorithm to forecast supply and demand based on inventory information received from the server. Inputs include the latest inventory data, historical sales data, and market trend information provided by the server. The terminal inputs this data into a generating AI model to analyze supply and demand trends. As a result, it outputs supply and demand forecast data predicting future inventory shortages or surpluses. The specific operation involves calling the AI ​​model, preprocessing the data, and executing the forecasting algorithm.

[0081] Step 3:

[0082] The server optimizes picking routes using drones based on supply and demand forecasts obtained from terminals. Inputs include supply and demand forecast data from terminals and information on the location of goods stored in the warehouse. The server uses this data to calculate and output the shortest route that allows the drone to efficiently sort and transport goods. Specifically, it generates a drone operation plan and sends instructions to the drone following the optimal route.

[0083] Step 4:

[0084] The user monitors all processes in real time and intervenes as needed. Inputs include inventory status and drone operation information provided by servers and terminals. Based on this information, the user can quickly detect abnormal inventory fluctuations or equipment malfunctions and take manual corrections or countermeasures. Specific actions include checking information displayed through the interface and entering commands as needed.

[0085] (Application Example 1)

[0086] 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."

[0087] Modern logistics centers require efficient inventory management and picking, but traditional methods have problems with real-time inventory tracking and accurate supply and demand forecasting, resulting in insufficient optimization of logistics. Furthermore, logistics center managers lack the means to quickly grasp inventory status and respond to abnormal situations.

[0088] 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.

[0089] In this invention, the server includes means for having multiple detectors for collecting product inventory data in real time, means for having a computing device for analyzing the inventory data collected by the detectors and predicting supply and demand, means for having a management device for controlling flight devices to optimize predetermined product picking routes, means for having an information management device for logistics center operators to monitor inventory status and picking status in real time, and means for having a warning system that immediately notifies of inventory fluctuations or equipment malfunctions. This enables more efficient inventory management and faster response in logistics centers.

[0090] "Product inventory data" refers to information used to record the status of each product in a logistics center in real time, including its quantity and location.

[0091] A "detector" is a device that uses technologies such as RFID sensors and video recognition cameras to identify the location and quantity of products in order to acquire product inventory data.

[0092] A "computational device" is a device that uses AI and algorithms to forecast supply and demand based on collected inventory data, and to optimize logistics.

[0093] A "management device" is a device that controls the operation of aircraft and product picking routes, and performs control to ensure that logistics operations proceed efficiently.

[0094] "Flying devices" refer to devices such as drones that automatically move around within a warehouse, sorting and transporting designated goods according to an optimized route.

[0095] An "information management device" is a system that allows logistics center operators to monitor inventory status and picking operations in real time and visualize necessary data.

[0096] A "warning system" is a system designed to promptly notify administrators of inventory fluctuations or equipment malfunctions, and to assist in resolving these issues.

[0097] One embodiment of this invention is to automate inventory management and picking operations in a logistics center, thereby improving their efficiency. The server collects product inventory data from multiple detectors installed in the warehouse. These detectors use RFID sensors and video recognition cameras to track the status of products in real time.

[0098] The terminal uses AI algorithms to forecast supply and demand based on collected inventory data. The AI ​​models used here utilize machine learning frameworks such as TENSORFLOW®. Historical transaction data and market trends are also considered during the supply and demand forecasting process, and logistics plans are optimized accordingly.

[0099] The management system optimizes the picking routes of the drones, which are the flying devices, based on supply and demand forecasts from the server. In this process, a cloud service using GOOGLE FI® rebase is used for storing and retrieving information, enabling efficient data exchange.

[0100] Users can monitor the inventory and picking status of the logistics center in real time through the information management system. If inventory fluctuations or equipment malfunctions occur, the warning system will immediately notify users and provide information to enable them to respond quickly. This notification function allows for a rapid response when problems arise.

[0101] For example, if demand for a particular product surges during a major sale, this system will immediately detect that demand, check inventory levels, and update supply and demand forecasts. Furthermore, it will use drones to efficiently pick products, enabling timely delivery of goods during the sale.

[0102] An example of a prompt for a generated AI model might be: "Explain how to forecast the supply and demand of a specific product inventory during a major sale period and optimize the drone picking route."

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] The server collects real-time product inventory data from various detectors within the warehouse. Inputs are data from RFID sensors and video recognition cameras, while outputs include inventory data such as product quantity and location information. This inventory data is centrally managed via a cloud service.

[0106] Step 2:

[0107] The terminal receives inventory data from the server and uses an AI algorithm to forecast supply and demand based on that data. It uses historical transaction history and market trend data as input and predicts the supply and demand balance for each product as output. Machine learning libraries such as TensorFlow are used in this process.

[0108] Step 3:

[0109] The server optimizes the picking routes for drones based on supply and demand forecasts. The input is supply and demand forecast data, and the output is optimized picking route information. Google® Firebase is used to manage route information and send commands to the drones.

[0110] Step 4:

[0111] Users monitor the inventory and picking status of the logistics center in real time through an information management device. Input is inventory and picking information from the server, and output is a status display on the monitor screen. If an anomaly is detected, the user receives an immediate notification from the system.

[0112] Step 5:

[0113] Users manually intervene in the system as needed to resolve problems. Inputs are notifications from the warning system, and outputs are actions taken based on user judgment. This allows for quick responses to abnormal inventory fluctuations and equipment malfunctions.

[0114] 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.

[0115] One embodiment of the present invention provides a system that automates the management of product inventory in a warehouse and optimizes operations by recognizing user emotions. This system collects product inventory data using multiple detectors and performs supply and demand forecasting using a computing device. Furthermore, picking routes are optimized through a management device that controls flight devices. In addition, an emotion engine is incorporated that recognizes user emotions in real time and adjusts the system's operation based on those emotions.

[0116] The server collects inventory data in real time from detectors installed in the warehouse. These detectors include RFID tags and cameras, each playing a role in transmitting product inventory counts and location information to the server.

[0117] Next, the terminal uses an AI algorithm to perform supply and demand forecasting based on the data collected by the server. This process includes historical sales data and trend analysis to predict future inventory demand. The terminal sends the results to the server, supporting the operation of the entire system.

[0118] Subsequently, the server utilizes predictive data to calculate the optimal picking route for the drones and controls them through a management system. This enables efficient sorting and transportation of goods within the warehouse.

[0119] Users are evaluated in real time by an emotion engine, which assesses their mental stress and satisfaction levels. This emotion engine acquires emotional data from the user's voice and facial expressions, and adjusts the system's operation accordingly. Specifically, if a user is experiencing high stress levels, the system will readjust their picking route or provide an interval for rest.

[0120] Furthermore, the system includes an emotion engine that collects user emotional data and provides relaxation content and feedback to reduce stress. Through this process, the user experience can be improved, and the system's operational efficiency and safety can be maximized.

[0121] For example, if a user starts working in a warehouse and is stressed because they are not yet accustomed to the work, the emotion engine analyzes the user's tone of voice and facial expressions to detect their stress level. The system automatically makes adjustments to reduce the workload and provides the user with appropriate content to alleviate stress. This allows the user to continue working with peace of mind.

[0122] The following describes the processing flow.

[0123] Step 1:

[0124] The server receives real-time inventory data collected by multiple detectors within the warehouse. These detectors use RFID sensors and cameras to determine the location and quantity of goods. The server stores this data in a database for later analysis.

[0125] Step 2:

[0126] The terminal analyzes inventory data transferred from the server and performs supply and demand forecasting using an AI algorithm. The terminal takes into account past sales data, seasonal influences, and current market trends to predict future demand for each product. The predicted supply and demand data is then sent to the server.

[0127] Step 3:

[0128] The server generates a list of items to be picked based on supply and demand forecast data and calculates the optimal picking route for the aircraft. This allows the aircraft to plan how to efficiently select and transport the items in the shortest possible time.

[0129] Step 4:

[0130] Users have their stress levels and emotional states monitored through an emotion engine. The emotion engine analyzes the user's voice tone and facial expressions in real time and notifies the server of the evaluation results.

[0131] Step 5:

[0132] The server dynamically adjusts its operational plan based on user emotion data obtained from the emotion engine. For example, if a user's stress level is high, the server will reduce the load on the picking route or suggest an interval.

[0133] Step 6:

[0134] The device utilizes user emotional data to provide relaxation content and feedback to reduce stress. The device selects and displays content based on the user's emotions, thereby reducing the user's mental burden.

[0135] (Example 2)

[0136] 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".

[0137] In recent years, there has been a growing demand for more efficient inventory management in warehouses. However, accurately managing inventory and forecasting supply and demand using automated systems without human intervention is extremely difficult, and adjusting the system to take into account the emotional state of the users is particularly challenging. Conventional systems have failed to consider the mental burden on workers during product sorting and transportation, which has sometimes led to decreased operational efficiency. The objective of this invention is to solve these problems, improve the efficiency of the inventory management process in warehouses, and adjust the system to accommodate the emotional state of the workers.

[0138] 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.

[0139] In this invention, the server includes a plurality of sensor means for collecting information on product inventory in real time, a calculation means for analyzing the inventory information collected by the sensor means and predicting supply and demand, a control means for controlling transport devices to optimize a predetermined product sorting route, and a means for detecting the user's emotional state and adjusting the operation of the system based on the detection results. This enables efficient inventory management and optimization of the user's work environment.

[0140] "Sensor means" is a general term for devices used to detect the inventory status of goods in a warehouse, and includes RFID tag readers and cameras.

[0141] A "computational means" refers to a computing device or software used to forecast supply and demand based on collected inventory information, and which analyzes data using AI algorithms.

[0142] A "control device" is a device that instructs and manages transport devices in order to sort and transport goods according to a predetermined route.

[0143] "Means for detecting a user's emotional state" refers to technologies and devices that collect and analyze data such as voice and facial expressions to evaluate a user's stress and satisfaction level in real time.

[0144] A "transportation device" refers to an aircraft or moving device used to automatically sort and transport goods within a warehouse.

[0145] This invention is a system for efficiently managing inventory in a warehouse, and it operates by combining multiple sensors and AI technology. This system grasps product inventory information in real time and optimizes warehouse operations while taking into account the emotional state of the user.

[0146] First, the server collects inventory information in real time from multiple sensors installed within the warehouse. These sensors include RFID tag readers and cameras, which acquire identification and location information for each product. The collected data is aggregated on the server and stored in a database.

[0147] Next, the terminal uses AI algorithms based on data obtained from the server to forecast supply and demand. These AI algorithms utilize machine learning frameworks such as TensorFlow and PyTorch, analyzing past sales data and current inventory information to predict future inventory demand. This information is then sent back to the server and used for inventory management and logistics optimization.

[0148] Furthermore, the server calculates the optimal route for the transport devices based on supply and demand forecast data obtained from the computing means. Geographic Information System (GIS) technology and route optimization algorithms are used to plan efficient product sorting and transport routes. The calculation results are transmitted to the transport devices via the control means, and the products are automatically sorted and transported.

[0149] The user's emotions are evaluated in real time by an emotion engine. The emotion engine acquires the user's voice and facial expression data and analyzes stress and satisfaction levels using natural language processing and image processing technologies. If the system determines that the user is in a high-stress state, it automatically adjusts the system to reduce the workload or provide break intervals. It also presents the user with relaxation content and feedback to provide a comfortable work environment.

[0150] For example, suppose a user starts a new job in a warehouse and is overwhelmed by the workload. In this case, the emotion engine uses voice analysis and facial recognition technology to detect the user's stress, adjust the operating schedule of the transport devices, and take action to reduce the workload.

[0151] An example of a prompt for a generative AI model is, "Explain how to automate warehouse inventory management and optimize operations by recognizing user emotions."

[0152] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0153] Step 1:

[0154] The server collects inventory information from multiple sensors installed within the warehouse. Specifically, sensors such as RFID tag readers and cameras acquire identification and location information for each product in real time and transmit the data to the server. The server receives raw data from the sensors as input, converts that data into a standard format, and stores it in a database. This process allows the server to constantly monitor the inventory status within the warehouse.

[0155] Step 2:

[0156] The terminal performs supply and demand forecasting using inventory information received from the server. The terminal uses inventory data and historical sales history from the server as input. This allows AI algorithms (e.g., TensorFlow or PyTorch) to analyze the data and predict future demand. The calculation results in the generation of supply and demand forecast data, which is then sent back to the server. The entire system uses these results to plan future inventory management.

[0157] Step 3:

[0158] The server calculates the optimal route for transport devices based on supply and demand forecast data received from terminals. Using supply and demand forecast data and current inventory information as input, it executes a route optimization algorithm utilizing a Geographic Information System (GIS). The optimal picking route is generated as output and sent to the transport devices. This ensures that goods are efficiently sorted and transported.

[0159] Step 4:

[0160] The user's emotions are evaluated in real time by an emotion engine. In this process, the user's voice and facial expressions are acquired from sensors as input, and natural language processing and image processing technologies are applied. As a result of the analysis, the user's stress level and satisfaction level are output. If high stress is detected, the system automatically adjusts and takes measures to reduce the workload.

[0161] (Application Example 2)

[0162] 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".

[0163] Traditionally, inventory management in logistics centers has relied heavily on human resources, leading to efficiency challenges. Furthermore, it has been difficult to manage stress and satisfaction levels effectively, as it has been challenging to consider the emotional state of workers. Therefore, there is a need for a system that is both efficient and worker-friendly.

[0164] 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.

[0165] In this invention, the server includes multiple sensing devices for collecting product inventory data in real time, a computing device for analyzing the collected inventory data and predicting supply and demand, and an emotion analysis device for recognizing the emotional state of workers and adjusting their workload. This enables efficient inventory management and reduction of worker stress.

[0166] A "sensing device" is a device used to acquire product inventory data in real time within a logistics center, and includes sensors, RFID tags, cameras, and other similar devices.

[0167] A "calculation unit" is a device that uses collected inventory data to perform calculations to forecast supply and demand.

[0168] "Mobile equipment" refers to equipment that automatically moves goods along a designated route so that they can be efficiently sorted and transported.

[0169] A "management device" is a device that controls mobile equipment and generates the optimal item collection route, and manages the entire system.

[0170] An "emotion analysis device" is a device that recognizes the emotional state of a worker from their voice and facial expressions and adjusts their workload accordingly.

[0171] A "display device" is a device that provides information based on an emotional state in order to give feedback to an operator.

[0172] The embodiment of this invention is a system for automating product inventory management in a logistics center and optimizing operations by recognizing the emotions of workers. The server collects inventory data in real time from multiple sensing devices installed in the environment. The sensing devices use sensors, RFID tags, cameras, etc., to accurately determine the number and location of products. The server analyzes this data and uses a computing device to predict supply and demand. The computing device calculates future inventory demand based on past sales data and market trend analysis.

[0173] Mobile devices use predictive data provided by the server to set the optimal item collection route. The management device controls the mobile devices to ensure efficient product picking and transportation. In addition, it monitors the emotional state of workers through an emotion analysis device and uses voice and facial recognition technology to determine stress levels as appropriate using an AI algorithm.

[0174] Users can receive emotionally-based feedback and relaxation content through the display device. For example, if work during a busy period is causing stress, the emotion analyzer can detect this and display a message such as, "Please take a 5-minute break." This can reduce the burden on workers and improve work efficiency and safety.

[0175] An example of a prompt for a generative AI model might be: "Generate recommendations regarding my current emotional state and coping strategies. Please suggest specific measures, such as relaxation during holidays or how to spend weekday evenings." This prompt is intended to help users manage stress and create a comfortable work environment.

[0176] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0177] Step 1:

[0178] The server collects inventory data in real time from multiple sensing devices. Inputs include the location and quantity of products obtained from the sensing devices. This data is collected from RFID tags and cameras to form an initial dataset for understanding the current inventory status. The output is an updated inventory information database.

[0179] Step 2:

[0180] The server analyzes the collected inventory data using a computing device to predict supply and demand. Inputs include inventory data updated in step 1 and historical sales data. An AI algorithm is used to perform trend analysis and calculate future demand forecasts. The output is a list of required inventory based on the supply and demand forecasting model.

[0181] Step 3:

[0182] Based on the supply and demand forecast, the server sends instructions via a management device to enable mobile equipment to set the optimal item collection route. Inputs include the supply and demand forecast model generated in step 2 and current warehouse layout data. The shortest path is calculated based on this data, optimizing the picking efficiency. The output generates optimized collection route configuration information for the mobile equipment.

[0183] Step 4:

[0184] The user is monitored by an emotion analysis device, and their emotional state is determined in real time. Inputs include the user's voice data and facial expression data. These are analyzed by an AI algorithm to quantify emotional states such as stress levels. The output is the worker's current emotional evaluation result.

[0185] Step 5:

[0186] The user receives feedback through a display device that corresponds to their emotional state. The input is the emotional assessment result obtained in step 4. If the worker is determined to be highly stressed, break suggestions and relaxation content are displayed through the UI. The output provides specific action suggestions for the worker.

[0187] This series of steps will improve the efficiency of inventory management and support workers at the logistics center.

[0188] 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.

[0189] Data generation model 58 is a 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.

[0190] 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.

[0191] [Second Embodiment]

[0192] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0193] 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.

[0194] 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).

[0195] 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.

[0196] 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.

[0197] 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).

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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".

[0204] One embodiment of the present invention provides a system for automating product inventory management and streamlining logistics processes. This system includes multiple detectors, computing devices, management devices, and flight devices located within a warehouse.

[0205] First, the server collects inventory data from detectors installed within the warehouse. These detectors consist of RFID sensors and video recognition cameras, and have the capability to detect product quantities in real time.

[0206] Next, the terminal uses an AI algorithm to forecast supply and demand for each product based on inventory data sent from the server. This process also takes into account past transaction data and market trend information. As a result, it predicts the supply and demand balance of the product and calculates the required inventory quantity.

[0207] Subsequently, the server receives supply and demand forecast results from the computing device and optimizes the picking routes for the drones. The drones are drones that fly autonomously within the warehouse and pick specified items. The management device controls these drones to ensure efficient picking.

[0208] Furthermore, users can monitor these processes in real time and intervene manually as needed. For example, if abnormal inventory fluctuations or sensor malfunctions are detected, users can take appropriate action.

[0209] Furthermore, the server can appropriately adjust the drone's flight schedule in accordance with laws and regulations. This ensures safe operation in compliance with regulations set by local governments and related organizations.

[0210] As a concrete example, if demand for product A suddenly increases in a warehouse, the server immediately checks the inventory data, and a terminal uses that information to create a new supply and demand forecast. Based on this data, the server quickly adjusts the picking route, and the flying device prioritizes the selection and transport of product A. This entire process is automated, minimizing human intervention.

[0211] The following describes the processing flow.

[0212] Step 1:

[0213] The server collects product inventory data in real time from multiple detectors installed within the warehouse. Each detector detects the quantity and location of products and transmits this information to the server. This data is stored in the inventory management system on the server.

[0214] Step 2:

[0215] The terminal uses an AI algorithm to perform supply and demand forecasting based on inventory data obtained from the server. The terminal analyzes past sales history and market trend data to predict future demand for each product. The forecast results are used for future inventory replenishment and picking plans.

[0216] Step 3:

[0217] The server uses supply and demand forecasts to create a priority list of items to be picked. A management system calculates the optimal picking route for the drones and issues instructions. This route is designed based on a map of the warehouse and information on the placement of goods.

[0218] Step 4:

[0219] The user monitors the product picking process performed by the autonomous flight of the aircraft. The aircraft follows a route instructed by the server and performs the picking task. If an anomaly occurs, the user intervenes manually to resolve the issue.

[0220] Step 5:

[0221] The server packs the picked items based on order information and optimizes the delivery route. Factors such as distance to the destination, priority, and traffic information are considered when determining the delivery route.

[0222] Step 6:

[0223] The terminal checks the flight plan for the aircraft in accordance with local laws and regulations and adjusts the plan as needed. If modifications are required based on the legal information, it notifies the server of the update.

[0224] (Example 1)

[0225] 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."

[0226] To streamline and automate logistics processes, accurate inventory management and planned delivery are essential. However, current systems struggle with accurate inventory data collection and supply and demand forecasting, resulting in frequent manual intervention and inefficiency. Furthermore, in automated picking using aircraft, real-time route optimization and compliance with legal flight restrictions are critical challenges.

[0227] 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.

[0228] In this invention, the server includes means using multiple sensors for collecting product inventory information in real time, means using a computing device for processing the collected inventory information and making supply and demand forecasts, and means using a control device for controlling aircraft to optimize the item sorting route. This enables improved inventory accuracy, automation of logistics, efficient route optimization, and implementation of safe operation plans in compliance with laws and regulations.

[0229] "Product inventory information" refers to information such as the type, quantity, and storage location of products in storage locations such as warehouses and stores.

[0230] A "sensor" is a device used to detect a specific object or state, and in this invention, it refers to RFID sensors, video recognition cameras, and the like.

[0231] A "processing unit" is a device used to process data and perform calculations and analyses, and includes processors and computers used for supply and demand forecasting and data analysis.

[0232] An "airplane" is a device that flies autonomously within a warehouse for the purpose of sorting and transporting goods, and in this invention, it refers to a drone.

[0233] A "control device" refers to a device used to manage and instruct the operation of specific equipment or systems.

[0234] A "generative AI model" refers to a model that uses artificial intelligence to generate and analyze data, and in this invention, it is used for supply and demand forecasting and route optimization.

[0235] "Real-time monitoring" means observing and managing the operation of systems and processes immediately and without delay.

[0236] "Legal information" refers to laws and regulations related to operations, including standards and rules that should be considered when planning the operation of aircraft.

[0237] This invention comprises a system combining multiple sensors, computing devices, control devices, aircraft, and generative AI models to streamline inventory management and logistics processes within a warehouse.

[0238] First, the server uses sensors, including RFID sensors and video recognition cameras, installed within the warehouse to collect product inventory information in real time. This makes it possible to instantly determine the current quantity and location of items in stock.

[0239] Next, the terminal uses an AI algorithm to perform supply and demand forecasting based on inventory information received from the server. In doing so, the terminal also refers to past sales data and market trend information, and performs data analysis using its computing device. Based on the supply and demand forecasting results, it predicts future inventory shortages or surpluses and calculates the required inventory levels.

[0240] Subsequently, the server uses the supply and demand forecast results obtained from the computing unit to manage the drones, which are aircraft, via a control device. The server optimizes the picking routes for goods while referring to warehouse map data. This uses advanced algorithms to achieve efficient and rapid handling of goods.

[0241] Users monitor this entire process in real time through an interface. In the event of unusual inventory fluctuations or sensor malfunctions, users can manually adjust the system to resolve the problem.

[0242] As a concrete example, if demand for a particular product surges, the server immediately checks inventory information, and the terminal uses an AI model to create a new supply and demand forecast. Based on this information, the server instantly recalculates the picking route, and the aircraft prioritizes the selection and transportation of the designated products. This entire process is automated, minimizing human intervention.

[0243] An example of a prompt for a generated AI model would be an instruction such as, "Use real-time warehouse inventory data to predict supply and demand using AI and optimize the route." This allows the AI ​​model to properly perform supply and demand forecasting and route calculations.

[0244] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0245] Step 1:

[0246] The server collects product inventory information in real time using RFID sensors and video recognition cameras installed within the warehouse. Inputs include product ID, quantity, and location information detected by each sensor. The server analyzes this data and outputs it as the current inventory status. Specifically, the server communicates with each sensor and updates the collected data at regular intervals.

[0247] Step 2:

[0248] The terminal uses an AI algorithm to forecast supply and demand based on inventory information received from the server. Inputs include the latest inventory data, historical sales data, and market trend information provided by the server. The terminal inputs this data into a generating AI model to analyze supply and demand trends. As a result, it outputs supply and demand forecast data predicting future inventory shortages or surpluses. The specific operation involves calling the AI ​​model, preprocessing the data, and executing the forecasting algorithm.

[0249] Step 3:

[0250] The server optimizes picking routes using drones based on supply and demand forecasts obtained from terminals. Inputs include supply and demand forecast data from terminals and information on the location of goods stored in the warehouse. The server uses this data to calculate and output the shortest route that allows the drone to efficiently sort and transport goods. Specifically, it generates a drone operation plan and sends instructions to the drone following the optimal route.

[0251] Step 4:

[0252] The user monitors all processes in real time and intervenes as needed. Inputs include inventory status and drone operation information provided by servers and terminals. Based on this information, the user can quickly detect abnormal inventory fluctuations or equipment malfunctions and take manual corrections or countermeasures. Specific actions include checking information displayed through the interface and entering commands as needed.

[0253] (Application Example 1)

[0254] 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."

[0255] Modern logistics centers require efficient inventory management and picking, but traditional methods have problems with real-time inventory tracking and accurate supply and demand forecasting, resulting in insufficient optimization of logistics. Furthermore, logistics center managers lack the means to quickly grasp inventory status and respond to abnormal situations.

[0256] 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.

[0257] In this invention, the server includes means for having multiple detectors for collecting product inventory data in real time, means for having a computing device for analyzing the inventory data collected by the detectors and predicting supply and demand, means for having a management device for controlling flight devices to optimize predetermined product picking routes, means for having an information management device for logistics center operators to monitor inventory status and picking status in real time, and means for having a warning system that immediately notifies of inventory fluctuations or equipment malfunctions. This enables more efficient inventory management and faster response in logistics centers.

[0258] "Product inventory data" refers to information used to record the status of each product in a logistics center in real time, including its quantity and location.

[0259] A "detector" is a device that uses technologies such as RFID sensors and video recognition cameras to identify the location and quantity of products in order to acquire product inventory data.

[0260] A "computational device" is a device that uses AI and algorithms to forecast supply and demand based on collected inventory data, and to optimize logistics.

[0261] A "management device" is a device that controls the operation of aircraft and product picking routes, and performs control to ensure that logistics operations proceed efficiently.

[0262] "Flying devices" refer to devices such as drones that automatically move around within a warehouse, sorting and transporting designated goods according to an optimized route.

[0263] An "information management device" is a system that allows logistics center operators to monitor inventory status and picking operations in real time and visualize necessary data.

[0264] A "warning system" is a system designed to promptly notify administrators of inventory fluctuations or equipment malfunctions, and to assist in resolving these issues.

[0265] One embodiment of this invention is to automate inventory management and picking operations in a logistics center, thereby improving their efficiency. The server collects product inventory data from multiple detectors installed in the warehouse. These detectors use RFID sensors and video recognition cameras to track the status of products in real time.

[0266] The terminal uses AI algorithms to forecast supply and demand based on collected inventory data. Machine learning frameworks such as TensorFlow are used in the AI ​​models employed. Historical transaction data and market trends are also considered during the forecasting process, and logistics plans are optimized accordingly.

[0267] The management system optimizes the picking routes of the drones, which are the flying devices, based on supply and demand forecasts from the server. In this process, a cloud service using Google Firebase is used for storing and retrieving information, enabling efficient data exchange.

[0268] Users can monitor the inventory and picking status of the logistics center in real time through the information management system. If inventory fluctuations or equipment malfunctions occur, the warning system will immediately notify users and provide information to enable them to respond quickly. This notification function allows for a rapid response when problems arise.

[0269] For example, if demand for a particular product surges during a major sale, this system will immediately detect that demand, check inventory levels, and update supply and demand forecasts. Furthermore, it will use drones to efficiently pick products, enabling timely delivery of goods during the sale.

[0270] An example of a prompt for a generated AI model might be: "Explain how to forecast the supply and demand of a specific product inventory during a major sale period and optimize the drone picking route."

[0271] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0272] Step 1:

[0273] The server collects real-time product inventory data from various detectors within the warehouse. Inputs are data from RFID sensors and video recognition cameras, while outputs include inventory data such as product quantity and location information. This inventory data is centrally managed via a cloud service.

[0274] Step 2:

[0275] The terminal receives inventory data from the server and uses an AI algorithm to forecast supply and demand based on that data. It uses historical transaction history and market trend data as input and predicts the supply and demand balance for each product as output. Machine learning libraries such as TensorFlow are used in this process.

[0276] Step 3:

[0277] The server optimizes the picking routes for drones based on supply and demand forecasts. The input is supply and demand forecast data, and the output is optimized picking route information. Google Firebase is used to manage the route information and send commands to the drones.

[0278] Step 4:

[0279] The user monitors the inventory status and picking status of the logistics center in real time through the information management device. The input is the inventory and picking information from the server, and the output is the status display on the monitor screen. When an abnormality is detected, the user receives a notification from the system immediately.

[0280] Step 5:

[0281] The user manually intervenes in the system as needed to solve the problem. The input is the notification from the warning system, and the output is the operation based on the user's judgment. This enables quick response to abnormal inventory fluctuations and equipment malfunctions.

[0282] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0283] The embodiments for implementing the present invention provide a system that automates the management of product inventory in a warehouse and optimizes operations by recognizing the user's emotion. This system collects product inventory data by a plurality of detectors and performs supply and demand prediction by a computing device. Also, the picking route is optimized through a management device that controls the flying device. Furthermore, an emotion engine that recognizes the user's emotion in real time and adjusts the operation of the system based on it is incorporated.

[0284] The server collects inventory data in real time from detectors installed in the warehouse. The detectors include RFID tags and cameras, each of which plays a role of transferring the inventory quantity and location information of products to the server.

[0285] Next, the terminal executes supply and demand prediction using an AI algorithm based on the data collected by the server. This process includes past sales data and trend analysis to predict future inventory demand. The terminal transmits the result to the server to support the operation of the entire system.

[0286] After that, the server utilizes the prediction data to calculate the optimal picking route for the flying device (drone) and controls it through the management device. This enables efficient sorting and transportation of goods within the warehouse.

[0287] The user is evaluated for mental stress and satisfaction in real time by the emotion engine. This emotion engine adjusts the operation of the system by acquiring emotion data from the user's voice and expression. Specifically, when the user is under high stress, the system either readjusts the picking route or provides an interval for rest.

[0288] Furthermore, the emotion engine includes the function of collecting the user's emotion data and providing relaxation content and feedback for stress reduction. Through this process, the user experience can be improved, and the working efficiency and safety of the system can be maximized.

[0289] As a specific example, at a certain time, when a user newly starts working in the warehouse and feels stressed due to unfamiliarity with the work, the emotion engine analyzes the tone of voice and expression and detects the user's stress level. The system automatically makes adjustments to reduce the work burden and provides appropriate content for the user to relieve stress. As a result, the user can continue the work with peace of mind.

[0290] The following describes the processing flow.

[0291] Step 1:

[0292] The server receives the real-time inventory data collected by multiple detectors in the warehouse. The detectors use RFID sensors or cameras to detect the position and quantity of goods. The server stores this data in the database for later analysis.

[0293] Step 2:

[0294] The terminal analyzes inventory data transferred from the server and performs supply and demand forecasting using an AI algorithm. The terminal takes into account past sales data, seasonal influences, and current market trends to predict future demand for each product. The predicted supply and demand data is then sent to the server.

[0295] Step 3:

[0296] The server generates a list of items to be picked based on supply and demand forecast data and calculates the optimal picking route for the aircraft. This allows the aircraft to plan how to efficiently select and transport the items in the shortest possible time.

[0297] Step 4:

[0298] Users have their stress levels and emotional states monitored through an emotion engine. The emotion engine analyzes the user's voice tone and facial expressions in real time and notifies the server of the evaluation results.

[0299] Step 5:

[0300] The server dynamically adjusts its operational plan based on user emotion data obtained from the emotion engine. For example, if a user's stress level is high, the server will reduce the load on the picking route or suggest an interval.

[0301] Step 6:

[0302] The device utilizes user emotional data to provide relaxation content and feedback to reduce stress. The device selects and displays content based on the user's emotions, thereby reducing the user's mental burden.

[0303] (Example 2)

[0304] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0305] In recent years, there has been a demand for improving the efficiency of inventory management in warehouses. On the other hand, it is extremely difficult to accurately manage inventory and predict supply and demand by an automated system without human intervention, especially the adjustment of the system considering the emotional state of the user is a difficult problem. In the conventional system, the mental burden of the operator is not considered in the sorting and transportation of goods, and the work efficiency may decrease. The object of the present invention is to solve these problems and to improve the efficiency of the inventory management process in the warehouse while adjusting the system according to the emotional state of the operator.

[0306] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0307] In this invention, the server includes a plurality of sensor means for collecting information on product inventory in real time, a computing means for analyzing the inventory information collected by the sensor means and predicting supply and demand, a control means for controlling a transportation device to optimize a predetermined product sorting route, and a means for detecting the emotional state of the user and adjusting the operation of the system based on the detection result. Thereby, it becomes possible to improve the efficiency of inventory management and optimize the working environment of the user.

[0308] The "sensor means" is a general term for devices used to detect the inventory status of products in the warehouse, and includes RFID tag readers, cameras, and the like.

[0309] The "computing means" is a computing device or software for predicting supply and demand based on the collected inventory information, and analyzes data using an AI algorithm.

[0310] The "control means" is a device that instructs and manages a transportation device to sort and transport products according to a predetermined route.

[0311] "Means for detecting a user's emotional state" refers to technologies and devices that collect and analyze data such as voice and facial expressions to evaluate a user's stress and satisfaction level in real time.

[0312] A "transportation device" refers to an aircraft or moving device used to automatically sort and transport goods within a warehouse.

[0313] This invention is a system for efficiently managing inventory in a warehouse, and it operates by combining multiple sensors and AI technology. This system grasps product inventory information in real time and optimizes warehouse operations while taking into account the emotional state of the user.

[0314] First, the server collects inventory information in real time from multiple sensors installed within the warehouse. These sensors include RFID tag readers and cameras, which acquire identification and location information for each product. The collected data is aggregated on the server and stored in a database.

[0315] Next, the terminal uses AI algorithms based on data obtained from the server to forecast supply and demand. These AI algorithms utilize machine learning frameworks such as TensorFlow and PyTorch, analyzing past sales data and current inventory information to predict future inventory demand. This information is then sent back to the server and used for inventory management and logistics optimization.

[0316] Furthermore, the server calculates the optimal route for the transport devices based on supply and demand forecast data obtained from the computing means. Geographic Information System (GIS) technology and route optimization algorithms are used to plan efficient product sorting and transport routes. The calculation results are transmitted to the transport devices via the control means, and the products are automatically sorted and transported.

[0317] The user's emotions are evaluated in real time by an emotion engine. The emotion engine acquires the user's voice and facial expression data and analyzes stress and satisfaction levels using natural language processing and image processing technologies. If the system determines that the user is in a high-stress state, it automatically adjusts the system to reduce the workload or provide break intervals. It also presents the user with relaxation content and feedback to provide a comfortable work environment.

[0318] For example, suppose a user starts a new job in a warehouse and is overwhelmed by the workload. In this case, the emotion engine uses voice analysis and facial recognition technology to detect the user's stress, adjust the operating schedule of the transport devices, and take action to reduce the workload.

[0319] An example of a prompt for a generative AI model is, "Explain how to automate warehouse inventory management and optimize operations by recognizing user emotions."

[0320] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0321] Step 1:

[0322] The server collects inventory information from multiple sensors installed within the warehouse. Specifically, sensors such as RFID tag readers and cameras acquire identification and location information for each product in real time and transmit the data to the server. The server receives raw data from the sensors as input, converts that data into a standard format, and stores it in a database. This process allows the server to constantly monitor the inventory status within the warehouse.

[0323] Step 2:

[0324] The terminal performs supply and demand forecasting using inventory information received from the server. The terminal uses inventory data and historical sales history from the server as input. This allows AI algorithms (e.g., TensorFlow or PyTorch) to analyze the data and predict future demand. The calculation results in the generation of supply and demand forecast data, which is then sent back to the server. The entire system uses these results to plan future inventory management.

[0325] Step 3:

[0326] The server calculates the optimal route for transport devices based on supply and demand forecast data received from terminals. Using supply and demand forecast data and current inventory information as input, it executes a route optimization algorithm utilizing a Geographic Information System (GIS). The optimal picking route is generated as output and sent to the transport devices. This ensures that goods are efficiently sorted and transported.

[0327] Step 4:

[0328] The user's emotions are evaluated in real time by an emotion engine. In this process, the user's voice and facial expressions are acquired from sensors as input, and natural language processing and image processing technologies are applied. As a result of the analysis, the user's stress level and satisfaction level are output. If high stress is detected, the system automatically adjusts and takes measures to reduce the workload.

[0329] (Application Example 2)

[0330] 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."

[0331] Traditionally, inventory management in logistics centers has relied heavily on human resources, leading to efficiency challenges. Furthermore, it has been difficult to manage stress and satisfaction levels effectively, as it has been challenging to consider the emotional state of workers. Therefore, there is a need for a system that is both efficient and worker-friendly.

[0332] 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.

[0333] In this invention, the server includes multiple sensing devices for collecting product inventory data in real time, a computing device for analyzing the collected inventory data and predicting supply and demand, and an emotion analysis device for recognizing the emotional state of workers and adjusting their workload. This enables efficient inventory management and reduction of worker stress.

[0334] A "sensing device" is a device used to acquire product inventory data in real time within a logistics center, and includes sensors, RFID tags, cameras, and other similar devices.

[0335] A "calculation unit" is a device that uses collected inventory data to perform calculations to forecast supply and demand.

[0336] "Mobile equipment" refers to equipment that automatically moves goods along a designated route so that they can be efficiently sorted and transported.

[0337] A "management device" is a device that controls mobile equipment and generates the optimal item collection route, and manages the entire system.

[0338] An "emotion analysis device" is a device that recognizes the emotional state of a worker from their voice and facial expressions and adjusts their workload accordingly.

[0339] A "display device" is a device that provides information based on an emotional state in order to give feedback to an operator.

[0340] The embodiment of this invention is a system for automating product inventory management in a logistics center and optimizing operations by recognizing the emotions of workers. The server collects inventory data in real time from multiple sensing devices installed in the environment. The sensing devices use sensors, RFID tags, cameras, etc., to accurately determine the number and location of products. The server analyzes this data and uses a computing device to predict supply and demand. The computing device calculates future inventory demand based on past sales data and market trend analysis.

[0341] Mobile devices use predictive data provided by the server to set the optimal item collection route. The management device controls the mobile devices to ensure efficient product picking and transportation. In addition, it monitors the emotional state of workers through an emotion analysis device and uses voice and facial recognition technology to determine stress levels as appropriate using an AI algorithm.

[0342] Users can receive emotionally-based feedback and relaxation content through the display device. For example, if work during a busy period is causing stress, the emotion analyzer can detect this and display a message such as, "Please take a 5-minute break." This can reduce the burden on workers and improve work efficiency and safety.

[0343] An example of a prompt for a generative AI model might be: "Generate recommendations regarding my current emotional state and coping strategies. Please suggest specific measures, such as relaxation during holidays or how to spend weekday evenings." This prompt is intended to help users manage stress and create a comfortable work environment.

[0344] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0345] Step 1:

[0346] The server collects inventory data in real time from multiple sensing devices. Inputs include the location and quantity of products obtained from the sensing devices. This data is collected from RFID tags and cameras to form an initial dataset for understanding the current inventory status. The output is an updated inventory information database.

[0347] Step 2:

[0348] The server analyzes the collected inventory data using a computing device to predict supply and demand. Inputs include inventory data updated in step 1 and historical sales data. An AI algorithm is used to perform trend analysis and calculate future demand forecasts. The output is a list of required inventory based on the supply and demand forecasting model.

[0349] Step 3:

[0350] Based on the supply and demand forecast, the server sends instructions via a management device to enable mobile equipment to set the optimal item collection route. Inputs include the supply and demand forecast model generated in step 2 and current warehouse layout data. The shortest path is calculated based on this data, optimizing the picking efficiency. The output generates optimized collection route configuration information for the mobile equipment.

[0351] Step 4:

[0352] The user is monitored by an emotion analysis device, and their emotional state is determined in real time. Inputs include the user's voice data and facial expression data. These are analyzed by an AI algorithm to quantify emotional states such as stress levels. The output is the worker's current emotional evaluation result.

[0353] Step 5:

[0354] The user receives feedback through a display device that corresponds to their emotional state. The input is the emotional assessment result obtained in step 4. If the worker is determined to be highly stressed, break suggestions and relaxation content are displayed through the UI. The output provides specific action suggestions for the worker.

[0355] This series of steps will improve the efficiency of inventory management and support workers at the logistics center.

[0356] 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.

[0357] 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.

[0358] 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.

[0359] [Third Embodiment]

[0360] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0361] 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.

[0362] 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).

[0363] 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.

[0364] 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.

[0365] 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).

[0366] 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.

[0367] 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.

[0368] 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.

[0369] 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.

[0370] 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.

[0371] 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".

[0372] One embodiment of the present invention provides a system for automating product inventory management and streamlining logistics processes. This system includes multiple detectors, computing devices, management devices, and flight devices located within a warehouse.

[0373] First, the server collects inventory data from detectors installed within the warehouse. These detectors consist of RFID sensors and video recognition cameras, and have the capability to detect product quantities in real time.

[0374] Next, the terminal uses an AI algorithm to forecast supply and demand for each product based on inventory data sent from the server. This process also takes into account past transaction data and market trend information. As a result, it predicts the supply and demand balance of the product and calculates the required inventory quantity.

[0375] Subsequently, the server receives supply and demand forecast results from the computing device and optimizes the picking routes for the drones. The drones are drones that fly autonomously within the warehouse and pick specified items. The management device controls these drones to ensure efficient picking.

[0376] Furthermore, users can monitor these processes in real time and intervene manually as needed. For example, if abnormal inventory fluctuations or sensor malfunctions are detected, users can take appropriate action.

[0377] Furthermore, the server can appropriately adjust the drone's flight schedule in accordance with laws and regulations. This ensures safe operation in compliance with regulations set by local governments and related organizations.

[0378] As a concrete example, if demand for product A suddenly increases in a warehouse, the server immediately checks the inventory data, and a terminal uses that information to create a new supply and demand forecast. Based on this data, the server quickly adjusts the picking route, and the flying device prioritizes the selection and transport of product A. This entire process is automated, minimizing human intervention.

[0379] The following describes the processing flow.

[0380] Step 1:

[0381] The server collects product inventory data in real time from multiple detectors installed within the warehouse. Each detector detects the quantity and location of products and transmits this information to the server. This data is stored in the inventory management system on the server.

[0382] Step 2:

[0383] The terminal uses an AI algorithm to perform supply and demand forecasting based on inventory data obtained from the server. The terminal analyzes past sales history and market trend data to predict future demand for each product. The forecast results are used for future inventory replenishment and picking plans.

[0384] Step 3:

[0385] The server uses supply and demand forecasts to create a priority list of items to be picked. A management system calculates the optimal picking route for the drones and issues instructions. This route is designed based on a map of the warehouse and information on the placement of goods.

[0386] Step 4:

[0387] The user monitors the product picking process performed by the autonomous flight of the aircraft. The aircraft follows a route instructed by the server and performs the picking task. If an anomaly occurs, the user intervenes manually to resolve the issue.

[0388] Step 5:

[0389] The server packs the picked items based on order information and optimizes the delivery route. Factors such as distance to the destination, priority, and traffic information are considered when determining the delivery route.

[0390] Step 6:

[0391] The terminal checks the flight plan for the aircraft in accordance with local laws and regulations and adjusts the plan as needed. If modifications are required based on the legal information, it notifies the server of the update.

[0392] (Example 1)

[0393] 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."

[0394] To streamline and automate logistics processes, accurate inventory management and planned delivery are essential. However, current systems struggle with accurate inventory data collection and supply and demand forecasting, resulting in frequent manual intervention and inefficiency. Furthermore, in automated picking using aircraft, real-time route optimization and compliance with legal flight restrictions are critical challenges.

[0395] 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.

[0396] In this invention, the server includes means using multiple sensors for collecting product inventory information in real time, means using a computing device for processing the collected inventory information and making supply and demand forecasts, and means using a control device for controlling aircraft to optimize the item sorting route. This enables improved inventory accuracy, automation of logistics, efficient route optimization, and implementation of safe operation plans in compliance with laws and regulations.

[0397] "Product inventory information" refers to information such as the type, quantity, and storage location of products in storage locations such as warehouses and stores.

[0398] A "sensor" is a device used to detect a specific object or state, and in this invention, it refers to RFID sensors, video recognition cameras, and the like.

[0399] A "processing unit" is a device used to process data and perform calculations and analyses, and includes processors and computers used for supply and demand forecasting and data analysis.

[0400] An "airplane" is a device that flies autonomously within a warehouse for the purpose of sorting and transporting goods, and in this invention, it refers to a drone.

[0401] A "control device" refers to a device used to manage and instruct the operation of specific equipment or systems.

[0402] A "generative AI model" refers to a model that uses artificial intelligence to generate and analyze data, and in this invention, it is used for supply and demand forecasting and route optimization.

[0403] "Real-time monitoring" means observing and managing the operation of systems and processes immediately and without delay.

[0404] "Legal information" refers to laws and regulations related to operations, including standards and rules that should be considered when planning the operation of aircraft.

[0405] This invention comprises a system combining multiple sensors, computing devices, control devices, aircraft, and generative AI models to streamline inventory management and logistics processes within a warehouse.

[0406] First, the server uses sensors, including RFID sensors and video recognition cameras, installed within the warehouse to collect product inventory information in real time. This makes it possible to instantly determine the current quantity and location of items in stock.

[0407] Next, the terminal uses an AI algorithm to perform supply and demand forecasting based on inventory information received from the server. In doing so, the terminal also refers to past sales data and market trend information, and performs data analysis using its computing device. Based on the supply and demand forecasting results, it predicts future inventory shortages or surpluses and calculates the required inventory levels.

[0408] Subsequently, the server uses the supply and demand forecast results obtained from the computing unit to manage the drones, which are aircraft, via a control device. The server optimizes the picking routes for goods while referring to warehouse map data. This uses advanced algorithms to achieve efficient and rapid handling of goods.

[0409] Users monitor this entire process in real time through an interface. In the event of unusual inventory fluctuations or sensor malfunctions, users can manually adjust the system to resolve the problem.

[0410] As a concrete example, if demand for a particular product surges, the server immediately checks inventory information, and the terminal uses an AI model to create a new supply and demand forecast. Based on this information, the server instantly recalculates the picking route, and the aircraft prioritizes the selection and transportation of the designated products. This entire process is automated, minimizing human intervention.

[0411] An example of a prompt for a generated AI model would be an instruction such as, "Use real-time warehouse inventory data to predict supply and demand using AI and optimize the route." This allows the AI ​​model to properly perform supply and demand forecasting and route calculations.

[0412] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0413] Step 1:

[0414] The server collects product inventory information in real time using RFID sensors and video recognition cameras installed within the warehouse. Inputs include product ID, quantity, and location information detected by each sensor. The server analyzes this data and outputs it as the current inventory status. Specifically, the server communicates with each sensor and updates the collected data at regular intervals.

[0415] Step 2:

[0416] The terminal uses an AI algorithm to forecast supply and demand based on inventory information received from the server. Inputs include the latest inventory data, historical sales data, and market trend information provided by the server. The terminal inputs this data into a generating AI model to analyze supply and demand trends. As a result, it outputs supply and demand forecast data predicting future inventory shortages or surpluses. The specific operation involves calling the AI ​​model, preprocessing the data, and executing the forecasting algorithm.

[0417] Step 3:

[0418] The server optimizes picking routes using drones based on supply and demand forecasts obtained from terminals. Inputs include supply and demand forecast data from terminals and information on the location of goods stored in the warehouse. The server uses this data to calculate and output the shortest route that allows the drone to efficiently sort and transport goods. Specifically, it generates a drone operation plan and sends instructions to the drone following the optimal route.

[0419] Step 4:

[0420] The user monitors all processes in real time and intervenes as needed. Inputs include inventory status and drone operation information provided by servers and terminals. Based on this information, the user can quickly detect abnormal inventory fluctuations or equipment malfunctions and take manual corrections or countermeasures. Specific actions include checking information displayed through the interface and entering commands as needed.

[0421] (Application Example 1)

[0422] 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."

[0423] Modern logistics centers require efficient inventory management and picking, but traditional methods have problems with real-time inventory tracking and accurate supply and demand forecasting, resulting in insufficient optimization of logistics. Furthermore, logistics center managers lack the means to quickly grasp inventory status and respond to abnormal situations.

[0424] 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.

[0425] In this invention, the server includes means for having multiple detectors for collecting product inventory data in real time, means for having a computing device for analyzing the inventory data collected by the detectors and predicting supply and demand, means for having a management device for controlling flight devices to optimize predetermined product picking routes, means for having an information management device for logistics center operators to monitor inventory status and picking status in real time, and means for having a warning system that immediately notifies of inventory fluctuations or equipment malfunctions. This enables more efficient inventory management and faster response in logistics centers.

[0426] "Product inventory data" refers to information used to record the status of each product in a logistics center in real time, including its quantity and location.

[0427] A "detector" is a device that uses technologies such as RFID sensors and video recognition cameras to identify the location and quantity of products in order to acquire product inventory data.

[0428] A "computational device" is a device that uses AI and algorithms to forecast supply and demand based on collected inventory data, and to optimize logistics.

[0429] A "management device" is a device that controls the operation of aircraft and product picking routes, and performs control to ensure that logistics operations proceed efficiently.

[0430] "Flying devices" refer to devices such as drones that automatically move around within a warehouse, sorting and transporting designated goods according to an optimized route.

[0431] An "information management device" is a system that allows logistics center operators to monitor inventory status and picking operations in real time and visualize necessary data.

[0432] A "warning system" is a system designed to promptly notify administrators of inventory fluctuations or equipment malfunctions, and to assist in resolving these issues.

[0433] One embodiment of this invention is to automate inventory management and picking operations in a logistics center, thereby improving their efficiency. The server collects product inventory data from multiple detectors installed in the warehouse. These detectors use RFID sensors and video recognition cameras to track the status of products in real time.

[0434] The terminal uses AI algorithms to forecast supply and demand based on collected inventory data. Machine learning frameworks such as TensorFlow are used in the AI ​​models employed. Historical transaction data and market trends are also considered during the forecasting process, and logistics plans are optimized accordingly.

[0435] The management system optimizes the picking routes of the drones, which are the flying devices, based on supply and demand forecasts from the server. In this process, a cloud service using Google Firebase is used for storing and retrieving information, enabling efficient data exchange.

[0436] Users can monitor the inventory and picking status of the logistics center in real time through the information management system. If inventory fluctuations or equipment malfunctions occur, the warning system will immediately notify users and provide information to enable them to respond quickly. This notification function allows for a rapid response when problems arise.

[0437] For example, if demand for a particular product surges during a major sale, this system will immediately detect that demand, check inventory levels, and update supply and demand forecasts. Furthermore, it will use drones to efficiently pick products, enabling timely delivery of goods during the sale.

[0438] An example of a prompt for a generated AI model might be: "Explain how to forecast the supply and demand of a specific product inventory during a major sale period and optimize the drone picking route."

[0439] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0440] Step 1:

[0441] The server collects real-time product inventory data from various detectors within the warehouse. Inputs are data from RFID sensors and video recognition cameras, while outputs include inventory data such as product quantity and location information. This inventory data is centrally managed via a cloud service.

[0442] Step 2:

[0443] The terminal receives inventory data from the server and uses an AI algorithm to forecast supply and demand based on that data. It uses historical transaction history and market trend data as input and predicts the supply and demand balance for each product as output. Machine learning libraries such as TensorFlow are used in this process.

[0444] Step 3:

[0445] The server optimizes the picking routes for drones based on supply and demand forecasts. The input is supply and demand forecast data, and the output is optimized picking route information. Google Firebase is used to manage the route information and send commands to the drones.

[0446] Step 4:

[0447] Users monitor the inventory and picking status of the logistics center in real time through an information management device. Input is inventory and picking information from the server, and output is a status display on the monitor screen. If an anomaly is detected, the user receives an immediate notification from the system.

[0448] Step 5:

[0449] Users manually intervene in the system as needed to resolve problems. Inputs are notifications from the warning system, and outputs are actions taken based on user judgment. This allows for quick responses to abnormal inventory fluctuations and equipment malfunctions.

[0450] 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.

[0451] One embodiment of the present invention provides a system that automates the management of product inventory in a warehouse and optimizes operations by recognizing user emotions. This system collects product inventory data using multiple detectors and performs supply and demand forecasting using a computing device. Furthermore, picking routes are optimized through a management device that controls flight devices. In addition, an emotion engine is incorporated that recognizes user emotions in real time and adjusts the system's operation based on those emotions.

[0452] The server collects inventory data in real time from detectors installed in the warehouse. These detectors include RFID tags and cameras, each playing a role in transmitting product inventory counts and location information to the server.

[0453] Next, the terminal uses an AI algorithm to perform supply and demand forecasting based on the data collected by the server. This process includes historical sales data and trend analysis to predict future inventory demand. The terminal sends the results to the server, supporting the operation of the entire system.

[0454] Subsequently, the server utilizes predictive data to calculate the optimal picking route for the drones and controls them through a management system. This enables efficient sorting and transportation of goods within the warehouse.

[0455] Users are evaluated in real time by an emotion engine, which assesses their mental stress and satisfaction levels. This emotion engine acquires emotional data from the user's voice and facial expressions, and adjusts the system's operation accordingly. Specifically, if a user is experiencing high stress levels, the system will readjust their picking route or provide an interval for rest.

[0456] Furthermore, the system includes an emotion engine that collects user emotional data and provides relaxation content and feedback to reduce stress. Through this process, the user experience can be improved, and the system's operational efficiency and safety can be maximized.

[0457] For example, if a user starts working in a warehouse and is stressed because they are not yet accustomed to the work, the emotion engine analyzes the user's tone of voice and facial expressions to detect their stress level. The system automatically makes adjustments to reduce the workload and provides the user with appropriate content to alleviate stress. This allows the user to continue working with peace of mind.

[0458] The following describes the processing flow.

[0459] Step 1:

[0460] The server receives real-time inventory data collected by multiple detectors within the warehouse. These detectors use RFID sensors and cameras to determine the location and quantity of goods. The server stores this data in a database for later analysis.

[0461] Step 2:

[0462] The terminal analyzes inventory data transferred from the server and performs supply and demand forecasting using an AI algorithm. The terminal takes into account past sales data, seasonal influences, and current market trends to predict future demand for each product. The predicted supply and demand data is then sent to the server.

[0463] Step 3:

[0464] The server generates a list of items to be picked based on supply and demand forecast data and calculates the optimal picking route for the aircraft. This allows the aircraft to plan how to efficiently select and transport the items in the shortest possible time.

[0465] Step 4:

[0466] Users have their stress levels and emotional states monitored through an emotion engine. The emotion engine analyzes the user's voice tone and facial expressions in real time and notifies the server of the evaluation results.

[0467] Step 5:

[0468] The server dynamically adjusts its operational plan based on user emotion data obtained from the emotion engine. For example, if a user's stress level is high, the server will reduce the load on the picking route or suggest an interval.

[0469] Step 6:

[0470] The device utilizes user emotional data to provide relaxation content and feedback to reduce stress. The device selects and displays content based on the user's emotions, thereby reducing the user's mental burden.

[0471] (Example 2)

[0472] 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."

[0473] In recent years, there has been a growing demand for more efficient inventory management in warehouses. However, accurately managing inventory and forecasting supply and demand using automated systems without human intervention is extremely difficult, and adjusting the system to take into account the emotional state of the users is particularly challenging. Conventional systems have failed to consider the mental burden on workers during product sorting and transportation, which has sometimes led to decreased operational efficiency. The objective of this invention is to solve these problems, improve the efficiency of the inventory management process in warehouses, and adjust the system to accommodate the emotional state of the workers.

[0474] 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.

[0475] In this invention, the server includes a plurality of sensor means for collecting information on product inventory in real time, a calculation means for analyzing the inventory information collected by the sensor means and predicting supply and demand, a control means for controlling transport devices to optimize a predetermined product sorting route, and a means for detecting the user's emotional state and adjusting the operation of the system based on the detection results. This enables efficient inventory management and optimization of the user's work environment.

[0476] "Sensor means" is a general term for devices used to detect the inventory status of goods in a warehouse, and includes RFID tag readers and cameras.

[0477] A "computational means" refers to a computing device or software used to forecast supply and demand based on collected inventory information, and which analyzes data using AI algorithms.

[0478] A "control device" is a device that instructs and manages transport devices in order to sort and transport goods according to a predetermined route.

[0479] "Means for detecting a user's emotional state" refers to technologies and devices that collect and analyze data such as voice and facial expressions to evaluate a user's stress and satisfaction level in real time.

[0480] A "transportation device" refers to an aircraft or moving device used to automatically sort and transport goods within a warehouse.

[0481] This invention is a system for efficiently managing inventory in a warehouse, and it operates by combining multiple sensors and AI technology. This system grasps product inventory information in real time and optimizes warehouse operations while taking into account the emotional state of the user.

[0482] First, the server collects inventory information in real time from multiple sensors installed within the warehouse. These sensors include RFID tag readers and cameras, which acquire identification and location information for each product. The collected data is aggregated on the server and stored in a database.

[0483] Next, the terminal uses AI algorithms based on data obtained from the server to forecast supply and demand. These AI algorithms utilize machine learning frameworks such as TensorFlow and PyTorch, analyzing past sales data and current inventory information to predict future inventory demand. This information is then sent back to the server and used for inventory management and logistics optimization.

[0484] Furthermore, the server calculates the optimal route for the transport devices based on supply and demand forecast data obtained from the computing means. Geographic Information System (GIS) technology and route optimization algorithms are used to plan efficient product sorting and transport routes. The calculation results are transmitted to the transport devices via the control means, and the products are automatically sorted and transported.

[0485] The user's emotions are evaluated in real time by an emotion engine. The emotion engine acquires the user's voice and facial expression data and analyzes stress and satisfaction levels using natural language processing and image processing technologies. If the system determines that the user is in a high-stress state, it automatically adjusts the system to reduce the workload or provide break intervals. It also presents the user with relaxation content and feedback to provide a comfortable work environment.

[0486] For example, suppose a user starts a new job in a warehouse and is overwhelmed by the workload. In this case, the emotion engine uses voice analysis and facial recognition technology to detect the user's stress, adjust the operating schedule of the transport devices, and take action to reduce the workload.

[0487] An example of a prompt for a generative AI model is, "Explain how to automate warehouse inventory management and optimize operations by recognizing user emotions."

[0488] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0489] Step 1:

[0490] The server collects inventory information from multiple sensors installed within the warehouse. Specifically, sensors such as RFID tag readers and cameras acquire identification and location information for each product in real time and transmit the data to the server. The server receives raw data from the sensors as input, converts that data into a standard format, and stores it in a database. This process allows the server to constantly monitor the inventory status within the warehouse.

[0491] Step 2:

[0492] The terminal performs supply and demand forecasting using inventory information received from the server. The terminal uses inventory data and historical sales history from the server as input. This allows AI algorithms (e.g., TensorFlow or PyTorch) to analyze the data and predict future demand. The calculation results in the generation of supply and demand forecast data, which is then sent back to the server. The entire system uses these results to plan future inventory management.

[0493] Step 3:

[0494] The server calculates the optimal route for transport devices based on supply and demand forecast data received from terminals. Using supply and demand forecast data and current inventory information as input, it executes a route optimization algorithm utilizing a Geographic Information System (GIS). The optimal picking route is generated as output and sent to the transport devices. This ensures that goods are efficiently sorted and transported.

[0495] Step 4:

[0496] The user's emotions are evaluated in real time by an emotion engine. In this process, the user's voice and facial expressions are acquired from sensors as input, and natural language processing and image processing technologies are applied. As a result of the analysis, the user's stress level and satisfaction level are output. If high stress is detected, the system automatically adjusts and takes measures to reduce the workload.

[0497] (Application Example 2)

[0498] 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."

[0499] Traditionally, inventory management in logistics centers has relied heavily on human resources, leading to efficiency challenges. Furthermore, it has been difficult to manage stress and satisfaction levels effectively, as it has been challenging to consider the emotional state of workers. Therefore, there is a need for a system that is both efficient and worker-friendly.

[0500] 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.

[0501] In this invention, the server includes multiple sensing devices for collecting product inventory data in real time, a computing device for analyzing the collected inventory data and predicting supply and demand, and an emotion analysis device for recognizing the emotional state of workers and adjusting their workload. This enables efficient inventory management and reduction of worker stress.

[0502] A "sensing device" is a device used to acquire product inventory data in real time within a logistics center, and includes sensors, RFID tags, cameras, and other similar devices.

[0503] A "calculation unit" is a device that uses collected inventory data to perform calculations to forecast supply and demand.

[0504] "Mobile equipment" refers to equipment that automatically moves goods along a designated route so that they can be efficiently sorted and transported.

[0505] A "management device" is a device that controls mobile equipment and generates the optimal item collection route, and manages the entire system.

[0506] An "emotion analysis device" is a device that recognizes the emotional state of a worker from their voice and facial expressions and adjusts their workload accordingly.

[0507] A "display device" is a device that provides information based on an emotional state in order to give feedback to an operator.

[0508] The embodiment of this invention is a system for automating product inventory management in a logistics center and optimizing operations by recognizing the emotions of workers. The server collects inventory data in real time from multiple sensing devices installed in the environment. The sensing devices use sensors, RFID tags, cameras, etc., to accurately determine the number and location of products. The server analyzes this data and uses a computing device to predict supply and demand. The computing device calculates future inventory demand based on past sales data and market trend analysis.

[0509] Mobile devices use predictive data provided by the server to set the optimal item collection route. The management device controls the mobile devices to ensure efficient product picking and transportation. In addition, it monitors the emotional state of workers through an emotion analysis device and uses voice and facial recognition technology to determine stress levels as appropriate using an AI algorithm.

[0510] Users can receive emotionally-based feedback and relaxation content through the display device. For example, if work during a busy period is causing stress, the emotion analyzer can detect this and display a message such as, "Please take a 5-minute break." This can reduce the burden on workers and improve work efficiency and safety.

[0511] An example of a prompt for a generative AI model might be: "Generate recommendations regarding my current emotional state and coping strategies. Please suggest specific measures, such as relaxation during holidays or how to spend weekday evenings." This prompt is intended to help users manage stress and create a comfortable work environment.

[0512] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0513] Step 1:

[0514] The server collects inventory data in real time from multiple sensing devices. Inputs include the location and quantity of products obtained from the sensing devices. This data is collected from RFID tags and cameras to form an initial dataset for understanding the current inventory status. The output is an updated inventory information database.

[0515] Step 2:

[0516] The server analyzes the collected inventory data using a computing device to predict supply and demand. Inputs include inventory data updated in step 1 and historical sales data. An AI algorithm is used to perform trend analysis and calculate future demand forecasts. The output is a list of required inventory based on the supply and demand forecasting model.

[0517] Step 3:

[0518] Based on the supply and demand forecast, the server sends instructions via a management device to enable mobile equipment to set the optimal item collection route. Inputs include the supply and demand forecast model generated in step 2 and current warehouse layout data. The shortest path is calculated based on this data, optimizing the picking efficiency. The output generates optimized collection route configuration information for the mobile equipment.

[0519] Step 4:

[0520] The user is monitored by an emotion analysis device, and their emotional state is determined in real time. Inputs include the user's voice data and facial expression data. These are analyzed by an AI algorithm to quantify emotional states such as stress levels. The output is the worker's current emotional evaluation result.

[0521] Step 5:

[0522] The user receives feedback through a display device that corresponds to their emotional state. The input is the emotional assessment result obtained in step 4. If the worker is determined to be highly stressed, break suggestions and relaxation content are displayed through the UI. The output provides specific action suggestions for the worker.

[0523] This series of steps will improve the efficiency of inventory management and support workers at the logistics center.

[0524] 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.

[0525] 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.

[0526] 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.

[0527] [Fourth Embodiment]

[0528] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0529] 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.

[0530] 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).

[0531] 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.

[0532] 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.

[0533] 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).

[0534] 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.

[0535] 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.

[0536] 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.

[0537] 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.

[0538] 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.

[0539] 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.

[0540] 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".

[0541] One embodiment of the present invention provides a system for automating product inventory management and streamlining logistics processes. This system includes multiple detectors, computing devices, management devices, and flight devices located within a warehouse.

[0542] First, the server collects inventory data from detectors installed within the warehouse. These detectors consist of RFID sensors and video recognition cameras, and have the capability to detect product quantities in real time.

[0543] Next, the terminal uses an AI algorithm to forecast supply and demand for each product based on inventory data sent from the server. This process also takes into account past transaction data and market trend information. As a result, it predicts the supply and demand balance of the product and calculates the required inventory quantity.

[0544] Subsequently, the server receives supply and demand forecast results from the computing device and optimizes the picking routes for the drones. The drones are drones that fly autonomously within the warehouse and pick specified items. The management device controls these drones to ensure efficient picking.

[0545] Furthermore, users can monitor these processes in real time and intervene manually as needed. For example, if abnormal inventory fluctuations or sensor malfunctions are detected, users can take appropriate action.

[0546] Furthermore, the server can appropriately adjust the drone's flight schedule in accordance with laws and regulations. This ensures safe operation in compliance with regulations set by local governments and related organizations.

[0547] As a concrete example, if demand for product A suddenly increases in a warehouse, the server immediately checks the inventory data, and a terminal uses that information to create a new supply and demand forecast. Based on this data, the server quickly adjusts the picking route, and the flying device prioritizes the selection and transport of product A. This entire process is automated, minimizing human intervention.

[0548] The following describes the processing flow.

[0549] Step 1:

[0550] The server collects product inventory data in real time from multiple detectors installed within the warehouse. Each detector detects the quantity and location of products and transmits this information to the server. This data is stored in the inventory management system on the server.

[0551] Step 2:

[0552] The terminal uses an AI algorithm to perform supply and demand forecasting based on inventory data obtained from the server. The terminal analyzes past sales history and market trend data to predict future demand for each product. The forecast results are used for future inventory replenishment and picking plans.

[0553] Step 3:

[0554] The server uses supply and demand forecasts to create a priority list of items to be picked. A management system calculates the optimal picking route for the drones and issues instructions. This route is designed based on a map of the warehouse and information on the placement of goods.

[0555] Step 4:

[0556] The user monitors the product picking process performed by the autonomous flight of the aircraft. The aircraft follows a route instructed by the server and performs the picking task. If an anomaly occurs, the user intervenes manually to resolve the issue.

[0557] Step 5:

[0558] The server packs the picked items based on order information and optimizes the delivery route. Factors such as distance to the destination, priority, and traffic information are considered when determining the delivery route.

[0559] Step 6:

[0560] The terminal checks the flight plan for the aircraft in accordance with local laws and regulations and adjusts the plan as needed. If modifications are required based on the legal information, it notifies the server of the update.

[0561] (Example 1)

[0562] 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".

[0563] To streamline and automate logistics processes, accurate inventory management and planned delivery are essential. However, current systems struggle with accurate inventory data collection and supply and demand forecasting, resulting in frequent manual intervention and inefficiency. Furthermore, in automated picking using aircraft, real-time route optimization and compliance with legal flight restrictions are critical challenges.

[0564] 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.

[0565] In this invention, the server includes means using multiple sensors for collecting product inventory information in real time, means using a computing device for processing the collected inventory information and making supply and demand forecasts, and means using a control device for controlling aircraft to optimize the item sorting route. This enables improved inventory accuracy, automation of logistics, efficient route optimization, and implementation of safe operation plans in compliance with laws and regulations.

[0566] "Product inventory information" refers to information such as the type, quantity, and storage location of products in storage locations such as warehouses and stores.

[0567] A "sensor" is a device used to detect a specific object or state, and in this invention, it refers to RFID sensors, video recognition cameras, and the like.

[0568] A "processing unit" is a device used to process data and perform calculations and analyses, and includes processors and computers used for supply and demand forecasting and data analysis.

[0569] An "airplane" is a device that flies autonomously within a warehouse for the purpose of sorting and transporting goods, and in this invention, it refers to a drone.

[0570] A "control device" refers to a device used to manage and instruct the operation of specific equipment or systems.

[0571] A "generative AI model" refers to a model that uses artificial intelligence to generate and analyze data, and in this invention, it is used for supply and demand forecasting and route optimization.

[0572] "Real-time monitoring" means observing and managing the operation of systems and processes immediately and without delay.

[0573] "Legal information" refers to laws and regulations related to operations, including standards and rules that should be considered when planning the operation of aircraft.

[0574] This invention comprises a system combining multiple sensors, computing devices, control devices, aircraft, and generative AI models to streamline inventory management and logistics processes within a warehouse.

[0575] First, the server uses sensors, including RFID sensors and video recognition cameras, installed within the warehouse to collect product inventory information in real time. This makes it possible to instantly determine the current quantity and location of items in stock.

[0576] Next, the terminal uses an AI algorithm to perform supply and demand forecasting based on inventory information received from the server. In doing so, the terminal also refers to past sales data and market trend information, and performs data analysis using its computing device. Based on the supply and demand forecasting results, it predicts future inventory shortages or surpluses and calculates the required inventory levels.

[0577] Subsequently, the server uses the supply and demand forecast results obtained from the computing unit to manage the drones, which are aircraft, via a control device. The server optimizes the picking routes for goods while referring to warehouse map data. This uses advanced algorithms to achieve efficient and rapid handling of goods.

[0578] Users monitor this entire process in real time through an interface. In the event of unusual inventory fluctuations or sensor malfunctions, users can manually adjust the system to resolve the problem.

[0579] As a concrete example, if demand for a particular product surges, the server immediately checks inventory information, and the terminal uses an AI model to create a new supply and demand forecast. Based on this information, the server instantly recalculates the picking route, and the aircraft prioritizes the selection and transportation of the designated products. This entire process is automated, minimizing human intervention.

[0580] An example of a prompt for a generated AI model would be an instruction such as, "Use real-time warehouse inventory data to predict supply and demand using AI and optimize the route." This allows the AI ​​model to properly perform supply and demand forecasting and route calculations.

[0581] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0582] Step 1:

[0583] The server collects product inventory information in real time using RFID sensors and video recognition cameras installed within the warehouse. Inputs include product ID, quantity, and location information detected by each sensor. The server analyzes this data and outputs it as the current inventory status. Specifically, the server communicates with each sensor and updates the collected data at regular intervals.

[0584] Step 2:

[0585] The terminal uses an AI algorithm to forecast supply and demand based on inventory information received from the server. Inputs include the latest inventory data, historical sales data, and market trend information provided by the server. The terminal inputs this data into a generating AI model to analyze supply and demand trends. As a result, it outputs supply and demand forecast data predicting future inventory shortages or surpluses. The specific operation involves calling the AI ​​model, preprocessing the data, and executing the forecasting algorithm.

[0586] Step 3:

[0587] The server optimizes picking routes using drones based on supply and demand forecasts obtained from terminals. Inputs include supply and demand forecast data from terminals and information on the location of goods stored in the warehouse. The server uses this data to calculate and output the shortest route that allows the drone to efficiently sort and transport goods. Specifically, it generates a drone operation plan and sends instructions to the drone following the optimal route.

[0588] Step 4:

[0589] The user monitors all processes in real time and intervenes as needed. Inputs include inventory status and drone operation information provided by servers and terminals. Based on this information, the user can quickly detect abnormal inventory fluctuations or equipment malfunctions and take manual corrections or countermeasures. Specific actions include checking information displayed through the interface and entering commands as needed.

[0590] (Application Example 1)

[0591] 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".

[0592] Modern logistics centers require efficient inventory management and picking, but traditional methods have problems with real-time inventory tracking and accurate supply and demand forecasting, resulting in insufficient optimization of logistics. Furthermore, logistics center managers lack the means to quickly grasp inventory status and respond to abnormal situations.

[0593] 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.

[0594] In this invention, the server includes means for having multiple detectors for collecting product inventory data in real time, means for having a computing device for analyzing the inventory data collected by the detectors and predicting supply and demand, means for having a management device for controlling flight devices to optimize predetermined product picking routes, means for having an information management device for logistics center operators to monitor inventory status and picking status in real time, and means for having a warning system that immediately notifies of inventory fluctuations or equipment malfunctions. This enables more efficient inventory management and faster response in logistics centers.

[0595] "Product inventory data" refers to information used to record the status of each product in a logistics center in real time, including its quantity and location.

[0596] A "detector" is a device that uses technologies such as RFID sensors and video recognition cameras to identify the location and quantity of products in order to acquire product inventory data.

[0597] A "computational device" is a device that uses AI and algorithms to forecast supply and demand based on collected inventory data, and to optimize logistics.

[0598] A "management device" is a device that controls the operation of aircraft and product picking routes, and performs control to ensure that logistics operations proceed efficiently.

[0599] "Flying devices" refer to devices such as drones that automatically move around within a warehouse, sorting and transporting designated goods according to an optimized route.

[0600] An "information management device" is a system that allows logistics center operators to monitor inventory status and picking operations in real time and visualize necessary data.

[0601] A "warning system" is a system designed to promptly notify administrators of inventory fluctuations or equipment malfunctions, and to assist in resolving these issues.

[0602] One embodiment of this invention is to automate inventory management and picking operations in a logistics center, thereby improving their efficiency. The server collects product inventory data from multiple detectors installed in the warehouse. These detectors use RFID sensors and video recognition cameras to track the status of products in real time.

[0603] The terminal uses AI algorithms to forecast supply and demand based on collected inventory data. Machine learning frameworks such as TensorFlow are used in the AI ​​models employed. Historical transaction data and market trends are also considered during the forecasting process, and logistics plans are optimized accordingly.

[0604] The management system optimizes the picking routes of the drones, which are the flying devices, based on supply and demand forecasts from the server. In this process, a cloud service using Google Firebase is used for storing and retrieving information, enabling efficient data exchange.

[0605] Users can monitor the inventory and picking status of the logistics center in real time through the information management system. If inventory fluctuations or equipment malfunctions occur, the warning system will immediately notify users and provide information to enable them to respond quickly. This notification function allows for a rapid response when problems arise.

[0606] For example, if demand for a particular product surges during a major sale, this system will immediately detect that demand, check inventory levels, and update supply and demand forecasts. Furthermore, it will use drones to efficiently pick products, enabling timely delivery of goods during the sale.

[0607] An example of a prompt for a generated AI model might be: "Explain how to forecast the supply and demand of a specific product inventory during a major sale period and optimize the drone picking route."

[0608] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0609] Step 1:

[0610] The server collects real-time product inventory data from various detectors within the warehouse. Inputs are data from RFID sensors and video recognition cameras, while outputs include inventory data such as product quantity and location information. This inventory data is centrally managed via a cloud service.

[0611] Step 2:

[0612] The terminal receives inventory data from the server and uses an AI algorithm to forecast supply and demand based on that data. It uses historical transaction history and market trend data as input and predicts the supply and demand balance for each product as output. Machine learning libraries such as TensorFlow are used in this process.

[0613] Step 3:

[0614] The server optimizes the picking routes for drones based on supply and demand forecasts. The input is supply and demand forecast data, and the output is optimized picking route information. Google Firebase is used to manage the route information and send commands to the drones.

[0615] Step 4:

[0616] Users monitor the inventory and picking status of the logistics center in real time through an information management device. Input is inventory and picking information from the server, and output is a status display on the monitor screen. If an anomaly is detected, the user receives an immediate notification from the system.

[0617] Step 5:

[0618] Users manually intervene in the system as needed to resolve problems. Inputs are notifications from the warning system, and outputs are actions taken based on user judgment. This allows for quick responses to abnormal inventory fluctuations and equipment malfunctions.

[0619] 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.

[0620] One embodiment of the present invention provides a system that automates the management of product inventory in a warehouse and optimizes operations by recognizing user emotions. This system collects product inventory data using multiple detectors and performs supply and demand forecasting using a computing device. Furthermore, picking routes are optimized through a management device that controls flight devices. In addition, an emotion engine is incorporated that recognizes user emotions in real time and adjusts the system's operation based on those emotions.

[0621] The server collects inventory data in real time from detectors installed in the warehouse. These detectors include RFID tags and cameras, each playing a role in transmitting product inventory counts and location information to the server.

[0622] Next, the terminal uses an AI algorithm to perform supply and demand forecasting based on the data collected by the server. This process includes historical sales data and trend analysis to predict future inventory demand. The terminal sends the results to the server, supporting the operation of the entire system.

[0623] Subsequently, the server utilizes predictive data to calculate the optimal picking route for the drones and controls them through a management system. This enables efficient sorting and transportation of goods within the warehouse.

[0624] Users are evaluated in real time by an emotion engine, which assesses their mental stress and satisfaction levels. This emotion engine acquires emotional data from the user's voice and facial expressions, and adjusts the system's operation accordingly. Specifically, if a user is experiencing high stress levels, the system will readjust their picking route or provide an interval for rest.

[0625] Furthermore, the system includes an emotion engine that collects user emotional data and provides relaxation content and feedback to reduce stress. Through this process, the user experience can be improved, and the system's operational efficiency and safety can be maximized.

[0626] For example, if a user starts working in a warehouse and is stressed because they are not yet accustomed to the work, the emotion engine analyzes the user's tone of voice and facial expressions to detect their stress level. The system automatically makes adjustments to reduce the workload and provides the user with appropriate content to alleviate stress. This allows the user to continue working with peace of mind.

[0627] The following describes the processing flow.

[0628] Step 1:

[0629] The server receives real-time inventory data collected by multiple detectors within the warehouse. These detectors use RFID sensors and cameras to determine the location and quantity of goods. The server stores this data in a database for later analysis.

[0630] Step 2:

[0631] The terminal analyzes inventory data transferred from the server and performs supply and demand forecasting using an AI algorithm. The terminal takes into account past sales data, seasonal influences, and current market trends to predict future demand for each product. The predicted supply and demand data is then sent to the server.

[0632] Step 3:

[0633] The server generates a list of items to be picked based on supply and demand forecast data and calculates the optimal picking route for the aircraft. This allows the aircraft to plan how to efficiently select and transport the items in the shortest possible time.

[0634] Step 4:

[0635] Users have their stress levels and emotional states monitored through an emotion engine. The emotion engine analyzes the user's voice tone and facial expressions in real time and notifies the server of the evaluation results.

[0636] Step 5:

[0637] The server dynamically adjusts its operational plan based on user emotion data obtained from the emotion engine. For example, if a user's stress level is high, the server will reduce the load on the picking route or suggest an interval.

[0638] Step 6:

[0639] The device utilizes user emotional data to provide relaxation content and feedback to reduce stress. The device selects and displays content based on the user's emotions, thereby reducing the user's mental burden.

[0640] (Example 2)

[0641] 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".

[0642] In recent years, there has been a growing demand for more efficient inventory management in warehouses. However, accurately managing inventory and forecasting supply and demand using automated systems without human intervention is extremely difficult, and adjusting the system to take into account the emotional state of the users is particularly challenging. Conventional systems have failed to consider the mental burden on workers during product sorting and transportation, which has sometimes led to decreased operational efficiency. The objective of this invention is to solve these problems, improve the efficiency of the inventory management process in warehouses, and adjust the system to accommodate the emotional state of the workers.

[0643] 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.

[0644] In this invention, the server includes a plurality of sensor means for collecting information on product inventory in real time, a calculation means for analyzing the inventory information collected by the sensor means and predicting supply and demand, a control means for controlling transport devices to optimize a predetermined product sorting route, and a means for detecting the user's emotional state and adjusting the operation of the system based on the detection results. This enables efficient inventory management and optimization of the user's work environment.

[0645] "Sensor means" is a general term for devices used to detect the inventory status of goods in a warehouse, and includes RFID tag readers and cameras.

[0646] A "computational means" refers to a computing device or software used to forecast supply and demand based on collected inventory information, and which analyzes data using AI algorithms.

[0647] A "control device" is a device that instructs and manages transport devices in order to sort and transport goods according to a predetermined route.

[0648] "Means for detecting a user's emotional state" refers to technologies and devices that collect and analyze data such as voice and facial expressions to evaluate a user's stress and satisfaction level in real time.

[0649] A "transportation device" refers to an aircraft or moving device used to automatically sort and transport goods within a warehouse.

[0650] This invention is a system for efficiently managing inventory in a warehouse, and it operates by combining multiple sensors and AI technology. This system grasps product inventory information in real time and optimizes warehouse operations while taking into account the emotional state of the user.

[0651] First, the server collects inventory information in real time from multiple sensors installed within the warehouse. These sensors include RFID tag readers and cameras, which acquire identification and location information for each product. The collected data is aggregated on the server and stored in a database.

[0652] Next, the terminal uses AI algorithms based on data obtained from the server to forecast supply and demand. These AI algorithms utilize machine learning frameworks such as TensorFlow and PyTorch, analyzing past sales data and current inventory information to predict future inventory demand. This information is then sent back to the server and used for inventory management and logistics optimization.

[0653] Furthermore, the server calculates the optimal route for the transport devices based on supply and demand forecast data obtained from the computing means. Geographic Information System (GIS) technology and route optimization algorithms are used to plan efficient product sorting and transport routes. The calculation results are transmitted to the transport devices via the control means, and the products are automatically sorted and transported.

[0654] The user's emotions are evaluated in real time by an emotion engine. The emotion engine acquires the user's voice and facial expression data and analyzes stress and satisfaction levels using natural language processing and image processing technologies. If the system determines that the user is in a high-stress state, it automatically adjusts the system to reduce the workload or provide break intervals. It also presents the user with relaxation content and feedback to provide a comfortable work environment.

[0655] For example, suppose a user starts a new job in a warehouse and is overwhelmed by the workload. In this case, the emotion engine uses voice analysis and facial recognition technology to detect the user's stress, adjust the operating schedule of the transport devices, and take action to reduce the workload.

[0656] An example of a prompt for a generative AI model is, "Explain how to automate warehouse inventory management and optimize operations by recognizing user emotions."

[0657] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0658] Step 1:

[0659] The server collects inventory information from multiple sensors installed within the warehouse. Specifically, sensors such as RFID tag readers and cameras acquire identification and location information for each product in real time and transmit the data to the server. The server receives raw data from the sensors as input, converts that data into a standard format, and stores it in a database. This process allows the server to constantly monitor the inventory status within the warehouse.

[0660] Step 2:

[0661] The terminal performs supply and demand forecasting using inventory information received from the server. The terminal uses inventory data and historical sales history from the server as input. This allows AI algorithms (e.g., TensorFlow or PyTorch) to analyze the data and predict future demand. The calculation results in the generation of supply and demand forecast data, which is then sent back to the server. The entire system uses these results to plan future inventory management.

[0662] Step 3:

[0663] The server calculates the optimal route for transport devices based on supply and demand forecast data received from terminals. Using supply and demand forecast data and current inventory information as input, it executes a route optimization algorithm utilizing a Geographic Information System (GIS). The optimal picking route is generated as output and sent to the transport devices. This ensures that goods are efficiently sorted and transported.

[0664] Step 4:

[0665] The user's emotions are evaluated in real time by an emotion engine. In this process, the user's voice and facial expressions are acquired from sensors as input, and natural language processing and image processing technologies are applied. As a result of the analysis, the user's stress level and satisfaction level are output. If high stress is detected, the system automatically adjusts and takes measures to reduce the workload.

[0666] (Application Example 2)

[0667] 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".

[0668] Traditionally, inventory management in logistics centers has relied heavily on human resources, leading to efficiency challenges. Furthermore, it has been difficult to manage stress and satisfaction levels effectively, as it has been challenging to consider the emotional state of workers. Therefore, there is a need for a system that is both efficient and worker-friendly.

[0669] 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.

[0670] In this invention, the server includes multiple sensing devices for collecting product inventory data in real time, a computing device for analyzing the collected inventory data and predicting supply and demand, and an emotion analysis device for recognizing the emotional state of workers and adjusting their workload. This enables efficient inventory management and reduction of worker stress.

[0671] A "sensing device" is a device used to acquire product inventory data in real time within a logistics center, and includes sensors, RFID tags, cameras, and other similar devices.

[0672] A "calculation unit" is a device that uses collected inventory data to perform calculations to forecast supply and demand.

[0673] "Mobile equipment" refers to equipment that automatically moves goods along a designated route so that they can be efficiently sorted and transported.

[0674] A "management device" is a device that controls mobile equipment and generates the optimal item collection route, and manages the entire system.

[0675] An "emotion analysis device" is a device that recognizes the emotional state of a worker from their voice and facial expressions and adjusts their workload accordingly.

[0676] A "display device" is a device that provides information based on an emotional state in order to give feedback to an operator.

[0677] The embodiment of this invention is a system for automating product inventory management in a logistics center and optimizing operations by recognizing the emotions of workers. The server collects inventory data in real time from multiple sensing devices installed in the environment. The sensing devices use sensors, RFID tags, cameras, etc., to accurately determine the number and location of products. The server analyzes this data and uses a computing device to predict supply and demand. The computing device calculates future inventory demand based on past sales data and market trend analysis.

[0678] Mobile devices use predictive data provided by the server to set the optimal item collection route. The management device controls the mobile devices to ensure efficient product picking and transportation. In addition, it monitors the emotional state of workers through an emotion analysis device and uses voice and facial recognition technology to determine stress levels as appropriate using an AI algorithm.

[0679] Users can receive emotionally-based feedback and relaxation content through the display device. For example, if work during a busy period is causing stress, the emotion analyzer can detect this and display a message such as, "Please take a 5-minute break." This can reduce the burden on workers and improve work efficiency and safety.

[0680] An example of a prompt for a generative AI model might be: "Generate recommendations regarding my current emotional state and coping strategies. Please suggest specific measures, such as relaxation during holidays or how to spend weekday evenings." This prompt is intended to help users manage stress and create a comfortable work environment.

[0681] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0682] Step 1:

[0683] The server collects inventory data in real time from multiple sensing devices. Inputs include the location and quantity of products obtained from the sensing devices. This data is collected from RFID tags and cameras to form an initial dataset for understanding the current inventory status. The output is an updated inventory information database.

[0684] Step 2:

[0685] The server analyzes the collected inventory data using a computing device to predict supply and demand. Inputs include inventory data updated in step 1 and historical sales data. An AI algorithm is used to perform trend analysis and calculate future demand forecasts. The output is a list of required inventory based on the supply and demand forecasting model.

[0686] Step 3:

[0687] Based on the supply and demand forecast, the server sends instructions via a management device to enable mobile equipment to set the optimal item collection route. Inputs include the supply and demand forecast model generated in step 2 and current warehouse layout data. The shortest path is calculated based on this data, optimizing the picking efficiency. The output generates optimized collection route configuration information for the mobile equipment.

[0688] Step 4:

[0689] The user is monitored by an emotion analysis device, and their emotional state is determined in real time. Inputs include the user's voice data and facial expression data. These are analyzed by an AI algorithm to quantify emotional states such as stress levels. The output is the worker's current emotional evaluation result.

[0690] Step 5:

[0691] The user receives feedback through a display device that corresponds to their emotional state. The input is the emotional assessment result obtained in step 4. If the worker is determined to be highly stressed, break suggestions and relaxation content are displayed through the UI. The output provides specific action suggestions for the worker.

[0692] This series of steps will improve the efficiency of inventory management and support workers at the logistics center.

[0693] 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.

[0694] 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.

[0695] In the above embodiment, an example was given in which the 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.

[0696] 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.

[0697] 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.

[0698] 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.

[0699] 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.

[0700] 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.

[0701] 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."

[0702] 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.

[0703] 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.

[0704] 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.

[0705] 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.

[0706] 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.

[0707] 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.

[0708] 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.

[0709] 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.

[0710] 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.

[0711] 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.

[0712] 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.

[0713] 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.

[0714] The following is further disclosed regarding the embodiments described above.

[0715] (Claim 1)

[0716] Multiple detectors for collecting product inventory data in real time,

[0717] A computing device that analyzes inventory data collected by the detector and predicts supply and demand,

[0718] A control device that controls the flight device to optimize a predetermined product picking route,

[0719] A means for automatically sorting and transporting goods using the aforementioned flying device,

[0720] A system that includes this.

[0721] (Claim 2)

[0722] The system according to claim 1, further comprising means for optimizing and prioritizing delivery plans based on supply and demand forecast data generated by the computing device.

[0723] (Claim 3)

[0724] The system according to claim 1, further comprising means for storing legal and regulatory information of the location area and appropriately adjusting the flight schedule of the aircraft based on said information.

[0725] "Example 1"

[0726] (Claim 1)

[0727] Multiple sensors for collecting product inventory information in real time,

[0728] A computing device that processes inventory information collected by the sensor and predicts supply and demand,

[0729] A means for generating supply and demand forecasts using past commercial transaction information and market trend information,

[0730] A control device that controls an aircraft to optimize a predetermined item sorting route,

[0731] A means for automatically handling and delivering articles using the aforementioned aircraft,

[0732] A method for performing supply and demand forecasting and route optimization using a generative AI model,

[0733] Means for users to monitor and intervene manually in real time,

[0734] A system that includes this.

[0735] (Claim 2)

[0736] The system according to claim 1, further comprising means for optimizing a delivery plan and setting priorities based on supply and demand forecast information generated by the computing device.

[0737] (Claim 3)

[0738] The system according to claim 1, wherein the control device further includes means for storing legal information of the location area and appropriately adjusting the flight plan of the aircraft based on said information.

[0739] "Application Example 1"

[0740] (Claim 1)

[0741] Multiple detectors for collecting product inventory data in real time,

[0742] A computing device that analyzes inventory data collected by the detector and predicts supply and demand,

[0743] A control device that controls the flight device to optimize a predetermined product picking route,

[0744] A means for automatically sorting and transporting goods using the aforementioned flying device,

[0745] An information management device for logistics center operators to monitor inventory status and picking status in real time,

[0746] A warning system that immediately notifies of inventory fluctuations and equipment malfunctions,

[0747] A system that includes this.

[0748] (Claim 2)

[0749] The system according to claim 1, further comprising means for optimizing and prioritizing delivery plans based on supply and demand forecast data generated by the computing device.

[0750] (Claim 3)

[0751] The system according to claim 1, further comprising means for storing legal and regulatory information of the location area and appropriately adjusting the flight schedule of the aircraft based on said information.

[0752] "Example 2 of combining an emotion engine"

[0753] (Claim 1)

[0754] Multiple sensor means for collecting information on product inventory in real time,

[0755] A calculation means for analyzing inventory information collected by the sensor means and predicting supply and demand,

[0756] A control means for controlling a transport device in order to optimize a predetermined product sorting route,

[0757] A means for automatically sorting and transporting goods using the aforementioned transport device,

[0758] A means for detecting the user's emotional state and adjusting the system's operation based on the detection results,

[0759] A system that includes this.

[0760] (Claim 2)

[0761] The system according to claim 1, further comprising means for optimizing and prioritizing logistics plans based on supply and demand forecast information generated by the calculation means.

[0762] (Claim 3)

[0763] The system according to claim 1, wherein the control means further includes means for recording information on local laws and regulations and for appropriately adjusting the operation schedule of the transport device based on said information.

[0764] "Application example 2 when combining with an emotional engine"

[0765] (Claim 1)

[0766] Multiple sensing devices for collecting product inventory data in real time,

[0767] A computing device that analyzes inventory data collected by the sensing device and predicts supply and demand,

[0768] A management device that controls mobile equipment to optimize a predetermined item collection route,

[0769] The aforementioned mobile device has a function for automatically sorting and transporting goods,

[0770] An emotion analysis device for recognizing the emotional state of workers and adjusting their workload,

[0771] A display device for providing feedback to workers based on their emotional state,

[0772] A system that includes this.

[0773] (Claim 2)

[0774] The system according to claim 1, further comprising means for optimizing and prioritizing transportation plans based on demand and supply forecast data generated by the computing device.

[0775] (Claim 3)

[0776] The system according to claim 1, further comprising a management device that stores regional regulatory information and adjusts the operating schedule of mobile devices appropriately based on said information. [Explanation of symbols]

[0777] 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. Multiple detectors for collecting product inventory data in real time, A computing device that analyzes inventory data collected by the detector and predicts supply and demand, A control device that controls the flight device to optimize a predetermined product picking route, A means for automatically sorting and transporting goods using the aforementioned flying device, A system that includes this.

2. The system according to claim 1, further comprising means for optimizing a delivery plan and setting priorities based on supply and demand forecast data generated by the aforementioned computing device.

3. The system according to claim 1, further comprising means for storing legal and regulatory information of the location area and appropriately adjusting the flight schedule of the aircraft based on said information.

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