system

Abstract

We provide the system. [Solution] A device that acquires inventory and order information in the warehouse in real time and generates the optimal picking route, A device that generates the optimal packaging design based on the shape and weight of the product, A device that shares information and performs logistics processing collaboratively among multiple autonomous transport machines, A device that monitors the operating status of transport machinery and outputs a warning if an abnormality is detected, 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, it is difficult to secure the labor force required for operations, and the decline in work efficiency and the increase in costs have become serious problems. In addition, it is difficult to optimize limited warehouse space, and there is a demand to improve the work efficiency of inventory management and shipping preparation. It is desired to effectively solve these multiple problems.

Means for Solving the Problems

[0005] According to the present invention, a system including means for acquiring inventory and order information in a warehouse in real time and generating an optimal picking route, means for generating an optimal packaging plan based on the shape and weight of the goods, and means for sharing information and coordinating logistics operations among multiple autonomous robots enables efficient and smooth execution of logistics operations. Furthermore, by incorporating a learning function to adapt to obstacles and environmental changes in logistics operations, flexible responses are possible, further enhancing efficiency.

[0006] "Inventory information" refers to data about the types, quantities, and locations of goods within a warehouse.

[0007] "Order information" refers to data related to the type, quantity, and delivery address of products based on purchase requests from customers.

[0008] A "picking route" is a planned movement path within a warehouse designed to efficiently collect goods.

[0009] A "packaging plan" is a plan for determining the optimal packaging method and packaging based on the shape and weight of the product.

[0010] An "autonomous robot" is a mechanical device that has the ability to move and perform tasks based on its own judgment, following programmed instructions.

[0011] "Collaborative work" refers to a process where multiple entities share information and cooperate with each other to achieve a common goal.

[0012] "Learning ability" refers to the capacity to improve one's own performance based on the results of past work and changes in the environment. [Brief explanation of the drawing]

[0013] [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]It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the 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.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the 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, etc.

[0019] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention relates to a system for automating logistics operations within a warehouse. This system consists of multiple autonomous robots, each utilizing AI to efficiently perform product picking, packaging, and preparation for shipment. A specific embodiment of this system is described below in natural language.

[0035] The server's initial role is to receive and process order information in real time from a general order management system. The server then combines this information with warehouse inventory data to perform analysis, which is then used by an AI agent to generate the optimal picking route. This optimized route and order information are then sent as instructions to autonomous robots.

[0036] Autonomous robots, acting as terminals, automatically move within the warehouse based on received instructions and pick each item. For example, the robots are designed to take the shortest route to retrieve items from shelves, taking into account factors such as the handling priority, weight, and shape of each item. This enables efficient and accurate processing.

[0037] After each picking is complete, the terminal's robot executes the optimal packing plan designed by the AI ​​agent. The robot automatically selects appropriate packing materials and boxes based on the shape and weight of the product, ensuring efficient and waste-free packing. For example, fragile items may be given additional cushioning material, and irregularly shaped items may be given special packaging.

[0038] Once the goods are ready for shipment, the robot transports them to the designated shipping area. The server monitors this entire process and records the progress at each stage. Furthermore, the user can make necessary adjustments and maintenance based on this information.

[0039] Through the above embodiments, logistics operations within the warehouse can be carried out efficiently and accurately, solving the problem of labor shortages and reducing operational costs.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server receives order information via API from external order management systems and online platforms. This information includes product ID, quantity, priority, and shipping destination.

[0043] Step 2:

[0044] The server sends the acquired order information and warehouse inventory information to the AI ​​agent, which then generates the optimal picking route. The AI ​​agent calculates the route considering the inventory location of the products, the warehouse layout, and the robot's current location.

[0045] Step 3:

[0046] The server sends the optimal route calculated by the AI ​​agent as instructions to the autonomous robot acting as a terminal. These instructions include the picking order, shelf location, and movement path.

[0047] Step 4:

[0048] The terminal robot follows a calculated route, moves to a designated location within the warehouse, and picks the corresponding product. The robot uses built-in sensors to scan and verify the product's barcode or RFID.

[0049] Step 5:

[0050] The robot quickly and efficiently packs products based on a packing plan provided by an AI agent. It selects the most suitable packing materials according to the shape, weight, and fragility of the product.

[0051] Step 6:

[0052] A robot transports the packaged goods to the shipping area. The server monitors this process and records its progress.

[0053] Step 7:

[0054] The user checks the robot's status and work progress based on information provided by the server, and performs system adjustments and maintenance as needed.

[0055] (Example 1)

[0056] 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."

[0057] The challenge lies in improving efficiency and accuracy in logistics operations within the warehouse. This requires optimizing the overall logistics process by implementing real-time processing of inventory and order information, enhancing coordination in goods transportation, and strengthening the ability to respond to environmental changes and obstacles.

[0058] 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.

[0059] This invention includes a server comprising means for acquiring inventory and order information in a warehouse in real time and generating an optimal route using an intelligent system for analyzing that information; means for selecting packaging materials based on the shape and weight of the goods and generating an optimal packaging plan; means for sharing information among multiple autonomous devices and processing items in a coordinated manner; and means for a monitoring system that improves accuracy by moving items to designated locations and confirming their locations. This improves the efficiency and accuracy of item processing in the warehouse, enabling faster operations and flexible responses to environmental changes.

[0060] "Warehouse" refers to the area inside a facility used for storing, managing, and preparing goods for delivery.

[0061] "Inventory information" refers to a collection of detailed data regarding the types, quantities, and locations of items held within a warehouse.

[0062] "Order information" refers to a collection of data that includes the contents, quantity, and delivery address of items based on a customer's purchase request.

[0063] "Real-time acquisition" refers to the process of collecting information continuously and without delay.

[0064] An "intelligent system" is software or hardware that uses artificial intelligence technology to analyze data and make optimal decisions.

[0065] An "optimal route" is the shortest or most efficient route calculated to ensure the efficient movement or delivery of goods.

[0066] "Packaging materials" is a general term for materials used to protect goods and ensure their safety during transportation.

[0067] A "packaging plan" is a plan for selecting appropriate packaging materials according to the characteristics of the goods and for efficiently protecting those goods.

[0068] An "autonomous device" is a machine or robot that can perform a specific task automatically or with minimal human intervention.

[0069] "Item processing" refers to a series of tasks including picking, packing, and preparing items for delivery.

[0070] A "monitoring system" is a mechanism for continuously observing the status of ongoing processes and equipment and collecting data.

[0071] This invention is a system for efficiently and accurately automating logistics operations within a warehouse. The system mainly consists of the following hardware and software.

[0072] The server uses an intelligent system to receive and analyze order and inventory information in real time. This intelligent system leverages generative AI models to calculate optimal product picking routes and develop optimal packaging plans based on product shape and weight. Specifically, the server manages a database and is equipped with a high-performance processor for rapid information processing. In addition, the software running on it includes AI-powered route optimization algorithms and data management applications.

[0073] The autonomous terminal devices automatically move around the warehouse based on instructions from the server, performing tasks such as picking, packing, and preparing items for shipment. The robots are equipped with sensors to scan barcodes on items, accurately identifying and picking specified items. Specifically, the robots have a lightweight structure with a wide range of motion, designed to smoothly pass through narrow gaps between shelves.

[0074] Users monitor and maintain the entire system, ensuring it is functioning correctly. The user interface is designed to display progress and log information in real time, allowing for adjustments as needed.

[0075] As an example of a prompt to maximize the system's usefulness, it might say, "Use the AI ​​agent to automate logistics within the warehouse and design the optimal picking route and packing plan for the products." This prompts the generated AI model to perform the required optimizations.

[0076] The system of the present invention, through its configuration and functions, can significantly improve the efficiency of logistics operations, compensate for labor shortages, and reduce operational costs.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The server receives order information in real time from the order management system. The inputs received are the order ID, product list, quantity, and delivery date. Based on this, the server updates the order database and performs the specific actions of listing the new orders.

[0080] Step 2:

[0081] The server retrieves inventory information from the warehouse and analyzes it in combination with order information. Inputs include the location, quantity, and characteristics of the inventory. Using a generative AI model, it calculates the optimal picking route and outputs the result as instructions. Specifically, the algorithm calculates the route to retrieve the goods, prioritizing routes that avoid congestion.

[0082] Step 3:

[0083] The terminal's autonomous device (robot) begins moving based on picking route information transmitted from the server. The input consists of route information and a list of items, and the robot accurately picks the correct items while reading barcodes using sensors. During this process, the route is fine-tuned through data calculations to achieve the most efficient movement.

[0084] Step 4:

[0085] After the terminal robot has finished collecting the items, it packs them according to the packing plan sent from the server. The input includes the dimensions, weight, and characteristics of the items. The output is a list of the completed, packed items. Specifically, the robot automatically selects the appropriate packing materials and uses additional cushioning material as needed.

[0086] Step 5:

[0087] The terminal robot transports the packaged goods to the designated shipping area. The input is the location information of the shipping area, and the robot uses sensors to confirm the route while transporting the goods. The output is that the goods are precisely placed in the shipping area. Specifically, the robot's movement is enhanced with obstacle avoidance capabilities.

[0088] Step 6:

[0089] The server monitors the entire logistics process and logs the status. Inputs include operation logs and work progress information for each robot, while outputs include historical data and progress reports. The user monitors this output and takes specific actions to adjust the system as needed.

[0090] (Application Example 1)

[0091] 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."

[0092] Modern logistics centers require systems that streamline processes such as product picking, packing, and shipping preparation, while also enabling immediate response to any anomalies. Traditional methods have limitations in terms of efficiency improvements and troubleshooting, making overall optimization difficult. In particular, there has been a lack of systems that integrate real-time information sharing and anomaly detection functions, necessitating a more comprehensive solution.

[0093] 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.

[0094] In this invention, the server includes a device that acquires inventory and order information in the warehouse in real time and generates an optimal picking route, a device that generates an optimal packaging design based on the shape and weight of the goods, a device that shares information among multiple autonomous transport machines and performs logistics processing in cooperation, and a device that monitors the operating status of the transport machines and outputs a warning when an abnormality is detected. This enables efficient process management in the logistics center and makes it possible to build a system that can respond immediately when an abnormality occurs.

[0095] "Inventory information" refers to data about the types and quantities of goods stored in a warehouse.

[0096] "Order information" refers to data concerning the details of product orders placed with the logistics center.

[0097] A "picking route" is the optimal path a robot takes to collect goods within a warehouse.

[0098] "Packaging design" refers to the appropriate packaging method selected based on the shape and weight of the product.

[0099] An "autonomous transport machine" is a robot that uses AI technology to automatically perform tasks.

[0100] "Logistics processing" refers to a series of tasks related to logistics, such as picking, packing, and preparing goods for shipment.

[0101] "Anomaly detection" is a function that detects abnormal conditions in the operation of a system or machine.

[0102] A "warning output device" is a mechanism that sends out notifications or alerts when an abnormality is detected.

[0103] To implement this invention, it is necessary to construct an autonomous transport machine system for managing logistics operations within a warehouse. The server first acquires inventory and order information from the warehouse in real time. This information is collected via a network using database technology. Based on this information, the server utilizes a generative AI model to generate the optimal picking route.

[0104] The autonomous transport machines, acting as terminals, move within the warehouse according to the picking routes received from the server and pick the specified items. The terminals execute packaging designs tailored to the characteristics of each product and apply appropriate packaging. In particular, the design takes into account the shape, weight, and fragility of the product.

[0105] Users can check the progress of work and the presence of any abnormalities on their smartphones or other devices based on the data collected by the transport machinery. When an abnormality is detected, the device sends a notification to the user, enabling immediate countermeasures to be taken. This improves work efficiency at the logistics center and allows for a quick response to unexpected problems.

[0106] For example, if the picking of a certain product is behind schedule, the user can immediately recognize this and make adjustments, such as deploying additional transport machinery. An example of a prompt to the generated AI model would be, "Optimize the routes so that autonomous transport machinery in the logistics center can perform efficient picking and packing." This prompt allows the AI ​​model to propose an optimal logistics process plan.

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] The server acquires real-time inventory and order information from the warehouse via the network. Inputs come from external databases and order management systems, and output is a dataset representing the latest state of the warehouse. Based on this data, prompts are sent to the generating AI model, preparing it to perform optimization calculations for picking routes.

[0110] Step 2:

[0111] The server generates the optimal picking route using an AI model based on the acquired data. The input is the inventory layout and order information within the warehouse, and the output is the picking order and route information for each product. In this process, the AI ​​model executes an algorithm that takes into account the priority of products and the distance they travel to optimize efficiency.

[0112] Step 3:

[0113] The autonomous transport machine, acting as the terminal, moves within the warehouse based on the picking route transmitted from the server. It receives generated route information as input and obtains actual location data and progress during movement as output. The transport machine efficiently collects items from designated shelves and performs the picking process.

[0114] Step 4:

[0115] The terminal performs optimal packaging design based on the shape and weight information of the product. The input is the collected product attribute data, and the output is a properly packaged product. In this step, the autonomous transport machine selects the packaging materials and performs packaging that takes into account the protection of the product and efficient transport.

[0116] Step 5:

[0117] Users can check information regarding work progress and the presence of anomalies provided by the terminal on a smartphone or other device. The system receives status data from the transport machinery as input and outputs visualized report-style information. Users receive immediate notifications when an anomaly is detected, allowing them to take necessary countermeasures.

[0118] 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.

[0119] This invention relates to a system for automating logistics operations within a warehouse and for recognizing user emotions to adjust system operation accordingly. This system consists of a combination of multiple autonomous robots, AI agents, and an emotion engine. A specific embodiment is described below in natural language.

[0120] The server's primary task is to receive order information from an external order management system and integrate it with warehouse inventory information. The server then passes this information to an AI agent to generate the optimal picking route and packing plan. The generated information is then sent as instructions to autonomous robots acting as terminals.

[0121] The autonomous robot at the terminal moves around the warehouse based on the received picking route and picks the specified items. Following the optimal plan provided by the AI ​​agent, it efficiently and appropriately packs the items. It selects packing materials according to the shape and weight of the items and transports them to the designated shipping area, completing the preparation for shipment.

[0122] Furthermore, the emotion engine detects in real time how users are feeling towards the system and flexibly adjusts the system's operation based on that. For example, if a user is feeling stressed, the emotion engine can reduce the frequency of notifications or make work instructions more concise. In this way, the system is attentive to the user's emotions and creates a comfortable work environment.

[0123] Thus, the present invention not only enables the automation of logistics operations, but also achieves more efficient and flexible operations by responding to user emotions.

[0124] The following describes the processing flow.

[0125] Step 1:

[0126] The server receives order information via API from external order management systems and online platforms. This information includes product ID, quantity, priority, and shipping destination.

[0127] Step 2:

[0128] The server sends the acquired order information and warehouse inventory information to the AI ​​agent, which then generates the optimal picking route and packing plan based on this information. The AI ​​agent calculates the route considering the inventory location of the products, the warehouse layout, and the current location of the robots.

[0129] Step 3:

[0130] The server sends instructions to the autonomous robot, which acts as a terminal, based on the optimal route and packing plan calculated by the AI ​​agent. These instructions include the picking order, shelf location, movement path, and packing method.

[0131] Step 4:

[0132] The terminal robot follows a calculated route, moves to a designated location within the warehouse, and picks the corresponding product. The robot uses built-in sensors to scan and verify the product's barcode or RFID.

[0133] Step 5:

[0134] The robot quickly and efficiently packs products based on a packing plan provided by an AI agent. It selects the most suitable packing materials according to the shape, weight, and fragility of the product.

[0135] Step 6:

[0136] A robot transports the packaged goods to the shipping area. The server monitors this process and records its progress.

[0137] Step 7:

[0138] The emotion engine detects the user's emotions in real time and adjusts system operations accordingly. For example, if the user is feeling stressed, it might instruct the system to reduce the frequency of notifications.

[0139] Step 8:

[0140] The user checks the robot's status and work progress based on information provided by the server, and performs system adjustments and maintenance as needed.

[0141] (Example 2)

[0142] 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".

[0143] The challenges in logistics operations include achieving efficient work processes and reducing the emotional burden on workers in the work environment. While conventional logistics systems have made progress in efficiency and automation, they have not adequately addressed the emotional needs of workers. There is a need for smoother operations by reducing tension and stress.

[0144] 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.

[0145] In this invention, the server includes means for instantly acquiring information on items and instructions within the warehouse and generating an optimized movement path, means for generating an optimized packaging plan based on the external shape and weight of the items, and means for analyzing the emotional state of the user and adjusting the instruction method accordingly. This enables increased efficiency in logistics operations and a reduction in the mental burden on workers.

[0146] "Item information" refers to data such as the type, quantity, and location of individual items stored in the warehouse.

[0147] "Instruction information" refers to data related to work instructions concerning orders, picking, and shipping.

[0148] A "workflow path" is an optimized route for picking, packing, and shipping goods.

[0149] "External shape" refers to the characteristics that indicate the shape and dimensions of an item.

[0150] "Weight" is a characteristic that indicates the mass of an object.

[0151] "Packaging planning" is the process of determining the optimal packaging method based on the shape and weight of the items.

[0152] "Mobile equipment" refers to devices that autonomously move around within a warehouse to pick and transport goods.

[0153] "Emotional state" refers to information indicating the user's psychological health, and work instructions are adjusted based on this information.

[0154] This invention is a system that automates logistics operations and realizes a flexible work environment based on user emotions. This system instantly acquires item and instruction information within the warehouse, and its server, terminal, and user components work together to generate optimal movement and packaging plans. Specific embodiments of the system are described below.

[0155] The server functions as the primary information processing unit. It interacts with an external order management system to acquire inventory and order information in real time. Using an AI agent, it calculates the optimal picking route based on the acquired information and generates an optimal packaging plan based on the shape and weight of the items. These functions are implemented through signal processing equipment and AI algorithms. A machine learning framework is implemented as the software.

[0156] The terminal operates as an autonomous mobile device, picking items within the warehouse based on instructions sent from the server. Equipped with sensors, the terminal accurately determines its own position and moves along a designated route via automatic control. Furthermore, during packaging, it dynamically selects the appropriate packaging material for efficient work.

[0157] The user is responsible for giving and managing work instructions, and uses an emotion engine to feed back their emotional state to the system. The emotion engine analyzes the user's facial expressions and voice, detecting stress and fatigue in real time, and plays a role in optimizing notifications and adjusting work instructions. This creates a less stressful work environment for the user.

[0158] As a concrete example, consider a scenario where a server retrieves order information, and an AI agent creates an optimized route and packing plan. The way terminals efficiently move around the warehouse based on this plan, quickly picking a large number of items, directly contributes to streamlining logistics and reducing processing time.

[0159] An example of a prompt message to enable these systems to function is: "Explain what emotion recognition technologies are used in this warehouse management system to reduce user stress. Also, explain the procedure for how autonomous robots streamline inventory management."

[0160] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0161] Step 1:

[0162] The server receives order information from an external order management system. This order information includes product name, quantity, and delivery date. Next, it extracts and integrates warehouse item information from a database. This provides a clear real-time inventory status. The server then prepares this integrated data as input data for the AI ​​agent.

[0163] Step 2:

[0164] The server uses an AI agent to generate the optimal picking route and packaging plan based on the received order and item information. Specifically, the AI ​​agent uses a generated AI model to calculate the shortest path considering the placement of items and the path length, and then selects appropriate packaging materials based on the item's shape and weight. This calculation result is sent as output data to the autonomous robot.

[0165] Step 3:

[0166] The autonomous robot, acting as the terminal, automatically begins moving within the warehouse according to the picking route information received from the server. Using its onboard sensors, the robot recognizes its current location and picks the designated items. After picking is complete, it packs the goods according to the packing plan. Specifically, it selects boxes and packing materials of the appropriate size and safely packs the goods.

[0167] Step 4:

[0168] The user monitors their work environment and feeds emotional data back to the system through an emotion engine. The system analyzes the user's facial expressions and voice to determine their stress levels and satisfaction, and requests adjustments to notification frequency or improvements to work instructions from the server as needed. This allows the system to operate in a more user-friendly manner based on emotional data.

[0169] Step 5:

[0170] Robots transport packaged goods to designated shipping areas. The robots plan and execute the optimal transport route based on destination information received from the server, resulting in efficient preparation for shipment and completion of the next shipping cycle.

[0171] (Application Example 2)

[0172] 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".

[0173] Modern logistics centers require efficient inventory management and order fulfillment, but current systems lack the flexibility to adjust to the mental state of workers. As a result, stress and fatigue accumulate among workers, leading to decreased productivity. Furthermore, while rapid and efficient route selection and packaging are necessary in logistics operations, effective systems for automating these processes have not yet been established.

[0174] 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.

[0175] In this invention, the server includes means for acquiring inventory and order information in a warehouse in real time and generating an optimal movement route, means for generating an optimal packaging plan based on the shape and weight of the goods, means for sharing information among multiple autonomous mobile devices and performing transportation operations in cooperation, and means for detecting the emotional state of workers and adjusting the frequency of notifications and the content of instructions based on that state. This promotes the automation of logistics operations and enables flexible work operations that are sensitive to the emotions of workers.

[0176] "Inventory information" refers to data about the quantity and location of goods stored within a warehouse.

[0177] "Order information" refers to data that includes the type and quantity of goods ordered by the customer, as well as delivery requests.

[0178] "Acquiring in real time" means instantly obtaining the latest information at the present moment.

[0179] An "optimal movement route" is a route designed to efficiently retrieve and deliver goods within a logistics center.

[0180] "Product shape" refers to the physical characteristics of each product, such as its shape and size.

[0181] "Product weight" refers to the weight characteristics of each individual product.

[0182] An "optimal packaging plan" is a plan that selects appropriate packaging materials and methods based on the shape and weight of the product.

[0183] An "autonomous mobile device" is a machine that moves independently based on pre-programmed routes and instructions, and performs logistics operations.

[0184] "Information sharing" means making data mutually available to multiple systems and devices.

[0185] "Cooperative transportation operations" means that multiple autonomous mobile devices work together to efficiently transport goods.

[0186] "Worker emotional state" refers to the psychological and emotional health of personnel involved in logistics operations.

[0187] "Notification frequency" refers to the number of times the system communicates information or instructions to the worker.

[0188] "Adjusting the content of instructions" means changing the details and difficulty level of the instructions communicated according to the emotional state of the worker.

[0189] The system for implementing this invention supports efficient operation in warehouse management and adjustments to operations that take into account the feelings of workers. Specific embodiments of this system are described below.

[0190] The server first retrieves real-time inventory and order information from the warehouse. This allows for immediate confirmation of the latest inventory status and customer order requests. The server then uses an AI agent to generate the optimal movement route and packaging plan, and sends instructions to the autonomous mobile device. These instructions include selecting packaging materials based on the shape and weight of the products.

[0191] The autonomous mobile devices move within the warehouse based on the instructions they receive, handling and packaging goods. This enables labor-saving and efficient logistics operations. Information sharing between devices allows for coordinated work, resulting in quick and effective operations.

[0192] For workers, the system monitors their emotional state in conjunction with smartphones and tablets. The emotion engine analyzes the worker's stress level and fatigue in real time, and if the worker is in an unstable state, the server reduces the frequency of notifications or simplifies instructions. This makes it possible to provide a worker-friendly work environment.

[0193] For example, if the system determines that a worker is in a highly stressed state and needs a break, it can suggest interrupting work and display an encouraging message on the worker's smartphone. In this way, the system balances improved work efficiency with worker health management.

[0194] By using a generative AI model, it is possible to design more precise prompts. As a concrete example, a prompt such as "Devise a concise and encouraging message to provide when the stress levels of staff working at a logistics center are high, and describe how to efficiently give work instructions to autonomous robots" can be generated and used.

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] The server retrieves real-time inventory and order information from the warehouse. Inputs include order data from an external order management system and inventory data from the warehouse management system. By retrieving and integrating information from the database, it obtains the latest inventory status and order requests. The output is a list of integrated inventory and order information.

[0198] Step 2:

[0199] The server uses an AI agent to generate the optimal travel route and packaging plan. Inputs include inventory and order information, as well as product shape and weight. The AI ​​algorithm calculates the shortest route and appropriate packaging method. The output is a set of instructions sent to the autonomous mobile device.

[0200] Step 3:

[0201] The autonomous mobile device moves around the warehouse based on instructions from the server, handling and packaging goods. The input is a set of instructions from the server. It picks the specified goods, selects packaging materials, and performs the actual packaging work. The output is the packaged goods and their placement information.

[0202] Step 4:

[0203] The server acquires emotional data from the worker's smartphone and analyzes it using an emotion engine. The input is biometric data acquired by the smartphone. The emotion engine processes the data and determines the worker's stress and fatigue levels. The output is the analysis result based on their emotional state.

[0204] Step 5:

[0205] The server adjusts the frequency and content of notifications based on the analysis results from the emotion engine. The input is the analysis results of the emotional state obtained in the previous step. The notification content is appropriately converted by the generation AI model and sent to the worker's smartphone. The output is the adjusted instructions and notifications.

[0206] Step 6:

[0207] The user (worker) receives coordinated instructions and notifications via their smartphone and proceeds with their work accordingly. Input consists of notifications and work instructions from the server. The worker checks the notifications and adjusts their work pace as needed. Output is the work performed by the user.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] [Second Embodiment]

[0212] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0213] 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.

[0214] 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).

[0215] 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.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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".

[0224] This invention relates to a system for automating logistics operations within a warehouse. This system consists of multiple autonomous robots, each utilizing AI to efficiently perform product picking, packaging, and preparation for shipment. A specific embodiment of this system is described below in natural language.

[0225] The server's initial role is to receive and process order information in real time from a general order management system. The server then combines this information with warehouse inventory data to perform analysis, which is then used by an AI agent to generate the optimal picking route. This optimized route and order information are then sent as instructions to autonomous robots.

[0226] Autonomous robots, acting as terminals, automatically move within the warehouse based on received instructions and pick each item. For example, the robots are designed to take the shortest route to retrieve items from shelves, taking into account factors such as the handling priority, weight, and shape of each item. This enables efficient and accurate processing.

[0227] After each picking is complete, the terminal's robot executes the optimal packing plan designed by the AI ​​agent. The robot automatically selects appropriate packing materials and boxes based on the shape and weight of the product, ensuring efficient and waste-free packing. For example, fragile items may be given additional cushioning material, and irregularly shaped items may be given special packaging.

[0228] Once the goods are ready for shipment, the robot transports them to the designated shipping area. The server monitors this entire process and records the progress at each stage. Furthermore, the user can make necessary adjustments and maintenance based on this information.

[0229] Through the above embodiments, logistics operations within the warehouse can be carried out efficiently and accurately, solving the problem of labor shortages and reducing operational costs.

[0230] The following describes the processing flow.

[0231] Step 1:

[0232] The server receives order information via API from external order management systems and online platforms. This information includes product ID, quantity, priority, and shipping destination.

[0233] Step 2:

[0234] The server sends the acquired order information and warehouse inventory information to the AI ​​agent, which then generates the optimal picking route. The AI ​​agent calculates the route considering the inventory location of the products, the warehouse layout, and the robot's current location.

[0235] Step 3:

[0236] The server sends the optimal route calculated by the AI ​​agent as instructions to the autonomous robot acting as a terminal. These instructions include the picking order, shelf location, and movement path.

[0237] Step 4:

[0238] The terminal robot follows a calculated route, moves to a designated location within the warehouse, and picks the corresponding product. The robot uses built-in sensors to scan and verify the product's barcode or RFID.

[0239] Step 5:

[0240] The robot quickly and efficiently packs products based on a packing plan provided by an AI agent. It selects the most suitable packing materials according to the shape, weight, and fragility of the product.

[0241] Step 6:

[0242] A robot transports the packaged goods to the shipping area. The server monitors this process and records its progress.

[0243] Step 7:

[0244] The user checks the robot's status and work progress based on information provided by the server, and performs system adjustments and maintenance as needed.

[0245] (Example 1)

[0246] 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".

[0247] The challenge lies in improving efficiency and accuracy in logistics operations within the warehouse. This requires optimizing the overall logistics process by implementing real-time processing of inventory and order information, enhancing coordination in goods transportation, and strengthening the ability to respond to environmental changes and obstacles.

[0248] 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.

[0249] This invention includes a server comprising means for acquiring inventory and order information in a warehouse in real time and generating an optimal route using an intelligent system for analyzing that information; means for selecting packaging materials based on the shape and weight of the goods and generating an optimal packaging plan; means for sharing information among multiple autonomous devices and processing items in a coordinated manner; and means for a monitoring system that improves accuracy by moving items to designated locations and confirming their locations. This improves the efficiency and accuracy of item processing in the warehouse, enabling faster operations and flexible responses to environmental changes.

[0250] "Warehouse" refers to the area inside a facility used for storing, managing, and preparing goods for delivery.

[0251] "Inventory information" refers to a collection of detailed data regarding the types, quantities, and locations of items held within a warehouse.

[0252] "Order information" refers to a collection of data that includes the contents, quantity, and delivery address of items based on a customer's purchase request.

[0253] "Real-time acquisition" refers to the process of collecting information continuously and without delay.

[0254] An "intelligent system" is software or hardware that uses artificial intelligence technology to analyze data and make optimal decisions.

[0255] An "optimal route" is the shortest or most efficient route calculated to ensure the efficient movement or delivery of goods.

[0256] "Packaging materials" is a general term for materials used to protect goods and ensure their safety during transportation.

[0257] A "packaging plan" is a plan for selecting appropriate packaging materials according to the characteristics of the goods and for efficiently protecting those goods.

[0258] An "autonomous device" is a machine or robot that can perform a specific task automatically or with minimal human intervention.

[0259] "Item processing" refers to a series of tasks including picking, packing, and preparing items for delivery.

[0260] A "monitoring system" is a mechanism for continuously observing the status of ongoing processes and equipment and collecting data.

[0261] This invention is a system for efficiently and accurately automating logistics operations within a warehouse. The system mainly consists of the following hardware and software.

[0262] The server uses an intelligent system to receive and analyze order and inventory information in real time. This intelligent system leverages generative AI models to calculate optimal product picking routes and develop optimal packaging plans based on product shape and weight. Specifically, the server manages a database and is equipped with a high-performance processor for rapid information processing. In addition, the software running on it includes AI-powered route optimization algorithms and data management applications.

[0263] The autonomous terminal devices automatically move around the warehouse based on instructions from the server, performing tasks such as picking, packing, and preparing items for shipment. The robots are equipped with sensors to scan barcodes on items, accurately identifying and picking specified items. Specifically, the robots have a lightweight structure with a wide range of motion, designed to smoothly pass through narrow gaps between shelves.

[0264] Users monitor and maintain the entire system, ensuring it is functioning correctly. The user interface is designed to display progress and log information in real time, allowing for adjustments as needed.

[0265] As an example of a prompt to maximize the system's usefulness, it might say, "Use the AI ​​agent to automate logistics within the warehouse and design the optimal picking route and packing plan for the products." This prompts the generated AI model to perform the required optimizations.

[0266] The system of the present invention, through its configuration and functions, can significantly improve the efficiency of logistics operations, compensate for labor shortages, and reduce operational costs.

[0267] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0268] Step 1:

[0269] The server receives order information in real time from the order management system. The inputs received are the order ID, product list, quantity, and delivery date. Based on this, the server updates the order database and performs the specific actions of listing the new orders.

[0270] Step 2:

[0271] The server retrieves inventory information from the warehouse and analyzes it in combination with order information. Inputs include the location, quantity, and characteristics of the inventory. Using a generative AI model, it calculates the optimal picking route and outputs the result as instructions. Specifically, the algorithm calculates the route to retrieve the goods, prioritizing routes that avoid congestion.

[0272] Step 3:

[0273] The terminal's autonomous device (robot) begins moving based on picking route information transmitted from the server. The input consists of route information and a list of items, and the robot accurately picks the correct items while reading barcodes using sensors. During this process, the route is fine-tuned through data calculations to achieve the most efficient movement.

[0274] Step 4:

[0275] After the terminal robot has finished collecting the items, it packs them according to the packing plan sent from the server. The input includes the dimensions, weight, and characteristics of the items. The output is a list of the completed, packed items. Specifically, the robot automatically selects the appropriate packing materials and uses additional cushioning material as needed.

[0276] Step 5:

[0277] The terminal robot transports the packaged goods to the designated shipping area. The input is the location information of the shipping area, and the robot uses sensors to confirm the route while transporting the goods. The output is that the goods are precisely placed in the shipping area. Specifically, the robot's movement is enhanced with obstacle avoidance capabilities.

[0278] Step 6:

[0279] The server monitors the entire logistics process and records the situation in the log. The input includes the operation logs and work progress information of each robot, and the output is historical data and progress reports. The user monitors this output and performs specific operations to adjust the system as needed.

[0280] (Application Example 1)

[0281] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0282] In modern logistics centers, there is a demand for building a system that can improve the efficiency of the processes of product picking, packing, and shipping preparation, and can immediately respond when an abnormality occurs. Conventional methods have limitations in optimizing efficiency and troubleshooting in limited parts, and it is difficult to achieve overall optimization. In particular, there has been a lack of a system that integrates real-time information sharing and abnormality detection functions, so a more comprehensive solution is needed.

[0283] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0284] In this invention, the server includes a device that acquires inventory information and order information in the warehouse in real time and generates an optimal picking route, a device that generates an optimal packing design based on the shape and weight of the product, a device that shares information among multiple autonomous transport machines and cooperates to perform logistics processing, and a device that monitors the operating state of the transport machine and outputs a warning when an abnormality is detected. As a result, efficient process management in the logistics center becomes possible, and it is possible to build a system that can immediately respond even when an abnormality occurs.

[0285] "Inventory information" is data regarding the types and quantities of products stored in the warehouse.

[0286] "Order information" refers to data related to the order details of goods issued to the logistics center.

[0287] "Picking route" refers to the optimal movement route when a robot collects goods in the warehouse.

[0288] "Packaging design" refers to an appropriate packaging method selected based on the shape and weight of the goods.

[0289] "Autonomous transport machinery" refers to a robot that utilizes AI technology to automatically perform operations.

[0290] "Logistics processing" refers to a series of logistics-related operations such as picking, packaging, and shipping preparation of goods.

[0291] "Abnormality detection" refers to a function that detects a state different from normal in the operation of a system or machine.

[0292] "Device for outputting warnings" refers to a mechanism that issues notifications or alerts when an abnormality is detected.

[0293] To implement this invention, it is necessary to construct an autonomous transport machinery system for managing logistics operations in the warehouse. The server first obtains the inventory information and order information in the warehouse in real time. These information are collected via a network using database technology. The server utilizes the generated AI model based on this information to generate an optimal picking route.

[0294] The autonomous transport machinery, which is a terminal, moves within the warehouse according to the picking route received from the server and performs picking of the specified goods. The terminal executes a packaging design according to the characteristics of the goods and applies appropriate packaging. In particular, a design considering the shape, weight, and fragility of the goods is carried out.

[0295] Users can check the progress of work and the presence of any abnormalities on their smartphones or other devices based on the data collected by the transport machinery. When an abnormality is detected, the device sends a notification to the user, enabling immediate countermeasures to be taken. This improves work efficiency at the logistics center and allows for a quick response to unexpected problems.

[0296] For example, if the picking of a certain product is behind schedule, the user can immediately recognize this and make adjustments, such as deploying additional transport machinery. An example of a prompt to the generated AI model would be, "Optimize the routes so that autonomous transport machinery in the logistics center can perform efficient picking and packing." This prompt allows the AI ​​model to propose an optimal logistics process plan.

[0297] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0298] Step 1:

[0299] The server acquires real-time inventory and order information from the warehouse via the network. Inputs come from external databases and order management systems, and output is a dataset representing the latest state of the warehouse. Based on this data, prompts are sent to the generating AI model, preparing it to perform optimization calculations for picking routes.

[0300] Step 2:

[0301] The server generates the optimal picking route using an AI model based on the acquired data. The input is the inventory layout and order information within the warehouse, and the output is the picking order and route information for each product. In this process, the AI ​​model executes an algorithm that takes into account the priority of products and the distance they travel to optimize efficiency.

[0302] Step 3:

[0303] The autonomous transport machine, which is a terminal, moves inside the warehouse based on the picking route transmitted from the server. It receives the generated route information as input and obtains the actual position data and progress during movement as output. The transport machine efficiently collects goods from the designated shelves and executes the picking process.

[0304] Step 4:

[0305] The terminal executes an optimal packaging design based on the shape and weight information of the goods. The input is the attribute data of the collected goods, and as output, a properly packaged package is obtained. In this step, the autonomous transport machine selects the packaging material and performs packaging considering the protection of the goods and efficient transportation.

[0306] Step 5:

[0307] The user checks information regarding the work progress and the presence or absence of abnormalities provided by the terminal on a terminal such as a smartphone. It receives the state data of the transport machine as input and obtains information in a visualized report format as output. The user can receive an immediate notification when an abnormality is detected and take necessary countermeasures.

[0308] 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.

[0309] The present invention relates to a system for automating logistics operations in a warehouse and further recognizing the user's emotion to adjust the operation of the system. This system is configured by combining a plurality of autonomous robots, AI agents, and an emotion engine. The following describes its specific embodiments in natural language.

[0310] The server's primary task is to receive order information from an external order management system and integrate it with warehouse inventory information. The server then passes this information to an AI agent to generate the optimal picking route and packing plan. The generated information is then sent as instructions to autonomous robots acting as terminals.

[0311] The autonomous robot at the terminal moves around the warehouse based on the received picking route and picks the specified items. Following the optimal plan provided by the AI ​​agent, it efficiently and appropriately packs the items. It selects packing materials according to the shape and weight of the items and transports them to the designated shipping area, completing the preparation for shipment.

[0312] Furthermore, the emotion engine detects in real time how users are feeling towards the system and flexibly adjusts the system's operation based on that. For example, if a user is feeling stressed, the emotion engine can reduce the frequency of notifications or make work instructions more concise. In this way, the system is attentive to the user's emotions and creates a comfortable work environment.

[0313] Thus, the present invention not only enables the automation of logistics operations, but also achieves more efficient and flexible operations by responding to user emotions.

[0314] The following describes the processing flow.

[0315] Step 1:

[0316] The server receives order information via API from external order management systems and online platforms. This information includes product ID, quantity, priority, and shipping destination.

[0317] Step 2:

[0318] The server sends the acquired order information and warehouse inventory information to the AI ​​agent, which then generates the optimal picking route and packing plan based on this information. The AI ​​agent calculates the route considering the inventory location of the products, the warehouse layout, and the current location of the robots.

[0319] Step 3:

[0320] The server sends instructions to the autonomous robot, which acts as a terminal, based on the optimal route and packing plan calculated by the AI ​​agent. These instructions include the picking order, shelf location, movement path, and packing method.

[0321] Step 4:

[0322] The terminal robot follows a calculated route, moves to a designated location within the warehouse, and picks the corresponding product. The robot uses built-in sensors to scan and verify the product's barcode or RFID.

[0323] Step 5:

[0324] The robot quickly and efficiently packs products based on a packing plan provided by an AI agent. It selects the most suitable packing materials according to the shape, weight, and fragility of the product.

[0325] Step 6:

[0326] A robot transports the packaged goods to the shipping area. The server monitors this process and records its progress.

[0327] Step 7:

[0328] The emotion engine detects the user's emotions in real time and adjusts system operations accordingly. For example, if the user is feeling stressed, it might instruct the system to reduce the frequency of notifications.

[0329] Step 8:

[0330] The user checks the robot's status and work progress based on information provided by the server, and performs system adjustments and maintenance as needed.

[0331] (Example 2)

[0332] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0333] The challenges in logistics operations include achieving efficient work processes and reducing the emotional burden on workers in the work environment. While conventional logistics systems have made progress in efficiency and automation, they have not adequately addressed the emotional needs of workers. There is a need for smoother operations by reducing tension and stress.

[0334] 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.

[0335] In this invention, the server includes means for instantly acquiring information on items and instructions within the warehouse and generating an optimized movement path, means for generating an optimized packaging plan based on the external shape and weight of the items, and means for analyzing the emotional state of the user and adjusting the instruction method accordingly. This enables increased efficiency in logistics operations and a reduction in the mental burden on workers.

[0336] "Item information" refers to data such as the type, quantity, and location of individual items stored in the warehouse.

[0337] "Instruction information" refers to data related to work instructions concerning orders, picking, and shipping.

[0338] A "workflow path" is an optimized route for picking, packing, and shipping goods.

[0339] "External shape" refers to the characteristics that indicate the shape and dimensions of an item.

[0340] "Weight" is a characteristic that indicates the mass of an object.

[0341] "Packaging planning" is the process of determining the optimal packaging method based on the shape and weight of the items.

[0342] "Mobile equipment" refers to devices that autonomously move around within a warehouse to pick and transport goods.

[0343] "Emotional state" refers to information indicating the user's psychological health, and work instructions are adjusted based on this information.

[0344] This invention is a system that automates logistics operations and realizes a flexible work environment based on user emotions. This system instantly acquires item and instruction information within the warehouse, and its server, terminal, and user components work together to generate optimal movement and packaging plans. Specific embodiments of the system are described below.

[0345] The server functions as the primary information processing unit. It interacts with an external order management system to acquire inventory and order information in real time. Using an AI agent, it calculates the optimal picking route based on the acquired information and generates an optimal packaging plan based on the shape and weight of the items. These functions are implemented through signal processing equipment and AI algorithms. A machine learning framework is implemented as the software.

[0346] The terminal operates as an autonomous mobile device, picking items within the warehouse based on instructions sent from the server. Equipped with sensors, the terminal accurately determines its own position and moves along a designated route via automatic control. Furthermore, during packaging, it dynamically selects the appropriate packaging material for efficient work.

[0347] The user is responsible for giving and managing work instructions, and uses an emotion engine to feed back their emotional state to the system. The emotion engine analyzes the user's facial expressions and voice, detecting stress and fatigue in real time, and plays a role in optimizing notifications and adjusting work instructions. This creates a less stressful work environment for the user.

[0348] As a concrete example, consider a scenario where a server retrieves order information, and an AI agent creates an optimized route and packing plan. The way terminals efficiently move around the warehouse based on this plan, quickly picking a large number of items, directly contributes to streamlining logistics and reducing processing time.

[0349] An example of a prompt message to enable these systems to function is: "Explain what emotion recognition technologies are used in this warehouse management system to reduce user stress. Also, explain the procedure for how autonomous robots streamline inventory management."

[0350] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0351] Step 1:

[0352] The server receives order information from an external order management system. This order information includes product name, quantity, and delivery date. Next, it extracts and integrates warehouse item information from a database. This provides a clear real-time inventory status. The server then prepares this integrated data as input data for the AI ​​agent.

[0353] Step 2:

[0354] The server uses an AI agent to generate the optimal picking route and packaging plan based on the received order and item information. Specifically, the AI ​​agent uses a generated AI model to calculate the shortest path considering the placement of items and the path length, and then selects appropriate packaging materials based on the item's shape and weight. This calculation result is sent as output data to the autonomous robot.

[0355] Step 3:

[0356] The autonomous robot, acting as the terminal, automatically begins moving within the warehouse according to the picking route information received from the server. Using its onboard sensors, the robot recognizes its current location and picks the designated items. After picking is complete, it packs the goods according to the packing plan. Specifically, it selects boxes and packing materials of the appropriate size and safely packs the goods.

[0357] Step 4:

[0358] The user monitors their work environment and feeds emotional data back to the system through an emotion engine. The system analyzes the user's facial expressions and voice to determine their stress levels and satisfaction, and requests adjustments to notification frequency or improvements to work instructions from the server as needed. This allows the system to operate in a more user-friendly manner based on emotional data.

[0359] Step 5:

[0360] Robots transport packaged goods to designated shipping areas. The robots plan and execute the optimal transport route based on destination information received from the server, resulting in efficient preparation for shipment and completion of the next shipping cycle.

[0361] (Application Example 2)

[0362] 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."

[0363] Modern logistics centers require efficient inventory management and order fulfillment, but current systems lack the flexibility to adjust to the mental state of workers. As a result, stress and fatigue accumulate among workers, leading to decreased productivity. Furthermore, while rapid and efficient route selection and packaging are necessary in logistics operations, effective systems for automating these processes have not yet been established.

[0364] 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.

[0365] In this invention, the server includes means for acquiring inventory and order information in a warehouse in real time and generating an optimal movement route, means for generating an optimal packaging plan based on the shape and weight of the goods, means for sharing information among multiple autonomous mobile devices and performing transportation operations in cooperation, and means for detecting the emotional state of workers and adjusting the frequency of notifications and the content of instructions based on that state. This promotes the automation of logistics operations and enables flexible work operations that are sensitive to the emotions of workers.

[0366] "Inventory information" refers to data about the quantity and location of goods stored within a warehouse.

[0367] "Order information" refers to data that includes the type and quantity of goods ordered by the customer, as well as delivery requests.

[0368] "Acquiring in real time" means instantly obtaining the latest information at the present moment.

[0369] An "optimal movement route" is a route designed to efficiently retrieve and deliver goods within a logistics center.

[0370] "Product shape" refers to the physical characteristics of each product, such as its shape and size.

[0371] "Product weight" refers to the weight characteristics of each individual product.

[0372] An "optimal packaging plan" is a plan that selects appropriate packaging materials and methods based on the shape and weight of the product.

[0373] An "autonomous mobile device" is a machine that moves independently based on pre-programmed routes and instructions, and performs logistics operations.

[0374] "Information sharing" means making data mutually available to multiple systems and devices.

[0375] "Cooperative transportation operations" means that multiple autonomous mobile devices work together to efficiently transport goods.

[0376] "Worker emotional state" refers to the psychological and emotional health of personnel involved in logistics operations.

[0377] "Notification frequency" refers to the number of times the system communicates information or instructions to the worker.

[0378] "Adjusting the content of instructions" means changing the details and difficulty level of the instructions communicated according to the emotional state of the worker.

[0379] The system for implementing this invention supports efficient operation in warehouse management and adjustments to operations that take into account the feelings of workers. Specific embodiments of this system are described below.

[0380] The server first retrieves real-time inventory and order information from the warehouse. This allows for immediate confirmation of the latest inventory status and customer order requests. The server then uses an AI agent to generate the optimal movement route and packaging plan, and sends instructions to the autonomous mobile device. These instructions include selecting packaging materials based on the shape and weight of the products.

[0381] The autonomous mobile devices move within the warehouse based on the instructions they receive, handling and packaging goods. This enables labor-saving and efficient logistics operations. Information sharing between devices allows for coordinated work, resulting in quick and effective operations.

[0382] For workers, the system monitors their emotional state in conjunction with smartphones and tablets. The emotion engine analyzes the worker's stress level and fatigue in real time, and if the worker is in an unstable state, the server reduces the frequency of notifications or simplifies instructions. This makes it possible to provide a worker-friendly work environment.

[0383] For example, if the system determines that a worker is in a highly stressed state and needs a break, it can suggest interrupting work and display an encouraging message on the worker's smartphone. In this way, the system balances improved work efficiency with worker health management.

[0384] By using a generative AI model, it is possible to design more precise prompts. As a concrete example, a prompt such as "Devise a concise and encouraging message to provide when the stress levels of staff working at a logistics center are high, and describe how to efficiently give work instructions to autonomous robots" can be generated and used.

[0385] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0386] Step 1:

[0387] The server retrieves real-time inventory and order information from the warehouse. Inputs include order data from an external order management system and inventory data from the warehouse management system. By retrieving and integrating information from the database, it obtains the latest inventory status and order requests. The output is a list of integrated inventory and order information.

[0388] Step 2:

[0389] The server uses an AI agent to generate the optimal travel route and packaging plan. Inputs include inventory and order information, as well as product shape and weight. The AI ​​algorithm calculates the shortest route and appropriate packaging method. The output is a set of instructions sent to the autonomous mobile device.

[0390] Step 3:

[0391] The autonomous mobile device moves around the warehouse based on instructions from the server, handling and packaging goods. The input is a set of instructions from the server. It picks the specified goods, selects packaging materials, and performs the actual packaging work. The output is the packaged goods and their placement information.

[0392] Step 4:

[0393] The server acquires emotional data from the worker's smartphone and analyzes it using an emotion engine. The input is biometric data acquired by the smartphone. The emotion engine processes the data and determines the worker's stress and fatigue levels. The output is the analysis result based on their emotional state.

[0394] Step 5:

[0395] The server adjusts the frequency and content of notifications based on the analysis results from the emotion engine. The input is the analysis results of the emotional state obtained in the previous step. The notification content is appropriately converted by the generation AI model and sent to the worker's smartphone. The output is the adjusted instructions and notifications.

[0396] Step 6:

[0397] The user (worker) receives coordinated instructions and notifications via their smartphone and proceeds with their work accordingly. Input consists of notifications and work instructions from the server. The worker checks the notifications and adjusts their work pace as needed. Output is the work performed by the user.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] [Third Embodiment]

[0402] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0403] 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.

[0404] 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).

[0405] 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.

[0406] 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.

[0407] 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).

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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.

[0413] 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".

[0414] This invention relates to a system for automating logistics operations within a warehouse. This system consists of multiple autonomous robots, each utilizing AI to efficiently perform product picking, packaging, and preparation for shipment. A specific embodiment of this system is described below in natural language.

[0415] The server's initial role is to receive and process order information in real time from a general order management system. The server then combines this information with warehouse inventory data to perform analysis, which is then used by an AI agent to generate the optimal picking route. This optimized route and order information are then sent as instructions to autonomous robots.

[0416] Autonomous robots, acting as terminals, automatically move within the warehouse based on received instructions and pick each item. For example, the robots are designed to take the shortest route to retrieve items from shelves, taking into account factors such as the handling priority, weight, and shape of each item. This enables efficient and accurate processing.

[0417] After each picking is complete, the terminal's robot executes the optimal packing plan designed by the AI ​​agent. The robot automatically selects appropriate packing materials and boxes based on the shape and weight of the product, ensuring efficient and waste-free packing. For example, fragile items may be given additional cushioning material, and irregularly shaped items may be given special packaging.

[0418] Once the goods are ready for shipment, the robot transports them to the designated shipping area. The server monitors this entire process and records the progress at each stage. Furthermore, the user can make necessary adjustments and maintenance based on this information.

[0419] Through the above embodiments, logistics operations within the warehouse can be carried out efficiently and accurately, solving the problem of labor shortages and reducing operational costs.

[0420] The following describes the processing flow.

[0421] Step 1:

[0422] The server receives order information via API from external order management systems and online platforms. This information includes product ID, quantity, priority, and shipping destination.

[0423] Step 2:

[0424] The server sends the acquired order information and warehouse inventory information to the AI ​​agent, which then generates the optimal picking route. The AI ​​agent calculates the route considering the inventory location of the products, the warehouse layout, and the robot's current location.

[0425] Step 3:

[0426] The server sends the optimal route calculated by the AI ​​agent as instructions to the autonomous robot acting as a terminal. These instructions include the picking order, shelf location, and movement path.

[0427] Step 4:

[0428] The terminal robot follows a calculated route, moves to a designated location within the warehouse, and picks the corresponding product. The robot uses built-in sensors to scan and verify the product's barcode or RFID.

[0429] Step 5:

[0430] The robot quickly and efficiently packs products based on a packing plan provided by an AI agent. It selects the most suitable packing materials according to the shape, weight, and fragility of the product.

[0431] Step 6:

[0432] A robot transports the packaged goods to the shipping area. The server monitors this process and records its progress.

[0433] Step 7:

[0434] The user checks the robot's status and work progress based on information provided by the server, and performs system adjustments and maintenance as needed.

[0435] (Example 1)

[0436] 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."

[0437] The challenge lies in improving efficiency and accuracy in logistics operations within the warehouse. This requires optimizing the overall logistics process by implementing real-time processing of inventory and order information, enhancing coordination in goods transportation, and strengthening the ability to respond to environmental changes and obstacles.

[0438] 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.

[0439] This invention includes a server comprising means for acquiring inventory and order information in a warehouse in real time and generating an optimal route using an intelligent system for analyzing that information; means for selecting packaging materials based on the shape and weight of the goods and generating an optimal packaging plan; means for sharing information among multiple autonomous devices and processing items in a coordinated manner; and means for a monitoring system that improves accuracy by moving items to designated locations and confirming their locations. This improves the efficiency and accuracy of item processing in the warehouse, enabling faster operations and flexible responses to environmental changes.

[0440] "Warehouse" refers to the area inside a facility used for storing, managing, and preparing goods for delivery.

[0441] "Inventory information" refers to a collection of detailed data regarding the types, quantities, and locations of items held within a warehouse.

[0442] "Order information" refers to a collection of data that includes the contents, quantity, and delivery address of items based on a customer's purchase request.

[0443] "Real-time acquisition" refers to the process of collecting information continuously and without delay.

[0444] An "intelligent system" is software or hardware that uses artificial intelligence technology to analyze data and make optimal decisions.

[0445] An "optimal route" is the shortest or most efficient route calculated to ensure the efficient movement or delivery of goods.

[0446] "Packaging materials" is a general term for materials used to protect goods and ensure their safety during transportation.

[0447] A "packaging plan" is a plan for selecting appropriate packaging materials according to the characteristics of the goods and for efficiently protecting those goods.

[0448] An "autonomous device" is a machine or robot that can perform a specific task automatically or with minimal human intervention.

[0449] "Item processing" refers to a series of tasks including picking, packing, and preparing items for delivery.

[0450] A "monitoring system" is a mechanism for continuously observing the status of ongoing processes and equipment and collecting data.

[0451] This invention is a system for efficiently and accurately automating logistics operations within a warehouse. The system mainly consists of the following hardware and software.

[0452] The server uses an intelligent system to receive and analyze order and inventory information in real time. This intelligent system leverages generative AI models to calculate optimal product picking routes and develop optimal packaging plans based on product shape and weight. Specifically, the server manages a database and is equipped with a high-performance processor for rapid information processing. In addition, the software running on it includes AI-powered route optimization algorithms and data management applications.

[0453] The autonomous terminal devices automatically move around the warehouse based on instructions from the server, performing tasks such as picking, packing, and preparing items for shipment. The robots are equipped with sensors to scan barcodes on items, accurately identifying and picking specified items. Specifically, the robots have a lightweight structure with a wide range of motion, designed to smoothly pass through narrow gaps between shelves.

[0454] Users monitor and maintain the entire system, ensuring it is functioning correctly. The user interface is designed to display progress and log information in real time, allowing for adjustments as needed.

[0455] As an example of a prompt to maximize the system's usefulness, it might say, "Use the AI ​​agent to automate logistics within the warehouse and design the optimal picking route and packing plan for the products." This prompts the generated AI model to perform the required optimizations.

[0456] The system of the present invention, through its configuration and functions, can significantly improve the efficiency of logistics operations, compensate for labor shortages, and reduce operational costs.

[0457] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0458] Step 1:

[0459] The server receives order information in real time from the order management system. The inputs received are the order ID, product list, quantity, and delivery date. Based on this, the server updates the order database and performs the specific actions of listing the new orders.

[0460] Step 2:

[0461] The server retrieves inventory information from the warehouse and analyzes it in combination with order information. Inputs include the location, quantity, and characteristics of the inventory. Using a generative AI model, it calculates the optimal picking route and outputs the result as instructions. Specifically, the algorithm calculates the route to retrieve the goods, prioritizing routes that avoid congestion.

[0462] Step 3:

[0463] The terminal's autonomous device (robot) begins moving based on picking route information transmitted from the server. The input consists of route information and a list of items, and the robot accurately picks the correct items while reading barcodes using sensors. During this process, the route is fine-tuned through data calculations to achieve the most efficient movement.

[0464] Step 4:

[0465] After the terminal robot has finished collecting the items, it packs them according to the packing plan sent from the server. The input includes the dimensions, weight, and characteristics of the items. The output is a list of the completed, packed items. Specifically, the robot automatically selects the appropriate packing materials and uses additional cushioning material as needed.

[0466] Step 5:

[0467] The terminal robot transports the packaged goods to the designated shipping area. The input is the location information of the shipping area, and the robot uses sensors to confirm the route while transporting the goods. The output is that the goods are precisely placed in the shipping area. Specifically, the robot's movement is enhanced with obstacle avoidance capabilities.

[0468] Step 6:

[0469] The server monitors the entire logistics process and logs the status. Inputs include operation logs and work progress information for each robot, while outputs include historical data and progress reports. The user monitors this output and takes specific actions to adjust the system as needed.

[0470] (Application Example 1)

[0471] 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."

[0472] Modern logistics centers require systems that streamline processes such as product picking, packing, and shipping preparation, while also enabling immediate response to any anomalies. Traditional methods have limitations in terms of efficiency improvements and troubleshooting, making overall optimization difficult. In particular, there has been a lack of systems that integrate real-time information sharing and anomaly detection functions, necessitating a more comprehensive solution.

[0473] 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.

[0474] In this invention, the server includes a device that acquires inventory and order information in the warehouse in real time and generates an optimal picking route, a device that generates an optimal packaging design based on the shape and weight of the goods, a device that shares information among multiple autonomous transport machines and performs logistics processing in cooperation, and a device that monitors the operating status of the transport machines and outputs a warning when an abnormality is detected. This enables efficient process management in the logistics center and makes it possible to build a system that can respond immediately when an abnormality occurs.

[0475] "Inventory information" refers to data about the types and quantities of goods stored in a warehouse.

[0476] "Order information" refers to data concerning the details of product orders placed with the logistics center.

[0477] A "picking route" is the optimal path a robot takes to collect goods within a warehouse.

[0478] "Packaging design" refers to the appropriate packaging method selected based on the shape and weight of the product.

[0479] An "autonomous transport machine" is a robot that uses AI technology to automatically perform tasks.

[0480] "Logistics processing" refers to a series of tasks related to logistics, such as picking, packing, and preparing goods for shipment.

[0481] "Anomaly detection" is a function that detects abnormal conditions in the operation of a system or machine.

[0482] A "warning output device" is a mechanism that sends out notifications or alerts when an abnormality is detected.

[0483] To implement this invention, it is necessary to construct an autonomous transport machine system for managing logistics operations within a warehouse. The server first acquires inventory and order information from the warehouse in real time. This information is collected via a network using database technology. Based on this information, the server utilizes a generative AI model to generate the optimal picking route.

[0484] The autonomous transport machines, acting as terminals, move within the warehouse according to the picking routes received from the server and pick the specified items. The terminals execute packaging designs tailored to the characteristics of each product and apply appropriate packaging. In particular, the design takes into account the shape, weight, and fragility of the product.

[0485] Users can check the progress of work and the presence of any abnormalities on their smartphones or other devices based on the data collected by the transport machinery. When an abnormality is detected, the device sends a notification to the user, enabling immediate countermeasures to be taken. This improves work efficiency at the logistics center and allows for a quick response to unexpected problems.

[0486] For example, if the picking of a certain product is behind schedule, the user can immediately recognize this and make adjustments, such as deploying additional transport machinery. An example of a prompt to the generated AI model would be, "Optimize the routes so that autonomous transport machinery in the logistics center can perform efficient picking and packing." This prompt allows the AI ​​model to propose an optimal logistics process plan.

[0487] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0488] Step 1:

[0489] The server acquires real-time inventory and order information from the warehouse via the network. Inputs come from external databases and order management systems, and output is a dataset representing the latest state of the warehouse. Based on this data, prompts are sent to the generating AI model, preparing it to perform optimization calculations for picking routes.

[0490] Step 2:

[0491] The server generates the optimal picking route using an AI model based on the acquired data. The input is the inventory layout and order information within the warehouse, and the output is the picking order and route information for each product. In this process, the AI ​​model executes an algorithm that takes into account the priority of products and the distance they travel to optimize efficiency.

[0492] Step 3:

[0493] The autonomous transport machine, acting as the terminal, moves within the warehouse based on the picking route transmitted from the server. It receives generated route information as input and obtains actual location data and progress during movement as output. The transport machine efficiently collects items from designated shelves and performs the picking process.

[0494] Step 4:

[0495] The terminal performs optimal packaging design based on the shape and weight information of the product. The input is the collected product attribute data, and the output is a properly packaged product. In this step, the autonomous transport machine selects the packaging materials and performs packaging that takes into account the protection of the product and efficient transport.

[0496] Step 5:

[0497] Users can check information regarding work progress and the presence of anomalies provided by the terminal on a smartphone or other device. The system receives status data from the transport machinery as input and outputs visualized report-style information. Users receive immediate notifications when an anomaly is detected, allowing them to take necessary countermeasures.

[0498] 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.

[0499] This invention relates to a system for automating logistics operations within a warehouse and for recognizing user emotions to adjust system operation accordingly. This system consists of a combination of multiple autonomous robots, AI agents, and an emotion engine. A specific embodiment is described below in natural language.

[0500] The server's primary task is to receive order information from an external order management system and integrate it with warehouse inventory information. The server then passes this information to an AI agent to generate the optimal picking route and packing plan. The generated information is then sent as instructions to autonomous robots acting as terminals.

[0501] The autonomous robot at the terminal moves around the warehouse based on the received picking route and picks the specified items. Following the optimal plan provided by the AI ​​agent, it efficiently and appropriately packs the items. It selects packing materials according to the shape and weight of the items and transports them to the designated shipping area, completing the preparation for shipment.

[0502] Furthermore, the emotion engine detects in real time how users are feeling towards the system and flexibly adjusts the system's operation based on that. For example, if a user is feeling stressed, the emotion engine can reduce the frequency of notifications or make work instructions more concise. In this way, the system is attentive to the user's emotions and creates a comfortable work environment.

[0503] Thus, the present invention not only enables the automation of logistics operations, but also achieves more efficient and flexible operations by responding to user emotions.

[0504] The following describes the processing flow.

[0505] Step 1:

[0506] The server receives order information via API from external order management systems and online platforms. This information includes product ID, quantity, priority, and shipping destination.

[0507] Step 2:

[0508] The server sends the acquired order information and warehouse inventory information to the AI ​​agent, which then generates the optimal picking route and packing plan based on this information. The AI ​​agent calculates the route considering the inventory location of the products, the warehouse layout, and the current location of the robots.

[0509] Step 3:

[0510] The server sends instructions to the autonomous robot, which acts as a terminal, based on the optimal route and packing plan calculated by the AI ​​agent. These instructions include the picking order, shelf location, movement path, and packing method.

[0511] Step 4:

[0512] The terminal robot follows a calculated route, moves to a designated location within the warehouse, and picks the corresponding product. The robot uses built-in sensors to scan and verify the product's barcode or RFID.

[0513] Step 5:

[0514] The robot quickly and efficiently packs products based on a packing plan provided by an AI agent. It selects the most suitable packing materials according to the shape, weight, and fragility of the product.

[0515] Step 6:

[0516] A robot transports the packaged goods to the shipping area. The server monitors this process and records its progress.

[0517] Step 7:

[0518] The emotion engine detects the user's emotions in real time and adjusts system operations accordingly. For example, if the user is feeling stressed, it might instruct the system to reduce the frequency of notifications.

[0519] Step 8:

[0520] The user checks the robot's status and work progress based on information provided by the server, and performs system adjustments and maintenance as needed.

[0521] (Example 2)

[0522] 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."

[0523] The challenges in logistics operations include achieving efficient work processes and reducing the emotional burden on workers in the work environment. While conventional logistics systems have made progress in efficiency and automation, they have not adequately addressed the emotional needs of workers. There is a need for smoother operations by reducing tension and stress.

[0524] 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.

[0525] In this invention, the server includes means for instantly acquiring information on items and instructions within the warehouse and generating an optimized movement path, means for generating an optimized packaging plan based on the external shape and weight of the items, and means for analyzing the emotional state of the user and adjusting the instruction method accordingly. This enables increased efficiency in logistics operations and a reduction in the mental burden on workers.

[0526] "Item information" refers to data such as the type, quantity, and location of individual items stored in the warehouse.

[0527] "Instruction information" refers to data related to work instructions concerning orders, picking, and shipping.

[0528] A "workflow path" is an optimized route for picking, packing, and shipping goods.

[0529] "External shape" refers to the characteristics that indicate the shape and dimensions of an item.

[0530] "Weight" is a characteristic that indicates the mass of an object.

[0531] "Packaging planning" is the process of determining the optimal packaging method based on the shape and weight of the items.

[0532] "Mobile equipment" refers to devices that autonomously move around within a warehouse to pick and transport goods.

[0533] "Emotional state" refers to information indicating the user's psychological health, and work instructions are adjusted based on this information.

[0534] This invention is a system that automates logistics operations and realizes a flexible work environment based on user emotions. This system instantly acquires item and instruction information within the warehouse, and its server, terminal, and user components work together to generate optimal movement and packaging plans. Specific embodiments of the system are described below.

[0535] The server functions as the primary information processing unit. It interacts with an external order management system to acquire inventory and order information in real time. Using an AI agent, it calculates the optimal picking route based on the acquired information and generates an optimal packaging plan based on the shape and weight of the items. These functions are implemented through signal processing equipment and AI algorithms. A machine learning framework is implemented as the software.

[0536] The terminal operates as an autonomous mobile device, picking items within the warehouse based on instructions sent from the server. Equipped with sensors, the terminal accurately determines its own position and moves along a designated route via automatic control. Furthermore, during packaging, it dynamically selects the appropriate packaging material for efficient work.

[0537] The user is responsible for giving and managing work instructions, and uses an emotion engine to feed back their emotional state to the system. The emotion engine analyzes the user's facial expressions and voice, detecting stress and fatigue in real time, and plays a role in optimizing notifications and adjusting work instructions. This creates a less stressful work environment for the user.

[0538] As a concrete example, consider a scenario where a server retrieves order information, and an AI agent creates an optimized route and packing plan. The way terminals efficiently move around the warehouse based on this plan, quickly picking a large number of items, directly contributes to streamlining logistics and reducing processing time.

[0539] An example of a prompt message to enable these systems to function is: "Explain what emotion recognition technologies are used in this warehouse management system to reduce user stress. Also, explain the procedure for how autonomous robots streamline inventory management."

[0540] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0541] Step 1:

[0542] The server receives order information from an external order management system. This order information includes product name, quantity, and delivery date. Next, it extracts and integrates warehouse item information from a database. This provides a clear real-time inventory status. The server then prepares this integrated data as input data for the AI ​​agent.

[0543] Step 2:

[0544] The server uses an AI agent to generate the optimal picking route and packaging plan based on the received order and item information. Specifically, the AI ​​agent uses a generated AI model to calculate the shortest path considering the placement of items and the path length, and then selects appropriate packaging materials based on the item's shape and weight. This calculation result is sent as output data to the autonomous robot.

[0545] Step 3:

[0546] The autonomous robot, acting as the terminal, automatically begins moving within the warehouse according to the picking route information received from the server. Using its onboard sensors, the robot recognizes its current location and picks the designated items. After picking is complete, it packs the goods according to the packing plan. Specifically, it selects boxes and packing materials of the appropriate size and safely packs the goods.

[0547] Step 4:

[0548] The user monitors their work environment and feeds emotional data back to the system through an emotion engine. The system analyzes the user's facial expressions and voice to determine their stress levels and satisfaction, and requests adjustments to notification frequency or improvements to work instructions from the server as needed. This allows the system to operate in a more user-friendly manner based on emotional data.

[0549] Step 5:

[0550] Robots transport packaged goods to designated shipping areas. The robots plan and execute the optimal transport route based on destination information received from the server, resulting in efficient preparation for shipment and completion of the next shipping cycle.

[0551] (Application Example 2)

[0552] 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."

[0553] Modern logistics centers require efficient inventory management and order fulfillment, but current systems lack the flexibility to adjust to the mental state of workers. As a result, stress and fatigue accumulate among workers, leading to decreased productivity. Furthermore, while rapid and efficient route selection and packaging are necessary in logistics operations, effective systems for automating these processes have not yet been established.

[0554] 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.

[0555] In this invention, the server includes means for acquiring inventory and order information in a warehouse in real time and generating an optimal movement route, means for generating an optimal packaging plan based on the shape and weight of the goods, means for sharing information among multiple autonomous mobile devices and performing transportation operations in cooperation, and means for detecting the emotional state of workers and adjusting the frequency of notifications and the content of instructions based on that state. This promotes the automation of logistics operations and enables flexible work operations that are sensitive to the emotions of workers.

[0556] "Inventory information" refers to data about the quantity and location of goods stored within a warehouse.

[0557] "Order information" refers to data that includes the type and quantity of goods ordered by the customer, as well as delivery requests.

[0558] "Acquiring in real time" means instantly obtaining the latest information at the present moment.

[0559] An "optimal movement route" is a route designed to efficiently retrieve and deliver goods within a logistics center.

[0560] "Product shape" refers to the physical characteristics of each product, such as its shape and size.

[0561] "Product weight" refers to the weight characteristics of each individual product.

[0562] An "optimal packaging plan" is a plan that selects appropriate packaging materials and methods based on the shape and weight of the product.

[0563] An "autonomous mobile device" is a machine that moves independently based on pre-programmed routes and instructions, and performs logistics operations.

[0564] "Information sharing" means making data mutually available to multiple systems and devices.

[0565] "Cooperative transportation operations" means that multiple autonomous mobile devices work together to efficiently transport goods.

[0566] "Worker emotional state" refers to the psychological and emotional health of personnel involved in logistics operations.

[0567] "Notification frequency" refers to the number of times the system communicates information or instructions to the worker.

[0568] "Adjusting the content of instructions" means changing the details and difficulty level of the instructions communicated according to the emotional state of the worker.

[0569] The system for implementing this invention supports efficient operation in warehouse management and adjustments to operations that take into account the feelings of workers. Specific embodiments of this system are described below.

[0570] The server first retrieves real-time inventory and order information from the warehouse. This allows for immediate confirmation of the latest inventory status and customer order requests. The server then uses an AI agent to generate the optimal movement route and packaging plan, and sends instructions to the autonomous mobile device. These instructions include selecting packaging materials based on the shape and weight of the products.

[0571] The autonomous mobile devices move within the warehouse based on the instructions they receive, handling and packaging goods. This enables labor-saving and efficient logistics operations. Information sharing between devices allows for coordinated work, resulting in quick and effective operations.

[0572] For workers, the system monitors their emotional state in conjunction with smartphones and tablets. The emotion engine analyzes the worker's stress level and fatigue in real time, and if the worker is in an unstable state, the server reduces the frequency of notifications or simplifies instructions. This makes it possible to provide a worker-friendly work environment.

[0573] For example, if the system determines that a worker is in a highly stressed state and needs a break, it can suggest interrupting work and display an encouraging message on the worker's smartphone. In this way, the system balances improved work efficiency with worker health management.

[0574] By using a generative AI model, it is possible to design more precise prompts. As a concrete example, a prompt such as "Devise a concise and encouraging message to provide when the stress levels of staff working at a logistics center are high, and describe how to efficiently give work instructions to autonomous robots" can be generated and used.

[0575] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0576] Step 1:

[0577] The server retrieves real-time inventory and order information from the warehouse. Inputs include order data from an external order management system and inventory data from the warehouse management system. By retrieving and integrating information from the database, it obtains the latest inventory status and order requests. The output is a list of integrated inventory and order information.

[0578] Step 2:

[0579] The server uses an AI agent to generate the optimal travel route and packaging plan. Inputs include inventory and order information, as well as product shape and weight. The AI ​​algorithm calculates the shortest route and appropriate packaging method. The output is a set of instructions sent to the autonomous mobile device.

[0580] Step 3:

[0581] The autonomous mobile device moves around the warehouse based on instructions from the server, handling and packaging goods. The input is a set of instructions from the server. It picks the specified goods, selects packaging materials, and performs the actual packaging work. The output is the packaged goods and their placement information.

[0582] Step 4:

[0583] The server acquires emotional data from the worker's smartphone and analyzes it using an emotion engine. The input is biometric data acquired by the smartphone. The emotion engine processes the data and determines the worker's stress and fatigue levels. The output is the analysis result based on their emotional state.

[0584] Step 5:

[0585] The server adjusts the frequency and content of notifications based on the analysis results from the emotion engine. The input is the analysis results of the emotional state obtained in the previous step. The notification content is appropriately converted by the generation AI model and sent to the worker's smartphone. The output is the adjusted instructions and notifications.

[0586] Step 6:

[0587] The user (worker) receives coordinated instructions and notifications via their smartphone and proceeds with their work accordingly. Input consists of notifications and work instructions from the server. The worker checks the notifications and adjusts their work pace as needed. Output is the work performed by the user.

[0588] 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.

[0589] 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.

[0590] 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.

[0591] [Fourth Embodiment]

[0592] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0593] 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.

[0594] 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).

[0595] 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.

[0596] 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.

[0597] 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).

[0598] 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.

[0599] 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.

[0600] 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.

[0601] 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.

[0602] 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.

[0603] 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.

[0604] 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".

[0605] This invention relates to a system for automating logistics operations within a warehouse. This system consists of multiple autonomous robots, each utilizing AI to efficiently perform product picking, packaging, and preparation for shipment. A specific embodiment of this system is described below in natural language.

[0606] The server's initial role is to receive and process order information in real time from a general order management system. The server then combines this information with warehouse inventory data to perform analysis, which is then used by an AI agent to generate the optimal picking route. This optimized route and order information are then sent as instructions to autonomous robots.

[0607] Autonomous robots, acting as terminals, automatically move within the warehouse based on received instructions and pick each item. For example, the robots are designed to take the shortest route to retrieve items from shelves, taking into account factors such as the handling priority, weight, and shape of each item. This enables efficient and accurate processing.

[0608] After each picking is complete, the terminal's robot executes the optimal packing plan designed by the AI ​​agent. The robot automatically selects appropriate packing materials and boxes based on the shape and weight of the product, ensuring efficient and waste-free packing. For example, fragile items may be given additional cushioning material, and irregularly shaped items may be given special packaging.

[0609] Once the goods are ready for shipment, the robot transports them to the designated shipping area. The server monitors this entire process and records the progress at each stage. Furthermore, the user can make necessary adjustments and maintenance based on this information.

[0610] Through the above embodiments, logistics operations within the warehouse can be carried out efficiently and accurately, solving the problem of labor shortages and reducing operational costs.

[0611] The following describes the processing flow.

[0612] Step 1:

[0613] The server receives order information via API from external order management systems and online platforms. This information includes product ID, quantity, priority, and shipping destination.

[0614] Step 2:

[0615] The server sends the acquired order information and warehouse inventory information to the AI ​​agent, which then generates the optimal picking route. The AI ​​agent calculates the route considering the inventory location of the products, the warehouse layout, and the robot's current location.

[0616] Step 3:

[0617] The server sends the optimal route calculated by the AI ​​agent as instructions to the autonomous robot acting as a terminal. These instructions include the picking order, shelf location, and movement path.

[0618] Step 4:

[0619] The terminal robot follows a calculated route, moves to a designated location within the warehouse, and picks the corresponding product. The robot uses built-in sensors to scan and verify the product's barcode or RFID.

[0620] Step 5:

[0621] The robot quickly and efficiently packs products based on a packing plan provided by an AI agent. It selects the most suitable packing materials according to the shape, weight, and fragility of the product.

[0622] Step 6:

[0623] A robot transports the packaged goods to the shipping area. The server monitors this process and records its progress.

[0624] Step 7:

[0625] The user checks the robot's status and work progress based on information provided by the server, and performs system adjustments and maintenance as needed.

[0626] (Example 1)

[0627] 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".

[0628] The challenge lies in improving efficiency and accuracy in logistics operations within the warehouse. This requires optimizing the overall logistics process by implementing real-time processing of inventory and order information, enhancing coordination in goods transportation, and strengthening the ability to respond to environmental changes and obstacles.

[0629] 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.

[0630] This invention includes a server comprising means for acquiring inventory and order information in a warehouse in real time and generating an optimal route using an intelligent system for analyzing that information; means for selecting packaging materials based on the shape and weight of the goods and generating an optimal packaging plan; means for sharing information among multiple autonomous devices and processing items in a coordinated manner; and means for a monitoring system that improves accuracy by moving items to designated locations and confirming their locations. This improves the efficiency and accuracy of item processing in the warehouse, enabling faster operations and flexible responses to environmental changes.

[0631] "Warehouse" refers to the area inside a facility used for storing, managing, and preparing goods for delivery.

[0632] "Inventory information" refers to a collection of detailed data regarding the types, quantities, and locations of items held within a warehouse.

[0633] "Order information" refers to a collection of data that includes the contents, quantity, and delivery address of items based on a customer's purchase request.

[0634] "Real-time acquisition" refers to the process of collecting information continuously and without delay.

[0635] An "intelligent system" is software or hardware that uses artificial intelligence technology to analyze data and make optimal decisions.

[0636] An "optimal route" is the shortest or most efficient route calculated to ensure the efficient movement or delivery of goods.

[0637] "Packaging materials" is a general term for materials used to protect goods and ensure their safety during transportation.

[0638] A "packaging plan" is a plan for selecting appropriate packaging materials according to the characteristics of the goods and for efficiently protecting those goods.

[0639] An "autonomous device" is a machine or robot that can perform a specific task automatically or with minimal human intervention.

[0640] "Item processing" refers to a series of tasks including picking, packing, and preparing items for delivery.

[0641] A "monitoring system" is a mechanism for continuously observing the status of ongoing processes and equipment and collecting data.

[0642] This invention is a system for efficiently and accurately automating logistics operations within a warehouse. The system mainly consists of the following hardware and software.

[0643] The server uses an intelligent system to receive and analyze order and inventory information in real time. This intelligent system leverages generative AI models to calculate optimal product picking routes and develop optimal packaging plans based on product shape and weight. Specifically, the server manages a database and is equipped with a high-performance processor for rapid information processing. In addition, the software running on it includes AI-powered route optimization algorithms and data management applications.

[0644] The autonomous terminal devices automatically move around the warehouse based on instructions from the server, performing tasks such as picking, packing, and preparing items for shipment. The robots are equipped with sensors to scan barcodes on items, accurately identifying and picking specified items. Specifically, the robots have a lightweight structure with a wide range of motion, designed to smoothly pass through narrow gaps between shelves.

[0645] Users monitor and maintain the entire system, ensuring it is functioning correctly. The user interface is designed to display progress and log information in real time, allowing for adjustments as needed.

[0646] As an example of a prompt to maximize the system's usefulness, it might say, "Use the AI ​​agent to automate logistics within the warehouse and design the optimal picking route and packing plan for the products." This prompts the generated AI model to perform the required optimizations.

[0647] The system of the present invention, through its configuration and functions, can significantly improve the efficiency of logistics operations, compensate for labor shortages, and reduce operational costs.

[0648] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0649] Step 1:

[0650] The server receives order information in real time from the order management system. The inputs received are the order ID, product list, quantity, and delivery date. Based on this, the server updates the order database and performs the specific actions of listing the new orders.

[0651] Step 2:

[0652] The server retrieves inventory information from the warehouse and analyzes it in combination with order information. Inputs include the location, quantity, and characteristics of the inventory. Using a generative AI model, it calculates the optimal picking route and outputs the result as instructions. Specifically, the algorithm calculates the route to retrieve the goods, prioritizing routes that avoid congestion.

[0653] Step 3:

[0654] The terminal's autonomous device (robot) begins moving based on picking route information transmitted from the server. The input consists of route information and a list of items, and the robot accurately picks the correct items while reading barcodes using sensors. During this process, the route is fine-tuned through data calculations to achieve the most efficient movement.

[0655] Step 4:

[0656] After the terminal robot has finished collecting the items, it packs them according to the packing plan sent from the server. The input includes the dimensions, weight, and characteristics of the items. The output is a list of the completed, packed items. Specifically, the robot automatically selects the appropriate packing materials and uses additional cushioning material as needed.

[0657] Step 5:

[0658] The terminal robot transports the packaged goods to the designated shipping area. The input is the location information of the shipping area, and the robot uses sensors to confirm the route while transporting the goods. The output is that the goods are precisely placed in the shipping area. Specifically, the robot's movement is enhanced with obstacle avoidance capabilities.

[0659] Step 6:

[0660] The server monitors the entire logistics process and logs the status. Inputs include operation logs and work progress information for each robot, while outputs include historical data and progress reports. The user monitors this output and takes specific actions to adjust the system as needed.

[0661] (Application Example 1)

[0662] 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".

[0663] Modern logistics centers require systems that streamline processes such as product picking, packing, and shipping preparation, while also enabling immediate response to any anomalies. Traditional methods have limitations in terms of efficiency improvements and troubleshooting, making overall optimization difficult. In particular, there has been a lack of systems that integrate real-time information sharing and anomaly detection functions, necessitating a more comprehensive solution.

[0664] 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.

[0665] In this invention, the server includes a device that acquires inventory and order information in the warehouse in real time and generates an optimal picking route, a device that generates an optimal packaging design based on the shape and weight of the goods, a device that shares information among multiple autonomous transport machines and performs logistics processing in cooperation, and a device that monitors the operating status of the transport machines and outputs a warning when an abnormality is detected. This enables efficient process management in the logistics center and makes it possible to build a system that can respond immediately when an abnormality occurs.

[0666] "Inventory information" refers to data about the types and quantities of goods stored in a warehouse.

[0667] "Order information" refers to data concerning the details of product orders placed with the logistics center.

[0668] A "picking route" is the optimal path a robot takes to collect goods within a warehouse.

[0669] "Packaging design" refers to the appropriate packaging method selected based on the shape and weight of the product.

[0670] An "autonomous transport machine" is a robot that uses AI technology to automatically perform tasks.

[0671] "Logistics processing" refers to a series of tasks related to logistics, such as picking, packing, and preparing goods for shipment.

[0672] "Anomaly detection" is a function that detects abnormal conditions in the operation of a system or machine.

[0673] A "warning output device" is a mechanism that sends out notifications or alerts when an abnormality is detected.

[0674] To implement this invention, it is necessary to construct an autonomous transport machine system for managing logistics operations within a warehouse. The server first acquires inventory and order information from the warehouse in real time. This information is collected via a network using database technology. Based on this information, the server utilizes a generative AI model to generate the optimal picking route.

[0675] The autonomous transport machines, acting as terminals, move within the warehouse according to the picking routes received from the server and pick the specified items. The terminals execute packaging designs tailored to the characteristics of each product and apply appropriate packaging. In particular, the design takes into account the shape, weight, and fragility of the product.

[0676] Users can check the progress of work and the presence of any abnormalities on their smartphones or other devices based on the data collected by the transport machinery. When an abnormality is detected, the device sends a notification to the user, enabling immediate countermeasures to be taken. This improves work efficiency at the logistics center and allows for a quick response to unexpected problems.

[0677] For example, if the picking of a certain product is behind schedule, the user can immediately recognize this and make adjustments, such as deploying additional transport machinery. An example of a prompt to the generated AI model would be, "Optimize the routes so that autonomous transport machinery in the logistics center can perform efficient picking and packing." This prompt allows the AI ​​model to propose an optimal logistics process plan.

[0678] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0679] Step 1:

[0680] The server acquires real-time inventory and order information from the warehouse via the network. Inputs come from external databases and order management systems, and output is a dataset representing the latest state of the warehouse. Based on this data, prompts are sent to the generating AI model, preparing it to perform optimization calculations for picking routes.

[0681] Step 2:

[0682] The server generates the optimal picking route using an AI model based on the acquired data. The input is the inventory layout and order information within the warehouse, and the output is the picking order and route information for each product. In this process, the AI ​​model executes an algorithm that takes into account the priority of products and the distance they travel to optimize efficiency.

[0683] Step 3:

[0684] The autonomous transport machine, acting as the terminal, moves within the warehouse based on the picking route transmitted from the server. It receives generated route information as input and obtains actual location data and progress during movement as output. The transport machine efficiently collects items from designated shelves and performs the picking process.

[0685] Step 4:

[0686] The terminal performs optimal packaging design based on the shape and weight information of the product. The input is the collected product attribute data, and the output is a properly packaged product. In this step, the autonomous transport machine selects the packaging materials and performs packaging that takes into account the protection of the product and efficient transport.

[0687] Step 5:

[0688] Users can check information regarding work progress and the presence of anomalies provided by the terminal on a smartphone or other device. The system receives status data from the transport machinery as input and outputs visualized report-style information. Users receive immediate notifications when an anomaly is detected, allowing them to take necessary countermeasures.

[0689] 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.

[0690] This invention relates to a system for automating logistics operations within a warehouse and for recognizing user emotions to adjust system operation accordingly. This system consists of a combination of multiple autonomous robots, AI agents, and an emotion engine. A specific embodiment is described below in natural language.

[0691] The server's primary task is to receive order information from an external order management system and integrate it with warehouse inventory information. The server then passes this information to an AI agent to generate the optimal picking route and packing plan. The generated information is then sent as instructions to autonomous robots acting as terminals.

[0692] The autonomous robot at the terminal moves around the warehouse based on the received picking route and picks the specified items. Following the optimal plan provided by the AI ​​agent, it efficiently and appropriately packs the items. It selects packing materials according to the shape and weight of the items and transports them to the designated shipping area, completing the preparation for shipment.

[0693] Furthermore, the emotion engine detects in real time how users are feeling towards the system and flexibly adjusts the system's operation based on that. For example, if a user is feeling stressed, the emotion engine can reduce the frequency of notifications or make work instructions more concise. In this way, the system is attentive to the user's emotions and creates a comfortable work environment.

[0694] Thus, the present invention not only enables the automation of logistics operations, but also achieves more efficient and flexible operations by responding to user emotions.

[0695] The following describes the processing flow.

[0696] Step 1:

[0697] The server receives order information via API from external order management systems and online platforms. This information includes product ID, quantity, priority, and shipping destination.

[0698] Step 2:

[0699] The server sends the acquired order information and warehouse inventory information to the AI ​​agent, which then generates the optimal picking route and packing plan based on this information. The AI ​​agent calculates the route considering the inventory location of the products, the warehouse layout, and the current location of the robots.

[0700] Step 3:

[0701] The server sends instructions to the autonomous robot, which acts as a terminal, based on the optimal route and packing plan calculated by the AI ​​agent. These instructions include the picking order, shelf location, movement path, and packing method.

[0702] Step 4:

[0703] The terminal robot follows a calculated route, moves to a designated location within the warehouse, and picks the corresponding product. The robot uses built-in sensors to scan and verify the product's barcode or RFID.

[0704] Step 5:

[0705] The robot quickly and efficiently packs products based on a packing plan provided by an AI agent. It selects the most suitable packing materials according to the shape, weight, and fragility of the product.

[0706] Step 6:

[0707] A robot transports the packaged goods to the shipping area. The server monitors this process and records its progress.

[0708] Step 7:

[0709] The emotion engine detects the user's emotions in real time and adjusts system operations accordingly. For example, if the user is feeling stressed, it might instruct the system to reduce the frequency of notifications.

[0710] Step 8:

[0711] The user checks the robot's status and work progress based on information provided by the server, and performs system adjustments and maintenance as needed.

[0712] (Example 2)

[0713] 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".

[0714] The challenges in logistics operations include achieving efficient work processes and reducing the emotional burden on workers in the work environment. While conventional logistics systems have made progress in efficiency and automation, they have not adequately addressed the emotional needs of workers. There is a need for smoother operations by reducing tension and stress.

[0715] 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.

[0716] In this invention, the server includes means for instantly acquiring information on items and instructions within the warehouse and generating an optimized movement path, means for generating an optimized packaging plan based on the external shape and weight of the items, and means for analyzing the emotional state of the user and adjusting the instruction method accordingly. This enables increased efficiency in logistics operations and a reduction in the mental burden on workers.

[0717] "Item information" refers to data such as the type, quantity, and location of individual items stored in the warehouse.

[0718] "Instruction information" refers to data related to work instructions concerning orders, picking, and shipping.

[0719] A "workflow path" is an optimized route for picking, packing, and shipping goods.

[0720] "External shape" refers to the characteristics that indicate the shape and dimensions of an item.

[0721] "Weight" is a characteristic that indicates the mass of an object.

[0722] "Packaging planning" is the process of determining the optimal packaging method based on the shape and weight of the items.

[0723] "Mobile equipment" refers to devices that autonomously move around within a warehouse to pick and transport goods.

[0724] "Emotional state" refers to information indicating the user's psychological health, and work instructions are adjusted based on this information.

[0725] This invention is a system that automates logistics operations and realizes a flexible work environment based on user emotions. This system instantly acquires item and instruction information within the warehouse, and its server, terminal, and user components work together to generate optimal movement and packaging plans. Specific embodiments of the system are described below.

[0726] The server functions as the primary information processing unit. It interacts with an external order management system to acquire inventory and order information in real time. Using an AI agent, it calculates the optimal picking route based on the acquired information and generates an optimal packaging plan based on the shape and weight of the items. These functions are implemented through signal processing equipment and AI algorithms. A machine learning framework is implemented as the software.

[0727] The terminal operates as an autonomous mobile device, picking items within the warehouse based on instructions sent from the server. Equipped with sensors, the terminal accurately determines its own position and moves along a designated route via automatic control. Furthermore, during packaging, it dynamically selects the appropriate packaging material for efficient work.

[0728] The user is responsible for giving and managing work instructions, and uses an emotion engine to feed back their emotional state to the system. The emotion engine analyzes the user's facial expressions and voice, detecting stress and fatigue in real time, and plays a role in optimizing notifications and adjusting work instructions. This creates a less stressful work environment for the user.

[0729] As a concrete example, consider a scenario where a server retrieves order information, and an AI agent creates an optimized route and packing plan. The way terminals efficiently move around the warehouse based on this plan, quickly picking a large number of items, directly contributes to streamlining logistics and reducing processing time.

[0730] An example of a prompt message to enable these systems to function is: "Explain what emotion recognition technologies are used in this warehouse management system to reduce user stress. Also, explain the procedure for how autonomous robots streamline inventory management."

[0731] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0732] Step 1:

[0733] The server receives order information from an external order management system. This order information includes product name, quantity, and delivery date. Next, it extracts and integrates warehouse item information from a database. This provides a clear real-time inventory status. The server then prepares this integrated data as input data for the AI ​​agent.

[0734] Step 2:

[0735] The server uses an AI agent to generate the optimal picking route and packaging plan based on the received order and item information. Specifically, the AI ​​agent uses a generated AI model to calculate the shortest path considering the placement of items and the path length, and then selects appropriate packaging materials based on the item's shape and weight. This calculation result is sent as output data to the autonomous robot.

[0736] Step 3:

[0737] The autonomous robot, acting as the terminal, automatically begins moving within the warehouse according to the picking route information received from the server. Using its onboard sensors, the robot recognizes its current location and picks the designated items. After picking is complete, it packs the goods according to the packing plan. Specifically, it selects boxes and packing materials of the appropriate size and safely packs the goods.

[0738] Step 4:

[0739] The user monitors their work environment and feeds emotional data back to the system through an emotion engine. The system analyzes the user's facial expressions and voice to determine their stress levels and satisfaction, and requests adjustments to notification frequency or improvements to work instructions from the server as needed. This allows the system to operate in a more user-friendly manner based on emotional data.

[0740] Step 5:

[0741] Robots transport packaged goods to designated shipping areas. The robots plan and execute the optimal transport route based on destination information received from the server, resulting in efficient preparation for shipment and completion of the next shipping cycle.

[0742] (Application Example 2)

[0743] 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".

[0744] Modern logistics centers require efficient inventory management and order fulfillment, but current systems lack the flexibility to adjust to the mental state of workers. As a result, stress and fatigue accumulate among workers, leading to decreased productivity. Furthermore, while rapid and efficient route selection and packaging are necessary in logistics operations, effective systems for automating these processes have not yet been established.

[0745] 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.

[0746] In this invention, the server includes means for acquiring inventory and order information in a warehouse in real time and generating an optimal movement route, means for generating an optimal packaging plan based on the shape and weight of the goods, means for sharing information among multiple autonomous mobile devices and performing transportation operations in cooperation, and means for detecting the emotional state of workers and adjusting the frequency of notifications and the content of instructions based on that state. This promotes the automation of logistics operations and enables flexible work operations that are sensitive to the emotions of workers.

[0747] "Inventory information" refers to data about the quantity and location of goods stored within a warehouse.

[0748] "Order information" refers to data that includes the type and quantity of goods ordered by the customer, as well as delivery requests.

[0749] "Acquiring in real time" means instantly obtaining the latest information at the present moment.

[0750] An "optimal movement route" is a route designed to efficiently retrieve and deliver goods within a logistics center.

[0751] "Product shape" refers to the physical characteristics of each product, such as its shape and size.

[0752] "Product weight" refers to the weight characteristics of each individual product.

[0753] An "optimal packaging plan" is a plan that selects appropriate packaging materials and methods based on the shape and weight of the product.

[0754] An "autonomous mobile device" is a machine that moves independently based on pre-programmed routes and instructions, and performs logistics operations.

[0755] "Information sharing" means making data mutually available to multiple systems and devices.

[0756] "Cooperative transportation operations" means that multiple autonomous mobile devices work together to efficiently transport goods.

[0757] "Worker emotional state" refers to the psychological and emotional health of personnel involved in logistics operations.

[0758] "Notification frequency" refers to the number of times the system communicates information or instructions to the worker.

[0759] "Adjusting the content of instructions" means changing the details and difficulty level of the instructions communicated according to the emotional state of the worker.

[0760] The system for implementing this invention supports efficient operation in warehouse management and adjustments to operations that take into account the feelings of workers. Specific embodiments of this system are described below.

[0761] The server first retrieves real-time inventory and order information from the warehouse. This allows for immediate confirmation of the latest inventory status and customer order requests. The server then uses an AI agent to generate the optimal movement route and packaging plan, and sends instructions to the autonomous mobile device. These instructions include selecting packaging materials based on the shape and weight of the products.

[0762] The autonomous mobile devices move within the warehouse based on the instructions they receive, handling and packaging goods. This enables labor-saving and efficient logistics operations. Information sharing between devices allows for coordinated work, resulting in quick and effective operations.

[0763] For workers, the system monitors their emotional state in conjunction with smartphones and tablets. The emotion engine analyzes the worker's stress level and fatigue in real time, and if the worker is in an unstable state, the server reduces the frequency of notifications or simplifies instructions. This makes it possible to provide a worker-friendly work environment.

[0764] For example, if the system determines that a worker is in a highly stressed state and needs a break, it can suggest interrupting work and display an encouraging message on the worker's smartphone. In this way, the system balances improved work efficiency with worker health management.

[0765] By using a generative AI model, it is possible to design more precise prompts. As a concrete example, a prompt such as "Devise a concise and encouraging message to provide when the stress levels of staff working at a logistics center are high, and describe how to efficiently give work instructions to autonomous robots" can be generated and used.

[0766] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0767] Step 1:

[0768] The server retrieves real-time inventory and order information from the warehouse. Inputs include order data from an external order management system and inventory data from the warehouse management system. By retrieving and integrating information from the database, it obtains the latest inventory status and order requests. The output is a list of integrated inventory and order information.

[0769] Step 2:

[0770] The server uses an AI agent to generate the optimal travel route and packaging plan. Inputs include inventory and order information, as well as product shape and weight. The AI ​​algorithm calculates the shortest route and appropriate packaging method. The output is a set of instructions sent to the autonomous mobile device.

[0771] Step 3:

[0772] The autonomous mobile device moves around the warehouse based on instructions from the server, handling and packaging goods. The input is a set of instructions from the server. It picks the specified goods, selects packaging materials, and performs the actual packaging work. The output is the packaged goods and their placement information.

[0773] Step 4:

[0774] The server acquires emotional data from the worker's smartphone and analyzes it using an emotion engine. The input is biometric data acquired by the smartphone. The emotion engine processes the data and determines the worker's stress and fatigue levels. The output is the analysis result based on their emotional state.

[0775] Step 5:

[0776] The server adjusts the frequency and content of notifications based on the analysis results from the emotion engine. The input is the analysis results of the emotional state obtained in the previous step. The notification content is appropriately converted by the generation AI model and sent to the worker's smartphone. The output is the adjusted instructions and notifications.

[0777] Step 6:

[0778] The user (worker) receives coordinated instructions and notifications via their smartphone and proceeds with their work accordingly. Input consists of notifications and work instructions from the server. The worker checks the notifications and adjusts their work pace as needed. Output is the work performed by the user.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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."

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] The following is further disclosed regarding the embodiments described above.

[0801] (Claim 1)

[0802] A means for acquiring inventory and order information within the warehouse in real time and generating the optimal picking route,

[0803] A means for generating an optimal packaging plan based on the shape and weight of the product,

[0804] A means of sharing information and coordinating logistics operations among multiple autonomous robots,

[0805] A system that includes this.

[0806] (Claim 2)

[0807] The system according to claim 1, which is equipped with a learning function to respond to obstacles and environmental changes in logistics operations.

[0808] (Claim 3)

[0809] The system according to claim 1, in which an autonomous robot performs picking, packing, and preparing goods for shipment.

[0810] "Example 1"

[0811] (Claim 1)

[0812] A means for acquiring inventory and order information within a warehouse in real time and generating the optimal route using an intelligent system for analyzing that information,

[0813] A means for selecting packaging materials based on the shape and weight of the product and generating an optimal packaging plan,

[0814] A means for sharing information and coordinating item processing among multiple autonomous devices,

[0815] A means comprising a monitoring system that improves accuracy by moving an item to a designated location and confirming its location,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, which includes a function for acquiring knowledge to respond to obstacles and environmental changes in logistics processing.

[0819] (Claim 3)

[0820] The system according to claim 1, in which a self-contained device performs the acquisition, packaging, and preparation of goods.

[0821] "Application Example 1"

[0822] (Claim 1)

[0823] A device that acquires inventory and order information in the warehouse in real time and generates the optimal picking route,

[0824] A device that generates the optimal packaging design based on the shape and weight of the product,

[0825] A device that shares information and performs logistics processing collaboratively among multiple autonomous transport machines,

[0826] A device that monitors the operating status of transport machinery and outputs a warning if an abnormality is detected,

[0827] A system that includes this.

[0828] (Claim 2)

[0829] The system according to claim 1, comprising an educational function for responding to obstacles and environmental changes in logistics processing, and a function for tracking the operating status of transport machinery and reporting work progress.

[0830] (Claim 3)

[0831] The system according to claim 1, in which an autonomous transport machine picks, packs, and prepares goods for shipment, and further outputs a warning in the event of an abnormality.

[0832] "Example 2 of combining an emotion engine"

[0833] (Claim 1)

[0834] A means for instantly acquiring information on items and instructions within a warehouse and generating an optimized workflow,

[0835] Means for generating an optimized packaging plan based on the external shape and weight of an article,

[0836] A means of sharing information and coordinating cargo operations among multiple mobile devices,

[0837] A means of analyzing the user's emotional state and adjusting the instruction method based on that analysis,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, which has the ability to learn in order to respond to obstacles and environmental changes in cargo handling.

[0841] (Claim 3)

[0842] The system according to claim 1, in which a mobile device acquires, packages, and prepares goods for shipment.

[0843] "Application example 2 when combining with an emotional engine"

[0844] (Claim 1)

[0845] A means for acquiring inventory and order information within a warehouse in real time and generating the optimal movement route,

[0846] A means for generating an optimal packaging plan based on the shape and weight of the product,

[0847] A means of sharing information among multiple autonomous mobile devices and performing transport operations in a coordinated manner,

[0848] A means for detecting the emotional state of a worker and adjusting the frequency of notifications and the content of instructions based on that state,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, which is equipped with a learning function to respond to obstacles and environmental changes during transport operations.

[0852] (Claim 3)

[0853] The system according to claim 1, wherein an autonomous mobile device handles, packages, and prepares goods for delivery. [Explanation of symbols]

[0854] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A device that acquires inventory and order information in the warehouse in real time and generates the optimal picking route, A device that generates the optimal packaging design based on the shape and weight of the product, A device that shares information and performs logistics processing collaboratively among multiple autonomous transport machines, A device that monitors the operating status of transport machinery and outputs a warning if an abnormality is detected, A system that includes this.

2. The system according to claim 1, which includes an educational function for responding to obstacles and environmental changes in logistics processing, and a function for tracking the operating status of transport machinery and reporting work progress.

3. The system according to claim 1, in which an autonomous transport machine picks, packs, and prepares goods for shipment, and further outputs a warning in the event of an abnormality.

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