system

Abstract

We provide the system. [Solution] A user terminal for entering luggage information, A computing device that analyzes the input package information and performs a process to identify the physical characteristics of the package, A computing device that performs a process to calculate the packing method in order to optimize the arrangement of luggage, A three-dimensional display means for visually displaying the calculated packing method, A system that includes this.

Description

Technical Field

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[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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003] [[ID=o]]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Efficiently packing luggage during travel is a problem for many people. In particular, unnecessary expansion of luggage and uneven weight distribution make movement inconvenient and make it difficult to take out the luggage. Also, learning and applying an efficient packing method is a difficult problem without experience.O

Means for Solving the Problems

[0005] The present invention provides a user terminal for inputting luggage information, and includes a computing device that analyzes the information to identify the characteristics of the luggage. Thereby, an optimal packing method is calculated and visually presented using three - dimensional display means. Also, by receiving user feedback, recalculating the packing method, and generating an adjusted proposal, a system that enables packing according to the needs of the user is provided.

[0006] "Luggage information" refers to data related to each individual item carried during travel, including information such as type, shape, and weight.

[0007] A "user terminal" is a device that allows users to perform input and output through operation, and functions as an interface with the system.

[0008] A "computer device" is a computer system used to process and analyze input data, particularly for data identification and calculation.

[0009] "Physical properties" refer to the shape, weight, material, and related parameters of an object, and are characteristics that affect how an object is handled and placed.

[0010] "Optimization" is the process of performing calculations and adjustments to satisfy a specific purpose or condition; in this case, it refers to making the way luggage is packed more efficiently.

[0011] A "three-dimensional display means" is a display device that visually represents the arrangement and structure of an object and provides users with three-dimensional visual information.

[0012] "Feedback" refers to information provided by users in response to the system's output, which is used to readjust or improve processing and results. [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] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4]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 language 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, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

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

[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 is a system for efficiently packing luggage, aiming to assist users in making optimal packing arrangements when traveling. The system consists of a user terminal and a server.

[0035] First, the user uses a user terminal to enter information about the items they plan to pack. This information is collected through the creation of a text-based list and by taking photos of the items using the terminal's camera. The terminal then formats this information and prepares it for transmission to the server.

[0036] Next, the server receives package information from the user's terminal. The server uses analysis algorithms to identify the physical characteristics of the package, such as its type, shape, and weight. In particular, for image data, it automatically extracts information using object recognition technology.

[0037] Furthermore, the server calculates the optimal placement of luggage based on this data. Using an optimization algorithm, it proposes an efficient packing method that takes into account factors such as space conservation and center of gravity balance. The calculation results are then transmitted from the server to the user's terminal.

[0038] The terminal uses a three-dimensional display to visually present the optimal arrangement of luggage to the user based on calculation results received from the server. This allows the user to intuitively understand how to pack their luggage. The terminal can also provide packing instructions through voice guidance.

[0039] For example, suppose a traveler is packing clothes, electronic devices, and documents of different sizes and shapes into a suitcase for a business trip. In this case, the user takes pictures of these items with a device and sends them to a server. The server analyzes the data, calculates the optimal packing method, and provides it as a three-dimensional model. The device displays this suggestion, and the user can arrange the items in the suitcase accordingly.

[0040] In this way, by providing an efficient way to pack luggage, travelers can achieve comfortable and easy-to-travel packing. Due to its flexibility, this system can customize suggestions based on user feedback and accommodate individual needs.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user uses a user terminal to input the items they plan to pack, or takes photos of them with the camera. The terminal then converts this information into a digital format and prepares to transfer it to the server.

[0044] Step 2:

[0045] The server receives package information from the terminal. The received data is processed by an analysis algorithm to identify the physical characteristics of the package, such as its shape, weight, and type.

[0046] Step 3:

[0047] The server uses an optimization algorithm to calculate the optimal way to pack the luggage based on the identified luggage information. The calculation takes into account the luggage's space efficiency and center of gravity balance.

[0048] Step 4:

[0049] The server generates a three-dimensional model of the calculated packing method. This model visually shows the placement of the packages, organizing the data in a way that is easy for the user to understand.

[0050] Step 5:

[0051] The terminal displays the 3D model and related information received from the server on its screen. It provides voice guidance as needed and instructs the user on specific packing procedures.

[0052] Step 6:

[0053] Users pack their belongings according to the instructions and guidance displayed on the device. If necessary, they can adjust the packing method to their liking and input that information into the device.

[0054] Step 7:

[0055] The device sends user feedback to the server. The server uses this information to readjust the packing method, generates a new 3D model, and sends it back to the device.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] Traditional travel preparations have presented challenges in efficiently packing luggage and maximizing limited space. Furthermore, manually planning the optimal packing method, considering the shape and weight of luggage, is time-consuming and laborious. Additionally, the lack of sufficient visual and intuitive guidance on specific arrangements and procedures makes it difficult to pack effectively according to user needs.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes means for acquiring luggage information and converting it into an input format, calculation means for analyzing the acquired luggage information and determining its physical characteristics, and calculation means for performing optimization processing to calculate the optimal arrangement of luggage. This enables users to intuitively arrange luggage by automatically analyzing the most efficient way to pack it and displaying it in three dimensions.

[0061] A "user-use device that acquires luggage information and converts it into an input format" is a device that allows users to input details of the luggage they are carrying and then formats that data into a standardized format.

[0062] "A computational means for analyzing acquired package information and determining its physical characteristics" refers to a means of performing computational processing to identify physical characteristics such as the type, shape, and weight of a package from the input data.

[0063] "A computational means for performing an optimization process to calculate the optimal placement of luggage" refers to a means for calculating an efficient way to pack luggage, taking into account factors such as the center of gravity of the luggage and ease of retrieval, while making maximum use of available space.

[0064] A "three-dimensional visualization method" is a means of presenting a calculated arrangement of luggage to the user in a three-dimensional and visual manner, enabling intuitive understanding.

[0065] A "guidance system that provides instructions for luggage placement via voice" is a means of showing the user specific placement procedures via voice based on the calculated luggage placement results.

[0066] This invention is a support system for efficiently organizing and arranging users' belongings. The system primarily consists of a user terminal and a server. The following details an embodiment of this system.

[0067] First, when preparing for a trip, users use a user terminal to enter details of their luggage. Through the terminal's application, users can not only enter a luggage list as text, but also take photos of their luggage using the terminal's camera function. This information is formatted on the terminal and ready to be sent to the server.

[0068] Next, the server analyzes the received package information. Specifically, the server uses machine learning algorithms and object recognition technology to identify the physical characteristics of the package, such as its type, shape, and weight. This analysis generates the data necessary to determine the efficient placement of the packages.

[0069] Furthermore, the server calculates the optimal placement of the luggage based on the acquired physical characteristics. This optimization process utilizes a generative AI model to create placement plans that consider the balance of the luggage's center of gravity and the efficient use of space. The calculated placement plans are then sent to the user's terminal.

[0070] The terminal receives the suggested placement from the server and presents it visually to the user using 3D display technology. This allows the user to intuitively understand the suggested luggage placement. The terminal can also provide voice guidance and instruct the user on specific placement steps. Through this process, the user can actually place the luggage while referring to the suggestion, making preparation more efficient and less burdensome.

[0071] A concrete example is when a user needs to pack clothing, electronic devices, and documents of different shapes and sizes into a suitcase for a business trip. In such a scenario, the user can list or photograph their belongings on a device and arrange them in the suitcase based on the optimal packing method suggested by the system. This results in a space-saving and easily accessible arrangement of individual items.

[0072] An example of a prompt message is: "How can I most efficiently pack the following items into my suitcase? The items include a laptop, three shirts, a suit, and one pair of shoes." This prompt prompts the system to provide optimal placement suggestions for each type of item.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] Users enter information about the luggage they will be taking on their trip into a user terminal. Input methods include entering a list of luggage as text or taking images with the terminal's camera. This entered data is converted to a unified format within the terminal. Text data is converted to CSV format, and image data to JPEG format. This prepares the data for transmission to the server.

[0076] Step 2:

[0077] The terminal sends formatted package information to the server. The data is sent over the network to the server, and a handshake process is performed to confirm successful reception. To maintain data security, the information is encrypted using SSL / TLS before transmission.

[0078] Step 3:

[0079] The server analyzes the received package information. Specifically, the server utilizes a generative AI model to automatically extract the physical characteristics of the package from the input data. For text data, a natural language processing algorithm is used, and for image data, an image analysis algorithm is applied to identify objects and estimate their shape and weight. The analysis results are stored in a database and used in the next step.

[0080] Step 4:

[0081] The server calculates the optimal placement of packages based on the analyzed package information. Using an optimization algorithm, it generates proposed package placements. This process considers space conservation and center of gravity balance to create an efficient placement plan. The calculation results are organized in a structured data format (e.g., JSON) and prepared as data for transmission to the terminal.

[0082] Step 5:

[0083] The server sends the calculation results to the user's terminal. In this process, as described above, the data is encrypted before transmission, ensuring that it is received accurately on the terminal. The transmitted data is used for 3D display on the terminal.

[0084] Step 6:

[0085] The terminal displays the package placement plan received from the server in three dimensions. The placement method is visually presented on the screen through the user interface, rendered in a way that is easy for the user to understand intuitively. The visually displayed package placement plan can be adjusted by the user as desired. The terminal also uses voice guidance to provide the user with detailed work procedures.

[0086] This will allow users to easily perform efficient and stress-free packing procedures by utilizing servers and terminals.

[0087] (Application Example 1)

[0088] 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."

[0089] In logistics operations, efficiently storing goods is crucial, but quickly and appropriately arranging items of diverse shapes and sizes places a significant burden on workers. Furthermore, human error and wasted time are common, often preventing efficient storage from being achieved. Technologies to address this are needed.

[0090] 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.

[0091] In this invention, the server includes a user terminal for inputting luggage information, a computing device that analyzes the input luggage information and performs processing to identify the physical characteristics of the luggage, a computing device that performs processing to calculate the packing method in order to optimize the arrangement of luggage, and an observation device that visually displays the arrangement information to support the worker's work. This makes it possible to support workers in efficiently storing luggage.

[0092] "Cargo information" refers to data used to identify the characteristics of various types of cargo involved in logistics, and includes attributes such as shape, size, and weight.

[0093] A "user terminal" is an information device used by logistics workers to input package information, and it has an interface for collecting and inputting data.

[0094] A "processing unit" is a computing device that processes input data and performs analysis and calculations, utilizing algorithms to identify physical properties and optimize placement.

[0095] A "spatial display means" is a device that visually presents the calculated packing method to the user, and provides it to the worker as three-dimensional visual information.

[0096] An "observation device" is a visual support device that allows workers to quickly understand the placement information of packages, and assists in efficient and accurate package storage on site.

[0097] The system for realizing this invention consists of a user terminal, a server, and an observation device used by the worker. This system collects, analyzes, optimizes, and provides instructions for efficiently storing packages.

[0098] Users use a user terminal to collect information about the items to be stored, utilizing scanners and cameras. This information is acquired as image and text data and transmitted to the server via the user terminal. The user terminal can use smart devices or similar as an interface for data collection.

[0099] The server uses image recognition software on a computing device (e.g., Google® Cloud Vision) to analyze the received package information. This identifies attributes such as the shape, size, and weight of the package. Furthermore, based on this data, the computing unit uses optimization algorithm libraries such as SciPy to calculate efficient package storage patterns. The calculated results are generated as a three-dimensional model.

[0100] This three-dimensional model is transmitted to the worker's observation device (e.g., smart glasses) and visually presented by a spatial display system. The worker can then accurately position the materials on-site according to this presentation. Audio guidance is also used to support efficient placement.

[0101] As a concrete example, when workers at a logistics center load packages of various sizes and shapes onto trucks, they use smart glasses to scan the packages and send the data to a server. The server analyzes the attributes of the packages and calculates the optimal loading plan. Workers can then visually confirm this plan and accurately position the packages on the truck.

[0102] Examples of prompt statements to input into a generative AI model include the following:

[0103] "Please calculate the optimal arrangement for efficiently loading this cargo."

[0104] "Please provide a 3D rendering of a space-saving and safe way to store cargo in a truck."

[0105] This system is expected to improve the efficiency of cargo placement in logistics operations, leading to increased accuracy and speed in work.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] Users input package information using a user terminal. Specifically, they use the terminal's camera to photograph or scan the package, collecting the data as image data. Input can include images of the package or a text list, and this data is collected.

[0109] Step 2:

[0110] The terminal formats the entered package information and prepares it for transmission to the server. During this process, image data is formatted to enable optimal analysis. Input consists of data captured or entered by the user, and output is the formatted data sent to the server.

[0111] Step 3:

[0112] The server analyzes the package information received from the terminal. Using image recognition software, it performs calculations to identify the physical characteristics of the package, such as its shape, size, and weight. The input is formatted image data, and the output is characteristic data.

[0113] Step 4:

[0114] The server uses an optimization algorithm to calculate the placement of identified packages based on their characteristic data. The computing unit performs numerous simulations to generate a plan that considers space-saving placement and center of gravity balance. The input is characteristic data, and the output is an optimized placement plan.

[0115] Step 5:

[0116] The server converts the calculated layout plan into a three-dimensional model that can be visually displayed. This model is sent to an observation device and presented to the operator. The input is optimized plan data, and the output is a three-dimensional model.

[0117] Step 6:

[0118] The terminal or observation device uses this three-dimensional model to provide visual guidance to the worker. The specific placement and order of packages are displayed through devices such as smart glasses, and instructions are given via voice guidance. The input is the three-dimensional model, and the output provides visual and voice instructions.

[0119] 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.

[0120] This invention provides a system for assisting with the efficient packing of luggage, offering packing suggestions that take the user's emotions into consideration. The system consists of a user terminal, a server, and an emotion engine.

[0121] The user enters luggage information or takes a photo of their luggage and sends it to the system using a user terminal. The terminal prepares this data to send to the server and, at the same time, uses its built-in emotion engine to recognize the user's current emotional state.

[0122] The server receives package information sent from the terminal and analyzes the data. This analysis includes identifying the physical characteristics and type of the package. Using feedback from the emotion engine, the server calculates the optimal packing method based on the user's emotional state. The emotion engine considers the user's stress level and comfort level, and adjusts suggestions to make the interaction as effective as possible.

[0123] In this system, the emotion engine, as a method to reduce user stress, can suggest multiple options for how to arrange luggage, if possible, and allow the user to choose. The system also presents packing advice through voice guidance and visual models, providing information in a way that is easy for the user to understand.

[0124] For example, suppose a user is preparing for a business trip and needs to pack their bags quickly. The emotion engine detects the user's stress level. In this case, the server calculates an intuitive and easy-to-follow packing procedure and provides it quickly through the terminal. The user can then follow the guide and pack their bags efficiently.

[0125] By incorporating an emotion engine, this system provides a more personalized experience that takes into account the user's motivation and emotional burden, resulting in a comfortable and efficient packing experience.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] The user uses a terminal to input information about the items they plan to pack or to take photos of the items. The terminal then formats this information and prepares it to be sent to the server. The terminal also uses a built-in emotion engine to recognize the user's emotional state from their facial expressions and voice.

[0129] Step 2:

[0130] The device sends user emotion data detected by the emotion engine, along with package information, to the server. Emotion data includes stress levels and changes in emotional state.

[0131] Step 3:

[0132] The server receives package information from the terminal and uses an analysis algorithm to identify the type, shape, and weight of the package. This clarifies its physical characteristics.

[0133] Step 4:

[0134] The server considers user emotion data obtained from the emotion engine and uses an optimization algorithm to calculate the best way to pack the luggage. It also adjusts the complexity of the procedure to reduce user stress.

[0135] Step 5:

[0136] The server generates an optimized packing method as a 3D model and an audio guide, and sends them to the user's terminal. The 3D model visually shows the placement of the items, and the audio guide verbally explains the packing procedure.

[0137] Step 6:

[0138] The terminal displays a 3D model and audio guidance received from the server, suggesting efficient ways to pack luggage to the user.

[0139] Step 7:

[0140] Users pack their belongings by following the on-screen instructions and voice guidance on the device. If necessary, users can provide feedback and further adjust their packing method.

[0141] Step 8:

[0142] The device sends user feedback to the server. Based on this information, the server recalculates the packing method as needed and sends new suggestions to the device.

[0143] (Example 2)

[0144] 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".

[0145] Conventional luggage packing assistance systems make packing suggestions without considering the user's emotional state, and may not be effective when used in stressful situations. In such circumstances, the challenge is to provide personalized suggestions that reflect the user's psychological state.

[0146] 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.

[0147] In this invention, the server includes terminal means for inputting luggage information and recognizing emotional states; information processing means for analyzing the input luggage information and performing processing to identify the characteristics of the luggage; information processing means for calculating packing methods to optimize luggage placement using the user's emotional data; and presentation means for displaying the calculated packing method audibly or visually. This enables the provision of optimal luggage placement suggestions based on the user's emotional state, resulting in less stressful and more efficient packing.

[0148] A "terminal device" is a device designed to input luggage information and recognize the user's emotional state.

[0149] An "information processing device" is a device that analyzes input luggage information, identifies the characteristics of the luggage, and further calculates the optimal luggage placement using emotional data.

[0150] "Presentation means" refers to a medium or device for presenting the calculated optimal packing method to the user, either audibly or visually.

[0151] "Emotional state" refers to data that indicates the user's psychological and physiological state, including information such as stress levels and mood.

[0152] "Characteristics" refers to physical characteristics related to the package, such as size, shape, and weight.

[0153] This invention comprises a device and method for assisting in the efficient packing of luggage. First, the user inputs luggage information using a user terminal. This can be done by text input through an interface provided by the terminal device, or by taking a photograph of the luggage and sending it to the terminal as image data.

[0154] The device uses a built-in emotion engine to recognize the user's emotional state. This involves collecting emotional data using facial recognition software and voice analysis technology to understand the user's stress level and psychological state.

[0155] When the server receives information sent from the terminal, it starts data analysis using a high-performance processor. Specifically, it uses machine learning algorithms to identify the physical properties of the luggage and calculate how to best arrange it. It also generates personalized packing suggestions based on the user's emotional state, using data obtained from the emotion engine.

[0156] The calculation results are provided to the user via a terminal. This presentation is done through audio guidance and visual models (e.g., visual displays using augmented reality technology), making it possible to practically demonstrate how to arrange luggage ideally.

[0157] For example, when a user is preparing for a business trip and needs to pack their luggage quickly, they can take a photo of their luggage and send it to the system. The terminal then detects the user's stress level. The server then calculates a packing procedure that can be quickly executed and provides an intuitively understandable guide. In this way, the user can pack their luggage efficiently and with less stress.

[0158] An example of a prompt message would be, "I'm trying to pack three days' worth of clothes for a business trip, but I'm feeling stressed about it. I'd like some easy and efficient packing suggestions."

[0159] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0160] Step 1:

[0161] The user enters package information using a terminal. This input can be in the form of specific text information (e.g., package name, quantity) or by taking and sending a photograph of the package. This input serves as basic data for subsequent processing, representing the package's physical characteristics and identification information.

[0162] Step 2:

[0163] Upon receiving the entered package information, the terminal activates its built-in emotion engine to analyze the user's emotional state. Specifically, it analyzes facial expressions captured by the camera and voice tone using the microphone, extracting the user's stress level and mood as numerical data. This provides data on the user's psychological state.

[0164] Step 3:

[0165] The terminal sends the acquired package information and sentiment data to the server. At this stage, the terminal uses an image processing algorithm to convert the shape and size of the package from the photo into text data. As output, a set of package characteristic data and user sentiment data is provided to the server.

[0166] Step 4:

[0167] The server uses the received data to analyze the packages. Specifically, it uses machine learning models to calculate the features of each package and performs calculations to classify similar packages and identify space-efficient placement methods. As output, it generates an optimal package placement plan.

[0168] Step 5:

[0169] The server creates packing suggestions tailored to the user based on analysis results and sentiment data. For users experiencing stress, it generates suggestions that include multiple options and simple steps. This step also prepares data for voice output and visual models.

[0170] Step 6:

[0171] The terminal receives packing suggestions sent from the server and presents them to the user. Specifically, it generates navigation voices using a speech synthesis engine or displays how to arrange luggage as augmented reality through a visual display. Based on this information, the user can pack their luggage efficiently.

[0172] (Application Example 2)

[0173] 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".

[0174] When considering efficient ways to pack goods, there is a challenge in proposing the optimal arrangement while taking into account the emotional state of the workers. It is necessary to solve this problem and provide efficient work procedures while reducing worker stress.

[0175] 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.

[0176] In this invention, the server includes an information processing device for inputting luggage information, a data processing device that performs processing to analyze the input information and identify physical characteristics, and an information processing device equipped with a sensor for recognizing emotional states. This makes it possible to suggest the optimal luggage placement according to the emotional state.

[0177] An "information processing device" is a device that inputs package information and sends and receives data between the user and the system.

[0178] A "data processing device" is a device that performs analysis and calculations based on input information, and is responsible for identifying physical characteristics and calculating the optimal placement.

[0179] A "sensor" is a device used to detect a worker's emotional state and measure their stress level and comfort level.

[0180] A "three-dimensional display device" is a device that visually presents the calculated packing method to the user, allowing them to intuitively understand the arrangement of their belongings.

[0181] An "emotion analysis device" is a device that analyzes the emotional state of a worker in detail and reflects that state as feedback into the system.

[0182] To carry out the present invention, a system comprising an information processing device, a data processing device, a sensor, a three-dimensional display device, and an emotion analysis device is used. Specifically, it is configured as follows.

[0183] The information processing device inputs package information and receives instructions from the user. This can include wearable devices such as smart glasses, and the input information is transmitted to a server.

[0184] The server processes the data based on the received information. The data processing unit analyzes the input information and performs calculations to identify the physical characteristics of the package. The sensor also detects emotional states and provides information to the emotion analysis device for analysis. This process utilizes image analysis software (e.g., OpenCV, TENSORFLOW®) and emotion recognition APIs (e.g., Affectiva).

[0185] The server calculates the optimal luggage placement, including feedback on emotional state, and presents the results via a 3D display. This display is designed to be intuitively understandable to the user and is shown in real time on the smart glasses' display.

[0186] As a concrete example, the system can sense information about the size and shape of packages, as well as the stress levels of workers in a logistics center. For instance, if a worker is emotionally stressed, the server creates a simple and intuitive packing guide to alleviate the stress, which is then visually displayed on smart glasses.

[0187] An example of a prompt for a generated AI model is: "Design a system that suggests the optimal way to pack packages to minimize worker stress in a logistics center. It will provide real-time visual guidance to workers based on emotion sensors and package information."

[0188] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0189] Step 1:

[0190] The terminal uses smart glasses to input package information (size, shape, weight, etc.). Input is done using voice commands or an on-screen touch panel. The entered information is sent to a server as digital data.

[0191] Step 2:

[0192] The server receives package information transmitted from the terminal and begins analysis using a data processing device. Here, image analysis software (e.g., OpenCV) is used to identify the physical characteristics of the package. The output is a detailed dataset containing information about the type and shape of the package.

[0193] Step 3:

[0194] Sensors installed in the device acquire the user's emotional state in real time. An emotion recognition API (e.g., Affectiva) is used to measure emotional data such as tension and stress levels. The measurement results are sent to a server.

[0195] Step 4:

[0196] The server calculates the optimal placement of luggage based on luggage information and emotional data. In this process, a generative AI model is used to devise personalized packing methods, and a specific packing plan is generated as the output of the calculation.

[0197] Step 5:

[0198] The generated luggage placement plan is visually displayed on the smart glasses' screen via a 3D display device. Users can then review this plan and pack their luggage according to the instructions, enabling efficient work.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] [Second Embodiment]

[0203] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0204] 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.

[0205] 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).

[0206] 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.

[0207] 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.

[0208] 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).

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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".

[0215] This invention is a system for efficiently packing luggage, aiming to assist users in making optimal packing arrangements when traveling. The system consists of a user terminal and a server.

[0216] First, the user uses a user terminal to enter information about the items they plan to pack. This information is collected through the creation of a text-based list and by taking photos of the items using the terminal's camera. The terminal then formats this information and prepares it for transmission to the server.

[0217] Next, the server receives package information from the user's terminal. The server uses analysis algorithms to identify the physical characteristics of the package, such as its type, shape, and weight. In particular, for image data, it automatically extracts information using object recognition technology.

[0218] Furthermore, the server calculates the optimal placement of luggage based on this data. Using an optimization algorithm, it proposes an efficient packing method that takes into account factors such as space conservation and center of gravity balance. The calculation results are then transmitted from the server to the user's terminal.

[0219] The terminal uses a three-dimensional display to visually present the optimal arrangement of luggage to the user based on calculation results received from the server. This allows the user to intuitively understand how to pack their luggage. The terminal can also provide packing instructions through voice guidance.

[0220] For example, suppose a traveler is packing clothes, electronic devices, and documents of different sizes and shapes into a suitcase for a business trip. In this case, the user takes pictures of these items with a device and sends them to a server. The server analyzes the data, calculates the optimal packing method, and provides it as a three-dimensional model. The device displays this suggestion, and the user can arrange the items in the suitcase accordingly.

[0221] In this way, by providing an efficient way to pack luggage, travelers can achieve comfortable and easy-to-travel packing. Due to its flexibility, this system can customize suggestions based on user feedback and accommodate individual needs.

[0222] The following describes the processing flow.

[0223] Step 1:

[0224] The user uses a user terminal to input the items they plan to pack, or takes photos of them with the camera. The terminal then converts this information into a digital format and prepares to transfer it to the server.

[0225] Step 2:

[0226] The server receives package information from the terminal. The received data is processed by an analysis algorithm to identify the physical characteristics of the package, such as its shape, weight, and type.

[0227] Step 3:

[0228] The server uses an optimization algorithm to calculate the optimal way to pack the luggage based on the identified luggage information. The calculation takes into account the luggage's space efficiency and center of gravity balance.

[0229] Step 4:

[0230] The server generates a three-dimensional model of the calculated packing method. This model visually shows the placement of the packages, organizing the data in a way that is easy for the user to understand.

[0231] Step 5:

[0232] The terminal displays the 3D model and related information received from the server on its screen. It provides voice guidance as needed and instructs the user on specific packing procedures.

[0233] Step 6:

[0234] Users pack their belongings according to the instructions and guidance displayed on the device. If necessary, they can adjust the packing method to their liking and input that information into the device.

[0235] Step 7:

[0236] The device sends user feedback to the server. The server uses this information to readjust the packing method, generates a new 3D model, and sends it back to the device.

[0237] (Example 1)

[0238] 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."

[0239] Traditional travel preparations have presented challenges in efficiently packing luggage and maximizing limited space. Furthermore, manually planning the optimal packing method, considering the shape and weight of luggage, is time-consuming and laborious. Additionally, the lack of sufficient visual and intuitive guidance on specific arrangements and procedures makes it difficult to pack effectively according to user needs.

[0240] 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.

[0241] In this invention, the server includes means for acquiring luggage information and converting it into an input format, calculation means for analyzing the acquired luggage information and determining its physical characteristics, and calculation means for performing optimization processing to calculate the optimal arrangement of luggage. This enables users to intuitively arrange luggage by automatically analyzing the most efficient way to pack it and displaying it in three dimensions.

[0242] A "user-use device that acquires luggage information and converts it into an input format" is a device that allows users to input details of the luggage they are carrying and then formats that data into a standardized format.

[0243] "A computational means for analyzing acquired package information and determining its physical characteristics" refers to a means of performing computational processing to identify physical characteristics such as the type, shape, and weight of a package from the input data.

[0244] "A computational means for performing an optimization process to calculate the optimal placement of luggage" refers to a means for calculating an efficient way to pack luggage, taking into account factors such as the center of gravity of the luggage and ease of retrieval, while making maximum use of available space.

[0245] A "three-dimensional visualization method" is a means of presenting a calculated arrangement of luggage to the user in a three-dimensional and visual manner, enabling intuitive understanding.

[0246] A "guidance system that provides instructions for luggage placement via voice" is a means of showing the user specific placement procedures via voice based on the calculated luggage placement results.

[0247] This invention is a support system for efficiently organizing and arranging users' belongings. The system primarily consists of a user terminal and a server. The following details an embodiment of this system.

[0248] First, when preparing for a trip, users use a user terminal to enter details of their luggage. Through the terminal's application, users can not only enter a luggage list as text, but also take photos of their luggage using the terminal's camera function. This information is formatted on the terminal and ready to be sent to the server.

[0249] Next, the server analyzes the received package information. Specifically, the server uses machine learning algorithms and object recognition technology to identify the physical characteristics of the package, such as its type, shape, and weight. This analysis generates the data necessary to determine the efficient placement of the packages.

[0250] Furthermore, the server calculates the optimal placement of the luggage based on the acquired physical characteristics. This optimization process utilizes a generative AI model to create placement plans that consider the balance of the luggage's center of gravity and the efficient use of space. The calculated placement plans are then sent to the user's terminal.

[0251] The terminal receives the suggested placement from the server and presents it visually to the user using 3D display technology. This allows the user to intuitively understand the suggested luggage placement. The terminal can also provide voice guidance and instruct the user on specific placement steps. Through this process, the user can actually place the luggage while referring to the suggestion, making preparation more efficient and less burdensome.

[0252] A concrete example is when a user needs to pack clothing, electronic devices, and documents of different shapes and sizes into a suitcase for a business trip. In such a scenario, the user can list or photograph their belongings on a device and arrange them in the suitcase based on the optimal packing method suggested by the system. This results in a space-saving and easily accessible arrangement of individual items.

[0253] An example of a prompt message is: "How can I most efficiently pack the following items into my suitcase? The items include a laptop, three shirts, a suit, and one pair of shoes." This prompt prompts the system to provide optimal placement suggestions for each type of item.

[0254] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0255] Step 1:

[0256] Users enter information about the luggage they will be taking on their trip into a user terminal. Input methods include entering a list of luggage as text or taking images with the terminal's camera. This entered data is converted to a unified format within the terminal. Text data is converted to CSV format, and image data to JPEG format. This prepares the data for transmission to the server.

[0257] Step 2:

[0258] The terminal sends formatted package information to the server. The data is sent over the network to the server, and a handshake process is performed to confirm successful reception. To maintain data security, the information is encrypted using SSL / TLS before transmission.

[0259] Step 3:

[0260] The server analyzes the received package information. Specifically, the server utilizes a generative AI model to automatically extract the physical characteristics of the package from the input data. For text data, a natural language processing algorithm is used, and for image data, an image analysis algorithm is applied to identify objects and estimate their shape and weight. The analysis results are stored in a database and used in the next step.

[0261] Step 4:

[0262] The server calculates the optimal placement of packages based on the analyzed package information. Using an optimization algorithm, it generates proposed package placements. This process considers space conservation and center of gravity balance to create an efficient placement plan. The calculation results are organized in a structured data format (e.g., JSON) and prepared as data for transmission to the terminal.

[0263] Step 5:

[0264] The server sends the calculation results to the user's terminal. In this process, as described above, the data is encrypted before transmission, ensuring that it is received accurately on the terminal. The transmitted data is used for 3D display on the terminal.

[0265] Step 6:

[0266] The terminal displays the package placement plan received from the server in three dimensions. The placement method is visually presented on the screen through the user interface, rendered in a way that is easy for the user to understand intuitively. The visually displayed package placement plan can be adjusted by the user as desired. The terminal also uses voice guidance to provide the user with detailed work procedures.

[0267] This will allow users to easily perform efficient and stress-free packing procedures by utilizing servers and terminals.

[0268] (Application Example 1)

[0269] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0270] In logistics operations, efficiently storing goods is crucial, but quickly and appropriately arranging items of diverse shapes and sizes places a significant burden on workers. Furthermore, human error and wasted time are common, often preventing efficient storage from being achieved. Technologies to address this are needed.

[0271] 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.

[0272] In this invention, the server includes a user terminal for inputting luggage information, a computing device that analyzes the input luggage information and performs processing to identify the physical characteristics of the luggage, a computing device that performs processing to calculate the packing method in order to optimize the arrangement of luggage, and an observation device that visually displays the arrangement information to support the worker's work. This makes it possible to support workers in efficiently storing luggage.

[0273] "Cargo information" refers to data used to identify the characteristics of various types of cargo involved in logistics, and includes attributes such as shape, size, and weight.

[0274] A "user terminal" is an information device used by logistics workers to input package information, and it has an interface for collecting and inputting data.

[0275] A "processing unit" is a computing device that processes input data and performs analysis and calculations, utilizing algorithms to identify physical properties and optimize placement.

[0276] A "spatial display means" is a device that visually presents the calculated packing method to the user, and provides it to the worker as three-dimensional visual information.

[0277] An "observation device" is a visual support device that allows workers to quickly understand the placement information of packages, and assists in efficient and accurate package storage on site.

[0278] The system for realizing this invention consists of a user terminal, a server, and an observation device used by the worker. This system collects, analyzes, optimizes, and provides instructions for efficiently storing packages.

[0279] Users use a user terminal to collect information about the items to be stored, utilizing scanners and cameras. This information is acquired as image and text data and transmitted to the server via the user terminal. The user terminal can use smart devices or similar as an interface for data collection.

[0280] The server uses image recognition software (e.g., Google Cloud Vision) on a computing device to analyze the received package information. This identifies attributes such as the shape, size, and weight of the package. Furthermore, based on this data, the computing unit uses optimization algorithm libraries such as SciPy to calculate efficient package storage patterns. The calculated results are generated as a three-dimensional model.

[0281] This 3D model is transmitted to the operator's observation device (e.g., smart glasses) and visually presented by the spatial display means. The operator can accurately place the goods on-site according to this presentation. Also, an audio guide is used in combination to support efficient placement.

[0282] As a specific example, when an operator at a logistics center loads packages of various sizes and shapes onto a truck, they use smart glasses to scan the goods and transmit the data to the server. The server analyzes the attributes of the goods and calculates an optimal loading plan. The operator can accurately place the goods on the truck while visually confirming it.

[0283] Examples of prompt texts input into the generation AI model can be as follows:

[0284] "Please calculate the optimal placement method for efficiently loading this package."

[0285] "Please display a 3D plan for storing the goods on the truck in a space-saving and safe manner."

[0286] It is expected that this system will improve the placement efficiency of goods at the logistics site and enhance the accuracy and speed of operations.

[0287] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0288] Step 1:

[0289] The user inputs information about the goods using the user terminal. Specifically, the user uses the camera of the terminal to photograph the goods or reads them with a scanner and collects them as image data as image data. The inputs include images of the goods and text-based lists, and these data are collected.

[0290] Step 2:

[0291] The terminal formats the entered package information and prepares it for transmission to the server. During this process, image data is formatted to enable optimal analysis. Input consists of data captured or entered by the user, and output is the formatted data sent to the server.

[0292] Step 3:

[0293] The server analyzes the package information received from the terminal. Using image recognition software, it performs calculations to identify the physical characteristics of the package, such as its shape, size, and weight. The input is formatted image data, and the output is characteristic data.

[0294] Step 4:

[0295] The server uses an optimization algorithm to calculate the placement of identified packages based on their characteristic data. The computing unit performs numerous simulations to generate a plan that considers space-saving placement and center of gravity balance. The input is characteristic data, and the output is an optimized placement plan.

[0296] Step 5:

[0297] The server converts the calculated layout plan into a three-dimensional model that can be visually displayed. This model is sent to an observation device and presented to the operator. The input is optimized plan data, and the output is a three-dimensional model.

[0298] Step 6:

[0299] The terminal or observation device uses this three-dimensional model to provide visual guidance to the worker. The specific placement and order of packages are displayed through devices such as smart glasses, and instructions are given via voice guidance. The input is the three-dimensional model, and the output provides visual and voice instructions.

[0300] 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.

[0301] This invention provides a system for assisting with the efficient packing of luggage, offering packing suggestions that take the user's emotions into consideration. The system consists of a user terminal, a server, and an emotion engine.

[0302] The user enters luggage information or takes a photo of their luggage and sends it to the system using a user terminal. The terminal prepares this data to send to the server and, at the same time, uses its built-in emotion engine to recognize the user's current emotional state.

[0303] The server receives package information sent from the terminal and analyzes the data. This analysis includes identifying the physical characteristics and type of the package. Using feedback from the emotion engine, the server calculates the optimal packing method based on the user's emotional state. The emotion engine considers the user's stress level and comfort level, and adjusts suggestions to make the interaction as effective as possible.

[0304] In this system, the emotion engine, as a method to reduce user stress, can suggest multiple options for how to arrange luggage, if possible, and allow the user to choose. The system also presents packing advice through voice guidance and visual models, providing information in a way that is easy for the user to understand.

[0305] For example, suppose a user is preparing for a business trip and needs to pack their bags quickly. The emotion engine detects the user's stress level. In this case, the server calculates an intuitive and easy-to-follow packing procedure and provides it quickly through the terminal. The user can then follow the guide and pack their bags efficiently.

[0306] By integrating the emotion engine, this system provides a more personalized experience considering the user's motivation and emotional burden, achieving comfortable and efficient packing.

[0307] The following describes the processing flow.

[0308] Step 1:

[0309] The user uses the user terminal to input information about the luggage to be packed or take a photo of the luggage. The terminal formats this information and prepares to send it to the server. Also, the terminal uses the built-in emotion engine to recognize the emotional state from the user's expression and voice.

[0310] Step 2:

[0311] The terminal sends the user's emotion data detected by the emotion engine and the luggage information to the server. The emotion data includes the stress level and changes in the emotional state.

[0312] Step 3:

[0313] The server receives the luggage information from the terminal and uses an analysis algorithm to identify the type, shape, and weight of the luggage. Thereby, the physical characteristics are clarified.

[0314] Step 4:

[0315] The server calculates the optimal way to pack the luggage using an optimization algorithm considering the user's emotion data obtained from the emotion engine. It also adjusts the complexity of the procedure to reduce the user's stress.

[0316] Step 5:

[0317] The server generates the optimized packing method as a three-dimensional model and an audio guide and sends it to the user terminal. The three-dimensional model visually shows the arrangement of the luggage, and the audio guide verbally explains the packing procedure.

[0318] Step 6:

[0319] The terminal displays a 3D model and audio guidance received from the server, suggesting efficient ways to pack luggage to the user.

[0320] Step 7:

[0321] Users pack their belongings by following the on-screen instructions and voice guidance on the device. If necessary, users can provide feedback and further adjust their packing method.

[0322] Step 8:

[0323] The device sends user feedback to the server. Based on this information, the server recalculates the packing method as needed and sends new suggestions to the device.

[0324] (Example 2)

[0325] 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".

[0326] Conventional luggage packing assistance systems make packing suggestions without considering the user's emotional state, and may not be effective when used in stressful situations. In such circumstances, the challenge is to provide personalized suggestions that reflect the user's psychological state.

[0327] 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.

[0328] In this invention, the server includes terminal means for inputting luggage information and recognizing emotional states; information processing means for analyzing the input luggage information and performing processing to identify the characteristics of the luggage; information processing means for calculating packing methods to optimize luggage placement using the user's emotional data; and presentation means for displaying the calculated packing method audibly or visually. This enables the provision of optimal luggage placement suggestions based on the user's emotional state, resulting in less stressful and more efficient packing.

[0329] A "terminal device" is a device designed to input luggage information and recognize the user's emotional state.

[0330] An "information processing device" is a device that analyzes input luggage information, identifies the characteristics of the luggage, and further calculates the optimal luggage placement using emotional data.

[0331] "Presentation means" refers to a medium or device for presenting the calculated optimal packing method to the user, either audibly or visually.

[0332] "Emotional state" refers to data that indicates the user's psychological and physiological state, including information such as stress levels and mood.

[0333] "Characteristics" refers to physical characteristics related to the package, such as size, shape, and weight.

[0334] This invention comprises a device and method for assisting in the efficient packing of luggage. First, the user inputs luggage information using a user terminal. This can be done by text input through an interface provided by the terminal device, or by taking a photograph of the luggage and sending it to the terminal as image data.

[0335] The device uses a built-in emotion engine to recognize the user's emotional state. This involves collecting emotional data using facial recognition software and voice analysis technology to understand the user's stress level and psychological state.

[0336] When the server receives information sent from the terminal, it starts data analysis using a high-performance processor. Specifically, it uses machine learning algorithms to identify the physical properties of the luggage and calculate how to best arrange it. It also generates personalized packing suggestions based on the user's emotional state, using data obtained from the emotion engine.

[0337] The calculation results are provided to the user via a terminal. This presentation is done through audio guidance and visual models (e.g., visual displays using augmented reality technology), making it possible to practically demonstrate how to arrange luggage ideally.

[0338] For example, when a user is preparing for a business trip and needs to pack their luggage quickly, they can take a photo of their luggage and send it to the system. The terminal then detects the user's stress level. The server then calculates a packing procedure that can be quickly executed and provides an intuitively understandable guide. In this way, the user can pack their luggage efficiently and with less stress.

[0339] An example of a prompt message would be, "I'm trying to pack three days' worth of clothes for a business trip, but I'm feeling stressed about it. I'd like some easy and efficient packing suggestions."

[0340] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0341] Step 1:

[0342] The user enters package information using a terminal. This input can be in the form of specific text information (e.g., package name, quantity) or by taking and sending a photograph of the package. This input serves as basic data for subsequent processing, representing the package's physical characteristics and identification information.

[0343] Step 2:

[0344] Upon receiving the entered package information, the terminal activates its built-in emotion engine to analyze the user's emotional state. Specifically, it analyzes facial expressions captured by the camera and voice tone using the microphone, extracting the user's stress level and mood as numerical data. This provides data on the user's psychological state.

[0345] Step 3:

[0346] The terminal sends the acquired package information and sentiment data to the server. At this stage, the terminal uses an image processing algorithm to convert the shape and size of the package from the photo into text data. As output, a set of package characteristic data and user sentiment data is provided to the server.

[0347] Step 4:

[0348] The server uses the received data to analyze the packages. Specifically, it uses machine learning models to calculate the features of each package and performs calculations to classify similar packages and identify space-efficient placement methods. As output, it generates an optimal package placement plan.

[0349] Step 5:

[0350] The server creates packing suggestions tailored to the user based on analysis results and sentiment data. For users experiencing stress, it generates suggestions that include multiple options and simple steps. This step also prepares data for voice output and visual models.

[0351] Step 6:

[0352] The terminal receives packing suggestions sent from the server and presents them to the user. Specifically, it generates navigation voices using a speech synthesis engine or displays how to arrange luggage as augmented reality through a visual display. Based on this information, the user can pack their luggage efficiently.

[0353] (Application Example 2)

[0354] 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 as the "terminal".

[0355] When considering efficient ways to pack goods, there is a challenge in proposing the optimal arrangement while taking into account the emotional state of the workers. It is necessary to solve this problem and provide efficient work procedures while reducing worker stress.

[0356] 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.

[0357] In this invention, the server includes an information processing device for inputting luggage information, a data processing device that performs processing to analyze the input information and identify physical characteristics, and an information processing device equipped with a sensor for recognizing emotional states. This makes it possible to suggest the optimal luggage placement according to the emotional state.

[0358] An "information processing device" is a device that inputs package information and sends and receives data between the user and the system.

[0359] A "data processing device" is a device that performs analysis and calculations based on input information, and is responsible for identifying physical characteristics and calculating the optimal placement.

[0360] A "sensor" is a device used to detect a worker's emotional state and measure their stress level and comfort level.

[0361] A "three-dimensional display device" is a device that visually presents the calculated packing method to the user, allowing them to intuitively understand the arrangement of their belongings.

[0362] An "emotion analysis device" is a device that analyzes the emotional state of a worker in detail and reflects that state as feedback into the system.

[0363] To carry out the present invention, a system comprising an information processing device, a data processing device, a sensor, a three-dimensional display device, and an emotion analysis device is used. Specifically, it is configured as follows.

[0364] The information processing device inputs package information and receives instructions from the user. This can include wearable devices such as smart glasses, and the input information is transmitted to a server.

[0365] The server processes the received information. The data processing unit analyzes the input information and performs calculations to identify the physical characteristics of the package. The sensor also detects emotional states and provides information to the emotion analysis device for analysis. This process utilizes image analysis software (e.g., OpenCV, TensorFlow) and emotion recognition APIs (e.g., Affectiva).

[0366] The server calculates the optimal luggage placement, including feedback on emotional state, and presents the results via a 3D display. This display is designed to be intuitively understandable to the user and is shown in real time on the smart glasses' display.

[0367] As a concrete example, the system can sense information about the size and shape of packages, as well as the stress levels of workers in a logistics center. For instance, if a worker is emotionally stressed, the server creates a simple and intuitive packing guide to alleviate the stress, which is then visually displayed on smart glasses.

[0368] An example of a prompt for a generated AI model is: "Design a system that suggests the optimal way to pack packages to minimize worker stress in a logistics center. It will provide real-time visual guidance to workers based on emotion sensors and package information."

[0369] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0370] Step 1:

[0371] The terminal uses smart glasses to input package information (size, shape, weight, etc.). Input is done using voice commands or an on-screen touch panel. The entered information is sent to a server as digital data.

[0372] Step 2:

[0373] The server receives package information transmitted from the terminal and begins analysis using a data processing device. Here, image analysis software (e.g., OpenCV) is used to identify the physical characteristics of the package. The output is a detailed dataset containing information about the type and shape of the package.

[0374] Step 3:

[0375] Sensors installed in the device acquire the user's emotional state in real time. An emotion recognition API (e.g., Affectiva) is used to measure emotional data such as tension and stress levels. The measurement results are sent to a server.

[0376] Step 4:

[0377] The server calculates the optimal placement of luggage based on luggage information and emotional data. In this process, a generative AI model is used to devise personalized packing methods, and a specific packing plan is generated as the output of the calculation.

[0378] Step 5:

[0379] The generated luggage placement plan is visually displayed on the smart glasses' screen via a 3D display device. Users can then review this plan and pack their luggage according to the instructions, enabling efficient work.

[0380] 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.

[0381] 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.

[0382] 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.

[0383] [Third Embodiment]

[0384] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0385] 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.

[0386] 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).

[0387] 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.

[0388] 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.

[0389] 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).

[0390] 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.

[0391] 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.

[0392] 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.

[0393] 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.

[0394] 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.

[0395] 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".

[0396] This invention is a system for efficiently packing luggage, aiming to assist users in making optimal packing arrangements when traveling. The system consists of a user terminal and a server.

[0397] First, the user uses a user terminal to enter information about the items they plan to pack. This information is collected through the creation of a text-based list and by taking photos of the items using the terminal's camera. The terminal then formats this information and prepares it for transmission to the server.

[0398] Next, the server receives package information from the user's terminal. The server uses analysis algorithms to identify the physical characteristics of the package, such as its type, shape, and weight. In particular, for image data, it automatically extracts information using object recognition technology.

[0399] Furthermore, the server calculates the optimal placement of luggage based on this data. Using an optimization algorithm, it proposes an efficient packing method that takes into account factors such as space conservation and center of gravity balance. The calculation results are then transmitted from the server to the user's terminal.

[0400] The terminal uses a three-dimensional display to visually present the optimal arrangement of luggage to the user based on calculation results received from the server. This allows the user to intuitively understand how to pack their luggage. The terminal can also provide packing instructions through voice guidance.

[0401] For example, suppose a traveler is packing clothes, electronic devices, and documents of different sizes and shapes into a suitcase for a business trip. In this case, the user takes pictures of these items with a device and sends them to a server. The server analyzes the data, calculates the optimal packing method, and provides it as a three-dimensional model. The device displays this suggestion, and the user can arrange the items in the suitcase accordingly.

[0402] In this way, by providing an efficient way to pack luggage, travelers can achieve comfortable and easy-to-travel packing. Due to its flexibility, this system can customize suggestions based on user feedback and accommodate individual needs.

[0403] The following describes the processing flow.

[0404] Step 1:

[0405] The user uses a user terminal to input the items they plan to pack, or takes photos of them with the camera. The terminal then converts this information into a digital format and prepares to transfer it to the server.

[0406] Step 2:

[0407] The server receives package information from the terminal. The received data is processed by an analysis algorithm to identify the physical characteristics of the package, such as its shape, weight, and type.

[0408] Step 3:

[0409] The server uses an optimization algorithm to calculate the optimal way to pack the luggage based on the identified luggage information. The calculation takes into account the luggage's space efficiency and center of gravity balance.

[0410] Step 4:

[0411] The server generates a three-dimensional model of the calculated packing method. This model visually shows the placement of the packages, organizing the data in a way that is easy for the user to understand.

[0412] Step 5:

[0413] The terminal displays the 3D model and related information received from the server on its screen. It provides voice guidance as needed and instructs the user on specific packing procedures.

[0414] Step 6:

[0415] Users pack their belongings according to the instructions and guidance displayed on the device. If necessary, they can adjust the packing method to their liking and input that information into the device.

[0416] Step 7:

[0417] The device sends user feedback to the server. The server uses this information to readjust the packing method, generates a new 3D model, and sends it back to the device.

[0418] (Example 1)

[0419] 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."

[0420] Traditional travel preparations have presented challenges in efficiently packing luggage and maximizing limited space. Furthermore, manually planning the optimal packing method, considering the shape and weight of luggage, is time-consuming and laborious. Additionally, the lack of sufficient visual and intuitive guidance on specific arrangements and procedures makes it difficult to pack effectively according to user needs.

[0421] 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.

[0422] In this invention, the server includes means for acquiring luggage information and converting it into an input format, calculation means for analyzing the acquired luggage information and determining its physical characteristics, and calculation means for performing optimization processing to calculate the optimal arrangement of luggage. This enables users to intuitively arrange luggage by automatically analyzing the most efficient way to pack it and displaying it in three dimensions.

[0423] A "user-use device that acquires luggage information and converts it into an input format" is a device that allows users to input details of the luggage they are carrying and then formats that data into a standardized format.

[0424] "A computational means for analyzing acquired package information and determining its physical characteristics" refers to a means of performing computational processing to identify physical characteristics such as the type, shape, and weight of a package from the input data.

[0425] "A computational means for performing an optimization process to calculate the optimal placement of luggage" refers to a means for calculating an efficient way to pack luggage, taking into account factors such as the center of gravity of the luggage and ease of retrieval, while making maximum use of available space.

[0426] A "three-dimensional visualization method" is a means of presenting a calculated arrangement of luggage to the user in a three-dimensional and visual manner, enabling intuitive understanding.

[0427] A "guidance system that provides instructions for luggage placement via voice" is a means of showing the user specific placement procedures via voice based on the calculated luggage placement results.

[0428] This invention is a support system for efficiently organizing and arranging users' belongings. The system primarily consists of a user terminal and a server. The following details an embodiment of this system.

[0429] First, when preparing for a trip, users use a user terminal to enter details of their luggage. Through the terminal's application, users can not only enter a luggage list as text, but also take photos of their luggage using the terminal's camera function. This information is formatted on the terminal and ready to be sent to the server.

[0430] Next, the server analyzes the received package information. Specifically, the server uses machine learning algorithms and object recognition technology to identify the physical characteristics of the package, such as its type, shape, and weight. This analysis generates the data necessary to determine the efficient placement of the packages.

[0431] Furthermore, the server calculates the optimal placement of the luggage based on the acquired physical characteristics. This optimization process utilizes a generative AI model to create placement plans that consider the balance of the luggage's center of gravity and the efficient use of space. The calculated placement plans are then sent to the user's terminal.

[0432] The terminal receives the suggested placement from the server and presents it visually to the user using 3D display technology. This allows the user to intuitively understand the suggested luggage placement. The terminal can also provide voice guidance and instruct the user on specific placement steps. Through this process, the user can actually place the luggage while referring to the suggestion, making preparation more efficient and less burdensome.

[0433] A concrete example is when a user needs to pack clothing, electronic devices, and documents of different shapes and sizes into a suitcase for a business trip. In such a scenario, the user can list or photograph their belongings on a device and arrange them in the suitcase based on the optimal packing method suggested by the system. This results in a space-saving and easily accessible arrangement of individual items.

[0434] An example of a prompt message is: "How can I most efficiently pack the following items into my suitcase? The items include a laptop, three shirts, a suit, and one pair of shoes." This prompt prompts the system to provide optimal placement suggestions for each type of item.

[0435] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0436] Step 1:

[0437] Users enter information about the luggage they will be taking on their trip into a user terminal. Input methods include entering a list of luggage as text or taking images with the terminal's camera. This entered data is converted to a unified format within the terminal. Text data is converted to CSV format, and image data to JPEG format. This prepares the data for transmission to the server.

[0438] Step 2:

[0439] The terminal sends formatted package information to the server. The data is sent over the network to the server, and a handshake process is performed to confirm successful reception. To maintain data security, the information is encrypted using SSL / TLS before transmission.

[0440] Step 3:

[0441] The server analyzes the received package information. Specifically, the server utilizes a generative AI model to automatically extract the physical characteristics of the package from the input data. For text data, a natural language processing algorithm is used, and for image data, an image analysis algorithm is applied to identify objects and estimate their shape and weight. The analysis results are stored in a database and used in the next step.

[0442] Step 4:

[0443] The server calculates the optimal placement of packages based on the analyzed package information. Using an optimization algorithm, it generates proposed package placements. This process considers space conservation and center of gravity balance to create an efficient placement plan. The calculation results are organized in a structured data format (e.g., JSON) and prepared as data for transmission to the terminal.

[0444] Step 5:

[0445] The server sends the calculation results to the user's terminal. In this process, as described above, the data is encrypted before transmission, ensuring that it is received accurately on the terminal. The transmitted data is used for 3D display on the terminal.

[0446] Step 6:

[0447] The terminal displays the package placement plan received from the server in three dimensions. The placement method is visually presented on the screen through the user interface, rendered in a way that is easy for the user to understand intuitively. The visually displayed package placement plan can be adjusted by the user as desired. The terminal also uses voice guidance to provide the user with detailed work procedures.

[0448] This will allow users to easily perform efficient and stress-free packing procedures by utilizing servers and terminals.

[0449] (Application Example 1)

[0450] 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."

[0451] In logistics operations, efficiently storing goods is crucial, but quickly and appropriately arranging items of diverse shapes and sizes places a significant burden on workers. Furthermore, human error and wasted time are common, often preventing efficient storage from being achieved. Technologies to address this are needed.

[0452] 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.

[0453] In this invention, the server includes a user terminal for inputting luggage information, a computing device that analyzes the input luggage information and performs processing to identify the physical characteristics of the luggage, a computing device that performs processing to calculate the packing method in order to optimize the arrangement of luggage, and an observation device that visually displays the arrangement information to support the worker's work. This makes it possible to support workers in efficiently storing luggage.

[0454] "Cargo information" refers to data used to identify the characteristics of various types of cargo involved in logistics, and includes attributes such as shape, size, and weight.

[0455] A "user terminal" is an information device used by logistics workers to input package information, and it has an interface for collecting and inputting data.

[0456] A "processing unit" is a computing device that processes input data and performs analysis and calculations, utilizing algorithms to identify physical properties and optimize placement.

[0457] A "spatial display means" is a device that visually presents the calculated packing method to the user, and provides it to the worker as three-dimensional visual information.

[0458] An "observation device" is a visual support device that allows workers to quickly understand the placement information of packages, and assists in efficient and accurate package storage on site.

[0459] The system for realizing this invention consists of a user terminal, a server, and an observation device used by the worker. This system collects, analyzes, optimizes, and provides instructions for efficiently storing packages.

[0460] Users use a user terminal to collect information about the items to be stored, utilizing scanners and cameras. This information is acquired as image and text data and transmitted to the server via the user terminal. The user terminal can use smart devices or similar as an interface for data collection.

[0461] The server uses image recognition software (e.g., Google Cloud Vision) on a computing device to analyze the received package information. This identifies attributes such as the shape, size, and weight of the package. Furthermore, based on this data, the computing unit uses optimization algorithm libraries such as SciPy to calculate efficient package storage patterns. The calculated results are generated as a three-dimensional model.

[0462] This three-dimensional model is transmitted to the worker's observation device (e.g., smart glasses) and visually presented by a spatial display system. The worker can then accurately position the materials on-site according to this presentation. Audio guidance is also used to support efficient placement.

[0463] As a concrete example, when workers at a logistics center load packages of various sizes and shapes onto trucks, they use smart glasses to scan the packages and send the data to a server. The server analyzes the attributes of the packages and calculates the optimal loading plan. Workers can then visually confirm this plan and accurately position the packages on the truck.

[0464] Examples of prompt statements to input into a generative AI model include the following:

[0465] "Please calculate the optimal arrangement for efficiently loading this cargo."

[0466] "Please provide a 3D rendering of a space-saving and safe way to store cargo in a truck."

[0467] This system is expected to improve the efficiency of cargo placement in logistics operations, leading to increased accuracy and speed in work.

[0468] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0469] Step 1:

[0470] Users input package information using a user terminal. Specifically, they use the terminal's camera to photograph or scan the package, collecting the data as image data. Input can include images of the package or a text list, and this data is collected.

[0471] Step 2:

[0472] The terminal formats the entered package information and prepares it for transmission to the server. During this process, image data is formatted to enable optimal analysis. Input consists of data captured or entered by the user, and output is the formatted data sent to the server.

[0473] Step 3:

[0474] The server analyzes the package information received from the terminal. Using image recognition software, it performs calculations to identify the physical characteristics of the package, such as its shape, size, and weight. The input is formatted image data, and the output is characteristic data.

[0475] Step 4:

[0476] The server uses an optimization algorithm to calculate the placement of identified packages based on their characteristic data. The computing unit performs numerous simulations to generate a plan that considers space-saving placement and center of gravity balance. The input is characteristic data, and the output is an optimized placement plan.

[0477] Step 5:

[0478] The server converts the calculated layout plan into a three-dimensional model that can be visually displayed. This model is sent to an observation device and presented to the operator. The input is optimized plan data, and the output is a three-dimensional model.

[0479] Step 6:

[0480] The terminal or observation device uses this three-dimensional model to provide visual guidance to the worker. The specific placement and order of packages are displayed through devices such as smart glasses, and instructions are given via voice guidance. The input is the three-dimensional model, and the output provides visual and voice instructions.

[0481] 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.

[0482] This invention provides a system for assisting with the efficient packing of luggage, offering packing suggestions that take the user's emotions into consideration. The system consists of a user terminal, a server, and an emotion engine.

[0483] The user enters luggage information or takes a photo of their luggage and sends it to the system using a user terminal. The terminal prepares this data to send to the server and, at the same time, uses its built-in emotion engine to recognize the user's current emotional state.

[0484] The server receives package information sent from the terminal and analyzes the data. This analysis includes identifying the physical characteristics and type of the package. Using feedback from the emotion engine, the server calculates the optimal packing method based on the user's emotional state. The emotion engine considers the user's stress level and comfort level, and adjusts suggestions to make the interaction as effective as possible.

[0485] In this system, the emotion engine, as a method to reduce user stress, can suggest multiple options for how to arrange luggage, if possible, and allow the user to choose. The system also presents packing advice through voice guidance and visual models, providing information in a way that is easy for the user to understand.

[0486] For example, suppose a user is preparing for a business trip and needs to pack their bags quickly. The emotion engine detects the user's stress level. In this case, the server calculates an intuitive and easy-to-follow packing procedure and provides it quickly through the terminal. The user can then follow the guide and pack their bags efficiently.

[0487] By incorporating an emotion engine, this system provides a more personalized experience that takes into account the user's motivation and emotional burden, resulting in a comfortable and efficient packing experience.

[0488] The following describes the processing flow.

[0489] Step 1:

[0490] The user uses a terminal to input information about the items they plan to pack or to take photos of the items. The terminal then formats this information and prepares it to be sent to the server. The terminal also uses a built-in emotion engine to recognize the user's emotional state from their facial expressions and voice.

[0491] Step 2:

[0492] The device sends user emotion data detected by the emotion engine, along with package information, to the server. Emotion data includes stress levels and changes in emotional state.

[0493] Step 3:

[0494] The server receives package information from the terminal and uses an analysis algorithm to identify the type, shape, and weight of the package. This clarifies its physical characteristics.

[0495] Step 4:

[0496] The server considers user emotion data obtained from the emotion engine and uses an optimization algorithm to calculate the best way to pack the luggage. It also adjusts the complexity of the procedure to reduce user stress.

[0497] Step 5:

[0498] The server generates an optimized packing method as a 3D model and an audio guide, and sends them to the user's terminal. The 3D model visually shows the placement of the items, and the audio guide verbally explains the packing procedure.

[0499] Step 6:

[0500] The terminal displays a 3D model and audio guidance received from the server, suggesting efficient ways to pack luggage to the user.

[0501] Step 7:

[0502] Users pack their belongings by following the on-screen instructions and voice guidance on the device. If necessary, users can provide feedback and further adjust their packing method.

[0503] Step 8:

[0504] The device sends user feedback to the server. Based on this information, the server recalculates the packing method as needed and sends new suggestions to the device.

[0505] (Example 2)

[0506] 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."

[0507] Conventional luggage packing assistance systems make packing suggestions without considering the user's emotional state, and may not be effective when used in stressful situations. In such circumstances, the challenge is to provide personalized suggestions that reflect the user's psychological state.

[0508] 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.

[0509] In this invention, the server includes terminal means for inputting luggage information and recognizing emotional states; information processing means for analyzing the input luggage information and performing processing to identify the characteristics of the luggage; information processing means for calculating packing methods to optimize luggage placement using the user's emotional data; and presentation means for displaying the calculated packing method audibly or visually. This enables the provision of optimal luggage placement suggestions based on the user's emotional state, resulting in less stressful and more efficient packing.

[0510] A "terminal device" is a device designed to input luggage information and recognize the user's emotional state.

[0511] An "information processing device" is a device that analyzes input luggage information, identifies the characteristics of the luggage, and further calculates the optimal luggage placement using emotional data.

[0512] "Presentation means" refers to a medium or device for presenting the calculated optimal packing method to the user, either audibly or visually.

[0513] "Emotional state" refers to data that indicates the user's psychological and physiological state, including information such as stress levels and mood.

[0514] "Characteristics" refers to physical characteristics related to the package, such as size, shape, and weight.

[0515] This invention comprises a device and method for assisting in the efficient packing of luggage. First, the user inputs luggage information using a user terminal. This can be done by text input through an interface provided by the terminal device, or by taking a photograph of the luggage and sending it to the terminal as image data.

[0516] The device uses a built-in emotion engine to recognize the user's emotional state. This involves collecting emotional data using facial recognition software and voice analysis technology to understand the user's stress level and psychological state.

[0517] When the server receives information sent from the terminal, it starts data analysis using a high-performance processor. Specifically, it uses machine learning algorithms to identify the physical properties of the luggage and calculate how to best arrange it. It also generates personalized packing suggestions based on the user's emotional state, using data obtained from the emotion engine.

[0518] The calculation results are provided to the user via a terminal. This presentation is done through audio guidance and visual models (e.g., visual displays using augmented reality technology), making it possible to practically demonstrate how to arrange luggage ideally.

[0519] For example, when a user is preparing for a business trip and needs to pack their luggage quickly, they can take a photo of their luggage and send it to the system. The terminal then detects the user's stress level. The server then calculates a packing procedure that can be quickly executed and provides an intuitively understandable guide. In this way, the user can pack their luggage efficiently and with less stress.

[0520] An example of a prompt message would be, "I'm trying to pack three days' worth of clothes for a business trip, but I'm feeling stressed about it. I'd like some easy and efficient packing suggestions."

[0521] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0522] Step 1:

[0523] The user enters package information using a terminal. This input can be in the form of specific text information (e.g., package name, quantity) or by taking and sending a photograph of the package. This input serves as basic data for subsequent processing, representing the package's physical characteristics and identification information.

[0524] Step 2:

[0525] Upon receiving the entered package information, the terminal activates its built-in emotion engine to analyze the user's emotional state. Specifically, it analyzes facial expressions captured by the camera and voice tone using the microphone, extracting the user's stress level and mood as numerical data. This provides data on the user's psychological state.

[0526] Step 3:

[0527] The terminal sends the acquired package information and sentiment data to the server. At this stage, the terminal uses an image processing algorithm to convert the shape and size of the package from the photo into text data. As output, a set of package characteristic data and user sentiment data is provided to the server.

[0528] Step 4:

[0529] The server uses the received data to analyze the packages. Specifically, it uses machine learning models to calculate the features of each package and performs calculations to classify similar packages and identify space-efficient placement methods. As output, it generates an optimal package placement plan.

[0530] Step 5:

[0531] The server creates packing suggestions tailored to the user based on analysis results and sentiment data. For users experiencing stress, it generates suggestions that include multiple options and simple steps. This step also prepares data for voice output and visual models.

[0532] Step 6:

[0533] The terminal receives packing suggestions sent from the server and presents them to the user. Specifically, it generates navigation voices using a speech synthesis engine or displays how to arrange luggage as augmented reality through a visual display. Based on this information, the user can pack their luggage efficiently.

[0534] (Application Example 2)

[0535] 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."

[0536] When considering efficient ways to pack goods, there is a challenge in proposing the optimal arrangement while taking into account the emotional state of the workers. It is necessary to solve this problem and provide efficient work procedures while reducing worker stress.

[0537] 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.

[0538] In this invention, the server includes an information processing device for inputting luggage information, a data processing device that performs processing to analyze the input information and identify physical characteristics, and an information processing device equipped with a sensor for recognizing emotional states. This makes it possible to suggest the optimal luggage placement according to the emotional state.

[0539] An "information processing device" is a device that inputs package information and sends and receives data between the user and the system.

[0540] A "data processing device" is a device that performs analysis and calculations based on input information, and is responsible for identifying physical characteristics and calculating the optimal placement.

[0541] A "sensor" is a device used to detect a worker's emotional state and measure their stress level and comfort level.

[0542] A "three-dimensional display device" is a device that visually presents the calculated packing method to the user, allowing them to intuitively understand the arrangement of their belongings.

[0543] An "emotion analysis device" is a device that analyzes the emotional state of a worker in detail and reflects that state as feedback into the system.

[0544] To carry out the present invention, a system comprising an information processing device, a data processing device, a sensor, a three-dimensional display device, and an emotion analysis device is used. Specifically, it is configured as follows.

[0545] The information processing device inputs package information and receives instructions from the user. This can include wearable devices such as smart glasses, and the input information is transmitted to a server.

[0546] The server processes the received information. The data processing unit analyzes the input information and performs calculations to identify the physical characteristics of the package. The sensor also detects emotional states and provides information to the emotion analysis device for analysis. This process utilizes image analysis software (e.g., OpenCV, TensorFlow) and emotion recognition APIs (e.g., Affectiva).

[0547] The server calculates the optimal luggage placement, including feedback on emotional state, and presents the results via a 3D display. This display is designed to be intuitively understandable to the user and is shown in real time on the smart glasses' display.

[0548] As a concrete example, the system can sense information about the size and shape of packages, as well as the stress levels of workers in a logistics center. For instance, if a worker is emotionally stressed, the server creates a simple and intuitive packing guide to alleviate the stress, which is then visually displayed on smart glasses.

[0549] An example of a prompt for a generated AI model is: "Design a system that suggests the optimal way to pack packages to minimize worker stress in a logistics center. It will provide real-time visual guidance to workers based on emotion sensors and package information."

[0550] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0551] Step 1:

[0552] The terminal uses smart glasses to input package information (size, shape, weight, etc.). Input is done using voice commands or an on-screen touch panel. The entered information is sent to a server as digital data.

[0553] Step 2:

[0554] The server receives package information transmitted from the terminal and begins analysis using a data processing device. Here, image analysis software (e.g., OpenCV) is used to identify the physical characteristics of the package. The output is a detailed dataset containing information about the type and shape of the package.

[0555] Step 3:

[0556] Sensors installed in the device acquire the user's emotional state in real time. An emotion recognition API (e.g., Affectiva) is used to measure emotional data such as tension and stress levels. The measurement results are sent to a server.

[0557] Step 4:

[0558] The server calculates the optimal placement of luggage based on luggage information and emotional data. In this process, a generative AI model is used to devise personalized packing methods, and a specific packing plan is generated as the output of the calculation.

[0559] Step 5:

[0560] The generated luggage placement plan is visually displayed on the smart glasses' screen via a 3D display device. Users can then review this plan and pack their luggage according to the instructions, enabling efficient work.

[0561] 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.

[0562] 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.

[0563] 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.

[0564] [Fourth Embodiment]

[0565] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0566] 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.

[0567] 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).

[0568] 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.

[0569] 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.

[0570] 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).

[0571] 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.

[0572] 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.

[0573] 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.

[0574] 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.

[0575] 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.

[0576] 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.

[0577] 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".

[0578] This invention is a system for efficiently packing luggage, aiming to assist users in making optimal packing arrangements when traveling. The system consists of a user terminal and a server.

[0579] First, the user uses a user terminal to enter information about the items they plan to pack. This information is collected through the creation of a text-based list and by taking photos of the items using the terminal's camera. The terminal then formats this information and prepares it for transmission to the server.

[0580] Next, the server receives package information from the user's terminal. The server uses analysis algorithms to identify the physical characteristics of the package, such as its type, shape, and weight. In particular, for image data, it automatically extracts information using object recognition technology.

[0581] Furthermore, the server calculates the optimal placement of luggage based on this data. Using an optimization algorithm, it proposes an efficient packing method that takes into account factors such as space conservation and center of gravity balance. The calculation results are then transmitted from the server to the user's terminal.

[0582] The terminal uses a three-dimensional display to visually present the optimal arrangement of luggage to the user based on calculation results received from the server. This allows the user to intuitively understand how to pack their luggage. The terminal can also provide packing instructions through voice guidance.

[0583] For example, suppose a traveler is packing clothes, electronic devices, and documents of different sizes and shapes into a suitcase for a business trip. In this case, the user takes pictures of these items with a device and sends them to a server. The server analyzes the data, calculates the optimal packing method, and provides it as a three-dimensional model. The device displays this suggestion, and the user can arrange the items in the suitcase accordingly.

[0584] In this way, by providing an efficient way to pack luggage, travelers can achieve comfortable and easy-to-travel packing. Due to its flexibility, this system can customize suggestions based on user feedback and accommodate individual needs.

[0585] The following describes the processing flow.

[0586] Step 1:

[0587] The user uses a user terminal to input the items they plan to pack, or takes photos of them with the camera. The terminal then converts this information into a digital format and prepares to transfer it to the server.

[0588] Step 2:

[0589] The server receives package information from the terminal. The received data is processed by an analysis algorithm to identify the physical characteristics of the package, such as its shape, weight, and type.

[0590] Step 3:

[0591] The server uses an optimization algorithm to calculate the optimal way to pack the luggage based on the identified luggage information. The calculation takes into account the luggage's space efficiency and center of gravity balance.

[0592] Step 4:

[0593] The server generates a three-dimensional model of the calculated packing method. This model visually shows the placement of the packages, organizing the data in a way that is easy for the user to understand.

[0594] Step 5:

[0595] The terminal displays the 3D model and related information received from the server on its screen. It provides voice guidance as needed and instructs the user on specific packing procedures.

[0596] Step 6:

[0597] Users pack their belongings according to the instructions and guidance displayed on the device. If necessary, they can adjust the packing method to their liking and input that information into the device.

[0598] Step 7:

[0599] The device sends user feedback to the server. The server uses this information to readjust the packing method, generates a new 3D model, and sends it back to the device.

[0600] (Example 1)

[0601] 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".

[0602] Traditional travel preparations have presented challenges in efficiently packing luggage and maximizing limited space. Furthermore, manually planning the optimal packing method, considering the shape and weight of luggage, is time-consuming and laborious. Additionally, the lack of sufficient visual and intuitive guidance on specific arrangements and procedures makes it difficult to pack effectively according to user needs.

[0603] 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.

[0604] In this invention, the server includes means for acquiring luggage information and converting it into an input format, calculation means for analyzing the acquired luggage information and determining its physical characteristics, and calculation means for performing optimization processing to calculate the optimal arrangement of luggage. This enables users to intuitively arrange luggage by automatically analyzing the most efficient way to pack it and displaying it in three dimensions.

[0605] A "user-use device that acquires luggage information and converts it into an input format" is a device that allows users to input details of the luggage they are carrying and then formats that data into a standardized format.

[0606] "A computational means for analyzing acquired package information and determining its physical characteristics" refers to a means of performing computational processing to identify physical characteristics such as the type, shape, and weight of a package from the input data.

[0607] "A computational means for performing an optimization process to calculate the optimal placement of luggage" refers to a means for calculating an efficient way to pack luggage, taking into account factors such as the center of gravity of the luggage and ease of retrieval, while making maximum use of available space.

[0608] A "three-dimensional visualization method" is a means of presenting a calculated arrangement of luggage to the user in a three-dimensional and visual manner, enabling intuitive understanding.

[0609] A "guidance system that provides instructions for luggage placement via voice" is a means of showing the user specific placement procedures via voice based on the calculated luggage placement results.

[0610] This invention is a support system for efficiently organizing and arranging users' belongings. The system primarily consists of a user terminal and a server. The following details an embodiment of this system.

[0611] First, when preparing for a trip, users use a user terminal to enter details of their luggage. Through the terminal's application, users can not only enter a luggage list as text, but also take photos of their luggage using the terminal's camera function. This information is formatted on the terminal and ready to be sent to the server.

[0612] Next, the server analyzes the received package information. Specifically, the server uses machine learning algorithms and object recognition technology to identify the physical characteristics of the package, such as its type, shape, and weight. This analysis generates the data necessary to determine the efficient placement of the packages.

[0613] Furthermore, the server calculates the optimal placement of the luggage based on the acquired physical characteristics. This optimization process utilizes a generative AI model to create placement plans that consider the balance of the luggage's center of gravity and the efficient use of space. The calculated placement plans are then sent to the user's terminal.

[0614] The terminal receives the suggested placement from the server and presents it visually to the user using 3D display technology. This allows the user to intuitively understand the suggested luggage placement. The terminal can also provide voice guidance and instruct the user on specific placement steps. Through this process, the user can actually place the luggage while referring to the suggestion, making preparation more efficient and less burdensome.

[0615] A concrete example is when a user needs to pack clothing, electronic devices, and documents of different shapes and sizes into a suitcase for a business trip. In such a scenario, the user can list or photograph their belongings on a device and arrange them in the suitcase based on the optimal packing method suggested by the system. This results in a space-saving and easily accessible arrangement of individual items.

[0616] An example of a prompt message is: "How can I most efficiently pack the following items into my suitcase? The items include a laptop, three shirts, a suit, and one pair of shoes." This prompt prompts the system to provide optimal placement suggestions for each type of item.

[0617] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0618] Step 1:

[0619] Users enter information about the luggage they will be taking on their trip into a user terminal. Input methods include entering a list of luggage as text or taking images with the terminal's camera. This entered data is converted to a unified format within the terminal. Text data is converted to CSV format, and image data to JPEG format. This prepares the data for transmission to the server.

[0620] Step 2:

[0621] The terminal sends formatted package information to the server. The data is sent over the network to the server, and a handshake process is performed to confirm successful reception. To maintain data security, the information is encrypted using SSL / TLS before transmission.

[0622] Step 3:

[0623] The server analyzes the received package information. Specifically, the server utilizes a generative AI model to automatically extract the physical characteristics of the package from the input data. For text data, a natural language processing algorithm is used, and for image data, an image analysis algorithm is applied to identify objects and estimate their shape and weight. The analysis results are stored in a database and used in the next step.

[0624] Step 4:

[0625] The server calculates the optimal placement of packages based on the analyzed package information. Using an optimization algorithm, it generates proposed package placements. This process considers space conservation and center of gravity balance to create an efficient placement plan. The calculation results are organized in a structured data format (e.g., JSON) and prepared as data for transmission to the terminal.

[0626] Step 5:

[0627] The server sends the calculation results to the user's terminal. In this process, as described above, the data is encrypted before transmission, ensuring that it is received accurately on the terminal. The transmitted data is used for 3D display on the terminal.

[0628] Step 6:

[0629] The terminal displays the package placement plan received from the server in three dimensions. The placement method is visually presented on the screen through the user interface, rendered in a way that is easy for the user to understand intuitively. The visually displayed package placement plan can be adjusted by the user as desired. The terminal also uses voice guidance to provide the user with detailed work procedures.

[0630] This will allow users to easily perform efficient and stress-free packing procedures by utilizing servers and terminals.

[0631] (Application Example 1)

[0632] 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".

[0633] In logistics operations, efficiently storing goods is crucial, but quickly and appropriately arranging items of diverse shapes and sizes places a significant burden on workers. Furthermore, human error and wasted time are common, often preventing efficient storage from being achieved. Technologies to address this are needed.

[0634] 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.

[0635] In this invention, the server includes a user terminal for inputting luggage information, a computing device that analyzes the input luggage information and performs processing to identify the physical characteristics of the luggage, a computing device that performs processing to calculate the packing method in order to optimize the arrangement of luggage, and an observation device that visually displays the arrangement information to support the worker's work. This makes it possible to support workers in efficiently storing luggage.

[0636] "Cargo information" refers to data used to identify the characteristics of various types of cargo involved in logistics, and includes attributes such as shape, size, and weight.

[0637] A "user terminal" is an information device used by logistics workers to input package information, and it has an interface for collecting and inputting data.

[0638] A "processing unit" is a computing device that processes input data and performs analysis and calculations, utilizing algorithms to identify physical properties and optimize placement.

[0639] A "spatial display means" is a device that visually presents the calculated packing method to the user, and provides it to the worker as three-dimensional visual information.

[0640] An "observation device" is a visual support device that allows workers to quickly understand the placement information of packages, and assists in efficient and accurate package storage on site.

[0641] The system for realizing this invention consists of a user terminal, a server, and an observation device used by the worker. This system collects, analyzes, optimizes, and provides instructions for efficiently storing packages.

[0642] Users use a user terminal to collect information about the items to be stored, utilizing scanners and cameras. This information is acquired as image and text data and transmitted to the server via the user terminal. The user terminal can use smart devices or similar as an interface for data collection.

[0643] The server uses image recognition software (e.g., Google Cloud Vision) on a computing device to analyze the received package information. This identifies attributes such as the shape, size, and weight of the package. Furthermore, based on this data, the computing unit uses optimization algorithm libraries such as SciPy to calculate efficient package storage patterns. The calculated results are generated as a three-dimensional model.

[0644] This three-dimensional model is transmitted to the worker's observation device (e.g., smart glasses) and visually presented by a spatial display system. The worker can then accurately position the materials on-site according to this presentation. Audio guidance is also used to support efficient placement.

[0645] As a concrete example, when workers at a logistics center load packages of various sizes and shapes onto trucks, they use smart glasses to scan the packages and send the data to a server. The server analyzes the attributes of the packages and calculates the optimal loading plan. Workers can then visually confirm this plan and accurately position the packages on the truck.

[0646] Examples of prompt statements to input into a generative AI model include the following:

[0647] "Please calculate the optimal arrangement for efficiently loading this cargo."

[0648] "Please provide a 3D rendering of a space-saving and safe way to store cargo in a truck."

[0649] This system is expected to improve the efficiency of cargo placement in logistics operations, leading to increased accuracy and speed in work.

[0650] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0651] Step 1:

[0652] Users input package information using a user terminal. Specifically, they use the terminal's camera to photograph or scan the package, collecting the data as image data. Input can include images of the package or a text list, and this data is collected.

[0653] Step 2:

[0654] The terminal formats the entered package information and prepares it for transmission to the server. During this process, image data is formatted to enable optimal analysis. Input consists of data captured or entered by the user, and output is the formatted data sent to the server.

[0655] Step 3:

[0656] The server analyzes the package information received from the terminal. Using image recognition software, it performs calculations to identify the physical characteristics of the package, such as its shape, size, and weight. The input is formatted image data, and the output is characteristic data.

[0657] Step 4:

[0658] The server uses an optimization algorithm to calculate the placement of identified packages based on their characteristic data. The computing unit performs numerous simulations to generate a plan that considers space-saving placement and center of gravity balance. The input is characteristic data, and the output is an optimized placement plan.

[0659] Step 5:

[0660] The server converts the calculated layout plan into a three-dimensional model that can be visually displayed. This model is sent to an observation device and presented to the operator. The input is optimized plan data, and the output is a three-dimensional model.

[0661] Step 6:

[0662] The terminal or observation device uses this three-dimensional model to provide visual guidance to the worker. The specific placement and order of packages are displayed through devices such as smart glasses, and instructions are given via voice guidance. The input is the three-dimensional model, and the output provides visual and voice instructions.

[0663] 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.

[0664] This invention provides a system for assisting with the efficient packing of luggage, offering packing suggestions that take the user's emotions into consideration. The system consists of a user terminal, a server, and an emotion engine.

[0665] The user enters luggage information or takes a photo of their luggage and sends it to the system using a user terminal. The terminal prepares this data to send to the server and, at the same time, uses its built-in emotion engine to recognize the user's current emotional state.

[0666] The server receives package information sent from the terminal and analyzes the data. This analysis includes identifying the physical characteristics and type of the package. Using feedback from the emotion engine, the server calculates the optimal packing method based on the user's emotional state. The emotion engine considers the user's stress level and comfort level, and adjusts suggestions to make the interaction as effective as possible.

[0667] In this system, the emotion engine, as a method to reduce user stress, can suggest multiple options for how to arrange luggage, if possible, and allow the user to choose. The system also presents packing advice through voice guidance and visual models, providing information in a way that is easy for the user to understand.

[0668] For example, suppose a user is preparing for a business trip and needs to pack their bags quickly. The emotion engine detects the user's stress level. In this case, the server calculates an intuitive and easy-to-follow packing procedure and provides it quickly through the terminal. The user can then follow the guide and pack their bags efficiently.

[0669] By incorporating an emotion engine, this system provides a more personalized experience that takes into account the user's motivation and emotional burden, resulting in a comfortable and efficient packing experience.

[0670] The following describes the processing flow.

[0671] Step 1:

[0672] The user uses a terminal to input information about the items they plan to pack or to take photos of the items. The terminal then formats this information and prepares it to be sent to the server. The terminal also uses a built-in emotion engine to recognize the user's emotional state from their facial expressions and voice.

[0673] Step 2:

[0674] The device sends user emotion data detected by the emotion engine, along with package information, to the server. Emotion data includes stress levels and changes in emotional state.

[0675] Step 3:

[0676] The server receives package information from the terminal and uses an analysis algorithm to identify the type, shape, and weight of the package. This clarifies its physical characteristics.

[0677] Step 4:

[0678] The server considers user emotion data obtained from the emotion engine and uses an optimization algorithm to calculate the best way to pack the luggage. It also adjusts the complexity of the procedure to reduce user stress.

[0679] Step 5:

[0680] The server generates an optimized packing method as a 3D model and an audio guide, and sends them to the user's terminal. The 3D model visually shows the placement of the items, and the audio guide verbally explains the packing procedure.

[0681] Step 6:

[0682] The terminal displays a 3D model and audio guidance received from the server, suggesting efficient ways to pack luggage to the user.

[0683] Step 7:

[0684] Users pack their belongings by following the on-screen instructions and voice guidance on the device. If necessary, users can provide feedback and further adjust their packing method.

[0685] Step 8:

[0686] The device sends user feedback to the server. Based on this information, the server recalculates the packing method as needed and sends new suggestions to the device.

[0687] (Example 2)

[0688] 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".

[0689] Conventional luggage packing assistance systems make packing suggestions without considering the user's emotional state, and may not be effective when used in stressful situations. In such circumstances, the challenge is to provide personalized suggestions that reflect the user's psychological state.

[0690] 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.

[0691] In this invention, the server includes terminal means for inputting luggage information and recognizing emotional states; information processing means for analyzing the input luggage information and performing processing to identify the characteristics of the luggage; information processing means for calculating packing methods to optimize luggage placement using the user's emotional data; and presentation means for displaying the calculated packing method audibly or visually. This enables the provision of optimal luggage placement suggestions based on the user's emotional state, resulting in less stressful and more efficient packing.

[0692] A "terminal device" is a device designed to input luggage information and recognize the user's emotional state.

[0693] An "information processing device" is a device that analyzes input luggage information, identifies the characteristics of the luggage, and further calculates the optimal luggage placement using emotional data.

[0694] "Presentation means" refers to a medium or device for presenting the calculated optimal packing method to the user, either audibly or visually.

[0695] "Emotional state" refers to data that indicates the user's psychological and physiological state, including information such as stress levels and mood.

[0696] "Characteristics" refers to physical characteristics related to the package, such as size, shape, and weight.

[0697] This invention comprises a device and method for assisting in the efficient packing of luggage. First, the user inputs luggage information using a user terminal. This can be done by text input through an interface provided by the terminal device, or by taking a photograph of the luggage and sending it to the terminal as image data.

[0698] The device uses a built-in emotion engine to recognize the user's emotional state. This involves collecting emotional data using facial recognition software and voice analysis technology to understand the user's stress level and psychological state.

[0699] When the server receives information sent from the terminal, it starts data analysis using a high-performance processor. Specifically, it uses machine learning algorithms to identify the physical properties of the luggage and calculate how to best arrange it. It also generates personalized packing suggestions based on the user's emotional state, using data obtained from the emotion engine.

[0700] The calculation results are provided to the user via a terminal. This presentation is done through audio guidance and visual models (e.g., visual displays using augmented reality technology), making it possible to practically demonstrate how to arrange luggage ideally.

[0701] For example, when a user is preparing for a business trip and needs to pack their luggage quickly, they can take a photo of their luggage and send it to the system. The terminal then detects the user's stress level. The server then calculates a packing procedure that can be quickly executed and provides an intuitively understandable guide. In this way, the user can pack their luggage efficiently and with less stress.

[0702] An example of a prompt message would be, "I'm trying to pack three days' worth of clothes for a business trip, but I'm feeling stressed about it. I'd like some easy and efficient packing suggestions."

[0703] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0704] Step 1:

[0705] The user enters package information using a terminal. This input can be in the form of specific text information (e.g., package name, quantity) or by taking and sending a photograph of the package. This input serves as basic data for subsequent processing, representing the package's physical characteristics and identification information.

[0706] Step 2:

[0707] Upon receiving the entered package information, the terminal activates its built-in emotion engine to analyze the user's emotional state. Specifically, it analyzes facial expressions captured by the camera and voice tone using the microphone, extracting the user's stress level and mood as numerical data. This provides data on the user's psychological state.

[0708] Step 3:

[0709] The terminal sends the acquired package information and sentiment data to the server. At this stage, the terminal uses an image processing algorithm to convert the shape and size of the package from the photo into text data. As output, a set of package characteristic data and user sentiment data is provided to the server.

[0710] Step 4:

[0711] The server uses the received data to analyze the packages. Specifically, it uses machine learning models to calculate the features of each package and performs calculations to classify similar packages and identify space-efficient placement methods. As output, it generates an optimal package placement plan.

[0712] Step 5:

[0713] The server creates packing suggestions tailored to the user based on analysis results and sentiment data. For users experiencing stress, it generates suggestions that include multiple options and simple steps. This step also prepares data for voice output and visual models.

[0714] Step 6:

[0715] The terminal receives packing suggestions sent from the server and presents them to the user. Specifically, it generates navigation voices using a speech synthesis engine or displays how to arrange luggage as augmented reality through a visual display. Based on this information, the user can pack their luggage efficiently.

[0716] (Application Example 2)

[0717] 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".

[0718] When considering efficient ways to pack goods, there is a challenge in proposing the optimal arrangement while taking into account the emotional state of the workers. It is necessary to solve this problem and provide efficient work procedures while reducing worker stress.

[0719] 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.

[0720] In this invention, the server includes an information processing device for inputting luggage information, a data processing device that performs processing to analyze the input information and identify physical characteristics, and an information processing device equipped with a sensor for recognizing emotional states. This makes it possible to suggest the optimal luggage placement according to the emotional state.

[0721] An "information processing device" is a device that inputs package information and sends and receives data between the user and the system.

[0722] A "data processing device" is a device that performs analysis and calculations based on input information, and is responsible for identifying physical characteristics and calculating the optimal placement.

[0723] A "sensor" is a device used to detect a worker's emotional state and measure their stress level and comfort level.

[0724] A "three-dimensional display device" is a device that visually presents the calculated packing method to the user, allowing them to intuitively understand the arrangement of their belongings.

[0725] An "emotion analysis device" is a device that analyzes the emotional state of a worker in detail and reflects that state as feedback into the system.

[0726] To carry out the present invention, a system comprising an information processing device, a data processing device, a sensor, a three-dimensional display device, and an emotion analysis device is used. Specifically, it is configured as follows.

[0727] The information processing device inputs package information and receives instructions from the user. This can include wearable devices such as smart glasses, and the input information is transmitted to a server.

[0728] The server processes the received information. The data processing unit analyzes the input information and performs calculations to identify the physical characteristics of the package. The sensor also detects emotional states and provides information to the emotion analysis device for analysis. This process utilizes image analysis software (e.g., OpenCV, TensorFlow) and emotion recognition APIs (e.g., Affectiva).

[0729] The server calculates the optimal luggage placement, including feedback on emotional state, and presents the results via a 3D display. This display is designed to be intuitively understandable to the user and is shown in real time on the smart glasses' display.

[0730] As a concrete example, the system can sense information about the size and shape of packages, as well as the stress levels of workers in a logistics center. For instance, if a worker is emotionally stressed, the server creates a simple and intuitive packing guide to alleviate the stress, which is then visually displayed on smart glasses.

[0731] An example of a prompt for a generated AI model is: "Design a system that suggests the optimal way to pack packages to minimize worker stress in a logistics center. It will provide real-time visual guidance to workers based on emotion sensors and package information."

[0732] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0733] Step 1:

[0734] The terminal uses smart glasses to input package information (size, shape, weight, etc.). Input is done using voice commands or an on-screen touch panel. The entered information is sent to a server as digital data.

[0735] Step 2:

[0736] The server receives package information transmitted from the terminal and begins analysis using a data processing device. Here, image analysis software (e.g., OpenCV) is used to identify the physical characteristics of the package. The output is a detailed dataset containing information about the type and shape of the package.

[0737] Step 3:

[0738] Sensors installed in the device acquire the user's emotional state in real time. An emotion recognition API (e.g., Affectiva) is used to measure emotional data such as tension and stress levels. The measurement results are sent to a server.

[0739] Step 4:

[0740] The server calculates the optimal placement of luggage based on luggage information and emotional data. In this process, a generative AI model is used to devise personalized packing methods, and a specific packing plan is generated as the output of the calculation.

[0741] Step 5:

[0742] The generated luggage placement plan is visually displayed on the smart glasses' screen via a 3D display device. Users can then review this plan and pack their luggage according to the instructions, enabling efficient work.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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."

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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 as being incorporated by reference.

[0764] The following is further disclosed regarding the embodiments described above.

[0765] (Claim 1)

[0766] A user terminal for entering luggage information,

[0767] A computing device that analyzes the input package information and performs a process to identify the physical characteristics of the package,

[0768] A computing device that performs a process to calculate the packing method in order to optimize the arrangement of luggage,

[0769] A three-dimensional display means for visually displaying the calculated packing method,

[0770] A system that includes this.

[0771] (Claim 2)

[0772] The system according to claim 1, which recalculates the packing method based on user feedback and generates an adjusted suggestion.

[0773] (Claim 3)

[0774] The system according to claim 1, further comprising image analysis means for identifying the type and shape of luggage from input luggage images.

[0775] "Example 1"

[0776] (Claim 1)

[0777] A user device that acquires luggage information and converts it into an input format,

[0778] A computational means for analyzing acquired package information and determining its physical characteristics,

[0779] A computing means for performing an optimization process to calculate the optimal placement of luggage,

[0780] A visualization means for displaying the calculated placement results in three dimensions,

[0781] A guidance system that provides voice instructions for luggage placement,

[0782] A system that includes this.

[0783] (Claim 2)

[0784] The system according to claim 1, which modifies the luggage arrangement plan based on user input and proposes the desired arrangement.

[0785] (Claim 3)

[0786] The system according to claim 1, which automatically identifies the attributes of a package from a package image obtained using an image analysis means.

[0787] "Application Example 1"

[0788] (Claim 1)

[0789] A user terminal for entering luggage information,

[0790] A computing unit that analyzes the input package information and performs a process to identify the physical characteristics of the package,

[0791] A computing device that performs processing to calculate the packing method in order to optimize the arrangement of luggage,

[0792] A spatial display means for visually displaying the calculated packing method,

[0793] An observation device that visually displays layout information to support the worker's work,

[0794] A system that includes this.

[0795] (Claim 2)

[0796] The system according to claim 1, which recalculates the packing method based on user feedback and generates an adjusted suggestion.

[0797] (Claim 3)

[0798] The system according to claim 1, further comprising image analysis means for identifying the type and shape of luggage from input luggage images.

[0799] "Example 2 of combining an emotion engine"

[0800] (Claim 1)

[0801] A terminal device for inputting luggage information and recognizing emotional state,

[0802] Information processing device means that analyzes input luggage information and performs processing to identify the characteristics of the luggage,

[0803] An information processing device that performs a process to calculate how to pack items in order to optimize the arrangement of items using user emotion data,

[0804] A presentation means for displaying the calculated packing method audibly or visually,

[0805] A system that includes this.

[0806] (Claim 2)

[0807] The system according to claim 1, which adjusts luggage placement suggestions to reduce user stress based on emotional data.

[0808] (Claim 3)

[0809] The system according to claim 1, further comprising means for identifying the characteristics of luggage from input luggage images and optimizing the packing method based on emotional data.

[0810] "Application example 2 when combining with an emotional engine"

[0811] (Claim 1)

[0812] An information processing device for inputting luggage information,

[0813] A data processing device that analyzes input information and performs a process to identify physical characteristics,

[0814] An information processing device equipped with a sensor for recognizing emotional states,

[0815] A data processing device that performs a process to calculate a packing method to optimize the arrangement of luggage according to the emotional state,

[0816] A three-dimensional display device for visually displaying the calculated packing method,

[0817] A system that includes this.

[0818] (Claim 2)

[0819] The system according to claim 1, which recalculates the packing method based on user feedback, generates adjusted suggestions, and applies feedback based on emotional state.

[0820] (Claim 3)

[0821] The system according to claim 1, comprising an image analysis device for identifying the type and shape from an input image, and an emotion analysis device for understanding the emotional state. [Explanation of symbols]

[0822] 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 user terminal for entering luggage information, A computing device that analyzes the input package information and performs a process to identify the physical characteristics of the package, A computing device that performs a process to calculate the packing method in order to optimize the arrangement of luggage, A three-dimensional display means for visually displaying the calculated packing method, A system that includes this.

2. The system according to claim 1, which recalculates the packing method based on user feedback and generates an adjusted suggestion.

3. The system according to claim 1, further comprising image analysis means for identifying the type and shape of luggage from input luggage images.

Images