system

Abstract

We provide the system. [Solution] A means of identifying necessary supplies based on the generated information, A means of generating images of identified materials and obtaining user confirmation, Means of delivering the confirmed supplies, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In base stations and related facilities, shortages of necessary materials and spare parts frequently occur, which often hinders the provision of services to users. This problem becomes particularly serious in situations where quick responses are required because the procedures for physical material confirmation, ordering, and delivery are complicated and time-consuming. There is also a problem that misrecognition of components and delivery troubles cause further delays, resulting in a decrease in user satisfaction.

Means for Solving the Problems

[0005] This invention provides a system for identifying necessary materials by utilizing generated information. This system generates images of the identified materials and presents them to the user, enabling visual confirmation. After obtaining user consent, it has a function to quickly arrange for the delivery of the materials. This reduces errors in identifying components and improves delivery efficiency. Furthermore, it provides a means to ensure continuous supply by managing inventory status and automatically placing orders with external suppliers if shortages are detected.

[0006] "Generated information" refers to data used to identify specific materials needed for activities and plans, based on user requests and historical data.

[0007] "Supplies" refers to all commercial and spare parts necessary for the operation and maintenance of base stations and facilities, and includes items such as power cables and communication equipment.

[0008] "Means of identification" refers to the internal system processes and functions that find the necessary supplies based on the generated information and propose them to the user.

[0009] "Generating an image" refers to the process of creating identifiable visual information for a specific object and providing that visual information to the user.

[0010] "User verification" refers to the act of users viewing images and information about generated materials to confirm whether their content is accurate.

[0011] "Means of delivery" refers to the procedures and technologies used to efficiently deliver agreed-upon goods to designated locations.

[0012] "Managing inventory" refers to the actions taken to constantly monitor the quantity and condition of goods held and to replenish or readjust inventory as needed.

[0013] "Placing additional orders with external suppliers" refers to the process of requesting additional supplies from external suppliers when inventory becomes insufficient. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, when an emotion engine is combined. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include 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.

[0018] In the following embodiments, a RAM (Random Access Memory) with a reference number is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a storage with a reference number is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention provides a system for quickly and accurately securing and delivering supplies in the operation of base stations and related facilities. This system is configured as follows.

[0036] First, to resolve situations where necessary supplies are in short supply, users use a terminal to input their requests for supplies. These requests include information such as the type and quantity of supplies and the required delivery date. This allows users to easily generate requests without having to go through cumbersome and detailed procedures.

[0037] Next, the server that receives the request uses generative AI to identify the necessary supplies based on this information. The server analyzes past data and inventory status to select the appropriate supplies. This process involves accessing databases and using machine learning algorithms.

[0038] The server then generates an image of the identified item, creating information for visual confirmation. This image accurately reflects the actual shape and color of the item, preventing misidentification.

[0039] Next, this image is sent to the user's device for verification. The user views the image on their device, confirms that the items have been accurately identified, and gives their consent. Consent is indicated by a button press on the device screen, and once approved, the process proceeds to the next stage.

[0040] When it comes to actually securing supplies, after an agreement is reached, the server automatically checks the inventory. If the supplies are in stock, they are immediately dispatched; if the inventory is insufficient, an automatic order is placed with an external supplier. This automation reduces human error and delays in the supply chain.

[0041] Finally, the secured supplies are arranged for delivery, and the server works with the delivery company to quickly deliver the supplies to the designated location. During this time, the delivery status is notified to the user's terminal in real time.

[0042] As a concrete example, consider a situation where a base station is short of a specific cable. The user enters the shortage information into their terminal and immediately submits a request. The server processes the data, identifies the appropriate cable, verifies it with an image, checks inventory, and promptly places an order and arranges for delivery. This ensures that the necessary supplies reach the base station quickly, minimizing the impact on users.

[0043] The objective of this invention is to provide a system that supports the operation of base stations and enables the rapid and reliable supply of materials through such a process.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The user selects the necessary supplies on their terminal and enters a request including the required quantity and delivery date. The entered request is sent, and the server prepares to process it.

[0047] Step 2:

[0048] The server analyzes the received request and checks the inventory status of the requested materials by referring to the relevant database. Based on the situation, it determines whether the necessary materials are in internal inventory or whether an additional order is required.

[0049] Step 3:

[0050] Once the server identifies the items, it uses a generation AI to generate images of the identified items. These generated images are then prepared as data for user verification.

[0051] Step 4:

[0052] The terminal receives images from the server and presents them to the user. This allows the user to visually verify the materials and provides a function to select approval or request corrections on the confirmation screen.

[0053] Step 5:

[0054] The user reviews images and information about the supplies on their device, and if there are no issues, approves the agreement. The approval information is then sent from the device to the server.

[0055] Step 6:

[0056] After confirming the user's agreement, the server performs a final inventory check. If inventory is available, it immediately initiates the dispatch process; if inventory is insufficient, it automatically places an order with an external supplier.

[0057] Step 7:

[0058] The server completes the arrangements for outbound or inbound shipments and handles the delivery procedures for the goods. Delivery arrangement information is tracked in real time, and the user's terminal is notified of the progress.

[0059] Step 8:

[0060] The user confirms the arrival of the supplies and begins the actual work after receiving them. Once the work is completed, they send feedback to the system, and the entire process is finished.

[0061] (Example 1)

[0062] 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."

[0063] In resource supply processes, it is essential to quickly and accurately identify materials, manage inventory levels, and minimize human error and delays at each step from ordering from external sources to delivery. Conventional systems require users to spend a great deal of time selecting and verifying materials, and there is a lack of timely information sharing throughout the supply chain. Therefore, it is necessary to effectively solve these problems.

[0064] 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.

[0065] In this invention, the server includes means for receiving request information entered by a user and utilizing a generative AI model to identify the necessary resources based on this information; means for generating visual information about the identified resources and allowing the user to visually confirm them; and means for managing inventory status, automatically placing orders with external suppliers as needed, and delivering resources after user confirmation has been obtained. This enables improved efficiency and accuracy in the resource supply process.

[0066] A "user" is an individual or group that inputs resource request information and interacts with the system through the information provided and visual confirmations.

[0067] A "generative AI model" is an artificial intelligence system used to analyze input request information and identify the most suitable resources.

[0068] "Visual information" refers to image information that clearly represents the shape, color, and other characteristics of a specific resource.

[0069] "Inventory status" refers to information that indicates the current amount and condition of resources held, and is used to determine supply plans.

[0070] "External sources" refer to external manufacturers, vendors, and other entities used to procure resources that are lacking within the system.

[0071] "Automatic ordering" refers to the process of automatically ordering necessary resources from external suppliers based on inventory levels.

[0072] "Delivery method" refers to the method or system used to transport specific resources to a designated location.

[0073] "Real-time notification" is a method of providing information that informs users in real time about changes in resource delivery processes and inventory status.

[0074] This invention demonstrates how servers, terminals, and users interact within a resource supply system. This system uses a generative AI model to automate the process of identifying necessary resources from input request information.

[0075] First, the user enters resource request information through a terminal. This terminal has a user interface and allows the user to input data including the type of resource, quantity, and required delivery date. This request information is then sent to the server.

[0076] Next, the server inputs the received request information into a generative AI model to identify resources suitable for the request. The server is equipped with a database, which uses machine learning algorithms to analyze data, referencing historical data and inventory status. In this process, the generative AI model is used, and specific resources are identified by prompt statements.

[0077] Information about identified resources is generated as visual information from the server. This visual information accurately reflects the shape and color of the resources to prevent misidentification. The generated visual information is sent to the user's terminal and provided for verification.

[0078] The user reviews the visual information received via the device to confirm that the resources are accurate. This verification process allows the system to proceed to the next stage.

[0079] As a concrete example, consider a situation where a specific cable is in short supply at a certain facility. The user enters information about the cable type on a terminal and submits a request. The server uses a generative AI model to identify the appropriate cable and generates an image of it. The user reviews the image and approves the resource identification. After approval, the server automatically checks inventory, places an order with an external supplier if necessary, and proceeds with the delivery process.

[0080] An example of a prompt message for the generating AI model in this system is: "Identify the ID of a resource that matches this condition. Example: Cable X-500, red, 20 meters." This prompt is used as input data for the AI ​​model to function properly in identifying resources.

[0081] This invention enables rapid resource supply and accurate identification by having servers and terminals work together in a highly coordinated manner, allowing users to easily manage resources.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The user enters resource request information through a terminal. This input includes specific data such as the type of resource, quantity, and required delivery date. The terminal prepares to send this data to the server. The entered data serves as basic information necessary for subsequent processes in the system.

[0085] Step 2:

[0086] The server receives request information sent from the terminal. Based on this input data, it uses a generative AI model to identify the optimal resource. It sends a prompt to the generative AI asking, "Identify the ID of the resource that matches these conditions," and obtains the resource ID as output. The server uses this ID to search for additional data about the resource's characteristics.

[0087] Step 3:

[0088] The server generates visual information based on the identified resource's information. In this process, it retrieves detailed information such as the resource's shape and color from the database and outputs an accurate image. The generated visual information is stored on the server in a format easily accessible to the user.

[0089] Step 4:

[0090] Visual information generated from the server is sent to the user's terminal. The terminal displays this image on its screen and requests visual confirmation from the user. The image display function is appropriately adjusted so that the user can accurately recognize this information.

[0091] Step 5:

[0092] The user reviews the visual information on their device and scrutinizes the accuracy of the resources. Once agreement is reached, they press the confirmation button on their device to send a notification to the server. This action signals the system to proceed to the next process.

[0093] Step 6:

[0094] The server receives the user's confirmation notification and begins checking the inventory status. If the item is in stock, it immediately arranges for dispatch; otherwise, it automatically places an order with an external supplier. The order details are generated as output and sent to the supplier if necessary.

[0095] Step 7:

[0096] The server arranges delivery and manages its progress in real time. Delivery information is notified to the user's device, and the delivery status is displayed. Finally, the user receives the estimated delivery date and arrival confirmation on their device. This completes the resource supply process, and the user receives the resources.

[0097] (Application Example 1)

[0098] 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."

[0099] In the supply chain process, human error and delays in verification are common, which reduces the efficiency of the entire supply chain. In particular, accurate material management is required in manufacturing and other industrial settings, but traditional methods have limitations in terms of supply accuracy and responsiveness.

[0100] 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.

[0101] In this invention, the server includes means for identifying necessary supplies based on generated information, means for generating images of the identified supplies and obtaining user confirmation, means for supplying the confirmed supplies using an automated transport mechanism, and means for notifying the user's terminal of the real-time status of the supplied supplies. This streamlines the supply process and enables accurate and rapid supply.

[0102] "Generated information" refers to data used to identify necessary supplies based on request data entered by the user.

[0103] "Supplies" is a general term for the various materials and products required within a supply chain.

[0104] A "generative AI model" is a technology that uses machine learning algorithms to generate information from specific data and is used to identify materials.

[0105] "Image generation" is the process of creating image data to facilitate the visual identification of identified materials.

[0106] An "automated moving mechanism" is a system that utilizes robots and conveyor systems to supply specific materials to designated locations.

[0107] "Real-time status" refers to information that immediately monitors and transmits the current status and progress in the supply chain process.

[0108] "User terminal" refers to an electronic device used by the user to send and receive information and to verify and manage goods.

[0109] The system that implements this application is designed to automate material supply in manufacturing plants. The server analyzes the material requests received from the user's terminal and uses a generative AI model to identify the necessary materials. The generative AI model used here identifies the optimal type and quantity of requested materials based on historical data and inventory information.

[0110] The server generates images to facilitate visual confirmation of identified items. These images accurately reflect the shape and color of the items and are designed to prevent misidentification. The generated images are sent to the user's device, where the user can verify the items.

[0111] Once the supplies have been confirmed, the server controls an automated movement mechanism to deliver the necessary supplies to the appropriate location. Robotics and conveyor systems are utilized in this process. The progress of the supply process is monitored in real time and notified to the user's terminal. The main contents of the notification are the supply start time, the status in progress, and the supply completion time.

[0112] As a concrete example, consider a situation where a factory's production line is short of parts. The user uses a terminal to specify the missing parts and inputs the quantity and delivery date. The server then automatically selects the appropriate parts and can supply them quickly. Delays and errors in this supply process are minimized, improving production efficiency.

[0113] An example of a prompt message would be: "We are short on a specific part in the factory. Please tell us the name and quantity of the part. Also, if it is urgent, please specify the delivery date."

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

[0115] Step 1:

[0116] Users enter their material requests from a terminal. The data entered includes information such as the type and quantity of materials and the required delivery date. This information is sent to the server and forms the basis of the request.

[0117] Step 2:

[0118] Based on the received request, the server uses a generative AI model to identify the necessary supplies. In this step, historical data and inventory information are retrieved from the database and analyzed to identify the optimal supplies. As a result, the selected supplies are output.

[0119] Step 3:

[0120] The server uses the information of the identified items to generate a visual confirmation image. The generated image is visual data that accurately reproduces the shape and color of the items to prevent user misidentification. This image is obtained as the output of the generation process.

[0121] Step 4:

[0122] The server sends the generated supply images to the user's terminal. The user reviews the images on their terminal and determines whether the supplies are correct. User verification is performed through the terminal's GUI.

[0123] Step 5:

[0124] Once confirmation is received, the server controls the automated movement mechanism to supply the goods. This step involves checking inventory and using movement systems such as robots and conveyors to deliver the goods to the appropriate locations. It also includes checking inventory data and placing external orders if there are any shortages.

[0125] Step 6:

[0126] The server monitors the progress of the supply process in real time and notifies the user's terminal of its status. This notification includes the start time, ongoing status, and completion time of the supply, allowing the user to understand the entire process. The server uses supply status monitoring data as input and generates user notification data as output.

[0127] 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.

[0128] This invention provides a system for the rapid and efficient supply of necessary materials, and by taking user emotions into consideration, it enables the provision of better service. The system is configured as follows:

[0129] First, the user enters the shortage of supplies using a terminal. Here, they specify the type and quantity of supplies needed, as well as the desired delivery date. This request is sent to the server and processed.

[0130] Next, the server analyzes the received request data to identify the necessary supplies. Using generative AI technology, the server makes appropriate decisions based on past data and current inventory levels. During this process, the emotion engine references the user's past emotional data, taking into account the user's stress level and satisfaction level.

[0131] Once the items have been identified, the server generates an image of them. AI technology is used in this image generation process, and information is prepared to facilitate visual recognition. This image is then presented to the user via their device.

[0132] The device displays images to the user and provides a process to verify the accuracy of the supplies. The user reviews the presented information and agrees. During this agreement process, an emotion engine analyzes the user's emotions in real time and provides additional instructions or suggestions as needed.

[0133] Once the supplies have been confirmed, the server checks the inventory. If necessary, it automatically places orders with external suppliers. After securing the supplies, arrangements for delivery are made. During the delivery process, the emotion engine evaluates the user's emotions regarding the delivery status and provides appropriate notifications and follow-up.

[0134] For example, if a user reports a shortage of a specific communication cable, the server quickly identifies the cable and verifies it with images. If the user verifies and the emotion engine detects stress, priority delivery arrangements and enhanced support are made. This increases user satisfaction and achieves efficient supply of goods.

[0135] Thus, the invention provides a comprehensive material supply system that takes user emotions into consideration, thereby improving service quality.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] The user uses a terminal to enter requests for supplies that are lacking or needed. The request includes the type of supply, quantity, priority, and desired delivery date. The entered data is sent to the server.

[0139] Step 2:

[0140] The server analyzes the received request, consults the system's database to check the inventory status of the requested supplies, and simultaneously uses a generative AI to select the necessary supplies.

[0141] Step 3:

[0142] The server uses AI technology to generate visual confirmation images for identified items. The generated images should include detailed information about the items (e.g., model number, size, etc.).

[0143] Step 4:

[0144] The terminal displays images and material information sent from the server to the user. The user reviews the displayed information, judges its accuracy, and approves or requests corrections.

[0145] Step 5:

[0146] Upon receiving user confirmation, the server initiates a process to perform a final inventory check and, if necessary, automatically place orders with external suppliers.

[0147] Step 6:

[0148] Once the server confirms that the requested supplies have been secured, it will arrange for delivery. Delivery information is tracked in real time, and the user's terminal is notified of the progress.

[0149] Step 7:

[0150] The emotion engine embedded in the server analyzes user reactions and responses, and based on the emotion data, makes suggestions for improving the content and services offered.

[0151] Step 8:

[0152] The user receives the supplies and begins work after confirming receipt. After completing the work, they send feedback to the system, and data is collected to assess user satisfaction.

[0153] (Example 2)

[0154] 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".

[0155] In managing the supply chain of goods, there are challenges in optimizing effective supply methods that take into account user emotions and in ensuring rapid inventory replenishment. Furthermore, in the goods verification process, discrepancies between user requests and inventory information lead to decreased satisfaction. Addressing these challenges, reducing user stress, and improving satisfaction are crucial.

[0156] 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.

[0157] This invention includes a server that identifies necessary supplies based on generated information and optimizes the supply method considering the user's emotional data; a server that generates images of the identified supplies using AI technology and obtains user confirmation; and a server that analyzes the user's emotions during the delivery process and provides necessary notifications and follow-up. This enables rapid and accurate supply and inventory management of supplies that takes the user's emotions into consideration.

[0158] "Generated information" refers to the request data provided by the user and the analysis data based on that data, which serves as the basis for making decisions necessary for supplying goods.

[0159] "Required supplies" refers to the types and quantities of supplies identified by the user's requests and the system.

[0160] "User sentiment data" refers to the quantified or data-based representation of past and present user emotional responses, used to optimize delivery methods.

[0161] "Optimizing supply methods" is the process of determining the optimal supply route and timing for goods, taking into account the type of goods, the supply and demand balance, and user sentiment data.

[0162] "Images of materials generated using AI technology" refers to digital images generated by artificial intelligence based on the details and characteristics of materials, which assist users in visual verification.

[0163] "Re-evaluating inventory levels" is a process of verifying current inventory data against the latest information to determine the need for supply.

[0164] "Automatic ordering" is a function in which the system automatically places orders for necessary supplies from external suppliers when it detects a shortage in inventory.

[0165] "Analyzing user emotions during the delivery process" refers to activities that evaluate user emotional data in real time during the delivery of goods and use that data to improve the supply process.

[0166] "Notifications and follow-ups" refer to procedures and communications aimed at providing users with information and additional support for problem-solving.

[0167] To implement this invention, a server, a terminal, a generative AI model, and an emotion engine are required. The server is responsible for the main processing, utilizing the generative AI model to analyze historical data and current inventory status to identify materials and optimize supply methods. The emotion engine is used to analyze user emotion data in real time to aid in supply decision-making. Furthermore, AI technology is used to generate visual information of materials as images and present them to the user via the terminal.

[0168] The terminal provides an interface for users to input requests for supplies, receives generated images from the server, and presents them visually to the user. It also has the function to send feedback to the server based on the user's selections and confirmations.

[0169] Users input shortages of supplies via a terminal. The entered information is sent to a server where it is analyzed. After user confirmation, the server automatically places orders with external suppliers if necessary, and handles inventory management and delivery arrangements. The server also monitors the user's emotions during delivery and provides appropriate notifications and follow-ups through the terminal.

[0170] As a concrete example, suppose a user enters "I need 5 communication cables" into their terminal and specifies a delivery date. The server analyzes this request, checks inventory using a generative AI model, and places a new order if necessary. The generated image is sent to the terminal for the user to confirm. At this time, the user's emotional data is analyzed, and if stress is detected, priority delivery or support arrangements are made. An example of a prompt message could be, "I have received a report of a communication cable shortage; please prioritize this."

[0171] This invention aims to improve service quality by enabling dynamic supply of goods based on user requests and emotions.

[0172] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0173] Step 1:

[0174] The user accesses the terminal interface and enters the type of out-of-stock item, the required quantity, and the desired delivery date. This information is organized into data packets by the terminal and sent to the server. The more specific the information entered, the more efficient the analysis can be.

[0175] Step 2:

[0176] The server analyzes the data packets received from the user and uses a generating AI model to identify the necessary supplies based on historical data and current inventory levels. This data processing involves comparing the data with similar past request data and cross-referencing it with figures in the inventory management system. The identified supply information is then used for the following processes.

[0177] Step 3:

[0178] The server uses AI technology to generate images of the identified materials. It utilizes the material's specifications and detailed information as input data for image generation, outputting it in a visual format that the user can review. The generated images are then transferred to the terminal.

[0179] Step 4:

[0180] The device displays the received image to the user and prompts them to confirm if it matches the requested item. The user examines the displayed image and, if there is a mismatch, sends feedback to the server via the device. Once confirmation is received, the data is sent to the server as an approved status.

[0181] Step 5:

[0182] The server receives user confirmation data and re-evaluates the inventory status. If inventory is insufficient, it generates a prompt message and sends the order information to the supply system to automatically place an order with an external supplier. The output will appear as a notification of order completion or inventory availability.

[0183] Step 6:

[0184] During the delivery arrangement phase, the server analyzes the user's emotions in real time and provides appropriate notifications and follow-up information as the delivery progresses. It analyzes emotional data as input and outputs actions as needed, including providing alternatives and support in case of delays. It also provides progress notifications via the terminal.

[0185] (Application Example 2)

[0186] 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".

[0187] Traditional supply systems have struggled to respond flexibly to the emotional state of users, resulting in a lack of sufficient improvement in user satisfaction. In particular, services requiring immediate responses, such as food delivery, demand service provision that considers the user's emotions, but there has been a lack of means to achieve this.

[0188] 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.

[0189] In this invention, the server includes means for identifying necessary supplies based on generated information, means for generating images of the identified supplies and obtaining confirmation that takes into account the user's emotional state, and means for delivering the confirmed supplies with adjusted delivery priority based on emotion analysis. This enables adaptive supply of supplies that takes into account the user's emotions.

[0190] "Generated information" refers to data that is analyzed by the system and used to identify materials and provide them to users.

[0191] "Goods" refers to the items or services that users request to be supplied.

[0192] "Emotional state" refers to information that indicates the user's psychological reactions and emotions, and its analysis enables individualized responses.

[0193] "Means of obtaining confirmation" refers to the process of presenting information to users and obtaining their agreement or consent.

[0194] "Delivery priority" is an evaluation metric used to determine the order and speed of delivery of goods, and is adjusted based on the results of user sentiment analysis.

[0195] "Emotional analysis" refers to a technology that analyzes a user's emotional state as data and uses it for various system controls and feedback.

[0196] The system that implements this application works in cooperation with the user, terminal, and server. The user can use their smartphone to order goods, such as food, and input their desired delivery date at that time. Once the order is complete, the terminal sends this information to the server.

[0197] Based on the received information, the server uses a generative AI model to perform necessary data analysis and identify the items. This process involves analyzing big data, including the user's past history and current inventory status, to achieve efficient and accurate item identification. An image of the identified item is generated using AI technology and sent to the terminal.

[0198] The device visually presents the user with images of the generated items and prompts them to confirm. At the same time, the device utilizes an emotion analysis API (e.g., Microsoft® Emotion API) to evaluate the user's real-time emotional state. For example, if the user is experiencing high stress levels, it may present a special message or discount. This makes it possible to provide an emotionally sensitive user experience.

[0199] Once the supplies are identified and confirmed, the server arranges delivery. Based on information obtained from sentiment analysis, delivery priorities are adjusted; for example, users with high sentiment scores receive expedited delivery. Once delivery begins, the server also provides appropriate notifications regarding the delivery status, taking into account the user's sentiment.

[0200] (Specific example)

[0201] For example, if a user orders pasta at lunchtime and places the order via smartphone, and the system determines that their stress level is high, a message such as "We'll deliver it early today so you can relax!" will be displayed on the device.

[0202] (Example of prompt text to input to the generated AI model)

[0203] "Based on the user's ordered dish name, image, delivery date, and sentiment data, generate the optimal delivery method and additional message."

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] The user places an order for supplies by operating a terminal. The input includes the name, quantity, and delivery date of the specified supplies. The terminal then sends this information to the server.

[0207] Step 2:

[0208] The server analyzes the received order information. The input is the order information from the user, and the output is the analysis result. The server uses a generative AI model to refer to past user data and inventory information to identify the necessary supplies. In this process, data analysis technology is utilized to efficiently identify the supplies.

[0209] Step 3:

[0210] The server uses AI generation technology to create images of identified items. The input is data of the identified items, and the output is image data. The generated images are sent to the user's terminal for visual confirmation.

[0211] Step 4:

[0212] The device presents images to the user and evaluates the user's emotional state in real time. Input is image data from the server, and output is the user's response confirmation and emotional evaluation. Using an emotional analysis API, it retrieves emotional data such as the user's stress level and displays special messages or discounts as needed.

[0213] Step 5:

[0214] Once the user completes the image verification, the verification result is sent to the server. The input is the user's acknowledgment, and the output is the transmission of that result to the server.

[0215] Step 6:

[0216] The server arranges delivery for confirmed supplies. Inputs are the final confirmed order data and the user's sentiment rating; output is the delivery plan. Based on the sentiment analysis results, the server adjusts delivery priorities and, if necessary, prioritizes delivery.

[0217] Step 7:

[0218] The server also evaluates the user's emotions regarding goods in transit and sends appropriate notifications to the device. Inputs are delivery status and emotion evaluation data, while output is a notification message to the device. This provides users with a sense of security and improves their satisfaction.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] [Second Embodiment]

[0223] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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".

[0235] This invention provides a system for quickly and accurately securing and delivering supplies in the operation of base stations and related facilities. This system is configured as follows.

[0236] First, to resolve situations where necessary supplies are in short supply, users use a terminal to input their requests for supplies. These requests include information such as the type and quantity of supplies and the required delivery date. This allows users to easily generate requests without having to go through cumbersome and detailed procedures.

[0237] Next, the server that receives the request uses generative AI to identify the necessary supplies based on this information. The server analyzes past data and inventory status to select the appropriate supplies. This process involves accessing databases and using machine learning algorithms.

[0238] The server then generates an image of the identified item, creating information for visual confirmation. This image accurately reflects the actual shape and color of the item, preventing misidentification.

[0239] Next, this image is sent to the user's device for verification. The user views the image on their device, confirms that the items have been accurately identified, and gives their consent. Consent is indicated by a button press on the device screen, and once approved, the process proceeds to the next stage.

[0240] When it comes to actually securing supplies, after an agreement is reached, the server automatically checks the inventory. If the supplies are in stock, they are immediately dispatched; if the inventory is insufficient, an automatic order is placed with an external supplier. This automation reduces human error and delays in the supply chain.

[0241] Finally, the secured supplies are arranged for delivery, and the server works with the delivery company to quickly deliver the supplies to the designated location. During this time, the delivery status is notified to the user's terminal in real time.

[0242] As a concrete example, consider a situation where a base station is short of a specific cable. The user enters the shortage information into their terminal and immediately submits a request. The server processes the data, identifies the appropriate cable, verifies it with an image, checks inventory, and promptly places an order and arranges for delivery. This ensures that the necessary supplies reach the base station quickly, minimizing the impact on users.

[0243] The objective of this invention is to provide a system that supports the operation of base stations and enables the rapid and reliable supply of materials through such a process.

[0244] The following describes the processing flow.

[0245] Step 1:

[0246] The user selects the necessary supplies on their terminal and enters a request including the required quantity and delivery date. The entered request is sent, and the server prepares to process it.

[0247] Step 2:

[0248] The server analyzes the received request and checks the inventory status of the requested materials by referring to the relevant database. Based on the situation, it determines whether the necessary materials are in internal inventory or whether an additional order is required.

[0249] Step 3:

[0250] Once the server identifies the items, it uses a generation AI to generate images of the identified items. These generated images are then prepared as data for user verification.

[0251] Step 4:

[0252] The terminal receives images from the server and presents them to the user. This allows the user to visually verify the materials and provides a function to select approval or request corrections on the confirmation screen.

[0253] Step 5:

[0254] The user reviews images and information about the supplies on their device, and if there are no issues, approves the agreement. The approval information is then sent from the device to the server.

[0255] Step 6:

[0256] After confirming the user's agreement, the server performs a final inventory check. If inventory is available, it immediately initiates the dispatch process; if inventory is insufficient, it automatically places an order with an external supplier.

[0257] Step 7:

[0258] The server completes the arrangements for outbound or inbound shipments and handles the delivery procedures for the goods. Delivery arrangement information is tracked in real time, and the user's terminal is notified of the progress.

[0259] Step 8:

[0260] The user confirms the arrival of the supplies and begins the actual work after receiving them. Once the work is completed, they send feedback to the system, and the entire process is finished.

[0261] (Example 1)

[0262] 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."

[0263] In resource supply processes, it is essential to quickly and accurately identify materials, manage inventory levels, and minimize human error and delays at each step from ordering from external sources to delivery. Conventional systems require users to spend a great deal of time selecting and verifying materials, and there is a lack of timely information sharing throughout the supply chain. Therefore, it is necessary to effectively solve these problems.

[0264] 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.

[0265] In this invention, the server includes means for receiving request information entered by a user and utilizing a generative AI model to identify the necessary resources based on this information; means for generating visual information about the identified resources and allowing the user to visually confirm them; and means for managing inventory status, automatically placing orders with external suppliers as needed, and delivering resources after user confirmation has been obtained. This enables improved efficiency and accuracy in the resource supply process.

[0266] A "user" is an individual or group that inputs resource request information and interacts with the system through the information provided and visual confirmations.

[0267] A "generative AI model" is an artificial intelligence system used to analyze input request information and identify the most suitable resources.

[0268] "Visual information" refers to image information that clearly represents the shape, color, and other characteristics of a specific resource.

[0269] "Inventory status" refers to information that indicates the current amount and condition of resources held, and is used to determine supply plans.

[0270] "External sources" refer to external manufacturers, vendors, and other entities used to procure resources that are lacking within the system.

[0271] "Automatic ordering" refers to the process of automatically ordering necessary resources from external suppliers based on inventory levels.

[0272] "Delivery method" refers to the method or system used to transport specific resources to a designated location.

[0273] "Real-time notification" is a method of providing information that informs users in real time about changes in resource delivery processes and inventory status.

[0274] This invention demonstrates how servers, terminals, and users interact within a resource supply system. This system uses a generative AI model to automate the process of identifying necessary resources from input request information.

[0275] First, the user enters resource request information through a terminal. This terminal has a user interface and allows the user to input data including the type of resource, quantity, and required delivery date. This request information is then sent to the server.

[0276] Next, the server inputs the received request information into a generative AI model to identify resources suitable for the request. The server is equipped with a database, which uses machine learning algorithms to analyze data, referencing historical data and inventory status. In this process, the generative AI model is used, and specific resources are identified by prompt statements.

[0277] Information about identified resources is generated as visual information from the server. This visual information accurately reflects the shape and color of the resources to prevent misidentification. The generated visual information is sent to the user's terminal and provided for verification.

[0278] The user reviews the visual information received via the device to confirm that the resources are accurate. This verification process allows the system to proceed to the next stage.

[0279] As a concrete example, consider a situation where a specific cable is in short supply at a certain facility. The user enters information about the cable type on a terminal and submits a request. The server uses a generative AI model to identify the appropriate cable and generates an image of it. The user reviews the image and approves the resource identification. After approval, the server automatically checks inventory, places an order with an external supplier if necessary, and proceeds with the delivery process.

[0280] An example of a prompt message to the generating AI model in this system is: "Identify the ID of a resource that matches this condition. Example: Cable X-500, red, 20 meters." This prompt is used as input data for the AI ​​model to function properly in identifying resources.

[0281] This invention enables rapid resource supply and accurate identification by having servers and terminals work together in a highly coordinated manner, allowing users to easily manage resources.

[0282] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0283] Step 1:

[0284] The user inputs resource request information through the terminal. This input includes specific data such as the type of resource, quantity, required delivery date, etc. The terminal prepares to send this data to the server. The input data functions as the basic information required for the subsequent processes of the system.

[0285] Step 2:

[0286] The server receives the request information sent from the terminal. Based on this input data, it uses a generative AI model to identify the optimal resources. As a prompt sentence, a question "Please identify the resource ID that matches this condition" is sent to the generative AI, and the resource ID is obtained as the output. The server uses this ID to search for additional data regarding the characteristics of the resources.

[0287] Step 3:

[0288] The server generates visual information based on the identified resource information. In this process, detailed information such as the shape and color of the resource is obtained from the database, and an accurate image is output. The generated visual information is retained in the server in a format that can be easily confirmed by the user.

[0289] Step 4:

[0290] The visual information generated by the server is sent to the user's terminal. The terminal displays this image on the display and requests visual confirmation from the user. The image display function is appropriately adjusted so that the user can accurately recognize this information.

[0291] Step 5:

[0292] The user checks the visual information on the terminal and verifies the accuracy of the resources. When an agreement is reached, the user operates the confirmation button on the terminal to send a notification to the server. By this operation, the system receives a signal to proceed to the next process.

[0293] Step 6:

[0294] The server receives the user's confirmation notification and begins checking the inventory status. If the item is in stock, it immediately arranges for dispatch; otherwise, it automatically places an order with an external supplier. The order details are generated as output and sent to the supplier if necessary.

[0295] Step 7:

[0296] The server arranges delivery and manages its progress in real time. Delivery information is notified to the user's device, and the delivery status is displayed. Finally, the user receives the estimated delivery date and arrival confirmation on their device. This completes the resource supply process, and the user receives the resources.

[0297] (Application Example 1)

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

[0299] In the supply chain process, human error and delays in verification are common, which reduces the efficiency of the entire supply chain. In particular, accurate material management is required in manufacturing and other industrial settings, but traditional methods have limitations in terms of supply accuracy and responsiveness.

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

[0301] In this invention, the server includes means for identifying necessary supplies based on generated information, means for generating images of the identified supplies and obtaining user confirmation, means for supplying the confirmed supplies using an automated transport mechanism, and means for notifying the user's terminal of the real-time status of the supplied supplies. This streamlines the supply process and enables accurate and rapid supply.

[0302] "Generated information" refers to data used to identify necessary materials based on the requested data input by the user.

[0303] "Materials" is a general term for various materials and products required within the supply chain.

[0304] "Generative AI model" is a technology that uses machine learning algorithms to generate information from specific data and is used to identify materials.

[0305] "Image generation" is a process of creating image data to facilitate visual confirmation of identified materials.

[0306] "Automated movement mechanism" is a system that utilizes robots or conveyor systems to supply determined materials to designated locations.

[0307] "Real-time situation" refers to information that immediately monitors and transmits the current status and progress of the material supply process.

[0308] "User's terminal" is an electronic device used by the user to send and receive information and to confirm and manage materials.

[0309] The system for realizing this application example is designed to automate the material supply in the manufacturing site. The server analyzes the requirements of the materials received from the user's terminal and identifies the necessary materials using the generative AI model. The generative AI model used here identifies the optimal types and quantities of the requested materials based on past data and inventory information.

[0310] The server generates an image to facilitate visual confirmation of the identified materials. This image reflects the exact shape and color of the materials and is designed to prevent misidentification. The generated image is sent to the user's terminal, and the user confirms the materials on the terminal.

[0311] Once the supplies have been confirmed, the server controls an automated movement mechanism to deliver the necessary supplies to the appropriate location. Robotics and conveyor systems are utilized in this process. The progress of the supply process is monitored in real time and notified to the user's terminal. The main contents of the notification are the supply start time, the status in progress, and the supply completion time.

[0312] As a concrete example, consider a situation where a factory's production line is short of parts. The user uses a terminal to specify the missing parts and inputs the quantity and delivery date. The server then automatically selects the appropriate parts and can supply them quickly. Delays and errors in this supply process are minimized, improving production efficiency.

[0313] An example of a prompt message would be: "We are short on a specific part in the factory. Please tell us the name and quantity of the part. Also, if it is urgent, please specify the delivery date."

[0314] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0315] Step 1:

[0316] Users enter their material requests from a terminal. The data entered includes information such as the type and quantity of materials and the required delivery date. This information is sent to the server and forms the basis of the request.

[0317] Step 2:

[0318] Based on the received request, the server uses a generative AI model to identify the necessary supplies. In this step, historical data and inventory information are retrieved from the database and analyzed to identify the optimal supplies. As a result, the selected supplies are output.

[0319] Step 3:

[0320] The server uses the information of the identified items to generate a visual confirmation image. The generated image is visual data that accurately reproduces the shape and color of the items to prevent user misidentification. This image is obtained as the output of the generation process.

[0321] Step 4:

[0322] The server sends the generated supply images to the user's terminal. The user reviews the images on their terminal and determines whether the supplies are correct. User verification is performed through the terminal's GUI.

[0323] Step 5:

[0324] Once confirmation is received, the server controls the automated movement mechanism to supply the goods. This step involves checking inventory and using movement systems such as robots and conveyors to deliver the goods to the appropriate locations. It also includes checking inventory data and placing external orders if there are any shortages.

[0325] Step 6:

[0326] The server monitors the progress of the supply process in real time and notifies the user's terminal of its status. This notification includes the start time, ongoing status, and completion time of the supply, allowing the user to understand the entire process. The server uses supply status monitoring data as input and generates user notification data as output.

[0327] 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.

[0328] This invention provides a system for the rapid and efficient supply of necessary materials, and by taking user emotions into consideration, it enables the provision of better service. The system is configured as follows:

[0329] First, the user enters the shortage of supplies using a terminal. Here, they specify the type and quantity of supplies needed, as well as the desired delivery date. This request is sent to the server and processed.

[0330] Next, the server analyzes the received request data to identify the necessary supplies. Using generative AI technology, the server makes appropriate decisions based on past data and current inventory levels. During this process, the emotion engine references the user's past emotional data, taking into account the user's stress level and satisfaction level.

[0331] Once the items have been identified, the server generates an image of them. AI technology is used in this image generation process, and information is prepared to facilitate visual recognition. This image is then presented to the user via their device.

[0332] The device displays images to the user and provides a process to verify the accuracy of the supplies. The user reviews the presented information and agrees. During this agreement process, an emotion engine analyzes the user's emotions in real time and provides additional instructions or suggestions as needed.

[0333] Once the supplies have been confirmed, the server checks the inventory. If necessary, it automatically places orders with external suppliers. After securing the supplies, arrangements for delivery are made. During the delivery process, the emotion engine evaluates the user's emotions regarding the delivery status and provides appropriate notifications and follow-up.

[0334] For example, if a user reports a shortage of a specific communication cable, the server quickly identifies the cable and verifies it with images. If the user verifies and the emotion engine detects stress, priority delivery arrangements and enhanced support are made. This increases user satisfaction and achieves efficient supply of goods.

[0335] Thus, the invention provides a comprehensive material supply system that takes user emotions into consideration, thereby improving service quality.

[0336] The following describes the processing flow.

[0337] Step 1:

[0338] The user uses a terminal to enter requests for supplies that are lacking or needed. The request includes the type of supply, quantity, priority, and desired delivery date. The entered data is sent to the server.

[0339] Step 2:

[0340] The server analyzes the received request, consults the system's database to check the inventory status of the requested supplies, and simultaneously uses a generative AI to select the necessary supplies.

[0341] Step 3:

[0342] The server uses AI technology to generate visual confirmation images for identified items. The generated images should include detailed information about the items (e.g., model number, size, etc.).

[0343] Step 4:

[0344] The terminal displays images and material information sent from the server to the user. The user reviews the displayed information, judges its accuracy, and approves or requests corrections.

[0345] Step 5:

[0346] Upon receiving user confirmation, the server initiates a process to perform a final inventory check and, if necessary, automatically place orders with external suppliers.

[0347] Step 6:

[0348] Once the server confirms that the requested supplies have been secured, it will arrange for delivery. Delivery information is tracked in real time, and the user's terminal is notified of the progress.

[0349] Step 7:

[0350] The emotion engine embedded in the server analyzes user reactions and responses, and based on the emotion data, makes suggestions for improving the content and services offered.

[0351] Step 8:

[0352] The user receives the supplies and begins work after confirming receipt. After completing the work, they send feedback to the system, and data is collected to assess user satisfaction.

[0353] (Example 2)

[0354] 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".

[0355] In managing the supply chain of goods, there are challenges in optimizing effective supply methods that take into account user emotions and in ensuring rapid inventory replenishment. Furthermore, in the goods verification process, discrepancies between user requests and inventory information lead to decreased satisfaction. Addressing these challenges, reducing user stress, and improving satisfaction are crucial.

[0356] 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.

[0357] This invention includes a server that identifies necessary supplies based on generated information and optimizes the supply method considering the user's emotional data; a server that generates images of the identified supplies using AI technology and obtains user confirmation; and a server that analyzes the user's emotions during the delivery process and provides necessary notifications and follow-up. This enables rapid and accurate supply and inventory management of supplies that takes the user's emotions into consideration.

[0358] "Generated information" refers to the request data provided by the user and the analysis data based on that data, which serves as the basis for making decisions necessary for supplying goods.

[0359] "Required supplies" refers to the types and quantities of supplies identified by the user's requests and the system.

[0360] "User sentiment data" refers to the quantified or data-based representation of past and present user emotional responses, used to optimize delivery methods.

[0361] "Optimizing supply methods" is the process of determining the optimal supply route and timing for goods, taking into account the type of goods, the supply and demand balance, and user sentiment data.

[0362] "Images of materials generated using AI technology" refers to digital images generated by artificial intelligence based on the details and characteristics of materials, which assist users in visual verification.

[0363] "Re-evaluating inventory levels" is a process of verifying current inventory data against the latest information to determine the need for supply.

[0364] "Automatic ordering" is a function in which the system automatically places orders for necessary supplies from external suppliers when it detects a shortage in inventory.

[0365] "Analyzing user emotions during the delivery process" refers to activities that evaluate user emotional data in real time during the delivery of goods and use that data to improve the supply process.

[0366] "Notifications and follow-ups" refer to procedures and communications aimed at providing users with information and additional support for problem-solving.

[0367] To implement this invention, a server, a terminal, a generative AI model, and an emotion engine are required. The server is responsible for the main processing, utilizing the generative AI model to analyze historical data and current inventory status to identify materials and optimize supply methods. The emotion engine is used to analyze user emotion data in real time to aid in supply decision-making. Furthermore, AI technology is used to generate visual information of materials as images and present them to the user via the terminal.

[0368] The terminal provides an interface for users to input requests for supplies, receives generated images from the server, and presents them visually to the user. It also has the function to send feedback to the server based on the user's selections and confirmations.

[0369] Users input shortages of supplies via a terminal. The entered information is sent to a server where it is analyzed. After user confirmation, the server automatically places orders with external suppliers if necessary, and handles inventory management and delivery arrangements. The server also monitors the user's emotions during delivery and provides appropriate notifications and follow-ups through the terminal.

[0370] As a concrete example, suppose a user enters "I need 5 communication cables" into their terminal and specifies a delivery date. The server analyzes this request, checks inventory using a generative AI model, and places a new order if necessary. The generated image is sent to the terminal for the user to confirm. At this time, the user's emotional data is analyzed, and if stress is detected, priority delivery or support arrangements are made. An example of a prompt message could be, "I have received a report of a communication cable shortage; please prioritize this."

[0371] This invention aims to improve service quality by enabling dynamic supply of goods based on user requests and emotions.

[0372] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0373] Step 1:

[0374] The user accesses the terminal interface and enters the type of out-of-stock item, the required quantity, and the desired delivery date. This information is organized into data packets by the terminal and sent to the server. The more specific the information entered, the more efficient the analysis can be.

[0375] Step 2:

[0376] The server analyzes the data packets received from the user and uses a generating AI model to identify the necessary supplies based on historical data and current inventory levels. This data processing involves comparing the data with similar past request data and cross-referencing it with figures in the inventory management system. The identified supply information is then used for the following processes.

[0377] Step 3:

[0378] The server uses AI technology to generate images of the identified materials. It utilizes the material's specifications and detailed information as input data for image generation, outputting it in a visual format that the user can review. The generated images are then transferred to the terminal.

[0379] Step 4:

[0380] The device displays the received image to the user and prompts them to confirm if it matches the requested item. The user examines the displayed image and, if there is a mismatch, sends feedback to the server via the device. Once confirmation is received, the data is sent to the server as an approved status.

[0381] Step 5:

[0382] The server receives user confirmation data and re-evaluates the inventory status. If inventory is insufficient, it generates a prompt message and sends the order information to the supply system to automatically place an order with an external supplier. The output will appear as a notification of order completion or inventory availability.

[0383] Step 6:

[0384] During the delivery arrangement phase, the server analyzes the user's emotions in real time and provides appropriate notifications and follow-up information as the delivery progresses. It analyzes emotional data as input and outputs actions as needed, including providing alternatives and support in case of delays. It also provides progress notifications via the terminal.

[0385] (Application Example 2)

[0386] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0387] Traditional supply systems have struggled to respond flexibly to the emotional state of users, resulting in a lack of sufficient improvement in user satisfaction. In particular, services requiring immediate responses, such as food delivery, demand service provision that considers the user's emotions, but there has been a lack of means to achieve this.

[0388] 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.

[0389] In this invention, the server includes means for identifying necessary supplies based on generated information, means for generating images of the identified supplies and obtaining confirmation that takes into account the user's emotional state, and means for delivering the confirmed supplies with adjusted delivery priority based on emotion analysis. This enables adaptive supply of supplies that takes into account the user's emotions.

[0390] "Generated information" refers to data that is analyzed by the system and used to identify materials and provide them to users.

[0391] "Goods" refers to the items or services that users request to be supplied.

[0392] "Emotional state" refers to information that indicates the user's psychological reactions and emotions, and its analysis enables individualized responses.

[0393] "Means of obtaining confirmation" refers to the process of presenting information to users and obtaining their agreement or consent.

[0394] "Delivery priority" is an evaluation metric used to determine the order and speed of delivery of goods, and is adjusted based on the results of user sentiment analysis.

[0395] "Emotional analysis" refers to a technology that analyzes a user's emotional state as data and uses it for various system controls and feedback.

[0396] The system that implements this application works in cooperation with the user, terminal, and server. The user can use their smartphone to order goods, such as food, and input their desired delivery date at that time. Once the order is complete, the terminal sends this information to the server.

[0397] Based on the received information, the server uses a generative AI model to perform necessary data analysis and identify the items. This process involves analyzing big data, including the user's past history and current inventory status, to achieve efficient and accurate item identification. An image of the identified item is generated using AI technology and sent to the terminal.

[0398] The device visually presents the user with images of the generated items and prompts them to confirm. At the same time, the device utilizes an emotion analysis API (e.g., Microsoft Emotion API) to evaluate the user's real-time emotional state. For example, if the user is experiencing high stress levels, it may present a special message or discount. This makes it possible to provide an emotionally sensitive user experience.

[0399] Once the supplies are identified and confirmed, the server arranges delivery. Based on information obtained from sentiment analysis, delivery priorities are adjusted; for example, users with high sentiment scores receive expedited delivery. Once delivery begins, the server also provides appropriate notifications regarding the delivery status, taking into account the user's sentiment.

[0400] (Specific example)

[0401] For example, if a user orders pasta at lunchtime and places the order via smartphone, and the system determines that their stress level is high, a message such as "We'll deliver it early today so you can relax!" will be displayed on the device.

[0402] (Example of prompt text to input to the generated AI model)

[0403] "Based on the user's ordered dish name, image, delivery date, and sentiment data, generate the optimal delivery method and additional message."

[0404] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0405] Step 1:

[0406] The user places an order for supplies by operating a terminal. The input includes the name, quantity, and delivery date of the specified supplies. The terminal then sends this information to the server.

[0407] Step 2:

[0408] The server analyzes the received order information. The input is the order information from the user, and the output is the analysis result. The server uses a generative AI model to refer to past user data and inventory information to identify the necessary supplies. In this process, data analysis technology is utilized to efficiently identify the supplies.

[0409] Step 3:

[0410] The server uses AI generation technology to create images of identified items. The input is data of the identified items, and the output is image data. The generated images are sent to the user's terminal for visual confirmation.

[0411] Step 4:

[0412] The device presents images to the user and evaluates the user's emotional state in real time. Input is image data from the server, and output is the user's response confirmation and emotional evaluation. Using an emotional analysis API, it retrieves emotional data such as the user's stress level and displays special messages or discounts as needed.

[0413] Step 5:

[0414] Once the user completes the image verification, the verification result is sent to the server. The input is the user's acknowledgment, and the output is the transmission of that result to the server.

[0415] Step 6:

[0416] The server arranges delivery for confirmed supplies. Inputs are the final confirmed order data and the user's sentiment rating; output is the delivery plan. Based on the sentiment analysis results, the server adjusts delivery priorities and, if necessary, prioritizes delivery.

[0417] Step 7:

[0418] The server also evaluates the user's emotions regarding goods in transit and sends appropriate notifications to the device. Inputs are delivery status and emotion evaluation data, while output is a notification message to the device. This provides users with a sense of security and improves their satisfaction.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] [Third Embodiment]

[0423] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0424] 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.

[0425] 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).

[0426] 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.

[0427] 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.

[0428] 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).

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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".

[0435] This invention provides a system for quickly and accurately securing and delivering supplies in the operation of base stations and related facilities. This system is configured as follows.

[0436] First, to resolve situations where necessary supplies are in short supply, users use a terminal to input their requests for supplies. These requests include information such as the type and quantity of supplies and the required delivery date. This allows users to easily generate requests without having to go through cumbersome and detailed procedures.

[0437] Next, the server that receives the request uses generative AI to identify the necessary supplies based on this information. The server analyzes past data and inventory status to select the appropriate supplies. This process involves accessing databases and using machine learning algorithms.

[0438] The server then generates an image of the identified item, creating information for visual confirmation. This image accurately reflects the actual shape and color of the item, preventing misidentification.

[0439] Next, this image is sent to the user's device for verification. The user views the image on their device, confirms that the items have been accurately identified, and gives their consent. Consent is indicated by a button press on the device screen, and once approved, the process proceeds to the next stage.

[0440] When it comes to actually securing supplies, after an agreement is reached, the server automatically checks the inventory. If the supplies are in stock, they are immediately dispatched; if the inventory is insufficient, an automatic order is placed with an external supplier. This automation reduces human error and delays in the supply chain.

[0441] Finally, the secured supplies are arranged for delivery, and the server works with the delivery company to quickly deliver the supplies to the designated location. During this time, the delivery status is notified to the user's terminal in real time.

[0442] As a concrete example, consider a situation where a base station is short of a specific cable. The user enters the shortage information into their terminal and immediately submits a request. The server processes the data, identifies the appropriate cable, verifies it with an image, checks inventory, and promptly places an order and arranges for delivery. This ensures that the necessary supplies reach the base station quickly, minimizing the impact on users.

[0443] The objective of this invention is to provide a system that supports the operation of base stations and enables the rapid and reliable supply of materials through such a process.

[0444] The following describes the processing flow.

[0445] Step 1:

[0446] The user selects the necessary supplies on their terminal and enters a request including the required quantity and delivery date. The entered request is sent, and the server prepares to process it.

[0447] Step 2:

[0448] The server analyzes the received request and checks the inventory status of the requested materials by referring to the relevant database. Based on the situation, it determines whether the necessary materials are in internal inventory or whether an additional order is required.

[0449] Step 3:

[0450] Once the server identifies the items, it uses a generation AI to generate images of the identified items. These generated images are then prepared as data for user verification.

[0451] Step 4:

[0452] The terminal receives images from the server and presents them to the user. This allows the user to visually verify the materials and provides a function to select approval or request corrections on the confirmation screen.

[0453] Step 5:

[0454] The user reviews images and information about the supplies on their device, and if there are no issues, approves the agreement. The approval information is then sent from the device to the server.

[0455] Step 6:

[0456] After confirming the user's agreement, the server performs a final inventory check. If inventory is available, it immediately initiates the dispatch process; if inventory is insufficient, it automatically places an order with an external supplier.

[0457] Step 7:

[0458] The server completes the arrangements for outbound or inbound shipments and handles the delivery procedures for the goods. Delivery arrangement information is tracked in real time, and the user's terminal is notified of the progress.

[0459] Step 8:

[0460] The user confirms the arrival of the supplies and begins the actual work after receiving them. Once the work is completed, they send feedback to the system, and the entire process is finished.

[0461] (Example 1)

[0462] 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."

[0463] In resource supply processes, it is essential to quickly and accurately identify materials, manage inventory levels, and minimize human error and delays at each step from ordering from external sources to delivery. Conventional systems require users to spend a great deal of time selecting and verifying materials, and there is a lack of timely information sharing throughout the supply chain. Therefore, it is necessary to effectively solve these problems.

[0464] 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.

[0465] In this invention, the server includes means for receiving request information entered by a user and utilizing a generative AI model to identify the necessary resources based on this information; means for generating visual information about the identified resources and allowing the user to visually confirm them; and means for managing inventory status, automatically placing orders with external suppliers as needed, and delivering resources after user confirmation has been obtained. This enables improved efficiency and accuracy in the resource supply process.

[0466] A "user" is an individual or group that inputs resource request information and interacts with the system through the information provided and visual confirmations.

[0467] A "generative AI model" is an artificial intelligence system used to analyze input request information and identify the most suitable resources.

[0468] "Visual information" refers to image information that clearly represents the shape, color, and other characteristics of a specific resource.

[0469] "Inventory status" refers to information that indicates the current amount and condition of resources held, and is used to determine supply plans.

[0470] "External sources" refer to external manufacturers, vendors, and other entities used to procure resources that are lacking within the system.

[0471] "Automatic ordering" refers to the process of automatically ordering necessary resources from external suppliers based on inventory levels.

[0472] "Delivery method" refers to the method or system used to transport specific resources to a designated location.

[0473] "Real-time notification" is a method of providing information that informs users in real time about changes in resource delivery processes and inventory status.

[0474] This invention demonstrates how servers, terminals, and users interact within a resource supply system. This system uses a generative AI model to automate the process of identifying necessary resources from input request information.

[0475] First, the user enters resource request information through a terminal. This terminal has a user interface and allows the user to input data including the type of resource, quantity, and required delivery date. This request information is then sent to the server.

[0476] Next, the server inputs the received request information into a generative AI model to identify resources suitable for the request. The server is equipped with a database, which uses machine learning algorithms to analyze data, referencing historical data and inventory status. In this process, the generative AI model is used, and specific resources are identified by prompt statements.

[0477] Information about identified resources is generated as visual information from the server. This visual information accurately reflects the shape and color of the resources to prevent misidentification. The generated visual information is sent to the user's terminal and provided for verification.

[0478] The user reviews the visual information received via the device to confirm that the resources are accurate. This verification process allows the system to proceed to the next stage.

[0479] As a concrete example, consider a situation where a specific cable is in short supply at a certain facility. The user enters information about the cable type on a terminal and submits a request. The server uses a generative AI model to identify the appropriate cable and generates an image of it. The user reviews the image and approves the resource identification. After approval, the server automatically checks inventory, places an order with an external supplier if necessary, and proceeds with the delivery process.

[0480] An example of a prompt message to the generating AI model in this system is: "Identify the ID of a resource that matches this condition. Example: Cable X-500, red, 20 meters." This prompt is used as input data for the AI ​​model to function properly in identifying resources.

[0481] This invention enables rapid resource supply and accurate identification by having servers and terminals work together in a highly coordinated manner, allowing users to easily manage resources.

[0482] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0483] Step 1:

[0484] The user enters resource request information through a terminal. This input includes specific data such as the type of resource, quantity, and required delivery date. The terminal prepares to send this data to the server. The entered data serves as basic information necessary for subsequent processes in the system.

[0485] Step 2:

[0486] The server receives request information sent from the terminal. Based on this input data, it uses a generative AI model to identify the optimal resource. It sends a prompt to the generative AI asking, "Identify the ID of the resource that matches these conditions," and obtains the resource ID as output. The server uses this ID to search for additional data about the resource's characteristics.

[0487] Step 3:

[0488] The server generates visual information based on the identified resource's information. In this process, it retrieves detailed information such as the resource's shape and color from the database and outputs an accurate image. The generated visual information is stored on the server in a format easily accessible to the user.

[0489] Step 4:

[0490] Visual information generated from the server is sent to the user's terminal. The terminal displays this image on its screen and requests visual confirmation from the user. The image display function is appropriately adjusted so that the user can accurately recognize this information.

[0491] Step 5:

[0492] The user reviews the visual information on their device and scrutinizes the accuracy of the resources. Once agreement is reached, they press the confirmation button on their device to send a notification to the server. This action signals the system to proceed to the next process.

[0493] Step 6:

[0494] The server receives the user's confirmation notification and begins checking the inventory status. If the item is in stock, it immediately arranges for dispatch; otherwise, it automatically places an order with an external supplier. The order details are generated as output and sent to the supplier if necessary.

[0495] Step 7:

[0496] The server arranges delivery and manages its progress in real time. Delivery information is notified to the user's device, and the delivery status is displayed. Finally, the user receives the estimated delivery date and arrival confirmation on their device. This completes the resource supply process, and the user receives the resources.

[0497] (Application Example 1)

[0498] 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."

[0499] In the supply chain process, human error and delays in verification are common, which reduces the efficiency of the entire supply chain. In particular, accurate material management is required in manufacturing and other industrial settings, but traditional methods have limitations in terms of supply accuracy and responsiveness.

[0500] 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.

[0501] In this invention, the server includes means for identifying necessary supplies based on generated information, means for generating images of the identified supplies and obtaining user confirmation, means for supplying the confirmed supplies using an automated transport mechanism, and means for notifying the user's terminal of the real-time status of the supplied supplies. This streamlines the supply process and enables accurate and rapid supply.

[0502] "Generated information" refers to data used to identify necessary supplies based on request data entered by the user.

[0503] "Supplies" is a general term for the various materials and products required within a supply chain.

[0504] A "generative AI model" is a technology that uses machine learning algorithms to generate information from specific data and is used to identify materials.

[0505] "Image generation" is the process of creating image data to facilitate the visual identification of identified materials.

[0506] An "automated moving mechanism" is a system that utilizes robots and conveyor systems to supply specific materials to designated locations.

[0507] "Real-time status" refers to information that immediately monitors and transmits the current status and progress in the supply chain process.

[0508] "User terminal" refers to an electronic device used by the user to send and receive information and to verify and manage goods.

[0509] The system that implements this application is designed to automate material supply in manufacturing plants. The server analyzes the material requests received from the user's terminal and uses a generative AI model to identify the necessary materials. The generative AI model used here identifies the optimal type and quantity of requested materials based on historical data and inventory information.

[0510] The server generates images to facilitate visual confirmation of identified items. These images accurately reflect the shape and color of the items and are designed to prevent misidentification. The generated images are sent to the user's device, where the user can verify the items.

[0511] Once the supplies have been confirmed, the server controls an automated movement mechanism to deliver the necessary supplies to the appropriate location. Robotics and conveyor systems are utilized in this process. The progress of the supply process is monitored in real time and notified to the user's terminal. The main contents of the notification are the supply start time, the status in progress, and the supply completion time.

[0512] As a concrete example, consider a situation where a factory's production line is short of parts. The user uses a terminal to specify the missing parts and inputs the quantity and delivery date. The server then automatically selects the appropriate parts and can supply them quickly. Delays and errors in this supply process are minimized, improving production efficiency.

[0513] An example of a prompt message would be: "We are short on a specific part in the factory. Please tell us the name and quantity of the part. Also, if it is urgent, please specify the delivery date."

[0514] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0515] Step 1:

[0516] Users enter their material requests from a terminal. The data entered includes information such as the type and quantity of materials and the required delivery date. This information is sent to the server and forms the basis of the request.

[0517] Step 2:

[0518] Based on the received request, the server uses a generative AI model to identify the necessary supplies. In this step, historical data and inventory information are retrieved from the database and analyzed to identify the optimal supplies. As a result, the selected supplies are output.

[0519] Step 3:

[0520] The server uses the information of the identified items to generate a visual confirmation image. The generated image is visual data that accurately reproduces the shape and color of the items to prevent user misidentification. This image is obtained as the output of the generation process.

[0521] Step 4:

[0522] The server sends the generated supply images to the user's terminal. The user reviews the images on their terminal and determines whether the supplies are correct. User verification is performed through the terminal's GUI.

[0523] Step 5:

[0524] Once confirmation is received, the server controls the automated movement mechanism to supply the goods. This step involves checking inventory and using movement systems such as robots and conveyors to deliver the goods to the appropriate locations. It also includes checking inventory data and placing external orders if there are any shortages.

[0525] Step 6:

[0526] The server monitors the progress of the supply process in real time and notifies the user's terminal of its status. This notification includes the start time, ongoing status, and completion time of the supply, allowing the user to understand the entire process. The server uses supply status monitoring data as input and generates user notification data as output.

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

[0528] This invention provides a system for the rapid and efficient supply of necessary materials, and by taking user emotions into consideration, it enables the provision of better service. The system is configured as follows:

[0529] First, the user enters the shortage of supplies using a terminal. Here, they specify the type and quantity of supplies needed, as well as the desired delivery date. This request is sent to the server and processed.

[0530] Next, the server analyzes the received request data to identify the necessary supplies. Using generative AI technology, the server makes appropriate decisions based on past data and current inventory levels. During this process, the emotion engine references the user's past emotional data, taking into account the user's stress level and satisfaction level.

[0531] Once the items have been identified, the server generates an image of them. AI technology is used in this image generation process, and information is prepared to facilitate visual recognition. This image is then presented to the user via their device.

[0532] The device displays images to the user and provides a process to verify the accuracy of the supplies. The user reviews the presented information and agrees. During this agreement process, an emotion engine analyzes the user's emotions in real time and provides additional instructions or suggestions as needed.

[0533] Once the supplies have been confirmed, the server checks the inventory. If necessary, it automatically places orders with external suppliers. After securing the supplies, arrangements for delivery are made. During the delivery process, the emotion engine evaluates the user's emotions regarding the delivery status and provides appropriate notifications and follow-up.

[0534] For example, if a user reports a shortage of a specific communication cable, the server quickly identifies the cable and verifies it with images. If the user verifies and the emotion engine detects stress, priority delivery arrangements and enhanced support are made. This increases user satisfaction and achieves efficient supply of goods.

[0535] Thus, the invention provides a comprehensive material supply system that takes user emotions into consideration, thereby improving service quality.

[0536] The following describes the processing flow.

[0537] Step 1:

[0538] The user uses a terminal to enter requests for supplies that are lacking or needed. The request includes the type of supply, quantity, priority, and desired delivery date. The entered data is sent to the server.

[0539] Step 2:

[0540] The server analyzes the received request, consults the system's database to check the inventory status of the requested supplies, and simultaneously uses a generative AI to select the necessary supplies.

[0541] Step 3:

[0542] The server uses AI technology to generate visual confirmation images for identified items. The generated images should include detailed information about the items (e.g., model number, size, etc.).

[0543] Step 4:

[0544] The terminal displays images and material information sent from the server to the user. The user reviews the displayed information, judges its accuracy, and approves or requests corrections.

[0545] Step 5:

[0546] Upon receiving user confirmation, the server initiates a process to perform a final inventory check and, if necessary, automatically place orders with external suppliers.

[0547] Step 6:

[0548] Once the server confirms that the requested supplies have been secured, it will arrange for delivery. Delivery information is tracked in real time, and the user's terminal is notified of the progress.

[0549] Step 7:

[0550] The emotion engine embedded in the server analyzes user reactions and responses, and based on the emotion data, makes suggestions for improving the content and services offered.

[0551] Step 8:

[0552] The user receives the supplies and begins work after confirming receipt. After completing the work, they send feedback to the system, and data is collected to assess user satisfaction.

[0553] (Example 2)

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

[0555] In managing the supply chain of goods, there are challenges in optimizing effective supply methods that take into account user emotions and in ensuring rapid inventory replenishment. Furthermore, in the goods verification process, discrepancies between user requests and inventory information lead to decreased satisfaction. Addressing these challenges, reducing user stress, and improving satisfaction are crucial.

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

[0557] This invention includes a server that identifies necessary supplies based on generated information and optimizes the supply method considering the user's emotional data; a server that generates images of the identified supplies using AI technology and obtains user confirmation; and a server that analyzes the user's emotions during the delivery process and provides necessary notifications and follow-up. This enables rapid and accurate supply and inventory management of supplies that takes the user's emotions into consideration.

[0558] "Generated information" refers to the request data provided by the user and the analysis data based on that data, which serves as the basis for making decisions necessary for supplying goods.

[0559] "Required supplies" refers to the types and quantities of supplies identified by the user's requests and the system.

[0560] "User sentiment data" refers to the quantified or data-based representation of past and present user emotional responses, used to optimize delivery methods.

[0561] "Optimizing supply methods" is the process of determining the optimal supply route and timing for goods, taking into account the type of goods, the supply and demand balance, and user sentiment data.

[0562] "Images of materials generated using AI technology" refers to digital images generated by artificial intelligence based on the details and characteristics of materials, which assist users in visual verification.

[0563] "Re-evaluating inventory levels" is a process of verifying current inventory data against the latest information to determine the need for supply.

[0564] "Automatic ordering" is a function in which the system automatically places orders for necessary supplies from external suppliers when it detects a shortage in inventory.

[0565] "Analyzing user emotions during the delivery process" refers to activities that evaluate user emotional data in real time during the delivery of goods and use that data to improve the supply process.

[0566] "Notifications and follow-ups" refer to procedures and communications aimed at providing users with information and additional support for problem-solving.

[0567] To implement this invention, a server, a terminal, a generative AI model, and an emotion engine are required. The server is responsible for the main processing, utilizing the generative AI model to analyze historical data and current inventory status to identify materials and optimize supply methods. The emotion engine is used to analyze user emotion data in real time to aid in supply decision-making. Furthermore, AI technology is used to generate visual information of materials as images and present them to the user via the terminal.

[0568] The terminal provides an interface for users to input requests for supplies, receives generated images from the server, and presents them visually to the user. It also has the function to send feedback to the server based on the user's selections and confirmations.

[0569] Users input shortages of supplies via a terminal. The entered information is sent to a server where it is analyzed. After user confirmation, the server automatically places orders with external suppliers if necessary, and handles inventory management and delivery arrangements. The server also monitors the user's emotions during delivery and provides appropriate notifications and follow-ups through the terminal.

[0570] As a concrete example, suppose a user enters "I need 5 communication cables" into their terminal and specifies a delivery date. The server analyzes this request, checks inventory using a generative AI model, and places a new order if necessary. The generated image is sent to the terminal for the user to confirm. At this time, the user's emotional data is analyzed, and if stress is detected, priority delivery or support arrangements are made. An example of a prompt message could be, "I have received a report of a communication cable shortage; please prioritize this."

[0571] This invention aims to improve service quality by enabling dynamic supply of goods based on user requests and emotions.

[0572] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0573] Step 1:

[0574] The user accesses the terminal interface and enters the type of out-of-stock item, the required quantity, and the desired delivery date. This information is organized into data packets by the terminal and sent to the server. The more specific the information entered, the more efficient the analysis can be.

[0575] Step 2:

[0576] The server analyzes the data packets received from the user and uses a generating AI model to identify the necessary supplies based on historical data and current inventory levels. This data processing involves comparing the data with similar past request data and cross-referencing it with figures in the inventory management system. The identified supply information is then used for the following processes.

[0577] Step 3:

[0578] The server uses AI technology to generate images of the identified materials. It utilizes the material's specifications and detailed information as input data for image generation, outputting it in a visual format that the user can review. The generated images are then transferred to the terminal.

[0579] Step 4:

[0580] The device displays the received image to the user and prompts them to confirm if it matches the requested item. The user examines the displayed image and, if there is a mismatch, sends feedback to the server via the device. Once confirmation is received, the data is sent to the server as an approved status.

[0581] Step 5:

[0582] The server receives user confirmation data and re-evaluates the inventory status. If inventory is insufficient, it generates a prompt message and sends the order information to the supply system to automatically place an order with an external supplier. The output will appear as a notification of order completion or inventory availability.

[0583] Step 6:

[0584] During the delivery arrangement phase, the server analyzes the user's emotions in real time and provides appropriate notifications and follow-up information as the delivery progresses. It analyzes emotional data as input and outputs actions as needed, including providing alternatives and support in case of delays. It also provides progress notifications via the terminal.

[0585] (Application Example 2)

[0586] 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."

[0587] Traditional supply systems have struggled to respond flexibly to the emotional state of users, resulting in a lack of sufficient improvement in user satisfaction. In particular, services requiring immediate responses, such as food delivery, demand service provision that considers the user's emotions, but there has been a lack of means to achieve this.

[0588] 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.

[0589] In this invention, the server includes means for identifying necessary supplies based on generated information, means for generating images of the identified supplies and obtaining confirmation that takes into account the user's emotional state, and means for delivering the confirmed supplies with adjusted delivery priority based on emotion analysis. This enables adaptive supply of supplies that takes into account the user's emotions.

[0590] "Generated information" refers to data that is analyzed by the system and used to identify materials and provide them to users.

[0591] "Goods" refers to the items or services that users request to be supplied.

[0592] "Emotional state" refers to information that indicates the user's psychological reactions and emotions, and its analysis enables individualized responses.

[0593] "Means of obtaining confirmation" refers to the process of presenting information to users and obtaining their agreement or consent.

[0594] "Delivery priority" is an evaluation metric used to determine the order and speed of delivery of goods, and is adjusted based on the results of user sentiment analysis.

[0595] "Emotional analysis" refers to a technology that analyzes a user's emotional state as data and uses it for various system controls and feedback.

[0596] The system that implements this application works in cooperation with the user, terminal, and server. The user can use their smartphone to order goods, such as food, and input their desired delivery date at that time. Once the order is complete, the terminal sends this information to the server.

[0597] Based on the received information, the server uses a generative AI model to perform necessary data analysis and identify the items. This process involves analyzing big data, including the user's past history and current inventory status, to achieve efficient and accurate item identification. An image of the identified item is generated using AI technology and sent to the terminal.

[0598] The device visually presents the user with images of the generated items and prompts them to confirm. At the same time, the device utilizes an emotion analysis API (e.g., Microsoft Emotion API) to evaluate the user's real-time emotional state. For example, if the user is experiencing high stress levels, it may present a special message or discount. This makes it possible to provide an emotionally sensitive user experience.

[0599] Once the supplies are identified and confirmed, the server arranges delivery. Based on information obtained from sentiment analysis, delivery priorities are adjusted; for example, users with high sentiment scores receive expedited delivery. Once delivery begins, the server also provides appropriate notifications regarding the delivery status, taking into account the user's sentiment.

[0600] (Specific example)

[0601] For example, if a user orders pasta at lunchtime and places the order via smartphone, and the system determines that their stress level is high, a message such as "We'll deliver it early today so you can relax!" will be displayed on the device.

[0602] (Example of prompt text to input to the generated AI model)

[0603] "Based on the user's ordered dish name, image, delivery date, and sentiment data, generate the optimal delivery method and additional message."

[0604] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0605] Step 1:

[0606] The user places an order for supplies by operating a terminal. The input includes the name, quantity, and delivery date of the specified supplies. The terminal then sends this information to the server.

[0607] Step 2:

[0608] The server analyzes the received order information. The input is the order information from the user, and the output is the analysis result. The server uses a generative AI model to refer to past user data and inventory information to identify the necessary supplies. In this process, data analysis technology is utilized to efficiently identify the supplies.

[0609] Step 3:

[0610] The server uses AI generation technology to create images of identified items. The input is data of the identified items, and the output is image data. The generated images are sent to the user's terminal for visual confirmation.

[0611] Step 4:

[0612] The device presents images to the user and evaluates the user's emotional state in real time. Input is image data from the server, and output is the user's response confirmation and emotional evaluation. Using an emotional analysis API, it retrieves emotional data such as the user's stress level and displays special messages or discounts as needed.

[0613] Step 5:

[0614] Once the user completes the image verification, the verification result is sent to the server. The input is the user's acknowledgment, and the output is the transmission of that result to the server.

[0615] Step 6:

[0616] The server arranges delivery for confirmed supplies. Inputs are the final confirmed order data and the user's sentiment rating; output is the delivery plan. Based on the sentiment analysis results, the server adjusts delivery priorities and, if necessary, prioritizes delivery.

[0617] Step 7:

[0618] The server also evaluates the user's emotions regarding goods in transit and sends appropriate notifications to the device. Inputs are delivery status and emotion evaluation data, while output is a notification message to the device. This provides users with a sense of security and improves their satisfaction.

[0619] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

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

[0622] [Fourth Embodiment]

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

[0624] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0625] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0626] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0627] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0628] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0629] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0630] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0631] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0632] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0633] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

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

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

[0636] This invention provides a system for quickly and accurately securing and delivering supplies in the operation of base stations and related facilities. This system is configured as follows.

[0637] First, to resolve situations where necessary supplies are in short supply, users use a terminal to input their requests for supplies. These requests include information such as the type and quantity of supplies and the required delivery date. This allows users to easily generate requests without having to go through cumbersome and detailed procedures.

[0638] Next, the server that receives the request uses generative AI to identify the necessary supplies based on this information. The server analyzes past data and inventory status to select the appropriate supplies. This process involves accessing databases and using machine learning algorithms.

[0639] The server then generates an image of the identified item, creating information for visual confirmation. This image accurately reflects the actual shape and color of the item, preventing misidentification.

[0640] Next, this image is sent to the user's device for verification. The user views the image on their device, confirms that the items have been accurately identified, and gives their consent. Consent is indicated by a button press on the device screen, and once approved, the process proceeds to the next stage.

[0641] When it comes to actually securing supplies, after an agreement is reached, the server automatically checks the inventory. If the supplies are in stock, they are immediately dispatched; if the inventory is insufficient, an automatic order is placed with an external supplier. This automation reduces human error and delays in the supply chain.

[0642] Finally, the secured supplies are arranged for delivery, and the server works with the delivery company to quickly deliver the supplies to the designated location. During this time, the delivery status is notified to the user's terminal in real time.

[0643] As a concrete example, consider a situation where a base station is short of a specific cable. The user enters the shortage information into their terminal and immediately submits a request. The server processes the data, identifies the appropriate cable, verifies it with an image, checks inventory, and promptly places an order and arranges for delivery. This ensures that the necessary supplies reach the base station quickly, minimizing the impact on users.

[0644] The objective of this invention is to provide a system that supports the operation of base stations and enables the rapid and reliable supply of materials through such a process.

[0645] The following describes the processing flow.

[0646] Step 1:

[0647] The user selects the necessary supplies on their terminal and enters a request including the required quantity and delivery date. The entered request is sent, and the server prepares to process it.

[0648] Step 2:

[0649] The server analyzes the received request and checks the inventory status of the requested materials by referring to the relevant database. Based on the situation, it determines whether the necessary materials are in internal inventory or whether an additional order is required.

[0650] Step 3:

[0651] Once the server identifies the items, it uses a generation AI to generate images of the identified items. These generated images are then prepared as data for user verification.

[0652] Step 4:

[0653] The terminal receives images from the server and presents them to the user. This allows the user to visually verify the materials and provides a function to select approval or request corrections on the confirmation screen.

[0654] Step 5:

[0655] The user reviews images and information about the supplies on their device, and if there are no issues, approves the agreement. The approval information is then sent from the device to the server.

[0656] Step 6:

[0657] After confirming the user's agreement, the server performs a final inventory check. If inventory is available, it immediately initiates the dispatch process; if inventory is insufficient, it automatically places an order with an external supplier.

[0658] Step 7:

[0659] The server completes the arrangements for outbound or inbound shipments and handles the delivery procedures for the goods. Delivery arrangement information is tracked in real time, and the user's terminal is notified of the progress.

[0660] Step 8:

[0661] The user confirms the arrival of the supplies and begins the actual work after receiving them. Once the work is completed, they send feedback to the system, and the entire process is finished.

[0662] (Example 1)

[0663] 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".

[0664] In resource supply processes, it is essential to quickly and accurately identify materials, manage inventory levels, and minimize human error and delays at each step from ordering from external sources to delivery. Conventional systems require users to spend a great deal of time selecting and verifying materials, and there is a lack of timely information sharing throughout the supply chain. Therefore, it is necessary to effectively solve these problems.

[0665] 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.

[0666] In this invention, the server includes means for receiving request information entered by a user and utilizing a generative AI model to identify the necessary resources based on this information; means for generating visual information about the identified resources and allowing the user to visually confirm them; and means for managing inventory status, automatically placing orders with external suppliers as needed, and delivering resources after user confirmation has been obtained. This enables improved efficiency and accuracy in the resource supply process.

[0667] A "user" is an individual or group that inputs resource request information and interacts with the system through the information provided and visual confirmations.

[0668] A "generative AI model" is an artificial intelligence system used to analyze input request information and identify the most suitable resources.

[0669] "Visual information" refers to image information that clearly represents the shape, color, and other characteristics of a specific resource.

[0670] "Inventory status" refers to information that indicates the current amount and condition of resources held, and is used to determine supply plans.

[0671] "External sources" refer to external manufacturers, vendors, and other entities used to procure resources that are lacking within the system.

[0672] "Automatic ordering" refers to the process of automatically ordering necessary resources from external suppliers based on inventory levels.

[0673] "Delivery method" refers to the method or system used to transport specific resources to a designated location.

[0674] "Real-time notification" is a method of providing information that informs users in real time about changes in resource delivery processes and inventory status.

[0675] This invention demonstrates how servers, terminals, and users interact within a resource supply system. This system uses a generative AI model to automate the process of identifying necessary resources from input request information.

[0676] First, the user enters resource request information through a terminal. This terminal has a user interface and allows the user to input data including the type of resource, quantity, and required delivery date. This request information is then sent to the server.

[0677] Next, the server inputs the received request information into a generative AI model to identify resources suitable for the request. The server is equipped with a database, which uses machine learning algorithms to analyze data, referencing historical data and inventory status. In this process, the generative AI model is used, and specific resources are identified by prompt statements.

[0678] Information about identified resources is generated as visual information from the server. This visual information accurately reflects the shape and color of the resources to prevent misidentification. The generated visual information is sent to the user's terminal and provided for verification.

[0679] The user reviews the visual information received via the device to confirm that the resources are accurate. This verification process allows the system to proceed to the next stage.

[0680] As a concrete example, consider a situation where a specific cable is in short supply at a certain facility. The user enters information about the cable type on a terminal and submits a request. The server uses a generative AI model to identify the appropriate cable and generates an image of it. The user reviews the image and approves the resource identification. After approval, the server automatically checks inventory, places an order with an external supplier if necessary, and proceeds with the delivery process.

[0681] An example of a prompt message to the generating AI model in this system is: "Identify the ID of a resource that matches this condition. Example: Cable X-500, red, 20 meters." This prompt is used as input data for the AI ​​model to function properly in identifying resources.

[0682] This invention enables rapid resource supply and accurate identification by having servers and terminals work together in a highly coordinated manner, allowing users to easily manage resources.

[0683] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0684] Step 1:

[0685] The user enters resource request information through a terminal. This input includes specific data such as the type of resource, quantity, and required delivery date. The terminal prepares to send this data to the server. The entered data serves as basic information necessary for subsequent processes in the system.

[0686] Step 2:

[0687] The server receives request information sent from the terminal. Based on this input data, it uses a generative AI model to identify the optimal resource. It sends a prompt to the generative AI asking, "Identify the ID of the resource that matches these conditions," and obtains the resource ID as output. The server uses this ID to search for additional data about the resource's characteristics.

[0688] Step 3:

[0689] The server generates visual information based on the identified resource's information. In this process, it retrieves detailed information such as the resource's shape and color from the database and outputs an accurate image. The generated visual information is stored on the server in a format easily accessible to the user.

[0690] Step 4:

[0691] Visual information generated from the server is sent to the user's terminal. The terminal displays this image on its screen and requests visual confirmation from the user. The image display function is appropriately adjusted so that the user can accurately recognize this information.

[0692] Step 5:

[0693] The user reviews the visual information on their device and scrutinizes the accuracy of the resources. Once agreement is reached, they press the confirmation button on their device to send a notification to the server. This action signals the system to proceed to the next process.

[0694] Step 6:

[0695] The server receives the user's confirmation notification and begins checking the inventory status. If the item is in stock, it immediately arranges for dispatch; otherwise, it automatically places an order with an external supplier. The order details are generated as output and sent to the supplier if necessary.

[0696] Step 7:

[0697] The server arranges delivery and manages its progress in real time. Delivery information is notified to the user's device, and the delivery status is displayed. Finally, the user receives the estimated delivery date and arrival confirmation on their device. This completes the resource supply process, and the user receives the resources.

[0698] (Application Example 1)

[0699] 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".

[0700] In the supply chain process, human error and delays in verification are common, which reduces the efficiency of the entire supply chain. In particular, accurate material management is required in manufacturing and other industrial settings, but traditional methods have limitations in terms of supply accuracy and responsiveness.

[0701] 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.

[0702] In this invention, the server includes means for identifying necessary supplies based on generated information, means for generating images of the identified supplies and obtaining user confirmation, means for supplying the confirmed supplies using an automated transport mechanism, and means for notifying the user's terminal of the real-time status of the supplied supplies. This streamlines the supply process and enables accurate and rapid supply.

[0703] "Generated information" refers to data used to identify necessary supplies based on request data entered by the user.

[0704] "Supplies" is a general term for the various materials and products required within a supply chain.

[0705] A "generative AI model" is a technology that uses machine learning algorithms to generate information from specific data and is used to identify materials.

[0706] "Image generation" is the process of creating image data to facilitate the visual identification of identified materials.

[0707] An "automated moving mechanism" is a system that utilizes robots and conveyor systems to supply specific materials to designated locations.

[0708] "Real-time status" refers to information that immediately monitors and transmits the current status and progress in the supply chain process.

[0709] "User terminal" refers to an electronic device used by the user to send and receive information and to verify and manage goods.

[0710] The system that implements this application is designed to automate material supply in manufacturing plants. The server analyzes the material requests received from the user's terminal and uses a generative AI model to identify the necessary materials. The generative AI model used here identifies the optimal type and quantity of requested materials based on historical data and inventory information.

[0711] The server generates images to facilitate visual confirmation of identified items. These images accurately reflect the shape and color of the items and are designed to prevent misidentification. The generated images are sent to the user's device, where the user can verify the items.

[0712] Once the supplies have been confirmed, the server controls an automated movement mechanism to deliver the necessary supplies to the appropriate location. Robotics and conveyor systems are utilized in this process. The progress of the supply process is monitored in real time and notified to the user's terminal. The main contents of the notification are the supply start time, the status in progress, and the supply completion time.

[0713] As a concrete example, consider a situation where a factory's production line is short of parts. The user uses a terminal to specify the missing parts and inputs the quantity and delivery date. The server then automatically selects the appropriate parts and can supply them quickly. Delays and errors in this supply process are minimized, improving production efficiency.

[0714] An example of a prompt message would be: "We are short on a specific part in the factory. Please tell us the name and quantity of the part. Also, if it is urgent, please specify the delivery date."

[0715] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0716] Step 1:

[0717] Users enter their material requests from a terminal. The data entered includes information such as the type and quantity of materials and the required delivery date. This information is sent to the server and forms the basis of the request.

[0718] Step 2:

[0719] Based on the received request, the server uses a generative AI model to identify the necessary supplies. In this step, historical data and inventory information are retrieved from the database and analyzed to identify the optimal supplies. As a result, the selected supplies are output.

[0720] Step 3:

[0721] The server uses the information of the identified items to generate a visual confirmation image. The generated image is visual data that accurately reproduces the shape and color of the items to prevent user misidentification. This image is obtained as the output of the generation process.

[0722] Step 4:

[0723] The server sends the generated supply images to the user's terminal. The user reviews the images on their terminal and determines whether the supplies are correct. User verification is performed through the terminal's GUI.

[0724] Step 5:

[0725] Once confirmation is received, the server controls the automated movement mechanism to supply the goods. This step involves checking inventory and using movement systems such as robots and conveyors to deliver the goods to the appropriate locations. It also includes checking inventory data and placing external orders if there are any shortages.

[0726] Step 6:

[0727] The server monitors the progress of the supply process in real time and notifies the user's terminal of its status. This notification includes the start time, ongoing status, and completion time of the supply, allowing the user to understand the entire process. The server uses supply status monitoring data as input and generates user notification data as output.

[0728] 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.

[0729] This invention provides a system for the rapid and efficient supply of necessary materials, and by taking user emotions into consideration, it enables the provision of better service. The system is configured as follows:

[0730] First, the user enters the shortage of supplies using a terminal. Here, they specify the type and quantity of supplies needed, as well as the desired delivery date. This request is sent to the server and processed.

[0731] Next, the server analyzes the received request data to identify the necessary supplies. Using generative AI technology, the server makes appropriate decisions based on past data and current inventory levels. During this process, the emotion engine references the user's past emotional data, taking into account the user's stress level and satisfaction level.

[0732] Once the items have been identified, the server generates an image of them. AI technology is used for this image generation, and information is prepared to facilitate visual recognition. This image is then presented to the user via their device.

[0733] The device displays images to the user and provides a process to verify the accuracy of the supplies. The user reviews the presented information and agrees. During this agreement process, an emotion engine analyzes the user's emotions in real time and provides additional instructions or suggestions as needed.

[0734] Once the supplies have been confirmed, the server checks the inventory. If necessary, it automatically places orders with external suppliers. After securing the supplies, arrangements for delivery are made. During the delivery process, the emotion engine evaluates the user's emotions regarding the delivery status and provides appropriate notifications and follow-up.

[0735] For example, if a user reports a shortage of a specific communication cable, the server quickly identifies the cable and verifies it with images. If the user verifies and the emotion engine detects stress, priority delivery arrangements and enhanced support are made. This increases user satisfaction and achieves efficient supply of goods.

[0736] Thus, the invention provides a comprehensive material supply system that takes user emotions into consideration, thereby improving service quality.

[0737] The following describes the processing flow.

[0738] Step 1:

[0739] The user uses a terminal to enter requests for supplies that are lacking or needed. The request includes the type of supply, quantity, priority, and desired delivery date. The entered data is sent to the server.

[0740] Step 2:

[0741] The server analyzes the received request, consults the system's database to check the inventory status of the requested supplies, and simultaneously uses a generative AI to select the necessary supplies.

[0742] Step 3:

[0743] The server uses AI technology to generate visual confirmation images for identified items. The generated images should include detailed information about the items (e.g., model number, size, etc.).

[0744] Step 4:

[0745] The terminal displays images and material information sent from the server to the user. The user reviews the displayed information, judges its accuracy, and approves or requests corrections.

[0746] Step 5:

[0747] Upon receiving user confirmation, the server initiates a process to perform a final inventory check and, if necessary, automatically place orders with external suppliers.

[0748] Step 6:

[0749] Once the server confirms that the requested supplies have been secured, it will arrange for delivery. Delivery information is tracked in real time, and the user's terminal is notified of the progress.

[0750] Step 7:

[0751] The emotion engine embedded in the server analyzes user reactions and responses, and based on the emotion data, makes suggestions for improving the content and services offered.

[0752] Step 8:

[0753] The user receives the supplies and begins work after confirming receipt. After completing the work, they send feedback to the system, and data is collected to assess user satisfaction.

[0754] (Example 2)

[0755] 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".

[0756] In managing the supply chain of goods, there are challenges in optimizing effective supply methods that take into account user emotions and in ensuring rapid inventory replenishment. Furthermore, in the goods verification process, discrepancies between user requests and inventory information lead to decreased satisfaction. Addressing these challenges, reducing user stress, and improving satisfaction are crucial.

[0757] 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.

[0758] This invention includes a server that identifies necessary supplies based on generated information and optimizes the supply method considering the user's emotional data; a server that generates images of the identified supplies using AI technology and obtains user confirmation; and a server that analyzes the user's emotions during the delivery process and provides necessary notifications and follow-up. This enables rapid and accurate supply and inventory management of supplies that takes the user's emotions into consideration.

[0759] "Generated information" refers to the request data provided by the user and the analysis data based on that data, which serves as the basis for making decisions necessary for supplying goods.

[0760] "Required supplies" refers to the types and quantities of supplies identified by the user's requests and the system.

[0761] "User sentiment data" refers to the quantified or data-based representation of past and present user emotional responses, used to optimize delivery methods.

[0762] "Optimizing supply methods" is the process of determining the optimal supply route and timing for goods, taking into account the type of goods, the supply and demand balance, and user sentiment data.

[0763] "Images of materials generated using AI technology" refers to digital images generated by artificial intelligence based on the details and characteristics of materials, which assist users in visual verification.

[0764] "Re-evaluating inventory levels" is a process of verifying current inventory data against the latest information to determine the need for supply.

[0765] "Automatic ordering" is a function in which the system automatically places orders for necessary supplies from external suppliers when it detects a shortage in inventory.

[0766] "Analyzing user emotions during the delivery process" refers to activities that evaluate user emotional data in real time during the delivery of goods and use that data to improve the supply process.

[0767] "Notifications and follow-ups" refer to procedures and communications aimed at providing users with information and additional support for problem-solving.

[0768] To implement this invention, a server, a terminal, a generative AI model, and an emotion engine are required. The server is responsible for the main processing, utilizing the generative AI model to analyze historical data and current inventory status to identify materials and optimize supply methods. The emotion engine is used to analyze user emotion data in real time to aid in supply decision-making. Furthermore, AI technology is used to generate visual information of materials as images and present them to the user via the terminal.

[0769] The terminal provides an interface for users to input requests for supplies, receives generated images from the server, and presents them visually to the user. It also has the function to send feedback to the server based on the user's selections and confirmations.

[0770] Users input shortages of supplies via a terminal. The entered information is sent to a server where it is analyzed. After user confirmation, the server automatically places orders with external suppliers if necessary, and handles inventory management and delivery arrangements. The server also monitors the user's emotions during delivery and provides appropriate notifications and follow-ups through the terminal.

[0771] As a concrete example, suppose a user enters "I need 5 communication cables" into their terminal and specifies a delivery date. The server analyzes this request, checks inventory using a generative AI model, and places a new order if necessary. The generated image is sent to the terminal for the user to confirm. At this time, the user's emotional data is analyzed, and if stress is detected, priority delivery or support arrangements are made. An example of a prompt message could be, "I have received a report of a communication cable shortage; please prioritize this."

[0772] This invention aims to improve service quality by enabling dynamic supply of goods based on user requests and emotions.

[0773] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0774] Step 1:

[0775] The user accesses the terminal interface and enters the type of out-of-stock item, the required quantity, and the desired delivery date. This information is organized into data packets by the terminal and sent to the server. The more specific the information entered, the more efficient the analysis can be.

[0776] Step 2:

[0777] The server analyzes the data packets received from the user and uses a generating AI model to identify the necessary supplies based on historical data and current inventory levels. This data processing involves comparing the data with similar past request data and cross-referencing it with figures in the inventory management system. The identified supply information is then used for the following processes.

[0778] Step 3:

[0779] The server uses AI technology to generate images of the identified materials. It utilizes the material's specifications and detailed information as input data for image generation, outputting it in a visual format that the user can review. The generated images are then transferred to the terminal.

[0780] Step 4:

[0781] The device displays the received image to the user and prompts them to confirm if it matches the requested item. The user examines the displayed image and, if there is a mismatch, sends feedback to the server via the device. Once confirmation is received, the data is sent to the server as an approved status.

[0782] Step 5:

[0783] The server receives user confirmation data and re-evaluates the inventory status. If inventory is insufficient, it generates a prompt message and sends the order information to the supply system to automatically place an order with an external supplier. The output will appear as a notification of order completion or inventory availability.

[0784] Step 6:

[0785] During the delivery arrangement phase, the server analyzes the user's emotions in real time and provides appropriate notifications and follow-up information as the delivery progresses. It analyzes emotional data as input and outputs actions as needed, including providing alternatives and support in case of delays. It also provides progress notifications via the terminal.

[0786] (Application Example 2)

[0787] 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".

[0788] Traditional supply systems have struggled to respond flexibly to the emotional state of users, resulting in a lack of sufficient improvement in user satisfaction. In particular, services requiring immediate responses, such as food delivery, demand service provision that considers the user's emotions, but there has been a lack of means to achieve this.

[0789] 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.

[0790] In this invention, the server includes means for identifying necessary supplies based on generated information, means for generating images of the identified supplies and obtaining confirmation that takes into account the user's emotional state, and means for delivering the confirmed supplies with adjusted delivery priority based on emotion analysis. This enables adaptive supply of supplies that takes into account the user's emotions.

[0791] "Generated information" refers to data that is analyzed by the system and used to identify materials and provide them to users.

[0792] "Goods" refers to the items or services that users request to be supplied.

[0793] "Emotional state" refers to information that indicates the user's psychological reactions and emotions, and its analysis enables individualized responses.

[0794] "Means of obtaining confirmation" refers to the process of presenting information to users and obtaining their agreement or consent.

[0795] "Delivery priority" is an evaluation metric used to determine the order and speed of delivery of goods, and is adjusted based on the results of user sentiment analysis.

[0796] "Emotional analysis" refers to a technology that analyzes a user's emotional state as data and uses it for various system controls and feedback.

[0797] The system that implements this application works in cooperation with the user, terminal, and server. The user can use their smartphone to order goods, such as food, and input their desired delivery date at that time. Once the order is complete, the terminal sends this information to the server.

[0798] Based on the received information, the server uses a generative AI model to perform necessary data analysis and identify the items. This process involves analyzing big data, including the user's past history and current inventory status, to achieve efficient and accurate item identification. An image of the identified item is generated using AI technology and sent to the terminal.

[0799] The device visually presents the user with images of the generated items and prompts them to confirm. At the same time, the device utilizes an emotion analysis API (e.g., Microsoft Emotion API) to evaluate the user's real-time emotional state. For example, if the user is experiencing high stress levels, it may present a special message or discount. This makes it possible to provide an emotionally sensitive user experience.

[0800] Once the supplies are identified and confirmed, the server arranges delivery. Based on information obtained from sentiment analysis, delivery priorities are adjusted; for example, users with high sentiment scores receive expedited delivery. Once delivery begins, the server also provides appropriate notifications regarding the delivery status, taking into account the user's sentiment.

[0801] (Specific example)

[0802] For example, if a user orders pasta at lunchtime and places the order via smartphone, and the system determines that their stress level is high, a message such as "We'll deliver it early today so you can relax!" will be displayed on the device.

[0803] (Example of prompt text to input to the generated AI model)

[0804] "Based on the user's ordered dish name, image, delivery date, and sentiment data, generate the optimal delivery method and additional message."

[0805] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0806] Step 1:

[0807] The user places an order for supplies by operating a terminal. The input includes the name, quantity, and delivery date of the specified supplies. The terminal then sends this information to the server.

[0808] Step 2:

[0809] The server analyzes the received order information. The input is the order information from the user, and the output is the analysis result. The server uses a generative AI model to refer to past user data and inventory information to identify the necessary supplies. In this process, data analysis technology is utilized to efficiently identify the supplies.

[0810] Step 3:

[0811] The server uses AI generation technology to create images of identified items. The input is data of the identified items, and the output is image data. The generated images are sent to the user's terminal for visual confirmation.

[0812] Step 4:

[0813] The device presents images to the user and evaluates the user's emotional state in real time. Input is image data from the server, and output is the user's response confirmation and emotional evaluation. Using an emotional analysis API, it retrieves emotional data such as the user's stress level and displays special messages or discounts as needed.

[0814] Step 5:

[0815] Once the user completes the image verification, the verification result is sent to the server. The input is the user's acknowledgment, and the output is the transmission of that result to the server.

[0816] Step 6:

[0817] The server arranges delivery for confirmed supplies. Inputs are the final confirmed order data and the user's sentiment rating; output is the delivery plan. Based on the sentiment analysis results, the server adjusts delivery priorities and, if necessary, prioritizes delivery.

[0818] Step 7:

[0819] The server also evaluates the user's emotions regarding goods in transit and sends appropriate notifications to the device. Inputs are delivery status and emotion evaluation data, while output is a notification message to the device. This provides users with a sense of security and improves their satisfaction.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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."

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0841] The following is further disclosed regarding the embodiments described above.

[0842] (Claim 1)

[0843] A means of identifying necessary supplies based on the generated information,

[0844] A means of generating images of identified materials and obtaining user confirmation,

[0845] Means of delivering the confirmed supplies,

[0846] A system that includes this.

[0847] (Claim 2)

[0848] The system according to claim 1, further comprising means for prompting visual confirmation by providing the generated image to the user's terminal.

[0849] (Claim 3)

[0850] The system according to claim 1, which includes means for managing the inventory status of materials after confirmation and placing additional orders with external suppliers as necessary.

[0851] "Example 1"

[0852] (Claim 1)

[0853] A means of receiving request information entered by the user and utilizing a generative AI model to identify the necessary resources based on this information,

[0854] A means of generating visual information about identified resources and allowing users to visually confirm them,

[0855] After user verification is obtained, the system manages inventory levels, automatically places orders with external suppliers as needed, and delivers resources.

[0856] A means of tracking the progress of delivery and notifying the user's device in real time,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, comprising means for providing generated visual information to a user's terminal to enable visual confirmation.

[0860] (Claim 3)

[0861] The system according to claim 1, which includes means for managing the inventory status of resources and notifying the delivery status in real time after user verification.

[0862] "Application Example 1"

[0863] (Claim 1)

[0864] A means of identifying necessary supplies based on the generated information,

[0865] A means of generating images of identified materials and obtaining user confirmation,

[0866] A means of supplying the confirmed materials using an automated transport mechanism,

[0867] A means of notifying users' terminals of the real-time status of supplied goods,

[0868] A system that includes this.

[0869] (Claim 2)

[0870] The system according to claim 1, further comprising means for prompting visual confirmation by providing the generated image to the user's terminal.

[0871] (Claim 3)

[0872] The system according to claim 1, comprising means for managing the inventory status of goods after confirmation, placing additional orders with external supply sources as necessary, and optimizing delivery using a machine control system.

[0873] "Example 2 of combining an emotion engine"

[0874] (Claim 1)

[0875] A means to identify necessary supplies based on generated information and optimize the supply method considering user sentiment data,

[0876] A method for generating images of identified materials using AI technology and obtaining user confirmation,

[0877] We will reassess the inventory status of the confirmed supplies and, if necessary, automatically place orders with external suppliers.

[0878] A means of analyzing user emotions during the delivery process and providing necessary notifications and follow-ups,

[0879] A system that includes this.

[0880] (Claim 2)

[0881] The system according to claim 1, which includes means for providing the generated image to the user's terminal and prompting the user for additional instructions or suggestions based on a real-time sentiment analysis of the user.

[0882] (Claim 3)

[0883] The system according to claim 1, comprising means of using a generative AI model in the inventory management and ordering processes to external supply sources.

[0884] "Application example 2 when combining with an emotional engine"

[0885] (Claim 1)

[0886] A means of identifying necessary supplies based on the generated information,

[0887] A means of generating images of identified items and obtaining confirmation that takes into account the user's emotional state,

[0888] A method for delivering confirmed supplies by adjusting delivery priority based on sentiment analysis,

[0889] A system that includes this.

[0890] (Claim 2)

[0891] The system according to claim 1, comprising means for providing the generated image to the user's device to prompt visual confirmation and providing adaptive notifications in response to emotions.

[0892] (Claim 3)

[0893] The system according to claim 1, which includes means for managing the inventory status of goods after confirmation, placing additional orders with external suppliers as necessary, and providing feedback to users based on sentiment analysis. [Explanation of symbols]

[0894] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of identifying necessary supplies based on the generated information, A means of generating images of identified materials and obtaining user confirmation, Means of delivering the confirmed supplies, A system that includes this.

2. The system according to claim 1, further comprising means for providing the generated image to the user's terminal to encourage visual confirmation.

3. The system according to claim 1, which includes means for managing the inventory status of materials after confirmation and placing additional orders with external suppliers as necessary.

Images