system

Abstract

Provide a system. 【Solution means】 Means for the terminal to input a requirement for a component, Means for the server to receive the input requirement and search the inventory information of the component from the database, Means for the server to use a generative artificial intelligence to generate a proposal to procure components from an external source when the inventory is insufficient, Means for the server to obtain an image of the required component and send it to the terminal to request confirmation, Means for the server to arrange for delivery and installation based on the approved component information, Means for the terminal to collect feedback after delivery and installation, Means for optimizing the supplier and delivery route in the logistics business, A system including

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to the description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In companies that construct and maintain base stations, it is difficult to ensure necessary components and spare parts. Especially when important, the lack of components may reduce the service quality and user satisfaction. Business stops or delays caused by such component shortages not only lead to a decrease in customer satisfaction but may also have an adverse impact on the reliability and revenue of the company. There is a need for a system that can quickly and accurately ensure and supply components.

Means for Solving the Problems

[0005] This invention provides a means for a terminal to request necessary components, and a means for a server to receive these requests and search a database. It also includes a means for using artificial intelligence to select the optimal supply source when inventory is low, enabling rapid procurement of components. Furthermore, it transmits images of the components to the terminal, requests user confirmation, automates delivery and installation arrangements to prevent errors, and supports the collection of feedback after completion. In this way, it provides a system that improves user satisfaction and operational efficiency by ensuring the rapid and accurate securing and supply of components.

[0006] A "terminal" is a device used by a system user to input requests for components.

[0007] A "server" is a computer system that processes requests received from terminals and performs tasks such as database searches, suggestion generation, image transmission, and arrangement management.

[0008] "Generative artificial intelligence" refers to an algorithm or program used to select and generate suggestions for supply sources based on data.

[0009] A "database" is a collection of data where inventory information for components is stored and retrieved.

[0010] "Inventory information" refers to data that shows the quantity, location, and condition of specific components.

[0011] "External sources" refer to components supplied or provided by parties outside the system.

[0012] A "component image" is a photograph or diagram containing visual information about the requested component.

[0013] "Delivery" is the process of transporting materials to a designated location.

[0014] "Installation" refers to the process of attaching delivered components to their designated locations.

[0015] "Feedback" refers to the evaluation or opinion input made by the user after the completion of delivery and installation.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the 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.

Embodiment for Implementing the Invention

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of 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.

[0020] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, a storage with a reference numeral is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention relates to a system for quickly and accurately securing and delivering components for base stations. The system aims to smoothly supply components by having the user input information about the required components into a terminal, which is then received and processed by a server.

[0038] The user first inputs the type and quantity of required materials through the terminal's interface. This information is immediately transmitted to the server. Based on the received request, the server searches its database to check the current inventory status. If a shortage is detected in the inventory information, the server activates artificial intelligence to perform an analysis to identify the optimal external supply source. The optimal supplier is selected considering multiple factors, such as price, distance, and delivery time.

[0039] The server then retrieves images of the components from the database as needed and sends them to the user's terminal. The user reviews these images and either accepts them or requests modifications. If approved, the server automatically begins coordinating the delivery process and installation. For example, the server optimizes the delivery schedule and notifies delivery personnel and installation teams of relevant information.

[0040] Finally, after installation is complete, the terminal requests feedback from the user. This feedback is sent to the server and recorded for service improvement. This entire process can improve user satisfaction and increase operational efficiency.

[0041] By following the embodiments of this invention, telecommunications infrastructure companies and others can provide rapid and accurate component supply and deliver services that exceed customer expectations.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user accesses the terminal and enters the type and quantity of materials needed. Once the input is complete, pressing the submit button sends the information to the server.

[0045] Step 2:

[0046] The server receives a request from the user and searches its internal database. The search results confirm the inventory status of the parts and retrieve that information.

[0047] Step 3:

[0048] If the server detects a shortage of stock, it activates artificial intelligence. The AI ​​investigates external supply sources and generates suggestions to select the best supplier based on price, distance, and delivery time.

[0049] Step 4:

[0050] The server retrieves images of the requested components from the database and sends them to the user's terminal for verification.

[0051] Step 5:

[0052] The user reviews the component images sent via their device and requests approval or modification. If approval is granted, they click the approval button.

[0053] Step 6:

[0054] Once the server receives user approval, it begins arranging delivery and installation. It optimizes delivery routes, adjusts schedules, and notifies delivery personnel of relevant information.

[0055] Step 7:

[0056] The device notifies the user that delivery and installation are complete, and then requests feedback. The user enters and submits feedback, and the information is recorded on the server.

[0057] (Example 1)

[0058] 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."

[0059] Current supply systems are required to supply necessary goods quickly and efficiently, but delays and misdeliveries are problematic due to insufficient selection of external supply sources during times of inventory shortage and inadequate flexibility in responding to user requests.

[0060] 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.

[0061] In this invention, the server includes means for a terminal to input a request for an item, means for the server to receive the input request and retrieve stored information about the item from a data set, and means for generating a proposal to procure the item from an external source when the stored items are insufficient, using a generative intelligence model. This enables efficient and rapid selection of the optimal external source and flexible supply of items in response to user requests.

[0062] A "terminal" is an electronic device used by users to input requests for goods and is responsible for communicating with a server.

[0063] A "server" is a central computing device that performs data retrieval and analysis using generative intelligence models based on received item requests, and manages item information, including suggesting external supply sources.

[0064] "Items" refers to specific goods or materials that users request and receive through the system.

[0065] A "generative intelligence model" is an artificial intelligence model used to analyze multiple factors such as price, distance, and delivery time when selecting external supply sources.

[0066] A "data set" refers to a database or data storage area in which information about an item's storage and other related data are recorded.

[0067] "External sources" refer to external suppliers or traders selected by the server to procure goods when inventory is insufficient.

[0068] "Visual information" refers to images and other visual data sent to the terminal about the requested item.

[0069] "Transportation" refers to the process by which goods physically move from the supplier to the requester, and is an activity arranged by the server.

[0070] "Installation" refers to the process of appropriately placing requested items based on the location and conditions specified by the user, and is handled by the server.

[0071] "Evaluation" refers to feedback information provided by users after the transportation and installation of goods, and is used to improve the system's services.

[0072] One embodiment of this invention involves using a terminal in which the user inputs a request for goods. This terminal consists of common electronic devices such as PCs, tablets, and smartphones. When the user inputs the specific type and quantity of goods, that information is immediately transmitted to the server.

[0073] The server is a high-performance computer that uses an SQL database management system to query inventory information for goods for data collection. If there is a shortage in the stored information, it uses a generative AI model to identify external supply sources. In this process, the server inputs prompt statements into the generative AI model, which performs analysis to select the most efficient supplier. An example of a prompt statement is, "Based on the current inventory status and request details, identify the optimal supplier. Factors to consider are price, distance, and delivery time."

[0074] For example, if a user enters "I need 50 steel beams for construction by next week," the server searches the database based on this information and activates an AI model to generate supplies once a shortage is detected. It then selects the most suitable external supplier and generates a proposal. The selected supplier information, along with visual information of the items, is sent to the user's terminal. The user can review the images of the proposed items and request modifications as needed.

[0075] Based on the above process, the present invention can provide a flexible goods supply system that can quickly and accurately respond to the diverse needs of users.

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

[0077] Step 1:

[0078] The user inputs their item request using a terminal. Specifically, they input the type and quantity of items on the terminal's interface and confirm the request. This input becomes data that the terminal sends to the server. As output, the user's request details are prepared as data to be sent to the server.

[0079] Step 2:

[0080] The terminal sends the input request to the server. Data transfer from the terminal to the server takes place in real time via network communication. Specifically, the terminal converts the request content into a packet and sends it to the specified address on the server. The request content is received on the server side as output.

[0081] Step 3:

[0082] The server searches the database based on the received request. It executes SQL queries using the database management system to retrieve inventory information for the items. In this process, the server extracts inventory quantities and related information from the database and processes the results. The output confirms the current inventory status.

[0083] Step 4:

[0084] If inventory is insufficient, the server activates a generative AI model to analyze external supply sources. The server creates a prompt message and inputs it into the generative AI model. This prompt message includes factors to consider, such as price, distance, and delivery time. As output, the AI ​​model returns a list of the best suppliers.

[0085] Step 5:

[0086] The server sends the proposed supplier information and visual information of the item to the terminal. The server retrieves the visual information from the database and sends the data back to the terminal along with the supplier information. Based on this output data, the user can check the contents of the item on the terminal.

[0087] Step 6:

[0088] The user reviews the submitted information and either accepts it or requests corrections. If the user approves the information, the terminal sends the instructions to the server. If corrections are needed, the user enters the changes and sends them back to the server. The output then confirms the final instructions.

[0089] Step 7:

[0090] Based on the approved information, the server arranges the transportation and installation of the goods. Specifically, it notifies logistics companies and installation personnel and coordinates the schedule. As an output, the procedures required for transportation and installation are finalized and communicated to all relevant parties.

[0091] Step 8:

[0092] After installation, the terminal displays a screen requesting user feedback. Users input their evaluation and suggestions for improvement, and send this data from the terminal to the server. As output, user feedback information is stored on the server and used to improve the service in the future.

[0093] (Application Example 1)

[0094] 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."

[0095] There is a growing need for increased efficiency in inventory management and material supply in logistics operations. In particular, when inventory shortages occur, the rapid selection of appropriate supply sources and optimization of delivery routes are crucial. Traditional systems often require manual processes, which are time-consuming and labor-intensive, necessitating effective solutions.

[0096] 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.

[0097] In this invention, the server includes means for a terminal to input a request for a component, means for the server to receive the input request and retrieve component inventory information from a database, and means for optimizing the supplier and delivery route using generative artificial intelligence. This enables rapid procurement in times of inventory shortage and efficient management of logistics operations.

[0098] A "terminal" is a part of a computer or mobile device used by a user to input and receive information.

[0099] A "server" is a computer system that processes and manages data via a network.

[0100] "Components" refer to the items and materials necessary for a specific task or project.

[0101] "Inventory information" refers to data that shows the current status of stored items.

[0102] A "database" is a system for efficiently storing, searching, and managing data.

[0103] "Generative artificial intelligence" refers to algorithms or technologies that reduce manual intervention and perform information analysis and decision-making automatically.

[0104] A "supply source" is an external provider of necessary goods or services.

[0105] A "delivery route" is the path taken by goods from their source to their destination.

[0106] "Feedback" refers to information that includes opinions and evaluations from users regarding a completed process.

[0107] The system for realizing this invention uses a terminal, a server, and a generative AI model. The terminal provides an interface for the user to input component requests, and for the server to process them based on those requests. The terminal typically functions as a portable device such as a smartphone or tablet.

[0108] The server searches the database for inventory information based on the received request. Examples of databases used here include relational database management systems such as MySQL®. If an inventory shortage is detected, the server activates a generative AI model to determine the optimal supply source and delivery route. Generative AI models are typically built using deep learning frameworks such as Tensorflow® or PyTorch.

[0109] As a concrete example, when a user enters the type and quantity of a component from their terminal, the server immediately searches the database to check the inventory status. If an inventory shortage occurs, a generative AI model is used to make the optimal selection, taking into account the price, distance, and delivery time of the supply source. The server then sends an image of the component to the terminal and asks the user for confirmation. Once approval is received, the server automatically begins arranging delivery.

[0110] An example of a prompt would be: "Based on current inventory levels, identify suppliers with lower prices and faster delivery times. We would also like a detailed analysis, including the optimal delivery route." This prompt instructs the generating AI model to optimize supply sources and delivery.

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The user uses a terminal to input the type and quantity of required components. The input data, including detailed information such as name, quantity, and priority, is sent to the server through the terminal's application.

[0114] Step 2:

[0115] The server searches the database based on the request data received from the terminal. The database stores inventory information, and the server checks if the specified quantity of the relevant component exists. A flag indicating the presence or absence of inventory is output.

[0116] Step 3:

[0117] If inventory is insufficient, the server activates a generative AI model. The input quantities are information about the components and the current inventory status, and the output quantity is the selection of the optimal supply source. The generative AI model considers factors such as price, distance, and delivery time to analyze and propose the optimal supply strategy.

[0118] Step 4:

[0119] Based on the proposed supply strategy, the server retrieves images of the components from the database and sends them to the terminal. The user reviews the images and makes a final approval or requests for modifications. The user's input is then used in the server's next processing step.

[0120] Step 5:

[0121] Once user approval is obtained, the server automatically begins arranging delivery. The delivery route is efficiently set based on information proposed by the generating AI model. A notification is sent to the external system responsible for delivery operations.

[0122] Step 6:

[0123] After delivery and installation are complete, users submit feedback via a terminal. This feedback data is recorded on a server to improve service quality and is used as foundational information for future process improvements.

[0124] 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.

[0125] This invention enhances user psychological comfort by combining an emotion engine with a system that rapidly and accurately supplies components to base stations. When a user inputs a component request into the terminal, the emotion engine is simultaneously activated to recognize the user's input and the emotions associated with their actions. This emotion recognition is estimated from factors such as word choice and input speed.

[0126] When the server receives a request from a terminal, it retrieves inventory information from the database and, if necessary, activates artificial intelligence. The AI ​​selects the best external supply source when inventory is low and generates a suggestion. The selection of supply sources is optimized by considering factors such as price, distance, and delivery time.

[0127] Furthermore, the output of the emotion engine is reflected in the process of generating component suggestions. For example, if the user's emotion is estimated to be "stress" or "dissatisfaction," the suggested method will be adjusted to be more flexible or to prioritize a quick response.

[0128] The proposed component details are sent as images to the user's terminal, and the user is asked to confirm them. At this time, the user reviews the component images and approves or requests revisions. The server then takes appropriate action accordingly.

[0129] Delivery and installation arrangements are handled automatically after server approval, with efficient routes and schedules selected. After installation is complete, the device requests user feedback, and the emotion engine evaluates the user's emotions in this feedback. Any perceived dissatisfaction in the feedback is recorded on the server for future improvements.

[0130] For example, if a user inputs "I'm in a hurry, but I can't find the necessary parts," the emotion engine recognizes the urgency, and the AI ​​generates suggestions that prioritize quick delivery. This process allows the user to receive prompt and appropriate service, while also providing psychological reassurance.

[0131] This embodiment of the invention enables telecommunications infrastructure companies to provide services that are sensitive to user needs while also meeting user requirements.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The user inputs their component requests using a terminal. Upon receiving the input data, the terminal simultaneously sends interaction data to the emotion engine to analyze the user's emotional state.

[0135] Step 2:

[0136] The terminal sends user request data and sentiment data to the server. The server receives this data and searches its database for inventory information on the components.

[0137] Step 3:

[0138] If the server detects a shortage of stock, it activates the AI. The AI ​​considers emotional data and generates suggestions that prioritize urgency and quick responses if the user is experiencing stress.

[0139] Step 4:

[0140] The server sends information about the selected components and the proposed content to the user's terminal. The terminal displays the images and proposed content to the user and provides a screen for confirmation.

[0141] Step 5:

[0142] The user reviews the proposed components and content on their device and requests approval or modification. If approved, the user clicks the approval button. If modifications are needed, the user enters the necessary information.

[0143] Step 6:

[0144] When the server receives user approval or modification requests, it automatically arranges delivery and optimizes the installation schedule. Prioritizes arrangements, especially if emotional data indicates a high level of urgency.

[0145] Step 7:

[0146] After the device is installed, the user is asked for feedback. The feedback is analyzed by an emotion engine and sent to the server. The server records this information and uses it to improve the service in the future.

[0147] (Example 2)

[0148] 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".

[0149] Traditional material supply systems often procure materials without considering the user's feelings, making it difficult to adequately address urgent requests or anxieties from users. This results in a decline in service quality and a loss of user psychological comfort.

[0150] 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.

[0151] In this invention, the server includes means for the terminal to analyze the user's emotions using an emotion engine, means for generating suggestions adjusted based on those emotions, and means for analyzing the feedback content. This enables the adjustment of component procurement suggestions according to the user's emotions, allowing for the rapid and appropriate provision of services.

[0152] A "terminal" is an information processing device that a user operates to input requests for components and provide feedback.

[0153] An "emotion engine" is a software technology that analyzes a user's psychological state and emotions based on their input and actions.

[0154] A "server" is a central processing unit that receives requests and sentiment data from terminals and performs tasks such as optimizing component procurement and generating proposals.

[0155] "Generative artificial intelligence" is a technology that analyzes inventory status and information on external supply sources to automatically create optimal procurement proposals.

[0156] A "database" is an information recording device that centrally manages data such as inventory information and supplier information for materials.

[0157] "Feedback" refers to information provided by users regarding their experience and evaluation after requesting components.

[0158] An "external source" refers to an external supplier or company that procures materials when required components are in short supply.

[0159] A "proposal" is a proposal provided by the server, based on user requests, that includes information regarding component procurement and supply.

[0160] This invention provides a system that accurately and quickly supplies components while taking into account the user's psychological state. The system is started when the user inputs a request for components using a terminal. The terminal incorporates an emotion engine for analyzing the user's emotions, which estimates the user's emotions from the input content and operation status.

[0161] The server processes requests and sentiment data received from the terminal. It retrieves inventory information from the database and uses generative artificial intelligence to select the best external supply source if inventory is insufficient. The generative AI model optimizes the supply source selection by considering price, distance, and delivery time. Based on this information, it generates material procurement proposals that take the user's emotions into consideration.

[0162] Furthermore, the server sends the proposed content as an image to the user's terminal. The user reviews the image and either approves the proposal or requests revisions. Upon user approval, the server automatically arranges the delivery and installation of materials, selecting the optimal route and schedule.

[0163] After installation is complete, the terminal will request feedback from the user. The emotion engine will also evaluate the user's emotions in the feedback, and the information obtained will be used to improve the service in the future.

[0164] For example, if a user inputs "I'm in a hurry, but I can't find the necessary parts," the emotion engine recognizes the urgency, and the AI ​​generates parts supply suggestions that prioritize quick delivery. This process allows the user to receive prompt and appropriate service. This entire system enables telecommunications infrastructure companies to provide emotionally sensitive services while meeting user demands.

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1:

[0167] The user enters a request for materials using a terminal. The input includes the material name, quantity, urgency, and required deadline. This input is analyzed by an emotion engine to generate user emotion data. The emotion data and request details are sent from the terminal to the server.

[0168] Step 2:

[0169] The server retrieves inventory information from the database based on the received request and sentiment data. The database search results output the inventory status of the desired parts. If the inventory is insufficient, the server activates a generating AI model.

[0170] Step 3:

[0171] The server uses a generative AI model to select external supply sources. Factors such as price, distance, and delivery time are considered during this selection process. Based on the input inventory information and candidate supply source data, the optimal supply source is proposed. This proposal is then adjusted according to the user's sentiment data.

[0172] Step 4:

[0173] The server generates the adjusted parts procurement proposal as image data and sends it to the user's terminal. The image visually displays detailed information about the proposed parts and supply options. The user is then asked to review this output and can approve or request revisions.

[0174] Step 5:

[0175] The user reviews the proposed image displayed on the device and enters a request for approval or modification. If a revised proposal is required based on the request, that information is sent to the server via the device.

[0176] Step 6:

[0177] The server arranges delivery and installation based on user approval. Based on the approved information, the optimal delivery route and schedule are selected and output. This results in efficient delivery and installation of the components.

[0178] Step 7:

[0179] The terminal collects user feedback after installation is complete. An emotion engine analyzes the feedback input to evaluate user satisfaction and potential dissatisfaction. The data after emotion analysis is sent to a server and recorded for future service improvements.

[0180] (Application Example 2)

[0181] 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".

[0182] In modern logistics centers, there is a demand for efficient and rapid supply of materials. However, conventional systems have failed to consider the feelings of users during material supply, sometimes resulting in a lack of psychological comfort. As a result, the supply process lacked flexibility, leading to decreased user satisfaction. Therefore, there is a need for a system that can simultaneously achieve rapid material supply and flexible responses that consider the feelings of users.

[0183] 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.

[0184] In this invention, the server includes means for a terminal to input a request for components, means for identifying the user's emotions associated with the input using an emotion recognition engine, and means for adjusting the proposal based on the user's emotions. This enables flexible and rapid responses in the component supply process, taking into account the user's emotions.

[0185] A "terminal" is a device used by a user to input their requests for components, and usually refers to a computing device such as a smartphone or tablet.

[0186] A "server" refers to a central computing system that receives requests, retrieves information from a database, and generates processing and suggestions.

[0187] An "emotion recognition engine" is a software module that analyzes user input and uses natural language processing technology to identify the user's emotions.

[0188] "Generative artificial intelligence" refers to an algorithm or system that selects external supply sources and generates optimal suggestions when inventory is insufficient.

[0189] "External supply sources" refer to external suppliers that can be used to procure any missing parts at a logistics center.

[0190] "Inventory information" refers to data stored in a database that describes detailed information about the existence and quantity of each component.

[0191] "Proposal" refers to information about how to procure materials, generated by the server in response to a user's material request.

[0192] "Feedback" refers to reaction information, including opinions and feelings, collected from users after delivery and installation are complete.

[0193] The system for realizing this application includes a terminal, a server, an emotion recognition engine, generative artificial intelligence, and a database. The terminal is a device used by the user to input requests for materials, and is typically a smartphone or tablet. Once the user's request is entered, the terminal sends the information to the server. The server receives this input and searches the database for relevant inventory information.

[0194] The server utilizes an emotion recognition engine to analyze user input and uses natural language processing techniques to identify the user's emotions. For example, this emotion analysis can be implemented using SpaCy, a Python natural language processing library. The identified emotions are taken into consideration when adjusting component proposals.

[0195] If inventory is insufficient, the server uses generative artificial intelligence, such as the OpenAI® API, to generate suggestions for selecting the optimal external supply source. This generative AI model considers factors such as price, distance, and delivery time, and is further prioritized based on the user's sentiment. The AI-generated suggestions are sent to the terminal in the form of an image visualized with a library such as Mateplotlib, and the user is asked for confirmation.

[0196] For example, when a logistics center staff member enters "urgently needed," the system senses an emotion such as "urgent" in this input. Based on this, the AI ​​selects the fastest available external supply source and provides that information to the user.

[0197] For example, if the prompt is entered as "How long until my next order arrives?", the emotion engine identifies the emotion "worry," and the generative AI model generates a quick suggestion, providing details of that suggestion as an image. This process improves the overall efficiency of parts supply and ensures the user's psychological comfort.

[0198] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0199] Step 1:

[0200] The user enters the material request using a terminal. The system receives the user's request. The input data is sent to the server in text format.

[0201] Step 2:

[0202] The server analyzes the received request data and searches for inventory information using the database. The database query checks the inventory status of the requested component and retrieves that information.

[0203] Step 3:

[0204] The server activates an emotion recognition engine to identify emotions from the user's request input. It analyzes the text using a natural language processing library and determines the emotional state based on keywords and input speed. For example, it might use the Python SpaCy library to analyze emotions.

[0205] Step 4:

[0206] The generating artificial intelligence generates suggestions for external supply sources based on available inventory information and identified emotions. The AI ​​model considers price, distance, and delivery time, and also takes into account the user's emotions to generate an optimal supply plan. OpenAI APIs are used in this process.

[0207] Step 5:

[0208] The server visualizes the proposal results and sends them to the terminal in image format. The generated proposals are then converted into images using tools such as Matplotlib, allowing the user to visually review them.

[0209] Step 6:

[0210] The terminal presents the proposal to the user and accepts requests for approval or modification. It then retrieves the user's response and sends it to the server.

[0211] Step 7:

[0212] The server automatically arranges delivery and installation based on user approval. It optimizes delivery schedules and routes and sends instructions to the relevant transportation methods.

[0213] Step 8:

[0214] After the device has been delivered and installed, user feedback is collected. This feedback information is sent to a server and recorded for future improvements.

[0215] Step 9:

[0216] The server also evaluates the emotions included in the feedback and uses this information to improve the system. The emotion recognition engine then re-identifies the emotions and extracts elements of dissatisfaction and areas for improvement.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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".

[0233] This invention relates to a system for quickly and accurately securing and delivering components for base stations. The system aims to smoothly supply components by having the user input information about the required components into a terminal, which is then received and processed by a server.

[0234] The user first inputs the type and quantity of required materials through the terminal's interface. This information is immediately transmitted to the server. Based on the received request, the server searches its database to check the current inventory status. If a shortage is detected in the inventory information, the server activates artificial intelligence to perform an analysis to identify the optimal external supply source. The optimal supplier is selected considering multiple factors, such as price, distance, and delivery time.

[0235] The server then retrieves images of the components from the database as needed and sends them to the user's terminal. The user reviews these images and either accepts them or requests modifications. If approved, the server automatically begins coordinating the delivery process and installation. For example, the server optimizes the delivery schedule and notifies delivery personnel and installation teams of relevant information.

[0236] Finally, after installation is complete, the terminal requests feedback from the user. This feedback is sent to the server and recorded for service improvement. This entire process can improve user satisfaction and increase operational efficiency.

[0237] By following the embodiments of this invention, telecommunications infrastructure companies and others can provide rapid and accurate component supply and deliver services that exceed customer expectations.

[0238] The following describes the processing flow.

[0239] Step 1:

[0240] The user accesses the terminal and enters the type and quantity of materials needed. Once the input is complete, pressing the submit button sends the information to the server.

[0241] Step 2:

[0242] The server receives a request from the user and searches its internal database. The search results confirm the inventory status of the parts and retrieve that information.

[0243] Step 3:

[0244] If the server detects a shortage of stock, it activates artificial intelligence. The AI ​​investigates external supply sources and generates suggestions to select the best supplier based on price, distance, and delivery time.

[0245] Step 4:

[0246] The server retrieves images of the requested components from the database and sends them to the user's terminal for verification.

[0247] Step 5:

[0248] The user reviews the component images sent via their device and requests approval or modification. If approval is granted, they click the approval button.

[0249] Step 6:

[0250] Once the server receives user approval, it begins arranging delivery and installation. It optimizes delivery routes, adjusts schedules, and notifies delivery personnel of relevant information.

[0251] Step 7:

[0252] The device notifies the user that delivery and installation are complete, and then requests feedback. The user enters and submits feedback, and the information is recorded on the server.

[0253] (Example 1)

[0254] 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."

[0255] Current supply systems are required to supply necessary goods quickly and efficiently, but delays and misdeliveries are problematic due to insufficient selection of external supply sources during times of inventory shortage and inadequate flexibility in responding to user requests.

[0256] 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.

[0257] In this invention, the server includes means for a terminal to input a request for an item, means for the server to receive the input request and retrieve stored information about the item from a data set, and means for generating a proposal to procure the item from an external source when the stored items are insufficient, using a generative intelligence model. This enables efficient and rapid selection of the optimal external source and flexible supply of items in response to user requests.

[0258] A "terminal" is an electronic device used by users to input requests for goods and is responsible for communicating with a server.

[0259] A "server" is a central computing device that performs data retrieval and analysis using generative intelligence models based on received item requests, and manages item information, including suggesting external supply sources.

[0260] "Items" refers to specific goods or materials that users request and receive through the system.

[0261] A "generative intelligence model" is an artificial intelligence model used to analyze multiple factors such as price, distance, and delivery time when selecting external supply sources.

[0262] A "data set" refers to a database or data storage area in which information about an item's storage and other related data are recorded.

[0263] "External sources" refer to external suppliers or traders selected by the server to procure goods when inventory is insufficient.

[0264] "Visual information" refers to images and other visual data sent to the terminal about the requested item.

[0265] "Transportation" refers to the process by which goods physically move from the supplier to the requester, and is an activity arranged by the server.

[0266] "Installation" refers to the process of appropriately placing requested items based on the location and conditions specified by the user, and is handled by the server.

[0267] "Evaluation" refers to feedback information provided by users after the transportation and installation of goods, and is used to improve the system's services.

[0268] One embodiment of this invention involves using a terminal in which the user inputs a request for goods. This terminal consists of common electronic devices such as PCs, tablets, and smartphones. When the user inputs the specific type and quantity of goods, that information is immediately transmitted to the server.

[0269] The server is a high-performance computer that uses an SQL database management system to query inventory information for goods for data collection. If there is a shortage in the stored information, it uses a generative AI model to identify external supply sources. In this process, the server inputs prompt statements into the generative AI model, which performs analysis to select the most efficient supplier. An example of a prompt statement is, "Based on the current inventory status and request details, identify the optimal supplier. Factors to consider are price, distance, and delivery time."

[0270] For example, if a user enters "I need 50 steel beams for construction by next week," the server searches the database based on this information and activates an AI model to generate supplies once a shortage is detected. It then selects the most suitable external supplier and generates a proposal. The selected supplier information, along with visual information of the items, is sent to the user's terminal. The user can review the images of the proposed items and request modifications as needed.

[0271] Based on the above process, the present invention can provide a flexible goods supply system that can quickly and accurately respond to the diverse needs of users.

[0272] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0273] Step 1:

[0274] The user inputs their item request using a terminal. Specifically, they input the type and quantity of items on the terminal's interface and confirm the request. This input becomes data that the terminal sends to the server. As output, the user's request details are prepared as data to be sent to the server.

[0275] Step 2:

[0276] The terminal sends the input request to the server. Data transfer from the terminal to the server takes place in real time via network communication. Specifically, the terminal converts the request content into a packet and sends it to the specified address on the server. The request content is received on the server side as output.

[0277] Step 3:

[0278] The server searches the database based on the received request. It executes SQL queries using the database management system to retrieve inventory information for the items. In this process, the server extracts inventory quantities and related information from the database and processes the results. The output confirms the current inventory status.

[0279] Step 4:

[0280] If the inventory is insufficient, the server activates the generative AI model to analyze external supply sources. The server creates a prompt text and inputs it into the generative AI model. This prompt text includes factors such as price, distance, delivery date, etc. to be considered. As output, a list of optimal suppliers is returned from the AI model.

[0281] Step 5:

[0282] The server sends the proposed supplier information and the visual information of the item to the terminal. The server retrieves the visual information from the database and sends the data back to the terminal together with the supplier information. Based on this output data, the user can check the content of the item on the terminal.

[0283] Step 6:

[0284] The user checks the transmitted information and makes an acceptance or modification request. If the user approves the information, the terminal sends that instruction to the server. If modification is necessary, the content is input and sent to the server again. As output, the final instruction content is determined.

[0285] Step 7:

[0286] Based on the approved content, the server arranges for the transportation and installation of the item. Specifically, it notifies the logistics provider and the installation personnel and adjusts the schedule. As output, the procedures required for transportation and installation are determined and notified to the relevant parties.

[0287] Step 8:

[0288] After installation, the terminal displays a screen asking the user for an evaluation. The user inputs the evaluation of the service and improvement points and sends that data from the terminal to the server. As output, the feedback information from the user is accumulated in the server and utilized for future service improvement.

[0289] (Application Example 1)

[0290] 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."

[0291] There is a growing need for increased efficiency in inventory management and material supply in logistics operations. In particular, when inventory shortages occur, the rapid selection of appropriate supply sources and optimization of delivery routes are crucial. Traditional systems often require manual processes, which are time-consuming and labor-intensive, necessitating effective solutions.

[0292] 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.

[0293] In this invention, the server includes means for a terminal to input a request for a component, means for the server to receive the input request and retrieve component inventory information from a database, and means for optimizing the supplier and delivery route using generative artificial intelligence. This enables rapid procurement in times of inventory shortage and efficient management of logistics operations.

[0294] A "terminal" is a part of a computer or mobile device used by a user to input and receive information.

[0295] A "server" is a computer system that processes and manages data via a network.

[0296] "Components" refer to the items and materials necessary for a specific task or project.

[0297] "Inventory information" refers to data that shows the current status of stored items.

[0298] A "database" is a system for efficiently storing, searching, and managing data.

[0299] "Generative artificial intelligence" refers to algorithms or technologies that reduce manual intervention and perform information analysis and decision-making automatically.

[0300] A "supply source" is an external provider of necessary goods or services.

[0301] A "delivery route" is the path taken by goods from their source to their destination.

[0302] "Feedback" refers to information that includes opinions and evaluations from users regarding a completed process.

[0303] The system for realizing this invention uses a terminal, a server, and a generative AI model. The terminal provides an interface for the user to input component requests, and for the server to process them based on those requests. The terminal typically functions as a portable device such as a smartphone or tablet.

[0304] The server searches the database for inventory information based on the received request. Examples of databases used here include relational database management systems such as MySQL. If an inventory shortage is detected, the server activates a generative AI model to determine the optimal supply source and delivery route. Generative AI models are typically built using deep learning frameworks such as TensorFlow or PyTorch.

[0305] As a concrete example, when a user enters the type and quantity of a component from their terminal, the server immediately searches the database to check the inventory status. If an inventory shortage occurs, a generative AI model is used to make the optimal selection, taking into account the price, distance, and delivery time of the supply source. The server then sends an image of the component to the terminal and asks the user for confirmation. Once approval is received, the server automatically begins arranging delivery.

[0306] Examples of prompt texts include "Based on the current inventory status, please identify suppliers with low prices and early delivery times. A detailed analysis including the optimal delivery route is desired." This prompt text instructs the generative AI model to optimize the suppliers and delivery.

[0307] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0308] Step 1:

[0309] The user uses the terminal to input the types and quantities of the required components. The input data includes detailed information such as names, quantities, and priorities, and is sent to the server through the terminal application.

[0310] Step 2:

[0311] Based on the request data received by the server from the terminal, the database is searched. Inventory information is stored in the database to check whether the specified quantity of the corresponding component exists. As output, a flag for determining the presence or absence of inventory is output.

[0312] Step 3:

[0313] If the inventory is insufficient, the server activates the generative AI model. As input quantities, the component information and the current inventory status are input, and as output quantity, the optimal supplier is selected. The generative AI model considers factors such as price, distance, and delivery time, and analyzes and proposes an optimal supply strategy.

[0314] Step 4:

[0315] Based on the proposed supply strategy by the server, the image of the component is retrieved from the database and sent to the terminal. The user checks the image and makes a final approval or modification request. The input from the user is utilized for the next processing of the server.

[0316] Step 5:

[0317] Once user approval is obtained, the server automatically begins arranging delivery. The delivery route is efficiently set based on information proposed by the generating AI model. A notification is sent to the external system responsible for delivery operations.

[0318] Step 6:

[0319] After delivery and installation are complete, users submit feedback via a terminal. This feedback data is recorded on a server to improve service quality and is used as foundational information for future process improvements.

[0320] 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.

[0321] This invention enhances user psychological comfort by combining an emotion engine with a system that rapidly and accurately supplies components to base stations. When a user inputs a component request into the terminal, the emotion engine is simultaneously activated to recognize the user's input and the emotions associated with their actions. This emotion recognition is estimated from factors such as word choice and input speed.

[0322] When the server receives a request from a terminal, it retrieves inventory information from the database and, if necessary, activates artificial intelligence. The AI ​​selects the best external supply source when inventory is low and generates a suggestion. The selection of supply sources is optimized by considering factors such as price, distance, and delivery time.

[0323] Furthermore, the output of the emotion engine is reflected in the process of generating component suggestions. For example, if the user's emotion is estimated to be "stress" or "dissatisfaction," the suggested method will be adjusted to be more flexible or to prioritize a quick response.

[0324] The proposed component details are sent as images to the user's terminal, and the user is asked to confirm them. At this time, the user reviews the component images and approves or requests revisions. The server then takes appropriate action accordingly.

[0325] Delivery and installation arrangements are handled automatically after server approval, with efficient routes and schedules selected. After installation is complete, the device requests user feedback, and the emotion engine evaluates the user's emotions in this feedback. Any perceived dissatisfaction in the feedback is recorded on the server for future improvements.

[0326] For example, if a user inputs "I'm in a hurry, but I can't find the necessary parts," the emotion engine recognizes the urgency, and the AI ​​generates suggestions that prioritize quick delivery. This process allows the user to receive prompt and appropriate service, while also providing psychological reassurance.

[0327] This embodiment of the invention enables telecommunications infrastructure companies to provide services that are sensitive to user needs while also meeting user requirements.

[0328] The following describes the processing flow.

[0329] Step 1:

[0330] The user inputs their component requests using a terminal. Upon receiving the input data, the terminal simultaneously sends interaction data to the emotion engine to analyze the user's emotional state.

[0331] Step 2:

[0332] The terminal sends user request data and sentiment data to the server. The server receives this data and searches its database for inventory information on the components.

[0333] Step 3:

[0334] If the server detects a shortage of stock, it activates the AI. The AI ​​considers emotional data and generates suggestions that prioritize urgency and quick responses if the user is experiencing stress.

[0335] Step 4:

[0336] The server sends information about the selected components and the proposed content to the user's terminal. The terminal displays the images and proposed content to the user and provides a screen for confirmation.

[0337] Step 5:

[0338] The user reviews the proposed components and content on their device and requests approval or modification. If approved, the user clicks the approval button. If modifications are needed, the user enters the necessary information.

[0339] Step 6:

[0340] When the server receives user approval or modification requests, it automatically arranges delivery and optimizes the installation schedule. Prioritizes arrangements, especially if emotional data indicates a high level of urgency.

[0341] Step 7:

[0342] After the device is installed, the user is asked for feedback. The feedback is analyzed by an emotion engine and sent to the server. The server records this information and uses it to improve the service in the future.

[0343] (Example 2)

[0344] 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".

[0345] Traditional material supply systems often procure materials without considering the user's feelings, making it difficult to adequately address urgent requests or anxieties from users. This results in a decline in service quality and a loss of user psychological comfort.

[0346] 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.

[0347] In this invention, the server includes means for the terminal to analyze the user's emotions using an emotion engine, means for generating suggestions adjusted based on those emotions, and means for analyzing the feedback content. This enables the adjustment of component procurement suggestions according to the user's emotions, allowing for the rapid and appropriate provision of services.

[0348] A "terminal" is an information processing device that a user operates to input requests for components and provide feedback.

[0349] An "emotion engine" is a software technology that analyzes a user's psychological state and emotions based on their input and actions.

[0350] A "server" is a central processing unit that receives requests and sentiment data from terminals and performs tasks such as optimizing component procurement and generating proposals.

[0351] "Generative artificial intelligence" is a technology that analyzes inventory status and information on external supply sources to automatically create optimal procurement proposals.

[0352] A "database" is an information recording device that centrally manages data such as inventory information and supplier information for materials.

[0353] "Feedback" refers to information provided by users regarding their experience and evaluation after requesting components.

[0354] An "external source" refers to an external supplier or company that procures materials when required components are in short supply.

[0355] A "proposal" is a proposal provided by the server, based on user requests, that includes information regarding component procurement and supply.

[0356] This invention provides a system that accurately and quickly supplies components while taking into account the user's psychological state. The system is started when the user inputs a request for components using a terminal. The terminal incorporates an emotion engine for analyzing the user's emotions, which estimates the user's emotions from the input content and operation status.

[0357] The server processes requests and sentiment data received from the terminal. It retrieves inventory information from the database and uses generative artificial intelligence to select the best external supply source if inventory is insufficient. The generative AI model optimizes the supply source selection by considering price, distance, and delivery time. Based on this information, it generates material procurement proposals that take the user's emotions into consideration.

[0358] Furthermore, the server sends the proposed content as an image to the user's terminal. The user reviews the image and either approves the proposal or requests revisions. Upon user approval, the server automatically arranges the delivery and installation of materials, selecting the optimal route and schedule.

[0359] After installation is complete, the terminal will request feedback from the user. The emotion engine will also evaluate the user's emotions in the feedback, and the information obtained will be used to improve the service in the future.

[0360] For example, if a user inputs "I'm in a hurry, but I can't find the necessary parts," the emotion engine recognizes the urgency, and the AI ​​generates parts supply suggestions that prioritize quick delivery. This process allows the user to receive prompt and appropriate service. This entire system enables telecommunications infrastructure companies to provide emotionally sensitive services while meeting user demands.

[0361] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0362] Step 1:

[0363] The user enters a request for materials using a terminal. The input includes the material name, quantity, urgency, and required deadline. This input is analyzed by an emotion engine to generate user emotion data. The emotion data and request details are sent from the terminal to the server.

[0364] Step 2:

[0365] The server retrieves inventory information from the database based on the received request and sentiment data. The database search results output the inventory status of the desired parts. If the inventory is insufficient, the server activates a generating AI model.

[0366] Step 3:

[0367] The server uses a generative AI model to select external supply sources. Factors such as price, distance, and delivery time are considered during this selection process. Based on the input inventory information and candidate supply source data, the optimal supply source is proposed. This proposal is then adjusted according to the user's sentiment data.

[0368] Step 4:

[0369] The server generates the adjusted parts procurement proposal as image data and sends it to the user's terminal. The image visually displays detailed information about the proposed parts and supply options. The user is then asked to review this output and can approve or request revisions.

[0370] Step 5:

[0371] The user reviews the proposed image displayed on the device and enters a request for approval or modification. If a revised proposal is required based on the request, that information is sent to the server via the device.

[0372] Step 6:

[0373] The server arranges delivery and installation based on user approval. Based on the approved information, the optimal delivery route and schedule are selected and output. This results in efficient delivery and installation of the components.

[0374] Step 7:

[0375] The terminal collects user feedback after installation is complete. An emotion engine analyzes the feedback input to evaluate user satisfaction and potential dissatisfaction. The data after emotion analysis is sent to a server and recorded for future service improvements.

[0376] (Application Example 2)

[0377] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0378] In modern logistics centers, there is a demand for efficient and rapid supply of materials. However, conventional systems have failed to consider the feelings of users during material supply, sometimes resulting in a lack of psychological comfort. As a result, the supply process lacked flexibility, leading to decreased user satisfaction. Therefore, there is a need for a system that can simultaneously achieve rapid material supply and flexible responses that consider the feelings of users.

[0379] 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.

[0380] In this invention, the server includes means for a terminal to input a request for components, means for identifying the user's emotions associated with the input using an emotion recognition engine, and means for adjusting the proposal based on the user's emotions. This enables flexible and rapid responses in the component supply process, taking into account the user's emotions.

[0381] A "terminal" is a device used by a user to input their requests for components, and usually refers to a computing device such as a smartphone or tablet.

[0382] A "server" refers to a central computing system that receives requests, retrieves information from a database, and generates processing and suggestions.

[0383] An "emotion recognition engine" is a software module that analyzes user input and uses natural language processing technology to identify the user's emotions.

[0384] "Generative artificial intelligence" refers to an algorithm or system that selects external supply sources and generates optimal suggestions when inventory is insufficient.

[0385] "External supply sources" refer to external suppliers that can be used to procure any missing parts at a logistics center.

[0386] "Inventory information" refers to data stored in a database that describes detailed information about the existence and quantity of each component.

[0387] "Proposal" refers to information about how to procure materials, generated by the server in response to a user's material request.

[0388] "Feedback" refers to reaction information, including opinions and feelings, collected from users after delivery and installation are complete.

[0389] The system for realizing this application includes a terminal, a server, an emotion recognition engine, generative artificial intelligence, and a database. The terminal is a device used by the user to input requests for materials, and is typically a smartphone or tablet. Once the user's request is entered, the terminal sends the information to the server. The server receives this input and searches the database for relevant inventory information.

[0390] The server utilizes an emotion recognition engine to analyze user input and uses natural language processing techniques to identify the user's emotions. For example, this emotion analysis can be implemented using SpaCy, a Python natural language processing library. The identified emotions are taken into consideration when adjusting component proposals.

[0391] If inventory is insufficient, the server uses generative artificial intelligence, such as the OpenAI API, to generate suggestions for selecting the best external supply source. This generative AI model considers factors such as price, distance, and delivery time, and is further prioritized based on the user's sentiment. The AI-generated suggestions are sent to the terminal in the form of an image visualized with a library such as Mateplotlib, and the user is asked for confirmation.

[0392] For example, when a logistics center staff member enters "urgently needed," the system senses an emotion such as "urgent" in this input. Based on this, the AI ​​selects the fastest available external supply source and provides that information to the user.

[0393] For example, if the prompt is entered as "How long until my next order arrives?", the emotion engine identifies the emotion "worry," and the generative AI model generates a quick suggestion, providing details of that suggestion as an image. This process improves the overall efficiency of parts supply and ensures the user's psychological comfort.

[0394] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0395] Step 1:

[0396] The user enters the material request using a terminal. The system receives the user's request. The input data is sent to the server in text format.

[0397] Step 2:

[0398] The server analyzes the received request data and searches for inventory information using the database. The database query checks the inventory status of the requested component and retrieves that information.

[0399] Step 3:

[0400] The server activates an emotion recognition engine to identify emotions from the user's request input. It analyzes the text using a natural language processing library and determines the emotional state based on keywords and input speed. For example, it might use the Python SpaCy library to analyze emotions.

[0401] Step 4:

[0402] The generating artificial intelligence generates suggestions for external supply sources based on available inventory information and identified emotions. The AI ​​model considers price, distance, and delivery time, and also takes into account the user's emotions to generate an optimal supply plan. OpenAI APIs are used in this process.

[0403] Step 5:

[0404] The server visualizes the proposal results and sends them to the terminal in image format. The generated proposals are then converted into images using tools such as Matplotlib, allowing the user to visually review them.

[0405] Step 6:

[0406] The terminal presents the proposal to the user and accepts requests for approval or modification. It then retrieves the user's response and sends it to the server.

[0407] Step 7:

[0408] The server automatically arranges delivery and installation based on user approval. It optimizes delivery schedules and routes and sends instructions to the relevant transportation methods.

[0409] Step 8:

[0410] After the device has been delivered and installed, user feedback is collected. This feedback information is sent to a server and recorded for future improvements.

[0411] Step 9:

[0412] The server also evaluates the emotions included in the feedback and uses this information to improve the system. The emotion recognition engine then re-identifies the emotions and extracts elements of dissatisfaction and areas for improvement.

[0413] 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.

[0414] 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.

[0415] 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.

[0416] [Third Embodiment]

[0417] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0418] 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.

[0419] 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).

[0420] 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.

[0421] 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.

[0422] 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).

[0423] 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.

[0424] 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.

[0425] 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.

[0426] 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.

[0427] 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.

[0428] 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".

[0429] This invention relates to a system for quickly and accurately securing and delivering components for base stations. The system aims to smoothly supply components by having the user input information about the required components into a terminal, which is then received and processed by a server.

[0430] The user first inputs the type and quantity of required materials through the terminal's interface. This information is immediately transmitted to the server. Based on the received request, the server searches its database to check the current inventory status. If a shortage is detected in the inventory information, the server activates artificial intelligence to perform an analysis to identify the optimal external supply source. The optimal supplier is selected considering multiple factors, such as price, distance, and delivery time.

[0431] The server then retrieves images of the components from the database as needed and sends them to the user's terminal. The user reviews these images and either accepts them or requests modifications. If approved, the server automatically begins coordinating the delivery process and installation. For example, the server optimizes the delivery schedule and notifies delivery personnel and installation teams of relevant information.

[0432] Finally, after installation is complete, the terminal requests feedback from the user. This feedback is sent to the server and recorded for service improvement. This entire process can improve user satisfaction and increase operational efficiency.

[0433] By following the embodiments of this invention, telecommunications infrastructure companies and others can provide rapid and accurate component supply and deliver services that exceed customer expectations.

[0434] The following describes the processing flow.

[0435] Step 1:

[0436] The user accesses the terminal and enters the type and quantity of materials needed. Once the input is complete, pressing the submit button sends the information to the server.

[0437] Step 2:

[0438] The server receives a request from the user and searches its internal database. The search results confirm the inventory status of the parts and retrieve that information.

[0439] Step 3:

[0440] If the server detects a shortage of stock, it activates artificial intelligence. The AI ​​investigates external supply sources and generates suggestions to select the best supplier based on price, distance, and delivery time.

[0441] Step 4:

[0442] The server retrieves images of the requested components from the database and sends them to the user's terminal for verification.

[0443] Step 5:

[0444] The user reviews the component images sent via their device and requests approval or modification. If approval is granted, they click the approval button.

[0445] Step 6:

[0446] Once the server receives user approval, it begins arranging delivery and installation. It optimizes delivery routes, adjusts schedules, and notifies delivery personnel of relevant information.

[0447] Step 7:

[0448] The device notifies the user that delivery and installation are complete, and then requests feedback. The user enters and submits feedback, and the information is recorded on the server.

[0449] (Example 1)

[0450] 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."

[0451] Current supply systems are required to supply necessary goods quickly and efficiently, but delays and misdeliveries are problematic due to insufficient selection of external supply sources during times of inventory shortage and inadequate flexibility in responding to user requests.

[0452] 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.

[0453] In this invention, the server includes means for a terminal to input a request for an item, means for the server to receive the input request and retrieve stored information about the item from a data set, and means for generating a proposal to procure the item from an external source when the stored items are insufficient, using a generative intelligence model. This enables efficient and rapid selection of the optimal external source and flexible supply of items in response to user requests.

[0454] A "terminal" is an electronic device used by users to input requests for goods and is responsible for communicating with a server.

[0455] A "server" is a central computing device that performs data retrieval and analysis using generative intelligence models based on received item requests, and manages item information, including suggesting external supply sources.

[0456] "Items" refers to specific goods or materials that users request and receive through the system.

[0457] A "generative intelligence model" is an artificial intelligence model used to analyze multiple factors such as price, distance, and delivery time when selecting external supply sources.

[0458] A "data set" refers to a database or data storage area in which information about an item's storage and other related data are recorded.

[0459] "External sources" refer to external suppliers or traders selected by the server to procure goods when inventory is insufficient.

[0460] "Visual information" refers to images and other visual data sent to the terminal about the requested item.

[0461] "Transportation" refers to the process by which goods physically move from the supplier to the requester, and is an activity arranged by the server.

[0462] "Installation" refers to the process of appropriately placing requested items based on the location and conditions specified by the user, and is handled by the server.

[0463] "Evaluation" refers to feedback information provided by users after the transportation and installation of goods, and is used to improve the system's services.

[0464] One embodiment of this invention involves using a terminal in which the user inputs a request for goods. This terminal consists of common electronic devices such as PCs, tablets, and smartphones. When the user inputs the specific type and quantity of goods, that information is immediately transmitted to the server.

[0465] The server is a high-performance computer that uses an SQL database management system to query inventory information for goods for data collection. If there is a shortage in the stored information, it uses a generative AI model to identify external supply sources. In this process, the server inputs prompt statements into the generative AI model, which performs analysis to select the most efficient supplier. An example of a prompt statement is, "Based on the current inventory status and request details, identify the optimal supplier. Factors to consider are price, distance, and delivery time."

[0466] For example, if a user enters "I need 50 steel beams for construction by next week," the server searches the database based on this information and activates an AI model to generate supplies once a shortage is detected. It then selects the most suitable external supplier and generates a proposal. The selected supplier information, along with visual information of the items, is sent to the user's terminal. The user can review the images of the proposed items and request modifications as needed.

[0467] Based on the above process, the present invention can provide a flexible goods supply system that can quickly and accurately respond to the diverse needs of users.

[0468] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0469] Step 1:

[0470] The user inputs their item request using a terminal. Specifically, they input the type and quantity of items on the terminal's interface and confirm the request. This input becomes data that the terminal sends to the server. As output, the user's request details are prepared as data to be sent to the server.

[0471] Step 2:

[0472] The terminal sends the input request to the server. Data transfer from the terminal to the server takes place in real time via network communication. Specifically, the terminal converts the request content into a packet and sends it to the specified address on the server. The request content is received on the server side as output.

[0473] Step 3:

[0474] The server searches the database based on the received request. It executes SQL queries using the database management system to retrieve inventory information for the items. In this process, the server extracts inventory quantities and related information from the database and processes the results. The output confirms the current inventory status.

[0475] Step 4:

[0476] If inventory is insufficient, the server activates a generative AI model to analyze external supply sources. The server creates a prompt message and inputs it into the generative AI model. This prompt message includes factors to consider, such as price, distance, and delivery time. As output, the AI ​​model returns a list of the best suppliers.

[0477] Step 5:

[0478] The server sends the proposed supplier information and visual information of the item to the terminal. The server retrieves the visual information from the database and sends the data back to the terminal along with the supplier information. Based on this output data, the user can check the contents of the item on the terminal.

[0479] Step 6:

[0480] The user reviews the submitted information and either accepts it or requests corrections. If the user approves the information, the terminal sends the instructions to the server. If corrections are needed, the user enters the changes and sends them back to the server. The output then confirms the final instructions.

[0481] Step 7:

[0482] Based on the approved information, the server arranges the transportation and installation of the goods. Specifically, it notifies logistics companies and installation personnel and coordinates the schedule. As an output, the procedures required for transportation and installation are finalized and communicated to all relevant parties.

[0483] Step 8:

[0484] After installation, the terminal displays a screen requesting user feedback. Users input their evaluation and suggestions for improvement, and send this data from the terminal to the server. As output, user feedback information is stored on the server and used to improve the service in the future.

[0485] (Application Example 1)

[0486] 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."

[0487] There is a growing need for increased efficiency in inventory management and material supply in logistics operations. In particular, when inventory shortages occur, the rapid selection of appropriate supply sources and optimization of delivery routes are crucial. Traditional systems often require manual processes, which are time-consuming and labor-intensive, necessitating effective solutions.

[0488] 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.

[0489] In this invention, the server includes means for a terminal to input a request for a component, means for the server to receive the input request and retrieve component inventory information from a database, and means for optimizing the supplier and delivery route using generative artificial intelligence. This enables rapid procurement in times of inventory shortage and efficient management of logistics operations.

[0490] A "terminal" is a part of a computer or mobile device used by a user to input and receive information.

[0491] A "server" is a computer system that processes and manages data via a network.

[0492] "Components" refer to the items and materials necessary for a specific task or project.

[0493] "Inventory information" refers to data that shows the current status of stored items.

[0494] A "database" is a system for efficiently storing, searching, and managing data.

[0495] "Generative artificial intelligence" refers to algorithms or technologies that reduce manual intervention and perform information analysis and decision-making automatically.

[0496] A "supply source" is an external provider of necessary goods or services.

[0497] A "delivery route" is the path taken by goods from their source to their destination.

[0498] "Feedback" refers to information that includes opinions and evaluations from users regarding a completed process.

[0499] The system for realizing this invention uses a terminal, a server, and a generative AI model. The terminal provides an interface for the user to input component requests, and for the server to process them based on those requests. The terminal typically functions as a portable device such as a smartphone or tablet.

[0500] The server searches the database for inventory information based on the received request. Examples of databases used here include relational database management systems such as MySQL. If an inventory shortage is detected, the server activates a generative AI model to determine the optimal supply source and delivery route. Generative AI models are typically built using deep learning frameworks such as TensorFlow or PyTorch.

[0501] As a concrete example, when a user enters the type and quantity of a component from their terminal, the server immediately searches the database to check the inventory status. If an inventory shortage occurs, a generative AI model is used to make the optimal selection, taking into account the price, distance, and delivery time of the supply source. The server then sends an image of the component to the terminal and asks the user for confirmation. Once approval is received, the server automatically begins arranging delivery.

[0502] An example of a prompt would be: "Based on current inventory levels, identify suppliers with lower prices and faster delivery times. We would also like a detailed analysis, including the optimal delivery route." This prompt instructs the generating AI model to optimize supply sources and delivery.

[0503] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0504] Step 1:

[0505] The user uses a terminal to input the type and quantity of required components. The input data, including detailed information such as name, quantity, and priority, is sent to the server through the terminal's application.

[0506] Step 2:

[0507] The server searches the database based on the request data received from the terminal. The database stores inventory information, and the server checks if the specified quantity of the relevant component exists. A flag indicating the presence or absence of inventory is output.

[0508] Step 3:

[0509] If inventory is insufficient, the server activates a generative AI model. The input quantities are information about the components and the current inventory status, and the output quantity is the selection of the optimal supply source. The generative AI model considers factors such as price, distance, and delivery time to analyze and propose the optimal supply strategy.

[0510] Step 4:

[0511] Based on the proposed supply strategy, the server retrieves images of the components from the database and sends them to the terminal. The user reviews the images and makes a final approval or requests for modifications. The user's input is then used in the server's next processing step.

[0512] Step 5:

[0513] Once user approval is obtained, the server automatically begins arranging delivery. The delivery route is efficiently set based on information proposed by the generating AI model. A notification is sent to the external system responsible for delivery operations.

[0514] Step 6:

[0515] After delivery and installation are complete, users submit feedback via a terminal. This feedback data is recorded on a server to improve service quality and is used as foundational information for future process improvements.

[0516] 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.

[0517] This invention enhances user psychological comfort by combining an emotion engine with a system that rapidly and accurately supplies components to base stations. When a user inputs a component request into the terminal, the emotion engine is simultaneously activated to recognize the user's input and the emotions associated with their actions. This emotion recognition is estimated from factors such as word choice and input speed.

[0518] When the server receives a request from a terminal, it retrieves inventory information from the database and, if necessary, activates artificial intelligence. The AI ​​selects the best external supply source when inventory is low and generates a suggestion. The selection of supply sources is optimized by considering factors such as price, distance, and delivery time.

[0519] Furthermore, the output of the emotion engine is reflected in the process of generating component suggestions. For example, if the user's emotion is estimated to be "stress" or "dissatisfaction," the suggested method will be adjusted to be more flexible or to prioritize a quick response.

[0520] The proposed component details are sent as images to the user's terminal, and the user is asked to confirm them. At this time, the user reviews the component images and approves or requests revisions. The server then takes appropriate action accordingly.

[0521] Delivery and installation arrangements are handled automatically after server approval, with efficient routes and schedules selected. After installation is complete, the device requests user feedback, and the emotion engine evaluates the user's emotions in this feedback. Any perceived dissatisfaction in the feedback is recorded on the server for future improvements.

[0522] For example, if a user inputs "I'm in a hurry, but I can't find the necessary parts," the emotion engine recognizes the urgency, and the AI ​​generates suggestions that prioritize quick delivery. This process allows the user to receive prompt and appropriate service, while also providing psychological reassurance.

[0523] This embodiment of the invention enables telecommunications infrastructure companies to provide services that are sensitive to user needs while also meeting user requirements.

[0524] The following describes the processing flow.

[0525] Step 1:

[0526] The user inputs their component requests using a terminal. Upon receiving the input data, the terminal simultaneously sends interaction data to the emotion engine to analyze the user's emotional state.

[0527] Step 2:

[0528] The terminal sends user request data and sentiment data to the server. The server receives this data and searches its database for inventory information on the components.

[0529] Step 3:

[0530] If the server detects a shortage of stock, it activates the AI. The AI ​​considers emotional data and generates suggestions that prioritize urgency and quick responses if the user is experiencing stress.

[0531] Step 4:

[0532] The server sends information about the selected components and the proposed content to the user's terminal. The terminal displays the images and proposed content to the user and provides a screen for confirmation.

[0533] Step 5:

[0534] The user reviews the proposed components and content on their device and requests approval or modification. If approved, the user clicks the approval button. If modifications are needed, the user enters the necessary information.

[0535] Step 6:

[0536] When the server receives user approval or modification requests, it automatically arranges delivery and optimizes the installation schedule. Prioritizes arrangements, especially if emotional data indicates a high level of urgency.

[0537] Step 7:

[0538] After the device is installed, the user is asked for feedback. The feedback is analyzed by an emotion engine and sent to the server. The server records this information and uses it to improve the service in the future.

[0539] (Example 2)

[0540] 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."

[0541] Traditional material supply systems often procure materials without considering the user's feelings, making it difficult to adequately address urgent requests or anxieties from users. This results in a decline in service quality and a loss of user psychological comfort.

[0542] 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.

[0543] In this invention, the server includes means for the terminal to analyze the user's emotions using an emotion engine, means for generating suggestions adjusted based on those emotions, and means for analyzing the feedback content. This enables the adjustment of component procurement suggestions according to the user's emotions, allowing for the rapid and appropriate provision of services.

[0544] A "terminal" is an information processing device that a user operates to input requests for components and provide feedback.

[0545] An "emotion engine" is a software technology that analyzes a user's psychological state and emotions based on their input and actions.

[0546] A "server" is a central processing unit that receives requests and sentiment data from terminals and performs tasks such as optimizing component procurement and generating proposals.

[0547] "Generative artificial intelligence" is a technology that analyzes inventory status and information on external supply sources to automatically create optimal procurement proposals.

[0548] A "database" is an information recording device that centrally manages data such as inventory information and supplier information for materials.

[0549] "Feedback" refers to information provided by users regarding their experience and evaluation after requesting components.

[0550] An "external source" refers to an external supplier or company that procures materials when required components are in short supply.

[0551] A "proposal" is a proposal provided by the server, based on user requests, that includes information regarding component procurement and supply.

[0552] This invention provides a system that accurately and quickly supplies components while taking into account the user's psychological state. The system is started when the user inputs a request for components using a terminal. The terminal incorporates an emotion engine for analyzing the user's emotions, which estimates the user's emotions from the input content and operation status.

[0553] The server processes requests and sentiment data received from the terminal. It retrieves inventory information from the database and uses generative artificial intelligence to select the best external supply source if inventory is insufficient. The generative AI model optimizes the supply source selection by considering price, distance, and delivery time. Based on this information, it generates material procurement proposals that take the user's emotions into consideration.

[0554] Furthermore, the server sends the proposed content as an image to the user's terminal. The user reviews the image and either approves the proposal or requests revisions. Upon user approval, the server automatically arranges the delivery and installation of materials, selecting the optimal route and schedule.

[0555] After installation is complete, the terminal will request feedback from the user. The emotion engine will also evaluate the user's emotions in the feedback, and the information obtained will be used to improve the service in the future.

[0556] For example, if a user inputs "I'm in a hurry, but I can't find the necessary parts," the emotion engine recognizes the urgency, and the AI ​​generates parts supply suggestions that prioritize quick delivery. This process allows the user to receive prompt and appropriate service. This entire system enables telecommunications infrastructure companies to provide emotionally sensitive services while meeting user demands.

[0557] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0558] Step 1:

[0559] The user enters a request for materials using a terminal. The input includes the material name, quantity, urgency, and required deadline. This input is analyzed by an emotion engine to generate user emotion data. The emotion data and request details are sent from the terminal to the server.

[0560] Step 2:

[0561] The server retrieves inventory information from the database based on the received request and sentiment data. The database search results output the inventory status of the desired parts. If the inventory is insufficient, the server activates a generating AI model.

[0562] Step 3:

[0563] The server uses a generative AI model to select external supply sources. Factors such as price, distance, and delivery time are considered during this selection process. Based on the input inventory information and candidate supply source data, the optimal supply source is proposed. This proposal is then adjusted according to the user's sentiment data.

[0564] Step 4:

[0565] The server generates the adjusted parts procurement proposal as image data and sends it to the user's terminal. The image visually displays detailed information about the proposed parts and supply options. The user is then asked to review this output and can approve or request revisions.

[0566] Step 5:

[0567] The user reviews the proposed image displayed on the device and enters a request for approval or modification. If a revised proposal is required based on the request, that information is sent to the server via the device.

[0568] Step 6:

[0569] The server arranges delivery and installation based on user approval. Based on the approved information, the optimal delivery route and schedule are selected and output. This results in efficient delivery and installation of the components.

[0570] Step 7:

[0571] The terminal collects user feedback after installation is complete. An emotion engine analyzes the feedback input to evaluate user satisfaction and potential dissatisfaction. The data after emotion analysis is sent to a server and recorded for future service improvements.

[0572] (Application Example 2)

[0573] 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."

[0574] In modern logistics centers, there is a demand for efficient and rapid supply of materials. However, conventional systems have failed to consider the feelings of users during material supply, sometimes resulting in a lack of psychological comfort. As a result, the supply process lacked flexibility, leading to decreased user satisfaction. Therefore, there is a need for a system that can simultaneously achieve rapid material supply and flexible responses that consider the feelings of users.

[0575] 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.

[0576] In this invention, the server includes means for a terminal to input a request for components, means for identifying the user's emotions associated with the input using an emotion recognition engine, and means for adjusting the proposal based on the user's emotions. This enables flexible and rapid responses in the component supply process, taking into account the user's emotions.

[0577] A "terminal" is a device used by a user to input their requests for components, and usually refers to a computing device such as a smartphone or tablet.

[0578] A "server" refers to a central computing system that receives requests, retrieves information from a database, and generates processing and suggestions.

[0579] An "emotion recognition engine" is a software module that analyzes user input and uses natural language processing technology to identify the user's emotions.

[0580] "Generative artificial intelligence" refers to an algorithm or system that selects external supply sources and generates optimal suggestions when inventory is insufficient.

[0581] "External supply sources" refer to external suppliers that can be used to procure any missing parts at a logistics center.

[0582] "Inventory information" refers to data stored in a database that describes detailed information about the existence and quantity of each component.

[0583] "Proposal" refers to information about how to procure materials, generated by the server in response to a user's material request.

[0584] "Feedback" refers to reaction information, including opinions and feelings, collected from users after delivery and installation are complete.

[0585] The system for realizing this application includes a terminal, a server, an emotion recognition engine, generative artificial intelligence, and a database. The terminal is a device used by the user to input requests for materials, and is typically a smartphone or tablet. Once the user's request is entered, the terminal sends the information to the server. The server receives this input and searches the database for relevant inventory information.

[0586] The server utilizes an emotion recognition engine to analyze user input and uses natural language processing techniques to identify the user's emotions. For example, this emotion analysis can be implemented using SpaCy, a Python natural language processing library. The identified emotions are taken into consideration when adjusting component proposals.

[0587] If inventory is insufficient, the server uses generative artificial intelligence, such as the OpenAI API, to generate suggestions for selecting the best external supply source. This generative AI model considers factors such as price, distance, and delivery time, and is further prioritized based on the user's sentiment. The AI-generated suggestions are sent to the terminal in the form of an image visualized with a library such as Mateplotlib, and the user is asked for confirmation.

[0588] For example, when a logistics center staff member enters "urgently needed," the system senses an emotion such as "urgent" in this input. Based on this, the AI ​​selects the fastest available external supply source and provides that information to the user.

[0589] For example, if the prompt is entered as "How long until my next order arrives?", the emotion engine identifies the emotion "worry," and the generative AI model generates a quick suggestion, providing details of that suggestion as an image. This process improves the overall efficiency of parts supply and ensures the user's psychological comfort.

[0590] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0591] Step 1:

[0592] The user enters the material request using a terminal. The system receives the user's request. The input data is sent to the server in text format.

[0593] Step 2:

[0594] The server analyzes the received request data and searches for inventory information using the database. The database query checks the inventory status of the requested component and retrieves that information.

[0595] Step 3:

[0596] The server activates an emotion recognition engine to identify emotions from the user's request input. It analyzes the text using a natural language processing library and determines the emotional state based on keywords and input speed. For example, it might use the Python SpaCy library to analyze emotions.

[0597] Step 4:

[0598] The generating artificial intelligence generates suggestions for external supply sources based on available inventory information and identified emotions. The AI ​​model considers price, distance, and delivery time, and also takes into account the user's emotions to generate an optimal supply plan. OpenAI APIs are used in this process.

[0599] Step 5:

[0600] The server visualizes the proposal results and sends them to the terminal in image format. The generated proposals are then converted into images using tools such as Matplotlib, allowing the user to visually review them.

[0601] Step 6:

[0602] The terminal presents the proposal to the user and accepts requests for approval or modification. It then retrieves the user's response and sends it to the server.

[0603] Step 7:

[0604] The server automatically arranges delivery and installation based on user approval. It optimizes delivery schedules and routes and sends instructions to the relevant transportation methods.

[0605] Step 8:

[0606] After the device has been delivered and installed, user feedback is collected. This feedback information is sent to a server and recorded for future improvements.

[0607] Step 9:

[0608] The server also evaluates the emotions included in the feedback and uses this information to improve the system. The emotion recognition engine then re-identifies the emotions and extracts elements of dissatisfaction and areas for improvement.

[0609] 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.

[0610] 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.

[0611] 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.

[0612] [Fourth Embodiment]

[0613] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0614] 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.

[0615] 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).

[0616] 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.

[0617] 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.

[0618] 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).

[0619] 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.

[0620] 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.

[0621] 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.

[0622] 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.

[0623] 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.

[0624] 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.

[0625] 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".

[0626] This invention relates to a system for quickly and accurately securing and delivering components for base stations. The system aims to smoothly supply components by having the user input information about the required components into a terminal, which is then received and processed by a server.

[0627] The user first inputs the type and quantity of required materials through the terminal's interface. This information is immediately transmitted to the server. Based on the received request, the server searches its database to check the current inventory status. If a shortage is detected in the inventory information, the server activates artificial intelligence to perform an analysis to identify the optimal external supply source. The optimal supplier is selected considering multiple factors, such as price, distance, and delivery time.

[0628] The server then retrieves images of the components from the database as needed and sends them to the user's terminal. The user reviews these images and either accepts them or requests modifications. If approved, the server automatically begins coordinating the delivery process and installation. For example, the server optimizes the delivery schedule and notifies delivery personnel and installation teams of relevant information.

[0629] Finally, after installation is complete, the terminal requests feedback from the user. This feedback is sent to the server and recorded for service improvement. This entire process can improve user satisfaction and increase operational efficiency.

[0630] By following the embodiments of this invention, telecommunications infrastructure companies and others can provide rapid and accurate component supply and deliver services that exceed customer expectations.

[0631] The following describes the processing flow.

[0632] Step 1:

[0633] The user accesses the terminal and enters the type and quantity of materials needed. Once the input is complete, pressing the submit button sends the information to the server.

[0634] Step 2:

[0635] The server receives a request from the user and searches its internal database. The search results confirm the inventory status of the parts and retrieve that information.

[0636] Step 3:

[0637] If the server detects a shortage of stock, it activates artificial intelligence. The AI ​​investigates external supply sources and generates suggestions to select the best supplier based on price, distance, and delivery time.

[0638] Step 4:

[0639] The server retrieves images of the requested components from the database and sends them to the user's terminal for verification.

[0640] Step 5:

[0641] The user reviews the component images sent via their device and requests approval or modification. If approval is granted, they click the approval button.

[0642] Step 6:

[0643] Once the server receives user approval, it begins arranging delivery and installation. It optimizes delivery routes, adjusts schedules, and notifies delivery personnel of relevant information.

[0644] Step 7:

[0645] The device notifies the user that delivery and installation are complete, and then requests feedback. The user enters and submits feedback, and the information is recorded on the server.

[0646] (Example 1)

[0647] 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".

[0648] Current supply systems are required to supply necessary goods quickly and efficiently, but delays and misdeliveries are problematic due to insufficient selection of external supply sources during times of inventory shortage and inadequate flexibility in responding to user requests.

[0649] 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.

[0650] In this invention, the server includes means for a terminal to input a request for an item, means for the server to receive the input request and retrieve stored information about the item from a data set, and means for generating a proposal to procure the item from an external source when the stored items are insufficient, using a generative intelligence model. This enables efficient and rapid selection of the optimal external source and flexible supply of items in response to user requests.

[0651] A "terminal" is an electronic device used by users to input requests for goods and is responsible for communicating with a server.

[0652] A "server" is a central computing device that performs data retrieval and analysis using generative intelligence models based on received item requests, and manages item information, including suggesting external supply sources.

[0653] "Items" refers to specific goods or materials that users request and receive through the system.

[0654] A "generative intelligence model" is an artificial intelligence model used to analyze multiple factors such as price, distance, and delivery time when selecting external supply sources.

[0655] A "data set" refers to a database or data storage area in which information about an item's storage and other related data are recorded.

[0656] "External sources" refer to external suppliers or traders selected by the server to procure goods when inventory is insufficient.

[0657] "Visual information" refers to images and other visual data sent to the terminal about the requested item.

[0658] "Transportation" refers to the process by which goods physically move from the supplier to the requester, and is an activity arranged by the server.

[0659] "Installation" refers to the process of appropriately placing requested items based on the location and conditions specified by the user, and is handled by the server.

[0660] "Evaluation" refers to feedback information provided by users after the transportation and installation of goods, and is used to improve the system's services.

[0661] One embodiment of this invention involves using a terminal in which the user inputs a request for goods. This terminal consists of common electronic devices such as PCs, tablets, and smartphones. When the user inputs the specific type and quantity of goods, that information is immediately transmitted to the server.

[0662] The server is a high-performance computer that uses an SQL database management system to query inventory information for goods for data collection. If there is a shortage in the stored information, it uses a generative AI model to identify external supply sources. In this process, the server inputs prompt statements into the generative AI model, which performs analysis to select the most efficient supplier. An example of a prompt statement is, "Based on the current inventory status and request details, identify the optimal supplier. Factors to consider are price, distance, and delivery time."

[0663] For example, if a user enters "I need 50 steel beams for construction by next week," the server searches the database based on this information and activates an AI model to generate supplies once a shortage is detected. It then selects the most suitable external supplier and generates a proposal. The selected supplier information, along with visual information of the items, is sent to the user's terminal. The user can review the images of the proposed items and request modifications as needed.

[0664] Based on the above process, the present invention can provide a flexible goods supply system that can quickly and accurately respond to the diverse needs of users.

[0665] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0666] Step 1:

[0667] The user inputs their item request using a terminal. Specifically, they input the type and quantity of items on the terminal's interface and confirm the request. This input becomes data that the terminal sends to the server. As output, the user's request details are prepared as data to be sent to the server.

[0668] Step 2:

[0669] The terminal sends the input request to the server. Data transfer from the terminal to the server takes place in real time via network communication. Specifically, the terminal converts the request content into a packet and sends it to the specified address on the server. The request content is received on the server side as output.

[0670] Step 3:

[0671] The server searches the database based on the received request. It executes SQL queries using the database management system to retrieve inventory information for the items. In this process, the server extracts inventory quantities and related information from the database and processes the results. The output confirms the current inventory status.

[0672] Step 4:

[0673] If inventory is insufficient, the server activates a generative AI model to analyze external supply sources. The server creates a prompt message and inputs it into the generative AI model. This prompt message includes factors to consider, such as price, distance, and delivery time. As output, the AI ​​model returns a list of the best suppliers.

[0674] Step 5:

[0675] The server sends the proposed supplier information and visual information of the item to the terminal. The server retrieves the visual information from the database and sends the data back to the terminal along with the supplier information. Based on this output data, the user can check the contents of the item on the terminal.

[0676] Step 6:

[0677] The user reviews the submitted information and either accepts it or requests corrections. If the user approves the information, the terminal sends the instructions to the server. If corrections are needed, the user enters the changes and sends them back to the server. The output then confirms the final instructions.

[0678] Step 7:

[0679] Based on the approved information, the server arranges the transportation and installation of the goods. Specifically, it notifies logistics companies and installation personnel and coordinates the schedule. As an output, the procedures required for transportation and installation are finalized and communicated to all relevant parties.

[0680] Step 8:

[0681] After installation, the terminal displays a screen requesting user feedback. Users input their evaluation and suggestions for improvement, and send this data from the terminal to the server. As output, user feedback information is stored on the server and used to improve the service in the future.

[0682] (Application Example 1)

[0683] 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".

[0684] There is a growing need for increased efficiency in inventory management and material supply in logistics operations. In particular, when inventory shortages occur, the rapid selection of appropriate supply sources and optimization of delivery routes are crucial. Traditional systems often require manual processes, which are time-consuming and labor-intensive, necessitating effective solutions.

[0685] 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.

[0686] In this invention, the server includes means for a terminal to input a request for a component, means for the server to receive the input request and retrieve component inventory information from a database, and means for optimizing the supplier and delivery route using generative artificial intelligence. This enables rapid procurement in times of inventory shortage and efficient management of logistics operations.

[0687] A "terminal" is a part of a computer or mobile device used by a user to input and receive information.

[0688] A "server" is a computer system that processes and manages data via a network.

[0689] "Components" refer to the items and materials necessary for a specific task or project.

[0690] "Inventory information" refers to data that shows the current status of stored items.

[0691] A "database" is a system for efficiently storing, searching, and managing data.

[0692] "Generative artificial intelligence" refers to algorithms or technologies that reduce manual intervention and perform information analysis and decision-making automatically.

[0693] A "supply source" is an external provider of necessary goods or services.

[0694] A "delivery route" is the path taken by goods from their source to their destination.

[0695] "Feedback" refers to information that includes opinions and evaluations from users regarding a completed process.

[0696] The system for realizing this invention uses a terminal, a server, and a generative AI model. The terminal provides an interface for the user to input component requests, and for the server to process them based on those requests. The terminal typically functions as a portable device such as a smartphone or tablet.

[0697] The server searches the database for inventory information based on the received request. Examples of databases used here include relational database management systems such as MySQL. If an inventory shortage is detected, the server activates a generative AI model to determine the optimal supply source and delivery route. Generative AI models are typically built using deep learning frameworks such as TensorFlow or PyTorch.

[0698] As a concrete example, when a user enters the type and quantity of a component from their terminal, the server immediately searches the database to check the inventory status. If an inventory shortage occurs, a generative AI model is used to make the optimal selection, taking into account the price, distance, and delivery time of the supply source. The server then sends an image of the component to the terminal and asks the user for confirmation. Once approval is received, the server automatically begins arranging delivery.

[0699] An example of a prompt would be: "Based on current inventory levels, identify suppliers with lower prices and faster delivery times. We would also like a detailed analysis, including the optimal delivery route." This prompt instructs the generating AI model to optimize supply sources and delivery.

[0700] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0701] Step 1:

[0702] The user uses a terminal to input the type and quantity of required components. The input data, including detailed information such as name, quantity, and priority, is sent to the server through the terminal's application.

[0703] Step 2:

[0704] The server searches the database based on the request data received from the terminal. The database stores inventory information, and the server checks if the specified quantity of the relevant component exists. A flag indicating the presence or absence of inventory is output.

[0705] Step 3:

[0706] If inventory is insufficient, the server activates a generative AI model. The input quantities are information about the components and the current inventory status, and the output quantity is the selection of the optimal supply source. The generative AI model considers factors such as price, distance, and delivery time to analyze and propose the optimal supply strategy.

[0707] Step 4:

[0708] Based on the proposed supply strategy, the server retrieves images of the components from the database and sends them to the terminal. The user reviews the images and makes a final approval or requests for modifications. The user's input is then used in the server's next processing step.

[0709] Step 5:

[0710] Once user approval is obtained, the server automatically begins arranging delivery. The delivery route is efficiently set based on information proposed by the generating AI model. A notification is sent to the external system responsible for delivery operations.

[0711] Step 6:

[0712] After delivery and installation are complete, users submit feedback via a terminal. This feedback data is recorded on a server to improve service quality and is used as foundational information for future process improvements.

[0713] 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.

[0714] This invention enhances user psychological comfort by combining an emotion engine with a system that rapidly and accurately supplies components to base stations. When a user inputs a component request into the terminal, the emotion engine is simultaneously activated to recognize the user's input and the emotions associated with their actions. This emotion recognition is estimated from factors such as word choice and input speed.

[0715] When the server receives a request from a terminal, it retrieves inventory information from the database and, if necessary, activates artificial intelligence. The AI ​​selects the best external supply source when inventory is low and generates a suggestion. The selection of supply sources is optimized by considering factors such as price, distance, and delivery time.

[0716] Furthermore, the output of the emotion engine is reflected in the process of generating component suggestions. For example, if the user's emotion is estimated to be "stress" or "dissatisfaction," the suggested method will be adjusted to be more flexible or to prioritize a quick response.

[0717] The proposed component details are sent as images to the user's terminal, and the user is asked to confirm them. At this time, the user reviews the component images and approves or requests revisions. The server then takes appropriate action accordingly.

[0718] Delivery and installation arrangements are handled automatically after server approval, with efficient routes and schedules selected. After installation is complete, the device requests user feedback, and the emotion engine evaluates the user's emotions in this feedback. Any perceived dissatisfaction in the feedback is recorded on the server for future improvements.

[0719] For example, if a user inputs "I'm in a hurry, but I can't find the necessary parts," the emotion engine recognizes the urgency, and the AI ​​generates suggestions that prioritize quick delivery. This process allows the user to receive prompt and appropriate service, while also providing psychological reassurance.

[0720] This embodiment of the invention enables telecommunications infrastructure companies to provide services that are sensitive to user needs while also meeting user requirements.

[0721] The following describes the processing flow.

[0722] Step 1:

[0723] The user inputs their component requests using a terminal. Upon receiving the input data, the terminal simultaneously sends interaction data to the emotion engine to analyze the user's emotional state.

[0724] Step 2:

[0725] The terminal sends user request data and sentiment data to the server. The server receives this data and searches its database for inventory information on the components.

[0726] Step 3:

[0727] If the server detects a shortage of stock, it activates the AI. The AI ​​considers emotional data and generates suggestions that prioritize urgency and quick responses if the user is experiencing stress.

[0728] Step 4:

[0729] The server sends information about the selected components and the proposed content to the user's terminal. The terminal displays the images and proposed content to the user and provides a screen for confirmation.

[0730] Step 5:

[0731] The user reviews the proposed components and content on their device and requests approval or modification. If approved, the user clicks the approval button. If modifications are needed, the user enters the necessary information.

[0732] Step 6:

[0733] When the server receives user approval or modification requests, it automatically arranges delivery and optimizes the installation schedule. Prioritizes arrangements, especially if emotional data indicates a high level of urgency.

[0734] Step 7:

[0735] After the device is installed, the user is asked for feedback. The feedback is analyzed by an emotion engine and sent to the server. The server records this information and uses it to improve the service in the future.

[0736] (Example 2)

[0737] 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".

[0738] Traditional material supply systems often procure materials without considering the user's feelings, making it difficult to adequately address urgent requests or anxieties from users. This results in a decline in service quality and a loss of user psychological comfort.

[0739] 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.

[0740] In this invention, the server includes means for the terminal to analyze the user's emotions using an emotion engine, means for generating suggestions adjusted based on those emotions, and means for analyzing the feedback content. This enables the adjustment of component procurement suggestions according to the user's emotions, allowing for the rapid and appropriate provision of services.

[0741] A "terminal" is an information processing device that a user operates to input requests for components and provide feedback.

[0742] An "emotion engine" is a software technology that analyzes a user's psychological state and emotions based on their input and actions.

[0743] A "server" is a central processing unit that receives requests and sentiment data from terminals and performs tasks such as optimizing component procurement and generating proposals.

[0744] "Generative artificial intelligence" is a technology that analyzes inventory status and information on external supply sources to automatically create optimal procurement proposals.

[0745] A "database" is an information recording device that centrally manages data such as inventory information and supplier information for materials.

[0746] "Feedback" refers to information provided by users regarding their experience and evaluation after requesting components.

[0747] An "external source" refers to an external supplier or company that procures materials when required components are in short supply.

[0748] A "proposal" is a proposal provided by the server, based on user requests, that includes information regarding component procurement and supply.

[0749] This invention provides a system that accurately and quickly supplies components while taking into account the user's psychological state. The system is started when the user inputs a request for components using a terminal. The terminal incorporates an emotion engine for analyzing the user's emotions, which estimates the user's emotions from the input content and operation status.

[0750] The server processes requests and sentiment data received from the terminal. It retrieves inventory information from the database and uses generative artificial intelligence to select the best external supply source if inventory is insufficient. The generative AI model optimizes the supply source selection by considering price, distance, and delivery time. Based on this information, it generates material procurement proposals that take the user's emotions into consideration.

[0751] Furthermore, the server sends the proposed content as an image to the user's terminal. The user reviews the image and either approves the proposal or requests revisions. Upon user approval, the server automatically arranges the delivery and installation of materials, selecting the optimal route and schedule.

[0752] After installation is complete, the terminal will request feedback from the user. The emotion engine will also evaluate the user's emotions in the feedback, and the information obtained will be used to improve the service in the future.

[0753] For example, if a user inputs "I'm in a hurry, but I can't find the necessary parts," the emotion engine recognizes the urgency, and the AI ​​generates parts supply suggestions that prioritize quick delivery. This process allows the user to receive prompt and appropriate service. This entire system enables telecommunications infrastructure companies to provide emotionally sensitive services while meeting user demands.

[0754] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0755] Step 1:

[0756] The user enters a request for materials using a terminal. The input includes the material name, quantity, urgency, and required deadline. This input is analyzed by an emotion engine to generate user emotion data. The emotion data and request details are sent from the terminal to the server.

[0757] Step 2:

[0758] The server retrieves inventory information from the database based on the received request and sentiment data. The database search results output the inventory status of the desired parts. If the inventory is insufficient, the server activates a generating AI model.

[0759] Step 3:

[0760] The server uses a generative AI model to select external supply sources. Factors such as price, distance, and delivery time are considered during this selection process. Based on the input inventory information and candidate supply source data, the optimal supply source is proposed. This proposal is then adjusted according to the user's sentiment data.

[0761] Step 4:

[0762] The server generates the adjusted parts procurement proposal as image data and sends it to the user's terminal. The image visually displays detailed information about the proposed parts and supply options. The user is then asked to review this output and can approve or request revisions.

[0763] Step 5:

[0764] The user reviews the proposed image displayed on the device and enters a request for approval or modification. If a revised proposal is required based on the request, that information is sent to the server via the device.

[0765] Step 6:

[0766] The server arranges delivery and installation based on user approval. Based on the approved information, the optimal delivery route and schedule are selected and output. This results in efficient delivery and installation of the components.

[0767] Step 7:

[0768] The terminal collects user feedback after installation is complete. An emotion engine analyzes the feedback input to evaluate user satisfaction and potential dissatisfaction. The data after emotion analysis is sent to a server and recorded for future service improvements.

[0769] (Application Example 2)

[0770] 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".

[0771] In modern logistics centers, there is a demand for efficient and rapid supply of materials. However, conventional systems have failed to consider the feelings of users during material supply, sometimes resulting in a lack of psychological comfort. As a result, the supply process lacked flexibility, leading to decreased user satisfaction. Therefore, there is a need for a system that can simultaneously achieve rapid material supply and flexible responses that consider the feelings of users.

[0772] 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.

[0773] In this invention, the server includes means for a terminal to input a request for components, means for identifying the user's emotions associated with the input using an emotion recognition engine, and means for adjusting the proposal based on the user's emotions. This enables flexible and rapid responses in the component supply process, taking into account the user's emotions.

[0774] A "terminal" is a device used by a user to input their requests for components, and usually refers to a computing device such as a smartphone or tablet.

[0775] A "server" refers to a central computing system that receives requests, retrieves information from a database, and generates processing and suggestions.

[0776] An "emotion recognition engine" is a software module that analyzes user input and uses natural language processing technology to identify the user's emotions.

[0777] "Generative artificial intelligence" refers to an algorithm or system that selects external supply sources and generates optimal suggestions when inventory is insufficient.

[0778] "External supply sources" refer to external suppliers that can be used to procure any missing parts at a logistics center.

[0779] "Inventory information" refers to data stored in a database that describes detailed information about the existence and quantity of each component.

[0780] "Proposal" refers to information about how to procure materials, generated by the server in response to a user's material request.

[0781] "Feedback" refers to reaction information, including opinions and feelings, collected from users after delivery and installation are complete.

[0782] The system for realizing this application includes a terminal, a server, an emotion recognition engine, generative artificial intelligence, and a database. The terminal is a device used by the user to input requests for materials, and is typically a smartphone or tablet. Once the user's request is entered, the terminal sends the information to the server. The server receives this input and searches the database for relevant inventory information.

[0783] The server utilizes an emotion recognition engine to analyze user input and uses natural language processing techniques to identify the user's emotions. For example, this emotion analysis can be implemented using SpaCy, a Python natural language processing library. The identified emotions are taken into consideration when adjusting component proposals.

[0784] If inventory is insufficient, the server uses generative artificial intelligence, such as the OpenAI API, to generate suggestions for selecting the best external supply source. This generative AI model considers factors such as price, distance, and delivery time, and is further prioritized based on the user's sentiment. The AI-generated suggestions are sent to the terminal in the form of an image visualized with a library such as Mateplotlib, and the user is asked for confirmation.

[0785] For example, when a logistics center staff member enters "urgently needed," the system senses an emotion such as "urgent" in this input. Based on this, the AI ​​selects the fastest available external supply source and provides that information to the user.

[0786] For example, if the prompt is entered as "How long until my next order arrives?", the emotion engine identifies the emotion "worry," and the generative AI model generates a quick suggestion, providing details of that suggestion as an image. This process improves the overall efficiency of parts supply and ensures the user's psychological comfort.

[0787] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0788] Step 1:

[0789] The user enters the material request using a terminal. The system receives the user's request. The input data is sent to the server in text format.

[0790] Step 2:

[0791] The server analyzes the received request data and searches for inventory information using the database. The database query checks the inventory status of the requested component and retrieves that information.

[0792] Step 3:

[0793] The server activates an emotion recognition engine to identify emotions from the user's request input. It analyzes the text using a natural language processing library and determines the emotional state based on keywords and input speed. For example, it might use the Python SpaCy library to analyze emotions.

[0794] Step 4:

[0795] The generating artificial intelligence generates suggestions for external supply sources based on available inventory information and identified emotions. The AI ​​model considers price, distance, and delivery time, and also takes into account the user's emotions to generate an optimal supply plan. OpenAI APIs are used in this process.

[0796] Step 5:

[0797] The server visualizes the proposal results and sends them to the terminal in image format. The generated proposals are then converted into images using tools such as Matplotlib, allowing the user to visually review them.

[0798] Step 6:

[0799] The terminal presents the proposal to the user and accepts requests for approval or modification. It then retrieves the user's response and sends it to the server.

[0800] Step 7:

[0801] The server automatically arranges delivery and installation based on user approval. It optimizes delivery schedules and routes and sends instructions to the relevant transportation methods.

[0802] Step 8:

[0803] After the device has been delivered and installed, user feedback is collected. This feedback information is sent to a server and recorded for future improvements.

[0804] Step 9:

[0805] The server also evaluates the emotions included in the feedback and uses this information to improve the system. The emotion recognition engine then re-identifies the emotions and extracts elements of dissatisfaction and areas for improvement.

[0806] 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.

[0807] 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.

[0808] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0809] 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.

[0810] 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. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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."

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] The following is further disclosed regarding the embodiments described above.

[0828] (Claim 1)

[0829] A means by which the terminal inputs the request for components,

[0830] The server receives the input request and retrieves the inventory information of the component from the database.

[0831] A means for generating proposals to procure materials from external sources when inventory is insufficient, using server-generated artificial intelligence.

[0832] A means of obtaining an image of the requested component from the server and sending it to the terminal for confirmation,

[0833] The server provides a means to arrange for delivery and installation based on approved component information.

[0834] A means for collecting feedback after the device has been delivered and installed,

[0835] A system that includes this.

[0836] (Claim 2)

[0837] The system according to claim 1, wherein the generating artificial intelligence includes means for optimizing the selection of a supply source, taking into account price, distance, and delivery time.

[0838] (Claim 3)

[0839] The system according to claim 1, wherein the server includes means for receiving modification requests from users when verifying component information.

[0840] "Example 1"

[0841] (Claim 1)

[0842] A means by which the terminal inputs the request for an item,

[0843] The server receives the input request and retrieves the stored information of the item from the data set.

[0844] A means by which a server uses a generative intelligence model to generate proposals for procuring goods from external sources when storage is insufficient,

[0845] A means for the server to acquire visual information of the requested item and transmit it to the terminal for confirmation,

[0846] The server provides a means to arrange for transportation and installation based on approved item information.

[0847] A means for collecting evaluations after the terminal has been transported and installed,

[0848] A system that includes this.

[0849] (Claim 2)

[0850] The system according to claim 1, wherein the generative intelligence model includes means for optimizing the selection of a supply source, taking into account price, distance, and delivery time.

[0851] (Claim 3)

[0852] The system according to claim 1, wherein the server includes means for receiving correction requests from users when verifying item information.

[0853] "Application Example 1"

[0854] (Claim 1)

[0855] A means by which the terminal inputs the request for components,

[0856] The server receives the input request and retrieves the inventory information of the component from the database.

[0857] A means for generating proposals to procure materials from external sources when inventory is insufficient, using server-generated artificial intelligence.

[0858] A means of obtaining an image of the requested component from the server and sending it to the terminal for confirmation,

[0859] The server provides a means to arrange for delivery and installation based on approved component information.

[0860] A means for collecting feedback after the device has been delivered and installed,

[0861] In logistics operations, means to optimize supply sources and delivery routes,

[0862] A system that includes this.

[0863] (Claim 2)

[0864] The system according to claim 1, wherein the generating artificial intelligence includes means for optimizing the selection of a supply source, taking into account price, distance, and delivery time.

[0865] (Claim 3)

[0866] The system according to claim 1, wherein the server includes means for receiving modification requests from users when verifying component information.

[0867] "Example 2 of combining an emotion engine"

[0868] (Claim 1)

[0869] A means by which the terminal inputs the request for components,

[0870] A means by which a device analyzes the user's emotions using an emotion engine,

[0871] The server receives the input request and emotion information, and retrieves the inventory information of the components from the database.

[0872] A means for generating proposals to procure materials from external sources when inventory is insufficient, using server-generated artificial intelligence.

[0873] A means by which the server generates emotionally-adjusted suggestions,

[0874] A means of obtaining an image of the requested component from the server and sending it to the terminal for confirmation,

[0875] The server provides a means to arrange for delivery and installation based on approved component information.

[0876] A means for collecting feedback after the terminal has been delivered and installed, and for analyzing the content of the feedback using an emotion engine,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The system according to claim 1, wherein the generating artificial intelligence includes means for optimizing the selection of a supply source, taking into account price, distance, and delivery time.

[0880] (Claim 3)

[0881] The system according to claim 1, wherein the server includes means for receiving modification requests from users when verifying component information.

[0882] "Application example 2 when combining with an emotional engine"

[0883] (Claim 1)

[0884] A means by which the terminal inputs the request for components,

[0885] A means of identifying the user's emotions associated with input using an emotion recognition engine,

[0886] The server receives the input request and retrieves the inventory information of the component from the database.

[0887] A means for generating proposals to procure materials from external sources when inventory is insufficient, using server-generated artificial intelligence.

[0888] A means of adjusting the aforementioned proposal based on the user's feelings,

[0889] A means of obtaining an image of the requested component from the server and sending it to the terminal for confirmation,

[0890] The server provides a means to arrange for delivery and installation based on approved component information.

[0891] A means for collecting feedback after the terminal has been delivered and installed, and for evaluating the emotions contained in the feedback using an emotion recognition engine,

[0892] A system that includes this.

[0893] (Claim 2)

[0894] The system according to claim 1, wherein the generating artificial intelligence includes means for optimizing the selection of a supply source based on the user's emotional state, taking into account price, distance, and delivery time.

[0895] (Claim 3)

[0896] The system according to claim 1, wherein the server includes means for evaluating the user's emotions and receiving feedback for improvement when verifying component information. [Explanation of symbols]

[0897] 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 by which the terminal inputs the request for components, The server receives the input request and retrieves the inventory information of the component from the database. A means for generating proposals to procure materials from external sources when inventory is insufficient, using server-generated artificial intelligence. A means of obtaining an image of the requested component from the server and sending it to the terminal for confirmation, The server provides a means to arrange for delivery and installation based on approved component information. A means for collecting feedback after the device has been delivered and installed, In logistics operations, means to optimize supply sources and delivery routes, A system that includes this.

2. The system according to claim 1, wherein the generating artificial intelligence includes means for optimizing the selection of a supply source, taking into account price, distance, and delivery time.

3. The system according to claim 1, wherein the server includes means for receiving modification requests from users when verifying component information.

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