System

Abstract

A system is provided.SOLUTION: A system, comprising: means for obtaining item quotes via a user terminal; means for generating quotes and suggestions based on user input using a generative model; means for optimizing delivery routes and making suggestions for cost and environmental savings; and means for suggesting deliveries using autonomous flight devices for rapid delivery needs.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional wholesale ordering systems often impede efficient business operations by taking time to quote and order products. They also increase delivery costs and are unable to meet the need for rapid delivery. Furthermore, they have limited options for delivery methods that reduce environmental impact. The present invention aims to solve these issues and provide a platform that allows users to quote, order, and manage deliveries in an efficient and sustainable manner. [Means for solving the problem]

[0005] The present invention provides a system including a means for obtaining product quotes via a user terminal, a means for generating quotes and proposals based on user input using a generative model, a means for optimizing delivery routes and making proposals for reducing costs and environmental impact, and a means for proposing delivery using an autonomous aircraft device for rapid delivery needs. This allows users to obtain quotes in real time and select the optimal delivery method. Furthermore, by using the generative model, it is possible to propose optimal products and delivery methods in response to user questions, thereby achieving cost reduction and environmental impact reduction through delivery route optimization. In particular, proposing delivery using an autonomous aircraft device for rapid delivery needs enables efficient and rapid delivery.

[0006] A "user terminal" is a device that a user operates to input and output data.

[0007] A "generative model" is a type of artificial intelligence technology that generates answers or suggestions based on specific input information.

[0008] A "quote" is the act or result of calculating and presenting an expected price for a particular product or service.

[0009] A "recommendation" is the act or result of providing information to recommend a particular choice or course of action.

[0010] A "delivery route" is a route taken to transport goods or services to a specified location.

[0011] "Cost reduction" refers to minimizing financial expenses and costs.

[0012] "Reducing environmental burden" refers to minimizing the burden and impact on the environment.

[0013] "Expedited delivery" refers to getting goods or services to their destination as quickly as possible.

[0014] An "autonomous flying device" is an unmanned aircraft that flies automatically without human operation and delivers goods to designated locations. [Brief explanation of the drawings]

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

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

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

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

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

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

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

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

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0032] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] This invention is a smart wholesale platform that allows users to obtain product quotes and select and manage optimal delivery methods. The system of the present invention includes a user terminal, a generative model, a server, a delivery route optimization function, and a delivery means using an autonomous flying device.

[0037] Overall Platform Configuration

[0038] 1. User Device:

[0039] A device that users interact with to request product quotes and enter shipping requirements.

[0040] The terminal transmits the user's input to the server and displays the quotes and delivery proposals returned by the server.

[0041] 2. Generative Model:

[0042] The server uses the generative model to generate estimates and suggestions based on the user's input.

[0043] The generative model responds to user questions by suggesting appropriate products and delivery methods.

[0044] 3. Server:

[0045] It receives requests from users and passes them to a generative model to generate estimates and proposals.

[0046] The quotation results and delivery proposal are returned to the terminal.

[0047] 4. Delivery route optimization function:

[0048] The server optimizes delivery routes and makes suggestions for reducing costs and environmental impact.

[0049] Calculate the best delivery route based on user requirements.

[0050] 5. Autonomous Aircraft:

[0051] This is a drone designed to provide fast and efficient delivery for emergency delivery needs.

[0052] The server proposes autonomous flying devices based on delivery needs and delivers goods via the optimal route.

[0053] Program processing explanation

[0054] 1. User Inquiry Processing:

[0055] A user uses a terminal to request a quote for a product (for example, "Can I get a quote for 200 units of product A?").

[0056] The terminal sends this request to the server.

[0057] The server queries the generative model to compute an estimate.

[0058] The generative model calculates the estimate and sends it back to the server.

[0059] The server sends the estimate back to the device, which displays the results to the user.

[0060] Example: "The estimated price for this item is 200,000 yen."

[0061] 2. Delivery route optimization and suggestions:

[0062] The user enters delivery requirements (e.g., "please deliver as soon as possible") into the terminal.

[0063] The terminal sends this requirement to the server.

[0064] The server performs delivery route optimization and determines the best delivery method based on the user's requirements.

[0065] The server proposes the optimal delivery method (e.g., drone delivery) and returns the result to the device.

[0066] The terminal displays delivery offers to the user.

[0067] Example: "The best delivery method is drone delivery, using the shortest route."

[0068] This concludes the program process and concrete example of a smart wholesale platform that integrates generative models and drone delivery services. The system provides users with real-time quotes and selects and manages optimal delivery methods, enabling efficient and sustainable business operations.

[0069] The processing flow will be explained below.

[0070] Program processing flow explanation

[0071] User inquiry processing

[0072] Step 1:

[0073] A user uses a terminal to request a quote for a product, for example, by typing, "Please give me a quote for 200 units of product A."

[0074] Step 2:

[0075] The terminal sends the user's request to the server, which includes the type and quantity of the product.

[0076] Step 3:

[0077] The server receives the user request and passes it to the generative model.

[0078] Step 4:

[0079] The generative model calculates the estimated price of product A based on the user request.

[0080] Step 5:

[0081] The generative model sends the calculated price estimate back to the server.

[0082] Step 6:

[0083] The server receives the estimate and returns it to the user's device.

[0084] Step 7:

[0085] The terminal displays the estimate results received from the server to the user.

[0086] Delivery route optimization and proposals

[0087] Step 1:

[0088] The user uses the terminal to enter delivery requirements, for example, "please deliver as soon as possible."

[0089] Step 2:

[0090] The terminal sends the user's delivery requirements to the server, including delivery speed and special requests.

[0091] Step 3:

[0092] The server receives the delivery requirements and invokes the delivery route optimization function.

[0093] Step 4:

[0094] The server calculates delivery routes and finds the optimal route to reduce costs and environmental impact.

[0095] Step 5:

[0096] The server decides whether to suggest an autonomous flying device (drone delivery) based on the user's requirements (e.g., urgent delivery).

[0097] Step 6:

[0098] The server returns the optimal delivery method and route to the user's device.

[0099] Step 7:

[0100] The terminal displays the delivery proposal and route information received from the server to the user.

[0101] Comprehensive Processing

[0102] Step 1:

[0103] A user uses a terminal to request both a quote and urgent delivery of a product, for example, "Please give me a quote for 200 units of product A and deliver it as soon as possible."

[0104] Step 2:

[0105] The terminal sends this overall request to the server.

[0106] Step 3:

[0107] The server receives the synthetic request and first passes it to the generative model to calculate the estimated price.

[0108] Step 4:

[0109] The generative model calculates the estimated price and sends it back to the server.

[0110] Step 5:

[0111] The server receives the estimate results and then calls the delivery route optimization function to calculate the route.

[0112] Step 6:

[0113] Based on the calculated route, the server considers the user's requirements (e.g., urgent delivery) and suggests the optimal delivery method (e.g., drone delivery).

[0114] Step 7:

[0115] The server returns the overall results (estimated price, optimal delivery method, and route) to the user's device.

[0116] Step 8:

[0117] The terminal displays the overall results received from the server to the user.

[0118] Example 1

[0119] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0120] Traditional wholesale platforms face challenges in being unable to respond quickly to user requests when obtaining product quotes or proposing delivery methods. Delivery routes are often not sufficiently optimized, preventing cost reduction and environmental impact reduction. Furthermore, the lack of efficient delivery methods for urgent delivery needs prevents business efficiency and sustainability from improving.

[0121] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0122] In this invention, the server includes a means for transmitting user-entered data to the server, a means for generating estimates and proposals based on the user's input using a generative model, and a means for executing a delivery route optimization function. This not only enables quick and accurate product estimates based on the user's input, but also calculates optimal delivery routes, reducing costs and reducing environmental impact. Furthermore, efficient delivery using unmanned aerial vehicles can be proposed for urgent delivery needs, improving business efficiency and sustainability.

[0123] "User Terminal" means a device operated by a User to input product quote requests and delivery requirements.

[0124] A "generative model" is an artificial intelligence model used by the server to generate estimates and suggestions based on user input.

[0125] The "Delivery route optimization function" is a function in which the server optimizes delivery routes and makes suggestions for reducing costs and environmental impact.

[0126] A "drone" is an autonomous flying delivery device used for rapid delivery needs.

[0127] The "means for transmitting data entered by the user to the server" refers to a communication means for transmitting request information from the user terminal to the server.

[0128] The "means for the server to send a quote request to the generative model" is a function that passes the user's request received by the server to the generative model and requests processing.

[0129] The "means by which the generative model calculates an estimate" is the function by which the generative model calculates the price and proposal details for a specified product or service.

[0130] "Means for the server to return the estimation results to the user terminal" refers to means by which the server transmits the estimation results received from the generative model to the user terminal.

[0131] The "means for suggesting optimal products and delivery methods" is a function that enables the generative model to suggest optimal products or delivery methods based on the user's requests.

[0132] "Means to streamline the process from quotation to delivery" refers to a system that efficiently carries out the entire process from estimating and measuring products to final delivery.

[0133] This invention is a smart wholesale platform that allows users to obtain product quotes and select and manage optimal delivery methods. The system of the present invention includes a user terminal, a generative model, a server, a delivery route optimization function, and a delivery means using an unmanned aerial vehicle.

[0134] Overall Platform Configuration

[0135] 1. User Device:

[0136] A device that users interact with to request product quotes and enter shipping requirements.

[0137] The terminals are intended to be devices that can connect to the Internet, such as PCs, smartphones, and tablets, and will run on operating systems such as iOS and Android.

[0138] Example: User types, "Please give me a quote for 200 units of product A."

[0139] 2. Generative Model:

[0140] The server uses the generative model to generate estimates and suggestions based on the user's input.

[0141] This generative model uses AI models such as OpenAI's GPT (Generative Pre-trained Transformer).

[0142] Example: "The estimate for 200 units of product A is 200,000 yen."

[0143] 3. Server:

[0144] The server receives requests from users and passes them to a generative model to generate estimates and proposals.

[0145] The server uses cloud services such as AWS (Amazon Web Services) and Microsoft Azure.

[0146] Example: Taking a user request and passing it to a generative model.

[0147] 4. Delivery route optimization function:

[0148] The server optimizes delivery routes and makes suggestions for reducing costs and environmental impact.

[0149] The server will implement route optimization algorithms such as Google Maps API and Route4Me.

[0150] Example: Calculate the optimal route and display "The best delivery method is drone delivery, and we will deliver via the shortest route."

[0151] 5. Unmanned aircraft:

[0152] This is an autonomous flying delivery device that provides fast and efficient delivery for urgent delivery needs.

[0153] The server will suggest an unmanned aircraft based on delivery needs and deliver the goods via the optimal route.

[0154] Example: For urgent deliveries, we suggest, "The best delivery method is drone delivery, and we will deliver it via the shortest route."

[0155] Program processing explanation

[0156] 1. User Inquiry Processing:

[0157] A user uses a device to request a quote for a product.

[0158] The terminal sends this request to the server.

[0159] The server queries the generative model to compute an estimate.

[0160] The generative model calculates the estimate and sends it back to the server.

[0161] The server sends the estimate back to the device, which displays the results to the user.

[0162] Example: "The estimated price for this item is 200,000 yen."

[0163] 2. Delivery route optimization and suggestions:

[0164] The user enters their delivery requirements (e.g., "please deliver as soon as possible") into the terminal.

[0165] The terminal sends this requirement to the server.

[0166] The server performs delivery route optimization and determines the best delivery method based on the user's requirements.

[0167] The server proposes the optimal delivery method (e.g., drone delivery) and returns the result to the device.

[0168] The terminal displays delivery offers to the user.

[0169] Example: "The best delivery method is drone delivery, using the shortest route."

[0170] Prompt sentences for specific examples of operation

[0171] Prompt for product quote:

[0172] "Please give me a quote for 200 units of product A."

[0173] Prompt for delivery requirements:

[0174] "Please deliver it quickly."

[0175] This concludes the explanation of the structure and operation of a smart wholesale platform that integrates generative models and unmanned aerial vehicles. The system provides users with real-time quotes and selects and manages optimal delivery methods, enabling efficient and sustainable business operations.

[0176] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0177] Step 1:

[0178] A user requests a quote for a product. The user enters specific product information into the terminal (e.g., "Please give me a quote for 200 units of product A") and sends the input data to the terminal. The terminal receives this data and converts it into JSON format. This makes the user's input in a format that can be processed by the next step. The input is "Product name: Product A, Quantity: 200", and the output is JSON format data.

[0179] Step 2:

[0180] The terminal sends the user's input to the server. The terminal sends the JSON format data entered by the user to the server as an HTTP request. In this operation, the application inside the terminal adds the appropriate URL and header information to send the data. The input is the user data in JSON format, and the output is a request to send the requested quote data.

[0181] Step 3:

[0182] The server sends a request for an estimate to the generative model. The server analyzes the received user request and makes an API call to the generative model to calculate an estimate. The input is the user request data in JSON format, and the output is the API request to be passed to the generative model. The server calls the "generateEstimate(data)" function as an internal process.

[0183] Step 4:

[0184] The generative model calculates the estimate. Based on the provided product information, the generative AI model calculates the appropriate price and sends the result back to the server. This calculation is performed quickly based on an internal algorithm. The input is product information and quantity, and the output is the estimated price. Example: "The estimate for 200 units of product A is 200,000 yen."

[0185] Step 5:

[0186] The server returns the estimation results to the terminal. The server converts the estimation results received from the generative model back into JSON format and sends it to the terminal as an HTTP response. The input is the estimation result from the generative model, and the output is the JSON-formatted estimation data sent to the terminal.

[0187] Step 6:

[0188] The terminal displays the quotation results to the user. The terminal parses the quotation results received from the server and displays them in the user interface. The input is the quotation data in JSON format, and the output is what is displayed to the user. For example, the message "The quotation for the product is 200,000 yen" is displayed.

[0189] Step 7:

[0190] The user inputs the delivery requirements. The user inputs the delivery requirements (e.g., "Please deliver as soon as possible") into the terminal and sends the input data to the terminal. The terminal receives the delivery requirements and converts them into JSON format. This makes the input from the user in a format that can be processed in the next step. The input is "Delivery method: urgent delivery" and the output is JSON format data.

[0191] Step 8:

[0192] The terminal sends the delivery requirements to the server. The terminal sends the JSON format delivery requirement data entered by the user to the server as an HTTP request. The input is the user delivery requirement data in JSON format, and the output is a request to send the requested delivery data.

[0193] Step 9:

[0194] The server executes the delivery route optimization function. The server passes the received delivery requirements to the delivery route optimization function and calculates the optimal delivery route. This uses algorithms such as Google Maps API and Route4Me. The input is the user's delivery requirement data, and the output is the optimized delivery route data.

[0195] Step 10:

[0196] The server determines the optimal delivery method and returns it to the terminal. The server analyzes the results of the delivery route optimization function and determines the optimal delivery method based on the user's requirements. It then converts the result into JSON format and sends it to the terminal as an HTTP response. The input is the result data of the delivery route optimization, and the output is the optimal delivery method data sent to the terminal.

[0197] Step 11:

[0198] The terminal displays the delivery suggestions to the user. The terminal analyzes the results of the optimal delivery method received from the server and displays them on the user interface. The input is the delivery method data in JSON format, and the output is what is displayed to the user. Example: A message is displayed saying, "The optimal delivery method is drone delivery, and it will be delivered via the shortest route."

[0199] (Application example 1)

[0200] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0201] In conventional logistics centers, optimizing product estimates and delivery routes takes time, making efficient operation difficult. Furthermore, the lack of a system that users can operate intuitively and visually can lead to confusion in estimate results and delivery method selection. Furthermore, manual management is important for urgent deliveries, and urgent delivery methods are often not proposed in a timely manner.

[0202] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0203] In this invention, the server includes a means for obtaining product quotes via a user terminal, a means for generating quotes and proposals based on user input using a generative model, a means for optimizing delivery routes and making proposals for reducing costs and environmental impact, a means for proposing delivery using autonomous flying devices for rapid delivery needs, and a means for visualizing and manipulating quotes and delivery proposals through an application installed on a smart device or robot. This allows users to visually check and manipulate quote results and delivery methods, enabling them to obtain product quotes and manage deliveries efficiently and intuitively.

[0204] "User terminal" means a device for inputting product quote requests and delivery requirements and receiving the results.

[0205] A "generative model" is an AI system for generating estimates and recommendations based on user input.

[0206] "Delivery route optimization" involves calculating delivery routes and proposing optimal routes to reduce costs and environmental impact.

[0207] An "autonomous flying device" is an unmanned aircraft designed to quickly deliver goods to meet urgent delivery needs.

[0208] A "smart device" is a portable device with information processing and communication capabilities.

[0209] A "robot" is a mechanical device that can autonomously perform specified actions or tasks.

[0210] "Application" means software installed on a smart device or robot for visualizing and manipulating quotes and delivery proposals.

[0211] The system for realizing this application example includes a user terminal, a generative model, a server, a delivery route optimization function, an autonomous flying device, and an application installed on a smart device or robot. Specific implementation methods are described below.

[0212] Overall system configuration

[0213] 1. User Device

[0214] Users use their smartphones or tablets to input product quote requests and delivery requirements, which are then sent to the server, which then displays the quotes and delivery proposals returned by the server.

[0215] 2. Generative Model

[0216] The server uses a generative model, such as OpenAI GPT-3, to generate quotes and recommendations based on the user's input, and then responds to the user's query with appropriate product and delivery suggestions.

[0217] 3. Server

[0218] The server receives requests from users, passes them to the generative model to generate estimates and proposals, receives the results from the generative model, and returns them to the user's device. The server also optimizes delivery routes and determines the optimal delivery method.

[0219] 4. Delivery route optimization function

[0220] The server uses Python's NetworkX library to optimize delivery routes, calculating the best route for cost savings and environmental conservation.

[0221] 5. Autonomous aircraft

[0222] For urgent delivery needs, Sarver proposes autonomous flying devices, which enable fast and efficient delivery.

[0223] 6. Smart Device or Robot Applications

[0224] The application is installed on a smartphone, tablet, smart glasses, or logistics robot, and allows users to visually check and manipulate quotation results and delivery proposals.

[0225] Examples of concrete examples and prompts

[0226] A specific example is shown below.

[0227] Request a product quote

[0228] A user uses a smartphone to enter a product quote request as follows:

[0229] Please let me know the estimate for 200 items.

[0230] Displaying the estimate results

[0231] The server uses the generative model to generate an estimate and displays the result as follows:

[0232] Estimated price: 200,000 yen.

[0233] Delivery method: Drone delivery is recommended.

[0234] Optimal delivery route: The shortest route from the origin to the destination was calculated.

[0235] This system allows users to visually check and operate quotation results and delivery methods, enabling them to efficiently and intuitively obtain product quotes and manage deliveries.

[0236] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0237] Step 1:

[0238] A user uses a smartphone or tablet to enter a request for a quote on a product. For example, the user enters a request such as "Please give me a quote for 200 units of this product."

[0239] Input: User input (e.g. "Please give me a quote for 200 units of this product").

[0240] Output: Request data (JSON format).

[0241] Step 2:

[0242] The device sends this request to the server, and the device sends the user's input data in JSON format to the server.

[0243] Input: User-entered information.

[0244] Output: Sending a request to the server.

[0245] Step 3:

[0246] The server receives a request from the user and passes it to the generative model to generate an estimate or proposal. The server passes a prompt to the generative model to calculate an estimate.

[0247] Input: The request data sent from the device.

[0248] Output: Prompt statement and quote request to the generative model.

[0249] Step 4:

[0250] A generative model (e.g., OpenAI GPT-3) generates an estimate based on the user's input and sends the result back to the server.

[0251] Input: Prompt text from the server.

[0252] Output: Estimation results (text format).

[0253] Step 5:

[0254] The server receives the estimation results from the generative model and sends them back to the user device. The server receives the estimation results and sends them to the device.

[0255] Input: Estimation results from the generative model.

[0256] Output: Send the estimate results to the user's device.

[0257] Step 6:

[0258] The terminal displays the estimate results to the user, who can then check the estimate results on the terminal.

[0259] Input: Estimate result from the server.

[0260] Output: Display the estimate result to the user (e.g., "Estimate result: 200,000 yen.").

[0261] Step 7:

[0262] The user enters their delivery requirements (e.g., "please deliver quickly") into the terminal.

[0263] Input: User delivery requirement input (e.g. "Please hurry delivery").

[0264] Output: Delivery requirements data (JSON format).

[0265] Step 8:

[0266] The device sends these delivery requirements to the server. The device sends the user's delivery requirements data to the server in JSON format.

[0267] Input: User delivery requirement information.

[0268] Output: Send delivery requirements data to the server.

[0269] Step 9:

[0270] The server performs delivery route optimization and determines the best delivery method based on the user's requirements. The server uses the NetworkX library to calculate the optimal delivery route.

[0271] Input: Delivery requirement data sent from the terminal.

[0272] Output: Optimal delivery route information and delivery method.

[0273] Step 10:

[0274] The server proposes the optimal delivery method (e.g., drone delivery) and returns the result to the terminal.

[0275] Input: Optimized delivery route information.

[0276] Output: Sends delivery method proposal to user.

[0277] Step 11:

[0278] The device displays delivery suggestions to the user, who can then review and manage the optimal delivery method on the device.

[0279] Input: Delivery method suggestions from the server.

[0280] Output: Display delivery method to user (e.g. "The best delivery method is drone delivery, using the shortest route.").

[0281] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0282] This invention is a smart wholesale platform that allows users to obtain product quotes and select and manage optimal delivery methods. The system of the present invention includes a user terminal, a generative model, a server, a delivery route optimization function, an autonomous flying device, and an emotion engine that recognizes user emotions.

[0283] Overall Platform Configuration

[0284] 1. User Device:

[0285] It is a device that users can operate to input and output data.

[0286] The terminal transmits the user's input to the server and displays the quotes and delivery proposals returned by the server.

[0287] 2. Generative Model:

[0288] The server uses the generative model to generate estimates and suggestions based on the user's input.

[0289] This generative model responds to user queries by suggesting optimal products and delivery methods.

[0290] 3. Server:

[0291] It receives requests from users and passes them to a generative model to generate estimates and proposals.

[0292] The quotation results and delivery proposal are returned to the user's terminal.

[0293] 4. Delivery route optimization function:

[0294] The server optimizes delivery routes and makes suggestions for reducing costs and environmental impact.

[0295] Calculate the best delivery route based on user requirements.

[0296] 5. Autonomous Aircraft:

[0297] This is a drone designed to provide fast and efficient delivery for emergency delivery needs.

[0298] The server proposes autonomous flying devices based on delivery needs and delivers goods via the optimal route.

[0299] 6. Emotion Engine:

[0300] The server recognizes the user's emotions via the user's terminal.

[0301] The emotion engine adjusts suggestions and determines the urgency of delivery based on the user's emotions.

[0302] Program processing explanation

[0303] 1. User Inquiry Processing:

[0304] A user uses a terminal to request a quote for a product, for example, by typing, "Please give me a quote for 200 units of product A."

[0305] The terminal sends this request to the server.

[0306] The server queries the generative model to compute an estimate.

[0307] The generative model calculates the estimate and sends it back to the server.

[0308] The server receives the estimate and returns it to the user's device.

[0309] The terminal displays the estimate results to the user.

[0310] Example: "The estimated price for this item is 200,000 yen."

[0311] 2. Delivery route optimization and suggestions:

[0312] The user enters delivery requirements (e.g., "please deliver as soon as possible") into the terminal.

[0313] The terminal sends this requirement to the server.

[0314] The server performs delivery route optimization and determines the best delivery method based on the user's requirements.

[0315] The server proposes the optimal delivery method (e.g., drone delivery) and returns the result to the device.

[0316] The terminal displays delivery offers to the user.

[0317] Example: "The best delivery method is drone delivery, using the shortest route."

[0318] 3. Emotional engine regulation:

[0319] As users enter product quotes and delivery requirements, an emotion engine recognizes their emotions (e.g., "impatient" or "excited").

[0320] The server analyzes the user's emotional information through an emotion engine and adjusts the suggestions.

[0321] The emotion engine will more frequently suggest faster delivery methods (e.g. drone delivery) if the user is in a hurry, and more cost-saving suggestions if the user is relaxed.

[0322] The server generates suggestions based on the emotion and sends them back to the user's device.

[0323] The device displays tailored suggestions to the user.

[0324] Example display: "Based on the user's urgent requirements, the best delivery method is drone delivery, using the shortest route."

[0325] The above is a program process and example of a smart wholesale platform that integrates user terminals, generative models, and an emotion engine. By taking user emotions into account, it can make suggestions based on individual needs and provide more personalized services.

[0326] The processing flow will be explained below.

[0327] Program processing flow explanation

[0328] User inquiry processing

[0329] Step 1:

[0330] A user uses a terminal to request a quote for a product, for example, by typing, "Please give me a quote for 200 units of product A."

[0331] Step 2:

[0332] The terminal sends the request content to the server.

[0333] Step 3:

[0334] The server receives the user's request and passes it to the generative model.

[0335] Step 4:

[0336] The generative model calculates the estimated price of product A based on the user request.

[0337] Step 5:

[0338] The generative model sends the calculated price estimate back to the server.

[0339] Step 6:

[0340] The server receives the estimate and returns it to the user's device.

[0341] Step 7:

[0342] The terminal displays the estimate results received from the server to the user.

[0343] Example: "The estimated price for this item is 200,000 yen."

[0344] Delivery route optimization and proposals

[0345] Step 1:

[0346] The user enters their delivery requirements, for example, "Please deliver as soon as possible."

[0347] Step 2:

[0348] The terminal sends this delivery requirement to the server.

[0349] Step 3:

[0350] The server receives the delivery requirements and invokes the delivery route optimization function.

[0351] Step 4:

[0352] The server calculates delivery routes and finds the optimal route to reduce costs and environmental impact.

[0353] Step 5:

[0354] The server decides whether to suggest an autonomous flying device (drone delivery) based on the user's requirements (e.g., urgent delivery).

[0355] Step 6:

[0356] The server returns the optimal delivery method and route to the user's device.

[0357] Step 7:

[0358] The terminal displays the delivery proposal and route information received from the server to the user.

[0359] Example: "The best delivery method is drone delivery, using the shortest route."

[0360] Emotional Engine Adjustment

[0361] Step 1:

[0362] As users enter product quotes and delivery requirements, an emotion engine recognizes their emotions, such as "impatience" or "excitement."

[0363] Step 2:

[0364] The device sends the user's emotional information to the server.

[0365] Step 3:

[0366] The server analyzes the user's emotions through an emotion engine and adjusts the suggestions.

[0367] Step 4:

[0368] Based on the emotional information, the server will suggest a faster delivery method (e.g., drone delivery) if the user is in a hurry, or suggest cost-saving solutions if the user is relaxed.

[0369] Step 5:

[0370] The server sends suggestions based on the emotion back to the user's device.

[0371] Step 6:

[0372] The device displays the tailored proposals received from the server to the user.

[0373] Example display: "Based on the user's urgent requirements, the best delivery method is drone delivery, using the shortest route."

[0374] These are the program processing steps of the smart wholesale platform, which integrates user terminals, generative models, and emotion engines. By taking user emotions into account, the system can make suggestions tailored to individual needs and provide more personalized services.

[0375] Example 2

[0376] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0377] Traditional wholesale platforms struggle to provide users with a fully personalized service when obtaining product quotes and selecting the optimal delivery method. Fast and efficient responses are required, especially for urgent delivery needs and optimizing complex delivery routes. Another issue is the inability to adjust recommendations based on user sentiment, resulting in a lack of an improved user experience.

[0378] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0379] In this invention, the server

[0380] a means for obtaining product quotes via a user terminal;

[0381] means for generating estimates and recommendations based on user input using the generative model;

[0382] A means to optimize delivery routes and make suggestions to reduce costs and environmental impact;

[0383] A way to recognize user emotions and adjust suggestions accordingly;

[0384] A means for proposing delivery using autonomous flying devices to meet rapid delivery needs;

[0385] This allows us to provide you with personalized quotes and delivery suggestions, and ensure fast and efficient delivery.

[0386] A "user terminal" is a device through which a user provides input and output via an interface.

[0387] A "generative model" is an algorithm or system that generates estimates or recommendations based on user input data.

[0388] "Delivery route" refers to the route along which goods are delivered, and is a route that is optimized to reduce costs and environmental impact.

[0389] An "autonomous flying device" is a device that flies unmanned and is used to meet specific delivery needs.

[0390] An "emotion engine" is a system or algorithm that recognizes a user's emotions and adjusts suggestions based on those emotions.

[0391] "Rapid delivery needs" are needs that require immediate or very short delivery.

[0392] "Quote" means an estimate or calculation of the price or quantity of a particular commodity.

[0393] A "suggestion" is a choice or recommendation presented to a user.

[0394] The present invention is a smart wholesale platform that allows users to obtain product quotes and select and manage optimal delivery methods. The system includes a user terminal, a generative model, a server, a delivery route optimization function, an autonomous flying device, and an emotion engine that recognizes user emotions.

[0395] Overall Platform Configuration

[0396] 1. User Device:

[0397] A device that a user operates to input and output data. User terminals include smartphones, tablets, and desktop computers.

[0398] The terminal transmits the user's input to the server and displays the quotes and delivery proposals returned by the server.

[0399] 2. Generative Model:

[0400] The server uses the generative model to generate estimates and suggestions based on the user's input.

[0401] This generative model is based on a large-scale language model (e.g., GPT-3) and suggests optimal products and delivery methods in response to user questions.

[0402] 3. Server:

[0403] It receives requests from users and passes them to a generative model to generate estimates and proposals.

[0404] The quotation results and delivery proposal are returned to the user's terminal.

[0405] 4. Delivery route optimization function:

[0406] The server optimizes delivery routes and makes suggestions for reducing costs and environmental impact.

[0407] Calculate the optimal delivery route based on user requirements using route optimization algorithms such as the Dijkstra algorithm.

[0408] 5. Autonomous Aircraft:

[0409] This is a drone designed to provide fast and efficient delivery for emergency delivery needs.

[0410] The server proposes autonomous flying devices based on delivery needs and delivers goods via the optimal route.

[0411] 6. Emotion Engine:

[0412] The server recognizes the user's emotions via the user's terminal.

[0413] The emotion engine uses NLP (natural language processing) technology to analyze the user's emotions and tailor its recommendations accordingly: if the user is in a hurry, it will suggest a fast delivery option, and if the user is relaxed, it will offer suggestions focused on cost savings.

[0414] Examples of concrete examples and prompts

[0415] Example 1:

[0416] User Input: "Can you give me a quote for 500 units of product B?"

[0417] The server uses the generative model to generate a quote (e.g., 1,000,000 yen).

[0418] Terminal display: "The estimated price for this item is 1,000,000 yen."

[0419] Example 2:

[0420] User input: "Please deliver the item as soon as possible."

[0421] The server optimizes delivery routes and suggests drone delivery.

[0422] Display on device: "The best delivery method is drone delivery, using the shortest route."

[0423] Prompt Sentence Examples

[0424] "Please calculate the price for the product. Please tell me the product name, quantity, and total price."

[0425] "Please suggest the best delivery method. For urgent deliveries, we recommend drone delivery."

[0426] The system of the present invention aims to provide personalized services that take into account user emotions and achieve efficient and fast delivery, thereby improving user experience and increasing business productivity.

[0427] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0428] Step 1:

[0429] A user uses a terminal to request a quote for a product.

[0430] Input: The user types into the terminal, "Please give me a quote for 200 units of product A."

[0431] Behavior: The terminal processes user input data in text format.

[0432] Output: The request data is generated and sent to the server.

[0433] Step 2:

[0434] The device sends a request to the server.

[0435] Input: Request data (user's quote request)

[0436] How it works: The device transfers the request data to the server using the HTTPS protocol.

[0437] Output: The expected action reaches the server.

[0438] Step 3:

[0439] The server queries the generative model to compute an estimate.

[0440] Input: User quote request data

[0441] How it works: The server passes a quote request to the generative AI model, which then starts the calculation. The generative model calculates a quote based on the input product information and quantity.

[0442] Output: The calculated quote result (e.g. amount) is sent back to the server in JSON format.

[0443] Step 4:

[0444] The server receives the estimate and returns it to the user's device.

[0445] Input: JSON data of the estimation results received from the generative model

[0446] How it works: The server converts the received JSON data into a user-friendly format and prepares it for transfer to the device.

[0447] Output: The processed quote results are sent to the terminal.

[0448] Step 5:

[0449] The terminal displays the estimate results to the user.

[0450] Input: Quote result received from the server

[0451] Action: The device displays the received quote on the screen. Specifically, it updates the display area to make the quote more visible to the user.

[0452] Output: The estimation results are displayed visually on the terminal and provided to the user.

[0453] Step 6:

[0454] The user enters delivery requirements into the terminal.

[0455] Input: The user types "urgent delivery please" into the terminal.

[0456] How it works: The terminal processes the user's input data in text format and generates data for further processing.

[0457] Output: Delivery requirements are ready to be sent to the server.

[0458] Step 7:

[0459] The terminal sends delivery requirements to the server.

[0460] Input: Delivery requirement data (user delivery request)

[0461] Operation: The terminal sends delivery requirement data to the server using the HTTPS protocol.

[0462] Output: Delivery requirement data arrives at the server.

[0463] Step 8:

[0464] The server performs delivery route optimization and determines the best delivery method based on the user's requirements.

[0465] Input: Delivery requirement data

[0466] How it works: The server uses a delivery route optimization algorithm (e.g., Dijkstra algorithm) to calculate the optimal route, and also filters the candidate delivery methods based on requirements.

[0467] Output: The optimal delivery route and method (e.g. drone delivery) is determined.

[0468] Step 9:

[0469] The server proposes the optimal delivery method and returns the result to the terminal.

[0470] Input: Optimized delivery route and method

[0471] How it works: The server generates data on the optimal delivery method and sends it to the device in JSON format.

[0472] Output: Proposed delivery method data arrives at the terminal.

[0473] Step 10:

[0474] The terminal displays delivery offers to the user.

[0475] Input: Proposal data received from the server

[0476] Action: The device displays the proposed data on the screen, updates the display area, and presents the appropriate delivery method to the user.

[0477] Output: The user is provided with a visual representation of the delivery proposal displayed on the terminal.

[0478] Step 11:

[0479] An emotion engine recognizes the user's emotions as they enter product quotes and delivery requirements.

[0480] Input: User-entered text

[0481] How it works: The emotion engine uses NLP techniques to analyze input text and identify the user's emotion (e.g., "anxious").

[0482] Output: The identified emotion data is transmitted to the server.

[0483] Step 12:

[0484] The server analyzes the user's emotional information through an emotion engine and adjusts the suggestions.

[0485] Input: Emotion data

[0486] How it works: The server adjusts its suggestions based on the emotion data it receives from the emotion engine, such as prioritizing faster delivery methods if the customer is in a hurry.

[0487] Output: The adjusted proposal is generated.

[0488] Step 13:

[0489] The server sends the adjusted proposal back to the user terminal.

[0490] Input: Adjusted proposal

[0491] Operation: The server prepares the adjusted proposal to be sent to the user's device.

[0492] Output: Data is sent to the terminal.

[0493] Step 14:

[0494] The device displays tailored suggestions to the user.

[0495] Input: Adjusted proposal data received from the server

[0496] Action: The device displays the suggestion data on the screen and updates the display area to make the personalized suggestions more visible to the user.

[0497] Output: The user is provided with a visual representation of the suggestions displayed on the device.

[0498] (Application example 2)

[0499] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0500] On conventional wholesale platforms, the process from obtaining product quotes to determining the optimal delivery route and selecting a fast delivery method was often inefficient, resulting in a poor user experience. Furthermore, there was a lack of consideration for user feelings in the service, leading to issues with not being able to fully meet individual needs. In particular, there were cases where an appropriate response was not given to urgent delivery requirements, resulting in a decline in reliability.

[0501] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for obtaining product quotes via a user terminal, means for generating quotes and proposals based on user input using a generative model, means for optimizing delivery routes and making proposals for reducing costs and environmental impact, means for proposing delivery using autonomous aircraft devices to meet rapid delivery needs, and means for recognizing user emotions using an emotion recognition engine and adjusting the content of the proposals. This enables users to quickly obtain optimal product quotes and receive personalized proposals based on their emotional state.

[0502] A "user terminal" is a device that a user operates to input and output data.

[0503] A "generative model" is an algorithm or software that generates estimates or recommendations based on input data.

[0504] A "Quote" is a calculation of the price or cost of a product based on information entered by the user.

[0505] "Delivery route optimization" is the process of calculating and proposing efficient and cost-effective delivery routes.

[0506] An "autonomous flying device" is an unmanned aerial vehicle that flies automatically and delivers goods without the need for human operation.

[0507] An "emotion recognition engine" is a technology or software that identifies emotions from a user's facial expressions, voice, etc.

[0508] "Suggestions" are recommendations of products and services provided based on user input using generative models, etc.

[0509] "Cost reduction" refers to reducing the cost of providing a product or service.

[0510] "Environmental mitigation" refers to efforts and measures taken to minimize the environmental impact of the delivery process.

[0511] This invention is a system that provides a smart wholesale platform by combining a user terminal, a generative model, a server, a delivery route optimization function, an autonomous flying device, and an emotion recognition engine. The specific implementation method will be described below.

[0512] User Device

[0513] A user terminal is a device that a user operates to input and output data. Examples include smartphones, tablets, and PCs. Users use this terminal to input product quote requests and delivery requirements.

[0514] Generative Model

[0515] Generative models are AI tools that generate quotes and proposals based on user input. The server uses the generative models to quickly respond to user inquiries. The models perform text analysis to calculate optimal product suggestions and quote prices.

[0516] server

[0517] The server receives requests from users and generates estimates and proposals based on the content of the requests using a generative model.The server also has a delivery route optimization function that calculates delivery routes based on user requirements.Furthermore, it is equipped with an emotion recognition engine to provide personalized services according to the user's emotional state.

[0518] Delivery route optimization function

[0519] The delivery route optimization function is a technology for efficiently calculating delivery routes. The server analyzes the user's delivery requirements (e.g., "Please deliver quickly") and determines the most efficient delivery route. This reduces costs and the environmental impact.

[0520] autonomous aircraft equipment

[0521] An autonomous flying device is an unmanned aircraft for rapid delivery. The server proposes delivery by the autonomous flying device taking into account the generative model and delivery requirements. The device automatically flies the specified route and achieves rapid delivery.

[0522] Emotion Recognition Engine

[0523] The emotion recognition engine is a technology that identifies emotions from the user's facial expressions and voice and adjusts the content of suggestions based on that information. Using the emotion recognition engine, the server prioritizes fast delivery methods if the user is in a hurry, and makes suggestions that emphasize cost reduction if the user is relaxed.

[0524] Examples of concrete examples and prompts

[0525] Specific examples

[0526] 1. Get a quote: User types, "Please give me a quote for 300 units of product B."

[0527] 2. Delivery Route Suggestion: User types, "What is the cheapest standard delivery route?"

[0528] 3. Emotion Recognition: Using images captured by the camera, the system recognizes the user's emotions and suggests a faster delivery method if the user is in a hurry.

[0529] Prompt Sentence Examples

[0530] Please let me know the estimate for product B.

[0531] Please suggest the best route for urgent delivery.

[0532] Recognize user emotions from images and provide appropriate suggestions

[0533] The above is an embodiment of the present invention. This smart wholesale platform provides fast and efficient services tailored to the individual needs of users.

[0534] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0535] Step 1:

[0536] A user uses a terminal to request a quote for a product. For example, they type, "Please give me a quote for 200 units of product A." The terminal sends this request to the server. The input is the user's request, and the output is the submission of the request.

[0537] Step 2:

[0538] The server receives a request from the user and queries the generative model. The generative model calculates an estimate based on the input request and sends it back to the server. The input is the user's request, and the output is the estimate result.

[0539] Step 3:

[0540] The server receives the estimate results returned from the generative model and sends them back to the user's terminal. The terminal displays the estimate results to the user. The input is the estimate results, and the output is the display to the user. Specifically, the user can confirm, "The estimate for the product is 200,000 yen."

[0541] Step 4:

[0542] The user inputs delivery requirements into the terminal, for example, "please deliver as soon as possible." The terminal then sends these requirements to the server. The input is the user's delivery requirements, and the output is the request submission.

[0543] Step 5:

[0544] The server receives the delivery requirements and calculates the optimal delivery route using the delivery route optimization function. The calculation results are sent to the server and returned to the user's device. The input is the delivery requirements, and the output is the optimal delivery route. Specifically, the server suggests, "The optimal delivery method is drone delivery, using the shortest route."

[0545] Step 6:

[0546] The server acquires the user's image and passes it to the emotion recognition engine to recognize the user's emotion. The emotion recognition engine analyzes the user's emotional information and adjusts the suggestions accordingly. The input is the user's image, and the output is the emotion data and the adjusted suggestions.

[0547] Step 7:

[0548] The server generates a proposal based on the emotion data and sends it back to the user's device. For example, if data indicates that the user is in a hurry, it will propose a faster delivery method. The device then displays this proposal to the user. The input is the adjusted proposal, and the output is the display to the user. Specifically, the user can confirm, "Based on the user's urgency requirements, the optimal delivery method is drone delivery, and it will be delivered via the shortest route."

[0549] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0550] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0551] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0552] [Second embodiment]

[0553] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0554] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0555] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0556] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0557] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0558] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0559] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0560] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0561] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0562] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0563] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0564] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0565] This invention is a smart wholesale platform that allows users to obtain product quotes and select and manage optimal delivery methods. The system of the present invention includes a user terminal, a generative model, a server, a delivery route optimization function, and a delivery means using an autonomous flying device.

[0566] Overall Platform Configuration

[0567] 1. User Device:

[0568] A device that users interact with to request product quotes and enter shipping requirements.

[0569] The terminal transmits the user's input to the server and displays the quotes and delivery proposals returned by the server.

[0570] 2. Generative Model:

[0571] The server uses the generative model to generate estimates and suggestions based on the user's input.

[0572] The generative model responds to user questions by suggesting appropriate products and delivery methods.

[0573] 3. Server:

[0574] It receives requests from users and passes them to a generative model to generate estimates and proposals.

[0575] The quotation results and delivery proposal are returned to the terminal.

[0576] 4. Delivery route optimization function:

[0577] The server optimizes delivery routes and makes suggestions for reducing costs and environmental impact.

[0578] Calculate the best delivery route based on user requirements.

[0579] 5. Autonomous Aircraft:

[0580] This is a drone designed to provide fast and efficient delivery for emergency delivery needs.

[0581] The server proposes autonomous flying devices based on delivery needs and delivers goods via the optimal route.

[0582] Program processing explanation

[0583] 1. User Inquiry Processing:

[0584] A user uses a terminal to request a quote for a product (for example, "Can I get a quote for 200 units of product A?").

[0585] The terminal sends this request to the server.

[0586] The server queries the generative model to compute an estimate.

[0587] The generative model calculates the estimate and sends it back to the server.

[0588] The server sends the estimate back to the device, which displays the results to the user.

[0589] Example: "The estimated price for this item is 200,000 yen."

[0590] 2. Delivery route optimization and suggestions:

[0591] The user enters delivery requirements (e.g., "please deliver as soon as possible") into the terminal.

[0592] The terminal sends this requirement to the server.

[0593] The server performs delivery route optimization and determines the best delivery method based on the user's requirements.

[0594] The server proposes the optimal delivery method (e.g., drone delivery) and returns the result to the device.

[0595] The terminal displays delivery offers to the user.

[0596] Example: "The best delivery method is drone delivery, using the shortest route."

[0597] This concludes the program process and concrete example of a smart wholesale platform that integrates generative models and drone delivery services. The system provides users with real-time quotes and selects and manages optimal delivery methods, enabling efficient and sustainable business operations.

[0598] The processing flow will be explained below.

[0599] Program processing flow explanation

[0600] User inquiry processing

[0601] Step 1:

[0602] A user uses a terminal to request a quote for a product, for example, by typing, "Please give me a quote for 200 units of product A."

[0603] Step 2:

[0604] The terminal sends the user's request to the server, which includes the type and quantity of the product.

[0605] Step 3:

[0606] The server receives the user request and passes it to the generative model.

[0607] Step 4:

[0608] The generative model calculates the estimated price of product A based on the user request.

[0609] Step 5:

[0610] The generative model sends the calculated price estimate back to the server.

[0611] Step 6:

[0612] The server receives the estimate and returns it to the user's device.

[0613] Step 7:

[0614] The terminal displays the estimate results received from the server to the user.

[0615] Delivery route optimization and proposals

[0616] Step 1:

[0617] The user uses the terminal to enter delivery requirements, for example, "please deliver as soon as possible."

[0618] Step 2:

[0619] The terminal sends the user's delivery requirements to the server, including delivery speed and special requests.

[0620] Step 3:

[0621] The server receives the delivery requirements and invokes the delivery route optimization function.

[0622] Step 4:

[0623] The server calculates delivery routes and finds the optimal route to reduce costs and environmental impact.

[0624] Step 5:

[0625] The server decides whether to suggest an autonomous flying device (drone delivery) based on the user's requirements (e.g., urgent delivery).

[0626] Step 6:

[0627] The server returns the optimal delivery method and route to the user's device.

[0628] Step 7:

[0629] The terminal displays the delivery proposal and route information received from the server to the user.

[0630] Comprehensive Processing

[0631] Step 1:

[0632] A user uses a terminal to request both a quote and urgent delivery of a product, for example, "Please give me a quote for 200 units of product A and deliver it as soon as possible."

[0633] Step 2:

[0634] The terminal sends this overall request to the server.

[0635] Step 3:

[0636] The server receives the synthetic request and first passes it to the generative model to calculate the estimated price.

[0637] Step 4:

[0638] The generative model calculates the estimated price and sends it back to the server.

[0639] Step 5:

[0640] The server receives the estimate results and then calls the delivery route optimization function to calculate the route.

[0641] Step 6:

[0642] Based on the calculated route, the server considers the user's requirements (e.g., urgent delivery) and suggests the optimal delivery method (e.g., drone delivery).

[0643] Step 7:

[0644] The server returns the overall results (estimated price, optimal delivery method, and route) to the user's device.

[0645] Step 8:

[0646] The terminal displays the overall results received from the server to the user.

[0647] Example 1

[0648] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0649] Traditional wholesale platforms face challenges in being unable to respond quickly to user requests when obtaining product quotes or proposing delivery methods. Delivery routes are often not sufficiently optimized, preventing cost reduction and environmental impact reduction. Furthermore, the lack of efficient delivery methods for urgent delivery needs prevents business efficiency and sustainability from improving.

[0650] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0651] In this invention, the server includes a means for transmitting user-entered data to the server, a means for generating estimates and proposals based on the user's input using a generative model, and a means for executing a delivery route optimization function. This not only enables quick and accurate product estimates based on the user's input, but also calculates optimal delivery routes, reducing costs and reducing environmental impact. Furthermore, efficient delivery using unmanned aerial vehicles can be proposed for urgent delivery needs, improving business efficiency and sustainability.

[0652] "User Terminal" means a device operated by a User to input product quote requests and delivery requirements.

[0653] A "generative model" is an artificial intelligence model used by the server to generate estimates and suggestions based on user input.

[0654] The "Delivery route optimization function" is a function in which the server optimizes delivery routes and makes suggestions for reducing costs and environmental impact.

[0655] A "drone" is an autonomous flying delivery device used for rapid delivery needs.

[0656] The "means for transmitting data entered by the user to the server" refers to a communication means for transmitting request information from the user terminal to the server.

[0657] The "means for the server to send a quote request to the generative model" is a function that passes the user's request received by the server to the generative model and requests processing.

[0658] The "means by which the generative model calculates an estimate" is the function by which the generative model calculates the price and proposal details for a specified product or service.

[0659] "Means for the server to return the estimation results to the user terminal" refers to means by which the server transmits the estimation results received from the generative model to the user terminal.

[0660] The "means for suggesting optimal products and delivery methods" is a function that enables the generative model to suggest optimal products or delivery methods based on the user's requests.

[0661] "Means to streamline the process from quotation to delivery" refers to a system that efficiently carries out the entire process from estimating and measuring products to final delivery.

[0662] This invention is a smart wholesale platform that allows users to obtain product quotes and select and manage optimal delivery methods. The system of the present invention includes a user terminal, a generative model, a server, a delivery route optimization function, and a delivery means using an unmanned aerial vehicle.

[0663] Overall Platform Configuration

[0664] 1. User Device:

[0665] A device that users interact with to request product quotes and enter shipping requirements.

[0666] The terminals are intended to be devices that can connect to the Internet, such as PCs, smartphones, and tablets, and will run on operating systems such as iOS and Android.

[0667] Example: User types, "Please give me a quote for 200 units of product A."

[0668] 2. Generative Model:

[0669] The server uses the generative model to generate estimates and suggestions based on the user's input.

[0670] This generative model uses AI models such as OpenAI's GPT (Generative Pre-trained Transformer).

[0671] Example: "The estimate for 200 units of product A is 200,000 yen."

[0672] 3. Server:

[0673] The server receives requests from users and passes them to a generative model to generate estimates and proposals.

[0674] The server uses cloud services such as AWS (Amazon Web Services) and Microsoft Azure.

[0675] Example: Taking a user request and passing it to a generative model.

[0676] 4. Delivery route optimization function:

[0677] The server optimizes delivery routes and makes suggestions for reducing costs and environmental impact.

[0678] The server will implement route optimization algorithms such as Google Maps API and Route4Me.

[0679] Example: Calculate the optimal route and display "The best delivery method is drone delivery, and we will deliver via the shortest route."

[0680] 5. Unmanned aircraft:

[0681] This is an autonomous flying delivery device that provides fast and efficient delivery for urgent delivery needs.

[0682] The server will suggest an unmanned aircraft based on delivery needs and deliver the goods via the optimal route.

[0683] Example: For urgent deliveries, we suggest, "The best delivery method is drone delivery, and we will deliver it via the shortest route."

[0684] Program processing explanation

[0685] 1. User Inquiry Processing:

[0686] A user uses a device to request a quote for a product.

[0687] The terminal sends this request to the server.

[0688] The server queries the generative model to compute an estimate.

[0689] The generative model calculates the estimate and sends it back to the server.

[0690] The server sends the estimate back to the device, which displays the results to the user.

[0691] Example: "The estimated price for this item is 200,000 yen."

[0692] 2. Delivery route optimization and suggestions:

[0693] The user enters their delivery requirements (e.g., "please deliver as soon as possible") into the terminal.

[0694] The terminal sends this requirement to the server.

[0695] The server performs delivery route optimization and determines the best delivery method based on the user's requirements.

[0696] The server proposes the optimal delivery method (e.g., drone delivery) and returns the result to the device.

[0697] The terminal displays delivery offers to the user.

[0698] Example: "The best delivery method is drone delivery, using the shortest route."

[0699] Prompt sentences for specific examples of operation

[0700] Prompt for product quote:

[0701] "Please give me a quote for 200 units of product A."

[0702] Prompt for delivery requirements:

[0703] "Please deliver it quickly."

[0704] This concludes the explanation of the structure and operation of a smart wholesale platform that integrates generative models and unmanned aerial vehicles. The system provides users with real-time quotes and selects and manages optimal delivery methods, enabling efficient and sustainable business operations.

[0705] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0706] Step 1:

[0707] A user requests a quote for a product. The user enters specific product information into the terminal (e.g., "Please give me a quote for 200 units of product A") and sends the input data to the terminal. The terminal receives this data and converts it into JSON format. This makes the user's input in a format that can be processed by the next step. The input is "Product name: Product A, Quantity: 200", and the output is JSON format data.

[0708] Step 2:

[0709] The terminal sends the user's input to the server. The terminal sends the JSON format data entered by the user to the server as an HTTP request. In this operation, the application inside the terminal adds the appropriate URL and header information to send the data. The input is the user data in JSON format, and the output is a request to send the requested quote data.

[0710] Step 3:

[0711] The server sends a request for an estimate to the generative model. The server analyzes the received user request and makes an API call to the generative model to calculate an estimate. The input is the user request data in JSON format, and the output is the API request to be passed to the generative model. The server calls the "generateEstimate(data)" function as an internal process.

[0712] Step 4:

[0713] The generative model calculates the estimate. Based on the provided product information, the generative AI model calculates the appropriate price and sends the result back to the server. This calculation is performed quickly based on an internal algorithm. The input is product information and quantity, and the output is the estimated price. Example: "The estimate for 200 units of product A is 200,000 yen."

[0714] Step 5:

[0715] The server returns the estimation results to the terminal. The server converts the estimation results received from the generative model back into JSON format and sends it to the terminal as an HTTP response. The input is the estimation result from the generative model, and the output is the JSON-formatted estimation data sent to the terminal.

[0716] Step 6:

[0717] The terminal displays the quotation results to the user. The terminal parses the quotation results received from the server and displays them in the user interface. The input is the quotation data in JSON format, and the output is what is displayed to the user. For example, the message "The quotation for the product is 200,000 yen" is displayed.

[0718] Step 7:

[0719] The user inputs the delivery requirements. The user inputs the delivery requirements (e.g., "Please deliver as soon as possible") into the terminal and sends the input data to the terminal. The terminal receives the delivery requirements and converts them into JSON format. This makes the input from the user in a format that can be processed in the next step. The input is "Delivery method: urgent delivery" and the output is JSON format data.

[0720] Step 8:

[0721] The terminal sends the delivery requirements to the server. The terminal sends the JSON format delivery requirement data entered by the user to the server as an HTTP request. The input is the user delivery requirement data in JSON format, and the output is a request to send the requested delivery data.

[0722] Step 9:

[0723] The server executes the delivery route optimization function. The server passes the received delivery requirements to the delivery route optimization function and calculates the optimal delivery route. This uses algorithms such as Google Maps API and Route4Me. The input is the user's delivery requirement data, and the output is the optimized delivery route data.

[0724] Step 10:

[0725] The server determines the optimal delivery method and returns it to the terminal. The server analyzes the results of the delivery route optimization function and determines the optimal delivery method based on the user's requirements. It then converts the result into JSON format and sends it to the terminal as an HTTP response. The input is the result data of the delivery route optimization, and the output is the optimal delivery method data sent to the terminal.

[0726] Step 11:

[0727] The terminal displays the delivery suggestions to the user. The terminal analyzes the results of the optimal delivery method received from the server and displays them on the user interface. The input is the delivery method data in JSON format, and the output is what is displayed to the user. Example: A message is displayed saying, "The optimal delivery method is drone delivery, and it will be delivered via the shortest route."

[0728] (Application example 1)

[0729] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0730] In conventional logistics centers, optimizing product estimates and delivery routes takes time, making efficient operation difficult. Furthermore, the lack of a system that users can operate intuitively and visually can lead to confusion in estimate results and delivery method selection. Furthermore, manual management is important for urgent deliveries, and urgent delivery methods are often not proposed in a timely manner.

[0731] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0732] In this invention, the server includes a means for obtaining product quotes via a user terminal, a means for generating quotes and proposals based on user input using a generative model, a means for optimizing delivery routes and making proposals for reducing costs and environmental impact, a means for proposing delivery using autonomous flying devices for rapid delivery needs, and a means for visualizing and manipulating quotes and delivery proposals through an application installed on a smart device or robot. This allows users to visually check and manipulate quote results and delivery methods, enabling them to obtain product quotes and manage deliveries efficiently and intuitively.

[0733] "User terminal" means a device for inputting product quote requests and delivery requirements and receiving the results.

[0734] A "generative model" is an AI system for generating estimates and recommendations based on user input.

[0735] "Delivery route optimization" involves calculating delivery routes and proposing optimal routes to reduce costs and environmental impact.

[0736] An "autonomous flying device" is an unmanned aircraft designed to quickly deliver goods to meet urgent delivery needs.

[0737] A "smart device" is a portable device with information processing and communication capabilities.

[0738] A "robot" is a mechanical device that can autonomously perform specified actions or tasks.

[0739] "Application" means software installed on a smart device or robot for visualizing and manipulating quotes and delivery proposals.

[0740] The system for realizing this application example includes a user terminal, a generative model, a server, a delivery route optimization function, an autonomous flying device, and an application installed on a smart device or robot. Specific implementation methods are described below.

[0741] Overall system configuration

[0742] 1. User Device

[0743] Users use their smartphones or tablets to input product quote requests and delivery requirements, which are then sent to the server, which then displays the quotes and delivery proposals returned by the server.

[0744] 2. Generative Model

[0745] The server uses a generative model, such as OpenAI GPT-3, to generate quotes and recommendations based on the user's input, and then responds to the user's query with appropriate product and delivery suggestions.

[0746] 3. Server

[0747] The server receives requests from users, passes them to the generative model to generate estimates and proposals, receives the results from the generative model, and returns them to the user's device. The server also optimizes delivery routes and determines the optimal delivery method.

[0748] 4. Delivery route optimization function

[0749] The server uses Python's NetworkX library to optimize delivery routes, calculating the best route for cost savings and environmental conservation.

[0750] 5. Autonomous aircraft

[0751] For urgent delivery needs, Sarver proposes autonomous flying devices, which enable fast and efficient delivery.

[0752] 6. Smart Device or Robot Applications

[0753] The application is installed on a smartphone, tablet, smart glasses, or logistics robot, and allows users to visually check and manipulate quotation results and delivery proposals.

[0754] Examples of concrete examples and prompts

[0755] A specific example is shown below.

[0756] Request a product quote

[0757] A user uses a smartphone to enter a product quote request as follows:

[0758] Please let me know the estimate for 200 items.

[0759] Displaying the estimate results

[0760] The server uses the generative model to generate an estimate and displays the result as follows:

[0761] Estimated price: 200,000 yen.

[0762] Delivery method: Drone delivery is recommended.

[0763] Optimal delivery route: The shortest route from the origin to the destination was calculated.

[0764] This system allows users to visually check and operate quotation results and delivery methods, enabling them to efficiently and intuitively obtain product quotes and manage deliveries.

[0765] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0766] Step 1:

[0767] A user uses a smartphone or tablet to enter a request for a quote on a product. For example, the user enters a request such as "Please give me a quote for 200 units of this product."

[0768] Input: User input (e.g. "Please give me a quote for 200 units of this product").

[0769] Output: Request data (JSON format).

[0770] Step 2:

[0771] The device sends this request to the server, and the device sends the user's input data in JSON format to the server.

[0772] Input: User-entered information.

[0773] Output: Sending a request to the server.

[0774] Step 3:

[0775] The server receives a request from the user and passes it to the generative model to generate an estimate or proposal. The server passes a prompt to the generative model to calculate an estimate.

[0776] Input: The request data sent from the device.

[0777] Output: Prompt statement and quote request to the generative model.

[0778] Step 4:

[0779] A generative model (e.g., OpenAI GPT-3) generates an estimate based on the user's input and sends the result back to the server.

[0780] Input: Prompt text from the server.

[0781] Output: Estimation results (text format).

[0782] Step 5:

[0783] The server receives the estimation results from the generative model and sends them back to the user device. The server receives the estimation results and sends them to the device.

[0784] Input: Estimation results from the generative model.

[0785] Output: Send the estimate results to the user's device.

[0786] Step 6:

[0787] The terminal displays the estimate results to the user, who can then check the estimate results on the terminal.

[0788] Input: Estimate result from the server.

[0789] Output: Display the estimate result to the user (e.g., "Estimate result: 200,000 yen.").

[0790] Step 7:

[0791] The user enters their delivery requirements (e.g., "please deliver quickly") into the terminal.

[0792] Input: User delivery requirement input (e.g. "Please hurry delivery").

[0793] Output: Delivery requirements data (JSON format).

[0794] Step 8:

[0795] The device sends these delivery requirements to the server. The device sends the user's delivery requirements data to the server in JSON format.

[0796] Input: User delivery requirement information.

[0797] Output: Send delivery requirements data to the server.

[0798] Step 9:

[0799] The server performs delivery route optimization and determines the best delivery method based on the user's requirements. The server uses the NetworkX library to calculate the optimal delivery route.

[0800] Input: Delivery requirement data sent from the terminal.

[0801] Output: Optimal delivery route information and delivery method.

[0802] Step 10:

[0803] The server proposes the optimal delivery method (e.g., drone delivery) and returns the result to the terminal.

[0804] Input: Optimized delivery route information.

[0805] Output: Sends delivery method proposal to user.

[0806] Step 11:

[0807] The device displays delivery suggestions to the user, who can then review and manage the optimal delivery method on the device.

[0808] Input: Delivery method suggestions from the server.

[0809] Output: Display delivery method to user (e.g. "The best delivery method is drone delivery, using the shortest route.").

[0810] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0811] This invention is a smart wholesale platform that allows users to obtain product quotes and select and manage optimal delivery methods. The system of the present invention includes a user terminal, a generative model, a server, a delivery route optimization function, an autonomous flying device, and an emotion engine that recognizes user emotions.

[0812] Overall Platform Configuration

[0813] 1. User Device:

[0814] It is a device that users can operate to input and output data.

[0815] The terminal transmits the user's input to the server and displays the quotes and delivery proposals returned by the server.

[0816] 2. Generative Model:

[0817] The server uses the generative model to generate estimates and suggestions based on the user's input.

[0818] This generative model responds to user queries by suggesting optimal products and delivery methods.

[0819] 3. Server:

[0820] It receives requests from users and passes them to a generative model to generate estimates and proposals.

[0821] The quotation results and delivery proposal are returned to the user's terminal.

[0822] 4. Delivery route optimization function:

[0823] The server optimizes delivery routes and makes suggestions for reducing costs and environmental impact.

[0824] Calculate the best delivery route based on user requirements.

[0825] 5. Autonomous Aircraft:

[0826] This is a drone designed to provide fast and efficient delivery for emergency delivery needs.

[0827] The server proposes autonomous flying devices based on delivery needs and delivers goods via the optimal route.

[0828] 6. Emotion Engine:

[0829] The server recognizes the user's emotions via the user's terminal.

[0830] The emotion engine adjusts suggestions and determines the urgency of delivery based on the user's emotions.

[0831] Program processing explanation

[0832] 1. User Inquiry Processing:

[0833] A user uses a terminal to request a quote for a product, for example, by typing, "Please give me a quote for 200 units of product A."

[0834] The terminal sends this request to the server.

[0835] The server queries the generative model to compute an estimate.

[0836] The generative model calculates the estimate and sends it back to the server.

[0837] The server receives the estimate and returns it to the user's device.

[0838] The terminal displays the estimate results to the user.

[0839] Example: "The estimated price for this item is 200,000 yen."

[0840] 2. Delivery route optimization and suggestions:

[0841] The user enters delivery requirements (e.g., "please deliver as soon as possible") into the terminal.

[0842] The terminal sends this requirement to the server.

[0843] The server performs delivery route optimization and determines the best delivery method based on the user's requirements.

[0844] The server proposes the optimal delivery method (e.g., drone delivery) and returns the result to the device.

[0845] The terminal displays delivery offers to the user.

[0846] Example: "The best delivery method is drone delivery, using the shortest route."

[0847] 3. Emotional engine regulation:

[0848] As users enter product quotes and delivery requirements, an emotion engine recognizes their emotions (e.g., "impatient" or "excited").

[0849] The server analyzes the user's emotional information through an emotion engine and adjusts the suggestions.

[0850] The emotion engine will more frequently suggest faster delivery methods (e.g. drone delivery) if the user is in a hurry, and more cost-saving suggestions if the user is relaxed.

[0851] The server generates suggestions based on the emotion and sends them back to the user's device.

[0852] The device displays tailored suggestions to the user.

[0853] Example display: "Based on the user's urgent requirements, the best delivery method is drone delivery, using the shortest route."

[0854] The above is a program process and example of a smart wholesale platform that integrates user terminals, generative models, and an emotion engine. By taking user emotions into account, it can make suggestions based on individual needs and provide more personalized services.

[0855] The processing flow will be explained below.

[0856] Program processing flow explanation

[0857] User inquiry processing

[0858] Step 1:

[0859] A user uses a terminal to request a quote for a product, for example, by typing, "Please give me a quote for 200 units of product A."

[0860] Step 2:

[0861] The terminal sends the request content to the server.

[0862] Step 3:

[0863] The server receives the user's request and passes it to the generative model.

[0864] Step 4:

[0865] The generative model calculates the estimated price of product A based on the user request.

[0866] Step 5:

[0867] The generative model sends the calculated price estimate back to the server.

[0868] Step 6:

[0869] The server receives the estimate and returns it to the user's device.

[0870] Step 7:

[0871] The terminal displays the estimate results received from the server to the user.

[0872] Example: "The estimated price for this item is 200,000 yen."

[0873] Delivery route optimization and proposals

[0874] Step 1:

[0875] The user enters their delivery requirements, for example, "Please deliver as soon as possible."

[0876] Step 2:

[0877] The terminal sends this delivery requirement to the server.

[0878] Step 3:

[0879] The server receives the delivery requirements and invokes the delivery route optimization function.

[0880] Step 4:

[0881] The server calculates delivery routes and finds the optimal route to reduce costs and environmental impact.

[0882] Step 5:

[0883] The server decides whether to suggest an autonomous flying device (drone delivery) based on the user's requirements (e.g., urgent delivery).

[0884] Step 6:

[0885] The server returns the optimal delivery method and route to the user's device.

[0886] Step 7:

[0887] The terminal displays the delivery proposal and route information received from the server to the user.

[0888] Example: "The best delivery method is drone delivery, using the shortest route."

[0889] Emotional Engine Adjustment

[0890] Step 1:

[0891] As users enter product quotes and delivery requirements, an emotion engine recognizes their emotions, such as "impatience" or "excitement."

[0892] Step 2:

[0893] The device sends the user's emotional information to the server.

[0894] Step 3:

[0895] The server analyzes the user's emotions through an emotion engine and adjusts the suggestions.

[0896] Step 4:

[0897] Based on the emotional information, the server will suggest a faster delivery method (e.g., drone delivery) if the user is in a hurry, or suggest cost-saving solutions if the user is relaxed.

[0898] Step 5:

[0899] The server sends suggestions based on the emotion back to the user's device.

[0900] Step 6:

[0901] The device displays the tailored proposals received from the server to the user.

[0902] Example display: "Based on the user's urgent requirements, the best delivery method is drone delivery, using the shortest route."

[0903] These are the program processing steps of the smart wholesale platform, which integrates user terminals, generative models, and emotion engines. By taking user emotions into account, the system can make suggestions tailored to individual needs and provide more personalized services.

[0904] Example 2

[0905] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0906] Traditional wholesale platforms struggle to provide users with a fully personalized service when obtaining product quotes and selecting the optimal delivery method. Fast and efficient responses are required, especially for urgent delivery needs and optimizing complex delivery routes. Another issue is the inability to adjust recommendations based on user sentiment, resulting in a lack of an improved user experience.

[0907] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0908] In this invention, the server

[0909] a means for obtaining product quotes via a user terminal;

[0910] means for generating estimates and recommendations based on user input using the generative model;

[0911] A means to optimize delivery routes and make suggestions to reduce costs and environmental impact;

[0912] A way to recognize user emotions and adjust suggestions accordingly;

[0913] A means for proposing delivery using autonomous flying devices to meet rapid delivery needs;

[0914] This allows us to provide you with personalized quotes and delivery suggestions, and ensure fast and efficient delivery.

[0915] A "user terminal" is a device through which a user provides input and output via an interface.

[0916] A "generative model" is an algorithm or system that generates estimates or recommendations based on user input data.

[0917] "Delivery route" refers to the route along which goods are delivered, and is a route that is optimized to reduce costs and environmental impact.

[0918] An "autonomous flying device" is a device that flies unmanned and is used to meet specific delivery needs.

[0919] An "emotion engine" is a system or algorithm that recognizes a user's emotions and adjusts suggestions based on those emotions.

[0920] "Rapid delivery needs" are needs that require immediate or very short delivery.

[0921] "Quote" means an estimate or calculation of the price or quantity of a particular commodity.

[0922] A "suggestion" is a choice or recommendation presented to a user.

[0923] The present invention is a smart wholesale platform that allows users to obtain product quotes and select and manage optimal delivery methods. The system includes a user terminal, a generative model, a server, a delivery route optimization function, an autonomous flying device, and an emotion engine that recognizes user emotions.

[0924] Overall Platform Configuration

[0925] 1. User Device:

[0926] A device that a user operates to input and output data. User terminals include smartphones, tablets, and desktop computers.

[0927] The terminal transmits the user's input to the server and displays the quotes and delivery proposals returned by the server.

[0928] 2. Generative Model:

[0929] The server uses the generative model to generate estimates and suggestions based on the user's input.

[0930] This generative model is based on a large-scale language model (e.g., GPT-3) and suggests optimal products and delivery methods in response to user questions.

[0931] 3. Server:

[0932] It receives requests from users and passes them to a generative model to generate estimates and proposals.

[0933] The quotation results and delivery proposal are returned to the user's terminal.

[0934] 4. Delivery route optimization function:

[0935] The server optimizes delivery routes and makes suggestions for reducing costs and environmental impact.

[0936] Calculate the optimal delivery route based on user requirements using route optimization algorithms such as the Dijkstra algorithm.

[0937] 5. Autonomous Aircraft:

[0938] This is a drone designed to provide fast and efficient delivery for emergency delivery needs.

[0939] The server proposes autonomous flying devices based on delivery needs and delivers goods via the optimal route.

[0940] 6. Emotion Engine:

[0941] The server recognizes the user's emotions via the user's terminal.

[0942] The emotion engine uses NLP (natural language processing) technology to analyze the user's emotions and tailor its recommendations accordingly: if the user is in a hurry, it will suggest a fast delivery option, and if the user is relaxed, it will offer suggestions focused on cost savings.

[0943] Examples of concrete examples and prompts

[0944] Example 1:

[0945] User Input: "Can you give me a quote for 500 units of product B?"

[0946] The server uses the generative model to generate a quote (e.g., 1,000,000 yen).

[0947] Terminal display: "The estimated price for this item is 1,000,000 yen."

[0948] Example 2:

[0949] User input: "Please deliver the item as soon as possible."

[0950] The server optimizes delivery routes and suggests drone delivery.

[0951] Display on device: "The best delivery method is drone delivery, using the shortest route."

[0952] Prompt Sentence Examples

[0953] "Please calculate the price for the product. Please tell me the product name, quantity, and total price."

[0954] "Please suggest the best delivery method. For urgent deliveries, we recommend drone delivery."

[0955] The system of the present invention aims to provide personalized services that take into account user emotions and achieve efficient and fast delivery, thereby improving user experience and increasing business productivity.

[0956] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0957] Step 1:

[0958] A user uses a terminal to request a quote for a product.

[0959] Input: The user types into the terminal, "Please give me a quote for 200 units of product A."

[0960] Behavior: The terminal processes user input data in text format.

[0961] Output: The request data is generated and sent to the server.

[0962] Step 2:

[0963] The device sends a request to the server.

[0964] Input: Request data (user's quote request)

[0965] How it works: The device transfers the request data to the server using the HTTPS protocol.

[0966] Output: The expected action reaches the server.

[0967] Step 3:

[0968] The server queries the generative model to compute an estimate.

[0969] Input: User quote request data

[0970] How it works: The server passes a quote request to the generative AI model, which then starts the calculation. The generative model calculates a quote based on the input product information and quantity.

[0971] Output: The calculated quote result (e.g. amount) is sent back to the server in JSON format.

[0972] Step 4:

[0973] The server receives the estimate and returns it to the user's device.

[0974] Input: JSON data of the estimation results received from the generative model

[0975] How it works: The server converts the received JSON data into a user-friendly format and prepares it for transfer to the device.

[0976] Output: The processed quote results are sent to the terminal.

[0977] Step 5:

[0978] The terminal displays the estimate results to the user.

[0979] Input: Quote result received from the server

[0980] Action: The device displays the received quote on the screen. Specifically, it updates the display area to make the quote more visible to the user.

[0981] Output: The estimation results are displayed visually on the terminal and provided to the user.

[0982] Step 6:

[0983] The user enters delivery requirements into the terminal.

[0984] Input: The user types "urgent delivery please" into the terminal.

[0985] How it works: The terminal processes the user's input data in text format and generates data for further processing.

[0986] Output: Delivery requirements are ready to be sent to the server.

[0987] Step 7:

[0988] The terminal sends delivery requirements to the server.

[0989] Input: Delivery requirement data (user delivery request)

[0990] Operation: The terminal sends delivery requirement data to the server using the HTTPS protocol.

[0991] Output: Delivery requirement data arrives at the server.

[0992] Step 8:

[0993] The server performs delivery route optimization and determines the best delivery method based on the user's requirements.

[0994] Input: Delivery requirement data

[0995] How it works: The server uses a delivery route optimization algorithm (e.g., Dijkstra algorithm) to calculate the optimal route, and also filters the candidate delivery methods based on requirements.

[0996] Output: The optimal delivery route and method (e.g. drone delivery) is determined.

[0997] Step 9:

[0998] The server proposes the optimal delivery method and returns the result to the terminal.

[0999] Input: Optimized delivery route and method

[1000] How it works: The server generates data on the optimal delivery method and sends it to the device in JSON format.

[1001] Output: Proposed delivery method data arrives at the terminal.

[1002] Step 10:

[1003] The terminal displays delivery offers to the user.

[1004] Input: Proposal data received from the server

[1005] Action: The device displays the proposed data on the screen, updates the display area, and presents the appropriate delivery method to the user.

[1006] Output: The user is provided with a visual representation of the delivery proposal displayed on the terminal.

[1007] Step 11:

[1008] An emotion engine recognizes the user's emotions as they enter product quotes and delivery requirements.

[1009] Input: User-entered text

[1010] How it works: The emotion engine uses NLP techniques to analyze input text and identify the user's emotion (e.g., "anxious").

[1011] Output: The identified emotion data is transmitted to the server.

[1012] Step 12:

[1013] The server analyzes the user's emotional information through an emotion engine and adjusts the suggestions.

[1014] Input: Emotion data

[1015] How it works: The server adjusts its suggestions based on the emotion data it receives from the emotion engine, such as prioritizing faster delivery methods if the customer is in a hurry.

[1016] Output: The adjusted proposal is generated.

[1017] Step 13:

[1018] The server sends the adjusted proposal back to the user terminal.

[1019] Input: Adjusted proposal

[1020] Operation: The server prepares the adjusted proposal to be sent to the user's device.

[1021] Output: Data is sent to the terminal.

[1022] Step 14:

[1023] The device displays tailored suggestions to the user.

[1024] Input: Adjusted proposal data received from the server

[1025] Action: The device displays the suggestion data on the screen and updates the display area to make the personalized suggestions more visible to the user.

[1026] Output: The user is provided with a visual representation of the suggestions displayed on the device.

[1027] (Application example 2)

[1028] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1029] On conventional wholesale platforms, the process from obtaining product quotes to determining the optimal delivery route and selecting a fast delivery method was often inefficient, resulting in a poor user experience. Furthermore, there was a lack of consideration for user feelings in the service, leading to issues with not being able to fully meet individual needs. In particular, there were cases where an appropriate response was not given to urgent delivery requirements, resulting in a decline in reliability.

[1030] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for obtaining product quotes via a user terminal, means for generating quotes and proposals based on user input using a generative model, means for optimizing delivery routes and making proposals for reducing costs and environmental impact, means for proposing delivery using autonomous aircraft devices to meet rapid delivery needs, and means for recognizing user emotions using an emotion recognition engine and adjusting the content of the proposals. This enables users to quickly obtain optimal product quotes and receive personalized proposals based on their emotional state.

[1031] A "user terminal" is a device that a user operates to input and output data.

[1032] A "generative model" is an algorithm or software that generates estimates or recommendations based on input data.

[1033] A "Quote" is a calculation of the price or cost of a product based on information entered by the user.

[1034] "Delivery route optimization" is the process of calculating and proposing efficient and cost-effective delivery routes.

[1035] An "autonomous flying device" is an unmanned aerial vehicle that flies automatically and delivers goods without the need for human operation.

[1036] An "emotion recognition engine" is a technology or software that identifies emotions from a user's facial expressions, voice, etc.

[1037] "Suggestions" are recommendations of products and services provided based on user input using generative models, etc.

[1038] "Cost reduction" refers to reducing the cost of providing a product or service.

[1039] "Environmental mitigation" refers to efforts and measures taken to minimize the environmental impact of the delivery process.

[1040] This invention is a system that provides a smart wholesale platform by combining a user terminal, a generative model, a server, a delivery route optimization function, an autonomous flying device, and an emotion recognition engine. The specific implementation method will be described below.

[1041] User Device

[1042] A user terminal is a device that a user operates to input and output data. Examples include smartphones, tablets, and PCs. Users use this terminal to input product quote requests and delivery requirements.

[1043] Generative Model

[1044] Generative models are AI tools that generate quotes and proposals based on user input. The server uses the generative models to quickly respond to user inquiries. The models perform text analysis to calculate optimal product suggestions and quote prices.

[1045] server

[1046] The server receives requests from users and generates estimates and proposals based on the content of the requests using a generative model.The server also has a delivery route optimization function that calculates delivery routes based on user requirements.Furthermore, it is equipped with an emotion recognition engine to provide personalized services according to the user's emotional state.

[1047] Delivery route optimization function

[1048] The delivery route optimization function is a technology for efficiently calculating delivery routes. The server analyzes the user's delivery requirements (e.g., "Please deliver quickly") and determines the most efficient delivery route. This reduces costs and the environmental impact.

[1049] autonomous aircraft equipment

[1050] An autonomous flying device is an unmanned aircraft for rapid delivery. The server proposes delivery by the autonomous flying device taking into account the generative model and delivery requirements. The device automatically flies the specified route and achieves rapid delivery.

[1051] Emotion Recognition Engine

[1052] The emotion recognition engine is a technology that identifies emotions from the user's facial expressions and voice and adjusts the content of suggestions based on that information. Using the emotion recognition engine, the server prioritizes fast delivery methods if the user is in a hurry, and makes suggestions that emphasize cost reduction if the user is relaxed.

[1053] Examples of concrete examples and prompts

[1054] Specific examples

[1055] 1. Get a quote: User types, "Please give me a quote for 300 units of product B."

[1056] 2. Delivery Route Suggestion: User types, "What is the cheapest standard delivery route?"

[1057] 3. Emotion Recognition: Using images captured by the camera, the system recognizes the user's emotions and suggests a faster delivery method if the user is in a hurry.

[1058] Prompt Sentence Examples

[1059] Please let me know the estimate for product B.

[1060] Please suggest the best route for urgent delivery.

[1061] Recognize user emotions from images and provide appropriate suggestions

[1062] The above is an embodiment of the present invention. This smart wholesale platform provides fast and efficient services tailored to the individual needs of users.

[1063] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1064] Step 1:

[1065] A user uses a terminal to request a quote for a product. For example, they type, "Please give me a quote for 200 units of product A." The terminal sends this request to the server. The input is the user's request, and the output is the submission of the request.

[1066] Step 2:

[1067] The server receives a request from the user and queries the generative model. The generative model calculates an estimate based on the input request and sends it back to the server. The input is the user's request, and the output is the estimate result.

[1068] Step 3:

[1069] The server receives the estimate results returned from the generative model and sends them back to the user's terminal. The terminal displays the estimate results to the user. The input is the estimate results, and the output is the display to the user. Specifically, the user can confirm, "The estimate for the product is 200,000 yen."

[1070] Step 4:

[1071] The user inputs delivery requirements into the terminal, for example, "please deliver as soon as possible." The terminal then sends these requirements to the server. The input is the user's delivery requirements, and the output is the request submission.

[1072] Step 5:

[1073] The server receives the delivery requirements and calculates the optimal delivery route using the delivery route optimization function. The calculation results are sent to the server and returned to the user's device. The input is the delivery requirements, and the output is the optimal delivery route. Specifically, the server suggests, "The optimal delivery method is drone delivery, using the shortest route."

[1074] Step 6:

[1075] The server acquires the user's image and passes it to the emotion recognition engine to recognize the user's emotion. The emotion recognition engine analyzes the user's emotional information and adjusts the suggestions accordingly. The input is the user's image, and the output is the emotion data and the adjusted suggestions.

[1076] Step 7:

[1077] The server generates a proposal based on the emotion data and sends it back to the user's device. For example, if data indicates that the user is in a hurry, it will propose a faster delivery method. The device then displays this proposal to the user. The input is the adjusted proposal, and the output is the display to the user. Specifically, the user can confirm, "Based on the user's urgency requirements, the optimal delivery method is drone delivery, and it will be delivered via the shortest route."

[1078] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1079] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1080] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1081] [Third embodiment]

[1082] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1083] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1084] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1085] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1086] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1087] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1088] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1089] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1090] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1091] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1092] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1093] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[1094] This invention is a smart wholesale platform that allows users to obtain product quotes and select and manage optimal delivery methods. The system of the present invention includes a user terminal, a generative model, a server, a delivery route optimization function, and a delivery means using an autonomous flying device.

[1095] Overall Platform Configuration

[1096] 1. User Device:

[1097] A device that users interact with to request product quotes and enter shipping requirements.

[1098] The terminal transmits the user's input to the server and displays the quotes and delivery proposals returned by the server.

[1099] 2. Generative Model:

[1100] The server uses the generative model to generate estimates and suggestions based on the user's input.

[1101] The generative model responds to user questions by suggesting appropriate products and delivery methods.

[1102] 3. Server:

[1103] It receives requests from users and passes them to a generative model to generate estimates and proposals.

[1104] The quotation results and delivery proposal are returned to the terminal.

[1105] 4. Delivery route optimization function:

[1106] The server optimizes delivery routes and makes suggestions for reducing costs and environmental impact.

[1107] Calculate the best delivery route based on user requirements.

[1108] 5. Autonomous Aircraft:

[1109] This is a drone designed to provide fast and efficient delivery for emergency delivery needs.

[1110] The server proposes autonomous flying devices based on delivery needs and delivers goods via the optimal route.

[1111] Program processing explanation

[1112] 1. User Inquiry Processing:

[1113] A user uses a terminal to request a quote for a product (for example, "Can I get a quote for 200 units of product A?").

[1114] The terminal sends this request to the server.

[1115] The server queries the generative model to compute an estimate.

[1116] The generative model calculates the estimate and sends it back to the server.

[1117] The server sends the estimate back to the device, which displays the results to the user.

[1118] Example: "The estimated price for this item is 200,000 yen."

[1119] 2. Delivery route optimization and suggestions:

[1120] The user enters delivery requirements (e.g., "please deliver as soon as possible") into the terminal.

[1121] The terminal sends this requirement to the server.

[1122] The server performs delivery route optimization and determines the best delivery method based on the user's requirements.

[1123] The server proposes the optimal delivery method (e.g., drone delivery) and returns the result to the device.

[1124] The terminal displays delivery offers to the user.

[1125] Example: "The best delivery method is drone delivery, using the shortest route."

[1126] This concludes the program process and concrete example of a smart wholesale platform that integrates generative models and drone delivery services. The system provides users with real-time quotes and selects and manages optimal delivery methods, enabling efficient and sustainable business operations.

[1127] The processing flow will be explained below.

[1128] Program processing flow explanation

[1129] User inquiry processing

[1130] Step 1:

[1131] A user uses a terminal to request a quote for a product, for example, by typing, "Please give me a quote for 200 units of product A."

[1132] Step 2:

[1133] The terminal sends the user's request to the server, which includes the type and quantity of the product.

[1134] Step 3:

[1135] The server receives the user request and passes it to the generative model.

[1136] Step 4:

[1137] The generative model calculates the estimated price of product A based on the user request.

[1138] Step 5:

[1139] The generative model sends the calculated price estimate back to the server.

[1140] Step 6:

[1141] The server receives the estimate and returns it to the user's device.

[1142] Step 7:

[1143] The terminal displays the estimate results received from the server to the user.

[1144] Delivery route optimization and proposals

[1145] Step 1:

[1146] The user uses the terminal to enter delivery requirements, for example, "please deliver as soon as possible."

[1147] Step 2:

[1148] The terminal sends the user's delivery requirements to the server, including delivery speed and special requests.

[1149] Step 3:

[1150] The server receives the delivery requirements and invokes the delivery route optimization function.

[1151] Step 4:

[1152] The server calculates delivery routes and finds the optimal route to reduce costs and environmental impact.

[1153] Step 5:

[1154] The server decides whether to suggest an autonomous flying device (drone delivery) based on the user's requirements (e.g., urgent delivery).

[1155] Step 6:

[1156] The server returns the optimal delivery method and route to the user's device.

[1157] Step 7:

[1158] The terminal displays the delivery proposal and route information received from the server to the user.

[1159] Comprehensive Processing

[1160] Step 1:

[1161] A user uses a terminal to request both a quote and urgent delivery of a product, for example, "Please give me a quote for 200 units of product A and deliver it as soon as possible."

[1162] Step 2:

[1163] The terminal sends this overall request to the server.

[1164] Step 3:

[1165] The server receives the synthetic request and first passes it to the generative model to calculate the estimated price.

[1166] Step 4:

[1167] The generative model calculates the estimated price and sends it back to the server.

[1168] Step 5:

[1169] The server receives the estimate results and then calls the delivery route optimization function to calculate the route.

[1170] Step 6:

[1171] Based on the calculated route, the server considers the user's requirements (e.g., urgent delivery) and suggests the optimal delivery method (e.g., drone delivery).

[1172] Step 7:

[1173] The server returns the overall results (estimated price, optimal delivery method, and route) to the user's device.

[1174] Step 8:

[1175] The terminal displays the overall results received from the server to the user.

[1176] Example 1

[1177] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1178] Traditional wholesale platforms face challenges in being unable to respond quickly to user requests when obtaining product quotes or proposing delivery methods. Delivery routes are often not sufficiently optimized, preventing cost reduction and environmental impact reduction. Furthermore, the lack of efficient delivery methods for urgent delivery needs prevents business efficiency and sustainability from improving.

[1179] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1180] In this invention, the server includes a means for transmitting user-entered data to the server, a means for generating estimates and proposals based on the user's input using a generative model, and a means for executing a delivery route optimization function. This not only enables quick and accurate product estimates based on the user's input, but also calculates optimal delivery routes, reducing costs and reducing environmental impact. Furthermore, efficient delivery using unmanned aerial vehicles can be proposed for urgent delivery needs, improving business efficiency and sustainability.

[1181] "User Terminal" means a device operated by a User to input product quote requests and delivery requirements.

[1182] A "generative model" is an artificial intelligence model used by the server to generate estimates and suggestions based on user input.

[1183] The "Delivery route optimization function" is a function in which the server optimizes delivery routes and makes suggestions for reducing costs and environmental impact.

[1184] A "drone" is an autonomous flying delivery device used for rapid delivery needs.

[1185] The "means for transmitting data entered by the user to the server" refers to a communication means for transmitting request information from the user terminal to the server.

[1186] The "means for the server to send a quote request to the generative model" is a function that passes the user's request received by the server to the generative model and requests processing.

[1187] The "means by which the generative model calculates an estimate" is the function by which the generative model calculates the price and proposal details for a specified product or service.

[1188] "Means for the server to return the estimation results to the user terminal" refers to means by which the server transmits the estimation results received from the generative model to the user terminal.

[1189] The "means for suggesting optimal products and delivery methods" is a function that enables the generative model to suggest optimal products or delivery methods based on the user's requests.

[1190] "Means to streamline the process from quotation to delivery" refers to a system that efficiently carries out the entire process from estimating and measuring products to final delivery.

[1191] This invention is a smart wholesale platform that allows users to obtain product quotes and select and manage optimal delivery methods. The system of the present invention includes a user terminal, a generative model, a server, a delivery route optimization function, and a delivery means using an unmanned aerial vehicle.

[1192] Overall Platform Configuration

[1193] 1. User Device:

[1194] A device that users interact with to request product quotes and enter shipping requirements.

[1195] The terminals are intended to be devices that can connect to the Internet, such as PCs, smartphones, and tablets, and will run on operating systems such as iOS and Android.

[1196] Example: User types, "Please give me a quote for 200 units of product A."

[1197] 2. Generative Model:

[1198] The server uses the generative model to generate estimates and suggestions based on the user's input.

[1199] This generative model uses AI models such as OpenAI's GPT (Generative Pre-trained Transformer).

[1200] Example: "The estimate for 200 units of product A is 200,000 yen."

[1201] 3. Server:

[1202] The server receives requests from users and passes them to a generative model to generate estimates and proposals.

[1203] The server uses cloud services such as AWS (Amazon Web Services) and Microsoft Azure.

[1204] Example: Taking a user request and passing it to a generative model.

[1205] 4. Delivery route optimization function:

[1206] The server optimizes delivery routes and makes suggestions for reducing costs and environmental impact.

[1207] The server will implement route optimization algorithms such as Google Maps API and Route4Me.

[1208] Example: Calculate the optimal route and display "The best delivery method is drone delivery, and we will deliver via the shortest route."

[1209] 5. Unmanned aircraft:

[1210] This is an autonomous flying delivery device that provides fast and efficient delivery for urgent delivery needs.

[1211] The server will suggest an unmanned aircraft based on delivery needs and deliver the goods via the optimal route.

[1212] Example: For urgent deliveries, we suggest, "The best delivery method is drone delivery, and we will deliver it via the shortest route."

[1213] Program processing explanation

[1214] 1. User Inquiry Processing:

[1215] A user uses a device to request a quote for a product.

[1216] The terminal sends this request to the server.

[1217] The server queries the generative model to compute an estimate.

[1218] The generative model calculates the estimate and sends it back to the server.

[1219] The server sends the estimate back to the device, which displays the results to the user.

[1220] Example: "The estimated price for this item is 200,000 yen."

[1221] 2. Delivery route optimization and suggestions:

[1222] The user enters their delivery requirements (e.g., "please deliver as soon as possible") into the terminal.

[1223] The terminal sends this requirement to the server.

[1224] The server performs delivery route optimization and determines the best delivery method based on the user's requirements.

[1225] The server proposes the optimal delivery method (e.g., drone delivery) and returns the result to the device.

[1226] The terminal displays delivery offers to the user.

[1227] Example: "The best delivery method is drone delivery, using the shortest route."

[1228] Prompt sentences for specific examples of operation

[1229] Prompt for product quote:

[1230] "Please give me a quote for 200 units of product A."

[1231] Prompt for delivery requirements:

[1232] "Please deliver it quickly."

[1233] This concludes the explanation of the structure and operation of a smart wholesale platform that integrates generative models and unmanned aerial vehicles. The system provides users with real-time quotes and selects and manages optimal delivery methods, enabling efficient and sustainable business operations.

[1234] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1235] Step 1:

[1236] A user requests a quote for a product. The user enters specific product information into the terminal (e.g., "Please give me a quote for 200 units of product A") and sends the input data to the terminal. The terminal receives this data and converts it into JSON format. This makes the user's input in a format that can be processed by the next step. The input is "Product name: Product A, Quantity: 200", and the output is JSON format data.

[1237] Step 2:

[1238] The terminal sends the user's input to the server. The terminal sends the JSON format data entered by the user to the server as an HTTP request. In this operation, the application inside the terminal adds the appropriate URL and header information to send the data. The input is the user data in JSON format, and the output is a request to send the requested quote data.

[1239] Step 3:

[1240] The server sends a request for an estimate to the generative model. The server analyzes the received user request and makes an API call to the generative model to calculate an estimate. The input is the user request data in JSON format, and the output is the API request to be passed to the generative model. The server calls the "generateEstimate(data)" function as an internal process.

[1241] Step 4:

[1242] The generative model calculates the estimate. Based on the provided product information, the generative AI model calculates the appropriate price and sends the result back to the server. This calculation is performed quickly based on an internal algorithm. The input is product information and quantity, and the output is the estimated price. Example: "The estimate for 200 units of product A is 200,000 yen."

[1243] Step 5:

[1244] The server returns the estimation results to the terminal. The server converts the estimation results received from the generative model back into JSON format and sends it to the terminal as an HTTP response. The input is the estimation result from the generative model, and the output is the JSON-formatted estimation data sent to the terminal.

[1245] Step 6:

[1246] The terminal displays the quotation results to the user. The terminal parses the quotation results received from the server and displays them in the user interface. The input is the quotation data in JSON format, and the output is what is displayed to the user. For example, the message "The quotation for the product is 200,000 yen" is displayed.

[1247] Step 7:

[1248] The user inputs the delivery requirements. The user inputs the delivery requirements (e.g., "Please deliver as soon as possible") into the terminal and sends the input data to the terminal. The terminal receives the delivery requirements and converts them into JSON format. This makes the input from the user in a format that can be processed in the next step. The input is "Delivery method: urgent delivery" and the output is JSON format data.

[1249] Step 8:

[1250] The terminal sends the delivery requirements to the server. The terminal sends the JSON format delivery requirement data entered by the user to the server as an HTTP request. The input is the user delivery requirement data in JSON format, and the output is a request to send the requested delivery data.

[1251] Step 9:

[1252] The server executes the delivery route optimization function. The server passes the received delivery requirements to the delivery route optimization function and calculates the optimal delivery route. This uses algorithms such as Google Maps API and Route4Me. The input is the user's delivery requirement data, and the output is the optimized delivery route data.

[1253] Step 10:

[1254] The server determines the optimal delivery method and returns it to the terminal. The server analyzes the results of the delivery route optimization function and determines the optimal delivery method based on the user's requirements. It then converts the result into JSON format and sends it to the terminal as an HTTP response. The input is the result data of the delivery route optimization, and the output is the optimal delivery method data sent to the terminal.

[1255] Step 11:

[1256] The terminal displays the delivery suggestions to the user. The terminal analyzes the results of the optimal delivery method received from the server and displays them on the user interface. The input is the delivery method data in JSON format, and the output is what is displayed to the user. Example: A message is displayed saying, "The optimal delivery method is drone delivery, and it will be delivered via the shortest route."

[1257] (Application example 1)

[1258] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1259] In conventional logistics centers, optimizing product estimates and delivery routes takes time, making efficient operation difficult. Furthermore, the lack of a system that users can operate intuitively and visually can lead to confusion in estimate results and delivery method selection. Furthermore, manual management is important for urgent deliveries, and urgent delivery methods are often not proposed in a timely manner.

[1260] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1261] In this invention, the server includes a means for obtaining product quotes via a user terminal, a means for generating quotes and proposals based on user input using a generative model, a means for optimizing delivery routes and making proposals for reducing costs and environmental impact, a means for proposing delivery using autonomous flying devices for rapid delivery needs, and a means for visualizing and manipulating quotes and delivery proposals through an application installed on a smart device or robot. This allows users to visually check and manipulate quote results and delivery methods, enabling them to obtain product quotes and manage deliveries efficiently and intuitively.

[1262] "User terminal" means a device for inputting product quote requests and delivery requirements and receiving the results.

[1263] A "generative model" is an AI system for generating estimates and recommendations based on user input.

[1264] "Delivery route optimization" involves calculating delivery routes and proposing optimal routes to reduce costs and environmental impact.

[1265] An "autonomous flying device" is an unmanned aircraft designed to quickly deliver goods to meet urgent delivery needs.

[1266] A "smart device" is a portable device with information processing and communication capabilities.

[1267] A "robot" is a mechanical device that can autonomously perform specified actions or tasks.

[1268] "Application" means software installed on a smart device or robot for visualizing and manipulating quotes and delivery proposals.

[1269] The system for realizing this application example includes a user terminal, a generative model, a server, a delivery route optimization function, an autonomous flying device, and an application installed on a smart device or robot. Specific implementation methods are described below.

[1270] Overall system configuration

[1271] 1. User Device

[1272] Users use their smartphones or tablets to input product quote requests and delivery requirements, which are then sent to the server, which then displays the quotes and delivery proposals returned by the server.

[1273] 2. Generative Model

[1274] The server uses a generative model, such as OpenAI GPT-3, to generate quotes and recommendations based on the user's input, and then responds to the user's query with appropriate product and delivery suggestions.

[1275] 3. Server

[1276] The server receives requests from users, passes them to the generative model to generate estimates and proposals, receives the results from the generative model, and returns them to the user's device. The server also optimizes delivery routes and determines the optimal delivery method.

[1277] 4. Delivery route optimization function

[1278] The server uses Python's NetworkX library to optimize delivery routes, calculating the best route for cost savings and environmental conservation.

[1279] 5. Autonomous aircraft

[1280] For urgent delivery needs, Sarver proposes autonomous flying devices, which enable fast and efficient delivery.

[1281] 6. Smart Device or Robot Applications

[1282] The application is installed on a smartphone, tablet, smart glasses, or logistics robot, and allows users to visually check and manipulate quotation results and delivery proposals.

[1283] Examples of concrete examples and prompts

[1284] A specific example is shown below.

[1285] Request a product quote

[1286] A user uses a smartphone to enter a product quote request as follows:

[1287] Please let me know the estimate for 200 items.

[1288] Displaying the estimate results

[1289] The server uses the generative model to generate an estimate and displays the result as follows:

[1290] Estimated price: 200,000 yen.

[1291] Delivery method: Drone delivery is recommended.

[1292] Optimal delivery route: The shortest route from the origin to the destination was calculated.

[1293] This system allows users to visually check and operate quotation results and delivery methods, enabling them to efficiently and intuitively obtain product quotes and manage deliveries.

[1294] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1295] Step 1:

[1296] A user uses a smartphone or tablet to enter a request for a quote on a product. For example, the user enters a request such as "Please give me a quote for 200 units of this product."

[1297] Input: User input (e.g. "Please give me a quote for 200 units of this product").

[1298] Output: Request data (JSON format).

[1299] Step 2:

[1300] The device sends this request to the server, and the device sends the user's input data in JSON format to the server.

[1301] Input: User-entered information.

[1302] Output: Sending a request to the server.

[1303] Step 3:

[1304] The server receives a request from the user and passes it to the generative model to generate an estimate or proposal. The server passes a prompt to the generative model to calculate an estimate.

[1305] Input: The request data sent from the device.

[1306] Output: Prompt statement and quote request to the generative model.

[1307] Step 4:

[1308] A generative model (e.g., OpenAI GPT-3) generates an estimate based on the user's input and sends the result back to the server.

[1309] Input: Prompt text from the server.

[1310] Output: Estimation results (text format).

[1311] Step 5:

[1312] The server receives the estimation results from the generative model and sends them back to the user device. The server receives the estimation results and sends them to the device.

[1313] Input: Estimation results from the generative model.

[1314] Output: Send the estimate results to the user's device.

[1315] Step 6:

[1316] The terminal displays the estimate results to the user, who can then check the estimate results on the terminal.

[1317] Input: Estimate result from the server.

[1318] Output: Display the estimate result to the user (e.g., "Estimate result: 200,000 yen.").

[1319] Step 7:

[1320] The user enters their delivery requirements (e.g., "please deliver quickly") into the terminal.

[1321] Input: User delivery requirement input (e.g. "Please hurry delivery").

[1322] Output: Delivery requirements data (JSON format).

[1323] Step 8:

[1324] The device sends these delivery requirements to the server. The device sends the user's delivery requirements data to the server in JSON format.

[1325] Input: User delivery requirement information.

[1326] Output: Send delivery requirements data to the server.

[1327] Step 9:

[1328] The server performs delivery route optimization and determines the best delivery method based on the user's requirements. The server uses the NetworkX library to calculate the optimal delivery route.

[1329] Input: Delivery requirement data sent from the terminal.

[1330] Output: Optimal delivery route information and delivery method.

[1331] Step 10:

[1332] The server proposes the optimal delivery method (e.g., drone delivery) and returns the result to the terminal.

[1333] Input: Optimized delivery route information.

[1334] Output: Sends delivery method proposal to user.

[1335] Step 11:

[1336] The device displays delivery suggestions to the user, who can then review and manage the optimal delivery method on the device.

[1337] Input: Delivery method suggestions from the server.

[1338] Output: Display delivery method to user (e.g. "The best delivery method is drone delivery, using the shortest route.").

[1339] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1340] This invention is a smart wholesale platform that allows users to obtain product quotes and select and manage optimal delivery methods. The system of the present invention includes a user terminal, a generative model, a server, a delivery route optimization function, an autonomous flying device, and an emotion engine that recognizes user emotions.

[1341] Overall Platform Configuration

[1342] 1. User Device:

[1343] It is a device that users can operate to input and output data.

[1344] The terminal transmits the user's input to the server and displays the quotes and delivery proposals returned by the server.

[1345] 2. Generative Model:

[1346] The server uses the generative model to generate estimates and suggestions based on the user's input.

[1347] This generative model responds to user queries by suggesting optimal products and delivery methods.

[1348] 3. Server:

[1349] It receives requests from users and passes them to a generative model to generate estimates and proposals.

[1350] The quotation results and delivery proposal are returned to the user's terminal.

[1351] 4. Delivery route optimization function:

[1352] The server optimizes delivery routes and makes suggestions for reducing costs and environmental impact.

[1353] Calculate the best delivery route based on user requirements.

[1354] 5. Autonomous Aircraft:

[1355] This is a drone designed to provide fast and efficient delivery for emergency delivery needs.

[1356] The server proposes autonomous flying devices based on delivery needs and delivers goods via the optimal route.

[1357] 6. Emotion Engine:

[1358] The server recognizes the user's emotions via the user's terminal.

[1359] The emotion engine adjusts suggestions and determines the urgency of delivery based on the user's emotions.

[1360] Program processing explanation

[1361] 1. User Inquiry Processing:

[1362] A user uses a terminal to request a quote for a product, for example, by typing, "Please give me a quote for 200 units of product A."

[1363] The terminal sends this request to the server.

[1364] The server queries the generative model to compute an estimate.

[1365] The generative model calculates the estimate and sends it back to the server.

[1366] The server receives the estimate and returns it to the user's device.

[1367] The terminal displays the estimate results to the user.

[1368] Example: "The estimated price for this item is 200,000 yen."

[1369] 2. Delivery route optimization and suggestions:

[1370] The user enters delivery requirements (e.g., "please deliver as soon as possible") into the terminal.

[1371] The terminal sends this requirement to the server.

[1372] The server performs delivery route optimization and determines the best delivery method based on the user's requirements.

[1373] The server proposes the optimal delivery method (e.g., drone delivery) and returns the result to the device.

[1374] The terminal displays delivery offers to the user.

[1375] Example: "The best delivery method is drone delivery, using the shortest route."

[1376] 3. Emotional engine regulation:

[1377] As users enter product quotes and delivery requirements, an emotion engine recognizes their emotions (e.g., "impatient" or "excited").

[1378] The server analyzes the user's emotional information through an emotion engine and adjusts the suggestions.

[1379] The emotion engine will more frequently suggest faster delivery methods (e.g. drone delivery) if the user is in a hurry, and more cost-saving suggestions if the user is relaxed.

[1380] The server generates suggestions based on the emotion and sends them back to the user's device.

[1381] The device displays tailored suggestions to the user.

[1382] Example display: "Based on the user's urgent requirements, the best delivery method is drone delivery, using the shortest route."

[1383] The above is a program process and example of a smart wholesale platform that integrates user terminals, generative models, and an emotion engine. By taking user emotions into account, it can make suggestions based on individual needs and provide more personalized services.

[1384] The processing flow will be explained below.

[1385] Program processing flow explanation

[1386] User inquiry processing

[1387] Step 1:

[1388] A user uses a terminal to request a quote for a product, for example, by typing, "Please give me a quote for 200 units of product A."

[1389] Step 2:

[1390] The terminal sends the request content to the server.

[1391] Step 3:

[1392] The server receives the user's request and passes it to the generative model.

[1393] Step 4:

[1394] The generative model calculates the estimated price of product A based on the user request.

[1395] Step 5:

[1396] The generative model sends the calculated price estimate back to the server.

[1397] Step 6:

[1398] The server receives the estimate and returns it to the user's device.

[1399] Step 7:

[1400] The terminal displays the estimate results received from the server to the user.

[1401] Example: "The estimated price for this item is 200,000 yen."

[1402] Delivery route optimization and proposals

[1403] Step 1:

[1404] The user enters their delivery requirements, for example, "Please deliver as soon as possible."

[1405] Step 2:

[1406] The terminal sends this delivery requirement to the server.

[1407] Step 3:

[1408] The server receives the delivery requirements and invokes the delivery route optimization function.

[1409] Step 4:

[1410] The server calculates delivery routes and finds the optimal route to reduce costs and environmental impact.

[1411] Step 5:

[1412] The server decides whether to suggest an autonomous flying device (drone delivery) based on the user's requirements (e.g., urgent delivery).

[1413] Step 6:

[1414] The server returns the optimal delivery method and route to the user's device.

[1415] Step 7:

[1416] The terminal displays the delivery proposal and route information received from the server to the user.

[1417] Example: "The best delivery method is drone delivery, using the shortest route."

[1418] Emotional Engine Adjustment

[1419] Step 1:

[1420] As users enter product quotes and delivery requirements, an emotion engine recognizes their emotions, such as "impatience" or "excitement."

[1421] Step 2:

[1422] The device sends the user's emotional information to the server.

[1423] Step 3:

[1424] The server analyzes the user's emotions through an emotion engine and adjusts the suggestions.

[1425] Step 4:

[1426] Based on the emotional information, the server will suggest a faster delivery method (e.g., drone delivery) if the user is in a hurry, or suggest cost-saving solutions if the user is relaxed.

[1427] Step 5:

[1428] The server sends suggestions based on the emotion back to the user's device.

[1429] Step 6:

[1430] The device displays the tailored proposals received from the server to the user.

[1431] Example display: "Based on the user's urgent requirements, the best delivery method is drone delivery, using the shortest route."

[1432] These are the program processing steps of the smart wholesale platform, which integrates user terminals, generative models, and emotion engines. By taking user emotions into account, the system can make suggestions tailored to individual needs and provide more personalized services.

[1433] Example 2

[1434] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1435] Traditional wholesale platforms struggle to provide users with a fully personalized service when obtaining product quotes and selecting the optimal delivery method. Fast and efficient responses are required, especially for urgent delivery needs and optimizing complex delivery routes. Another issue is the inability to adjust recommendations based on user sentiment, resulting in a lack of an improved user experience.

[1436] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1437] In this invention, the server

[1438] a means for obtaining product quotes via a user terminal;

[1439] means for generating estimates and recommendations based on user input using the generative model;

[1440] A means to optimize delivery routes and make suggestions to reduce costs and environmental impact;

[1441] A way to recognize user emotions and adjust suggestions accordingly;

[1442] A means for proposing delivery using autonomous flying devices to meet rapid delivery needs;

[1443] This allows us to provide you with personalized quotes and delivery suggestions, and ensure fast and efficient delivery.

[1444] A "user terminal" is a device through which a user provides input and output via an interface.

[1445] A "generative model" is an algorithm or system that generates estimates or recommendations based on user input data.

[1446] "Delivery route" refers to the route along which goods are delivered, and is a route that is optimized to reduce costs and environmental impact.

[1447] An "autonomous flying device" is a device that flies unmanned and is used to meet specific delivery needs.

[1448] An "emotion engine" is a system or algorithm that recognizes a user's emotions and adjusts suggestions based on those emotions.

[1449] "Rapid delivery needs" are needs that require immediate or very short delivery.

[1450] "Quote" means an estimate or calculation of the price or quantity of a particular commodity.

[1451] A "suggestion" is a choice or recommendation presented to a user.

[1452] The present invention is a smart wholesale platform that allows users to obtain product quotes and select and manage optimal delivery methods. The system includes a user terminal, a generative model, a server, a delivery route optimization function, an autonomous flying device, and an emotion engine that recognizes user emotions.

[1453] Overall Platform Configuration

[1454] 1. User Device:

[1455] A device that a user operates to input and output data. User terminals include smartphones, tablets, and desktop computers.

[1456] The terminal transmits the user's input to the server and displays the quotes and delivery proposals returned by the server.

[1457] 2. Generative Model:

[1458] The server uses the generative model to generate estimates and suggestions based on the user's input.

[1459] This generative model is based on a large-scale language model (e.g., GPT-3) and suggests optimal products and delivery methods in response to user questions.

[1460] 3. Server:

[1461] It receives requests from users and passes them to a generative model to generate estimates and proposals.

[1462] The quotation results and delivery proposal are returned to the user's terminal.

[1463] 4. Delivery route optimization function:

[1464] The server optimizes delivery routes and makes suggestions for reducing costs and environmental impact.

[1465] Calculate the optimal delivery route based on user requirements using route optimization algorithms such as the Dijkstra algorithm.

[1466] 5. Autonomous Aircraft:

[1467] This is a drone designed to provide fast and efficient delivery for emergency delivery needs.

[1468] The server proposes autonomous flying devices based on delivery needs and delivers goods via the optimal route.

[1469] 6. Emotion Engine:

[1470] The server recognizes the user's emotions via the user's terminal.

[1471] The emotion engine uses NLP (natural language processing) technology to analyze the user's emotions and tailor its recommendations accordingly: if the user is in a hurry, it will suggest a fast delivery option, and if the user is relaxed, it will offer suggestions focused on cost savings.

[1472] Examples of concrete examples and prompts

[1473] Example 1:

[1474] User Input: "Can you give me a quote for 500 units of product B?"

[1475] The server uses the generative model to generate a quote (e.g., 1,000,000 yen).

[1476] Terminal display: "The estimated price for this item is 1,000,000 yen."

[1477] Example 2:

[1478] User input: "Please deliver the item as soon as possible."

[1479] The server optimizes delivery routes and suggests drone delivery.

[1480] Display on device: "The best delivery method is drone delivery, using the shortest route."

[1481] Prompt Sentence Examples

[1482] "Please calculate the price for the product. Please tell me the product name, quantity, and total price."

[1483] "Please suggest the best delivery method. For urgent deliveries, we recommend drone delivery."

[1484] The system of the present invention aims to provide personalized services that take into account user emotions and achieve efficient and fast delivery, thereby improving user experience and increasing business productivity.

[1485] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1486] Step 1:

[1487] A user uses a terminal to request a quote for a product.

[1488] Input: The user types into the terminal, "Please give me a quote for 200 units of product A."

[1489] Behavior: The terminal processes user input data in text format.

[1490] Output: The request data is generated and sent to the server.

[1491] Step 2:

[1492] The device sends a request to the server.

[1493] Input: Request data (user's quote request)

[1494] How it works: The device transfers the request data to the server using the HTTPS protocol.

[1495] Output: The expected action reaches the server.

[1496] Step 3:

[1497] The server queries the generative model to compute an estimate.

[1498] Input: User quote request data

[1499] How it works: The server passes a quote request to the generative AI model, which then starts the calculation. The generative model calculates a quote based on the input product information and quantity.

[1500] Output: The calculated quote result (e.g. amount) is sent back to the server in JSON format.

[1501] Step 4:

[1502] The server receives the estimate and returns it to the user's device.

[1503] Input: JSON data of the estimation results received from the generative model

[1504] How it works: The server converts the received JSON data into a user-friendly format and prepares it for transfer to the device.

[1505] Output: The processed quote results are sent to the terminal.

[1506] Step 5:

[1507] The terminal displays the estimate results to the user.

[1508] Input: Quote result received from the server

[1509] Action: The device displays the received quote on the screen. Specifically, it updates the display area to make the quote more visible to the user.

[1510] Output: The estimation results are displayed visually on the terminal and provided to the user.

[1511] Step 6:

[1512] The user enters delivery requirements into the terminal.

[1513] Input: The user types "urgent delivery please" into the terminal.

[1514] How it works: The terminal processes the user's input data in text format and generates data for further processing.

[1515] Output: Delivery requirements are ready to be sent to the server.

[1516] Step 7:

[1517] The terminal sends delivery requirements to the server.

[1518] Input: Delivery requirement data (user delivery request)

[1519] Operation: The terminal sends delivery requirement data to the server using the HTTPS protocol.

[1520] Output: Delivery requirement data arrives at the server.

[1521] Step 8:

[1522] The server performs delivery route optimization and determines the best delivery method based on the user's requirements.

[1523] Input: Delivery requirement data

[1524] How it works: The server uses a delivery route optimization algorithm (e.g., Dijkstra algorithm) to calculate the optimal route, and also filters the candidate delivery methods based on requirements.

[1525] Output: The optimal delivery route and method (e.g. drone delivery) is determined.

[1526] Step 9:

[1527] The server proposes the optimal delivery method and returns the result to the terminal.

[1528] Input: Optimized delivery route and method

[1529] How it works: The server generates data on the optimal delivery method and sends it to the device in JSON format.

[1530] Output: Proposed delivery method data arrives at the terminal.

[1531] Step 10:

[1532] The terminal displays delivery offers to the user.

[1533] Input: Proposal data received from the server

[1534] Action: The device displays the proposed data on the screen, updates the display area, and presents the appropriate delivery method to the user.

[1535] Output: The user is provided with a visual representation of the delivery proposal displayed on the terminal.

[1536] Step 11:

[1537] An emotion engine recognizes the user's emotions as they enter product quotes and delivery requirements.

[1538] Input: User-entered text

[1539] How it works: The emotion engine uses NLP techniques to analyze input text and identify the user's emotion (e.g., "anxious").

[1540] Output: The identified emotion data is transmitted to the server.

[1541] Step 12:

[1542] The server analyzes the user's emotional information through an emotion engine and adjusts the suggestions.

[1543] Input: Emotion data

[1544] How it works: The server adjusts its suggestions based on the emotion data it receives from the emotion engine, such as prioritizing faster delivery methods if the customer is in a hurry.

[1545] Output: The adjusted proposal is generated.

[1546] Step 13:

[1547] The server sends the adjusted proposal back to the user terminal.

[1548] Input: Adjusted proposal

[1549] Operation: The server prepares the adjusted proposal to be sent to the user's device.

[1550] Output: Data is sent to the terminal.

[1551] Step 14:

[1552] The device displays tailored suggestions to the user.

[1553] Input: Adjusted proposal data received from the server

[1554] Action: The device displays the suggestion data on the screen and updates the display area to make the personalized suggestions more visible to the user.

[1555] Output: The user is provided with a visual representation of the suggestions displayed on the device.

[1556] (Application example 2)

[1557] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1558] On conventional wholesale platforms, the process from obtaining product quotes to determining the optimal delivery route and selecting a fast delivery method was often inefficient, resulting in a poor user experience. Furthermore, there was a lack of consideration for user feelings in the service, leading to issues with not being able to fully meet individual needs. In particular, there were cases where an appropriate response was not given to urgent delivery requirements, resulting in a decline in reliability.

[1559] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for obtaining product quotes via a user terminal, means for generating quotes and proposals based on user input using a generative model, means for optimizing delivery routes and making proposals for reducing costs and environmental impact, means for proposing delivery using autonomous aircraft devices to meet rapid delivery needs, and means for recognizing user emotions using an emotion recognition engine and adjusting the content of the proposals. This enables users to quickly obtain optimal product quotes and receive personalized proposals based on their emotional state.

[1560] A "user terminal" is a device that a user operates to input and output data.

[1561] A "generative model" is an algorithm or software that generates estimates or recommendations based on input data.

[1562] A "Quote" is a calculation of the price or cost of a product based on information entered by the user.

[1563] "Delivery route optimization" is the process of calculating and proposing efficient and cost-effective delivery routes.

[1564] An "autonomous flying device" is an unmanned aerial vehicle that flies automatically and delivers goods without the need for human operation.

[1565] An "emotion recognition engine" is a technology or software that identifies emotions from a user's facial expressions, voice, etc.

[1566] "Suggestions" are recommendations of products and services provided based on user input using generative models, etc.

[1567] "Cost reduction" refers to reducing the cost of providing a product or service.

[1568] "Environmental mitigation" refers to efforts and measures taken to minimize the environmental impact of the delivery process.

[1569] This invention is a system that provides a smart wholesale platform by combining a user terminal, a generative model, a server, a delivery route optimization function, an autonomous flying device, and an emotion recognition engine. The specific implementation method will be described below.

[1570] User Device

[1571] A user terminal is a device that a user operates to input and output data. Examples include smartphones, tablets, and PCs. Users use this terminal to input product quote requests and delivery requirements.

[1572] Generative Model

[1573] Generative models are AI tools that generate quotes and proposals based on user input. The server uses the generative models to quickly respond to user inquiries. The models perform text analysis to calculate optimal product suggestions and quote prices.

[1574] server

[1575] The server receives requests from users and generates estimates and proposals based on the content of the requests using a generative model.The server also has a delivery route optimization function that calculates delivery routes based on user requirements.Furthermore, it is equipped with an emotion recognition engine to provide personalized services according to the user's emotional state.

[1576] Delivery route optimization function

[1577] The delivery route optimization function is a technology for efficiently calculating delivery routes. The server analyzes the user's delivery requirements (e.g., "Please deliver quickly") and determines the most efficient delivery route. This reduces costs and the environmental impact.

[1578] autonomous aircraft equipment

[1579] An autonomous flying device is an unmanned aircraft for rapid delivery. The server proposes delivery by the autonomous flying device taking into account the generative model and delivery requirements. The device automatically flies the specified route and achieves rapid delivery.

[1580] Emotion Recognition Engine

[1581] The emotion recognition engine is a technology that identifies emotions from the user's facial expressions and voice and adjusts the content of suggestions based on that information. Using the emotion recognition engine, the server prioritizes fast delivery methods if the user is in a hurry, and makes suggestions that emphasize cost reduction if the user is relaxed.

[1582] Examples of concrete examples and prompts

[1583] Specific examples

[1584] 1. Get a quote: User types, "Please give me a quote for 300 units of product B."

[1585] 2. Delivery Route Suggestion: User types, "What is the cheapest standard delivery route?"

[1586] 3. Emotion Recognition: Using images captured by the camera, the system recognizes the user's emotions and suggests a faster delivery method if the user is in a hurry.

[1587] Prompt Sentence Examples

[1588] Please let me know the estimate for product B.

[1589] Please suggest the best route for urgent delivery.

[1590] Recognize user emotions from images and provide appropriate suggestions

[1591] The above is an embodiment of the present invention. This smart wholesale platform provides fast and efficient services tailored to the individual needs of users.

[1592] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1593] Step 1:

[1594] A user uses a terminal to request a quote for a product. For example, they type, "Please give me a quote for 200 units of product A." The terminal sends this request to the server. The input is the user's request, and the output is the submission of the request.

[1595] Step 2:

[1596] The server receives a request from the user and queries the generative model. The generative model calculates an estimate based on the input request and sends it back to the server. The input is the user's request, and the output is the estimate result.

[1597] Step 3:

[1598] The server receives the estimate results returned from the generative model and sends them back to the user's terminal. The terminal displays the estimate results to the user. The input is the estimate results, and the output is the display to the user. Specifically, the user can confirm, "The estimate for the product is 200,000 yen."

[1599] Step 4:

[1600] The user inputs delivery requirements into the terminal, for example, "please deliver as soon as possible." The terminal then sends these requirements to the server. The input is the user's delivery requirements, and the output is the request submission.

[1601] Step 5:

[1602] The server receives the delivery requirements and calculates the optimal delivery route using the delivery route optimization function. The calculation results are sent to the server and returned to the user's device. The input is the delivery requirements, and the output is the optimal delivery route. Specifically, the server suggests, "The optimal delivery method is drone delivery, using the shortest route."

[1603] Step 6:

[1604] The server acquires the user's image and passes it to the emotion recognition engine to recognize the user's emotion. The emotion recognition engine analyzes the user's emotional information and adjusts the suggestions accordingly. The input is the user's image, and the output is the emotion data and the adjusted suggestions.

[1605] Step 7:

[1606] The server generates a proposal based on the emotion data and sends it back to the user's device. For example, if data indicates that the user is in a hurry, it will propose a faster delivery method. The device then displays this proposal to the user. The input is the adjusted proposal, and the output is the display to the user. Specifically, the user can confirm, "Based on the user's urgency requirements, the optimal delivery method is drone delivery, and it will be delivered via the shortest route."

[1607] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1608] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1609] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1610] [Fourth embodiment]

[1611] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1612] 7, a 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.

[1613] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1614] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1615] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1616] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1617] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1618] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1619] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1620] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1621] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1622] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1623] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1624] This invention is a smart wholesale platform that allows users to obtain product quotes and select and manage optimal delivery methods. The system of the present invention includes a user terminal, a generative model, a server, a delivery route optimization function, and a delivery means using an autonomous flying device.

[1625] Overall Platform Configuration

[1626] 1. User Device:

[1627] A device that users interact with to request product quotes and enter shipping requirements.

[1628] The terminal transmits the user's input to the server and displays the quotes and delivery proposals returned by the server.

[1629] 2. Generative Model:

[1630] The server uses the generative model to generate estimates and suggestions based on the user's input.

[1631] The generative model responds to user questions by suggesting appropriate products and delivery methods.

[1632] 3. Server:

[1633] It receives requests from users and passes them to a generative model to generate estimates and proposals.

[1634] The quotation results and delivery proposal are returned to the terminal.

[1635] 4. Delivery route optimization function:

[1636] The server optimizes delivery routes and makes suggestions for reducing costs and environmental impact.

[1637] Calculate the best delivery route based on user requirements.

[1638] 5. Autonomous Aircraft:

[1639] This is a drone designed to provide fast and efficient delivery for emergency delivery needs.

[1640] The server proposes autonomous flying devices based on delivery needs and delivers goods via the optimal route.

[1641] Program processing explanation

[1642] 1. User Inquiry Processing:

[1643] A user uses a terminal to request a quote for a product (for example, "Can I get a quote for 200 units of product A?").

[1644] The terminal sends this request to the server.

[1645] The server queries the generative model to compute an estimate.

[1646] The generative model calculates the estimate and sends it back to the server.

[1647] The server sends the estimate back to the device, which displays the results to the user.

[1648] Example: "The estimated price for this item is 200,000 yen."

[1649] 2. Delivery route optimization and suggestions:

[1650] The user enters delivery requirements (e.g., "please deliver as soon as possible") into the terminal.

[1651] The terminal sends this requirement to the server.

[1652] The server performs delivery route optimization and determines the best delivery method based on the user's requirements.

[1653] The server proposes the optimal delivery method (e.g., drone delivery) and returns the result to the device.

[1654] The terminal displays delivery offers to the user.

[1655] Example: "The best delivery method is drone delivery, using the shortest route."

[1656] This concludes the program process and concrete example of a smart wholesale platform that integrates generative models and drone delivery services. The system provides users with real-time quotes and selects and manages optimal delivery methods, enabling efficient and sustainable business operations.

[1657] The processing flow will be explained below.

[1658] Program processing flow explanation

[1659] User inquiry processing

[1660] Step 1:

[1661] A user uses a terminal to request a quote for a product, for example, by typing, "Please give me a quote for 200 units of product A."

[1662] Step 2:

[1663] The terminal sends the user's request to the server, which includes the type and quantity of the product.

[1664] Step 3:

[1665] The server receives the user request and passes it to the generative model.

[1666] Step 4:

[1667] The generative model calculates the estimated price of product A based on the user request.

[1668] Step 5:

[1669] The generative model sends the calculated price estimate back to the server.

[1670] Step 6:

[1671] The server receives the estimate and returns it to the user's device.

[1672] Step 7:

[1673] The terminal displays the estimate results received from the server to the user.

[1674] Delivery route optimization and proposals

[1675] Step 1:

[1676] The user uses the terminal to enter delivery requirements, for example, "please deliver as soon as possible."

[1677] Step 2:

[1678] The terminal sends the user's delivery requirements to the server, including delivery speed and special requests.

[1679] Step 3:

[1680] The server receives the delivery requirements and invokes the delivery route optimization function.

[1681] Step 4:

[1682] The server calculates delivery routes and finds the optimal route to reduce costs and environmental impact.

[1683] Step 5:

[1684] The server decides whether to suggest an autonomous flying device (drone delivery) based on the user's requirements (e.g., urgent delivery).

[1685] Step 6:

[1686] The server returns the optimal delivery method and route to the user's device.

[1687] Step 7:

[1688] The terminal displays the delivery proposal and route information received from the server to the user.

[1689] Comprehensive Processing

[1690] Step 1:

[1691] A user uses a terminal to request both a quote and urgent delivery of a product, for example, "Please give me a quote for 200 units of product A and deliver it as soon as possible."

[1692] Step 2:

[1693] The terminal sends this overall request to the server.

[1694] Step 3:

[1695] The server receives the synthetic request and first passes it to the generative model to calculate the estimated price.

[1696] Step 4:

[1697] The generative model calculates the estimated price and sends it back to the server.

[1698] Step 5:

[1699] The server receives the estimate results and then calls the delivery route optimization function to calculate the route.

[1700] Step 6:

[1701] Based on the calculated route, the server considers the user's requirements (e.g., urgent delivery) and suggests the optimal delivery method (e.g., drone delivery).

[1702] Step 7:

[1703] The server returns the overall results (estimated price, optimal delivery method, and route) to the user's device.

[1704] Step 8:

[1705] The terminal displays the overall results received from the server to the user.

[1706] Example 1

[1707] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1708] Traditional wholesale platforms face challenges in being unable to respond quickly to user requests when obtaining product quotes or proposing delivery methods. Delivery routes are often not sufficiently optimized, preventing cost reduction and environmental impact reduction. Furthermore, the lack of efficient delivery methods for urgent delivery needs prevents business efficiency and sustainability from improving.

[1709] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1710] In this invention, the server includes a means for transmitting user-entered data to the server, a means for generating estimates and proposals based on the user's input using a generative model, and a means for executing a delivery route optimization function. This not only enables quick and accurate product estimates based on the user's input, but also calculates optimal delivery routes, reducing costs and reducing environmental impact. Furthermore, efficient delivery using unmanned aerial vehicles can be proposed for urgent delivery needs, improving business efficiency and sustainability.

[1711] "User Terminal" means a device operated by a User to input product quote requests and delivery requirements.

[1712] A "generative model" is an artificial intelligence model used by the server to generate estimates and suggestions based on user input.

[1713] The "Delivery route optimization function" is a function in which the server optimizes delivery routes and makes suggestions for reducing costs and environmental impact.

[1714] A "drone" is an autonomous flying delivery device used for rapid delivery needs.

[1715] The "means for transmitting data entered by the user to the server" refers to a communication means for transmitting request information from the user terminal to the server.

[1716] The "means for the server to send a quote request to the generative model" is a function that passes the user's request received by the server to the generative model and requests processing.

[1717] The "means by which the generative model calculates an estimate" is the function by which the generative model calculates the price and proposal details for a specified product or service.

[1718] "Means for the server to return the estimation results to the user terminal" refers to means by which the server transmits the estimation results received from the generative model to the user terminal.

[1719] The "means for suggesting optimal products and delivery methods" is a function that enables the generative model to suggest optimal products or delivery methods based on the user's requests.

[1720] "Means to streamline the process from quotation to delivery" refers to a system that efficiently carries out the entire process from estimating and measuring products to final delivery.

[1721] This invention is a smart wholesale platform that allows users to obtain product quotes and select and manage optimal delivery methods. The system of the present invention includes a user terminal, a generative model, a server, a delivery route optimization function, and a delivery means using an unmanned aerial vehicle.

[1722] Overall Platform Configuration

[1723] 1. User Device:

[1724] A device that users interact with to request product quotes and enter shipping requirements.

[1725] The terminals are intended to be devices that can connect to the Internet, such as PCs, smartphones, and tablets, and will run on operating systems such as iOS and Android.

[1726] Example: User types, "Please give me a quote for 200 units of product A."

[1727] 2. Generative Model:

[1728] The server uses the generative model to generate estimates and suggestions based on the user's input.

[1729] This generative model uses AI models such as OpenAI's GPT (Generative Pre-trained Transformer).

[1730] Example: "The estimate for 200 units of product A is 200,000 yen."

[1731] 3. Server:

[1732] The server receives requests from users and passes them to a generative model to generate estimates and proposals.

[1733] The server uses cloud services such as AWS (Amazon Web Services) and Microsoft Azure.

[1734] Example: Taking a user request and passing it to a generative model.

[1735] 4. Delivery route optimization function:

[1736] The server optimizes delivery routes and makes suggestions for reducing costs and environmental impact.

[1737] The server will implement route optimization algorithms such as Google Maps API and Route4Me.

[1738] Example: Calculate the optimal route and display "The best delivery method is drone delivery, and we will deliver via the shortest route."

[1739] 5. Unmanned aircraft:

[1740] This is an autonomous flying delivery device that provides fast and efficient delivery for urgent delivery needs.

[1741] The server will suggest an unmanned aircraft based on delivery needs and deliver the goods via the optimal route.

[1742] Example: For urgent deliveries, we suggest, "The best delivery method is drone delivery, and we will deliver it via the shortest route."

[1743] Program processing explanation

[1744] 1. User Inquiry Processing:

[1745] A user uses a device to request a quote for a product.

[1746] The terminal sends this request to the server.

[1747] The server queries the generative model to compute an estimate.

[1748] The generative model calculates the estimate and sends it back to the server.

[1749] The server sends the estimate back to the device, which displays the results to the user.

[1750] Example: "The estimated price for this item is 200,000 yen."

[1751] 2. Delivery route optimization and suggestions:

[1752] The user enters their delivery requirements (e.g., "please deliver as soon as possible") into the terminal.

[1753] The terminal sends this requirement to the server.

[1754] The server performs delivery route optimization and determines the best delivery method based on the user's requirements.

[1755] The server proposes the optimal delivery method (e.g., drone delivery) and returns the result to the device.

[1756] The terminal displays delivery offers to the user.

[1757] Example: "The best delivery method is drone delivery, using the shortest route."

[1758] Prompt sentences for specific examples of operation

[1759] Prompt for product quote:

[1760] "Please give me a quote for 200 units of product A."

[1761] Prompt for delivery requirements:

[1762] "Please deliver it quickly."

[1763] This concludes the explanation of the structure and operation of a smart wholesale platform that integrates generative models and unmanned aerial vehicles. The system provides users with real-time quotes and selects and manages optimal delivery methods, enabling efficient and sustainable business operations.

[1764] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1765] Step 1:

[1766] A user requests a quote for a product. The user enters specific product information into the terminal (e.g., "Please give me a quote for 200 units of product A") and sends the input data to the terminal. The terminal receives this data and converts it into JSON format. This makes the user's input in a format that can be processed by the next step. The input is "Product name: Product A, Quantity: 200", and the output is JSON format data.

[1767] Step 2:

[1768] The terminal sends the user's input to the server. The terminal sends the JSON format data entered by the user to the server as an HTTP request. In this operation, the application inside the terminal adds the appropriate URL and header information to send the data. The input is the user data in JSON format, and the output is a request to send the requested quote data.

[1769] Step 3:

[1770] The server sends a request for an estimate to the generative model. The server analyzes the received user request and makes an API call to the generative model to calculate an estimate. The input is the user request data in JSON format, and the output is the API request to be passed to the generative model. The server calls the "generateEstimate(data)" function as an internal process.

[1771] Step 4:

[1772] The generative model calculates the estimate. Based on the provided product information, the generative AI model calculates the appropriate price and sends the result back to the server. This calculation is performed quickly based on an internal algorithm. The input is product information and quantity, and the output is the estimated price. Example: "The estimate for 200 units of product A is 200,000 yen."

[1773] Step 5:

[1774] The server returns the estimation results to the terminal. The server converts the estimation results received from the generative model back into JSON format and sends it to the terminal as an HTTP response. The input is the estimation result from the generative model, and the output is the JSON-formatted estimation data sent to the terminal.

[1775] Step 6:

[1776] The terminal displays the quotation results to the user. The terminal parses the quotation results received from the server and displays them in the user interface. The input is the quotation data in JSON format, and the output is what is displayed to the user. For example, the message "The quotation for the product is 200,000 yen" is displayed.

[1777] Step 7:

[1778] The user inputs the delivery requirements. The user inputs the delivery requirements (e.g., "Please deliver as soon as possible") into the terminal and sends the input data to the terminal. The terminal receives the delivery requirements and converts them into JSON format. This makes the input from the user in a format that can be processed in the next step. The input is "Delivery method: urgent delivery" and the output is JSON format data.

[1779] Step 8:

[1780] The terminal sends the delivery requirements to the server. The terminal sends the JSON format delivery requirement data entered by the user to the server as an HTTP request. The input is the user delivery requirement data in JSON format, and the output is a request to send the requested delivery data.

[1781] Step 9:

[1782] The server executes the delivery route optimization function. The server passes the received delivery requirements to the delivery route optimization function and calculates the optimal delivery route. This uses algorithms such as Google Maps API and Route4Me. The input is the user's delivery requirement data, and the output is the optimized delivery route data.

[1783] Step 10:

[1784] The server determines the optimal delivery method and returns it to the terminal. The server analyzes the results of the delivery route optimization function and determines the optimal delivery method based on the user's requirements. It then converts the result into JSON format and sends it to the terminal as an HTTP response. The input is the result data of the delivery route optimization, and the output is the optimal delivery method data sent to the terminal.

[1785] Step 11:

[1786] The terminal displays the delivery suggestions to the user. The terminal analyzes the results of the optimal delivery method received from the server and displays them on the user interface. The input is the delivery method data in JSON format, and the output is what is displayed to the user. Example: A message is displayed saying, "The optimal delivery method is drone delivery, and it will be delivered via the shortest route."

[1787] (Application example 1)

[1788] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1789] In conventional logistics centers, optimizing product estimates and delivery routes takes time, making efficient operation difficult. Furthermore, the lack of a system that users can operate intuitively and visually can lead to confusion in estimate results and delivery method selection. Furthermore, manual management is important for urgent deliveries, and urgent delivery methods are often not proposed in a timely manner.

[1790] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1791] In this invention, the server includes a means for obtaining product quotes via a user terminal, a means for generating quotes and proposals based on user input using a generative model, a means for optimizing delivery routes and making proposals for reducing costs and environmental impact, a means for proposing delivery using autonomous flying devices for rapid delivery needs, and a means for visualizing and manipulating quotes and delivery proposals through an application installed on a smart device or robot. This allows users to visually check and manipulate quote results and delivery methods, enabling them to obtain product quotes and manage deliveries efficiently and intuitively.

[1792] "User terminal" means a device for inputting product quote requests and delivery requirements and receiving the results.

[1793] A "generative model" is an AI system for generating estimates and recommendations based on user input.

[1794] "Delivery route optimization" involves calculating delivery routes and proposing optimal routes to reduce costs and environmental impact.

[1795] An "autonomous flying device" is an unmanned aircraft designed to quickly deliver goods to meet urgent delivery needs.

[1796] A "smart device" is a portable device with information processing and communication capabilities.

[1797] A "robot" is a mechanical device that can autonomously perform specified actions or tasks.

[1798] "Application" means software installed on a smart device or robot for visualizing and manipulating quotes and delivery proposals.

[1799] The system for realizing this application example includes a user terminal, a generative model, a server, a delivery route optimization function, an autonomous flying device, and an application installed on a smart device or robot. Specific implementation methods are described below.

[1800] Overall system configuration

[1801] 1. User Device

[1802] Users use their smartphones or tablets to input product quote requests and delivery requirements, which are then sent to the server, which then displays the quotes and delivery proposals returned by the server.

[1803] 2. Generative Model

[1804] The server uses a generative model, such as OpenAI GPT-3, to generate quotes and recommendations based on the user's input, and then responds to the user's query with appropriate product and delivery suggestions.

[1805] 3. Server

[1806] The server receives requests from users, passes them to the generative model to generate estimates and proposals, receives the results from the generative model, and returns them to the user's device. The server also optimizes delivery routes and determines the optimal delivery method.

[1807] 4. Delivery route optimization function

[1808] The server uses Python's NetworkX library to optimize delivery routes, calculating the best route for cost savings and environmental conservation.

[1809] 5. Autonomous aircraft

[1810] For urgent delivery needs, Sarver proposes autonomous flying devices, which enable fast and efficient delivery.

[1811] 6. Smart Device or Robot Applications

[1812] The application is installed on a smartphone, tablet, smart glasses, or logistics robot, and allows users to visually check and manipulate quotation results and delivery proposals.

[1813] Examples of concrete examples and prompts

[1814] A specific example is shown below.

[1815] Request a product quote

[1816] A user uses a smartphone to enter a product quote request as follows:

[1817] Please let me know the estimate for 200 items.

[1818] Displaying the estimate results

[1819] The server uses the generative model to generate an estimate and displays the result as follows:

[1820] Estimated price: 200,000 yen.

[1821] Delivery method: Drone delivery is recommended.

[1822] Optimal delivery route: The shortest route from the origin to the destination was calculated.

[1823] This system allows users to visually check and operate quotation results and delivery methods, enabling them to efficiently and intuitively obtain product quotes and manage deliveries.

[1824] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1825] Step 1:

[1826] A user uses a smartphone or tablet to enter a request for a quote on a product. For example, the user enters a request such as "Please give me a quote for 200 units of this product."

[1827] Input: User input (e.g. "Please give me a quote for 200 units of this product").

[1828] Output: Request data (JSON format).

[1829] Step 2:

[1830] The device sends this request to the server, and the device sends the user's input data in JSON format to the server.

[1831] Input: User-entered information.

[1832] Output: Sending a request to the server.

[1833] Step 3:

[1834] The server receives a request from the user and passes it to the generative model to generate an estimate or proposal. The server passes a prompt to the generative model to calculate an estimate.

[1835] Input: The request data sent from the device.

[1836] Output: Prompt statement and quote request to the generative model.

[1837] Step 4:

[1838] A generative model (e.g., OpenAI GPT-3) generates an estimate based on the user's input and sends the result back to the server.

[1839] Input: Prompt text from the server.

[1840] Output: Estimation results (text format).

[1841] Step 5:

[1842] The server receives the estimation results from the generative model and sends them back to the user device. The server receives the estimation results and sends them to the device.

[1843] Input: Estimation results from the generative model.

[1844] Output: Send the estimate results to the user's device.

[1845] Step 6:

[1846] The terminal displays the estimate results to the user, who can then check the estimate results on the terminal.

[1847] Input: Estimate result from the server.

[1848] Output: Display the estimate result to the user (e.g., "Estimate result: 200,000 yen.").

[1849] Step 7:

[1850] The user enters their delivery requirements (e.g., "please deliver quickly") into the terminal.

[1851] Input: User delivery requirement input (e.g. "Please hurry delivery").

[1852] Output: Delivery requirements data (JSON format).

[1853] Step 8:

[1854] The device sends these delivery requirements to the server. The device sends the user's delivery requirements data to the server in JSON format.

[1855] Input: User delivery requirement information.

[1856] Output: Send delivery requirements data to the server.

[1857] Step 9:

[1858] The server performs delivery route optimization and determines the best delivery method based on the user's requirements. The server uses the NetworkX library to calculate the optimal delivery route.

[1859] Input: Delivery requirement data sent from the terminal.

[1860] Output: Optimal delivery route information and delivery method.

[1861] Step 10:

[1862] The server proposes the optimal delivery method (e.g., drone delivery) and returns the result to the terminal.

[1863] Input: Optimized delivery route information.

[1864] Output: Sends delivery method proposal to user.

[1865] Step 11:

[1866] The device displays delivery suggestions to the user, who can then review and manage the optimal delivery method on the device.

[1867] Input: Delivery method suggestions from the server.

[1868] Output: Display delivery method to user (e.g. "The best delivery method is drone delivery, using the shortest route.").

[1869] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1870] This invention is a smart wholesale platform that allows users to obtain product quotes and select and manage optimal delivery methods. The system of the present invention includes a user terminal, a generative model, a server, a delivery route optimization function, an autonomous flying device, and an emotion engine that recognizes user emotions.

[1871] Overall Platform Configuration

[1872] 1. User Device:

[1873] It is a device that users can operate to input and output data.

[1874] The terminal transmits the user's input to the server and displays the quotes and delivery proposals returned by the server.

[1875] 2. Generative Model:

[1876] The server uses the generative model to generate estimates and suggestions based on the user's input.

[1877] This generative model responds to user queries by suggesting optimal products and delivery methods.

[1878] 3. Server:

[1879] It receives requests from users and passes them to a generative model to generate estimates and proposals.

[1880] The quotation results and delivery proposal are returned to the user's terminal.

[1881] 4. Delivery route optimization function:

[1882] The server optimizes delivery routes and makes suggestions for reducing costs and environmental impact.

[1883] Calculate the best delivery route based on user requirements.

[1884] 5. Autonomous Aircraft:

[1885] This is a drone designed to provide fast and efficient delivery for emergency delivery needs.

[1886] The server proposes autonomous flying devices based on delivery needs and delivers goods via the optimal route.

[1887] 6. Emotion Engine:

[1888] The server recognizes the user's emotions via the user's terminal.

[1889] The emotion engine adjusts suggestions and determines the urgency of delivery based on the user's emotions.

[1890] Program processing explanation

[1891] 1. User Inquiry Processing:

[1892] A user uses a terminal to request a quote for a product, for example, by typing, "Please give me a quote for 200 units of product A."

[1893] The terminal sends this request to the server.

[1894] The server queries the generative model to compute an estimate.

[1895] The generative model calculates the estimate and sends it back to the server.

[1896] The server receives the estimate and returns it to the user's device.

[1897] The terminal displays the estimate results to the user.

[1898] Example: "The estimated price for this item is 200,000 yen."

[1899] 2. Delivery route optimization and suggestions:

[1900] The user enters delivery requirements (e.g., "please deliver as soon as possible") into the terminal.

[1901] The terminal sends this requirement to the server.

[1902] The server performs delivery route optimization and determines the best delivery method based on the user's requirements.

[1903] The server proposes the optimal delivery method (e.g., drone delivery) and returns the result to the device.

[1904] The terminal displays delivery offers to the user.

[1905] Example: "The best delivery method is drone delivery, using the shortest route."

[1906] 3. Emotional engine regulation:

[1907] As users enter product quotes and delivery requirements, an emotion engine recognizes their emotions (e.g., "impatient" or "excited").

[1908] The server analyzes the user's emotional information through an emotion engine and adjusts the suggestions.

[1909] The emotion engine will more frequently suggest faster delivery methods (e.g. drone delivery) if the user is in a hurry, and more cost-saving suggestions if the user is relaxed.

[1910] The server generates suggestions based on the emotion and sends them back to the user's device.

[1911] The device displays tailored suggestions to the user.

[1912] Example display: "Based on the user's urgent requirements, the best delivery method is drone delivery, using the shortest route."

[1913] The above is a program process and example of a smart wholesale platform that integrates user terminals, generative models, and an emotion engine. By taking user emotions into account, it can make suggestions based on individual needs and provide more personalized services.

[1914] The processing flow will be explained below.

[1915] Program processing flow explanation

[1916] User inquiry processing

[1917] Step 1:

[1918] A user uses a terminal to request a quote for a product, for example, by typing, "Please give me a quote for 200 units of product A."

[1919] Step 2:

[1920] The terminal sends the request content to the server.

[1921] Step 3:

[1922] The server receives the user's request and passes it to the generative model.

[1923] Step 4:

[1924] The generative model calculates the estimated price of product A based on the user request.

[1925] Step 5:

[1926] The generative model sends the calculated price estimate back to the server.

[1927] Step 6:

[1928] The server receives the estimate and returns it to the user's device.

[1929] Step 7:

[1930] The terminal displays the estimate results received from the server to the user.

[1931] Example: "The estimated price for this item is 200,000 yen."

[1932] Delivery route optimization and proposals

[1933] Step 1:

[1934] The user enters their delivery requirements, for example, "Please deliver as soon as possible."

[1935] Step 2:

[1936] The terminal sends this delivery requirement to the server.

[1937] Step 3:

[1938] The server receives the delivery requirements and invokes the delivery route optimization function.

[1939] Step 4:

[1940] The server calculates delivery routes and finds the optimal route to reduce costs and environmental impact.

[1941] Step 5:

[1942] The server decides whether to suggest an autonomous flying device (drone delivery) based on the user's requirements (e.g., urgent delivery).

[1943] Step 6:

[1944] The server returns the optimal delivery method and route to the user's device.

[1945] Step 7:

[1946] The terminal displays the delivery proposal and route information received from the server to the user.

[1947] Example: "The best delivery method is drone delivery, using the shortest route."

[1948] Emotional Engine Adjustment

[1949] Step 1:

[1950] As users enter product quotes and delivery requirements, an emotion engine recognizes their emotions, such as "impatience" or "excitement."

[1951] Step 2:

[1952] The device sends the user's emotional information to the server.

[1953] Step 3:

[1954] The server analyzes the user's emotions through an emotion engine and adjusts the suggestions.

[1955] Step 4:

[1956] Based on the emotional information, the server will suggest a faster delivery method (e.g., drone delivery) if the user is in a hurry, or suggest cost-saving solutions if the user is relaxed.

[1957] Step 5:

[1958] The server sends suggestions based on the emotion back to the user's device.

[1959] Step 6:

[1960] The device displays the tailored proposals received from the server to the user.

[1961] Example display: "Based on the user's urgent requirements, the best delivery method is drone delivery, using the shortest route."

[1962] These are the program processing steps of the smart wholesale platform, which integrates user terminals, generative models, and emotion engines. By taking user emotions into account, the system can make suggestions tailored to individual needs and provide more personalized services.

[1963] Example 2

[1964] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1965] Traditional wholesale platforms struggle to provide users with a fully personalized service when obtaining product quotes and selecting the optimal delivery method. Fast and efficient responses are required, especially for urgent delivery needs and optimizing complex delivery routes. Another issue is the inability to adjust recommendations based on user sentiment, resulting in a lack of an improved user experience.

[1966] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1967] In this invention, the server

[1968] a means for obtaining product quotes via a user terminal;

[1969] means for generating estimates and recommendations based on user input using the generative model;

[1970] A means to optimize delivery routes and make suggestions to reduce costs and environmental impact;

[1971] A way to recognize user emotions and adjust suggestions accordingly;

[1972] A means for proposing delivery using autonomous flying devices to meet rapid delivery needs;

[1973] This allows us to provide you with personalized quotes and delivery suggestions, and ensure fast and efficient delivery.

[1974] A "user terminal" is a device through which a user provides input and output via an interface.

[1975] A "generative model" is an algorithm or system that generates estimates or recommendations based on user input data.

[1976] "Delivery route" refers to the route along which goods are delivered, and is a route that is optimized to reduce costs and environmental impact.

[1977] An "autonomous flying device" is a device that flies unmanned and is used to meet specific delivery needs.

[1978] An "emotion engine" is a system or algorithm that recognizes a user's emotions and adjusts suggestions based on those emotions.

[1979] "Rapid delivery needs" are needs that require immediate or very short delivery.

[1980] "Quote" means an estimate or calculation of the price or quantity of a particular commodity.

[1981] A "suggestion" is a choice or recommendation presented to a user.

[1982] The present invention is a smart wholesale platform that allows users to obtain product quotes and select and manage optimal delivery methods. The system includes a user terminal, a generative model, a server, a delivery route optimization function, an autonomous flying device, and an emotion engine that recognizes user emotions.

[1983] Overall Platform Configuration

[1984] 1. User Device:

[1985] A device that a user operates to input and output data. User terminals include smartphones, tablets, and desktop computers.

[1986] The terminal transmits the user's input to the server and displays the quotes and delivery proposals returned by the server.

[1987] 2. Generative Model:

[1988] The server uses the generative model to generate estimates and suggestions based on the user's input.

[1989] This generative model is based on a large-scale language model (e.g., GPT-3) and suggests optimal products and delivery methods in response to user questions.

[1990] 3. Server:

[1991] It receives requests from users and passes them to a generative model to generate estimates and proposals.

[1992] The quotation results and delivery proposal are returned to the user's terminal.

[1993] 4. Delivery route optimization function:

[1994] The server optimizes delivery routes and makes suggestions for reducing costs and environmental impact.

[1995] Calculate the optimal delivery route based on user requirements using route optimization algorithms such as the Dijkstra algorithm.

[1996] 5. Autonomous Aircraft:

[1997] This is a drone designed to provide fast and efficient delivery for emergency delivery needs.

[1998] The server proposes autonomous flying devices based on delivery needs and delivers goods via the optimal route.

[1999] 6. Emotion Engine:

[2000] The server recognizes the user's emotions via the user's terminal.

[2001] The emotion engine uses NLP (natural language processing) technology to analyze the user's emotions and tailor its recommendations accordingly: if the user is in a hurry, it will suggest a fast delivery option, and if the user is relaxed, it will offer suggestions focused on cost savings.

[2002] Examples of concrete examples and prompts

[2003] Example 1:

[2004] User Input: "Can you give me a quote for 500 units of product B?"

[2005] The server uses the generative model to generate a quote (e.g., 1,000,000 yen).

[2006] Terminal display: "The estimated price for this item is 1,000,000 yen."

[2007] Example 2:

[2008] User input: "Please deliver the item as soon as possible."

[2009] The server optimizes delivery routes and suggests drone delivery.

[2010] Display on device: "The best delivery method is drone delivery, using the shortest route."

[2011] Prompt Sentence Examples

[2012] "Please calculate the price for the product. Please tell me the product name, quantity, and total price."

[2013] "Please suggest the best delivery method. For urgent deliveries, we recommend drone delivery."

[2014] The system of the present invention aims to provide personalized services that take into account user emotions and achieve efficient and fast delivery, thereby improving user experience and increasing business productivity.

[2015] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2016] Step 1:

[2017] A user uses a terminal to request a quote for a product.

[2018] Input: The user types into the terminal, "Please give me a quote for 200 units of product A."

[2019] Behavior: The terminal processes user input data in text format.

[2020] Output: The request data is generated and sent to the server.

[2021] Step 2:

[2022] The device sends a request to the server.

[2023] Input: Request data (user's quote request)

[2024] How it works: The device transfers the request data to the server using the HTTPS protocol.

[2025] Output: The expected action reaches the server.

[2026] Step 3:

[2027] The server queries the generative model to compute an estimate.

[2028] Input: User quote request data

[2029] How it works: The server passes a quote request to the generative AI model, which then starts the calculation. The generative model calculates a quote based on the input product information and quantity.

[2030] Output: The calculated quote result (e.g. amount) is sent back to the server in JSON format.

[2031] Step 4:

[2032] The server receives the estimate and returns it to the user's device.

[2033] Input: JSON data of the estimation results received from the generative model

[2034] How it works: The server converts the received JSON data into a user-friendly format and prepares it for transfer to the device.

[2035] Output: The processed quote results are sent to the terminal.

[2036] Step 5:

[2037] The terminal displays the estimate results to the user.

[2038] Input: Quote result received from the server

[2039] Action: The device displays the received quote on the screen. Specifically, it updates the display area to make the quote more visible to the user.

[2040] Output: The estimation results are displayed visually on the terminal and provided to the user.

[2041] Step 6:

[2042] The user enters delivery requirements into the terminal.

[2043] Input: The user types "urgent delivery please" into the terminal.

[2044] How it works: The terminal processes the user's input data in text format and generates data for further processing.

[2045] Output: Delivery requirements are ready to be sent to the server.

[2046] Step 7:

[2047] The terminal sends delivery requirements to the server.

[2048] Input: Delivery requirement data (user delivery request)

[2049] Operation: The terminal sends delivery requirement data to the server using the HTTPS protocol.

[2050] Output: Delivery requirement data arrives at the server.

[2051] Step 8:

[2052] The server performs delivery route optimization and determines the best delivery method based on the user's requirements.

[2053] Input: Delivery requirement data

[2054] How it works: The server uses a delivery route optimization algorithm (e.g., Dijkstra algorithm) to calculate the optimal route, and also filters the candidate delivery methods based on requirements.

[2055] Output: The optimal delivery route and method (e.g. drone delivery) is determined.

[2056] Step 9:

[2057] The server proposes the optimal delivery method and returns the result to the terminal.

[2058] Input: Optimized delivery route and method

[2059] How it works: The server generates data on the optimal delivery method and sends it to the device in JSON format.

[2060] Output: Proposed delivery method data arrives at the terminal.

[2061] Step 10:

[2062] The terminal displays delivery offers to the user.

[2063] Input: Proposal data received from the server

[2064] Action: The device displays the proposed data on the screen, updates the display area, and presents the appropriate delivery method to the user.

[2065] Output: The user is provided with a visual representation of the delivery proposal displayed on the terminal.

[2066] Step 11:

[2067] An emotion engine recognizes the user's emotions as they enter product quotes and delivery requirements.

[2068] Input: User-entered text

[2069] How it works: The emotion engine uses NLP techniques to analyze input text and identify the user's emotion (e.g., "anxious").

[2070] Output: The identified emotion data is transmitted to the server.

[2071] Step 12:

[2072] The server analyzes the user's emotional information through an emotion engine and adjusts the suggestions.

[2073] Input: Emotion data

[2074] How it works: The server adjusts its suggestions based on the emotion data it receives from the emotion engine, such as prioritizing faster delivery methods if the customer is in a hurry.

[2075] Output: The adjusted proposal is generated.

[2076] Step 13:

[2077] The server sends the adjusted proposal back to the user terminal.

[2078] Input: Adjusted proposal

[2079] Operation: The server prepares the adjusted proposal to be sent to the user's device.

[2080] Output: Data is sent to the terminal.

[2081] Step 14:

[2082] The device displays tailored suggestions to the user.

[2083] Input: Adjusted proposal data received from the server

[2084] Action: The device displays the suggestion data on the screen and updates the display area to make the personalized suggestions more visible to the user.

[2085] Output: The user is provided with a visual representation of the suggestions displayed on the device.

[2086] (Application example 2)

[2087] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2088] On conventional wholesale platforms, the process from obtaining product quotes to determining the optimal delivery route and selecting a fast delivery method was often inefficient, resulting in a poor user experience. Furthermore, there was a lack of consideration for user feelings in the service, leading to issues with not being able to fully meet individual needs. In particular, there were cases where an appropriate response was not given to urgent delivery requirements, resulting in a decline in reliability.

[2089] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for obtaining product quotes via a user terminal, means for generating quotes and proposals based on user input using a generative model, means for optimizing delivery routes and making proposals for reducing costs and environmental impact, means for proposing delivery using autonomous aircraft devices to meet rapid delivery needs, and means for recognizing user emotions using an emotion recognition engine and adjusting the content of the proposals. This enables users to quickly obtain optimal product quotes and receive personalized proposals based on their emotional state.

[2090] A "user terminal" is a device that a user operates to input and output data.

[2091] A "generative model" is an algorithm or software that generates estimates or recommendations based on input data.

[2092] A "Quote" is a calculation of the price or cost of a product based on information entered by the user.

[2093] "Delivery route optimization" is the process of calculating and proposing efficient and cost-effective delivery routes.

[2094] An "autonomous flying device" is an unmanned aerial vehicle that flies automatically and delivers goods without the need for human operation.

[2095] An "emotion recognition engine" is a technology or software that identifies emotions from a user's facial expressions, voice, etc.

[2096] "Suggestions" are recommendations of products and services provided based on user input using generative models, etc.

[2097] "Cost reduction" refers to reducing the cost of providing a product or service.

[2098] "Environmental mitigation" refers to efforts and measures taken to minimize the environmental impact of the delivery process.

[2099] This invention is a system that provides a smart wholesale platform by combining a user terminal, a generative model, a server, a delivery route optimization function, an autonomous flying device, and an emotion recognition engine. The specific implementation method will be described below.

[2100] User Device

[2101] A user terminal is a device that a user operates to input and output data. Examples include smartphones, tablets, and PCs. Users use this terminal to input product quote requests and delivery requirements.

[2102] Generative Model

[2103] Generative models are AI tools that generate quotes and proposals based on user input. The server uses the generative models to quickly respond to user inquiries. The models perform text analysis to calculate optimal product suggestions and quote prices.

[2104] server

[2105] The server receives requests from users and generates estimates and proposals based on the content of the requests using a generative model.The server also has a delivery route optimization function that calculates delivery routes based on user requirements.Furthermore, it is equipped with an emotion recognition engine to provide personalized services according to the user's emotional state.

[2106] Delivery route optimization function

[2107] The delivery route optimization function is a technology for efficiently calculating delivery routes. The server analyzes the user's delivery requirements (e.g., "Please deliver quickly") and determines the most efficient delivery route. This reduces costs and the environmental impact.

[2108] autonomous aircraft equipment

[2109] An autonomous flying device is an unmanned aircraft for rapid delivery. The server proposes delivery by the autonomous flying device taking into account the generative model and delivery requirements. The device automatically flies the specified route and achieves rapid delivery.

[2110] Emotion Recognition Engine

[2111] The emotion recognition engine is a technology that identifies emotions from the user's facial expressions and voice and adjusts the content of suggestions based on that information. Using the emotion recognition engine, the server prioritizes fast delivery methods if the user is in a hurry, and makes suggestions that emphasize cost reduction if the user is relaxed.

[2112] Examples of concrete examples and prompts

[2113] Specific examples

[2114] 1. Get a quote: User types, "Please give me a quote for 300 units of product B."

[2115] 2. Delivery Route Suggestion: User types, "What is the cheapest standard delivery route?"

[2116] 3. Emotion Recognition: Using images captured by the camera, the system recognizes the user's emotions and suggests a faster delivery method if the user is in a hurry.

[2117] Prompt Sentence Examples

[2118] Please let me know the estimate for product B.

[2119] Please suggest the best route for urgent delivery.

[2120] Recognize user emotions from images and provide appropriate suggestions

[2121] The above is an embodiment of the present invention. This smart wholesale platform provides fast and efficient services tailored to the individual needs of users.

[2122] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2123] Step 1:

[2124] A user uses a terminal to request a quote for a product. For example, they type, "Please give me a quote for 200 units of product A." The terminal sends this request to the server. The input is the user's request, and the output is the submission of the request.

[2125] Step 2:

[2126] The server receives a request from the user and queries the generative model. The generative model calculates an estimate based on the input request and sends it back to the server. The input is the user's request, and the output is the estimate result.

[2127] Step 3:

[2128] The server receives the estimate results returned from the generative model and sends them back to the user's terminal. The terminal displays the estimate results to the user. The input is the estimate results, and the output is the display to the user. Specifically, the user can confirm, "The estimate for the product is 200,000 yen."

[2129] Step 4:

[2130] The user inputs delivery requirements into the terminal, for example, "please deliver as soon as possible." The terminal then sends these requirements to the server. The input is the user's delivery requirements, and the output is the request submission.

[2131] Step 5:

[2132] The server receives the delivery requirements and calculates the optimal delivery route using the delivery route optimization function. The calculation results are sent to the server and returned to the user's device. The input is the delivery requirements, and the output is the optimal delivery route. Specifically, the server suggests, "The optimal delivery method is drone delivery, using the shortest route."

[2133] Step 6:

[2134] The server acquires the user's image and passes it to the emotion recognition engine to recognize the user's emotion. The emotion recognition engine analyzes the user's emotional information and adjusts the suggestions accordingly. The input is the user's image, and the output is the emotion data and the adjusted suggestions.

[2135] Step 7:

[2136] The server generates a proposal based on the emotion data and sends it back to the user's device. For example, if data indicates that the user is in a hurry, it will propose a faster delivery method. The device then displays this proposal to the user. The input is the adjusted proposal, and the output is the display to the user. Specifically, the user can confirm, "Based on the user's urgency requirements, the optimal delivery method is drone delivery, and it will be delivered via the shortest route."

[2137] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2138] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2139] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2140] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2141] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2142] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2143] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2144] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2145] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2146] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2147] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2148] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2149] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2150] 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.

[2151] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2152] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2153] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[2154] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2155] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2156] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2157] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2158] The following is further disclosed regarding the above embodiment.

[2159] (Claim 1)

[2160] a means for obtaining product quotes via a user terminal;

[2161] means for generating estimates and recommendations based on user input using the generative model;

[2162] A means to optimize delivery routes and make suggestions to reduce costs and environmental impact;

[2163] A means for proposing delivery using autonomous flying devices to meet rapid delivery needs;

[2164] A system including:

[2165] (Claim 2)

[2166] 10. The system of claim 1,

[2167] Equipped with a means to propose the most suitable product and delivery method,

[2168] A system in which a generative model answers user questions.

[2169] (Claim 3)

[2170] 10. The system of claim 1,

[2171] means for calculating delivery routes and suggesting autonomous flying devices for urgent delivery needs;

[2172] A system that streamlines the process from quotation to delivery.

[2173] "Example 1"

[2174] (Claim 1)

[2175] a means for obtaining product quotes via a user terminal;

[2176] means for generating estimates and recommendations based on user input using the generative model;

[2177] A means to optimize delivery routes and make suggestions to reduce costs and environmental impact;

[2178] A method to propose delivery using unmanned aircraft to meet the needs for rapid delivery,

[2179] means for transmitting user-entered data to a server;

[2180] A means for the server to send a quote request to the generative model;

[2181] The means by which the generative model computes the estimate; and

[2182] A means for the server to return the estimate result to the user terminal;

[2183] The system includes a means for a server to perform a delivery route optimization function.

[2184] (Claim 2)

[2185] The system of claim 1, further comprising means for suggesting optimal products and delivery methods, wherein the generative model answers user questions.

[2186] (Claim 3)

[2187] The system of claim 1 includes a means for calculating delivery routes and proposing drones for urgent delivery needs, thereby streamlining the process from quotation to delivery.

[2188] "Application Example 1"

[2189] (Claim 1)

[2190] a means for obtaining product quotes via a user terminal;

[2191] means for generating estimates and recommendations based on user input using the generative model;

[2192] A means to optimize delivery routes and make suggestions to reduce costs and environmental impact;

[2193] A means for proposing delivery using autonomous flying devices to meet rapid delivery needs;

[2194] a means of visualizing and manipulating the quote and delivery proposal through an application installed on a smart device or robot;

[2195] A system including:

[2196] (Claim 2)

[2197] The system of claim 1, wherein the visualization of quotation results and delivery proposals is performed using an application embedded in a smart device or robot.

[2198] (Claim 3)

[2199] The system according to claim 1, wherein the delivery route optimization information and delivery method can be checked and operated via an application installed on a smart device or a robot.

[2200] "Example 2: Combining Emotion Engines"

[2201] (Claim 1)

[2202] a means for obtaining product quotes via a user terminal;

[2203] means for generating estimates and recommendations based on user input using the generative model;

[2204] A means to optimize delivery routes and make suggestions to reduce costs and environmental impact;

[2205] A way to recognize user emotions and adjust suggestions accordingly;

[2206] A means for proposing delivery using autonomous flying devices to meet rapid delivery needs;

[2207] A system including:

[2208] (Claim 2)

[2209] 2. The system of claim 1, wherein the user terminal comprises means for recognizing emotions and adjusting the suggestions.

[2210] (Claim 3)

[2211] The system of claim 1, further comprising means for the generative model to suggest optimal products and delivery methods based on user input and sentiment.

[2212] "Application example 2 when combining emotion engines"

[2213] Claiming a new invention

[2214] (Claim 1)

[2215] a means for obtaining product quotes via a user terminal;

[2216] means for generating estimates and recommendations based on user input using the generative model;

[2217] A means to optimize delivery routes and make suggestions to reduce costs and environmental impact;

[2218] A means for proposing delivery using autonomous flying devices to meet rapid delivery needs;

[2219] a means for recognizing a user's emotion using an emotion recognition engine and adjusting the content of the suggestions;

[2220] A system including:

[2221] (Claim 2)

[2222] The system of claim 1, further comprising means for suggesting optimal products and delivery methods, wherein the generative model answers user questions.

[2223] (Claim 3)

[2224] 10. The system of claim 1, further comprising means for calculating delivery routes and proposing autonomous flying devices for urgent delivery needs, thereby streamlining the quote-to-delivery process. [Explanation of symbols]

[2225] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for obtaining product quotes via a user terminal; means for generating estimates and recommendations based on user input using the generative model; A means to optimize delivery routes and make suggestions to reduce costs and environmental impact; A means for proposing delivery using autonomous flying devices to meet rapid delivery needs; A system including:

2. 10. The system of claim 1, Equipped with a means to propose the most suitable product and delivery method, A system in which a generative model answers user questions.

3. 10. The system of claim 1, means for calculating delivery routes and suggesting autonomous flying devices for urgent delivery needs; A system that streamlines the process from quotation to delivery.

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