System

Abstract

A system is provided.SOLUTION: A logistics system including a means for acquiring an inventory status in logistics, a means for acquiring a securing status of a driver in logistics, a means for acquiring road conditions and weather data, a means for preprocessing the acquired data and inputting the preprocessed data to a generative AI model, a means for generating a price proposal according to a desired delivery date, a means for setting a price of a commodity based on the generated price proposal, a means for presenting the price to a user, and a means for receiving an order depending on selection of the user and creating a delivery plan.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] In today's logistics industry, improving working conditions and diversifying customer demands are major challenges. In particular, external factors such as warehouse inventory management, driver availability, road conditions, and weather have a significant impact on logistics efficiency. At the same time, customers are demanding fast delivery and flexible pricing, making it difficult to meet these demands simultaneously. There are also concerns about the impact of labor shortages, known as the 2024 problem. Against this backdrop, there is a need for methods to optimize logistics center productivity while improving customer satisfaction. [Means for solving the problem]

[0005] This invention provides a dynamic pricing system that collects data on inventory status, driver availability, road conditions, and weather in logistics, and uses a generative AI model to dynamically set product prices based on desired delivery dates. This system includes means for preprocessing the acquired data, handling outliers, and imputing missing values. Furthermore, the system displays the user's desired delivery date and pricing information, accepts orders based on selection, creates an optimal delivery plan, and transmits it to the logistics center, enabling flexible responses to diverse customer needs. This also optimizes the efficiency of logistics centers and promotes a healthier working environment.

[0006] "Logistics" is the process of safely and efficiently transporting goods and materials from the place of production to the place of consumption.

[0007] "Stock status" is information indicating the quantity and status of products currently held in a logistics center or warehouse.

[0008] "Driver availability status" is information indicating the current availability and number of transport drivers engaged in logistics operations.

[0009] "Road conditions" refers to information on traffic volume, congestion, accidents, etc. on roads during transportation.

[0010] "Weather Data" means information about current and forecast weather conditions.

[0011] A "generative AI model" is an algorithm or system that uses artificial intelligence techniques to analyze data and generate predictive results.

[0012] A "price proposal" is a suggested price for a product that is presented to a customer, calculated using a generative AI model.

[0013] The "desired delivery date" is the date on which the customer wishes the product to be delivered.

[0014] "Dynamic configuration" refers to real-time or immediate changes and adjustments depending on the situation or conditions.

[0015] A "dynamic pricing system" is a pricing system that changes prices depending on supply and demand and various other conditions.

[0016] "Preprocessing" is the process of converting or preparing raw data into an analyzable format.

[0017] "Outlier treatment" refers to the detection and correction or removal of values ​​in a data set that fall outside the normal range.

[0018] "Missing value imputation" is the process of predicting and filling in values ​​that are not present in a dataset.

[0019] "Accepting an order" refers to receiving and confirming order information from a customer.

[0020] "Distribution planning" is the process of optimizing schedules and routes for delivering goods to customers.

[0021] A "logistics center" is a facility that carries out logistics activities such as receiving, storing, and shipping goods and materials. [Brief explanation of the drawings]

[0022] [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

[0023] 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.

[0024] First, the terms used in the following description will be explained.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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."

[0030] [First embodiment]

[0031] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0032] 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.

[0033] 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).

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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."

[0043] This invention is a dynamic pricing system aimed at improving efficiency and customer satisfaction in the logistics industry. This system utilizes a generative AI model based on inventory status at logistics centers, driver availability, road conditions, and weather data to dynamically set product prices according to the customer's desired delivery date.

[0044] The system consists of the following steps:

[0045] 1. Data Collection

[0046] The server accesses the distribution center database and obtains current inventory information, such as 100 units of product A and 50 units of product B.

[0047] The server accesses the driver resource management system to obtain the number and status of available drivers, for example, information that five drivers are available.

[0048] The server calls external APIs to collect current road and weather data, such as local traffic congestion information and weather information such as whether it's raining or sunny.

[0049] 2. Data Analysis

[0050] The server preprocesses the collected data, consolidating it into a single format, filling in missing values, and processing outliers. This preprocessed data is suitable for input into a generative AI model.

[0051] The server inputs the preprocessed data into a generative AI model to generate price suggestions based on the desired delivery date, such as 1,500 yen for next-day delivery and 1,200 yen for three days later.

[0052] 3. Pricing

[0053] The server sets available delivery dates and prices based on the generated price proposals.

[0054] The server transmits the set price information to the terminal.

[0055] 4. Price quote

[0056] The terminal displays the received price information to the user, for example, showing options such as "Next day delivery: 1,500 yen" or "3 days later delivery: 1,200 yen."

[0057] Users select their desired delivery date and price.

[0058] 5. Order acceptance and delivery planning

[0059] The server receives the delivery date and price information selected by the user and updates the order data.

[0060] The server creates an optimal delivery plan and sends it to the distribution center. For example, for an order with delivery in three days, it plans the most efficient driver and route for that delivery.

[0061] This series of processes will streamline each step of logistics and enable flexible responses to diverse customer needs. Furthermore, this system will contribute to a healthier working environment and improved productivity at logistics centers. It is also expected to address the labor shortage known as the 2024 problem.

[0062] The processing flow will be explained below.

[0063] Step 1: Data collection

[0064] The server accesses the distribution center database and obtains the current inventory information for each product. For example, it collects information such as the number of units of product A in stock being 100 and the number of units of product B being 50.

[0065] The server accesses the driver resource management system to obtain the number and status of available drivers. For example, it obtains data that five drivers are available.

[0066] The server calls external APIs to collect current road conditions and weather data, such as traffic congestion information and weather data (rainy, sunny, etc.) for a specific area.

[0067] Step 2: Data Preprocessing

[0068] The server consolidates collected inventory data, driver availability data, road conditions and weather data.

[0069] The server analyzes this integrated data and performs preprocessing such as removing outliers and imputing missing values. For example, it predicts and imputes missing data points using a certain algorithm.

[0070] Step 3: Analysis by generative AI model

[0071] The server inputs the preprocessed integrated data into the generative AI model.

[0072] The server uses the generative AI model to generate price suggestions based on the desired delivery date, such as "next day delivery: 1,500 yen" or "3-day delivery: 1,200 yen."

[0073] Step 4: Pricing and Presentation

[0074] The server sets available delivery dates and prices based on the price suggestions output by the generative AI model.

[0075] The server transmits the set price information to the terminal and configures the display elements.

[0076] The terminal displays the received price information to the user, for example, showing options such as "Next day delivery: 1,500 yen" or "3 days later delivery: 1,200 yen."

[0077] Step 5: Selecting Users

[0078] The user selects the desired delivery date and price from the presented price and delivery date options.

[0079] The terminal transmits the user's selection to the server.

[0080] Step 6: Order acceptance and updates

[0081] The server receives the selection information sent by the user and updates the order data in the distribution center database. For example, the order data is confirmed as "Product A, delivery in 3 days, price 1,200 yen."

[0082] The server prepares to optimize the delivery plan using the confirmed order data.

[0083] Step 7: Optimize delivery plans

[0084] The server creates an optimal delivery plan based on the received order data and pre-processed driver availability data. Specifically, it determines the optimal driver and route for the selected desired delivery date.

[0085] The server then sends the created optimal delivery plan to the logistics center system. For example, it sends a plan that says, "Driver B will deliver product A to the specified destination in three days."

[0086] The above are the specific processing steps in the dynamic pricing system of the present invention. This series of processes makes it possible to optimize the efficiency of the logistics center and flexibly respond to the diverse needs of customers.

[0087] Example 1

[0088] 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."

[0089] Conventional logistics systems lack efficient integration of many factors, such as inventory management, driver allocation, and fluctuations in road conditions and weather, resulting in delivery delays and increased costs. Furthermore, it is difficult to offer appropriate pricing to customers, making it difficult to improve customer satisfaction. The purpose of this invention is to solve these problems, improve the efficiency of logistics operations, and increase customer satisfaction.

[0090] 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.

[0091] In this invention, the server includes means for acquiring inventory status in logistics, means for acquiring driver availability status in logistics, means for acquiring road condition and weather data, means for preprocessing the acquired data and inputting it into a generative AI model, means for generating a price proposal according to a desired delivery date, means for setting a product price based on the generated price proposal and presenting it to the user, means for accepting an order and creating a delivery plan according to the user's selection, means for displaying the presented price information to the user and allowing the user to select a desired delivery date and price, means for receiving the desired delivery date and price information selected by the user and updating the order data, and means for creating an optimal delivery plan and transmitting it to a logistics center. This makes it possible to efficiently integrate various elements related to logistics and provide customers with optimal delivery options through dynamic price proposals.

[0092] The "means for acquiring inventory status in logistics" is a part of a program that runs within a server to collect inventory information from logistics centers and warehouses.

[0093] The "means for acquiring driver availability status for logistics" is a server function that enables logistics companies to acquire the number of drivers available and their working status from the management system.

[0094] The "means for acquiring road condition and weather data" is a server function for acquiring road congestion information and weather forecasts from external data providing services.

[0095] "Means for preprocessing acquired data and inputting it into the generative AI model" refers to the process of organizing collected data into a unified format and inputting it into the generative AI model in the optimal data format.

[0096] "Means for generating price proposals based on desired delivery date" refers to a function that uses a generative AI model to calculate the optimal price based on the user's desired delivery date, based on collected and pre-processed data.

[0097] The "means of setting a product price based on the generated price proposal and presenting it to the user" refers to a server function that determines the delivery fee for the product using the price proposed by the AI ​​and displays it through the user interface.

[0098] "Means for accepting orders and creating delivery plans based on user selections" refers to the process of storing order information in the system and creating an efficient delivery plan based on the delivery options selected by the user.

[0099] "Means for displaying the proposed price information to the user and allowing the user to select the desired delivery date and price" refers to a function that displays price information in a user interface and allows the user to select the desired delivery date and price.

[0100] The "means for receiving the desired delivery date and price information selected by the user and updating the order data" is a function of the server that records and updates the order information in the database based on the user's selection.

[0101] "Means for creating optimal delivery plans and transmitting them to logistics centers" refers to the process of calculating the most efficient delivery routes and driver allocations based on order data, and sending that information to logistics centers.

[0102] This invention is a dynamic pricing system aimed at improving efficiency and customer satisfaction in the logistics industry. The main components of the system include obtaining inventory status in logistics, obtaining driver availability, obtaining road and weather data, and using a generative AI model. The steps based on an embodiment of the present invention are as follows.

[0103] Required Hardware and Software

[0104] 1. Server:

[0105] Database Management System (DBMS)

[0106] Resource Management System

[0107] External APIs (e.g. Google Maps API, OpenWeatherMap API)

[0108] Data preprocessing software (e.g., Pandas)

[0109] Generative AI models (e.g., GPT-3)

[0110] 2. Terminal:

[0111] Front-end applications (e.g. web applications and mobile apps)

[0112] Data collection and preprocessing

[0113] The server collects the following data:

[0114] 1. Obtaining inventory status

[0115] The server queries the distribution center's database to retrieve inventory information.

[0116] Example: SELECT product, inventory FROM inventory table

[0117] Example: 100 units of product A in stock, 50 units of product B in stock

[0118] 2. Obtaining driver availability information

[0119] The server sends an API request to the driver resource management system to obtain the number and status of available drivers.

[0120] Example: 5 drivers available

[0121] 3. Obtaining road and weather data

[0122] The server calls external APIs (e.g., Google Maps API or OpenWeatherMap API) to collect road conditions and weather data.

[0123] Example: There is traffic congestion and it is raining.

[0124] After collecting the data, the server preprocesses them, consolidating them into a single format, imputing missing values, and handling outliers.

[0125] Price proposal based on desired delivery date

[0126] The server inputs the preprocessed data into a generative AI model to generate a price suggestion based on the desired delivery date. Below is an example of a prompt.

[0127] Example prompt sentence:

[0128] Stock information: Product A: 100 units, Product B: 50 units

[0129] Drivers: 5

[0130] Road conditions: Traffic jams

[0131] Weather: rain

[0132] Desired delivery date: Next day

[0133] Based on the price suggested by the generative AI model, the server sets the price of the product and presents it to the user.

[0134] User Presentation and Selection

[0135] The terminal displays the price information received from the server to the user, who then selects the desired delivery date and price.

[0136] Specific user interface examples:

[0137] Select delivery date and price:

[0138] Next day delivery: 1,500 yen

[0139] 3-day delivery: 1,200 yen

[0140] Once the user selects the desired delivery date and price, the server receives that information, updates the order data, creates an efficient delivery plan, and transmits it to the logistics center.

[0141] This will make each step of logistics more efficient and enable flexible responses to diverse customer needs. The system as a whole is expected to improve efficiency in the logistics industry and increase customer satisfaction.

[0142] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0143] Step 1:

[0144] The server sends a query to the distribution center's database to obtain inventory status. An example query is SELECT product, inventory FROM inventory table. This query obtains current inventory information. For example, if there are 100 units of product A in stock and 50 units of product B in stock, this information is obtained. The input is database connection information, and the output is inventory information. The specific operation is that the server sends an SQL query to the database and receives a response.

[0145] Step 2:

[0146] The server sends an API request to the driver resource management system to obtain the number and status of available drivers. An example request is GET / api / drivers / available. This request obtains information about currently available drivers. For example, if there are five drivers available, that information is obtained. The input is the API endpoint of the resource management system, and the output is driver availability information. In concrete terms, the server sends an HTTP request to the resource management system and receives a response.

[0147] Step 3:

[0148] The server calls an external API (e.g., Google Maps API or OpenWeatherMap API) to collect current road conditions and weather data. Example requests are GET / maps / api / traffic and GET / weather / api / current. This request obtains road conditions and weather data. For example, information about road congestion or rainy weather is obtained. The input is the endpoint of the external API, and the output is road conditions and weather data. Specifically, the server sends an HTTP request to the external API and receives a response.

[0149] Step 4:

[0150] The server preprocesses the collected inventory information, driver information, road conditions, and weather data. Specifically, it converts the data into a unified format, fills in missing values, and processes outliers. The input is the various collected data, and the output is data in a format suitable for the generative AI model. For example, if there are missing values, it performs operations such as filling in the average value.

[0151] Step 5:

[0152] The server inputs the preprocessed data into a generative AI model to generate price suggestions based on the desired delivery date. The input is preprocessed data in a unified format, and the output is price suggestions based on the desired delivery date. An example prompt is:

[0153] Stock information: Product A: 100 units, Product B: 50 units

[0154] Drivers: 5

[0155] Road conditions: Traffic jams

[0156] Weather: rain

[0157] Desired delivery date: Next day

[0158] An example of a price suggestion generated based on this prompt would be ¥1,500 for next day delivery or ¥1,200 for three days later.

[0159] Step 6:

[0160] The server sets selectable delivery dates and prices based on the generated price proposals and sends them to the terminal. The input is the price proposal from the generative AI model, and the output is the delivery date and price information to be presented to the user. Specifically, the server sends the price information to the terminal via an HTTP request.

[0161] Step 7:

[0162] The terminal displays the received price information to the user. The input is the price information sent from the server, and the output is the price information displayed on the user interface. A concrete example of how it is displayed is:

[0163] Select delivery date and price:

[0164] Next day delivery: 1,500 yen

[0165] 3-day delivery: 1,200 yen

[0166] Step 8:

[0167] The user selects the desired delivery date and price. The input is the price information displayed on the terminal, and the output is the user's selection. As a specific selection example, the user selects "Delivery in 3 days: 1,200 yen."

[0168] Step 9:

[0169] The server receives the delivery date and price information selected by the user and updates the order data. The input is the user's selection information, and the output is the updated order data. The specific operation is that the server updates the order database.

[0170] Step 10:

[0171] The server creates an optimal delivery plan and transmits it to the logistics center. The input is the updated order data, and the output is the optimal delivery plan. Specifically, the server calculates the delivery plan and sends it to the logistics center via an HTTP request.

[0172] In this way, each step of logistics can be made more efficient, enabling flexible responses to diverse customer needs.

[0173] (Application example 1)

[0174] 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."

[0175] In the logistics industry, it is difficult to create fast and efficient delivery plans due to factors such as inventory management, driver availability, and road and weather conditions. There is also a lack of methods to flexibly respond to customer delivery dates and dynamically set prices. Furthermore, the lack of tools to support real-time decision-making can lead to reduced operational efficiency. To solve these issues, a system is needed that allows logistics managers to instantly obtain information using smart devices and apply dynamic pricing using generative AI models.

[0176] 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.

[0177] In this invention, the server includes: a means for acquiring inventory status for logistics; a means for acquiring driver availability status for logistics; a means for acquiring road condition and weather data; a means for preprocessing the acquired data and inputting it into a generative AI model; a means for generating a price proposal according to a desired delivery date; a means for setting a product price based on the generated price proposal and presenting it to a user; a means for accepting orders and creating a delivery plan according to a user's selection; and a means for displaying the price proposal and delivery plan in real time using smart glasses and allowing the user to make a selection. This enables logistics managers to make efficient decisions in real time, improving the efficiency of each step of logistics and improving customer satisfaction.

[0178] "Stock status" is data indicating the current quantity and type of products at the logistics center.

[0179] "Driver availability status" is data that indicates the number of drivers available to work delivery tasks at a logistics center and their available working hours.

[0180] "Road conditions" is data that shows information such as current road congestion, closures, and road closures.

[0181] "Weather data" is information indicating the current weather conditions, and includes data such as sunny, rainy, and snowy weather.

[0182] A "generative AI model" is an artificial intelligence model that dynamically generates price proposals and delivery plans based on acquired data.

[0183] "Smart glasses" are wearable devices that are worn on the head and can display information in real time.

[0184] "Price proposal" is a product pricing proposal calculated by the generative AI model based on the desired delivery date.

[0185] A "delivery plan" is a specific plan for delivering goods to customers, including driver allocation and route optimization.

[0186] "Preprocessing" refers to data processing, such as processing outliers and filling in missing values, that is carried out before inputting the acquired data into a generative AI model.

[0187] "User" refers to the customer who receives a price quote and selects the desired delivery date and price.

[0188] "Terminal" refers to an information processing device for displaying price information and delivery schedules, including smart glasses.

[0189] This invention relates to a dynamic pricing system and its specific embodiments, which aim to improve efficiency and customer satisfaction in the logistics industry. In this embodiment, hardware such as a server or smart glasses is used to collect and process data, and price proposals and delivery plans are provided to users in real time. The specific configuration and operation are described below.

[0190] The server accesses the distribution center database to obtain inventory status. For example, it obtains information that there are 100 units of product A in stock and 50 units of product B in stock. The server also accesses the driver resource management system to obtain the number and status of available drivers. For example, it may obtain information that five drivers are available. Furthermore, the server calls an external API to collect road condition and weather data. For example, it may obtain information on traffic congestion and rain.

[0191] The collected data is managed centrally and undergoes preprocessing, which involves standardizing the data format, filling in missing values, and handling outliers. This preprocessing is primarily carried out using software libraries such as Python and Pandas. The preprocessed data is then input into a generative AI model, which generates price proposals. For example, the proposed price may be 1,500 yen for next-day delivery or 1,200 yen for delivery three days later.

[0192] The generated price proposal is sent to the smart glasses via a cloud server, which then displays the proposed price information to the logistics manager in real time. The logistics manager can then view the delivery date and price options through the smart glasses and instantly decide on the best option.

[0193] Once the user selects the desired delivery date and price, the information is sent back to the cloud server. The server updates the order data based on the received information and generates an optimal delivery plan. This delivery plan includes efficient driver assignment and the shortest route. This delivery plan is then transmitted to the logistics center, where the actual delivery takes place.

[0194] Specific examples

[0195] For example, input the following prompt into the generative AI model:

[0196] "We have 100 units of product A in stock and 50 units of product B in stock. Five drivers are available. The roads are congested and it's currently raining. Please provide pricing for next-day delivery and three-day delivery under these conditions."

[0197] This allows logistics managers to make efficient decisions in real time, improving the efficiency of each step of logistics and customer satisfaction.

[0198] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0199] Step 1:

[0200] The server accesses the distribution center database to obtain inventory status. For example, the server obtains data such as 100 units of product A and 50 units of product B. This inventory information is used in the next processing step.

[0201] Step 2:

[0202] The server accesses the driver resource management system to obtain the number and status of available drivers. For example, it may find that five drivers are available. This driver information is also used in the next processing step.

[0203] Step 3:

[0204] The server calls external APIs to collect current road conditions and weather data, such as traffic congestion and rainy weather. This road condition and weather data is used in the next data analysis.

[0205] Step 4:

[0206] The server preprocesses the acquired inventory status, driver information, road conditions, and weather data. It also performs data processing such as standardizing the data format, filling in missing values, and handling outliers. Specifically, it uses Python and Pandas to preprocess the data and prepare it in a format that can be input into the generative AI model.

[0207] Step 5:

[0208] The server inputs the preprocessed data into the generative AI model and generates a price proposal based on the desired delivery date. For example, it generates a price proposal of 1,500 yen for next-day delivery and 1,200 yen for three-day delivery. This generated price proposal is used in the next step.

[0209] Step 6:

[0210] The server sends the generated price proposal to the smart glasses, which then display delivery date and price options in real time based on the received price proposal. This information is then provided to the logistics manager as input for the next step.

[0211] Step 7:

[0212] The user (logistics manager) checks the price and delivery date options presented through the smart glasses and selects the desired option. This selected information is then sent back to the server.

[0213] Step 8:

[0214] The server updates the order data based on the delivery date and price information received from the user, thereby keeping the order information up to date.

[0215] Step 9:

[0216] The server creates an optimal delivery plan based on the delivery date and price information selected by the user, including driver assignment and route optimization, and then transmits this delivery plan to the distribution center.

[0217] Step 10:

[0218] The distribution center then begins actual delivery operations based on the optimal delivery plan received from the server, ensuring that products are delivered to customers efficiently.

[0219] 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.

[0220] This invention combines a dynamic pricing system, which aims to improve efficiency and customer satisfaction in the logistics industry, with an emotion engine that recognizes user emotions. This system utilizes a generative AI model based on inventory status at logistics centers, driver availability, road conditions, and weather data to dynamically set product prices according to the customer's desired delivery date. Furthermore, the emotion engine is used to adjust prices and suggest services according to the user's emotional state.

[0221] The system consists of the following steps:

[0222] 1. Data Collection

[0223] The server accesses the distribution center database and obtains current inventory information, such as 100 units of product A and 50 units of product B.

[0224] The server accesses the driver resource management system to obtain the number and status of available drivers. For example, it obtains data that five drivers are available.

[0225] The server calls external APIs to collect current road conditions and weather data, such as traffic congestion information and weather data (rainy, sunny, etc.) for a specific area.

[0226] 2. Data Preprocessing

[0227] The server consolidates collected inventory data, driver availability data, road conditions and weather data.

[0228] The server analyzes this integrated data and performs preprocessing such as removing outliers and filling in missing values.

[0229] 3. Analysis using generative AI models

[0230] The server inputs the preprocessed integrated data into the generative AI model.

[0231] The server uses the generative AI model to generate price suggestions based on the desired delivery date, such as "next day delivery: 1,500 yen" or "3-day delivery: 1,200 yen."

[0232] 4. Emotion Recognition by Emotion Engine

[0233] The device uses an emotion engine to recognize the emotions of users when they operate the system. The emotion engine collects emotion data from users' facial expressions, voice, text, etc.

[0234] The server then adjusts the generated price proposal based on the collected emotional data, for example, lowering the price if the user is feeling stressed.

[0235] 5. Pricing and Offerings

[0236] The server sets the final price based on the emotional data analyzed by the emotion engine.

[0237] The server transmits the set price information to the terminal and configures the display elements.

[0238] The terminal displays the received price information to the user.

[0239] 6. User Choices

[0240] The user selects the desired delivery date and price from the presented price and delivery date options.

[0241] The terminal transmits the user's selection to the server.

[0242] 7. Order Acceptance and Updates

[0243] The server receives the selection information sent by the user and updates the order data in the distribution center database.

[0244] 8. Optimizing delivery plans

[0245] The server creates an optimal delivery plan based on the received order data and pre-processed driver availability data.

[0246] The server sends the created optimal delivery plan to the logistics center's system.

[0247] This series of processes allows the system to streamline each step of logistics and flexibly respond to diverse customer needs. Furthermore, by incorporating an emotion engine, it becomes possible to provide services based on the user's emotional state, which is expected to further improve customer satisfaction. In this way, by adding an emotional element to the dynamic pricing system, embodiments of the invention can provide more personalized logistics services.

[0248] The processing flow will be explained below.

[0249] Step 1: Data collection

[0250] The server accesses the distribution center database to obtain current inventory information, such as detailed inventory status, such as 100 units of product A and 50 units of product B.

[0251] The server accesses the driver resource management system to obtain the number and status of currently available drivers. For example, it obtains data that five drivers are available.

[0252] The server calls external APIs to collect current road conditions and weather data, such as traffic congestion information for a certain area and weather data such as whether it will rain or shine.

[0253] Step 2: Data Preprocessing

[0254] The server consolidates the collected inventory data, driver availability data, road conditions and weather data.

[0255] The server detects outliers in the aggregated data, corrects or removes them as needed, and imputes missing values ​​using predictive algorithms.

[0256] Step 3: Analysis by generative AI model

[0257] The server inputs the preprocessed data into a generative AI model.

[0258] The server uses a generative AI model to generate price suggestions based on the desired delivery date based on the collected data, such as "next day delivery: 1,500 yen" or "three days later delivery: 1,200 yen."

[0259] Step 4: Emotion Recognition with the Emotion Engine

[0260] The device uses an emotion engine to recognize the user's emotions, collecting emotional data based on the user's tone of voice and facial expression recognition technology.

[0261] The device analyzes the collected emotional data to determine the user's current emotional state (e.g., joy, sadness, stress, etc.).

[0262] The server dynamically adjusts the generated price proposal based on this emotional data, for example, by lowering the price if the user is feeling stressed.

[0263] Step 5: Pricing and Presentation

[0264] The server sets the final price that reflects the analysis results of the emotion engine.

[0265] The server transmits the set price information to the terminal and configures the user interface.

[0266] The terminal displays the price information to the user, for example, "Next day delivery: 1,450 yen" or "3 days later delivery: 1,150 yen."

[0267] Step 6: Selecting Users

[0268] The user selects the desired price and delivery date from the options provided. For example, they can select "Delivery in 3 days: 1,150 yen."

[0269] The terminal transmits the user's selection to the server.

[0270] Step 7: Order acceptance and updates

[0271] The server receives the selection information sent by the user and updates the order data in the distribution center database. For example, the order data is confirmed as "Product A, delivery in 3 days, price 1,150 yen."

[0272] Step 8: Optimize your delivery plan

[0273] The server creates an optimal delivery plan based on the updated order data and pre-processed driver availability data. Specifically, it determines the optimal driver and route for the selected desired delivery date.

[0274] The server sends the created optimal delivery plan to the logistics center system. For example, it sends a plan that says, "Driver A will deliver Product A to the specified destination in three days."

[0275] The above are the specific processing steps for combining the dynamic pricing system of the present invention with an emotion engine. This series of processes will improve the efficiency of logistics operations and customer satisfaction. Furthermore, it will enable individual responses based on the user's emotions, enabling more personalized operations.

[0276] Example 2

[0277] 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."

[0278] The logistics industry requires optimization of inventory management and delivery plans, but currently these processes are not carried out efficiently, resulting in problems such as lower customer satisfaction and increased costs. Furthermore, there is a lack of services that take into account the emotional state of customers, making it difficult to improve customer experience. The present invention aims to solve these problems.

[0279] 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.

[0280] In this invention, the server includes a means for acquiring inventory status for logistics, a means for acquiring driver availability status for logistics, a means for acquiring road condition and weather data, a means for preprocessing the acquired data and inputting it into a generative AI model, a means for generating a price proposal according to the desired delivery date, a means for recognizing a user's emotion, a means for adjusting the product price based on the user's emotional state based on the generated price proposal and presenting it to the user, and a means for accepting orders and creating a delivery plan depending on the user's selection. This makes it possible to streamline each step of logistics and provide personalized services based on the user's emotional state.

[0281] "Inventory status in logistics" refers to information such as the quantity, type, and storage status of products at logistics centers and warehouses.

[0282] "Driver availability status" refers to information such as the current availability and deployment status of drivers in charge of delivery.

[0283] "Road conditions" refers to data about road conditions, such as traffic flow, congestion, and accident information.

[0284] "Weather data" is data that indicates the weather conditions for each region, and includes information such as fine weather, rain, and snowfall.

[0285] "Preprocessing" refers to processing the acquired data such as removing outliers, filling in missing values, and integrating data.

[0286] A "generative AI model" is a model that uses artificial intelligence technology to generate data for specific purposes.

[0287] A "price suggestion" is a suggestion that dynamically sets the price of a product to be offered to a user based on collected data.

[0288] "Emotion recognition" is a technology that analyzes and recognizes a user's emotions from facial expressions, voice, text, etc.

[0289] A "delivery plan" is a plan that aims to optimize the entire delivery process, including the delivery order and route for ordered items and driver assignment.

[0290] "Personalized service" means providing services that are customized based on the individual characteristics, feelings, and requests of each user.

[0291] This invention is a system aimed at improving efficiency and customer satisfaction in the logistics industry. This system combines generative AI models and emotion recognition technology, and is implemented in the following steps:

[0292] Data collection

[0293] The server accesses the distribution center's database to obtain current inventory information. For example, it collects information such as 100 units of product A in stock and 50 units of product B in stock. It also accesses the driver resource management system to obtain the number and status of available drivers. For example, it obtains data that shows that five drivers are available. It also calls external APIs to collect current road conditions and weather data. For example, it obtains traffic congestion information and weather data (rain, sunny, etc.) in a specific area.

[0294] Data Preprocessing

[0295] The server integrates the collected inventory data, driver availability data, road conditions, and weather data. It analyzes this integrated data and performs preprocessing such as removing outliers and filling in missing values. Specifically, it rationally fills in missing values ​​in the dataset and filters out abnormal data.

[0296] Analysis using generative AI models

[0297] The server inputs the preprocessed integrated data into a generative AI model. The generative AI model is implemented using, for example, a Python library (TensorFlow or PyTorch). The server uses the generative AI model to generate a price proposal based on the desired delivery date. For example, it generates price proposals such as "next day delivery: 1,500 yen" and "3-day delivery: 1,200 yen."

[0298] Emotion recognition by emotion engine

[0299] The device uses an emotion engine to recognize the emotions expressed when the user operates the system. The emotion engine is a technology that collects and analyzes emotional data from the user's facial expressions, voice, text, etc. For example, it can use a camera and microphone to analyze the user's facial expressions and tone of voice to identify emotions such as "stress" or "joy."

[0300] Pricing and Offering

[0301] The server adjusts the generated price proposal based on the emotional data analyzed by the emotion engine. For example, if the user is feeling stressed, the price will be set lower. The server also sends the final price information to the terminal and presents it to the user. For example, it displays information such as "Next day delivery: 1,400 yen" and "3 days later delivery: 1,100 yen."

[0302] User Choices and Order Acceptance

[0303] The user selects the desired delivery date and price from the presented price and delivery date options. The terminal sends the user's selection to the server, which accepts the order data and updates it in the distribution center's database. The server also creates an optimal delivery plan based on the accepted order data and preprocessed driver availability data, and sends it to the distribution center's system.

[0304] In this way, the system of the present invention not only improves the efficiency of each step in logistics but also provides personalized services based on the emotional state of the user, which is expected to improve customer satisfaction.

[0305] Prompt Sentence Examples

[0306] "Dynamic pricing should be based on current inventory at distribution centers, driver availability, road congestion, and weather data. Also, adjust pricing based on the user's emotional state. Specifically, offer two pricing options: next-day delivery and three-day delivery."

[0307] This enables the system to simultaneously improve logistics efficiency and customer satisfaction.

[0308] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0309] Step 1:

[0310] The server accesses the distribution center's database and obtains current inventory information. Specifically, it obtains the inventory quantity and type for each product from the database and saves this information in list format. The input data is the database connection information and query statement, and the output data is a list of inventory information. For example, it obtains data such as "100 units of product A are in stock, and 50 units of product B are in stock."

[0311] Step 2:

[0312] The server accesses the driver resource management system to obtain the number and status of available drivers. Specifically, it obtains a list of currently available drivers from the resource management system via API. The input data is the API endpoint and authentication information, and the output data is driver resource information. For example, it obtains data such as "5 drivers are available."

[0313] Step 3:

[0314] The server calls external APIs to collect current road conditions and weather data. Specifically, it obtains and integrates data from traffic information services and weather information services. The input data is the endpoint and search conditions of the external API, and the output data is current road conditions and weather information. For example, it collects "traffic congestion information and weather data (rainy, sunny, etc.) in a specific area."

[0315] Step 4:

[0316] The server integrates the collected inventory data, driver availability data, road conditions, and weather data. Specifically, it combines the individually acquired data into a single dataset. The input data is the various data acquired at each step, and the output data is the integrated dataset. For example, it creates integrated data such as "100 units in stock, 5 drivers, traffic information available, rainy weather."

[0317] Step 5:

[0318] The server analyzes this integrated data and performs preprocessing such as removing outliers and filling missing values. Specifically, it applies a data cleansing process to improve the quality of the data. The input data is the integrated dataset, and the output data is a clean, preprocessed dataset. For example, it performs processes such as "removing outliers and filling missing data."

[0319] Step 6:

[0320] The server inputs the preprocessed integrated data into a generative AI model. Specifically, the data is input into a trained AI model using Python libraries (TensorFlow and PyTorch) and analyzed. The input data is a clean integrated dataset, and the output data is the model's prediction results. For example, the resulting price suggestions are "next day delivery: 1,500 yen" and "3-day delivery: 1,200 yen."

[0321] Step 7:

[0322] The device uses an emotion engine to recognize the emotions expressed when the user operates the system. Specifically, it uses a camera and microphone to analyze the user's facial expressions and voice in real time to collect emotional data. The input data is the user's facial expressions and voice data, and the output data is the analyzed emotional information. For example, it can identify emotions such as "stress" and "joy."

[0323] Step 8:

[0324] The server adjusts the generated price proposal based on the collected emotional data. Specifically, it applies an algorithm that changes the price according to the user's emotional state. The input data is the price proposal from the AI ​​model and emotional data, and the output data is the adjusted price information. For example, "If the user is feeling stressed, adjust the price to 1,400 yen for next-day delivery and 1,100 yen for three-day delivery."

[0325] Step 9:

[0326] The server sets the final price based on the emotion data analyzed by the emotion engine and sends the price information to the terminal. Specifically, it converts the price information into a user-friendly format and creates display elements. The input data is the adjusted price information, and the output data is the price information displayed to the user.

[0327] Step 10:

[0328] The user selects the desired delivery date and price from the presented price and delivery date options. Specifically, the user clicks on the desired plan from the options displayed on the terminal. The input data is the user's selection, and the output data is the selection information.

[0329] Step 11:

[0330] The terminal transmits the user's selection to the server. Specifically, the terminal transmits the selection data as a request to the server to trigger processing. The input data is the user's selection information, and the output data is the request to the server.

[0331] Step 12:

[0332] The server receives the selection information sent by the user and updates the order data in the distribution center database. Specifically, it updates inventory information based on the selected delivery date and price and confirms the order. The input data is the user's selection information, and the output data is the updated order data.

[0333] Step 13:

[0334] The server creates an optimal delivery plan based on the received order data and pre-processed driver availability data. Specifically, it calculates efficient delivery routes and assigns drivers. The input data are order data and driver resource information, and the output data is the optimal delivery plan.

[0335] Step 14:

[0336] The server sends the created optimal delivery plan to the logistics center's system. Specifically, the delivery plan is registered in a database and necessary instructions are sent to the driver. The input data is the optimal delivery plan, and the output data is updated information from the logistics center and instructions to the driver.

[0337] This will streamline each step of logistics and enable personalized services based on the user's emotional state.

[0338] (Application example 2)

[0339] 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."

[0340] In the logistics industry, not only is there a need for efficient inventory management and delivery resources, but there is also a need for personalized services based on user emotions. However, conventional systems are unable to set prices or offer services that take user emotions into account, limiting the improvement of customer satisfaction. Therefore, there is a need for a system that recognizes the user's emotional state and can adjust prices and offer services based on that.

[0341] 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 a means for acquiring inventory status in logistics, a means for acquiring driver availability status in logistics, a means for acquiring road and weather information, a means for preprocessing the acquired information and inputting it into a generative AI model, a means for generating a price proposal according to a desired delivery date, a means for setting a product price based on the generated price proposal and presenting it to the user, a means for accepting orders and creating a delivery plan depending on the user's selection, a means for recognizing and acquiring user emotion data, a means for adjusting the price proposal based on the acquired emotion data, and a means for presenting the adjusted price information to the user. This enables flexible pricing based on user emotions and personalized service provision.

[0342] "Inventory status in logistics" is information that indicates the quantity and condition of products stored in logistics centers and warehouses.

[0343] "Driver availability status" is information that indicates the number of drivers required for product delivery, their operating status, and available resources.

[0344] "Road conditions" refers to information that indicates the degree of road congestion, traffic jams, and traffic accident occurrences that affect logistics.

[0345] "Weather information" refers to weather data such as rainfall, snowfall, temperature, and wind speed that affect logistics planning.

[0346] "Preprocessing" refers to the process of processing the acquired data by methods such as removing outliers and filling in missing values, and preparing it in a format suitable for a generative AI model.

[0347] A "generative AI model" is an artificial intelligence model that generates price suggestions based on the desired delivery date based on input data.

[0348] A "price suggestion" is a suggestion that provides an appropriate product price based on the user's desired delivery date and other conditions.

[0349] "Emotional data" refers to information about a user's emotional state obtained from facial expressions, voice, text, etc.

[0350] "Price adjustment" refers to changing the price proposals generated by a generative AI model based on user sentiment data.

[0351] A "delivery plan" is a plan that creates optimal delivery routes and schedules based on received order information.

[0352] "Personalized" refers to providing services that are tailored to each user's individual feelings and needs.

[0353] To implement this invention, the logistics system must go through the following steps: data collection, data preprocessing, utilization of generative AI models, emotion recognition, price adjustment, and information presentation. This system consists of a server, terminals, and users. Specifically, the following hardware and software are used:

[0354] Hardware and software used

[0355] Server: Responsible for acquiring, processing, and analyzing data. A server with high-performance computing power is required.

[0356] Smart glasses: Used by workers at logistics centers, these devices display information and recognize emotions. They have built-in cameras, microphones, and displays.

[0357] Generative AI models: Built using deep learning frameworks such as TensorFlow and PyTorch.

[0358] Emotion recognition engine: Utilizing OpenCV, Emotion API, etc., it analyzes user emotions in real time.

[0359] System operation explanation

[0360] The server first accesses the distribution center's database to obtain current inventory information, including the current stock quantity of each product. Next, the server accesses the driver resource management system to obtain the number of available drivers and their operating status. Finally, it calls an external API to collect current road conditions and weather data.

[0361] The collected data is preprocessed on the server. Outliers are removed and missing values ​​are filled, and the integrated data is input into a generative AI model. This generative AI model generates price suggestions based on the collected data according to the desired delivery date. For example, it generates price suggestions such as "next day delivery: 1,500 yen" and "3-day delivery: 1,200 yen."

[0362] Next, when a worker wearing smart glasses checks the proposal, the emotion recognition engine works to collect emotional data from the worker's facial expressions and voice. The emotional data is sent to the server, and the price proposal is adjusted. For example, if the worker is feeling stressed, the price may be set lower. This allows for personalized price proposals.

[0363] Finally, the adjusted price information is displayed on the smart glasses' display for the worker to confirm. The user's desired delivery date and price information are sent to the server, which updates the order data. An optimal delivery plan is then created and transmitted to the logistics center.

[0364] Examples of concrete examples and prompts

[0365] As a concrete example, consider a scenario in which staff working in a logistics center are wearing smart glasses. As the staff picks each item, the smart glasses display inventory information and price suggestions in real time. If the emotion recognition engine determines that the staff member is tired, the smart glasses will adjust the price suggestions and delivery method.

[0366] Prompt Sentence Examples

[0367] "It retrieves up-to-date inventory information, integrates and processes driver resource information, road conditions and weather data, and uses generative AI models to make price suggestions based on the desired delivery date. It also recognizes the emotions of workers wearing smart glasses in real time and dynamically adjusts price suggestions."

[0368] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0369] Step 1: Data collection

[0370] The server accesses the distribution center's database to obtain current inventory information. This inventory information includes the current stock quantity of each product. The server also accesses the driver resource management system to obtain the number of available drivers and their operating status. It also calls an external API to collect current road conditions and weather information. This inputs inventory data, driver resource data, road condition data, and weather data.

[0371] Step 2: Data Preprocessing

[0372] The server preprocesses the data obtained in step 1. Specifically, it cleanses the data by removing outliers and imputing missing values. This generates an integrated dataset that is in a suitable format for the generative AI model. The input is all the datasets obtained in the previous steps, and the output is the preprocessed integrated data.

[0373] Step 3: Generate price proposals

[0374] The server inputs the preprocessed integrated data into a generative AI model. This AI model generates price proposals based on the desired delivery date and other conditions. Specifically, it proposes prices such as "next day delivery: 1,500 yen" and "three-day delivery: 1,200 yen." The input is the preprocessed integrated data, and the output is the generated price proposal.

[0375] Step 4: Obtaining emotion data

[0376] An emotion recognition engine is operated to obtain real-time emotional data from the terminal, specifically the worker wearing the smart glasses. Emotional data is collected from the worker's facial expressions and voice and sent to the server. The input is the worker's facial expressions and voice data, and the output is analyzed emotional data.

[0377] Step 5: Adjust your pricing proposal

[0378] The server adjusts the price proposal based on the emotional data obtained in step 4. For example, if the worker is feeling stressed, it may set the price 10% lower. The input is the emotional data and the generated price proposal, and the output is the adjusted price proposal.

[0379] Step 6: Information presentation

[0380] The terminal displays the adjusted price proposal on the smart glasses, which the worker can review and make appropriate updates or check the delivery plan. The input is the adjusted price proposal, and the output is the displayed price information.

[0381] Step 7: User selection and order data update

[0382] The user selects the desired delivery date and price information, and sends it from the terminal to the server. The server updates the order data based on the received information and creates a new delivery plan. The input is the user's selection, and the output is the updated order data and the new delivery plan.

[0383] Step 8: Optimize your delivery plan

[0384] The server creates an optimal delivery plan based on the updated order data and pre-processed driver resource data. This plan is transmitted to the distribution center. The input is the updated order data and driver data, and the output is the optimal delivery plan.

[0385] 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.

[0386] 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.

[0387] 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.

[0388] [Second embodiment]

[0389] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0390] 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.

[0391] 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).

[0392] 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.

[0393] 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.

[0394] 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).

[0395] 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.

[0396] 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.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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."

[0401] This invention is a dynamic pricing system aimed at improving efficiency and customer satisfaction in the logistics industry. This system utilizes a generative AI model based on inventory status at logistics centers, driver availability, road conditions, and weather data to dynamically set product prices according to the customer's desired delivery date.

[0402] The system consists of the following steps:

[0403] 1. Data Collection

[0404] The server accesses the distribution center database and obtains current inventory information, such as 100 units of product A and 50 units of product B.

[0405] The server accesses the driver resource management system to obtain the number and status of available drivers, for example, information that five drivers are available.

[0406] The server calls external APIs to collect current road and weather data, such as local traffic congestion information and weather information such as whether it's raining or sunny.

[0407] 2. Data Analysis

[0408] The server preprocesses the collected data, consolidating it into a single format, filling in missing values, and processing outliers. This preprocessed data is suitable for input into a generative AI model.

[0409] The server inputs the preprocessed data into a generative AI model to generate price suggestions based on the desired delivery date, such as 1,500 yen for next-day delivery and 1,200 yen for three days later.

[0410] 3. Pricing

[0411] The server sets available delivery dates and prices based on the generated price proposals.

[0412] The server transmits the set price information to the terminal.

[0413] 4. Price quote

[0414] The terminal displays the received price information to the user, for example, showing options such as "Next day delivery: 1,500 yen" or "3 days later delivery: 1,200 yen."

[0415] Users select their desired delivery date and price.

[0416] 5. Order acceptance and delivery planning

[0417] The server receives the delivery date and price information selected by the user and updates the order data.

[0418] The server creates an optimal delivery plan and sends it to the distribution center. For example, for an order with delivery in three days, it plans the most efficient driver and route for that delivery.

[0419] This series of processes will streamline each step of logistics and enable flexible responses to diverse customer needs. Furthermore, this system will contribute to a healthier working environment and improved productivity at logistics centers. It is also expected to address the labor shortage known as the 2024 problem.

[0420] The processing flow will be explained below.

[0421] Step 1: Data collection

[0422] The server accesses the distribution center database and obtains the current inventory information for each product. For example, it collects information such as the number of units of product A in stock being 100 and the number of units of product B being 50.

[0423] The server accesses the driver resource management system to obtain the number and status of available drivers. For example, it obtains data that five drivers are available.

[0424] The server calls external APIs to collect current road conditions and weather data, such as traffic congestion information and weather data (rainy, sunny, etc.) for a specific area.

[0425] Step 2: Data Preprocessing

[0426] The server consolidates collected inventory data, driver availability data, road conditions and weather data.

[0427] The server analyzes this integrated data and performs preprocessing such as removing outliers and imputing missing values. For example, it predicts and imputes missing data points using a certain algorithm.

[0428] Step 3: Analysis by generative AI model

[0429] The server inputs the preprocessed integrated data into the generative AI model.

[0430] The server uses the generative AI model to generate price suggestions based on the desired delivery date, such as "next day delivery: 1,500 yen" or "3-day delivery: 1,200 yen."

[0431] Step 4: Pricing and Presentation

[0432] The server sets available delivery dates and prices based on the price suggestions output by the generative AI model.

[0433] The server transmits the set price information to the terminal and configures the display elements.

[0434] The terminal displays the received price information to the user, for example, showing options such as "Next day delivery: 1,500 yen" or "3 days later delivery: 1,200 yen."

[0435] Step 5: Selecting Users

[0436] The user selects the desired delivery date and price from the presented price and delivery date options.

[0437] The terminal transmits the user's selection to the server.

[0438] Step 6: Order acceptance and updates

[0439] The server receives the selection information sent by the user and updates the order data in the distribution center database. For example, the order data is confirmed as "Product A, delivery in 3 days, price 1,200 yen."

[0440] The server prepares to optimize the delivery plan using the confirmed order data.

[0441] Step 7: Optimize delivery plans

[0442] The server creates an optimal delivery plan based on the received order data and pre-processed driver availability data. Specifically, it determines the optimal driver and route for the selected desired delivery date.

[0443] The server then sends the created optimal delivery plan to the logistics center system. For example, it sends a plan that says, "Driver B will deliver product A to the specified destination in three days."

[0444] The above are the specific processing steps in the dynamic pricing system of the present invention. This series of processes makes it possible to optimize the efficiency of the logistics center and flexibly respond to the diverse needs of customers.

[0445] Example 1

[0446] 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."

[0447] Conventional logistics systems lack efficient integration of many factors, such as inventory management, driver allocation, and fluctuations in road conditions and weather, resulting in delivery delays and increased costs. Furthermore, it is difficult to offer appropriate pricing to customers, making it difficult to improve customer satisfaction. The purpose of this invention is to solve these problems, improve the efficiency of logistics operations, and increase customer satisfaction.

[0448] 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.

[0449] In this invention, the server includes means for acquiring inventory status in logistics, means for acquiring driver availability status in logistics, means for acquiring road condition and weather data, means for preprocessing the acquired data and inputting it into a generative AI model, means for generating a price proposal according to a desired delivery date, means for setting a product price based on the generated price proposal and presenting it to the user, means for accepting an order and creating a delivery plan according to the user's selection, means for displaying the presented price information to the user and allowing the user to select a desired delivery date and price, means for receiving the desired delivery date and price information selected by the user and updating the order data, and means for creating an optimal delivery plan and transmitting it to a logistics center. This makes it possible to efficiently integrate various elements related to logistics and provide customers with optimal delivery options through dynamic price proposals.

[0450] The "means for acquiring inventory status in logistics" is a part of a program that runs within a server to collect inventory information from logistics centers and warehouses.

[0451] The "means for acquiring driver availability status for logistics" is a server function that enables logistics companies to acquire the number of drivers available and their working status from the management system.

[0452] The "means for acquiring road condition and weather data" is a server function for acquiring road congestion information and weather forecasts from external data providing services.

[0453] "Means for preprocessing acquired data and inputting it into the generative AI model" refers to the process of organizing collected data into a unified format and inputting it into the generative AI model in the optimal data format.

[0454] "Means for generating price proposals based on desired delivery date" refers to a function that uses a generative AI model to calculate the optimal price based on the user's desired delivery date, based on collected and pre-processed data.

[0455] The "means of setting a product price based on the generated price proposal and presenting it to the user" refers to a server function that determines the delivery fee for the product using the price proposed by the AI ​​and displays it through the user interface.

[0456] "Means for accepting orders and creating delivery plans based on user selections" refers to the process of storing order information in the system and creating an efficient delivery plan based on the delivery options selected by the user.

[0457] "Means for displaying the proposed price information to the user and allowing the user to select the desired delivery date and price" refers to a function that displays price information in a user interface and allows the user to select the desired delivery date and price.

[0458] The "means for receiving the desired delivery date and price information selected by the user and updating the order data" is a function of the server that records and updates the order information in the database based on the user's selection.

[0459] "Means for creating optimal delivery plans and transmitting them to logistics centers" refers to the process of calculating the most efficient delivery routes and driver allocations based on order data, and sending that information to logistics centers.

[0460] This invention is a dynamic pricing system aimed at improving efficiency and customer satisfaction in the logistics industry. The main components of the system include obtaining inventory status in logistics, obtaining driver availability, obtaining road and weather data, and using a generative AI model. The steps based on an embodiment of the present invention are as follows.

[0461] Required Hardware and Software

[0462] 1. Server:

[0463] Database Management System (DBMS)

[0464] Resource Management System

[0465] External APIs (e.g. Google Maps API, OpenWeatherMap API)

[0466] Data preprocessing software (e.g., Pandas)

[0467] Generative AI models (e.g., GPT-3)

[0468] 2. Terminal:

[0469] Front-end applications (e.g. web applications and mobile apps)

[0470] Data collection and preprocessing

[0471] The server collects the following data:

[0472] 1. Obtaining inventory status

[0473] The server queries the distribution center's database to retrieve inventory information.

[0474] Example: SELECT product, inventory FROM inventory table

[0475] Example: 100 units of product A in stock, 50 units of product B in stock

[0476] 2. Obtaining driver availability information

[0477] The server sends an API request to the driver resource management system to obtain the number and status of available drivers.

[0478] Example: 5 drivers available

[0479] 3. Obtaining road and weather data

[0480] The server calls external APIs (e.g., Google Maps API or OpenWeatherMap API) to collect road conditions and weather data.

[0481] Example: There is traffic congestion and it is raining.

[0482] After collecting the data, the server preprocesses them, consolidating them into a single format, imputing missing values, and handling outliers.

[0483] Price proposal based on desired delivery date

[0484] The server inputs the preprocessed data into a generative AI model to generate a price suggestion based on the desired delivery date. Below is an example of a prompt.

[0485] Example prompt sentence:

[0486] Stock information: Product A: 100 units, Product B: 50 units

[0487] Drivers: 5

[0488] Road conditions: Traffic jams

[0489] Weather: rain

[0490] Desired delivery date: Next day

[0491] Based on the price suggested by the generative AI model, the server sets the price of the product and presents it to the user.

[0492] User Presentation and Selection

[0493] The terminal displays the price information received from the server to the user, who then selects the desired delivery date and price.

[0494] Specific user interface examples:

[0495] Select delivery date and price:

[0496] Next day delivery: 1,500 yen

[0497] 3-day delivery: 1,200 yen

[0498] Once the user selects the desired delivery date and price, the server receives that information, updates the order data, creates an efficient delivery plan, and transmits it to the logistics center.

[0499] This will make each step of logistics more efficient and enable flexible responses to diverse customer needs. The system as a whole is expected to improve efficiency in the logistics industry and increase customer satisfaction.

[0500] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0501] Step 1:

[0502] The server sends a query to the distribution center's database to obtain inventory status. An example query is SELECT product, inventory FROM inventory table. This query obtains current inventory information. For example, if there are 100 units of product A in stock and 50 units of product B in stock, this information is obtained. The input is database connection information, and the output is inventory information. The specific operation is that the server sends an SQL query to the database and receives a response.

[0503] Step 2:

[0504] The server sends an API request to the driver resource management system to obtain the number and status of available drivers. An example request is GET / api / drivers / available. This request obtains information about currently available drivers. For example, if there are five drivers available, that information is obtained. The input is the API endpoint of the resource management system, and the output is driver availability information. In concrete terms, the server sends an HTTP request to the resource management system and receives a response.

[0505] Step 3:

[0506] The server calls an external API (e.g., Google Maps API or OpenWeatherMap API) to collect current road conditions and weather data. Example requests are GET / maps / api / traffic and GET / weather / api / current. This request obtains road conditions and weather data. For example, information about road congestion or rainy weather is obtained. The input is the endpoint of the external API, and the output is road conditions and weather data. Specifically, the server sends an HTTP request to the external API and receives a response.

[0507] Step 4:

[0508] The server preprocesses the collected inventory information, driver information, road conditions, and weather data. Specifically, it converts the data into a unified format, fills in missing values, and processes outliers. The input is the various collected data, and the output is data in a format suitable for the generative AI model. For example, if there are missing values, it performs operations such as filling in the average value.

[0509] Step 5:

[0510] The server inputs the preprocessed data into a generative AI model to generate price suggestions based on the desired delivery date. The input is preprocessed data in a unified format, and the output is price suggestions based on the desired delivery date. An example prompt is:

[0511] Stock information: Product A: 100 units, Product B: 50 units

[0512] Drivers: 5

[0513] Road conditions: Traffic jams

[0514] Weather: rain

[0515] Desired delivery date: Next day

[0516] An example of a price suggestion generated based on this prompt would be ¥1,500 for next day delivery or ¥1,200 for three days later.

[0517] Step 6:

[0518] The server sets selectable delivery dates and prices based on the generated price proposals and sends them to the terminal. The input is the price proposal from the generative AI model, and the output is the delivery date and price information to be presented to the user. Specifically, the server sends the price information to the terminal via an HTTP request.

[0519] Step 7:

[0520] The terminal displays the received price information to the user. The input is the price information sent from the server, and the output is the price information displayed on the user interface. A concrete example of how it is displayed is:

[0521] Select delivery date and price:

[0522] Next day delivery: 1,500 yen

[0523] 3-day delivery: 1,200 yen

[0524] Step 8:

[0525] The user selects the desired delivery date and price. The input is the price information displayed on the terminal, and the output is the user's selection. As a specific selection example, the user selects "Delivery in 3 days: 1,200 yen."

[0526] Step 9:

[0527] The server receives the delivery date and price information selected by the user and updates the order data. The input is the user's selection information, and the output is the updated order data. The specific operation is that the server updates the order database.

[0528] Step 10:

[0529] The server creates an optimal delivery plan and transmits it to the logistics center. The input is the updated order data, and the output is the optimal delivery plan. Specifically, the server calculates the delivery plan and sends it to the logistics center via an HTTP request.

[0530] In this way, each step of logistics can be made more efficient, enabling flexible responses to diverse customer needs.

[0531] (Application example 1)

[0532] 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."

[0533] In the logistics industry, it is difficult to create fast and efficient delivery plans due to factors such as inventory management, driver availability, and road and weather conditions. There is also a lack of methods to flexibly respond to customer delivery dates and dynamically set prices. Furthermore, the lack of tools to support real-time decision-making can lead to reduced operational efficiency. To solve these issues, a system is needed that allows logistics managers to instantly obtain information using smart devices and apply dynamic pricing using generative AI models.

[0534] 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.

[0535] In this invention, the server includes: a means for acquiring inventory status for logistics; a means for acquiring driver availability status for logistics; a means for acquiring road condition and weather data; a means for preprocessing the acquired data and inputting it into a generative AI model; a means for generating a price proposal according to a desired delivery date; a means for setting a product price based on the generated price proposal and presenting it to a user; a means for accepting orders and creating a delivery plan according to a user's selection; and a means for displaying the price proposal and delivery plan in real time using smart glasses and allowing the user to make a selection. This enables logistics managers to make efficient decisions in real time, improving the efficiency of each step of logistics and improving customer satisfaction.

[0536] "Stock status" is data indicating the current quantity and type of products at the logistics center.

[0537] "Driver availability status" is data that indicates the number of drivers available to work delivery tasks at a logistics center and their available working hours.

[0538] "Road conditions" is data that shows information such as current road congestion, closures, and road closures.

[0539] "Weather data" is information indicating the current weather conditions, and includes data such as sunny, rainy, and snowy weather.

[0540] A "generative AI model" is an artificial intelligence model that dynamically generates price proposals and delivery plans based on acquired data.

[0541] "Smart glasses" are wearable devices that are worn on the head and can display information in real time.

[0542] "Price proposal" is a product pricing proposal calculated by the generative AI model based on the desired delivery date.

[0543] A "delivery plan" is a specific plan for delivering goods to customers, including driver allocation and route optimization.

[0544] "Preprocessing" refers to data processing, such as processing outliers and filling in missing values, that is carried out before inputting the acquired data into a generative AI model.

[0545] "User" refers to the customer who receives a price quote and selects the desired delivery date and price.

[0546] "Terminal" refers to an information processing device for displaying price information and delivery schedules, including smart glasses.

[0547] This invention relates to a dynamic pricing system and its specific embodiments, which aim to improve efficiency and customer satisfaction in the logistics industry. In this embodiment, hardware such as a server or smart glasses is used to collect and process data, and price proposals and delivery plans are provided to users in real time. The specific configuration and operation are described below.

[0548] The server accesses the distribution center database to obtain inventory status. For example, it obtains information that there are 100 units of product A in stock and 50 units of product B in stock. The server also accesses the driver resource management system to obtain the number and status of available drivers. For example, it may obtain information that five drivers are available. Furthermore, the server calls an external API to collect road condition and weather data. For example, it may obtain information on traffic congestion and rain.

[0549] The collected data is managed centrally and undergoes preprocessing, which involves standardizing the data format, filling in missing values, and handling outliers. This preprocessing is primarily carried out using software libraries such as Python and Pandas. The preprocessed data is then input into a generative AI model, which generates price proposals. For example, the proposed price may be 1,500 yen for next-day delivery or 1,200 yen for delivery three days later.

[0550] The generated price proposal is sent to the smart glasses via a cloud server, which then displays the proposed price information to the logistics manager in real time. The logistics manager can then view the delivery date and price options through the smart glasses and instantly decide on the best option.

[0551] Once the user selects the desired delivery date and price, the information is sent back to the cloud server. The server updates the order data based on the received information and generates an optimal delivery plan. This delivery plan includes efficient driver assignment and the shortest route. This delivery plan is then transmitted to the logistics center, where the actual delivery takes place.

[0552] Specific examples

[0553] For example, input the following prompt into the generative AI model:

[0554] "We have 100 units of product A in stock and 50 units of product B in stock. Five drivers are available. The roads are congested and it's currently raining. Please provide pricing for next-day delivery and three-day delivery under these conditions."

[0555] This allows logistics managers to make efficient decisions in real time, improving the efficiency of each step of logistics and customer satisfaction.

[0556] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0557] Step 1:

[0558] The server accesses the distribution center database to obtain inventory status. For example, the server obtains data such as 100 units of product A and 50 units of product B. This inventory information is used in the next processing step.

[0559] Step 2:

[0560] The server accesses the driver resource management system to obtain the number and status of available drivers. For example, it may find that five drivers are available. This driver information is also used in the next processing step.

[0561] Step 3:

[0562] The server calls external APIs to collect current road conditions and weather data, such as traffic congestion and rainy weather. This road condition and weather data is used in the next data analysis.

[0563] Step 4:

[0564] The server preprocesses the acquired inventory status, driver information, road conditions, and weather data. It also performs data processing such as standardizing the data format, filling in missing values, and handling outliers. Specifically, it uses Python and Pandas to preprocess the data and prepare it in a format that can be input into the generative AI model.

[0565] Step 5:

[0566] The server inputs the preprocessed data into the generative AI model and generates a price proposal based on the desired delivery date. For example, it generates a price proposal of 1,500 yen for next-day delivery and 1,200 yen for three-day delivery. This generated price proposal is used in the next step.

[0567] Step 6:

[0568] The server sends the generated price proposal to the smart glasses, which then display delivery date and price options in real time based on the received price proposal. This information is then provided to the logistics manager as input for the next step.

[0569] Step 7:

[0570] The user (logistics manager) checks the price and delivery date options presented through the smart glasses and selects the desired option. This selected information is then sent back to the server.

[0571] Step 8:

[0572] The server updates the order data based on the delivery date and price information received from the user, thereby keeping the order information up to date.

[0573] Step 9:

[0574] The server creates an optimal delivery plan based on the delivery date and price information selected by the user, including driver assignment and route optimization, and then transmits this delivery plan to the distribution center.

[0575] Step 10:

[0576] The distribution center then begins actual delivery operations based on the optimal delivery plan received from the server, ensuring that products are delivered to customers efficiently.

[0577] 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.

[0578] This invention combines a dynamic pricing system, which aims to improve efficiency and customer satisfaction in the logistics industry, with an emotion engine that recognizes user emotions. This system utilizes a generative AI model based on inventory status at logistics centers, driver availability, road conditions, and weather data to dynamically set product prices according to the customer's desired delivery date. Furthermore, the emotion engine is used to adjust prices and suggest services according to the user's emotional state.

[0579] The system consists of the following steps:

[0580] 1. Data Collection

[0581] The server accesses the distribution center database and obtains current inventory information, such as 100 units of product A and 50 units of product B.

[0582] The server accesses the driver resource management system to obtain the number and status of available drivers. For example, it obtains data that five drivers are available.

[0583] The server calls external APIs to collect current road conditions and weather data, such as traffic congestion information and weather data (rainy, sunny, etc.) for a specific area.

[0584] 2. Data Preprocessing

[0585] The server consolidates collected inventory data, driver availability data, road conditions and weather data.

[0586] The server analyzes this integrated data and performs preprocessing such as removing outliers and filling in missing values.

[0587] 3. Analysis using generative AI models

[0588] The server inputs the preprocessed integrated data into the generative AI model.

[0589] The server uses the generative AI model to generate price suggestions based on the desired delivery date, such as "next day delivery: 1,500 yen" or "3-day delivery: 1,200 yen."

[0590] 4. Emotion Recognition by Emotion Engine

[0591] The device uses an emotion engine to recognize the emotions of users when they operate the system. The emotion engine collects emotion data from users' facial expressions, voice, text, etc.

[0592] The server then adjusts the generated price proposal based on the collected emotional data, for example, lowering the price if the user is feeling stressed.

[0593] 5. Pricing and Offerings

[0594] The server sets the final price based on the emotional data analyzed by the emotion engine.

[0595] The server transmits the set price information to the terminal and configures the display elements.

[0596] The terminal displays the received price information to the user.

[0597] 6. User Choices

[0598] The user selects the desired delivery date and price from the presented price and delivery date options.

[0599] The terminal transmits the user's selection to the server.

[0600] 7. Order Acceptance and Updates

[0601] The server receives the selection information sent by the user and updates the order data in the distribution center database.

[0602] 8. Optimizing delivery plans

[0603] The server creates an optimal delivery plan based on the received order data and pre-processed driver availability data.

[0604] The server sends the created optimal delivery plan to the logistics center's system.

[0605] This series of processes allows the system to streamline each step of logistics and flexibly respond to diverse customer needs. Furthermore, by incorporating an emotion engine, it becomes possible to provide services based on the user's emotional state, which is expected to further improve customer satisfaction. In this way, by adding an emotional element to the dynamic pricing system, embodiments of the invention can provide more personalized logistics services.

[0606] The processing flow will be explained below.

[0607] Step 1: Data collection

[0608] The server accesses the distribution center database to obtain current inventory information, such as detailed inventory status, such as 100 units of product A and 50 units of product B.

[0609] The server accesses the driver resource management system to obtain the number and status of currently available drivers. For example, it obtains data that five drivers are available.

[0610] The server calls external APIs to collect current road conditions and weather data, such as traffic congestion information for a certain area and weather data such as whether it will rain or shine.

[0611] Step 2: Data Preprocessing

[0612] The server consolidates the collected inventory data, driver availability data, road conditions and weather data.

[0613] The server detects outliers in the aggregated data, corrects or removes them as needed, and imputes missing values ​​using predictive algorithms.

[0614] Step 3: Analysis by generative AI model

[0615] The server inputs the preprocessed data into a generative AI model.

[0616] The server uses a generative AI model to generate price suggestions based on the desired delivery date based on the collected data, such as "next day delivery: 1,500 yen" or "three days later delivery: 1,200 yen."

[0617] Step 4: Emotion Recognition with the Emotion Engine

[0618] The device uses an emotion engine to recognize the user's emotions, collecting emotional data based on the user's tone of voice and facial expression recognition technology.

[0619] The device analyzes the collected emotional data to determine the user's current emotional state (e.g., joy, sadness, stress, etc.).

[0620] The server dynamically adjusts the generated price proposal based on this emotional data, for example, by lowering the price if the user is feeling stressed.

[0621] Step 5: Pricing and Presentation

[0622] The server sets the final price that reflects the analysis results of the emotion engine.

[0623] The server transmits the set price information to the terminal and configures the user interface.

[0624] The terminal displays the price information to the user, for example, "Next day delivery: 1,450 yen" or "3 days later delivery: 1,150 yen."

[0625] Step 6: Selecting Users

[0626] The user selects the desired price and delivery date from the options provided. For example, they can select "Delivery in 3 days: 1,150 yen."

[0627] The terminal transmits the user's selection to the server.

[0628] Step 7: Order acceptance and updates

[0629] The server receives the selection information sent by the user and updates the order data in the distribution center database. For example, the order data is confirmed as "Product A, delivery in 3 days, price 1,150 yen."

[0630] Step 8: Optimize your delivery plan

[0631] The server creates an optimal delivery plan based on the updated order data and pre-processed driver availability data. Specifically, it determines the optimal driver and route for the selected desired delivery date.

[0632] The server sends the created optimal delivery plan to the logistics center system. For example, it sends a plan that says, "Driver A will deliver Product A to the specified destination in three days."

[0633] The above are the specific processing steps for combining the dynamic pricing system of the present invention with an emotion engine. This series of processes will improve the efficiency of logistics operations and customer satisfaction. Furthermore, it will enable individual responses based on the user's emotions, enabling more personalized operations.

[0634] Example 2

[0635] 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."

[0636] The logistics industry requires optimization of inventory management and delivery plans, but currently these processes are not carried out efficiently, resulting in problems such as lower customer satisfaction and increased costs. Furthermore, there is a lack of services that take into account the emotional state of customers, making it difficult to improve customer experience. The present invention aims to solve these problems.

[0637] 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.

[0638] In this invention, the server includes a means for acquiring inventory status for logistics, a means for acquiring driver availability status for logistics, a means for acquiring road condition and weather data, a means for preprocessing the acquired data and inputting it into a generative AI model, a means for generating a price proposal according to the desired delivery date, a means for recognizing a user's emotion, a means for adjusting the product price based on the user's emotional state based on the generated price proposal and presenting it to the user, and a means for accepting orders and creating a delivery plan depending on the user's selection. This makes it possible to streamline each step of logistics and provide personalized services based on the user's emotional state.

[0639] "Inventory status in logistics" refers to information such as the quantity, type, and storage status of products at logistics centers and warehouses.

[0640] "Driver availability status" refers to information such as the current availability and deployment status of drivers in charge of delivery.

[0641] "Road conditions" refers to data about road conditions, such as traffic flow, congestion, and accident information.

[0642] "Weather data" is data that indicates the weather conditions for each region, and includes information such as fine weather, rain, and snowfall.

[0643] "Preprocessing" refers to processing the acquired data such as removing outliers, filling in missing values, and integrating data.

[0644] A "generative AI model" is a model that uses artificial intelligence technology to generate data for specific purposes.

[0645] A "price suggestion" is a suggestion that dynamically sets the price of a product to be offered to a user based on collected data.

[0646] "Emotion recognition" is a technology that analyzes and recognizes a user's emotions from facial expressions, voice, text, etc.

[0647] A "delivery plan" is a plan that aims to optimize the entire delivery process, including the delivery order and route for ordered items and driver assignment.

[0648] "Personalized service" means providing services that are customized based on the individual characteristics, feelings, and requests of each user.

[0649] This invention is a system aimed at improving efficiency and customer satisfaction in the logistics industry. This system combines generative AI models and emotion recognition technology, and is implemented in the following steps:

[0650] Data collection

[0651] The server accesses the distribution center's database to obtain current inventory information. For example, it collects information such as 100 units of product A in stock and 50 units of product B in stock. It also accesses the driver resource management system to obtain the number and status of available drivers. For example, it obtains data that shows that five drivers are available. It also calls external APIs to collect current road conditions and weather data. For example, it obtains traffic congestion information and weather data (rain, sunny, etc.) in a specific area.

[0652] Data Preprocessing

[0653] The server integrates the collected inventory data, driver availability data, road conditions, and weather data. It analyzes this integrated data and performs preprocessing such as removing outliers and filling in missing values. Specifically, it rationally fills in missing values ​​in the dataset and filters out abnormal data.

[0654] Analysis using generative AI models

[0655] The server inputs the preprocessed integrated data into a generative AI model. The generative AI model is implemented using, for example, a Python library (TensorFlow or PyTorch). The server uses the generative AI model to generate a price proposal based on the desired delivery date. For example, it generates price proposals such as "next day delivery: 1,500 yen" and "3-day delivery: 1,200 yen."

[0656] Emotion recognition by emotion engine

[0657] The device uses an emotion engine to recognize the emotions expressed when the user operates the system. The emotion engine is a technology that collects and analyzes emotional data from the user's facial expressions, voice, text, etc. For example, it can use a camera and microphone to analyze the user's facial expressions and tone of voice to identify emotions such as "stress" or "joy."

[0658] Pricing and Offering

[0659] The server adjusts the generated price proposal based on the emotional data analyzed by the emotion engine. For example, if the user is feeling stressed, the price will be set lower. The server also sends the final price information to the terminal and presents it to the user. For example, it displays information such as "Next day delivery: 1,400 yen" and "3 days later delivery: 1,100 yen."

[0660] User Choices and Order Acceptance

[0661] The user selects the desired delivery date and price from the presented price and delivery date options. The terminal sends the user's selection to the server, which accepts the order data and updates it in the distribution center's database. The server also creates an optimal delivery plan based on the accepted order data and preprocessed driver availability data, and sends it to the distribution center's system.

[0662] In this way, the system of the present invention not only improves the efficiency of each step in logistics but also provides personalized services based on the emotional state of the user, which is expected to improve customer satisfaction.

[0663] Prompt Sentence Examples

[0664] "Dynamic pricing should be based on current inventory at distribution centers, driver availability, road congestion, and weather data. Also, adjust pricing based on the user's emotional state. Specifically, offer two pricing options: next-day delivery and three-day delivery."

[0665] This enables the system to simultaneously improve logistics efficiency and customer satisfaction.

[0666] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0667] Step 1:

[0668] The server accesses the distribution center's database and obtains current inventory information. Specifically, it obtains the inventory quantity and type for each product from the database and saves this information in list format. The input data is the database connection information and query statement, and the output data is a list of inventory information. For example, it obtains data such as "100 units of product A are in stock, and 50 units of product B are in stock."

[0669] Step 2:

[0670] The server accesses the driver resource management system to obtain the number and status of available drivers. Specifically, it obtains a list of currently available drivers from the resource management system via API. The input data is the API endpoint and authentication information, and the output data is driver resource information. For example, it obtains data such as "5 drivers are available."

[0671] Step 3:

[0672] The server calls external APIs to collect current road conditions and weather data. Specifically, it obtains and integrates data from traffic information services and weather information services. The input data is the endpoint and search conditions of the external API, and the output data is current road conditions and weather information. For example, it collects "traffic congestion information and weather data (rainy, sunny, etc.) in a specific area."

[0673] Step 4:

[0674] The server integrates the collected inventory data, driver availability data, road conditions, and weather data. Specifically, it combines the individually acquired data into a single dataset. The input data is the various data acquired at each step, and the output data is the integrated dataset. For example, it creates integrated data such as "100 units in stock, 5 drivers, traffic information available, rainy weather."

[0675] Step 5:

[0676] The server analyzes this integrated data and performs preprocessing such as removing outliers and filling missing values. Specifically, it applies a data cleansing process to improve the quality of the data. The input data is the integrated dataset, and the output data is a clean, preprocessed dataset. For example, it performs processes such as "removing outliers and filling missing data."

[0677] Step 6:

[0678] The server inputs the preprocessed integrated data into a generative AI model. Specifically, the data is input into a trained AI model using Python libraries (TensorFlow and PyTorch) and analyzed. The input data is a clean integrated dataset, and the output data is the model's prediction results. For example, the resulting price suggestions are "next day delivery: 1,500 yen" and "3-day delivery: 1,200 yen."

[0679] Step 7:

[0680] The device uses an emotion engine to recognize the emotions expressed when the user operates the system. Specifically, it uses a camera and microphone to analyze the user's facial expressions and voice in real time to collect emotional data. The input data is the user's facial expressions and voice data, and the output data is the analyzed emotional information. For example, it can identify emotions such as "stress" and "joy."

[0681] Step 8:

[0682] The server adjusts the generated price proposal based on the collected emotional data. Specifically, it applies an algorithm that changes the price according to the user's emotional state. The input data is the price proposal from the AI ​​model and emotional data, and the output data is the adjusted price information. For example, "If the user is feeling stressed, adjust the price to 1,400 yen for next-day delivery and 1,100 yen for three-day delivery."

[0683] Step 9:

[0684] The server sets the final price based on the emotion data analyzed by the emotion engine and sends the price information to the terminal. Specifically, it converts the price information into a user-friendly format and creates display elements. The input data is the adjusted price information, and the output data is the price information displayed to the user.

[0685] Step 10:

[0686] The user selects the desired delivery date and price from the presented price and delivery date options. Specifically, the user clicks on the desired plan from the options displayed on the terminal. The input data is the user's selection, and the output data is the selection information.

[0687] Step 11:

[0688] The terminal transmits the user's selection to the server. Specifically, the terminal transmits the selection data as a request to the server to trigger processing. The input data is the user's selection information, and the output data is the request to the server.

[0689] Step 12:

[0690] The server receives the selection information sent by the user and updates the order data in the distribution center database. Specifically, it updates inventory information based on the selected delivery date and price and confirms the order. The input data is the user's selection information, and the output data is the updated order data.

[0691] Step 13:

[0692] The server creates an optimal delivery plan based on the received order data and pre-processed driver availability data. Specifically, it calculates efficient delivery routes and assigns drivers. The input data are order data and driver resource information, and the output data is the optimal delivery plan.

[0693] Step 14:

[0694] The server sends the created optimal delivery plan to the logistics center's system. Specifically, the delivery plan is registered in a database and necessary instructions are sent to the driver. The input data is the optimal delivery plan, and the output data is updated information from the logistics center and instructions to the driver.

[0695] This will streamline each step of logistics and enable personalized services based on the user's emotional state.

[0696] (Application example 2)

[0697] 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."

[0698] In the logistics industry, not only is there a need for efficient inventory management and delivery resources, but there is also a need for personalized services based on user emotions. However, conventional systems are unable to set prices or offer services that take user emotions into account, limiting the improvement of customer satisfaction. Therefore, there is a need for a system that recognizes the user's emotional state and can adjust prices and offer services based on that.

[0699] 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 a means for acquiring inventory status in logistics, a means for acquiring driver availability status in logistics, a means for acquiring road and weather information, a means for preprocessing the acquired information and inputting it into a generative AI model, a means for generating a price proposal according to a desired delivery date, a means for setting a product price based on the generated price proposal and presenting it to the user, a means for accepting orders and creating a delivery plan depending on the user's selection, a means for recognizing and acquiring user emotion data, a means for adjusting the price proposal based on the acquired emotion data, and a means for presenting the adjusted price information to the user. This enables flexible pricing based on user emotions and personalized service provision.

[0700] "Inventory status in logistics" is information that indicates the quantity and condition of products stored in logistics centers and warehouses.

[0701] "Driver availability status" is information that indicates the number of drivers required for product delivery, their operating status, and available resources.

[0702] "Road conditions" refers to information that indicates the degree of road congestion, traffic jams, and traffic accident occurrences that affect logistics.

[0703] "Weather information" refers to weather data such as rainfall, snowfall, temperature, and wind speed that affect logistics planning.

[0704] "Preprocessing" refers to the process of processing the acquired data by methods such as removing outliers and filling in missing values, and preparing it in a format suitable for a generative AI model.

[0705] A "generative AI model" is an artificial intelligence model that generates price suggestions based on the desired delivery date based on input data.

[0706] A "price suggestion" is a suggestion that provides an appropriate product price based on the user's desired delivery date and other conditions.

[0707] "Emotional data" refers to information about a user's emotional state obtained from facial expressions, voice, text, etc.

[0708] "Price adjustment" refers to changing the price proposals generated by a generative AI model based on user sentiment data.

[0709] A "delivery plan" is a plan that creates optimal delivery routes and schedules based on received order information.

[0710] "Personalized" refers to providing services that are tailored to each user's individual feelings and needs.

[0711] To implement this invention, the logistics system must go through the following steps: data collection, data preprocessing, utilization of generative AI models, emotion recognition, price adjustment, and information presentation. This system consists of a server, terminals, and users. Specifically, the following hardware and software are used:

[0712] Hardware and software used

[0713] Server: Responsible for acquiring, processing, and analyzing data. A server with high-performance computing power is required.

[0714] Smart glasses: Used by workers at logistics centers, these devices display information and recognize emotions. They have built-in cameras, microphones, and displays.

[0715] Generative AI models: Built using deep learning frameworks such as TensorFlow and PyTorch.

[0716] Emotion recognition engine: Utilizing OpenCV, Emotion API, etc., it analyzes user emotions in real time.

[0717] System operation explanation

[0718] The server first accesses the distribution center's database to obtain current inventory information, including the current stock quantity of each product. Next, the server accesses the driver resource management system to obtain the number of available drivers and their operating status. Finally, it calls an external API to collect current road conditions and weather data.

[0719] The collected data is preprocessed on the server. Outliers are removed and missing values ​​are filled, and the integrated data is input into a generative AI model. This generative AI model generates price suggestions based on the collected data according to the desired delivery date. For example, it generates price suggestions such as "next day delivery: 1,500 yen" and "3-day delivery: 1,200 yen."

[0720] Next, when a worker wearing smart glasses checks the proposal, the emotion recognition engine works to collect emotional data from the worker's facial expressions and voice. The emotional data is sent to the server, and the price proposal is adjusted. For example, if the worker is feeling stressed, the price may be set lower. This allows for personalized price proposals.

[0721] Finally, the adjusted price information is displayed on the smart glasses' display for the worker to confirm. The user's desired delivery date and price information are sent to the server, which updates the order data. An optimal delivery plan is then created and transmitted to the logistics center.

[0722] Examples of concrete examples and prompts

[0723] As a concrete example, consider a scenario in which staff working in a logistics center are wearing smart glasses. As the staff picks each item, the smart glasses display inventory information and price suggestions in real time. If the emotion recognition engine determines that the staff member is tired, the smart glasses will adjust the price suggestions and delivery method.

[0724] Prompt Sentence Examples

[0725] "It retrieves up-to-date inventory information, integrates and processes driver resource information, road conditions and weather data, and uses generative AI models to make price suggestions based on the desired delivery date. It also recognizes the emotions of workers wearing smart glasses in real time and dynamically adjusts price suggestions."

[0726] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0727] Step 1: Data collection

[0728] The server accesses the distribution center's database to obtain current inventory information. This inventory information includes the current stock quantity of each product. The server also accesses the driver resource management system to obtain the number of available drivers and their operating status. It also calls an external API to collect current road conditions and weather information. This inputs inventory data, driver resource data, road condition data, and weather data.

[0729] Step 2: Data Preprocessing

[0730] The server preprocesses the data obtained in step 1. Specifically, it cleanses the data by removing outliers and imputing missing values. This generates an integrated dataset that is in a suitable format for the generative AI model. The input is all the datasets obtained in the previous steps, and the output is the preprocessed integrated data.

[0731] Step 3: Generate price proposals

[0732] The server inputs the preprocessed integrated data into a generative AI model. This AI model generates price proposals based on the desired delivery date and other conditions. Specifically, it proposes prices such as "next day delivery: 1,500 yen" and "three-day delivery: 1,200 yen." The input is the preprocessed integrated data, and the output is the generated price proposal.

[0733] Step 4: Obtaining emotion data

[0734] An emotion recognition engine is operated to obtain real-time emotional data from the terminal, specifically the worker wearing the smart glasses. Emotional data is collected from the worker's facial expressions and voice and sent to the server. The input is the worker's facial expressions and voice data, and the output is analyzed emotional data.

[0735] Step 5: Adjust your pricing proposal

[0736] The server adjusts the price proposal based on the emotional data obtained in step 4. For example, if the worker is feeling stressed, it may set the price 10% lower. The input is the emotional data and the generated price proposal, and the output is the adjusted price proposal.

[0737] Step 6: Information presentation

[0738] The terminal displays the adjusted price proposal on the smart glasses, which the worker can review and make appropriate updates or check the delivery plan. The input is the adjusted price proposal, and the output is the displayed price information.

[0739] Step 7: User selection and order data update

[0740] The user selects the desired delivery date and price information, and sends it from the terminal to the server. The server updates the order data based on the received information and creates a new delivery plan. The input is the user's selection, and the output is the updated order data and the new delivery plan.

[0741] Step 8: Optimize your delivery plan

[0742] The server creates an optimal delivery plan based on the updated order data and pre-processed driver resource data. This plan is transmitted to the distribution center. The input is the updated order data and driver data, and the output is the optimal delivery plan.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] [Third embodiment]

[0747] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0748] 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.

[0749] 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).

[0750] 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.

[0751] 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.

[0752] 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).

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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."

[0759] This invention is a dynamic pricing system aimed at improving efficiency and customer satisfaction in the logistics industry. This system utilizes a generative AI model based on inventory status at logistics centers, driver availability, road conditions, and weather data to dynamically set product prices according to the customer's desired delivery date.

[0760] The system consists of the following steps:

[0761] 1. Data Collection

[0762] The server accesses the distribution center database and obtains current inventory information, such as 100 units of product A and 50 units of product B.

[0763] The server accesses the driver resource management system to obtain the number and status of available drivers, for example, information that five drivers are available.

[0764] The server calls external APIs to collect current road and weather data, such as local traffic congestion information and weather information such as whether it's raining or sunny.

[0765] 2. Data Analysis

[0766] The server preprocesses the collected data, consolidating it into a single format, filling in missing values, and processing outliers. This preprocessed data is suitable for input into a generative AI model.

[0767] The server inputs the preprocessed data into a generative AI model to generate price suggestions based on the desired delivery date, such as 1,500 yen for next-day delivery and 1,200 yen for three days later.

[0768] 3. Pricing

[0769] The server sets available delivery dates and prices based on the generated price proposals.

[0770] The server transmits the set price information to the terminal.

[0771] 4. Price quote

[0772] The terminal displays the received price information to the user, for example, showing options such as "Next day delivery: 1,500 yen" or "3 days later delivery: 1,200 yen."

[0773] Users select their desired delivery date and price.

[0774] 5. Order acceptance and delivery planning

[0775] The server receives the delivery date and price information selected by the user and updates the order data.

[0776] The server creates an optimal delivery plan and sends it to the distribution center. For example, for an order with delivery in three days, it plans the most efficient driver and route for that delivery.

[0777] This series of processes will streamline each step of logistics and enable flexible responses to diverse customer needs. Furthermore, this system will contribute to a healthier working environment and improved productivity at logistics centers. It is also expected to address the labor shortage known as the 2024 problem.

[0778] The processing flow will be explained below.

[0779] Step 1: Data collection

[0780] The server accesses the distribution center database and obtains the current inventory information for each product. For example, it collects information such as the number of units of product A in stock being 100 and the number of units of product B being 50.

[0781] The server accesses the driver resource management system to obtain the number and status of available drivers. For example, it obtains data that five drivers are available.

[0782] The server calls external APIs to collect current road conditions and weather data, such as traffic congestion information and weather data (rainy, sunny, etc.) for a specific area.

[0783] Step 2: Data Preprocessing

[0784] The server consolidates collected inventory data, driver availability data, road conditions and weather data.

[0785] The server analyzes this integrated data and performs preprocessing such as removing outliers and imputing missing values. For example, it predicts and imputes missing data points using a certain algorithm.

[0786] Step 3: Analysis by generative AI model

[0787] The server inputs the preprocessed integrated data into the generative AI model.

[0788] The server uses the generative AI model to generate price suggestions based on the desired delivery date, such as "next day delivery: 1,500 yen" or "3-day delivery: 1,200 yen."

[0789] Step 4: Pricing and Presentation

[0790] The server sets available delivery dates and prices based on the price suggestions output by the generative AI model.

[0791] The server transmits the set price information to the terminal and configures the display elements.

[0792] The terminal displays the received price information to the user, for example, showing options such as "Next day delivery: 1,500 yen" or "3 days later delivery: 1,200 yen."

[0793] Step 5: Selecting Users

[0794] The user selects the desired delivery date and price from the presented price and delivery date options.

[0795] The terminal transmits the user's selection to the server.

[0796] Step 6: Order acceptance and updates

[0797] The server receives the selection information sent by the user and updates the order data in the distribution center database. For example, the order data is confirmed as "Product A, delivery in 3 days, price 1,200 yen."

[0798] The server prepares to optimize the delivery plan using the confirmed order data.

[0799] Step 7: Optimize delivery plans

[0800] The server creates an optimal delivery plan based on the received order data and pre-processed driver availability data. Specifically, it determines the optimal driver and route for the selected desired delivery date.

[0801] The server then sends the created optimal delivery plan to the logistics center system. For example, it sends a plan that says, "Driver B will deliver product A to the specified destination in three days."

[0802] The above are the specific processing steps in the dynamic pricing system of the present invention. This series of processes makes it possible to optimize the efficiency of the logistics center and flexibly respond to the diverse needs of customers.

[0803] Example 1

[0804] 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."

[0805] Conventional logistics systems lack efficient integration of many factors, such as inventory management, driver allocation, and fluctuations in road conditions and weather, resulting in delivery delays and increased costs. Furthermore, it is difficult to offer appropriate pricing to customers, making it difficult to improve customer satisfaction. The purpose of this invention is to solve these problems, improve the efficiency of logistics operations, and increase customer satisfaction.

[0806] 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.

[0807] In this invention, the server includes means for acquiring inventory status in logistics, means for acquiring driver availability status in logistics, means for acquiring road condition and weather data, means for preprocessing the acquired data and inputting it into a generative AI model, means for generating a price proposal according to a desired delivery date, means for setting a product price based on the generated price proposal and presenting it to the user, means for accepting an order and creating a delivery plan according to the user's selection, means for displaying the presented price information to the user and allowing the user to select a desired delivery date and price, means for receiving the desired delivery date and price information selected by the user and updating the order data, and means for creating an optimal delivery plan and transmitting it to a logistics center. This makes it possible to efficiently integrate various elements related to logistics and provide customers with optimal delivery options through dynamic price proposals.

[0808] The "means for acquiring inventory status in logistics" is a part of a program that runs within a server to collect inventory information from logistics centers and warehouses.

[0809] The "means for acquiring driver availability status for logistics" is a server function that enables logistics companies to acquire the number of drivers available and their working status from the management system.

[0810] The "means for acquiring road condition and weather data" is a server function for acquiring road congestion information and weather forecasts from external data providing services.

[0811] "Means for preprocessing acquired data and inputting it into the generative AI model" refers to the process of organizing collected data into a unified format and inputting it into the generative AI model in the optimal data format.

[0812] "Means for generating price proposals based on desired delivery date" refers to a function that uses a generative AI model to calculate the optimal price based on the user's desired delivery date, based on collected and pre-processed data.

[0813] The "means of setting a product price based on the generated price proposal and presenting it to the user" refers to a server function that determines the delivery fee for the product using the price proposed by the AI ​​and displays it through the user interface.

[0814] "Means for accepting orders and creating delivery plans based on user selections" refers to the process of storing order information in the system and creating an efficient delivery plan based on the delivery options selected by the user.

[0815] "Means for displaying the proposed price information to the user and allowing the user to select the desired delivery date and price" refers to a function that displays price information in a user interface and allows the user to select the desired delivery date and price.

[0816] The "means for receiving the desired delivery date and price information selected by the user and updating the order data" is a function of the server that records and updates the order information in the database based on the user's selection.

[0817] "Means for creating optimal delivery plans and transmitting them to logistics centers" refers to the process of calculating the most efficient delivery routes and driver allocations based on order data, and sending that information to logistics centers.

[0818] This invention is a dynamic pricing system aimed at improving efficiency and customer satisfaction in the logistics industry. The main components of the system include obtaining inventory status in logistics, obtaining driver availability, obtaining road and weather data, and using a generative AI model. The steps based on an embodiment of the present invention are as follows.

[0819] Required Hardware and Software

[0820] 1. Server:

[0821] Database Management System (DBMS)

[0822] Resource Management System

[0823] External APIs (e.g. Google Maps API, OpenWeatherMap API)

[0824] Data preprocessing software (e.g., Pandas)

[0825] Generative AI models (e.g., GPT-3)

[0826] 2. Terminal:

[0827] Front-end applications (e.g. web applications and mobile apps)

[0828] Data collection and preprocessing

[0829] The server collects the following data:

[0830] 1. Obtaining inventory status

[0831] The server queries the distribution center's database to retrieve inventory information.

[0832] Example: SELECT product, inventory FROM inventory table

[0833] Example: 100 units of product A in stock, 50 units of product B in stock

[0834] 2. Obtaining driver availability information

[0835] The server sends an API request to the driver resource management system to obtain the number and status of available drivers.

[0836] Example: 5 drivers available

[0837] 3. Obtaining road and weather data

[0838] The server calls external APIs (e.g., Google Maps API or OpenWeatherMap API) to collect road conditions and weather data.

[0839] Example: There is traffic congestion and it is raining.

[0840] After collecting the data, the server preprocesses them, consolidating them into a single format, imputing missing values, and handling outliers.

[0841] Price proposal based on desired delivery date

[0842] The server inputs the preprocessed data into a generative AI model to generate a price suggestion based on the desired delivery date. Below is an example of a prompt.

[0843] Example prompt sentence:

[0844] Stock information: Product A: 100 units, Product B: 50 units

[0845] Drivers: 5

[0846] Road conditions: Traffic jams

[0847] Weather: rain

[0848] Desired delivery date: Next day

[0849] Based on the price suggested by the generative AI model, the server sets the price of the product and presents it to the user.

[0850] User Presentation and Selection

[0851] The terminal displays the price information received from the server to the user, who then selects the desired delivery date and price.

[0852] Specific user interface examples:

[0853] Select delivery date and price:

[0854] Next day delivery: 1,500 yen

[0855] 3-day delivery: 1,200 yen

[0856] Once the user selects the desired delivery date and price, the server receives that information, updates the order data, creates an efficient delivery plan, and transmits it to the logistics center.

[0857] This will make each step of logistics more efficient and enable flexible responses to diverse customer needs. The system as a whole is expected to improve efficiency in the logistics industry and increase customer satisfaction.

[0858] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0859] Step 1:

[0860] The server sends a query to the distribution center's database to obtain inventory status. An example query is SELECT product, inventory FROM inventory table. This query obtains current inventory information. For example, if there are 100 units of product A in stock and 50 units of product B in stock, this information is obtained. The input is database connection information, and the output is inventory information. The specific operation is that the server sends an SQL query to the database and receives a response.

[0861] Step 2:

[0862] The server sends an API request to the driver resource management system to obtain the number and status of available drivers. An example request is GET / api / drivers / available. This request obtains information about currently available drivers. For example, if there are five drivers available, that information is obtained. The input is the API endpoint of the resource management system, and the output is driver availability information. In concrete terms, the server sends an HTTP request to the resource management system and receives a response.

[0863] Step 3:

[0864] The server calls an external API (e.g., Google Maps API or OpenWeatherMap API) to collect current road conditions and weather data. Example requests are GET / maps / api / traffic and GET / weather / api / current. This request obtains road conditions and weather data. For example, information about road congestion or rainy weather is obtained. The input is the endpoint of the external API, and the output is road conditions and weather data. Specifically, the server sends an HTTP request to the external API and receives a response.

[0865] Step 4:

[0866] The server preprocesses the collected inventory information, driver information, road conditions, and weather data. Specifically, it converts the data into a unified format, fills in missing values, and processes outliers. The input is the various collected data, and the output is data in a format suitable for the generative AI model. For example, if there are missing values, it performs operations such as filling in the average value.

[0867] Step 5:

[0868] The server inputs the preprocessed data into a generative AI model to generate price suggestions based on the desired delivery date. The input is preprocessed data in a unified format, and the output is price suggestions based on the desired delivery date. An example prompt is:

[0869] Stock information: Product A: 100 units, Product B: 50 units

[0870] Drivers: 5

[0871] Road conditions: Traffic jams

[0872] Weather: rain

[0873] Desired delivery date: Next day

[0874] An example of a price suggestion generated based on this prompt would be ¥1,500 for next day delivery or ¥1,200 for three days later.

[0875] Step 6:

[0876] The server sets selectable delivery dates and prices based on the generated price proposals and sends them to the terminal. The input is the price proposal from the generative AI model, and the output is the delivery date and price information to be presented to the user. Specifically, the server sends the price information to the terminal via an HTTP request.

[0877] Step 7:

[0878] The terminal displays the received price information to the user. The input is the price information sent from the server, and the output is the price information displayed on the user interface. A concrete example of how it is displayed is:

[0879] Select delivery date and price:

[0880] Next day delivery: 1,500 yen

[0881] 3-day delivery: 1,200 yen

[0882] Step 8:

[0883] The user selects the desired delivery date and price. The input is the price information displayed on the terminal, and the output is the user's selection. As a specific selection example, the user selects "Delivery in 3 days: 1,200 yen."

[0884] Step 9:

[0885] The server receives the delivery date and price information selected by the user and updates the order data. The input is the user's selection information, and the output is the updated order data. The specific operation is that the server updates the order database.

[0886] Step 10:

[0887] The server creates an optimal delivery plan and transmits it to the logistics center. The input is the updated order data, and the output is the optimal delivery plan. Specifically, the server calculates the delivery plan and sends it to the logistics center via an HTTP request.

[0888] In this way, each step of logistics can be made more efficient, enabling flexible responses to diverse customer needs.

[0889] (Application example 1)

[0890] 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."

[0891] In the logistics industry, it is difficult to create fast and efficient delivery plans due to factors such as inventory management, driver availability, and road and weather conditions. There is also a lack of methods to flexibly respond to customer delivery dates and dynamically set prices. Furthermore, the lack of tools to support real-time decision-making can lead to reduced operational efficiency. To solve these issues, a system is needed that allows logistics managers to instantly obtain information using smart devices and apply dynamic pricing using generative AI models.

[0892] 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.

[0893] In this invention, the server includes: a means for acquiring inventory status for logistics; a means for acquiring driver availability status for logistics; a means for acquiring road condition and weather data; a means for preprocessing the acquired data and inputting it into a generative AI model; a means for generating a price proposal according to a desired delivery date; a means for setting a product price based on the generated price proposal and presenting it to a user; a means for accepting orders and creating a delivery plan according to a user's selection; and a means for displaying the price proposal and delivery plan in real time using smart glasses and allowing the user to make a selection. This enables logistics managers to make efficient decisions in real time, improving the efficiency of each step of logistics and improving customer satisfaction.

[0894] "Stock status" is data indicating the current quantity and type of products at the logistics center.

[0895] "Driver availability status" is data that indicates the number of drivers available to work delivery tasks at a logistics center and their available working hours.

[0896] "Road conditions" is data that shows information such as current road congestion, closures, and road closures.

[0897] "Weather data" is information indicating the current weather conditions, and includes data such as sunny, rainy, and snowy weather.

[0898] A "generative AI model" is an artificial intelligence model that dynamically generates price proposals and delivery plans based on acquired data.

[0899] "Smart glasses" are wearable devices that are worn on the head and can display information in real time.

[0900] "Price proposal" is a product pricing proposal calculated by the generative AI model based on the desired delivery date.

[0901] A "delivery plan" is a specific plan for delivering goods to customers, including driver allocation and route optimization.

[0902] "Preprocessing" refers to data processing, such as processing outliers and filling in missing values, that is carried out before inputting the acquired data into a generative AI model.

[0903] "User" refers to the customer who receives a price quote and selects the desired delivery date and price.

[0904] "Terminal" refers to an information processing device for displaying price information and delivery schedules, including smart glasses.

[0905] This invention relates to a dynamic pricing system and its specific embodiments, which aim to improve efficiency and customer satisfaction in the logistics industry. In this embodiment, hardware such as a server or smart glasses is used to collect and process data, and price proposals and delivery plans are provided to users in real time. The specific configuration and operation are described below.

[0906] The server accesses the distribution center database to obtain inventory status. For example, it obtains information that there are 100 units of product A in stock and 50 units of product B in stock. The server also accesses the driver resource management system to obtain the number and status of available drivers. For example, it may obtain information that five drivers are available. Furthermore, the server calls an external API to collect road condition and weather data. For example, it may obtain information on traffic congestion and rain.

[0907] The collected data is managed centrally and undergoes preprocessing, which involves standardizing the data format, filling in missing values, and handling outliers. This preprocessing is primarily carried out using software libraries such as Python and Pandas. The preprocessed data is then input into a generative AI model, which generates price proposals. For example, the proposed price may be 1,500 yen for next-day delivery or 1,200 yen for delivery three days later.

[0908] The generated price proposal is sent to the smart glasses via a cloud server, which then displays the proposed price information to the logistics manager in real time. The logistics manager can then view the delivery date and price options through the smart glasses and instantly decide on the best option.

[0909] Once the user selects the desired delivery date and price, the information is sent back to the cloud server. The server updates the order data based on the received information and generates an optimal delivery plan. This delivery plan includes efficient driver assignment and the shortest route. This delivery plan is then transmitted to the logistics center, where the actual delivery takes place.

[0910] Specific examples

[0911] For example, input the following prompt into the generative AI model:

[0912] "We have 100 units of product A in stock and 50 units of product B in stock. Five drivers are available. The roads are congested and it's currently raining. Please provide pricing for next-day delivery and three-day delivery under these conditions."

[0913] This allows logistics managers to make efficient decisions in real time, improving the efficiency of each step of logistics and customer satisfaction.

[0914] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0915] Step 1:

[0916] The server accesses the distribution center database to obtain inventory status. For example, the server obtains data such as 100 units of product A and 50 units of product B. This inventory information is used in the next processing step.

[0917] Step 2:

[0918] The server accesses the driver resource management system to obtain the number and status of available drivers. For example, it may find that five drivers are available. This driver information is also used in the next processing step.

[0919] Step 3:

[0920] The server calls external APIs to collect current road conditions and weather data, such as traffic congestion and rainy weather. This road condition and weather data is used in the next data analysis.

[0921] Step 4:

[0922] The server preprocesses the acquired inventory status, driver information, road conditions, and weather data. It also performs data processing such as standardizing the data format, filling in missing values, and handling outliers. Specifically, it uses Python and Pandas to preprocess the data and prepare it in a format that can be input into the generative AI model.

[0923] Step 5:

[0924] The server inputs the preprocessed data into the generative AI model and generates a price proposal based on the desired delivery date. For example, it generates a price proposal of 1,500 yen for next-day delivery and 1,200 yen for three-day delivery. This generated price proposal is used in the next step.

[0925] Step 6:

[0926] The server sends the generated price proposal to the smart glasses, which then display delivery date and price options in real time based on the received price proposal. This information is then provided to the logistics manager as input for the next step.

[0927] Step 7:

[0928] The user (logistics manager) checks the price and delivery date options presented through the smart glasses and selects the desired option. This selected information is then sent back to the server.

[0929] Step 8:

[0930] The server updates the order data based on the delivery date and price information received from the user, thereby keeping the order information up to date.

[0931] Step 9:

[0932] The server creates an optimal delivery plan based on the delivery date and price information selected by the user, including driver assignment and route optimization, and then transmits this delivery plan to the distribution center.

[0933] Step 10:

[0934] The distribution center then begins actual delivery operations based on the optimal delivery plan received from the server, ensuring that products are delivered to customers efficiently.

[0935] 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.

[0936] This invention combines a dynamic pricing system, which aims to improve efficiency and customer satisfaction in the logistics industry, with an emotion engine that recognizes user emotions. This system utilizes a generative AI model based on inventory status at logistics centers, driver availability, road conditions, and weather data to dynamically set product prices according to the customer's desired delivery date. Furthermore, the emotion engine is used to adjust prices and suggest services according to the user's emotional state.

[0937] The system consists of the following steps:

[0938] 1. Data Collection

[0939] The server accesses the distribution center database and obtains current inventory information, such as 100 units of product A and 50 units of product B.

[0940] The server accesses the driver resource management system to obtain the number and status of available drivers. For example, it obtains data that five drivers are available.

[0941] The server calls external APIs to collect current road conditions and weather data, such as traffic congestion information and weather data (rainy, sunny, etc.) for a specific area.

[0942] 2. Data Preprocessing

[0943] The server consolidates collected inventory data, driver availability data, road conditions and weather data.

[0944] The server analyzes this integrated data and performs preprocessing such as removing outliers and filling in missing values.

[0945] 3. Analysis using generative AI models

[0946] The server inputs the preprocessed integrated data into the generative AI model.

[0947] The server uses the generative AI model to generate price suggestions based on the desired delivery date, such as "next day delivery: 1,500 yen" or "3-day delivery: 1,200 yen."

[0948] 4. Emotion Recognition by Emotion Engine

[0949] The device uses an emotion engine to recognize the emotions of users when they operate the system. The emotion engine collects emotion data from users' facial expressions, voice, text, etc.

[0950] The server then adjusts the generated price proposal based on the collected emotional data, for example, lowering the price if the user is feeling stressed.

[0951] 5. Pricing and Offerings

[0952] The server sets the final price based on the emotional data analyzed by the emotion engine.

[0953] The server transmits the set price information to the terminal and configures the display elements.

[0954] The terminal displays the received price information to the user.

[0955] 6. User Choices

[0956] The user selects the desired delivery date and price from the presented price and delivery date options.

[0957] The terminal transmits the user's selection to the server.

[0958] 7. Order Acceptance and Updates

[0959] The server receives the selection information sent by the user and updates the order data in the distribution center database.

[0960] 8. Optimizing delivery plans

[0961] The server creates an optimal delivery plan based on the received order data and pre-processed driver availability data.

[0962] The server sends the created optimal delivery plan to the logistics center's system.

[0963] This series of processes allows the system to streamline each step of logistics and flexibly respond to diverse customer needs. Furthermore, by incorporating an emotion engine, it becomes possible to provide services based on the user's emotional state, which is expected to further improve customer satisfaction. In this way, by adding an emotional element to the dynamic pricing system, embodiments of the invention can provide more personalized logistics services.

[0964] The processing flow will be explained below.

[0965] Step 1: Data collection

[0966] The server accesses the distribution center database to obtain current inventory information, such as detailed inventory status, such as 100 units of product A and 50 units of product B.

[0967] The server accesses the driver resource management system to obtain the number and status of currently available drivers. For example, it obtains data that five drivers are available.

[0968] The server calls external APIs to collect current road conditions and weather data, such as traffic congestion information for a certain area and weather data such as whether it will rain or shine.

[0969] Step 2: Data Preprocessing

[0970] The server consolidates the collected inventory data, driver availability data, road conditions and weather data.

[0971] The server detects outliers in the aggregated data, corrects or removes them as needed, and imputes missing values ​​using predictive algorithms.

[0972] Step 3: Analysis by generative AI model

[0973] The server inputs the preprocessed data into a generative AI model.

[0974] The server uses a generative AI model to generate price suggestions based on the desired delivery date based on the collected data, such as "next day delivery: 1,500 yen" or "three days later delivery: 1,200 yen."

[0975] Step 4: Emotion Recognition with the Emotion Engine

[0976] The device uses an emotion engine to recognize the user's emotions, collecting emotional data based on the user's tone of voice and facial expression recognition technology.

[0977] The device analyzes the collected emotional data to determine the user's current emotional state (e.g., joy, sadness, stress, etc.).

[0978] The server dynamically adjusts the generated price proposal based on this emotional data, for example, by lowering the price if the user is feeling stressed.

[0979] Step 5: Pricing and Presentation

[0980] The server sets the final price that reflects the analysis results of the emotion engine.

[0981] The server transmits the set price information to the terminal and configures the user interface.

[0982] The terminal displays the price information to the user, for example, "Next day delivery: 1,450 yen" or "3 days later delivery: 1,150 yen."

[0983] Step 6: Selecting Users

[0984] The user selects the desired price and delivery date from the options provided. For example, they can select "Delivery in 3 days: 1,150 yen."

[0985] The terminal transmits the user's selection to the server.

[0986] Step 7: Order acceptance and updates

[0987] The server receives the selection information sent by the user and updates the order data in the distribution center database. For example, the order data is confirmed as "Product A, delivery in 3 days, price 1,150 yen."

[0988] Step 8: Optimize your delivery plan

[0989] The server creates an optimal delivery plan based on the updated order data and pre-processed driver availability data. Specifically, it determines the optimal driver and route for the selected desired delivery date.

[0990] The server sends the created optimal delivery plan to the logistics center system. For example, it sends a plan that says, "Driver A will deliver Product A to the specified destination in three days."

[0991] The above are the specific processing steps for combining the dynamic pricing system of the present invention with an emotion engine. This series of processes will improve the efficiency of logistics operations and customer satisfaction. Furthermore, it will enable individual responses based on the user's emotions, enabling more personalized operations.

[0992] Example 2

[0993] 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."

[0994] The logistics industry requires optimization of inventory management and delivery plans, but currently these processes are not carried out efficiently, resulting in problems such as lower customer satisfaction and increased costs. Furthermore, there is a lack of services that take into account the emotional state of customers, making it difficult to improve customer experience. The present invention aims to solve these problems.

[0995] 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.

[0996] In this invention, the server includes a means for acquiring inventory status for logistics, a means for acquiring driver availability status for logistics, a means for acquiring road condition and weather data, a means for preprocessing the acquired data and inputting it into a generative AI model, a means for generating a price proposal according to the desired delivery date, a means for recognizing a user's emotion, a means for adjusting the product price based on the user's emotional state based on the generated price proposal and presenting it to the user, and a means for accepting orders and creating a delivery plan depending on the user's selection. This makes it possible to streamline each step of logistics and provide personalized services based on the user's emotional state.

[0997] "Inventory status in logistics" refers to information such as the quantity, type, and storage status of products at logistics centers and warehouses.

[0998] "Driver availability status" refers to information such as the current availability and deployment status of drivers in charge of delivery.

[0999] "Road conditions" refers to data about road conditions, such as traffic flow, congestion, and accident information.

[1000] "Weather data" is data that indicates the weather conditions for each region, and includes information such as fine weather, rain, and snowfall.

[1001] "Preprocessing" refers to processing the acquired data such as removing outliers, filling in missing values, and integrating data.

[1002] A "generative AI model" is a model that uses artificial intelligence technology to generate data for specific purposes.

[1003] A "price suggestion" is a suggestion that dynamically sets the price of a product to be offered to a user based on collected data.

[1004] "Emotion recognition" is a technology that analyzes and recognizes a user's emotions from facial expressions, voice, text, etc.

[1005] A "delivery plan" is a plan that aims to optimize the entire delivery process, including the delivery order and route for ordered items and driver assignment.

[1006] "Personalized service" means providing services that are customized based on the individual characteristics, feelings, and requests of each user.

[1007] This invention is a system aimed at improving efficiency and customer satisfaction in the logistics industry. This system combines generative AI models and emotion recognition technology, and is implemented in the following steps:

[1008] Data collection

[1009] The server accesses the distribution center's database to obtain current inventory information. For example, it collects information such as 100 units of product A in stock and 50 units of product B in stock. It also accesses the driver resource management system to obtain the number and status of available drivers. For example, it obtains data that shows that five drivers are available. It also calls external APIs to collect current road conditions and weather data. For example, it obtains traffic congestion information and weather data (rain, sunny, etc.) in a specific area.

[1010] Data Preprocessing

[1011] The server integrates the collected inventory data, driver availability data, road conditions, and weather data. It analyzes this integrated data and performs preprocessing such as removing outliers and filling in missing values. Specifically, it rationally fills in missing values ​​in the dataset and filters out abnormal data.

[1012] Analysis using generative AI models

[1013] The server inputs the preprocessed integrated data into a generative AI model. The generative AI model is implemented using, for example, a Python library (TensorFlow or PyTorch). The server uses the generative AI model to generate a price proposal based on the desired delivery date. For example, it generates price proposals such as "next day delivery: 1,500 yen" and "3-day delivery: 1,200 yen."

[1014] Emotion recognition by emotion engine

[1015] The device uses an emotion engine to recognize the emotions expressed when the user operates the system. The emotion engine is a technology that collects and analyzes emotional data from the user's facial expressions, voice, text, etc. For example, it can use a camera and microphone to analyze the user's facial expressions and tone of voice to identify emotions such as "stress" or "joy."

[1016] Pricing and Offering

[1017] The server adjusts the generated price proposal based on the emotional data analyzed by the emotion engine. For example, if the user is feeling stressed, the price will be set lower. The server also sends the final price information to the terminal and presents it to the user. For example, it displays information such as "Next day delivery: 1,400 yen" and "3 days later delivery: 1,100 yen."

[1018] User Choices and Order Acceptance

[1019] The user selects the desired delivery date and price from the presented price and delivery date options. The terminal sends the user's selection to the server, which accepts the order data and updates it in the distribution center's database. The server also creates an optimal delivery plan based on the accepted order data and preprocessed driver availability data, and sends it to the distribution center's system.

[1020] In this way, the system of the present invention not only improves the efficiency of each step in logistics but also provides personalized services based on the emotional state of the user, which is expected to improve customer satisfaction.

[1021] Prompt Sentence Examples

[1022] "Dynamic pricing should be based on current inventory at distribution centers, driver availability, road congestion, and weather data. Also, adjust pricing based on the user's emotional state. Specifically, offer two pricing options: next-day delivery and three-day delivery."

[1023] This enables the system to simultaneously improve logistics efficiency and customer satisfaction.

[1024] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1025] Step 1:

[1026] The server accesses the distribution center's database and obtains current inventory information. Specifically, it obtains the inventory quantity and type for each product from the database and saves this information in list format. The input data is the database connection information and query statement, and the output data is a list of inventory information. For example, it obtains data such as "100 units of product A are in stock, and 50 units of product B are in stock."

[1027] Step 2:

[1028] The server accesses the driver resource management system to obtain the number and status of available drivers. Specifically, it obtains a list of currently available drivers from the resource management system via API. The input data is the API endpoint and authentication information, and the output data is driver resource information. For example, it obtains data such as "5 drivers are available."

[1029] Step 3:

[1030] The server calls external APIs to collect current road conditions and weather data. Specifically, it obtains and integrates data from traffic information services and weather information services. The input data is the endpoint and search conditions of the external API, and the output data is current road conditions and weather information. For example, it collects "traffic congestion information and weather data (rainy, sunny, etc.) in a specific area."

[1031] Step 4:

[1032] The server integrates the collected inventory data, driver availability data, road conditions, and weather data. Specifically, it combines the individually acquired data into a single dataset. The input data is the various data acquired at each step, and the output data is the integrated dataset. For example, it creates integrated data such as "100 units in stock, 5 drivers, traffic information available, rainy weather."

[1033] Step 5:

[1034] The server analyzes this integrated data and performs preprocessing such as removing outliers and filling missing values. Specifically, it applies a data cleansing process to improve the quality of the data. The input data is the integrated dataset, and the output data is a clean, preprocessed dataset. For example, it performs processes such as "removing outliers and filling missing data."

[1035] Step 6:

[1036] The server inputs the preprocessed integrated data into a generative AI model. Specifically, the data is input into a trained AI model using Python libraries (TensorFlow and PyTorch) and analyzed. The input data is a clean integrated dataset, and the output data is the model's prediction results. For example, the resulting price suggestions are "next day delivery: 1,500 yen" and "3-day delivery: 1,200 yen."

[1037] Step 7:

[1038] The device uses an emotion engine to recognize the emotions expressed when the user operates the system. Specifically, it uses a camera and microphone to analyze the user's facial expressions and voice in real time to collect emotional data. The input data is the user's facial expressions and voice data, and the output data is the analyzed emotional information. For example, it can identify emotions such as "stress" and "joy."

[1039] Step 8:

[1040] The server adjusts the generated price proposal based on the collected emotional data. Specifically, it applies an algorithm that changes the price according to the user's emotional state. The input data is the price proposal from the AI ​​model and emotional data, and the output data is the adjusted price information. For example, "If the user is feeling stressed, adjust the price to 1,400 yen for next-day delivery and 1,100 yen for three-day delivery."

[1041] Step 9:

[1042] The server sets the final price based on the emotion data analyzed by the emotion engine and sends the price information to the terminal. Specifically, it converts the price information into a user-friendly format and creates display elements. The input data is the adjusted price information, and the output data is the price information displayed to the user.

[1043] Step 10:

[1044] The user selects the desired delivery date and price from the presented price and delivery date options. Specifically, the user clicks on the desired plan from the options displayed on the terminal. The input data is the user's selection, and the output data is the selection information.

[1045] Step 11:

[1046] The terminal transmits the user's selection to the server. Specifically, the terminal transmits the selection data as a request to the server to trigger processing. The input data is the user's selection information, and the output data is the request to the server.

[1047] Step 12:

[1048] The server receives the selection information sent by the user and updates the order data in the distribution center database. Specifically, it updates inventory information based on the selected delivery date and price and confirms the order. The input data is the user's selection information, and the output data is the updated order data.

[1049] Step 13:

[1050] The server creates an optimal delivery plan based on the received order data and pre-processed driver availability data. Specifically, it calculates efficient delivery routes and assigns drivers. The input data are order data and driver resource information, and the output data is the optimal delivery plan.

[1051] Step 14:

[1052] The server sends the created optimal delivery plan to the logistics center's system. Specifically, the delivery plan is registered in a database and necessary instructions are sent to the driver. The input data is the optimal delivery plan, and the output data is updated information from the logistics center and instructions to the driver.

[1053] This will streamline each step of logistics and enable personalized services based on the user's emotional state.

[1054] (Application example 2)

[1055] 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."

[1056] In the logistics industry, not only is there a need for efficient inventory management and delivery resources, but there is also a need for personalized services based on user emotions. However, conventional systems are unable to set prices or offer services that take user emotions into account, limiting the improvement of customer satisfaction. Therefore, there is a need for a system that recognizes the user's emotional state and can adjust prices and offer services based on that.

[1057] 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 a means for acquiring inventory status in logistics, a means for acquiring driver availability status in logistics, a means for acquiring road and weather information, a means for preprocessing the acquired information and inputting it into a generative AI model, a means for generating a price proposal according to a desired delivery date, a means for setting a product price based on the generated price proposal and presenting it to the user, a means for accepting orders and creating a delivery plan depending on the user's selection, a means for recognizing and acquiring user emotion data, a means for adjusting the price proposal based on the acquired emotion data, and a means for presenting the adjusted price information to the user. This enables flexible pricing based on user emotions and personalized service provision.

[1058] "Inventory status in logistics" is information that indicates the quantity and condition of products stored in logistics centers and warehouses.

[1059] "Driver availability status" is information that indicates the number of drivers required for product delivery, their operating status, and available resources.

[1060] "Road conditions" refers to information that indicates the degree of road congestion, traffic jams, and traffic accident occurrences that affect logistics.

[1061] "Weather information" refers to weather data such as rainfall, snowfall, temperature, and wind speed that affect logistics planning.

[1062] "Preprocessing" refers to the process of processing the acquired data by methods such as removing outliers and filling in missing values, and preparing it in a format suitable for a generative AI model.

[1063] A "generative AI model" is an artificial intelligence model that generates price suggestions based on the desired delivery date based on input data.

[1064] A "price suggestion" is a suggestion that provides an appropriate product price based on the user's desired delivery date and other conditions.

[1065] "Emotional data" refers to information about a user's emotional state obtained from facial expressions, voice, text, etc.

[1066] "Price adjustment" refers to changing the price proposals generated by a generative AI model based on user sentiment data.

[1067] A "delivery plan" is a plan that creates optimal delivery routes and schedules based on received order information.

[1068] "Personalized" refers to providing services that are tailored to each user's individual feelings and needs.

[1069] To implement this invention, the logistics system must go through the following steps: data collection, data preprocessing, utilization of generative AI models, emotion recognition, price adjustment, and information presentation. This system consists of a server, terminals, and users. Specifically, the following hardware and software are used:

[1070] Hardware and software used

[1071] Server: Responsible for acquiring, processing, and analyzing data. A server with high-performance computing power is required.

[1072] Smart glasses: Used by workers at logistics centers, these devices display information and recognize emotions. They have built-in cameras, microphones, and displays.

[1073] Generative AI models: Built using deep learning frameworks such as TensorFlow and PyTorch.

[1074] Emotion recognition engine: Utilizing OpenCV, Emotion API, etc., it analyzes user emotions in real time.

[1075] System operation explanation

[1076] The server first accesses the distribution center's database to obtain current inventory information, including the current stock quantity of each product. Next, the server accesses the driver resource management system to obtain the number of available drivers and their operating status. Finally, it calls an external API to collect current road conditions and weather data.

[1077] The collected data is preprocessed on the server. Outliers are removed and missing values ​​are filled, and the integrated data is input into a generative AI model. This generative AI model generates price suggestions based on the collected data according to the desired delivery date. For example, it generates price suggestions such as "next day delivery: 1,500 yen" and "3-day delivery: 1,200 yen."

[1078] Next, when a worker wearing smart glasses checks the proposal, the emotion recognition engine works to collect emotional data from the worker's facial expressions and voice. The emotional data is sent to the server, and the price proposal is adjusted. For example, if the worker is feeling stressed, the price may be set lower. This allows for personalized price proposals.

[1079] Finally, the adjusted price information is displayed on the smart glasses' display for the worker to confirm. The user's desired delivery date and price information are sent to the server, which updates the order data. An optimal delivery plan is then created and transmitted to the logistics center.

[1080] Examples of concrete examples and prompts

[1081] As a concrete example, consider a scenario in which staff working in a logistics center are wearing smart glasses. As the staff picks each item, the smart glasses display inventory information and price suggestions in real time. If the emotion recognition engine determines that the staff member is tired, the smart glasses will adjust the price suggestions and delivery method.

[1082] Prompt Sentence Examples

[1083] "It retrieves up-to-date inventory information, integrates and processes driver resource information, road conditions and weather data, and uses generative AI models to make price suggestions based on the desired delivery date. It also recognizes the emotions of workers wearing smart glasses in real time and dynamically adjusts price suggestions."

[1084] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1085] Step 1: Data collection

[1086] The server accesses the distribution center's database to obtain current inventory information. This inventory information includes the current stock quantity of each product. The server also accesses the driver resource management system to obtain the number of available drivers and their operating status. It also calls an external API to collect current road conditions and weather information. This inputs inventory data, driver resource data, road condition data, and weather data.

[1087] Step 2: Data Preprocessing

[1088] The server preprocesses the data obtained in step 1. Specifically, it cleanses the data by removing outliers and imputing missing values. This generates an integrated dataset that is in a suitable format for the generative AI model. The input is all the datasets obtained in the previous steps, and the output is the preprocessed integrated data.

[1089] Step 3: Generate price proposals

[1090] The server inputs the preprocessed integrated data into a generative AI model. This AI model generates price proposals based on the desired delivery date and other conditions. Specifically, it proposes prices such as "next day delivery: 1,500 yen" and "three-day delivery: 1,200 yen." The input is the preprocessed integrated data, and the output is the generated price proposal.

[1091] Step 4: Obtaining emotion data

[1092] An emotion recognition engine is operated to obtain real-time emotional data from the terminal, specifically the worker wearing the smart glasses. Emotional data is collected from the worker's facial expressions and voice and sent to the server. The input is the worker's facial expressions and voice data, and the output is analyzed emotional data.

[1093] Step 5: Adjust your pricing proposal

[1094] The server adjusts the price proposal based on the emotional data obtained in step 4. For example, if the worker is feeling stressed, it may set the price 10% lower. The input is the emotional data and the generated price proposal, and the output is the adjusted price proposal.

[1095] Step 6: Information presentation

[1096] The terminal displays the adjusted price proposal on the smart glasses, which the worker can review and make appropriate updates or check the delivery plan. The input is the adjusted price proposal, and the output is the displayed price information.

[1097] Step 7: User selection and order data update

[1098] The user selects the desired delivery date and price information, and sends it from the terminal to the server. The server updates the order data based on the received information and creates a new delivery plan. The input is the user's selection, and the output is the updated order data and the new delivery plan.

[1099] Step 8: Optimize your delivery plan

[1100] The server creates an optimal delivery plan based on the updated order data and pre-processed driver resource data. This plan is transmitted to the distribution center. The input is the updated order data and driver data, and the output is the optimal delivery plan.

[1101] 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.

[1102] 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.

[1103] 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.

[1104] [Fourth embodiment]

[1105] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1106] 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.

[1107] 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).

[1108] 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.

[1109] 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.

[1110] 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).

[1111] 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.

[1112] 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.

[1113] 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.

[1114] 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.

[1115] 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.

[1116] 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.

[1117] 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."

[1118] This invention is a dynamic pricing system aimed at improving efficiency and customer satisfaction in the logistics industry. This system utilizes a generative AI model based on inventory status at logistics centers, driver availability, road conditions, and weather data to dynamically set product prices according to the customer's desired delivery date.

[1119] The system consists of the following steps:

[1120] 1. Data Collection

[1121] The server accesses the distribution center database and obtains current inventory information, such as 100 units of product A and 50 units of product B.

[1122] The server accesses the driver resource management system to obtain the number and status of available drivers, for example, information that five drivers are available.

[1123] The server calls external APIs to collect current road and weather data, such as local traffic congestion information and weather information such as whether it's raining or sunny.

[1124] 2. Data Analysis

[1125] The server preprocesses the collected data, consolidating it into a single format, filling in missing values, and processing outliers. This preprocessed data is suitable for input into a generative AI model.

[1126] The server inputs the preprocessed data into a generative AI model to generate price suggestions based on the desired delivery date, such as 1,500 yen for next-day delivery and 1,200 yen for three days later.

[1127] 3. Pricing

[1128] The server sets available delivery dates and prices based on the generated price proposals.

[1129] The server transmits the set price information to the terminal.

[1130] 4. Price quote

[1131] The terminal displays the received price information to the user, for example, showing options such as "Next day delivery: 1,500 yen" or "3 days later delivery: 1,200 yen."

[1132] Users select their desired delivery date and price.

[1133] 5. Order acceptance and delivery planning

[1134] The server receives the delivery date and price information selected by the user and updates the order data.

[1135] The server creates an optimal delivery plan and sends it to the distribution center. For example, for an order with delivery in three days, it plans the most efficient driver and route for that delivery.

[1136] This series of processes will streamline each step of logistics and enable flexible responses to diverse customer needs. Furthermore, this system will contribute to a healthier working environment and improved productivity at logistics centers. It is also expected to address the labor shortage known as the 2024 problem.

[1137] The processing flow will be explained below.

[1138] Step 1: Data collection

[1139] The server accesses the distribution center database and obtains the current inventory information for each product. For example, it collects information such as the number of units of product A in stock being 100 and the number of units of product B being 50.

[1140] The server accesses the driver resource management system to obtain the number and status of available drivers. For example, it obtains data that five drivers are available.

[1141] The server calls external APIs to collect current road conditions and weather data, such as traffic congestion information and weather data (rainy, sunny, etc.) for a specific area.

[1142] Step 2: Data Preprocessing

[1143] The server consolidates collected inventory data, driver availability data, road conditions and weather data.

[1144] The server analyzes this integrated data and performs preprocessing such as removing outliers and imputing missing values. For example, it predicts and imputes missing data points using a certain algorithm.

[1145] Step 3: Analysis by generative AI model

[1146] The server inputs the preprocessed integrated data into the generative AI model.

[1147] The server uses the generative AI model to generate price suggestions based on the desired delivery date, such as "next day delivery: 1,500 yen" or "3-day delivery: 1,200 yen."

[1148] Step 4: Pricing and Presentation

[1149] The server sets available delivery dates and prices based on the price suggestions output by the generative AI model.

[1150] The server transmits the set price information to the terminal and configures the display elements.

[1151] The terminal displays the received price information to the user, for example, showing options such as "Next day delivery: 1,500 yen" or "3 days later delivery: 1,200 yen."

[1152] Step 5: Selecting Users

[1153] The user selects the desired delivery date and price from the presented price and delivery date options.

[1154] The terminal transmits the user's selection to the server.

[1155] Step 6: Order acceptance and updates

[1156] The server receives the selection information sent by the user and updates the order data in the distribution center database. For example, the order data is confirmed as "Product A, delivery in 3 days, price 1,200 yen."

[1157] The server prepares to optimize the delivery plan using the confirmed order data.

[1158] Step 7: Optimize delivery plans

[1159] The server creates an optimal delivery plan based on the received order data and pre-processed driver availability data. Specifically, it determines the optimal driver and route for the selected desired delivery date.

[1160] The server then sends the created optimal delivery plan to the logistics center system. For example, it sends a plan that says, "Driver B will deliver product A to the specified destination in three days."

[1161] The above are the specific processing steps in the dynamic pricing system of the present invention. This series of processes makes it possible to optimize the efficiency of the logistics center and flexibly respond to the diverse needs of customers.

[1162] Example 1

[1163] 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."

[1164] Conventional logistics systems lack efficient integration of many factors, such as inventory management, driver allocation, and fluctuations in road conditions and weather, resulting in delivery delays and increased costs. Furthermore, it is difficult to offer appropriate pricing to customers, making it difficult to improve customer satisfaction. The purpose of this invention is to solve these problems, improve the efficiency of logistics operations, and increase customer satisfaction.

[1165] 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.

[1166] In this invention, the server includes means for acquiring inventory status in logistics, means for acquiring driver availability status in logistics, means for acquiring road condition and weather data, means for preprocessing the acquired data and inputting it into a generative AI model, means for generating a price proposal according to a desired delivery date, means for setting a product price based on the generated price proposal and presenting it to the user, means for accepting an order and creating a delivery plan according to the user's selection, means for displaying the presented price information to the user and allowing the user to select a desired delivery date and price, means for receiving the desired delivery date and price information selected by the user and updating the order data, and means for creating an optimal delivery plan and transmitting it to a logistics center. This makes it possible to efficiently integrate various elements related to logistics and provide customers with optimal delivery options through dynamic price proposals.

[1167] The "means for acquiring inventory status in logistics" is a part of a program that runs within a server to collect inventory information from logistics centers and warehouses.

[1168] The "means for acquiring driver availability status for logistics" is a server function that enables logistics companies to acquire the number of drivers available and their working status from the management system.

[1169] The "means for acquiring road condition and weather data" is a server function for acquiring road congestion information and weather forecasts from external data providing services.

[1170] "Means for preprocessing acquired data and inputting it into the generative AI model" refers to the process of organizing collected data into a unified format and inputting it into the generative AI model in the optimal data format.

[1171] "Means for generating price proposals based on desired delivery date" refers to a function that uses a generative AI model to calculate the optimal price based on the user's desired delivery date, based on collected and pre-processed data.

[1172] The "means of setting a product price based on the generated price proposal and presenting it to the user" refers to a server function that determines the delivery fee for the product using the price proposed by the AI ​​and displays it through the user interface.

[1173] "Means for accepting orders and creating delivery plans based on user selections" refers to the process of storing order information in the system and creating an efficient delivery plan based on the delivery options selected by the user.

[1174] "Means for displaying the proposed price information to the user and allowing the user to select the desired delivery date and price" refers to a function that displays price information in a user interface and allows the user to select the desired delivery date and price.

[1175] The "means for receiving the desired delivery date and price information selected by the user and updating the order data" is a function of the server that records and updates the order information in the database based on the user's selection.

[1176] "Means for creating optimal delivery plans and transmitting them to logistics centers" refers to the process of calculating the most efficient delivery routes and driver allocations based on order data, and sending that information to logistics centers.

[1177] This invention is a dynamic pricing system aimed at improving efficiency and customer satisfaction in the logistics industry. The main components of the system include obtaining inventory status in logistics, obtaining driver availability, obtaining road and weather data, and using a generative AI model. The steps based on an embodiment of the present invention are as follows.

[1178] Required Hardware and Software

[1179] 1. Server:

[1180] Database Management System (DBMS)

[1181] Resource Management System

[1182] External APIs (e.g. Google Maps API, OpenWeatherMap API)

[1183] Data preprocessing software (e.g., Pandas)

[1184] Generative AI models (e.g., GPT-3)

[1185] 2. Terminal:

[1186] Front-end applications (e.g. web applications and mobile apps)

[1187] Data collection and preprocessing

[1188] The server collects the following data:

[1189] 1. Obtaining inventory status

[1190] The server queries the distribution center's database to retrieve inventory information.

[1191] Example: SELECT product, inventory FROM inventory table

[1192] Example: 100 units of product A in stock, 50 units of product B in stock

[1193] 2. Obtaining driver availability information

[1194] The server sends an API request to the driver resource management system to obtain the number and status of available drivers.

[1195] Example: 5 drivers available

[1196] 3. Obtaining road and weather data

[1197] The server calls external APIs (e.g., Google Maps API or OpenWeatherMap API) to collect road conditions and weather data.

[1198] Example: There is traffic congestion and it is raining.

[1199] After collecting the data, the server preprocesses them, consolidating them into a single format, imputing missing values, and handling outliers.

[1200] Price proposal based on desired delivery date

[1201] The server inputs the preprocessed data into a generative AI model to generate a price suggestion based on the desired delivery date. Below is an example of a prompt.

[1202] Example prompt sentence:

[1203] Stock information: Product A: 100 units, Product B: 50 units

[1204] Drivers: 5

[1205] Road conditions: Traffic jams

[1206] Weather: rain

[1207] Desired delivery date: Next day

[1208] Based on the price suggested by the generative AI model, the server sets the price of the product and presents it to the user.

[1209] User Presentation and Selection

[1210] The terminal displays the price information received from the server to the user, who then selects the desired delivery date and price.

[1211] Specific user interface examples:

[1212] Select delivery date and price:

[1213] Next day delivery: 1,500 yen

[1214] 3-day delivery: 1,200 yen

[1215] Once the user selects the desired delivery date and price, the server receives that information, updates the order data, creates an efficient delivery plan, and transmits it to the logistics center.

[1216] This will make each step of logistics more efficient and enable flexible responses to diverse customer needs. The system as a whole is expected to improve efficiency in the logistics industry and increase customer satisfaction.

[1217] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1218] Step 1:

[1219] The server sends a query to the distribution center's database to obtain inventory status. An example query is SELECT product, inventory FROM inventory table. This query obtains current inventory information. For example, if there are 100 units of product A in stock and 50 units of product B in stock, this information is obtained. The input is database connection information, and the output is inventory information. The specific operation is that the server sends an SQL query to the database and receives a response.

[1220] Step 2:

[1221] The server sends an API request to the driver resource management system to obtain the number and status of available drivers. An example request is GET / api / drivers / available. This request obtains information about currently available drivers. For example, if there are five drivers available, that information is obtained. The input is the API endpoint of the resource management system, and the output is driver availability information. In concrete terms, the server sends an HTTP request to the resource management system and receives a response.

[1222] Step 3:

[1223] The server calls an external API (e.g., Google Maps API or OpenWeatherMap API) to collect current road conditions and weather data. Example requests are GET / maps / api / traffic and GET / weather / api / current. This request obtains road conditions and weather data. For example, information about road congestion or rainy weather is obtained. The input is the endpoint of the external API, and the output is road conditions and weather data. Specifically, the server sends an HTTP request to the external API and receives a response.

[1224] Step 4:

[1225] The server preprocesses the collected inventory information, driver information, road conditions, and weather data. Specifically, it converts the data into a unified format, fills in missing values, and processes outliers. The input is the various collected data, and the output is data in a format suitable for the generative AI model. For example, if there are missing values, it performs operations such as filling in the average value.

[1226] Step 5:

[1227] The server inputs the preprocessed data into a generative AI model to generate price suggestions based on the desired delivery date. The input is preprocessed data in a unified format, and the output is price suggestions based on the desired delivery date. An example prompt is:

[1228] Stock information: Product A: 100 units, Product B: 50 units

[1229] Drivers: 5

[1230] Road conditions: Traffic jams

[1231] Weather: rain

[1232] Desired delivery date: Next day

[1233] An example of a price suggestion generated based on this prompt would be ¥1,500 for next day delivery or ¥1,200 for three days later.

[1234] Step 6:

[1235] The server sets selectable delivery dates and prices based on the generated price proposals and sends them to the terminal. The input is the price proposal from the generative AI model, and the output is the delivery date and price information to be presented to the user. Specifically, the server sends the price information to the terminal via an HTTP request.

[1236] Step 7:

[1237] The terminal displays the received price information to the user. The input is the price information sent from the server, and the output is the price information displayed on the user interface. A concrete example of how it is displayed is:

[1238] Select delivery date and price:

[1239] Next day delivery: 1,500 yen

[1240] 3-day delivery: 1,200 yen

[1241] Step 8:

[1242] The user selects the desired delivery date and price. The input is the price information displayed on the terminal, and the output is the user's selection. As a specific selection example, the user selects "Delivery in 3 days: 1,200 yen."

[1243] Step 9:

[1244] The server receives the delivery date and price information selected by the user and updates the order data. The input is the user's selection information, and the output is the updated order data. The specific operation is that the server updates the order database.

[1245] Step 10:

[1246] The server creates an optimal delivery plan and transmits it to the logistics center. The input is the updated order data, and the output is the optimal delivery plan. Specifically, the server calculates the delivery plan and sends it to the logistics center via an HTTP request.

[1247] In this way, each step of logistics can be made more efficient, enabling flexible responses to diverse customer needs.

[1248] (Application example 1)

[1249] 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."

[1250] In the logistics industry, it is difficult to create fast and efficient delivery plans due to factors such as inventory management, driver availability, and road and weather conditions. There is also a lack of methods to flexibly respond to customer delivery dates and dynamically set prices. Furthermore, the lack of tools to support real-time decision-making can lead to reduced operational efficiency. To solve these issues, a system is needed that allows logistics managers to instantly obtain information using smart devices and apply dynamic pricing using generative AI models.

[1251] 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.

[1252] In this invention, the server includes: a means for acquiring inventory status for logistics; a means for acquiring driver availability status for logistics; a means for acquiring road condition and weather data; a means for preprocessing the acquired data and inputting it into a generative AI model; a means for generating a price proposal according to a desired delivery date; a means for setting a product price based on the generated price proposal and presenting it to a user; a means for accepting orders and creating a delivery plan according to a user's selection; and a means for displaying the price proposal and delivery plan in real time using smart glasses and allowing the user to make a selection. This enables logistics managers to make efficient decisions in real time, improving the efficiency of each step of logistics and improving customer satisfaction.

[1253] "Stock status" is data indicating the current quantity and type of products at the logistics center.

[1254] "Driver availability status" is data that indicates the number of drivers available to work delivery tasks at a logistics center and their available working hours.

[1255] "Road conditions" is data that shows information such as current road congestion, closures, and road closures.

[1256] "Weather data" is information indicating the current weather conditions, and includes data such as sunny, rainy, and snowy weather.

[1257] A "generative AI model" is an artificial intelligence model that dynamically generates price proposals and delivery plans based on acquired data.

[1258] "Smart glasses" are wearable devices that are worn on the head and can display information in real time.

[1259] "Price proposal" is a product pricing proposal calculated by the generative AI model based on the desired delivery date.

[1260] A "delivery plan" is a specific plan for delivering goods to customers, including driver allocation and route optimization.

[1261] "Preprocessing" refers to data processing, such as processing outliers and filling in missing values, that is carried out before inputting the acquired data into a generative AI model.

[1262] "User" refers to the customer who receives a price quote and selects the desired delivery date and price.

[1263] "Terminal" refers to an information processing device for displaying price information and delivery schedules, including smart glasses.

[1264] This invention relates to a dynamic pricing system and its specific embodiments, which aim to improve efficiency and customer satisfaction in the logistics industry. In this embodiment, hardware such as a server or smart glasses is used to collect and process data, and price proposals and delivery plans are provided to users in real time. The specific configuration and operation are described below.

[1265] The server accesses the distribution center database to obtain inventory status. For example, it obtains information that there are 100 units of product A in stock and 50 units of product B in stock. The server also accesses the driver resource management system to obtain the number and status of available drivers. For example, it may obtain information that five drivers are available. Furthermore, the server calls an external API to collect road condition and weather data. For example, it may obtain information on traffic congestion and rain.

[1266] The collected data is managed centrally and undergoes preprocessing, which involves standardizing the data format, filling in missing values, and handling outliers. This preprocessing is primarily carried out using software libraries such as Python and Pandas. The preprocessed data is then input into a generative AI model, which generates price proposals. For example, the proposed price may be 1,500 yen for next-day delivery or 1,200 yen for delivery three days later.

[1267] The generated price proposal is sent to the smart glasses via a cloud server, which then displays the proposed price information to the logistics manager in real time. The logistics manager can then view the delivery date and price options through the smart glasses and instantly decide on the best option.

[1268] Once the user selects the desired delivery date and price, the information is sent back to the cloud server. The server updates the order data based on the received information and generates an optimal delivery plan. This delivery plan includes efficient driver assignment and the shortest route. This delivery plan is then transmitted to the logistics center, where the actual delivery takes place.

[1269] Specific examples

[1270] For example, input the following prompt into the generative AI model:

[1271] "We have 100 units of product A in stock and 50 units of product B in stock. Five drivers are available. The roads are congested and it's currently raining. Please provide pricing for next-day delivery and three-day delivery under these conditions."

[1272] This allows logistics managers to make efficient decisions in real time, improving the efficiency of each step of logistics and customer satisfaction.

[1273] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1274] Step 1:

[1275] The server accesses the distribution center database to obtain inventory status. For example, the server obtains data such as 100 units of product A and 50 units of product B. This inventory information is used in the next processing step.

[1276] Step 2:

[1277] The server accesses the driver resource management system to obtain the number and status of available drivers. For example, it may find that five drivers are available. This driver information is also used in the next processing step.

[1278] Step 3:

[1279] The server calls external APIs to collect current road conditions and weather data, such as traffic congestion and rainy weather. This road condition and weather data is used in the next data analysis.

[1280] Step 4:

[1281] The server preprocesses the acquired inventory status, driver information, road conditions, and weather data. It also performs data processing such as standardizing the data format, filling in missing values, and handling outliers. Specifically, it uses Python and Pandas to preprocess the data and prepare it in a format that can be input into the generative AI model.

[1282] Step 5:

[1283] The server inputs the preprocessed data into the generative AI model and generates a price proposal based on the desired delivery date. For example, it generates a price proposal of 1,500 yen for next-day delivery and 1,200 yen for three-day delivery. This generated price proposal is used in the next step.

[1284] Step 6:

[1285] The server sends the generated price proposal to the smart glasses, which then display delivery date and price options in real time based on the received price proposal. This information is then provided to the logistics manager as input for the next step.

[1286] Step 7:

[1287] The user (logistics manager) checks the price and delivery date options presented through the smart glasses and selects the desired option. This selected information is then sent back to the server.

[1288] Step 8:

[1289] The server updates the order data based on the delivery date and price information received from the user, thereby keeping the order information up to date.

[1290] Step 9:

[1291] The server creates an optimal delivery plan based on the delivery date and price information selected by the user, including driver assignment and route optimization, and then transmits this delivery plan to the distribution center.

[1292] Step 10:

[1293] The distribution center then begins actual delivery operations based on the optimal delivery plan received from the server, ensuring that products are delivered to customers efficiently.

[1294] 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.

[1295] This invention combines a dynamic pricing system, which aims to improve efficiency and customer satisfaction in the logistics industry, with an emotion engine that recognizes user emotions. This system utilizes a generative AI model based on inventory status at logistics centers, driver availability, road conditions, and weather data to dynamically set product prices according to the customer's desired delivery date. Furthermore, the emotion engine is used to adjust prices and suggest services according to the user's emotional state.

[1296] The system consists of the following steps:

[1297] 1. Data Collection

[1298] The server accesses the distribution center database and obtains current inventory information, such as 100 units of product A and 50 units of product B.

[1299] The server accesses the driver resource management system to obtain the number and status of available drivers. For example, it obtains data that five drivers are available.

[1300] The server calls external APIs to collect current road conditions and weather data, such as traffic congestion information and weather data (rainy, sunny, etc.) for a specific area.

[1301] 2. Data Preprocessing

[1302] The server consolidates collected inventory data, driver availability data, road conditions and weather data.

[1303] The server analyzes this integrated data and performs preprocessing such as removing outliers and filling in missing values.

[1304] 3. Analysis using generative AI models

[1305] The server inputs the preprocessed integrated data into the generative AI model.

[1306] The server uses the generative AI model to generate price suggestions based on the desired delivery date, such as "next day delivery: 1,500 yen" or "3-day delivery: 1,200 yen."

[1307] 4. Emotion Recognition by Emotion Engine

[1308] The device uses an emotion engine to recognize the emotions of users when they operate the system. The emotion engine collects emotion data from users' facial expressions, voice, text, etc.

[1309] The server then adjusts the generated price proposal based on the collected emotional data, for example, lowering the price if the user is feeling stressed.

[1310] 5. Pricing and Offerings

[1311] The server sets the final price based on the emotional data analyzed by the emotion engine.

[1312] The server transmits the set price information to the terminal and configures the display elements.

[1313] The terminal displays the received price information to the user.

[1314] 6. User Choices

[1315] The user selects the desired delivery date and price from the presented price and delivery date options.

[1316] The terminal transmits the user's selection to the server.

[1317] 7. Order Acceptance and Updates

[1318] The server receives the selection information sent by the user and updates the order data in the distribution center database.

[1319] 8. Optimizing delivery plans

[1320] The server creates an optimal delivery plan based on the received order data and pre-processed driver availability data.

[1321] The server sends the created optimal delivery plan to the logistics center's system.

[1322] This series of processes allows the system to streamline each step of logistics and flexibly respond to diverse customer needs. Furthermore, by incorporating an emotion engine, it becomes possible to provide services based on the user's emotional state, which is expected to further improve customer satisfaction. In this way, by adding an emotional element to the dynamic pricing system, embodiments of the invention can provide more personalized logistics services.

[1323] The processing flow will be explained below.

[1324] Step 1: Data collection

[1325] The server accesses the distribution center database to obtain current inventory information, such as detailed inventory status, such as 100 units of product A and 50 units of product B.

[1326] The server accesses the driver resource management system to obtain the number and status of currently available drivers. For example, it obtains data that five drivers are available.

[1327] The server calls external APIs to collect current road conditions and weather data, such as traffic congestion information for a certain area and weather data such as whether it will rain or shine.

[1328] Step 2: Data Preprocessing

[1329] The server consolidates the collected inventory data, driver availability data, road conditions and weather data.

[1330] The server detects outliers in the aggregated data, corrects or removes them as needed, and imputes missing values ​​using predictive algorithms.

[1331] Step 3: Analysis by generative AI model

[1332] The server inputs the preprocessed data into a generative AI model.

[1333] The server uses a generative AI model to generate price suggestions based on the desired delivery date based on the collected data, such as "next day delivery: 1,500 yen" or "three days later delivery: 1,200 yen."

[1334] Step 4: Emotion Recognition with the Emotion Engine

[1335] The device uses an emotion engine to recognize the user's emotions, collecting emotional data based on the user's tone of voice and facial expression recognition technology.

[1336] The device analyzes the collected emotional data to determine the user's current emotional state (e.g., joy, sadness, stress, etc.).

[1337] The server dynamically adjusts the generated price proposal based on this emotional data, for example, by lowering the price if the user is feeling stressed.

[1338] Step 5: Pricing and Presentation

[1339] The server sets the final price that reflects the analysis results of the emotion engine.

[1340] The server transmits the set price information to the terminal and configures the user interface.

[1341] The terminal displays the price information to the user, for example, "Next day delivery: 1,450 yen" or "3 days later delivery: 1,150 yen."

[1342] Step 6: Selecting Users

[1343] The user selects the desired price and delivery date from the options provided. For example, they can select "Delivery in 3 days: 1,150 yen."

[1344] The terminal transmits the user's selection to the server.

[1345] Step 7: Order acceptance and updates

[1346] The server receives the selection information sent by the user and updates the order data in the distribution center database. For example, the order data is confirmed as "Product A, delivery in 3 days, price 1,150 yen."

[1347] Step 8: Optimize your delivery plan

[1348] The server creates an optimal delivery plan based on the updated order data and pre-processed driver availability data. Specifically, it determines the optimal driver and route for the selected desired delivery date.

[1349] The server sends the created optimal delivery plan to the logistics center system. For example, it sends a plan that says, "Driver A will deliver Product A to the specified destination in three days."

[1350] The above are the specific processing steps for combining the dynamic pricing system of the present invention with an emotion engine. This series of processes will improve the efficiency of logistics operations and customer satisfaction. Furthermore, it will enable individual responses based on the user's emotions, enabling more personalized operations.

[1351] Example 2

[1352] 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."

[1353] The logistics industry requires optimization of inventory management and delivery plans, but currently these processes are not carried out efficiently, resulting in problems such as lower customer satisfaction and increased costs. Furthermore, there is a lack of services that take into account the emotional state of customers, making it difficult to improve customer experience. The present invention aims to solve these problems.

[1354] 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.

[1355] In this invention, the server includes a means for acquiring inventory status for logistics, a means for acquiring driver availability status for logistics, a means for acquiring road condition and weather data, a means for preprocessing the acquired data and inputting it into a generative AI model, a means for generating a price proposal according to the desired delivery date, a means for recognizing a user's emotion, a means for adjusting the product price based on the user's emotional state based on the generated price proposal and presenting it to the user, and a means for accepting orders and creating a delivery plan depending on the user's selection. This makes it possible to streamline each step of logistics and provide personalized services based on the user's emotional state.

[1356] "Inventory status in logistics" refers to information such as the quantity, type, and storage status of products at logistics centers and warehouses.

[1357] "Driver availability status" refers to information such as the current availability and deployment status of drivers in charge of delivery.

[1358] "Road conditions" refers to data about road conditions, such as traffic flow, congestion, and accident information.

[1359] "Weather data" is data that indicates the weather conditions for each region, and includes information such as fine weather, rain, and snowfall.

[1360] "Preprocessing" refers to processing the acquired data such as removing outliers, filling in missing values, and integrating data.

[1361] A "generative AI model" is a model that uses artificial intelligence technology to generate data for specific purposes.

[1362] A "price suggestion" is a suggestion that dynamically sets the price of a product to be offered to a user based on collected data.

[1363] "Emotion recognition" is a technology that analyzes and recognizes a user's emotions from facial expressions, voice, text, etc.

[1364] A "delivery plan" is a plan that aims to optimize the entire delivery process, including the delivery order and route for ordered items and driver assignment.

[1365] "Personalized service" means providing services that are customized based on the individual characteristics, feelings, and requests of each user.

[1366] This invention is a system aimed at improving efficiency and customer satisfaction in the logistics industry. This system combines generative AI models and emotion recognition technology, and is implemented in the following steps:

[1367] Data collection

[1368] The server accesses the distribution center's database to obtain current inventory information. For example, it collects information such as 100 units of product A in stock and 50 units of product B in stock. It also accesses the driver resource management system to obtain the number and status of available drivers. For example, it obtains data that shows that five drivers are available. It also calls external APIs to collect current road conditions and weather data. For example, it obtains traffic congestion information and weather data (rain, sunny, etc.) in a specific area.

[1369] Data Preprocessing

[1370] The server integrates the collected inventory data, driver availability data, road conditions, and weather data. It analyzes this integrated data and performs preprocessing such as removing outliers and filling in missing values. Specifically, it rationally fills in missing values ​​in the dataset and filters out abnormal data.

[1371] Analysis using generative AI models

[1372] The server inputs the preprocessed integrated data into a generative AI model. The generative AI model is implemented using, for example, a Python library (TensorFlow or PyTorch). The server uses the generative AI model to generate a price proposal based on the desired delivery date. For example, it generates price proposals such as "next day delivery: 1,500 yen" and "3-day delivery: 1,200 yen."

[1373] Emotion recognition by emotion engine

[1374] The device uses an emotion engine to recognize the emotions expressed when the user operates the system. The emotion engine is a technology that collects and analyzes emotional data from the user's facial expressions, voice, text, etc. For example, it can use a camera and microphone to analyze the user's facial expressions and tone of voice to identify emotions such as "stress" or "joy."

[1375] Pricing and Offering

[1376] The server adjusts the generated price proposal based on the emotional data analyzed by the emotion engine. For example, if the user is feeling stressed, the price will be set lower. The server also sends the final price information to the terminal and presents it to the user. For example, it displays information such as "Next day delivery: 1,400 yen" and "3 days later delivery: 1,100 yen."

[1377] User Choices and Order Acceptance

[1378] The user selects the desired delivery date and price from the presented price and delivery date options. The terminal sends the user's selection to the server, which accepts the order data and updates it in the distribution center's database. The server also creates an optimal delivery plan based on the accepted order data and preprocessed driver availability data, and sends it to the distribution center's system.

[1379] In this way, the system of the present invention not only improves the efficiency of each step in logistics but also provides personalized services based on the emotional state of the user, which is expected to improve customer satisfaction.

[1380] Prompt Sentence Examples

[1381] "Dynamic pricing should be based on current inventory at distribution centers, driver availability, road congestion, and weather data. Also, adjust pricing based on the user's emotional state. Specifically, offer two pricing options: next-day delivery and three-day delivery."

[1382] This enables the system to simultaneously improve logistics efficiency and customer satisfaction.

[1383] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1384] Step 1:

[1385] The server accesses the distribution center's database and obtains current inventory information. Specifically, it obtains the inventory quantity and type for each product from the database and saves this information in list format. The input data is the database connection information and query statement, and the output data is a list of inventory information. For example, it obtains data such as "100 units of product A are in stock, and 50 units of product B are in stock."

[1386] Step 2:

[1387] The server accesses the driver resource management system to obtain the number and status of available drivers. Specifically, it obtains a list of currently available drivers from the resource management system via API. The input data is the API endpoint and authentication information, and the output data is driver resource information. For example, it obtains data such as "5 drivers are available."

[1388] Step 3:

[1389] The server calls external APIs to collect current road conditions and weather data. Specifically, it obtains and integrates data from traffic information services and weather information services. The input data is the endpoint and search conditions of the external API, and the output data is current road conditions and weather information. For example, it collects "traffic congestion information and weather data (rainy, sunny, etc.) in a specific area."

[1390] Step 4:

[1391] The server integrates the collected inventory data, driver availability data, road conditions, and weather data. Specifically, it combines the individually acquired data into a single dataset. The input data is the various data acquired at each step, and the output data is the integrated dataset. For example, it creates integrated data such as "100 units in stock, 5 drivers, traffic information available, rainy weather."

[1392] Step 5:

[1393] The server analyzes this integrated data and performs preprocessing such as removing outliers and filling missing values. Specifically, it applies a data cleansing process to improve the quality of the data. The input data is the integrated dataset, and the output data is a clean, preprocessed dataset. For example, it performs processes such as "removing outliers and filling missing data."

[1394] Step 6:

[1395] The server inputs the preprocessed integrated data into a generative AI model. Specifically, the data is input into a trained AI model using Python libraries (TensorFlow and PyTorch) and analyzed. The input data is a clean integrated dataset, and the output data is the model's prediction results. For example, the resulting price suggestions are "next day delivery: 1,500 yen" and "3-day delivery: 1,200 yen."

[1396] Step 7:

[1397] The device uses an emotion engine to recognize the emotions expressed when the user operates the system. Specifically, it uses a camera and microphone to analyze the user's facial expressions and voice in real time to collect emotional data. The input data is the user's facial expressions and voice data, and the output data is the analyzed emotional information. For example, it can identify emotions such as "stress" and "joy."

[1398] Step 8:

[1399] The server adjusts the generated price proposal based on the collected emotional data. Specifically, it applies an algorithm that changes the price according to the user's emotional state. The input data is the price proposal from the AI ​​model and emotional data, and the output data is the adjusted price information. For example, "If the user is feeling stressed, adjust the price to 1,400 yen for next-day delivery and 1,100 yen for three-day delivery."

[1400] Step 9:

[1401] The server sets the final price based on the emotion data analyzed by the emotion engine and sends the price information to the terminal. Specifically, it converts the price information into a user-friendly format and creates display elements. The input data is the adjusted price information, and the output data is the price information displayed to the user.

[1402] Step 10:

[1403] The user selects the desired delivery date and price from the presented price and delivery date options. Specifically, the user clicks on the desired plan from the options displayed on the terminal. The input data is the user's selection, and the output data is the selection information.

[1404] Step 11:

[1405] The terminal transmits the user's selection to the server. Specifically, the terminal transmits the selection data as a request to the server to trigger processing. The input data is the user's selection information, and the output data is the request to the server.

[1406] Step 12:

[1407] The server receives the selection information sent by the user and updates the order data in the distribution center database. Specifically, it updates inventory information based on the selected delivery date and price and confirms the order. The input data is the user's selection information, and the output data is the updated order data.

[1408] Step 13:

[1409] The server creates an optimal delivery plan based on the received order data and pre-processed driver availability data. Specifically, it calculates efficient delivery routes and assigns drivers. The input data are order data and driver resource information, and the output data is the optimal delivery plan.

[1410] Step 14:

[1411] The server sends the created optimal delivery plan to the logistics center's system. Specifically, the delivery plan is registered in a database and necessary instructions are sent to the driver. The input data is the optimal delivery plan, and the output data is updated information from the logistics center and instructions to the driver.

[1412] This will streamline each step of logistics and enable personalized services based on the user's emotional state.

[1413] (Application example 2)

[1414] 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."

[1415] In the logistics industry, not only is there a need for efficient inventory management and delivery resources, but there is also a need for personalized services based on user emotions. However, conventional systems are unable to set prices or offer services that take user emotions into account, limiting the improvement of customer satisfaction. Therefore, there is a need for a system that recognizes the user's emotional state and can adjust prices and offer services based on that.

[1416] 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 a means for acquiring inventory status in logistics, a means for acquiring driver availability status in logistics, a means for acquiring road and weather information, a means for preprocessing the acquired information and inputting it into a generative AI model, a means for generating a price proposal according to a desired delivery date, a means for setting a product price based on the generated price proposal and presenting it to the user, a means for accepting orders and creating a delivery plan depending on the user's selection, a means for recognizing and acquiring user emotion data, a means for adjusting the price proposal based on the acquired emotion data, and a means for presenting the adjusted price information to the user. This enables flexible pricing based on user emotions and personalized service provision.

[1417] "Inventory status in logistics" is information that indicates the quantity and condition of products stored in logistics centers and warehouses.

[1418] "Driver availability status" is information that indicates the number of drivers required for product delivery, their operating status, and available resources.

[1419] "Road conditions" refers to information that indicates the degree of road congestion, traffic jams, and traffic accident occurrences that affect logistics.

[1420] "Weather information" refers to weather data such as rainfall, snowfall, temperature, and wind speed that affect logistics planning.

[1421] "Preprocessing" refers to the process of processing the acquired data by methods such as removing outliers and filling in missing values, and preparing it in a format suitable for a generative AI model.

[1422] A "generative AI model" is an artificial intelligence model that generates price suggestions based on the desired delivery date based on input data.

[1423] A "price suggestion" is a suggestion that provides an appropriate product price based on the user's desired delivery date and other conditions.

[1424] "Emotional data" refers to information about a user's emotional state obtained from facial expressions, voice, text, etc.

[1425] "Price adjustment" refers to changing the price proposals generated by a generative AI model based on user sentiment data.

[1426] A "delivery plan" is a plan that creates optimal delivery routes and schedules based on received order information.

[1427] "Personalized" refers to providing services that are tailored to each user's individual feelings and needs.

[1428] To implement this invention, the logistics system must go through the following steps: data collection, data preprocessing, utilization of generative AI models, emotion recognition, price adjustment, and information presentation. This system consists of a server, terminals, and users. Specifically, the following hardware and software are used:

[1429] Hardware and software used

[1430] Server: Responsible for acquiring, processing, and analyzing data. A server with high-performance computing power is required.

[1431] Smart glasses: Used by workers at logistics centers, these devices display information and recognize emotions. They have built-in cameras, microphones, and displays.

[1432] Generative AI models: Built using deep learning frameworks such as TensorFlow and PyTorch.

[1433] Emotion recognition engine: Utilizing OpenCV, Emotion API, etc., it analyzes user emotions in real time.

[1434] System operation explanation

[1435] The server first accesses the distribution center's database to obtain current inventory information, including the current stock quantity of each product. Next, the server accesses the driver resource management system to obtain the number of available drivers and their operating status. Finally, it calls an external API to collect current road conditions and weather data.

[1436] The collected data is preprocessed on the server. Outliers are removed and missing values ​​are filled, and the integrated data is input into a generative AI model. This generative AI model generates price suggestions based on the collected data according to the desired delivery date. For example, it generates price suggestions such as "next day delivery: 1,500 yen" and "3-day delivery: 1,200 yen."

[1437] Next, when a worker wearing smart glasses checks the proposal, the emotion recognition engine works to collect emotional data from the worker's facial expressions and voice. The emotional data is sent to the server, and the price proposal is adjusted. For example, if the worker is feeling stressed, the price may be set lower. This allows for personalized price proposals.

[1438] Finally, the adjusted price information is displayed on the smart glasses' display for the worker to confirm. The user's desired delivery date and price information are sent to the server, which updates the order data. An optimal delivery plan is then created and transmitted to the logistics center.

[1439] Examples of concrete examples and prompts

[1440] As a concrete example, consider a scenario in which staff working in a logistics center are wearing smart glasses. As the staff picks each item, the smart glasses display inventory information and price suggestions in real time. If the emotion recognition engine determines that the staff member is tired, the smart glasses will adjust the price suggestions and delivery method.

[1441] Prompt Sentence Examples

[1442] "It retrieves up-to-date inventory information, integrates and processes driver resource information, road conditions and weather data, and uses generative AI models to make price suggestions based on the desired delivery date. It also recognizes the emotions of workers wearing smart glasses in real time and dynamically adjusts price suggestions."

[1443] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1444] Step 1: Data collection

[1445] The server accesses the distribution center's database to obtain current inventory information. This inventory information includes the current stock quantity of each product. The server also accesses the driver resource management system to obtain the number of available drivers and their operating status. It also calls an external API to collect current road conditions and weather information. This inputs inventory data, driver resource data, road condition data, and weather data.

[1446] Step 2: Data Preprocessing

[1447] The server preprocesses the data obtained in step 1. Specifically, it cleanses the data by removing outliers and imputing missing values. This generates an integrated dataset that is in a suitable format for the generative AI model. The input is all the datasets obtained in the previous steps, and the output is the preprocessed integrated data.

[1448] Step 3: Generate price proposals

[1449] The server inputs the preprocessed integrated data into a generative AI model. This AI model generates price proposals based on the desired delivery date and other conditions. Specifically, it proposes prices such as "next day delivery: 1,500 yen" and "three-day delivery: 1,200 yen." The input is the preprocessed integrated data, and the output is the generated price proposal.

[1450] Step 4: Obtaining emotion data

[1451] An emotion recognition engine is operated to obtain real-time emotional data from the terminal, specifically the worker wearing the smart glasses. Emotional data is collected from the worker's facial expressions and voice and sent to the server. The input is the worker's facial expressions and voice data, and the output is analyzed emotional data.

[1452] Step 5: Adjust your pricing proposal

[1453] The server adjusts the price proposal based on the emotional data obtained in step 4. For example, if the worker is feeling stressed, it may set the price 10% lower. The input is the emotional data and the generated price proposal, and the output is the adjusted price proposal.

[1454] Step 6: Information presentation

[1455] The terminal displays the adjusted price proposal on the smart glasses, which the worker can review and make appropriate updates or check the delivery plan. The input is the adjusted price proposal, and the output is the displayed price information.

[1456] Step 7: User selection and order data update

[1457] The user selects the desired delivery date and price information, and sends it from the terminal to the server. The server updates the order data based on the received information and creates a new delivery plan. The input is the user's selection, and the output is the updated order data and the new delivery plan.

[1458] Step 8: Optimize your delivery plan

[1459] The server creates an optimal delivery plan based on the updated order data and pre-processed driver resource data. This plan is transmitted to the distribution center. The input is the updated order data and driver data, and the output is the optimal delivery plan.

[1460] 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.

[1461] 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.

[1462] 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.

[1463] 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.

[1464] 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.

[1465] 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.

[1466] 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).

[1467] 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.

[1468] 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."

[1469] 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.

[1470] 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).

[1471] 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.

[1472] 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.

[1473] 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.

[1474] 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.

[1475] 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.

[1476] 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.

[1477] 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.

[1478] 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.

[1479] 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.

[1480] 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.

[1481] The following is further disclosed regarding the above embodiment.

[1482] (Claim 1)

[1483] A means for acquiring inventory status in logistics;

[1484] A means for obtaining driver availability status for logistics;

[1485] means for obtaining road condition and weather data;

[1486] A means of preprocessing these acquired data and inputting them into a generative AI model;

[1487] means for generating price proposals according to desired delivery dates;

[1488] a means for pricing the product based on the generated price proposals and presenting the price to the user;

[1489] A means for accepting orders and creating delivery plans at the user's discretion;

[1490] Logistics system including.

[1491] (Claim 2)

[1492] 10. The system of claim 1, further comprising means for integrating the acquired inventory status, driver availability, road conditions, and weather data, and for processing outliers and imputing missing values.

[1493] (Claim 3)

[1494] 2. The system according to claim 1, further comprising means for receiving desired delivery date and price information selected by a user, updating order data, and creating an optimal delivery plan and transmitting it to a logistics center.

[1495] "Example 1"

[1496] (Claim 1)

[1497] A means for acquiring inventory status in logistics;

[1498] A means for obtaining driver availability status for logistics;

[1499] means for obtaining road condition and weather data;

[1500] A means of preprocessing these acquired data and inputting them into a generative AI model;

[1501] means for generating price proposals according to desired delivery dates;

[1502] a means for pricing the product based on the generated price proposals and presenting the price to the user;

[1503] A means for accepting orders and creating delivery plans at the user's discretion;

[1504] a means for displaying the offered price information to the user and allowing the user to select a desired delivery date and price;

[1505] A means for receiving the user's selected desired delivery date and price information and updating the order data;

[1506] A means to create an optimal delivery plan and communicate it to the logistics center;

[1507] A system including:

[1508] (Claim 2)

[1509] 10. The system of claim 1, further comprising means for integrating the acquired inventory status, driver availability, road conditions, and weather data, and for processing outliers and imputing missing values.

[1510] (Claim 3)

[1511] 2. The system according to claim 1, further comprising means for receiving desired delivery date and price information selected by a user, updating order data, and creating an optimal delivery plan and transmitting it to a logistics center.

[1512] "Application Example 1"

[1513] (Claim 1)

[1514] A means for acquiring inventory status in logistics;

[1515] A means for obtaining driver availability status for logistics;

[1516] means for obtaining road condition and weather data;

[1517] A means of preprocessing these acquired data and inputting them into a generative AI model;

[1518] means for generating price proposals according to desired delivery dates;

[1519] a means for pricing the product based on the generated price proposals and presenting the price to the user;

[1520] A means for accepting orders and creating delivery plans at the user's discretion;

[1521] means for displaying said price proposals and delivery plans in real time using smart glasses and for a user to make a selection;

[1522] A system including:

[1523] (Claim 2)

[1524] 10. The system of claim 1, further comprising means for integrating the acquired inventory status, driver availability, road conditions, and weather data, and for processing outliers and imputing missing values.

[1525] (Claim 3)

[1526] 2. The system according to claim 1, further comprising means for receiving desired delivery date and price information selected by a user, updating order data, and creating an optimal delivery plan and transmitting it to a logistics center.

[1527] "Example 2: Combining Emotion Engines"

[1528] (Claim 1)

[1529] A means for acquiring inventory status in logistics;

[1530] A means for obtaining driver availability status for logistics;

[1531] means for obtaining road condition and weather data;

[1532] A means of preprocessing these acquired data and inputting them into a generative AI model;

[1533] means for generating price proposals according to desired delivery dates;

[1534] a means of recognizing a user's emotions;

[1535] a means for adjusting the price of the product based on the generated price proposal and presenting it to the user based on the emotional state of the user;

[1536] A means for accepting orders and creating delivery plans at the user's discretion;

[1537] A system including:

[1538] (Claim 2)

[1539] 10. The system of claim 1, further comprising means for integrating the acquired inventory status, driver availability, road conditions, and weather data, and for processing outliers and imputing missing values.

[1540] (Claim 3)

[1541] 2. The system according to claim 1, further comprising means for receiving desired delivery date and price information selected by a user, updating order data, and creating an optimal delivery plan and transmitting it to a logistics center.

[1542] "Application example 2 when combining emotion engines"

[1543] (Claim 1)

[1544] A means for acquiring inventory status in logistics;

[1545] A means for obtaining driver availability status for logistics;

[1546] means for obtaining road and weather information;

[1547] A means of preprocessing this acquired information and inputting it into a generative AI model;

[1548] means for generating price proposals according to desired delivery dates;

[1549] a means for pricing the product based on the generated price proposals and presenting the price to the user;

[1550] A means for accepting orders and creating delivery plans at the user's discretion;

[1551] A means for recognizing and acquiring user emotional data;

[1552] means for adjusting price proposals based on the obtained sentiment data;

[1553] a means for presenting the adjusted pricing information to the user;

[1554] A system including:

[1555] (Claim 2)

[1556] The system of claim 1, further comprising means for integrating the acquired inventory status, driver availability status, road status, and weather information, and for processing outliers and completing missing values.

[1557] (Claim 3)

[1558] 2. The system according to claim 1, further comprising means for receiving desired delivery date and price information selected by a user, updating order data, and creating an optimal delivery plan and transmitting it to a logistics center. [Explanation of symbols]

[1559] 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 acquiring inventory status in logistics; A means for obtaining driver availability status for logistics; means for obtaining road condition and weather data; A means of preprocessing these acquired data and inputting them into a generative AI model; means for generating price proposals according to desired delivery dates; a means for pricing the product based on the generated price proposals and presenting the price to the user; A means for accepting orders and creating delivery plans at the user's discretion; Logistics system including.

2. 10. The system of claim 1, further comprising means for integrating the acquired inventory status, driver availability, road conditions, and weather data, and for processing outliers and imputing missing values.

3. 2. The system according to claim 1, further comprising means for receiving desired delivery date and price information selected by a user, updating order data, and creating an optimal delivery plan and transmitting it to a logistics center.

Images