system

Abstract

We provide the system. [Solution] Data acquisition method, A data analysis means for analyzing traffic information, weather information, and sales data acquired by the data acquisition means, A route optimization means that optimizes the delivery route based on the analysis results from the data analysis means, A communication means for notifying the carrier of the delivery route optimized by the route optimization means, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the logistics industry, there are problems such that due to changes in traffic conditions and weather, the delivery efficiency deteriorates, resulting in an increase in delivery costs. Also, when inventory management based on demand fluctuations is insufficient, stockouts or overstock may occur, causing economic losses and opportunity losses. Furthermore, when information transmission at the logistics site is not effectively carried out, communication among employees becomes insufficient, leading to a problem of reduced work efficiency.

Means for Solving the Problems

[0005] This invention was proposed to solve the above problems. It collects traffic information, weather information, and historical sales data in real time using data acquisition means and performs analysis using data analysis means. Based on the results of this analysis, it optimizes delivery routes using route optimization means and provides the optimized route information to the transporter, thereby improving delivery efficiency. Furthermore, it predicts fluctuations in demand using inventory forecasting means and automatically replenishes inventory using appropriate inventory management means, thereby resolving problems in inventory management. In addition, it facilitates communication with workers using dialogue means that use natural language and effectively transmits work instructions through instruction generation means, thereby significantly improving on-site operational efficiency.

[0006] "Data acquisition means" refers to a mechanism for collecting traffic information, weather information, and sales data in real time.

[0007] A "data analysis tool" is a mechanism for analyzing collected data and supporting decision-making based on the results.

[0008] A "route optimization mechanism" is a system that calculates and proposes the optimal delivery route based on analyzed data.

[0009] "Communication means" refers to a mechanism for notifying carriers of optimized delivery route information.

[0010] An "inventory forecasting tool" is a mechanism for predicting demand based on past sales data and current market trends.

[0011] An "inventory management system" is a mechanism for automatically replenishing inventory based on predicted demand.

[0012] A "dialogue mechanism" is a system for effectively communicating with workers using natural language.

[0013] An "instruction generation means" is a mechanism for generating and transmitting work instructions based on information obtained through dialogue means. [Brief explanation of the drawing]

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

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

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

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

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

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

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] To implement the present invention, it is necessary to construct an information processing system that includes data acquisition means, data analysis means, route optimization means, communication means, inventory forecasting means, inventory management means, dialogue means, and instruction generation means. This system is designed to streamline logistics operations.

[0036] Overall Operation Description

[0037] 1. Data collection:

[0038] Subject: Server

[0039] The server acquires traffic information, weather information, and sales data in real time through APIs and sensors.

[0040] 2. Data Analysis:

[0041] Subject: Server

[0042] The server inputs the collected data into an AI algorithm to perform demand forecasting. It also analyzes the optimal route to maximize delivery efficiency.

[0043] 3. Optimization and notification of delivery routes:

[0044] Subject: Server

[0045] Based on the analysis results, the server generates an optimized delivery route considering multiple variables. This route is then transmitted to the terminal via communication, notifying the driver. The terminal then relays the received instructions to the user, ensuring smooth delivery.

[0046] 4. Automating inventory management:

[0047] Subject: Server

[0048] The server uses inventory forecasting tools to predict demand and automatically replenishes inventory based on that forecast. This prevents inventory surpluses and shortages, improving economic efficiency.

[0049] 5. Use of conversational agents:

[0050] Subject: User

[0051] Users can communicate with the system using natural language through dialogue mechanisms. For example, if a user asks, "Where is the next delivery destination?", the system will respond with "Please head to Store C" using a command generation mechanism based on the analyzed optimal route. This dialogue can be conducted in multiple languages, making it suitable for global business operations.

[0052] By operating this system, it is possible to respond quickly to changes in traffic conditions, shorten delivery times, and reduce logistics costs. Furthermore, by streamlining inventory management and improving communication, overall operational efficiency can be dramatically increased.

[0053] The following describes the processing flow.

[0054] Step 1:

[0055] Subject: Server

[0056] The server acquires traffic information, weather information, and sales data in real time from external APIs and sensors. The acquired data is stored in a database and used for subsequent analysis.

[0057] Step 2:

[0058] Subject: Server

[0059] The server uses data analysis tools to input the collected data into an AI algorithm. Here, the machine learning model analyzes the data and creates a foundation for optimizing demand forecasting and delivery efficiency.

[0060] Step 3:

[0061] Subject: Server

[0062] Based on the analysis results, the server uses route optimization techniques to calculate the optimal delivery route. This is a process of selecting the best solution from multiple candidate routes, taking into account time, distance, and fuel efficiency.

[0063] Step 4:

[0064] Subject: Server

[0065] The server uses a communication method to send information about the calculated optimal route to the terminal. The terminal then receives the route information and prepares for the next step.

[0066] Step 5:

[0067] Subject: terminal

[0068] The terminal notifies the user of the received route information via voice or text message. This allows the transporter to clearly understand what action to take next.

[0069] Step 6:

[0070] Subject: Server

[0071] The server uses inventory forecasting tools to analyze demand based on collected data and automatically replenishes inventory. This helps mitigate inventory surpluses and shortages.

[0072] Step 7:

[0073] Subject: User

[0074] The user interacts with the system using natural language through a dialogue mechanism. For example, if the user requests specific instructions from the system, the dialogue mechanism utilizes an instruction generation mechanism to generate appropriate instructions based on the user's request.

[0075] (Example 1)

[0076] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0077] In logistics operations, there is a growing need to improve transportation efficiency through the effective use of traffic information, weather information, and sales data. However, systems that can process this data quickly and accurately and provide optimal transportation routes and inventory management in real time are still lacking. In particular, there is a need for comprehensive solutions that enable inventory replenishment based on demand forecasts and the generation of work instructions through multilingual natural language dialogue.

[0078] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0079] In this invention, the server includes a data acquisition device, an analysis device, a route optimization device, a notification device, and a demand forecasting device. This enables efficient optimization of transportation routes and automation of inventory management through real-time data collection, analysis, and notification. Furthermore, demand forecasting using a generative AI model and multilingual dialogue are supported, enabling improved efficiency of the entire logistics operation.

[0080] A "data acquisition device" is a device for collecting traffic-related data, weather-related data, and sales-related data, and has the function of acquiring necessary data in real time from multiple information sources.

[0081] An "analysis device" is a device that performs traffic condition analysis and demand forecasting based on acquired data, and it executes programs and algorithms that perform data analysis.

[0082] A "route optimization device" is a device that uses analyzed data to perform calculations to optimize transportation routes, considering route combinations to achieve efficient transportation.

[0083] A "notification device" is a device that notifies carriers of optimized transportation routes and is equipped with communication functions for sending instructions.

[0084] A "demand forecasting device" is a device that uses a generation AI model to predict future demand and generates information to improve the efficiency of logistics operations.

[0085] This invention provides an information processing system for streamlining logistics operations. The system includes multiple devices that perform data collection, analysis, notification, and demand forecasting. Examples of each device are described below.

[0086] The server collects traffic-related data, weather-related data, and sales-related data using data acquisition devices. To retrieve this data via APIs, the server establishes a network connection. Specifically, it uses the Google® Maps API to collect traffic data and weather information from weather data services such as OpenWeather.

[0087] The server processes the acquired data using an analysis device. Here, the data is preprocessed using the Python Pandas library and then analyzed using an AI algorithm. By using a generative AI model, it is possible to predict future demand and improve the efficiency of logistics activities based on the analysis results.

[0088] Furthermore, the server uses a route optimization device to calculate the optimal transportation route based on the collected and analyzed data. By utilizing Google OR-Tools to solve the shortest path problem, it generates efficient logistics routes.

[0089] Through the notification device, the server transmits optimized route information to the transporter. The information received by the terminal is conveyed to the user via a voice assistant or display and used as specific instructions. Based on this information, the user can carry out logistics activities smoothly.

[0090] Furthermore, the server automates inventory management based on demand forecasts. The forecasting model is built using a machine learning algorithm with TENSORFLOW®, thereby reducing the risk of inventory surpluses or shortages.

[0091] The system includes a conversational agent function for interacting with users. Using a generative AI model, it can respond to a user's natural language question, such as "Where is the next delivery destination?", with an instruction like "Please head to store C." An example of a prompt would be, "Please optimize the upcoming delivery route based on current traffic and weather data."

[0092] In this way, the server efficiently utilizes diverse information, thereby improving the overall efficiency of logistics operations.

[0093] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0094] Step 1:

[0095] The server collects traffic-related data, weather-related data, and sales-related data through data acquisition devices. It uses API access information and network connectivity as input. The server retrieves the necessary data via APIs and obtains it in real time. In this process, it directly obtains necessary information from Google Maps and common weather data services.

[0096] Step 2:

[0097] The server preprocesses the data acquired using the analysis device. The input is the raw data obtained in step 1. The server uses the Python Pandas library to format and normalize the data and impute missing values. This process transforms the data into a format suitable for analysis and prepares it for the next analysis step.

[0098] Step 3:

[0099] The server uses a generative AI model to forecast demand. Preprocessed data is used as input. The generative AI model is prompted to perform the forecast. In this step, TensorFlow is used to operate the AI ​​model and numerically predict future demand. The output is the predicted demand data.

[0100] Step 4:

[0101] The server uses a route optimization tool to optimize transportation routes based on predicted demand and current traffic conditions. It uses demand forecast data and real-time traffic data as input. Leveraging Google OR-Tools, it calculates the shortest route and generates efficient transportation routes. The output provides optimized route information.

[0102] Step 5:

[0103] The server sends optimized route information to the terminal via a notification device. It uses the route information generated in the previous step as input. The terminal receives this information and outputs specific instructions to the user. For example, it might send a notification such as "Go to point A next" via screen display or voice command.

[0104] Step 6:

[0105] The server automates inventory management based on demand forecast results. It uses demand forecast data as input. The server integrates with the inventory management system to calculate the required inventory levels and automatically places orders. This results in output that prevents inventory shortages or surpluses.

[0106] Step 7:

[0107] The user communicates with the server using dialogue methods and natural language. Input includes the user's voice and text questions. The server utilizes a generative AI model to analyze the questions, generate appropriate instructions, and respond to the user. Output includes instructions and information provided through the interaction with the user.

[0108] (Application Example 1)

[0109] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0110] In modern logistics systems, efficient inventory management and optimized transportation are crucial for reducing operating costs and improving operational efficiency at logistics centers. However, conventional methods struggle to obtain real-time information and provide optimal routes, resulting in insufficient information for workers to perform appropriate operations within the warehouse. Technologies are needed to solve these problems.

[0111] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0112] In this invention, the server includes data acquisition means, data analysis means for analyzing traffic information, weather information, and transaction data acquired by the data acquisition means, route optimization means for optimizing the transportation route based on the analysis results by the data analysis means, communication means for notifying the transportation manager of the transportation route optimized by the route optimization means, and movement route provision means for presenting the optimal in-warehouse movement route to workers using a visual device. This enables efficient movement of workers in a logistics center and automation of inventory management.

[0113] "Data acquisition methods" refer to means of collecting various types of information related to logistics in real time.

[0114] "Data analysis methods" refer to means of performing demand forecasting and route optimization based on acquired traffic information, weather information, and transaction data.

[0115] A "route optimization method" is a means of calculating and presenting an efficient transportation route based on data analysis results.

[0116] "Communication means" refers to means of notifying transportation managers and workers of optimized transportation routes and other important instructions.

[0117] A "means for providing movement routes" refers to a means of visually guiding workers to the optimal movement route within a warehouse via a visual device.

[0118] A "demand forecasting tool" is a means of predicting future demand by analyzing past and present data.

[0119] "Inventory management methods" refer to means for automatically managing and executing inventory replenishment in accordance with predicted demand.

[0120] A "dialogue support system" is a means of directly interacting with workers using natural language processing and providing them with necessary information.

[0121] An "instruction generation means" is a means for generating specific work instructions based on information from a dialogue support means and presenting them to the worker.

[0122] A "location identification support system" is a means of identifying the current location of a worker based on sensor information obtained from a visual device, and providing efficient work support.

[0123] According to this invention, in order to operate a system efficiently in a logistics center, it is necessary to collect and analyze various data in real time and provide optimal instructions to workers. Embodiments of this invention will be described in detail below.

[0124] The server first uses data acquisition methods to collect traffic information, weather information, and transaction data in real time. This data is obtained from mobile devices and sensors via APIs. This enables analysis based on the latest information.

[0125] Next, the server analyzes the data using AI algorithms such as TensorFlow to perform demand forecasting and route optimization. The results of this analysis are used as foundational data for inventory management and transportation planning at the logistics center. The optimal route calculated by the route optimization system is immediately communicated to the workers' terminals via communication.

[0126] The terminal displays the received optimal route information to the worker through a visual device. By using a visual device such as smart glasses, the worker can understand the optimal route within the warehouse in real time. This visual information improves work efficiency.

[0127] Furthermore, the server uses natural language processing-based dialogue support to instantly respond to voice input from workers and provide specific work instructions. This dialogue function supports multiple languages, making it possible to handle global operations.

[0128] For example, if a worker asks "What is the next task?" via voice, the server will generate an instruction based on the analyzed data, such as "Next, pick items in section B." Another example of a prompt message would be, "Retrieve inventory data and suggest the next optimal work route. Current location is the central aisle, target is picking area A."

[0129] In this way, the overall operation of the logistics center is highly streamlined by combining data analysis and optimized route guidance, ensuring efficient movement and significantly improving work efficiency.

[0130] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0131] Step 1:

[0132] The server uses data acquisition methods to collect traffic information, weather information, and transaction data in real time. Inputs are data from APIs and sensors, which are collected by polling at regular intervals. This data is then prepared for subsequent analysis.

[0133] Step 2:

[0134] The server inputs the collected data into an AI algorithm through data analysis tools to perform demand forecasting and route optimization. The input is the data collected in step 1, which is analyzed using an algorithm based on TensorFlow. The output is information on optimal inventory placement and delivery routes, which prepares the data for the next processing step.

[0135] Step 3:

[0136] Based on the analysis results, the server notifies the terminal of the optimal transport route calculated by the route optimization means via the communication means. The input is the analysis output from step 2. The optimal route information is sent to the terminal, and an actionable transport plan is formulated.

[0137] Step 4:

[0138] The terminal displays the optimal movement route in real time via a visual device worn by the worker. The input is the route information transmitted in step 3, which is presented to the worker as visual information using smart glasses or similar devices. The output is the worker's efficient movement within the warehouse.

[0139] Step 5:

[0140] The user queries the server in natural language using a dialogue support system, and receives specific work instructions from an instruction generation system. The input is voice data from the user, which is analyzed and processed by the server using natural language processing. As a result, work instructions are generated and presented to the user in either voice or text format.

[0141] Step 6:

[0142] Based on acquired sensor information, the server uses location identification support to determine the worker's current location and guides them to the optimal work position using a visual device. The input is real-time location data from the visual device, and the worker's position is calculated by a location identification algorithm. This allows the worker to move to the next task immediately, enabling efficient work.

[0143] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0144] The present invention is implemented by constructing an information processing system that includes data acquisition means, data analysis means, route optimization means, communication means, inventory forecasting means, inventory management means, dialogue means, instruction generation means, and an emotion engine. This system is designed to improve the efficiency of logistics operations and enable effective interaction with users.

[0145] Overall Operation Description

[0146] 1. Data collection and analysis:

[0147] Subject: Server

[0148] The server collects traffic information, weather information, and sales data in real time through external APIs and sensors, analyzes the data to forecast demand, and uses this information for inventory management and delivery route planning.

[0149] 2. Optimization and notification of delivery routes:

[0150] Subject: Server

[0151] The server calculates the optimal delivery route based on the analysis results and transmits it to the terminal via communication. The terminal then notifies the driver of the optimized route information and supports the transportation.

[0152] 3. Automating inventory forecasting and management:

[0153] Subject: Server

[0154] The server uses inventory forecasting tools to predict future demand, and based on that, automatically replenishes inventory using inventory management tools to maintain a balance between supply and demand.

[0155] 4. Recognition of user emotions by an emotion engine:

[0156] Subject: Server

[0157] The server uses an emotion engine to recognize emotions from the user's voice and text. This information is fed back into the entire system and influences responses to interactions and work instructions.

[0158] 5. Optimizing dialogue:

[0159] Subject: User

[0160] Users interact with the system through dialogue. The system generates responses that take the user's emotional state into account, enabling more personalized communication. For example, if the user sounds busy, the system will provide quick and concise instructions.

[0161] This system enables efficient decision-making based on real-time circumstances, while also facilitating emotion-based communication with users, thereby improving overall work efficiency and satisfaction.

[0162] The following describes the processing flow.

[0163] Step 1:

[0164] Subject: Server

[0165] The server collects real-time traffic information, weather information, and sales data through external APIs and sensors. This data is systematically stored in a database and forms the basis for subsequent data analysis.

[0166] Step 2:

[0167] Subject: Server

[0168] The server uses data analysis tools to analyze the collected data with AI algorithms. This analysis creates a demand forecasting model, which in turn generates conditions for optimizing delivery routes.

[0169] Step 3:

[0170] Subject: Server

[0171] The server uses route optimization techniques to evaluate various route options and calculate the optimal route considering time, fuel efficiency, and traffic conditions. The optimized route information is then used in the next step.

[0172] Step 4:

[0173] Subject: Server

[0174] The server transmits the calculated optimal delivery route to the terminal via communication means. This allows the transporter to obtain the information in a usable format.

[0175] Step 5:

[0176] Subject: terminal

[0177] The terminal notifies the user of delivery route information received from the server via voice or text. The user can then quickly take action by following the instructions.

[0178] Step 6:

[0179] Subject: Server

[0180] The server uses inventory forecasting tools to automatically replenish inventory based on sales data and demand forecasts. This prevents inventory shortages and surpluses, enabling efficient inventory management.

[0181] Step 7:

[0182] Subject: Server

[0183] The server uses an emotion engine to recognize emotions from the user's voice and text. The recognized emotion data is then fed back to the dialogue mechanism.

[0184] Step 8:

[0185] Subject: User

[0186] Users interact with the system using natural language through dialogue. The system analyzes the user's emotional state and adjusts its responses accordingly, resulting in more appropriate instructions and support.

[0187] (Example 2)

[0188] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0189] In logistics operations, accurately forecasting traffic conditions, weather conditions, and market demand, developing efficient delivery plans, and properly managing inventory are extremely important. Furthermore, smooth communication with customers, while being mindful of their feelings, is also necessary. However, traditional methods make it difficult to manage all of these elements in an integrated manner, which can lead to decreased operational efficiency.

[0190] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0191] In this invention, the server includes means for acquiring data, means for analyzing traffic-related data, weather-related data, and sales-related data acquired by the data acquisition means, means for optimizing the transportation route based on the analysis results by the data analysis means, means for notifying the transportation personnel of the optimized transportation route via a communication means, and an emotion analysis engine for recognizing human emotions. This enables increased efficiency in logistics operations and effective communication with users.

[0192] "Means of acquiring data" refers to mechanisms for collecting information related to traffic, weather, and sales from external sources.

[0193] "Means for analysis" refers to the process of analyzing acquired data to perform appropriate demand forecasting and trend analysis.

[0194] "Methods for optimizing transportation routes" refer to methods for calculating the shortest and most efficient delivery route, taking into account traffic information and weather conditions.

[0195] "Means for notifying transportation personnel via means of communication" refers to a communication system for informing transportation personnel of the calculated optimal route information.

[0196] The "emotion analysis engine" is a function that detects the user's emotions from their voice or text and reflects them in the dialogue response.

[0197] "Means for forecasting demand" refers to methodologies for predicting future demand based on past data and market trends.

[0198] "Means for automated supply management" refers to automated systems that appropriately replenish and manage inventory in accordance with predicted demand.

[0199] "A means of processing natural language and generating instructions through interaction with the user" refers to a system that understands user input in natural language and creates appropriate instructions accordingly.

[0200] "Means for generating responses based on generative AI models" refers to a method of generating the optimal response to user input using a machine learning model.

[0201] This invention provides an information processing system that streamlines logistics operations and enables effective communication with users. The system includes data acquisition means, data analysis means, route optimization means, communication means, inventory forecasting means, inventory management means, dialogue means, instruction generation means, and an emotion engine.

[0202] First, the server utilizes common APIs to obtain traffic, weather, and sales-related data from external data sources. For example, it can use traffic information APIs and weather information APIs. The server processes the acquired data in real time and performs analysis for demand forecasting. This analysis may involve using statistical methods and machine learning algorithms.

[0203] Next, the server calculates the optimal transport route based on the analysis results. This calculation can utilize route-finding algorithms such as Dijkstra's algorithm. The optimized route information is transmitted to a terminal via communication, and the terminal presents the information to the driver through voice guidance or display on a screen. This allows the driver to deliver packages efficiently.

[0204] Furthermore, the server utilizes inventory forecasting tools to manage inventory appropriately based on future demand. This involves using machine learning models that predict demand from historical sales data and market trends. Based on this information, the inventory management system automatically performs the necessary replenishment operations.

[0205] Furthermore, the server uses an emotion engine to detect emotions from the user's voice and text, and feeds this back to the dialogue and instruction generation systems. Specifically, it uses natural language processing technology to analyze the user's statements and emotions in real time, and uses a generative AI model to generate appropriate responses according to the user's emotional state. For example, a possible prompt might be "Prepare an answer that will help the user relax."

[0206] This system enables real-time decision-making regarding logistics and inventory management, and facilitates emotion-based communication with users. This, in turn, improves overall operational efficiency and customer satisfaction.

[0207] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0208] Step 1:

[0209] The server retrieves data from external traffic information APIs, weather information APIs, and sales databases. The input is raw data obtained from the APIs or databases. The server receives this raw data, converts and cleans the data format, and prepares it for analysis. The output is a structured dataset.

[0210] Step 2:

[0211] The server performs demand forecasting using the organized data. The input is the structured data obtained in Step 1. The server inputs this data into a machine learning model, such as a time series analysis or multiple regression model, to predict future demand. The output obtained from this analysis is the demand forecast result, which includes information on demand fluctuations over time.

[0212] Step 3:

[0213] The server optimizes transportation routes based on demand forecasts, traffic information, and weather information. Inputs are forecasted demand data and current traffic and weather data. The server calculates the optimal route using route-finding algorithms such as Dijkstra's algorithm. The output is optimized transportation route information, which includes the route from the starting point to the destination.

[0214] Step 4:

[0215] The server transmits optimized transport route information to the terminal. The input is the transport route information obtained in step 3. The terminal receives this information via communication and displays it on its screen, as well as using a voice guidance system to communicate the information to the driver. The output is instruction information for the driver.

[0216] Step 5:

[0217] The server performs inventory forecasting based on historical sales data and predicted demand. The inputs are the demand forecast results from step 2 and inventory data. The server uses an inventory forecasting model to calculate appropriate inventory levels. The output is inventory replenishment information for each product that needs to be replenished.

[0218] Step 6:

[0219] The inventory management system receives inventory replenishment information from the server and automatically processes orders. The input is the inventory replenishment information provided by the server. Based on this information, the inventory management system places orders with suppliers and adjusts inventory levels. The output is the new order data.

[0220] Step 7:

[0221] The server receives voice input or text messages from the user and recognizes their emotions using an emotion analysis engine. The input is raw voice or text data from the user. The server uses natural language processing techniques to analyze this data and identify the user's emotions. The output is the analyzed emotion data.

[0222] Step 8:

[0223] The server uses a generative AI model based on emotional data to generate appropriate responses. The input consists of the user's emotional data and dialogue content. The server uses prompts such as "Prepare an answer that will help the user relax" to generate responses for the generative AI model. The output is an optimized response message for the user.

[0224] (Application Example 2)

[0225] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0226] In modern logistics operations, efficient inventory management and optimal delivery are crucial, but achieving this in real time and in response to changing circumstances is difficult. Furthermore, flexible responses that consider the emotional state of users are required when communicating information to workers. The challenge lies in providing an integrated system to address these issues.

[0227] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0228] In this invention, the server includes means for collecting data, means for analyzing movement information, environmental information, and sales data, and means for optimizing the transportation route. This enables efficient real-time inventory management and optimal delivery, as well as the provision of personalized responses that take into account the emotional state of the user.

[0229] "Means of data collection" refers to a system for automatically acquiring data such as traffic conditions, weather conditions, and sales trends from external sources.

[0230] "Movement information" refers to data about the location and movement paths of objects and people, and is used to optimize the transportation routes of goods.

[0231] "Environmental information" refers to data about the external environment, such as weather, temperature, and traffic conditions, and is a factor that influences the planning and execution of logistics.

[0232] "Sales data" refers to numerical information obtained from sales activities over a specific period, and is useful for demand forecasting and inventory management.

[0233] "Means of analysis" refers to a system that performs a series of processes to analyze collected data and derive useful information.

[0234] "Means for optimizing transportation routes" refers to the process of calculating and proposing the most efficient route for transporting goods based on data analysis.

[0235] "Efficient real-time inventory management" refers to a management system that has the ability to instantly grasp the current inventory status and adjust inventory appropriately according to demand.

[0236] A "personalized response" is a method of providing appropriate information and instructions that take into account the user's individual emotional state and circumstances.

[0237] The system that realizes this invention is configured to exchange data primarily through a server, user terminals, and, if necessary, a communication network. The server collects traffic information, weather information, and sales data in real time from external APIs and sensors. In this process, the Python Requests library and various APIs can be utilized. The collected data is then analyzed using analysis software such as TensorFlow to perform demand forecasting and optimize transportation routes.

[0238] The server then uses map services such as the Google Maps API to calculate the optimal delivery route based on the analysis results and notifies the terminal. The terminal receives this information and displays it to drivers and administrators in real time. A smartphone app is often used for this display method.

[0239] Furthermore, the server uses voice analysis libraries such as IBM Watson® to recognize the user's emotional state from their voice and generates appropriate responses based on that state through the Amazon Alexa Skills Kit. This personalized response allows users to receive quick information when they are busy.

[0240] For example, when a logistics center manager asks their smartphone, "What is today's delivery route?", the system provides an accurate response such as, "Calculating the optimal route... Route A is recommended to avoid traffic congestion." An example of a prompt would be, "Please tell me the real-time inventory status of the logistics center and the optimal delivery route."

[0241] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0242] Step 1:

[0243] The server acquires traffic information, weather information, and sales data in real time via external APIs and sensors. It receives the information in the required data format and prepares it for storage in the internal database. Inputs consist of various data from external sources, while outputs are unanalyzed data stored within the server.

[0244] Step 2:

[0245] The server analyzes the acquired data. Here, a predictive model is applied using mature Python data analysis libraries and TensorFlow, and the data processing necessary for demand forecasting and transportation route optimization is performed. The input is the data acquired in step 1, and the output is the demand forecast and unoptimized transportation route information obtained from the predictive model.

[0246] Step 3:

[0247] The server optimizes the transportation route based on the analysis results. It uses map services such as the Google Maps API to calculate the optimal route considering traffic conditions and geographical features. The input is the analysis results from step 2, and the output is the optimized transportation route information.

[0248] Step 4:

[0249] The server notifies the terminal of the optimized delivery route. This process involves sending the calculated optimal route information to the terminal device via communication, preparing it for display and guidance. The input is the optimized route information, and the output is the instruction information displayed on the terminal.

[0250] Step 5:

[0251] The server receives voice input from the user and performs emotion recognition using voice analysis tools such as IBM Watson. The input is voice data from the user, and the output is the analyzed emotion information.

[0252] Step 6:

[0253] Based on the emotion recognition results, the server generates personalized responses using a dialogue engine such as the Amazon Alexa Skills Kit. The generated responses are designed to provide information that takes into account the user's emotional state. The input is the emotion information obtained in step 5, and the output is the appropriate response to present to the user.

[0254] Step 7:

[0255] Users can view and interact with information provided by the server in real time through a smartphone application. This allows for the display of transportation routes and inventory status, and immediate responses to questions via voice recognition. Input is data transmitted from the server, while output is information displayed on the user's screen and voice guidance.

[0256] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0257] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0258] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0259] [Second Embodiment]

[0260] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0261] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0262] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0263] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0264] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0265] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0266] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0267] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0268] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0269] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0270] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0271] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0272] To implement the present invention, it is necessary to construct an information processing system that includes data acquisition means, data analysis means, route optimization means, communication means, inventory forecasting means, inventory management means, dialogue means, and instruction generation means. This system is designed to streamline logistics operations.

[0273] Overall Operation Description

[0274] 1. Data collection:

[0275] Subject: Server

[0276] The server acquires traffic information, weather information, and sales data in real time through APIs and sensors.

[0277] 2. Data Analysis:

[0278] Subject: Server

[0279] The server inputs the collected data into an AI algorithm to perform demand forecasting. It also analyzes the optimal route to maximize delivery efficiency.

[0280] 3. Optimization and Notification of Delivery Routes:

[0281] Subject: Server

[0282] Based on the analysis results, the server generates an optimized delivery route considering multiple variables. This is transmitted to the terminal via communication means and notified to the driver. By specifically conveying the instructions received by the terminal to the user, smooth delivery is achieved.

[0283] 4. Automation of Inventory Management:

[0284] Subject: Server

[0285] The server uses inventory forecasting means to predict demand and automatically replenish inventory based on it. This prevents overstocking and understocking of inventory and improves economic efficiency.

[0286] 5. Use of Interactive Agents:

[0287] Subject: User

[0288] The user can communicate with the system in natural language through interactive means. For example, when the user asks "What is the next delivery destination?", the system uses the instruction generation means to reply "Please head to C Store" based on the analyzed optimal route. This interaction can be conducted in multiple languages and is thus adaptable to global business operations.

[0289] By operating this system, it is possible to respond quickly to changes in traffic conditions, shorten delivery times, and reduce logistics costs. Furthermore, by streamlining inventory management and improving communication, overall operational efficiency can be dramatically increased.

[0290] The following describes the processing flow.

[0291] Step 1:

[0292] Subject: Server

[0293] The server acquires traffic information, weather information, and sales data in real time from external APIs and sensors. The acquired data is stored in a database and used for subsequent analysis.

[0294] Step 2:

[0295] Subject: Server

[0296] The server uses data analysis tools to input the collected data into an AI algorithm. Here, the machine learning model analyzes the data and creates a foundation for optimizing demand forecasting and delivery efficiency.

[0297] Step 3:

[0298] Subject: Server

[0299] Based on the analysis results, the server uses route optimization techniques to calculate the optimal delivery route. This is a process of selecting the best solution from multiple candidate routes, taking into account time, distance, and fuel efficiency.

[0300] Step 4:

[0301] Subject: Server

[0302] The server uses communication means to transmit the information of the calculated optimal route to the terminal. Here, the terminal receives the route information and prepares for the next step.

[0303] Step 5:

[0304] Subject: Terminal

[0305] The terminal notifies the user of the received route information by voice or text message. Thereby, the transporter can clearly understand the actions to be taken next.

[0306] Step 6:

[0307] Subject: Server

[0308] The server uses inventory prediction means to analyze demand based on the collected data and perform automatic replenishment of inventory. This contributes to alleviating inventory shortages and surpluses.

[0309] Step 7:

[0310] Subject: User

[0311] The user interacts with the system in natural language through interaction means. For example, when the user requests a special instruction from the system, the interaction means utilizes instruction generation means to generate an appropriate instruction based on the user's request.

[0312] (Example 1)

[0313] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0314] In logistics operations, there is a growing need to improve transportation efficiency through the effective use of traffic information, weather information, and sales data. However, systems that can process this data quickly and accurately and provide optimal transportation routes and inventory management in real time are still lacking. In particular, there is a need for comprehensive solutions that enable inventory replenishment based on demand forecasts and the generation of work instructions through multilingual natural language dialogue.

[0315] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0316] In this invention, the server includes a data acquisition device, an analysis device, a route optimization device, a notification device, and a demand forecasting device. This enables efficient optimization of transportation routes and automation of inventory management through real-time data collection, analysis, and notification. Furthermore, demand forecasting using a generative AI model and multilingual dialogue are supported, enabling improved efficiency of the entire logistics operation.

[0317] A "data acquisition device" is a device for collecting traffic-related data, weather-related data, and sales-related data, and has the function of acquiring necessary data in real time from multiple information sources.

[0318] An "analysis device" is a device that performs traffic condition analysis and demand forecasting based on acquired data, and it executes programs and algorithms that perform data analysis.

[0319] A "route optimization device" is a device that uses analyzed data to perform calculations to optimize transportation routes, considering route combinations to achieve efficient transportation.

[0320] A "notification device" is a device that notifies carriers of optimized transportation routes and is equipped with communication functions for sending instructions.

[0321] A "demand forecasting device" is a device that uses a generation AI model to predict future demand and generates information to improve the efficiency of logistics operations.

[0322] This invention provides an information processing system for streamlining logistics operations. The system includes multiple devices that perform data collection, analysis, notification, and demand forecasting. Examples of each device are described below.

[0323] The server collects traffic-related data, weather-related data, and sales-related data using data acquisition devices. To retrieve data via APIs, the server establishes a network connection. Specifically, it uses the Google Maps API to collect traffic data and obtains weather information from weather data services such as OpenWeather.

[0324] The server processes the acquired data using an analysis device. Here, the data is preprocessed using the Python Pandas library and then analyzed using an AI algorithm. By using a generative AI model, it is possible to predict future demand and improve the efficiency of logistics activities based on the analysis results.

[0325] Furthermore, the server uses a route optimization device to calculate the optimal transportation route based on the collected and analyzed data. By utilizing Google OR-Tools to solve the shortest path problem, it generates efficient logistics routes.

[0326] Through the notification device, the server transmits optimized route information to the transporter. The information received by the terminal is conveyed to the user via a voice assistant or display and used as specific instructions. Based on this information, the user can carry out logistics activities smoothly.

[0327] Furthermore, the server automates inventory management based on demand forecasts. The forecasting model is built using a machine learning algorithm with TensorFlow, thereby reducing the risk of inventory surpluses or shortages.

[0328] The system includes a conversational agent function for interacting with users. Using a generative AI model, it can respond to a user's natural language question, such as "Where is the next delivery destination?", with an instruction like "Please head to store C." An example of a prompt would be, "Please optimize the upcoming delivery route based on current traffic and weather data."

[0329] In this way, the server efficiently utilizes diverse information, thereby improving the overall efficiency of logistics operations.

[0330] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0331] Step 1:

[0332] The server collects traffic-related data, weather-related data, and sales-related data through data acquisition devices. It uses API access information and network connectivity as input. The server retrieves the necessary data via APIs and obtains it in real time. In this process, it directly obtains necessary information from Google Maps and common weather data services.

[0333] Step 2:

[0334] The server preprocesses the data acquired using the analysis device. The input is the raw data obtained in step 1. The server uses the Python Pandas library to format and normalize the data and impute missing values. This process transforms the data into a format suitable for analysis and prepares it for the next analysis step.

[0335] Step 3:

[0336] The server uses a generative AI model to forecast demand. Preprocessed data is used as input. The generative AI model is prompted to perform the forecast. In this step, TensorFlow is used to operate the AI ​​model and numerically predict future demand. The output is the predicted demand data.

[0337] Step 4:

[0338] The server uses a route optimization tool to optimize transportation routes based on predicted demand and current traffic conditions. It uses demand forecast data and real-time traffic data as input. Leveraging Google OR-Tools, it calculates the shortest route and generates efficient transportation routes. The output provides optimized route information.

[0339] Step 5:

[0340] The server sends optimized route information to the terminal via a notification device. It uses the route information generated in the previous step as input. The terminal receives this information and outputs specific instructions to the user. For example, it might send a notification such as "Go to point A next" via screen display or voice command.

[0341] Step 6:

[0342] The server automates inventory management based on demand forecast results. It uses demand forecast data as input. The server integrates with the inventory management system to calculate the required inventory levels and automatically places orders. This results in output that prevents inventory shortages or surpluses.

[0343] Step 7:

[0344] The user communicates with the server using dialogue methods and natural language. Input includes the user's voice and text questions. The server utilizes a generative AI model to analyze the questions, generate appropriate instructions, and respond to the user. Output includes instructions and information provided through the interaction with the user.

[0345] (Application Example 1)

[0346] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0347] In modern logistics systems, efficient inventory management and optimized transportation are crucial for reducing operating costs and improving operational efficiency at logistics centers. However, conventional methods struggle to obtain real-time information and provide optimal routes, resulting in insufficient information for workers to perform appropriate operations within the warehouse. Technologies are needed to solve these problems.

[0348] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0349] In this invention, the server includes data acquisition means, data analysis means for analyzing traffic information, weather information, and transaction data acquired by the data acquisition means, route optimization means for optimizing the transportation route based on the analysis results by the data analysis means, communication means for notifying the transportation manager of the transportation route optimized by the route optimization means, and movement route provision means for presenting the optimal in-warehouse movement route to workers using a visual device. This enables efficient movement of workers in a logistics center and automation of inventory management.

[0350] "Data acquisition methods" refer to means of collecting various types of information related to logistics in real time.

[0351] "Data analysis methods" refer to means of performing demand forecasting and route optimization based on acquired traffic information, weather information, and transaction data.

[0352] A "route optimization method" is a means of calculating and presenting an efficient transportation route based on data analysis results.

[0353] "Communication means" refers to means of notifying transportation managers and workers of optimized transportation routes and other important instructions.

[0354] A "means for providing movement routes" refers to a means of visually guiding workers to the optimal movement route within a warehouse via a visual device.

[0355] A "demand forecasting tool" is a means of predicting future demand by analyzing past and present data.

[0356] "Inventory management methods" refer to means for automatically managing and executing inventory replenishment in accordance with predicted demand.

[0357] A "dialogue support system" is a means of directly interacting with workers using natural language processing and providing them with necessary information.

[0358] An "instruction generation means" is a means for generating specific work instructions based on information from a dialogue support means and presenting them to the worker.

[0359] A "location identification support system" is a means of identifying the current location of a worker based on sensor information obtained from a visual device, and providing efficient work support.

[0360] According to this invention, in order to operate a system efficiently in a logistics center, it is necessary to collect and analyze various data in real time and provide optimal instructions to workers. Embodiments of this invention will be described in detail below.

[0361] The server first uses data acquisition methods to collect traffic information, weather information, and transaction data in real time. This data is obtained from mobile devices and sensors via APIs. This enables analysis based on the latest information.

[0362] Next, the server analyzes the data using AI algorithms such as TensorFlow to perform demand forecasting and route optimization. The results of this analysis are used as foundational data for inventory management and transportation planning at the logistics center. The optimal route calculated by the route optimization system is immediately communicated to the workers' terminals via communication.

[0363] The terminal displays the received optimal route information to the worker through a visual device. By using a visual device such as smart glasses, the worker can understand the optimal route within the warehouse in real time. This visual information improves work efficiency.

[0364] Furthermore, the server uses natural language processing-based dialogue support to instantly respond to voice input from workers and provide specific work instructions. This dialogue function supports multiple languages, making it possible to handle global operations.

[0365] For example, if a worker asks "What is the next task?" via voice, the server will generate an instruction based on the analyzed data, such as "Next, pick items in section B." Another example of a prompt message would be, "Retrieve inventory data and suggest the next optimal work route. Current location is the central aisle, target is picking area A."

[0366] In this way, the overall operation of the logistics center is highly streamlined by combining data analysis and optimized route guidance, ensuring efficient movement and significantly improving work efficiency.

[0367] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0368] Step 1:

[0369] The server uses data acquisition methods to collect traffic information, weather information, and transaction data in real time. Inputs are data from APIs and sensors, which are collected by polling at regular intervals. This data is then prepared for subsequent analysis.

[0370] Step 2:

[0371] The server inputs the collected data into an AI algorithm through data analysis tools to perform demand forecasting and route optimization. The input is the data collected in step 1, which is analyzed using an algorithm based on TensorFlow. The output is information on optimal inventory placement and delivery routes, which prepares the data for the next processing step.

[0372] Step 3:

[0373] Based on the analysis results, the server notifies the terminal of the optimal transport route calculated by the route optimization means via the communication means. The input is the analysis output from step 2. The optimal route information is sent to the terminal, and an actionable transport plan is formulated.

[0374] Step 4:

[0375] The terminal displays the optimal movement route in real time via a visual device worn by the worker. The input is the route information transmitted in step 3, which is presented to the worker as visual information using smart glasses or similar devices. The output is the worker's efficient movement within the warehouse.

[0376] Step 5:

[0377] The user queries the server in natural language using a dialogue support system, and receives specific work instructions from an instruction generation system. The input is voice data from the user, which is analyzed and processed by the server using natural language processing. As a result, work instructions are generated and presented to the user in either voice or text format.

[0378] Step 6:

[0379] Based on acquired sensor information, the server uses location identification support to determine the worker's current location and guides them to the optimal work position using a visual device. The input is real-time location data from the visual device, and the worker's position is calculated by a location identification algorithm. This allows the worker to move to the next task immediately, enabling efficient work.

[0380] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0381] The present invention is implemented by constructing an information processing system that includes data acquisition means, data analysis means, route optimization means, communication means, inventory forecasting means, inventory management means, dialogue means, instruction generation means, and an emotion engine. This system is designed to improve the efficiency of logistics operations and enable effective interaction with users.

[0382] Overall Operation Description

[0383] 1. Data collection and analysis:

[0384] Subject: Server

[0385] The server collects traffic information, weather information, and sales data in real time through external APIs and sensors, analyzes the data to forecast demand, and uses this information for inventory management and delivery route planning.

[0386] 2. Optimization and notification of delivery routes:

[0387] Subject: Server

[0388] The server calculates the optimal delivery route based on the analysis results and transmits it to the terminal via communication. The terminal then notifies the driver of the optimized route information and supports the transportation.

[0389] 3. Automating inventory forecasting and management:

[0390] Subject: Server

[0391] The server uses inventory forecasting tools to predict future demand, and based on that, automatically replenishes inventory using inventory management tools to maintain a balance between supply and demand.

[0392] 4. Recognition of user emotions by an emotion engine:

[0393] Subject: Server

[0394] The server uses an emotion engine to recognize emotions from the user's voice and text. This information is fed back into the entire system and influences responses to interactions and work instructions.

[0395] 5. Optimizing dialogue:

[0396] Subject: User

[0397] Users interact with the system through dialogue. The system generates responses that take the user's emotional state into account, enabling more personalized communication. For example, if the user sounds busy, the system will provide quick and concise instructions.

[0398] This system enables efficient decision-making based on real-time circumstances, while also facilitating emotion-based communication with users, thereby improving overall work efficiency and satisfaction.

[0399] The following describes the processing flow.

[0400] Step 1:

[0401] Subject: Server

[0402] The server collects real-time traffic information, weather information, and sales data through external APIs and sensors. This data is systematically stored in a database and forms the basis for subsequent data analysis.

[0403] Step 2:

[0404] Subject: Server

[0405] The server uses data analysis tools to analyze the collected data with AI algorithms. This analysis creates a demand forecasting model, which in turn generates conditions for optimizing delivery routes.

[0406] Step 3:

[0407] Subject: Server

[0408] The server uses route optimization techniques to evaluate various route options and calculate the optimal route considering time, fuel efficiency, and traffic conditions. The optimized route information is then used in the next step.

[0409] Step 4:

[0410] Subject: Server

[0411] The server transmits the calculated optimal delivery route to the terminal via communication means. This allows the transporter to obtain the information in a usable format.

[0412] Step 5:

[0413] Subject: terminal

[0414] The terminal notifies the user of delivery route information received from the server via voice or text. The user can then quickly take action by following the instructions.

[0415] Step 6:

[0416] Subject: Server

[0417] The server uses inventory forecasting tools to automatically replenish inventory based on sales data and demand forecasts. This prevents inventory shortages and surpluses, enabling efficient inventory management.

[0418] Step 7:

[0419] Subject: Server

[0420] The server uses an emotion engine to recognize emotions from the user's voice and text. The recognized emotion data is then fed back to the dialogue mechanism.

[0421] Step 8:

[0422] Subject: User

[0423] Users interact with the system using natural language through dialogue. The system analyzes the user's emotional state and adjusts its responses accordingly, resulting in more appropriate instructions and support.

[0424] (Example 2)

[0425] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0426] In logistics operations, accurately forecasting traffic conditions, weather conditions, and market demand, developing efficient delivery plans, and properly managing inventory are extremely important. Furthermore, smooth communication with customers, while being mindful of their feelings, is also necessary. However, traditional methods make it difficult to manage all of these elements in an integrated manner, which can lead to decreased operational efficiency.

[0427] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0428] In this invention, the server includes means for acquiring data, means for analyzing traffic-related data, weather-related data, and sales-related data acquired by the data acquisition means, means for optimizing the transportation route based on the analysis results by the data analysis means, means for notifying the transportation personnel of the optimized transportation route via a communication means, and an emotion analysis engine for recognizing human emotions. This enables increased efficiency in logistics operations and effective communication with users.

[0429] "Means of acquiring data" refers to mechanisms for collecting information related to traffic, weather, and sales from external sources.

[0430] "Means for analysis" refers to the process of analyzing acquired data to perform appropriate demand forecasting and trend analysis.

[0431] "Methods for optimizing transportation routes" refer to methods for calculating the shortest and most efficient delivery route, taking into account traffic information and weather conditions.

[0432] "Means for notifying transportation personnel via means of communication" refers to a communication system for informing transportation personnel of the calculated optimal route information.

[0433] The "emotion analysis engine" is a function that detects the user's emotions from their voice or text and reflects them in the dialogue response.

[0434] "Means for forecasting demand" refers to methodologies for predicting future demand based on past data and market trends.

[0435] "Means for automated supply management" refers to automated systems that appropriately replenish and manage inventory in accordance with predicted demand.

[0436] "A means of processing natural language and generating instructions through interaction with the user" refers to a system that understands user input in natural language and creates appropriate instructions accordingly.

[0437] "Means for generating responses based on generative AI models" refers to a method of generating the optimal response to user input using a machine learning model.

[0438] This invention provides an information processing system that streamlines logistics operations and enables effective communication with users. The system includes data acquisition means, data analysis means, route optimization means, communication means, inventory forecasting means, inventory management means, dialogue means, instruction generation means, and an emotion engine.

[0439] First, the server utilizes common APIs to obtain traffic, weather, and sales-related data from external data sources. For example, it can use traffic information APIs and weather information APIs. The server processes the acquired data in real time and performs analysis for demand forecasting. This analysis may involve using statistical methods and machine learning algorithms.

[0440] Next, the server calculates the optimal transport route based on the analysis results. This calculation can utilize route-finding algorithms such as Dijkstra's algorithm. The optimized route information is transmitted to a terminal via communication, and the terminal presents the information to the driver through voice guidance or display on a screen. This allows the driver to deliver packages efficiently.

[0441] Furthermore, the server utilizes inventory forecasting tools to manage inventory appropriately based on future demand. This involves using machine learning models that predict demand from historical sales data and market trends. Based on this information, the inventory management system automatically performs the necessary replenishment operations.

[0442] Furthermore, the server uses an emotion engine to detect emotions from the user's voice and text, and feeds this back to the dialogue and instruction generation systems. Specifically, it uses natural language processing technology to analyze the user's statements and emotions in real time, and uses a generative AI model to generate appropriate responses according to the user's emotional state. For example, a possible prompt might be "Prepare an answer that will help the user relax."

[0443] This system enables real-time decision-making regarding logistics and inventory management, and facilitates emotion-based communication with users. This, in turn, improves overall operational efficiency and customer satisfaction.

[0444] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0445] Step 1:

[0446] The server retrieves data from external traffic information APIs, weather information APIs, and sales databases. The input is raw data obtained from the APIs or databases. The server receives this raw data, converts and cleans the data format, and prepares it for analysis. The output is a structured dataset.

[0447] Step 2:

[0448] The server performs demand forecasting using the organized data. The input is the structured data obtained in Step 1. The server inputs this data into a machine learning model, such as a time series analysis or multiple regression model, to predict future demand. The output obtained from this analysis is the demand forecast result, which includes information on demand fluctuations over time.

[0449] Step 3:

[0450] The server optimizes transportation routes based on demand forecasts, traffic information, and weather information. Inputs are forecasted demand data and current traffic and weather data. The server calculates the optimal route using route-finding algorithms such as Dijkstra's algorithm. The output is optimized transportation route information, which includes the route from the starting point to the destination.

[0451] Step 4:

[0452] The server transmits optimized transport route information to the terminal. The input is the transport route information obtained in step 3. The terminal receives this information via communication and displays it on its screen, as well as using a voice guidance system to communicate the information to the driver. The output is instruction information for the driver.

[0453] Step 5:

[0454] The server performs inventory forecasting based on historical sales data and predicted demand. The inputs are the demand forecast results from step 2 and inventory data. The server uses an inventory forecasting model to calculate appropriate inventory levels. The output is inventory replenishment information for each product that needs to be replenished.

[0455] Step 6:

[0456] The inventory management system receives inventory replenishment information from the server and automatically processes orders. The input is the inventory replenishment information provided by the server. Based on this information, the inventory management system places orders with suppliers and adjusts inventory levels. The output is the new order data.

[0457] Step 7:

[0458] The server receives voice input or text messages from the user and recognizes their emotions using an emotion analysis engine. The input is raw voice or text data from the user. The server uses natural language processing techniques to analyze this data and identify the user's emotions. The output is the analyzed emotion data.

[0459] Step 8:

[0460] The server uses a generative AI model based on emotional data to generate appropriate responses. The input consists of the user's emotional data and dialogue content. The server uses prompts such as "Prepare an answer that will help the user relax" to generate responses for the generative AI model. The output is an optimized response message for the user.

[0461] (Application Example 2)

[0462] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0463] In modern logistics operations, efficient inventory management and optimal delivery are crucial, but achieving this in real time and in response to changing circumstances is difficult. Furthermore, flexible responses that consider the emotional state of users are required when communicating information to workers. The challenge lies in providing an integrated system to address these issues.

[0464] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0465] In this invention, the server includes means for collecting data, means for analyzing movement information, environmental information, and sales data, and means for optimizing the transportation route. This enables efficient real-time inventory management and optimal delivery, as well as the provision of personalized responses that take into account the emotional state of the user.

[0466] "Means of data collection" refers to a system for automatically acquiring data such as traffic conditions, weather conditions, and sales trends from external sources.

[0467] "Movement information" refers to data about the location and movement paths of objects and people, and is used to optimize the transportation routes of goods.

[0468] "Environmental information" refers to data about the external environment, such as weather, temperature, and traffic conditions, and is a factor that influences the planning and execution of logistics.

[0469] "Sales data" refers to numerical information obtained from sales activities over a specific period, and is useful for demand forecasting and inventory management.

[0470] "Means of analysis" refers to a system that performs a series of processes to analyze collected data and derive useful information.

[0471] "Means for optimizing transportation routes" refers to the process of calculating and proposing the most efficient route for transporting goods based on data analysis.

[0472] "Efficient real-time inventory management" refers to a management system that has the ability to instantly grasp the current inventory status and adjust inventory appropriately according to demand.

[0473] A "personalized response" is a method of providing appropriate information and instructions that take into account the user's individual emotional state and circumstances.

[0474] The system that realizes this invention is configured to exchange data primarily through a server, user terminals, and, if necessary, a communication network. The server collects traffic information, weather information, and sales data in real time from external APIs and sensors. In this process, the Python Requests library and various APIs can be utilized. The collected data is then analyzed using analysis software such as TensorFlow to perform demand forecasting and optimize transportation routes.

[0475] The server then uses map services such as the Google Maps API to calculate the optimal delivery route based on the analysis results and notifies the terminal. The terminal receives this information and displays it to drivers and administrators in real time. A smartphone app is often used for this display method.

[0476] Furthermore, the server uses voice analysis libraries such as IBM Watson to recognize the user's emotional state from their voice and generates appropriate responses based on that state through the Amazon Alexa Skills Kit. This personalized response allows users to receive quick information when they are busy.

[0477] For example, when a logistics center manager asks their smartphone, "What is today's delivery route?", the system provides an accurate response such as, "Calculating the optimal route... Route A is recommended to avoid traffic congestion." An example of a prompt would be, "Please tell me the real-time inventory status of the logistics center and the optimal delivery route."

[0478] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0479] Step 1:

[0480] The server acquires traffic information, weather information, and sales data in real time via external APIs and sensors. It receives the information in the required data format and prepares it for storage in the internal database. Inputs consist of various data from external sources, while outputs are unanalyzed data stored within the server.

[0481] Step 2:

[0482] The server analyzes the acquired data. Here, a predictive model is applied using mature Python data analysis libraries and TensorFlow, and the data processing necessary for demand forecasting and transportation route optimization is performed. The input is the data acquired in step 1, and the output is the demand forecast and unoptimized transportation route information obtained from the predictive model.

[0483] Step 3:

[0484] The server optimizes the transportation route based on the analysis results. It uses map services such as the Google Maps API to calculate the optimal route considering traffic conditions and geographical features. The input is the analysis results from step 2, and the output is the optimized transportation route information.

[0485] Step 4:

[0486] The server notifies the terminal of the optimized delivery route. This process involves sending the calculated optimal route information to the terminal device via communication, preparing it for display and guidance. The input is the optimized route information, and the output is the instruction information displayed on the terminal.

[0487] Step 5:

[0488] The server receives voice input from the user and performs emotion recognition using voice analysis tools such as IBM Watson. The input is voice data from the user, and the output is the analyzed emotion information.

[0489] Step 6:

[0490] Based on the emotion recognition results, the server generates personalized responses using a dialogue engine such as the Amazon Alexa Skills Kit. The generated responses are designed to provide information that takes into account the user's emotional state. The input is the emotion information obtained in step 5, and the output is the appropriate response to present to the user.

[0491] Step 7:

[0492] Users can view and interact with information provided by the server in real time through a smartphone application. This allows for the display of transportation routes and inventory status, and immediate responses to questions via voice recognition. Input is data transmitted from the server, while output is information displayed on the user's screen and voice guidance.

[0493] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0494] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0495] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0496] [Third Embodiment]

[0497] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0498] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0499] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0500] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0501] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0502] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0503] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0504] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0505] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0506] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0507] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0508] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0509] To implement the present invention, it is necessary to construct an information processing system that includes data acquisition means, data analysis means, route optimization means, communication means, inventory forecasting means, inventory management means, dialogue means, and instruction generation means. This system is designed to streamline logistics operations.

[0510] Overall Operation Description

[0511] 1. Data collection:

[0512] Subject: Server

[0513] The server acquires traffic information, weather information, and sales data in real time through APIs and sensors.

[0514] 2. Data Analysis:

[0515] Subject: Server

[0516] The server inputs the collected data into an AI algorithm to perform demand forecasting. It also analyzes the optimal route to maximize delivery efficiency.

[0517] 3. Optimization and notification of delivery routes:

[0518] Subject: Server

[0519] Based on the analysis results, the server generates an optimized delivery route considering multiple variables. This route is then transmitted to the terminal via communication, notifying the driver. The terminal then relays the received instructions to the user, ensuring smooth delivery.

[0520] 4. Automating inventory management:

[0521] Subject: Server

[0522] The server uses inventory forecasting tools to predict demand and automatically replenishes inventory based on that forecast. This prevents inventory surpluses and shortages, improving economic efficiency.

[0523] 5. Use of conversational agents:

[0524] Subject: User

[0525] Users can communicate with the system using natural language through dialogue mechanisms. For example, if a user asks, "Where is the next delivery destination?", the system will respond with "Please head to Store C" using a command generation mechanism based on the analyzed optimal route. This dialogue can be conducted in multiple languages, making it suitable for global business operations.

[0526] By operating this system, it is possible to respond quickly to changes in traffic conditions, shorten delivery times, and reduce logistics costs. Furthermore, by streamlining inventory management and improving communication, overall operational efficiency can be dramatically increased.

[0527] The following describes the processing flow.

[0528] Step 1:

[0529] Subject: Server

[0530] The server acquires traffic information, weather information, and sales data in real time from external APIs and sensors. The acquired data is stored in a database and used for subsequent analysis.

[0531] Step 2:

[0532] Subject: Server

[0533] The server uses data analysis tools to input the collected data into an AI algorithm. Here, the machine learning model analyzes the data and creates a foundation for optimizing demand forecasting and delivery efficiency.

[0534] Step 3:

[0535] Subject: Server

[0536] Based on the analysis results, the server uses route optimization techniques to calculate the optimal delivery route. This is a process of selecting the best solution from multiple candidate routes, taking into account time, distance, and fuel efficiency.

[0537] Step 4:

[0538] Subject: Server

[0539] The server uses a communication method to send information about the calculated optimal route to the terminal. The terminal then receives the route information and prepares for the next step.

[0540] Step 5:

[0541] Subject: terminal

[0542] The terminal notifies the user of the received route information via voice or text message. This allows the transporter to clearly understand what action to take next.

[0543] Step 6:

[0544] Subject: Server

[0545] The server uses inventory forecasting tools to analyze demand based on collected data and automatically replenishes inventory. This helps mitigate inventory surpluses and shortages.

[0546] Step 7:

[0547] Subject: User

[0548] The user interacts with the system using natural language through a dialogue mechanism. For example, if the user requests specific instructions from the system, the dialogue mechanism utilizes an instruction generation mechanism to generate appropriate instructions based on the user's request.

[0549] (Example 1)

[0550] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0551] In logistics operations, there is a growing need to improve transportation efficiency through the effective use of traffic information, weather information, and sales data. However, systems that can process this data quickly and accurately and provide optimal transportation routes and inventory management in real time are still lacking. In particular, there is a need for comprehensive solutions that enable inventory replenishment based on demand forecasts and the generation of work instructions through multilingual natural language dialogue.

[0552] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0553] In this invention, the server includes a data acquisition device, an analysis device, a route optimization device, a notification device, and a demand forecasting device. This enables efficient optimization of transportation routes and automation of inventory management through real-time data collection, analysis, and notification. Furthermore, demand forecasting using a generative AI model and multilingual dialogue are supported, enabling improved efficiency of the entire logistics operation.

[0554] A "data acquisition device" is a device for collecting traffic-related data, weather-related data, and sales-related data, and has the function of acquiring necessary data in real time from multiple information sources.

[0555] An "analysis device" is a device that performs traffic condition analysis and demand forecasting based on acquired data, and it executes programs and algorithms that perform data analysis.

[0556] A "route optimization device" is a device that uses analyzed data to perform calculations to optimize transportation routes, considering route combinations to achieve efficient transportation.

[0557] A "notification device" is a device that notifies carriers of optimized transportation routes and is equipped with communication functions for sending instructions.

[0558] A "demand forecasting device" is a device that uses a generation AI model to predict future demand and generates information to improve the efficiency of logistics operations.

[0559] This invention provides an information processing system for streamlining logistics operations. The system includes multiple devices that perform data collection, analysis, notification, and demand forecasting. Examples of each device are described below.

[0560] The server collects traffic-related data, weather-related data, and sales-related data using data acquisition devices. To retrieve data via APIs, the server establishes a network connection. Specifically, it uses the Google Maps API to collect traffic data and obtains weather information from weather data services such as OpenWeather.

[0561] The server processes the acquired data using an analysis device. Here, the data is preprocessed using the Python Pandas library and then analyzed using an AI algorithm. By using a generative AI model, it is possible to predict future demand and improve the efficiency of logistics activities based on the analysis results.

[0562] Furthermore, the server uses a route optimization device to calculate the optimal transportation route based on the collected and analyzed data. By utilizing Google OR-Tools to solve the shortest path problem, it generates efficient logistics routes.

[0563] Through the notification device, the server transmits optimized route information to the transporter. The information received by the terminal is conveyed to the user via a voice assistant or display and used as specific instructions. Based on this information, the user can carry out logistics activities smoothly.

[0564] Furthermore, the server automates inventory management based on demand forecasts. The forecasting model is built using a machine learning algorithm with TensorFlow, thereby reducing the risk of inventory surpluses or shortages.

[0565] The system includes a conversational agent function for interacting with users. Using a generative AI model, it can respond to a user's natural language question, such as "Where is the next delivery destination?", with an instruction like "Please head to store C." An example of a prompt would be, "Please optimize the upcoming delivery route based on current traffic and weather data."

[0566] In this way, the server efficiently utilizes diverse information, thereby improving the overall efficiency of logistics operations.

[0567] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0568] Step 1:

[0569] The server collects traffic-related data, weather-related data, and sales-related data through data acquisition devices. It uses API access information and network connectivity as input. The server retrieves the necessary data via APIs and obtains it in real time. In this process, it directly obtains necessary information from Google Maps and common weather data services.

[0570] Step 2:

[0571] The server preprocesses the data acquired using the analysis device. The input is the raw data obtained in step 1. The server uses the Python Pandas library to format and normalize the data and impute missing values. This process transforms the data into a format suitable for analysis and prepares it for the next analysis step.

[0572] Step 3:

[0573] The server uses a generative AI model to forecast demand. Preprocessed data is used as input. The generative AI model is prompted to perform the forecast. In this step, TensorFlow is used to operate the AI ​​model and numerically predict future demand. The output is the predicted demand data.

[0574] Step 4:

[0575] The server uses a route optimization tool to optimize transportation routes based on predicted demand and current traffic conditions. It uses demand forecast data and real-time traffic data as input. Leveraging Google OR-Tools, it calculates the shortest route and generates efficient transportation routes. The output provides optimized route information.

[0576] Step 5:

[0577] The server sends optimized route information to the terminal via a notification device. It uses the route information generated in the previous step as input. The terminal receives this information and outputs specific instructions to the user. For example, it might send a notification such as "Go to point A next" via screen display or voice command.

[0578] Step 6:

[0579] The server automates inventory management based on demand forecast results. It uses demand forecast data as input. The server integrates with the inventory management system to calculate the required inventory levels and automatically places orders. This results in output that prevents inventory shortages or surpluses.

[0580] Step 7:

[0581] The user communicates with the server using dialogue methods and natural language. Input includes the user's voice and text questions. The server utilizes a generative AI model to analyze the questions, generate appropriate instructions, and respond to the user. Output includes instructions and information provided through the interaction with the user.

[0582] (Application Example 1)

[0583] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0584] In modern logistics systems, efficient inventory management and optimized transportation are crucial for reducing operating costs and improving operational efficiency at logistics centers. However, conventional methods struggle to obtain real-time information and provide optimal routes, resulting in insufficient information for workers to perform appropriate operations within the warehouse. Technologies are needed to solve these problems.

[0585] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0586] In this invention, the server includes data acquisition means, data analysis means for analyzing traffic information, weather information, and transaction data acquired by the data acquisition means, route optimization means for optimizing the transportation route based on the analysis results by the data analysis means, communication means for notifying the transportation manager of the transportation route optimized by the route optimization means, and movement route provision means for presenting the optimal in-warehouse movement route to workers using a visual device. This enables efficient movement of workers in a logistics center and automation of inventory management.

[0587] "Data acquisition methods" refer to means of collecting various types of information related to logistics in real time.

[0588] "Data analysis methods" refer to means of performing demand forecasting and route optimization based on acquired traffic information, weather information, and transaction data.

[0589] A "route optimization method" is a means of calculating and presenting an efficient transportation route based on data analysis results.

[0590] "Communication means" refers to means of notifying transportation managers and workers of optimized transportation routes and other important instructions.

[0591] A "means for providing movement routes" refers to a means of visually guiding workers to the optimal movement route within a warehouse via a visual device.

[0592] A "demand forecasting tool" is a means of predicting future demand by analyzing past and present data.

[0593] "Inventory management methods" refer to means for automatically managing and executing inventory replenishment in accordance with predicted demand.

[0594] A "dialogue support system" is a means of directly interacting with workers using natural language processing and providing them with necessary information.

[0595] An "instruction generation means" is a means for generating specific work instructions based on information from a dialogue support means and presenting them to the worker.

[0596] A "location identification support system" is a means of identifying the current location of a worker based on sensor information obtained from a visual device, and providing efficient work support.

[0597] According to this invention, in order to operate a system efficiently in a logistics center, it is necessary to collect and analyze various data in real time and provide optimal instructions to workers. Embodiments of this invention will be described in detail below.

[0598] The server first uses data acquisition methods to collect traffic information, weather information, and transaction data in real time. This data is obtained from mobile devices and sensors via APIs. This enables analysis based on the latest information.

[0599] Next, the server analyzes the data using AI algorithms such as TensorFlow to perform demand forecasting and route optimization. The results of this analysis are used as foundational data for inventory management and transportation planning at the logistics center. The optimal route calculated by the route optimization system is immediately communicated to the workers' terminals via communication.

[0600] The terminal displays the received optimal route information to the worker through a visual device. By using a visual device such as smart glasses, the worker can understand the optimal route within the warehouse in real time. This visual information improves work efficiency.

[0601] Furthermore, the server uses natural language processing-based dialogue support to instantly respond to voice input from workers and provide specific work instructions. This dialogue function supports multiple languages, making it possible to handle global operations.

[0602] For example, if a worker asks "What is the next task?" via voice, the server will generate an instruction based on the analyzed data, such as "Next, pick items in section B." Another example of a prompt message would be, "Retrieve inventory data and suggest the next optimal work route. Current location is the central aisle, target is picking area A."

[0603] In this way, the overall operation of the logistics center is highly streamlined by combining data analysis and optimized route guidance, ensuring efficient movement and significantly improving work efficiency.

[0604] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0605] Step 1:

[0606] The server uses data acquisition methods to collect traffic information, weather information, and transaction data in real time. Inputs are data from APIs and sensors, which are collected by polling at regular intervals. This data is then prepared for subsequent analysis.

[0607] Step 2:

[0608] The server inputs the collected data into an AI algorithm through data analysis tools to perform demand forecasting and route optimization. The input is the data collected in step 1, which is analyzed using an algorithm based on TensorFlow. The output is information on optimal inventory placement and delivery routes, which prepares the data for the next processing step.

[0609] Step 3:

[0610] Based on the analysis results, the server notifies the terminal of the optimal transport route calculated by the route optimization means via the communication means. The input is the analysis output from step 2. The optimal route information is sent to the terminal, and an actionable transport plan is formulated.

[0611] Step 4:

[0612] The terminal displays the optimal movement route in real time via a visual device worn by the worker. The input is the route information transmitted in step 3, which is presented to the worker as visual information using smart glasses or similar devices. The output is the worker's efficient movement within the warehouse.

[0613] Step 5:

[0614] The user queries the server in natural language using a dialogue support system, and receives specific work instructions from an instruction generation system. The input is voice data from the user, which is analyzed and processed by the server using natural language processing. As a result, work instructions are generated and presented to the user in either voice or text format.

[0615] Step 6:

[0616] Based on acquired sensor information, the server uses location identification support to determine the worker's current location and guides them to the optimal work position using a visual device. The input is real-time location data from the visual device, and the worker's position is calculated by a location identification algorithm. This allows the worker to move to the next task immediately, enabling efficient work.

[0617] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0618] The present invention is implemented by constructing an information processing system that includes data acquisition means, data analysis means, route optimization means, communication means, inventory forecasting means, inventory management means, dialogue means, instruction generation means, and an emotion engine. This system is designed to improve the efficiency of logistics operations and enable effective interaction with users.

[0619] Overall Operation Description

[0620] 1. Data collection and analysis:

[0621] Subject: Server

[0622] The server collects traffic information, weather information, and sales data in real time through external APIs and sensors, analyzes the data to forecast demand, and uses this information for inventory management and delivery route planning.

[0623] 2. Optimization and notification of delivery routes:

[0624] Subject: Server

[0625] The server calculates the optimal delivery route based on the analysis results and transmits it to the terminal via communication. The terminal then notifies the driver of the optimized route information and supports the transportation.

[0626] 3. Automating inventory forecasting and management:

[0627] Subject: Server

[0628] The server uses inventory forecasting tools to predict future demand, and based on that, automatically replenishes inventory using inventory management tools to maintain a balance between supply and demand.

[0629] 4. Recognition of user emotions by an emotion engine:

[0630] Subject: Server

[0631] The server uses an emotion engine to recognize emotions from the user's voice and text. This information is fed back into the entire system and influences responses to interactions and work instructions.

[0632] 5. Optimizing dialogue:

[0633] Subject: User

[0634] Users interact with the system through dialogue. The system generates responses that take the user's emotional state into account, enabling more personalized communication. For example, if the user sounds busy, the system will provide quick and concise instructions.

[0635] This system enables efficient decision-making based on real-time circumstances, while also facilitating emotion-based communication with users, thereby improving overall work efficiency and satisfaction.

[0636] The following describes the processing flow.

[0637] Step 1:

[0638] Subject: Server

[0639] The server collects real-time traffic information, weather information, and sales data through external APIs and sensors. This data is systematically stored in a database and forms the basis for subsequent data analysis.

[0640] Step 2:

[0641] Subject: Server

[0642] The server uses data analysis tools to analyze the collected data with AI algorithms. This analysis creates a demand forecasting model, which in turn generates conditions for optimizing delivery routes.

[0643] Step 3:

[0644] Subject: Server

[0645] The server uses route optimization techniques to evaluate various route options and calculate the optimal route considering time, fuel efficiency, and traffic conditions. The optimized route information is then used in the next step.

[0646] Step 4:

[0647] Subject: Server

[0648] The server transmits the calculated optimal delivery route to the terminal via communication means. This allows the transporter to obtain the information in a usable format.

[0649] Step 5:

[0650] Subject: terminal

[0651] The terminal notifies the user of delivery route information received from the server via voice or text. The user can then quickly take action by following the instructions.

[0652] Step 6:

[0653] Subject: Server

[0654] The server uses inventory forecasting tools to automatically replenish inventory based on sales data and demand forecasts. This prevents inventory shortages and surpluses, enabling efficient inventory management.

[0655] Step 7:

[0656] Subject: Server

[0657] The server uses an emotion engine to recognize emotions from the user's voice and text. The recognized emotion data is then fed back to the dialogue mechanism.

[0658] Step 8:

[0659] Subject: User

[0660] Users interact with the system using natural language through dialogue. The system analyzes the user's emotional state and adjusts its responses accordingly, resulting in more appropriate instructions and support.

[0661] (Example 2)

[0662] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0663] In logistics operations, accurately forecasting traffic conditions, weather conditions, and market demand, developing efficient delivery plans, and properly managing inventory are extremely important. Furthermore, smooth communication with customers, while being mindful of their feelings, is also necessary. However, traditional methods make it difficult to manage all of these elements in an integrated manner, which can lead to decreased operational efficiency.

[0664] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0665] In this invention, the server includes means for acquiring data, means for analyzing traffic-related data, weather-related data, and sales-related data acquired by the data acquisition means, means for optimizing the transportation route based on the analysis results by the data analysis means, means for notifying the transportation personnel of the optimized transportation route via a communication means, and an emotion analysis engine for recognizing human emotions. This enables increased efficiency in logistics operations and effective communication with users.

[0666] "Means of acquiring data" refers to mechanisms for collecting information related to traffic, weather, and sales from external sources.

[0667] "Means for analysis" refers to the process of analyzing acquired data to perform appropriate demand forecasting and trend analysis.

[0668] "Methods for optimizing transportation routes" refer to methods for calculating the shortest and most efficient delivery route, taking into account traffic information and weather conditions.

[0669] "Means for notifying transportation personnel via means of communication" refers to a communication system for informing transportation personnel of the calculated optimal route information.

[0670] The "emotion analysis engine" is a function that detects the user's emotions from their voice or text and reflects them in the dialogue response.

[0671] "Means for forecasting demand" refers to methodologies for predicting future demand based on past data and market trends.

[0672] "Means for automated supply management" refers to automated systems that appropriately replenish and manage inventory in accordance with predicted demand.

[0673] "A means of processing natural language and generating instructions through interaction with the user" refers to a system that understands user input in natural language and creates appropriate instructions accordingly.

[0674] "Means for generating responses based on generative AI models" refers to a method of generating the optimal response to user input using a machine learning model.

[0675] This invention provides an information processing system that streamlines logistics operations and enables effective communication with users. The system includes data acquisition means, data analysis means, route optimization means, communication means, inventory forecasting means, inventory management means, dialogue means, instruction generation means, and an emotion engine.

[0676] First, the server utilizes common APIs to obtain traffic, weather, and sales-related data from external data sources. For example, it can use traffic information APIs and weather information APIs. The server processes the acquired data in real time and performs analysis for demand forecasting. This analysis may involve using statistical methods and machine learning algorithms.

[0677] Next, the server calculates the optimal transport route based on the analysis results. This calculation can utilize route-finding algorithms such as Dijkstra's algorithm. The optimized route information is transmitted to a terminal via communication, and the terminal presents the information to the driver through voice guidance or display on a screen. This allows the driver to deliver packages efficiently.

[0678] Furthermore, the server utilizes inventory forecasting tools to manage inventory appropriately based on future demand. This involves using machine learning models that predict demand from historical sales data and market trends. Based on this information, the inventory management system automatically performs the necessary replenishment operations.

[0679] Furthermore, the server uses an emotion engine to detect emotions from the user's voice and text, and feeds this back to the dialogue and instruction generation systems. Specifically, it uses natural language processing technology to analyze the user's statements and emotions in real time, and uses a generative AI model to generate appropriate responses according to the user's emotional state. For example, a possible prompt might be "Prepare an answer that will help the user relax."

[0680] This system enables real-time decision-making regarding logistics and inventory management, and facilitates emotion-based communication with users. This, in turn, improves overall operational efficiency and customer satisfaction.

[0681] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0682] Step 1:

[0683] The server retrieves data from external traffic information APIs, weather information APIs, and sales databases. The input is raw data obtained from the APIs or databases. The server receives this raw data, converts and cleans the data format, and prepares it for analysis. The output is a structured dataset.

[0684] Step 2:

[0685] The server performs demand forecasting using the organized data. The input is the structured data obtained in Step 1. The server inputs this data into a machine learning model, such as a time series analysis or multiple regression model, to predict future demand. The output obtained from this analysis is the demand forecast result, which includes information on demand fluctuations over time.

[0686] Step 3:

[0687] The server optimizes transportation routes based on demand forecasts, traffic information, and weather information. Inputs are forecasted demand data and current traffic and weather data. The server calculates the optimal route using route-finding algorithms such as Dijkstra's algorithm. The output is optimized transportation route information, which includes the route from the starting point to the destination.

[0688] Step 4:

[0689] The server transmits optimized transport route information to the terminal. The input is the transport route information obtained in step 3. The terminal receives this information via communication and displays it on its screen, as well as using a voice guidance system to communicate the information to the driver. The output is instruction information for the driver.

[0690] Step 5:

[0691] The server performs inventory forecasting based on historical sales data and predicted demand. The inputs are the demand forecast results from step 2 and inventory data. The server uses an inventory forecasting model to calculate appropriate inventory levels. The output is inventory replenishment information for each product that needs to be replenished.

[0692] Step 6:

[0693] The inventory management system receives inventory replenishment information from the server and automatically processes orders. The input is the inventory replenishment information provided by the server. Based on this information, the inventory management system places orders with suppliers and adjusts inventory levels. The output is the new order data.

[0694] Step 7:

[0695] The server receives voice input or text messages from the user and recognizes their emotions using an emotion analysis engine. The input is raw voice or text data from the user. The server uses natural language processing techniques to analyze this data and identify the user's emotions. The output is the analyzed emotion data.

[0696] Step 8:

[0697] The server uses a generative AI model based on emotional data to generate appropriate responses. The input consists of the user's emotional data and dialogue content. The server uses prompts such as "Prepare an answer that will help the user relax" to generate responses for the generative AI model. The output is an optimized response message for the user.

[0698] (Application Example 2)

[0699] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0700] In modern logistics operations, efficient inventory management and optimal delivery are crucial, but achieving this in real time and in response to changing circumstances is difficult. Furthermore, flexible responses that consider the emotional state of users are required when communicating information to workers. The challenge lies in providing an integrated system to address these issues.

[0701] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0702] In this invention, the server includes means for collecting data, means for analyzing movement information, environmental information, and sales data, and means for optimizing the transportation route. This enables efficient real-time inventory management and optimal delivery, as well as the provision of personalized responses that take into account the emotional state of the user.

[0703] "Means of data collection" refers to a system for automatically acquiring data such as traffic conditions, weather conditions, and sales trends from external sources.

[0704] "Movement information" refers to data about the location and movement paths of objects and people, and is used to optimize the transportation routes of goods.

[0705] "Environmental information" refers to data about the external environment, such as weather, temperature, and traffic conditions, and is a factor that influences the planning and execution of logistics.

[0706] "Sales data" refers to numerical information obtained from sales activities over a specific period, and is useful for demand forecasting and inventory management.

[0707] "Means of analysis" refers to a system that performs a series of processes to analyze collected data and derive useful information.

[0708] "Means for optimizing transportation routes" refers to the process of calculating and proposing the most efficient route for transporting goods based on data analysis.

[0709] "Efficient real-time inventory management" refers to a management system that has the ability to instantly grasp the current inventory status and adjust inventory appropriately according to demand.

[0710] A "personalized response" is a method of providing appropriate information and instructions that take into account the user's individual emotional state and circumstances.

[0711] The system that realizes this invention is configured to exchange data primarily through a server, user terminals, and, if necessary, a communication network. The server collects traffic information, weather information, and sales data in real time from external APIs and sensors. In this process, the Python Requests library and various APIs can be utilized. The collected data is then analyzed using analysis software such as TensorFlow to perform demand forecasting and optimize transportation routes.

[0712] The server then uses map services such as the Google Maps API to calculate the optimal delivery route based on the analysis results and notifies the terminal. The terminal receives this information and displays it to drivers and administrators in real time. A smartphone app is often used for this display method.

[0713] Furthermore, the server uses voice analysis libraries such as IBM Watson to recognize the user's emotional state from their voice and generates appropriate responses based on that state through the Amazon Alexa Skills Kit. This personalized response allows users to receive quick information when they are busy.

[0714] For example, when a logistics center manager asks their smartphone, "What is today's delivery route?", the system provides an accurate response such as, "Calculating the optimal route... Route A is recommended to avoid traffic congestion." An example of a prompt would be, "Please tell me the real-time inventory status of the logistics center and the optimal delivery route."

[0715] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0716] Step 1:

[0717] The server acquires traffic information, weather information, and sales data in real time via external APIs and sensors. It receives the information in the required data format and prepares it for storage in the internal database. Inputs consist of various data from external sources, while outputs are unanalyzed data stored within the server.

[0718] Step 2:

[0719] The server analyzes the acquired data. Here, a predictive model is applied using mature Python data analysis libraries and TensorFlow, and the data processing necessary for demand forecasting and transportation route optimization is performed. The input is the data acquired in step 1, and the output is the demand forecast and unoptimized transportation route information obtained from the predictive model.

[0720] Step 3:

[0721] The server optimizes the transportation route based on the analysis results. It uses map services such as the Google Maps API to calculate the optimal route considering traffic conditions and geographical features. The input is the analysis results from step 2, and the output is the optimized transportation route information.

[0722] Step 4:

[0723] The server notifies the terminal of the optimized delivery route. This process involves sending the calculated optimal route information to the terminal device via communication, preparing it for display and guidance. The input is the optimized route information, and the output is the instruction information displayed on the terminal.

[0724] Step 5:

[0725] The server receives voice input from the user and performs emotion recognition using voice analysis tools such as IBM Watson. The input is voice data from the user, and the output is the analyzed emotion information.

[0726] Step 6:

[0727] Based on the emotion recognition results, the server generates personalized responses using a dialogue engine such as the Amazon Alexa Skills Kit. The generated responses are designed to provide information that takes into account the user's emotional state. The input is the emotion information obtained in step 5, and the output is the appropriate response to present to the user.

[0728] Step 7:

[0729] Users can view and interact with information provided by the server in real time through a smartphone application. This allows for the display of transportation routes and inventory status, and immediate responses to questions via voice recognition. Input is data transmitted from the server, while output is information displayed on the user's screen and voice guidance.

[0730] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0731] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0732] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0733] [Fourth Embodiment]

[0734] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0735] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0736] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0737] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0738] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0739] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0740] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0741] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0742] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0743] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0744] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0745] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0746] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0747] To implement the present invention, it is necessary to construct an information processing system that includes data acquisition means, data analysis means, route optimization means, communication means, inventory forecasting means, inventory management means, dialogue means, and instruction generation means. This system is designed to streamline logistics operations.

[0748] Overall Operation Description

[0749] 1. Data collection:

[0750] Subject: Server

[0751] The server acquires traffic information, weather information, and sales data in real time through APIs and sensors.

[0752] 2. Data Analysis:

[0753] Subject: Server

[0754] The server inputs the collected data into an AI algorithm to perform demand forecasting. It also analyzes the optimal route to maximize delivery efficiency.

[0755] 3. Optimization and notification of delivery routes:

[0756] Subject: Server

[0757] Based on the analysis results, the server generates an optimized delivery route considering multiple variables. This route is then transmitted to the terminal via communication, notifying the driver. The terminal then relays the received instructions to the user, ensuring smooth delivery.

[0758] 4. Automating inventory management:

[0759] Subject: Server

[0760] The server uses inventory forecasting tools to predict demand and automatically replenishes inventory based on that forecast. This prevents inventory surpluses and shortages, improving economic efficiency.

[0761] 5. Use of conversational agents:

[0762] Subject: User

[0763] Users can communicate with the system using natural language through dialogue mechanisms. For example, if a user asks, "Where is the next delivery destination?", the system will respond with "Please head to Store C" using a command generation mechanism based on the analyzed optimal route. This dialogue can be conducted in multiple languages, making it suitable for global business operations.

[0764] By operating this system, it is possible to respond quickly to changes in traffic conditions, shorten delivery times, and reduce logistics costs. Furthermore, by streamlining inventory management and improving communication, overall operational efficiency can be dramatically increased.

[0765] The following describes the processing flow.

[0766] Step 1:

[0767] Subject: Server

[0768] The server acquires traffic information, weather information, and sales data in real time from external APIs and sensors. The acquired data is stored in a database and used for subsequent analysis.

[0769] Step 2:

[0770] Subject: Server

[0771] The server uses data analysis tools to input the collected data into an AI algorithm. Here, the machine learning model analyzes the data and creates a foundation for optimizing demand forecasting and delivery efficiency.

[0772] Step 3:

[0773] Subject: Server

[0774] Based on the analysis results, the server uses route optimization techniques to calculate the optimal delivery route. This is a process of selecting the best solution from multiple candidate routes, taking into account time, distance, and fuel efficiency.

[0775] Step 4:

[0776] Subject: Server

[0777] The server uses a communication method to send information about the calculated optimal route to the terminal. The terminal then receives the route information and prepares for the next step.

[0778] Step 5:

[0779] Subject: terminal

[0780] The terminal notifies the user of the received route information via voice or text message. This allows the transporter to clearly understand what action to take next.

[0781] Step 6:

[0782] Subject: Server

[0783] The server uses inventory forecasting tools to analyze demand based on collected data and automatically replenishes inventory. This helps mitigate inventory surpluses and shortages.

[0784] Step 7:

[0785] Subject: User

[0786] The user interacts with the system using natural language through a dialogue mechanism. For example, if the user requests specific instructions from the system, the dialogue mechanism utilizes an instruction generation mechanism to generate appropriate instructions based on the user's request.

[0787] (Example 1)

[0788] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0789] In logistics operations, there is a growing need to improve transportation efficiency through the effective use of traffic information, weather information, and sales data. However, systems that can process this data quickly and accurately and provide optimal transportation routes and inventory management in real time are still lacking. In particular, there is a need for comprehensive solutions that enable inventory replenishment based on demand forecasts and the generation of work instructions through multilingual natural language dialogue.

[0790] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0791] In this invention, the server includes a data acquisition device, an analysis device, a route optimization device, a notification device, and a demand forecasting device. This enables efficient optimization of transportation routes and automation of inventory management through real-time data collection, analysis, and notification. Furthermore, demand forecasting using a generative AI model and multilingual dialogue are supported, enabling improved efficiency of the entire logistics operation.

[0792] A "data acquisition device" is a device for collecting traffic-related data, weather-related data, and sales-related data, and has the function of acquiring necessary data in real time from multiple information sources.

[0793] An "analysis device" is a device that performs traffic condition analysis and demand forecasting based on acquired data, and it executes programs and algorithms that perform data analysis.

[0794] A "route optimization device" is a device that uses analyzed data to perform calculations to optimize transportation routes, considering route combinations to achieve efficient transportation.

[0795] A "notification device" is a device that notifies carriers of optimized transportation routes and is equipped with communication functions for sending instructions.

[0796] A "demand forecasting device" is a device that uses a generation AI model to predict future demand and generates information to improve the efficiency of logistics operations.

[0797] This invention provides an information processing system for streamlining logistics operations. The system includes multiple devices that perform data collection, analysis, notification, and demand forecasting. Examples of each device are described below.

[0798] The server collects traffic-related data, weather-related data, and sales-related data using data acquisition devices. To retrieve data via APIs, the server establishes a network connection. Specifically, it uses the Google Maps API to collect traffic data and obtains weather information from weather data services such as OpenWeather.

[0799] The server processes the acquired data using an analysis device. Here, the data is preprocessed using the Python Pandas library and then analyzed using an AI algorithm. By using a generative AI model, it is possible to predict future demand and improve the efficiency of logistics activities based on the analysis results.

[0800] Furthermore, the server uses a route optimization device to calculate the optimal transportation route based on the collected and analyzed data. By utilizing Google OR-Tools to solve the shortest path problem, it generates efficient logistics routes.

[0801] Through the notification device, the server transmits optimized route information to the transporter. The information received by the terminal is conveyed to the user via a voice assistant or display and used as specific instructions. Based on this information, the user can carry out logistics activities smoothly.

[0802] Furthermore, the server automates inventory management based on demand forecasts. The forecasting model is built using a machine learning algorithm with TensorFlow, thereby reducing the risk of inventory surpluses or shortages.

[0803] The system includes a conversational agent function for interacting with users. Using a generative AI model, it can respond to a user's natural language question, such as "Where is the next delivery destination?", with an instruction like "Please head to store C." An example of a prompt would be, "Please optimize the upcoming delivery route based on current traffic and weather data."

[0804] In this way, the server efficiently utilizes diverse information, thereby improving the overall efficiency of logistics operations.

[0805] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0806] Step 1:

[0807] The server collects traffic-related data, weather-related data, and sales-related data through data acquisition devices. It uses API access information and network connectivity as input. The server retrieves the necessary data via APIs and obtains it in real time. In this process, it directly obtains necessary information from Google Maps and common weather data services.

[0808] Step 2:

[0809] The server preprocesses the data acquired using the analysis device. The input is the raw data obtained in step 1. The server uses the Python Pandas library to format and normalize the data and impute missing values. This process transforms the data into a format suitable for analysis and prepares it for the next analysis step.

[0810] Step 3:

[0811] The server uses a generative AI model to forecast demand. Preprocessed data is used as input. The generative AI model is prompted to perform the forecast. In this step, TensorFlow is used to operate the AI ​​model and numerically predict future demand. The output is the predicted demand data.

[0812] Step 4:

[0813] The server uses a route optimization tool to optimize transportation routes based on predicted demand and current traffic conditions. It uses demand forecast data and real-time traffic data as input. Leveraging Google OR-Tools, it calculates the shortest route and generates efficient transportation routes. The output provides optimized route information.

[0814] Step 5:

[0815] The server sends optimized route information to the terminal via a notification device. It uses the route information generated in the previous step as input. The terminal receives this information and outputs specific instructions to the user. For example, it might send a notification such as "Go to point A next" via screen display or voice command.

[0816] Step 6:

[0817] The server automates inventory management based on demand forecast results. It uses demand forecast data as input. The server integrates with the inventory management system to calculate the required inventory levels and automatically places orders. This results in output that prevents inventory shortages or surpluses.

[0818] Step 7:

[0819] The user communicates with the server using dialogue methods and natural language. Input includes the user's voice and text questions. The server utilizes a generative AI model to analyze the questions, generate appropriate instructions, and respond to the user. Output includes instructions and information provided through the interaction with the user.

[0820] (Application Example 1)

[0821] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0822] In modern logistics systems, efficient inventory management and optimized transportation are crucial for reducing operating costs and improving operational efficiency at logistics centers. However, conventional methods struggle to obtain real-time information and provide optimal routes, resulting in insufficient information for workers to perform appropriate operations within the warehouse. Technologies are needed to solve these problems.

[0823] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0824] In this invention, the server includes data acquisition means, data analysis means for analyzing traffic information, weather information, and transaction data acquired by the data acquisition means, route optimization means for optimizing the transportation route based on the analysis results by the data analysis means, communication means for notifying the transportation manager of the transportation route optimized by the route optimization means, and movement route provision means for presenting the optimal in-warehouse movement route to workers using a visual device. This enables efficient movement of workers in a logistics center and automation of inventory management.

[0825] "Data acquisition methods" refer to means of collecting various types of information related to logistics in real time.

[0826] "Data analysis methods" refer to means of performing demand forecasting and route optimization based on acquired traffic information, weather information, and transaction data.

[0827] A "route optimization method" is a means of calculating and presenting an efficient transportation route based on data analysis results.

[0828] "Communication means" refers to means of notifying transportation managers and workers of optimized transportation routes and other important instructions.

[0829] A "means for providing movement routes" refers to a means of visually guiding workers to the optimal movement route within a warehouse via a visual device.

[0830] A "demand forecasting tool" is a means of predicting future demand by analyzing past and present data.

[0831] "Inventory management methods" refer to means for automatically managing and executing inventory replenishment in accordance with predicted demand.

[0832] A "dialogue support system" is a means of directly interacting with workers using natural language processing and providing them with necessary information.

[0833] An "instruction generation means" is a means for generating specific work instructions based on information from a dialogue support means and presenting them to the worker.

[0834] A "location identification support system" is a means of identifying the current location of a worker based on sensor information obtained from a visual device, and providing efficient work support.

[0835] According to this invention, in order to operate a system efficiently in a logistics center, it is necessary to collect and analyze various data in real time and provide optimal instructions to workers. Embodiments of this invention will be described in detail below.

[0836] The server first uses data acquisition methods to collect traffic information, weather information, and transaction data in real time. This data is obtained from mobile devices and sensors via APIs. This enables analysis based on the latest information.

[0837] Next, the server analyzes the data using AI algorithms such as TensorFlow to perform demand forecasting and route optimization. The results of this analysis are used as foundational data for inventory management and transportation planning at the logistics center. The optimal route calculated by the route optimization system is immediately communicated to the workers' terminals via communication.

[0838] The terminal displays the received optimal route information to the worker through a visual device. By using a visual device such as smart glasses, the worker can understand the optimal route within the warehouse in real time. This visual information improves work efficiency.

[0839] Furthermore, the server uses natural language processing-based dialogue support to instantly respond to voice input from workers and provide specific work instructions. This dialogue function supports multiple languages, making it possible to handle global operations.

[0840] For example, if a worker asks "What is the next task?" via voice, the server will generate an instruction based on the analyzed data, such as "Next, pick items in section B." Another example of a prompt message would be, "Retrieve inventory data and suggest the next optimal work route. Current location is the central aisle, target is picking area A."

[0841] In this way, the overall operation of the logistics center is highly streamlined by combining data analysis and optimized route guidance, ensuring efficient movement and significantly improving work efficiency.

[0842] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0843] Step 1:

[0844] The server uses data acquisition methods to collect traffic information, weather information, and transaction data in real time. Inputs are data from APIs and sensors, which are collected by polling at regular intervals. This data is then prepared for subsequent analysis.

[0845] Step 2:

[0846] The server inputs the collected data into an AI algorithm through data analysis tools to perform demand forecasting and route optimization. The input is the data collected in step 1, which is analyzed using an algorithm based on TensorFlow. The output is information on optimal inventory placement and delivery routes, which prepares the data for the next processing step.

[0847] Step 3:

[0848] Based on the analysis results, the server notifies the terminal of the optimal transport route calculated by the route optimization means via the communication means. The input is the analysis output from step 2. The optimal route information is sent to the terminal, and an actionable transport plan is formulated.

[0849] Step 4:

[0850] The terminal displays the optimal movement route in real time via a visual device worn by the worker. The input is the route information transmitted in step 3, which is presented to the worker as visual information using smart glasses or similar devices. The output is the worker's efficient movement within the warehouse.

[0851] Step 5:

[0852] The user queries the server in natural language using a dialogue support system, and receives specific work instructions from an instruction generation system. The input is voice data from the user, which is analyzed and processed by the server using natural language processing. As a result, work instructions are generated and presented to the user in either voice or text format.

[0853] Step 6:

[0854] Based on acquired sensor information, the server uses location identification support to determine the worker's current location and guides them to the optimal work position using a visual device. The input is real-time location data from the visual device, and the worker's position is calculated by a location identification algorithm. This allows the worker to move to the next task immediately, enabling efficient work.

[0855] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0856] The present invention is implemented by constructing an information processing system that includes data acquisition means, data analysis means, route optimization means, communication means, inventory forecasting means, inventory management means, dialogue means, instruction generation means, and an emotion engine. This system is designed to improve the efficiency of logistics operations and enable effective interaction with users.

[0857] Overall Operation Description

[0858] 1. Data collection and analysis:

[0859] Subject: Server

[0860] The server collects traffic information, weather information, and sales data in real time through external APIs and sensors, analyzes the data to forecast demand, and uses this information for inventory management and delivery route planning.

[0861] 2. Optimization and notification of delivery routes:

[0862] Subject: Server

[0863] The server calculates the optimal delivery route based on the analysis results and transmits it to the terminal via communication. The terminal then notifies the driver of the optimized route information and supports the transportation.

[0864] 3. Automating inventory forecasting and management:

[0865] Subject: Server

[0866] The server uses inventory forecasting tools to predict future demand, and based on that, automatically replenishes inventory using inventory management tools to maintain a balance between supply and demand.

[0867] 4. Recognition of user emotions by an emotion engine:

[0868] Subject: Server

[0869] The server uses an emotion engine to recognize emotions from the user's voice and text. This information is fed back into the entire system and influences responses to interactions and work instructions.

[0870] 5. Optimizing dialogue:

[0871] Subject: User

[0872] Users interact with the system through dialogue. The system generates responses that take the user's emotional state into account, enabling more personalized communication. For example, if the user sounds busy, the system will provide quick and concise instructions.

[0873] This system enables efficient decision-making based on real-time circumstances, while also facilitating emotion-based communication with users, thereby improving overall work efficiency and satisfaction.

[0874] The following describes the processing flow.

[0875] Step 1:

[0876] Subject: Server

[0877] The server collects real-time traffic information, weather information, and sales data through external APIs and sensors. This data is systematically stored in a database and forms the basis for subsequent data analysis.

[0878] Step 2:

[0879] Subject: Server

[0880] The server uses data analysis tools to analyze the collected data with AI algorithms. This analysis creates a demand forecasting model, which in turn generates conditions for optimizing delivery routes.

[0881] Step 3:

[0882] Subject: Server

[0883] The server uses route optimization techniques to evaluate various route options and calculate the optimal route considering time, fuel efficiency, and traffic conditions. The optimized route information is then used in the next step.

[0884] Step 4:

[0885] Subject: Server

[0886] The server transmits the calculated optimal delivery route to the terminal via communication means. This allows the transporter to obtain the information in a usable format.

[0887] Step 5:

[0888] Subject: terminal

[0889] The terminal notifies the user of delivery route information received from the server via voice or text. The user can then quickly take action by following the instructions.

[0890] Step 6:

[0891] Subject: Server

[0892] The server uses inventory forecasting tools to automatically replenish inventory based on sales data and demand forecasts. This prevents inventory shortages and surpluses, enabling efficient inventory management.

[0893] Step 7:

[0894] Subject: Server

[0895] The server uses an emotion engine to recognize emotions from the user's voice and text. The recognized emotion data is then fed back to the dialogue mechanism.

[0896] Step 8:

[0897] Subject: User

[0898] Users interact with the system using natural language through dialogue. The system analyzes the user's emotional state and adjusts its responses accordingly, resulting in more appropriate instructions and support.

[0899] (Example 2)

[0900] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0901] In logistics operations, accurately forecasting traffic conditions, weather conditions, and market demand, developing efficient delivery plans, and properly managing inventory are extremely important. Furthermore, smooth communication with customers, while being mindful of their feelings, is also necessary. However, traditional methods make it difficult to manage all of these elements in an integrated manner, which can lead to decreased operational efficiency.

[0902] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0903] In this invention, the server includes means for acquiring data, means for analyzing traffic-related data, weather-related data, and sales-related data acquired by the data acquisition means, means for optimizing the transportation route based on the analysis results by the data analysis means, means for notifying the transportation personnel of the optimized transportation route via a communication means, and an emotion analysis engine for recognizing human emotions. This enables increased efficiency in logistics operations and effective communication with users.

[0904] "Means of acquiring data" refers to mechanisms for collecting information related to traffic, weather, and sales from external sources.

[0905] "Means for analysis" refers to the process of analyzing acquired data to perform appropriate demand forecasting and trend analysis.

[0906] "Methods for optimizing transportation routes" refer to methods for calculating the shortest and most efficient delivery route, taking into account traffic information and weather conditions.

[0907] "Means for notifying transportation personnel via means of communication" refers to a communication system for informing transportation personnel of the calculated optimal route information.

[0908] The "emotion analysis engine" is a function that detects the user's emotions from their voice or text and reflects them in the dialogue response.

[0909] "Means for forecasting demand" refers to methodologies for predicting future demand based on past data and market trends.

[0910] "Means for automated supply management" refers to automated systems that appropriately replenish and manage inventory in accordance with predicted demand.

[0911] "A means of processing natural language and generating instructions through interaction with the user" refers to a system that understands user input in natural language and creates appropriate instructions accordingly.

[0912] "Means for generating responses based on generative AI models" refers to a method of generating the optimal response to user input using a machine learning model.

[0913] This invention provides an information processing system that streamlines logistics operations and enables effective communication with users. The system includes data acquisition means, data analysis means, route optimization means, communication means, inventory forecasting means, inventory management means, dialogue means, instruction generation means, and an emotion engine.

[0914] First, the server utilizes common APIs to obtain traffic, weather, and sales-related data from external data sources. For example, it can use traffic information APIs and weather information APIs. The server processes the acquired data in real time and performs analysis for demand forecasting. This analysis may involve using statistical methods and machine learning algorithms.

[0915] Next, the server calculates the optimal transport route based on the analysis results. This calculation can utilize route-finding algorithms such as Dijkstra's algorithm. The optimized route information is transmitted to a terminal via communication, and the terminal presents the information to the driver through voice guidance or display on a screen. This allows the driver to deliver packages efficiently.

[0916] Furthermore, the server utilizes inventory forecasting tools to manage inventory appropriately based on future demand. This involves using machine learning models that predict demand from historical sales data and market trends. Based on this information, the inventory management system automatically performs the necessary replenishment operations.

[0917] Furthermore, the server uses an emotion engine to detect emotions from the user's voice and text, and feeds this back to the dialogue and instruction generation systems. Specifically, it uses natural language processing technology to analyze the user's statements and emotions in real time, and uses a generative AI model to generate appropriate responses according to the user's emotional state. For example, a possible prompt might be "Prepare an answer that will help the user relax."

[0918] This system enables real-time decision-making regarding logistics and inventory management, and facilitates emotion-based communication with users. This, in turn, improves overall operational efficiency and customer satisfaction.

[0919] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0920] Step 1:

[0921] The server retrieves data from external traffic information APIs, weather information APIs, and sales databases. The input is raw data obtained from the APIs or databases. The server receives this raw data, converts and cleans the data format, and prepares it for analysis. The output is a structured dataset.

[0922] Step 2:

[0923] The server performs demand forecasting using the organized data. The input is the structured data obtained in Step 1. The server inputs this data into a machine learning model, such as a time series analysis or multiple regression model, to predict future demand. The output obtained from this analysis is the demand forecast result, which includes information on demand fluctuations over time.

[0924] Step 3:

[0925] The server optimizes transportation routes based on demand forecasts, traffic information, and weather information. Inputs are forecasted demand data and current traffic and weather data. The server calculates the optimal route using route-finding algorithms such as Dijkstra's algorithm. The output is optimized transportation route information, which includes the route from the starting point to the destination.

[0926] Step 4:

[0927] The server transmits optimized transport route information to the terminal. The input is the transport route information obtained in step 3. The terminal receives this information via communication and displays it on its screen, as well as using a voice guidance system to communicate the information to the driver. The output is instruction information for the driver.

[0928] Step 5:

[0929] The server performs inventory forecasting based on historical sales data and predicted demand. The inputs are the demand forecast results from step 2 and inventory data. The server uses an inventory forecasting model to calculate appropriate inventory levels. The output is inventory replenishment information for each product that needs to be replenished.

[0930] Step 6:

[0931] The inventory management system receives inventory replenishment information from the server and automatically processes orders. The input is the inventory replenishment information provided by the server. Based on this information, the inventory management system places orders with suppliers and adjusts inventory levels. The output is the new order data.

[0932] Step 7:

[0933] The server receives voice input or text messages from the user and recognizes their emotions using an emotion analysis engine. The input is raw voice or text data from the user. The server uses natural language processing techniques to analyze this data and identify the user's emotions. The output is the analyzed emotion data.

[0934] Step 8:

[0935] The server uses a generative AI model based on emotional data to generate appropriate responses. The input consists of the user's emotional data and dialogue content. The server uses prompts such as "Prepare an answer that will help the user relax" to generate responses for the generative AI model. The output is an optimized response message for the user.

[0936] (Application Example 2)

[0937] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0938] In modern logistics operations, efficient inventory management and optimal delivery are crucial, but achieving this in real time and in response to changing circumstances is difficult. Furthermore, flexible responses that consider the emotional state of users are required when communicating information to workers. The challenge lies in providing an integrated system to address these issues.

[0939] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0940] In this invention, the server includes means for collecting data, means for analyzing movement information, environmental information, and sales data, and means for optimizing the transportation route. This enables efficient real-time inventory management and optimal delivery, as well as the provision of personalized responses that take into account the emotional state of the user.

[0941] "Means of data collection" refers to a system for automatically acquiring data such as traffic conditions, weather conditions, and sales trends from external sources.

[0942] "Movement information" refers to data about the location and movement paths of objects and people, and is used to optimize the transportation routes of goods.

[0943] "Environmental information" refers to data about the external environment, such as weather, temperature, and traffic conditions, and is a factor that influences the planning and execution of logistics.

[0944] "Sales data" refers to numerical information obtained from sales activities over a specific period, and is useful for demand forecasting and inventory management.

[0945] "Means of analysis" refers to a system that performs a series of processes to analyze collected data and derive useful information.

[0946] "Means for optimizing transportation routes" refers to the process of calculating and proposing the most efficient route for transporting goods based on data analysis.

[0947] "Efficient real-time inventory management" refers to a management system that has the ability to instantly grasp the current inventory status and adjust inventory appropriately according to demand.

[0948] A "personalized response" is a method of providing appropriate information and instructions that take into account the user's individual emotional state and circumstances.

[0949] The system that realizes this invention is configured to exchange data primarily through a server, user terminals, and, if necessary, a communication network. The server collects traffic information, weather information, and sales data in real time from external APIs and sensors. In this process, the Python Requests library and various APIs can be utilized. The collected data is then analyzed using analysis software such as TensorFlow to perform demand forecasting and optimize transportation routes.

[0950] The server then uses map services such as the Google Maps API to calculate the optimal delivery route based on the analysis results and notifies the terminal. The terminal receives this information and displays it to drivers and administrators in real time. A smartphone app is often used for this display method.

[0951] Furthermore, the server uses voice analysis libraries such as IBM Watson to recognize the user's emotional state from their voice and generates appropriate responses based on that state through the Amazon Alexa Skills Kit. This personalized response allows users to receive quick information when they are busy.

[0952] For example, when a logistics center manager asks their smartphone, "What is today's delivery route?", the system provides an accurate response such as, "Calculating the optimal route... Route A is recommended to avoid traffic congestion." An example of a prompt would be, "Please tell me the real-time inventory status of the logistics center and the optimal delivery route."

[0953] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0954] Step 1:

[0955] The server acquires traffic information, weather information, and sales data in real time via external APIs and sensors. It receives the information in the required data format and prepares it for storage in the internal database. Inputs consist of various data from external sources, while outputs are unanalyzed data stored within the server.

[0956] Step 2:

[0957] The server analyzes the acquired data. Here, a predictive model is applied using mature Python data analysis libraries and TensorFlow, and the data processing necessary for demand forecasting and transportation route optimization is performed. The input is the data acquired in step 1, and the output is the demand forecast and unoptimized transportation route information obtained from the predictive model.

[0958] Step 3:

[0959] The server optimizes the transportation route based on the analysis results. It uses map services such as the Google Maps API to calculate the optimal route considering traffic conditions and geographical features. The input is the analysis results from step 2, and the output is the optimized transportation route information.

[0960] Step 4:

[0961] The server notifies the terminal of the optimized delivery route. This process involves sending the calculated optimal route information to the terminal device via communication, preparing it for display and guidance. The input is the optimized route information, and the output is the instruction information displayed on the terminal.

[0962] Step 5:

[0963] The server receives voice input from the user and performs emotion recognition using voice analysis tools such as IBM Watson. The input is voice data from the user, and the output is the analyzed emotion information.

[0964] Step 6:

[0965] Based on the emotion recognition results, the server generates personalized responses using a dialogue engine such as the Amazon Alexa Skills Kit. The generated responses are designed to provide information that takes into account the user's emotional state. The input is the emotion information obtained in step 5, and the output is the appropriate response to present to the user.

[0966] Step 7:

[0967] Users can view and interact with information provided by the server in real time through a smartphone application. This allows for the display of transportation routes and inventory status, and immediate responses to questions via voice recognition. Input is data transmitted from the server, while output is information displayed on the user's screen and voice guidance.

[0968] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0969] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0970] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0971] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0972] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0973] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0974] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0975] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0976] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0977] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0978] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0979] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0980] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0981] 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.

[0982] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0983] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0984] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0985] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0986] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0987] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0988] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0989] The following is further disclosed regarding the embodiments described above.

[0990] (Claim 1)

[0991] Data acquisition method,

[0992] A data analysis means for analyzing traffic information, weather information, and sales data acquired by the data acquisition means,

[0993] A route optimization means that optimizes the delivery route based on the analysis results from the data analysis means,

[0994] A communication means for notifying the carrier of the delivery route optimized by the route optimization means,

[0995] A system that includes this.

[0996] (Claim 2)

[0997] A means of inventory forecasting that performs inventory forecasting,

[0998] An inventory management means that automatically replenishes inventory based on the forecast results from the inventory forecasting means,

[0999] The system according to claim 1, including the following:

[1000] (Claim 3)

[1001] A dialogue system that processes natural language and interacts with workers,

[1002] An instruction generation means that generates work instructions based on the information processed by the dialogue means,

[1003] The system according to claim 1, including the following:

[1004] "Example 1"

[1005] (Claim 1)

[1006] Data acquisition device,

[1007] An analysis device for analyzing traffic-related data, weather-related data, and sales-related data acquired by the data acquisition device,

[1008] A route optimization device that optimizes the transport route based on the analysis results from the analysis device,

[1009] A notification device that notifies the transporter of the transport route optimized by the route optimization device,

[1010] A demand forecasting device that receives the generated demand forecast results as input and performs calculations to improve the efficiency of logistics activities,

[1011] A system that includes this.

[1012] (Claim 2)

[1013] An inventory control system that automates inventory management,

[1014] A planning device that automatically replenishes goods based on operations performed by the inventory control device,

[1015] The system according to claim 1, including the following:

[1016] (Claim 3)

[1017] A communication device that processes natural language information and communicates with workers,

[1018] An instruction generation device that generates work instructions based on information processed by the communication device,

[1019] The system according to claim 1, including the following:

[1020] "Application Example 1"

[1021] (Claim 1)

[1022] Data acquisition method,

[1023] A data analysis means for analyzing traffic information, weather information, and transaction data acquired by the data acquisition means,

[1024] A route optimization means that optimizes the transport route based on the analysis results from the data analysis means,

[1025] A communication means for notifying the transportation manager of the transportation route optimized by the route optimization means,

[1026] A means of providing movement paths that presents workers with the optimal movement path within the warehouse using a visual device,

[1027] A system that includes this.

[1028] (Claim 2)

[1029] A demand forecasting tool for performing demand forecasting,

[1030] An inventory management means that automatically replenishes inventory based on the forecast results from the demand forecasting means,

[1031] The system according to claim 1, including the following:

[1032] (Claim 3)

[1033] A dialogue support system that processes natural language and engages in conversation with workers,

[1034] An instruction generation means that generates work instructions based on information processed by the dialogue support means,

[1035] A location identification support means that identifies the current location of a worker based on sensor information from a visual device and supports efficient work,

[1036] The system according to claim 1, including the following:

[1037] "Example 2 of combining an emotion engine"

[1038] (Claim 1)

[1039] Means of acquiring data,

[1040] A means for analyzing traffic-related data, weather-related data, and sales-related data acquired by the data acquisition means,

[1041] A means for optimizing the transport route based on the analysis results from the data analysis means,

[1042] Means for notifying the transporter of the optimized transport route via a communication means,

[1043] An emotion analysis engine for recognizing human emotions,

[1044] A system that includes this.

[1045] (Claim 2)

[1046] Means for forecasting demand,

[1047] A means for automatically managing supply based on the forecast results from the demand forecasting means,

[1048] The system according to claim 1, including the following:

[1049] (Claim 3)

[1050] A means for processing natural language and generating instructions through interaction with the user,

[1051] Means for generating responses based on a generative AI model,

[1052] The system according to claim 1, including the following:

[1053] "Application example 2 when combining with an emotional engine"

[1054] (Claim 1)

[1055] Means of collecting data,

[1056] Means for analyzing movement information, environmental information, and sales data acquired by the data collection means,

[1057] A means for optimizing the transport route based on the analysis results from the analysis means,

[1058] A means for notifying the carrier of the transportation route optimized by the optimization means,

[1059] A means of recognizing emotions from the user's voice,

[1060] A means for generating a quick and concise response based on the user's emotional state,

[1061] A system that includes this.

[1062] (Claim 2)

[1063] Methods for forecasting inventory,

[1064] A means for replenishing inventory based on the forecast results from the inventory forecasting means,

[1065] The system according to claim 1, comprising means for monitoring inventory and transportation status in real time.

[1066] (Claim 3)

[1067] A means of processing information in natural language and interacting with workers,

[1068] A means for generating work instructions based on information processed by the dialogue means,

[1069] The system according to claim 1, including response optimization based on emotion recognition. [Explanation of symbols]

[1070] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Data acquisition method, A data analysis means for analyzing traffic information, weather information, and sales data acquired by the data acquisition means, A route optimization means that optimizes the delivery route based on the analysis results from the data analysis means, A communication means for notifying the carrier of the delivery route optimized by the route optimization means, A system that includes this.

2. A means of inventory forecasting that performs inventory forecasting, An inventory management means that automatically replenishes inventory based on the forecast results from the inventory forecasting means, The system according to claim 1, including the following:

3. A dialogue system that processes natural language and interacts with workers, An instruction generation means that generates work instructions based on the information processed by the dialogue means, The system according to claim 1, including the following:

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