system

Abstract

The system according to this embodiment aims to analyze logistics data and propose and monitor optimal operating methods. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and a monitoring unit. The collection unit collects logistics data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes an optimal operating method based on the analysis results obtained by the analysis unit. The monitoring unit monitors in real time based on the operating method proposed by the proposal unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 conventional technology, optimization and real-time monitoring of operation methods based on logistics data have not been sufficiently performed, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze logistics data and propose and monitor an optimal operation method.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a monitoring unit. The data collection unit collects logistics data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes an optimal operating method based on the analysis results obtained by the analysis unit. The monitoring unit monitors in real time based on the operating method proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze logistics data and propose and monitor optimal operating methods. [Brief explanation of the drawing]

[0008] [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. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The Logistics Revolution Digital Twin System according to an embodiment of the present invention is a system that reproduces actual logistics processes using digital twin technology and performs simulations in real time. Based on logistics data held by a company, this Logistics Revolution Digital Twin System uses AI to propose the optimal operating method. For example, the Logistics Revolution Digital Twin System monitors warehouse inventory status and delivery routes in real time and immediately identifies delays and problems. This enables rapid decision-making, resulting in cost reductions and shorter delivery times. Furthermore, the Logistics Revolution Digital Twin System also optimizes demand forecasting and inventory management by analyzing historical data. For example, the Logistics Revolution Digital Twin System can predict future demand based on past sales data and manage inventory appropriately. This prevents excess inventory and stockouts, enabling efficient operations. Thus, the Logistics Revolution Digital Twin System is an innovative solution for companies to enhance their competitiveness. For example, the Logistics Revolution Digital Twin System monitors warehouse inventory status and delivery routes in real time and immediately identifies delays and problems, enabling rapid decision-making. Furthermore, the Logistics Revolution Digital Twin System also optimizes demand forecasting and inventory management by analyzing historical data. This results in cost reductions and shorter delivery times, improving the competitiveness of companies. This enables the logistics revolution through digital twin systems, which can streamline a company's logistics processes and enhance its competitiveness.

[0029] The logistics revolution digital twin system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a monitoring unit. The data collection unit collects logistics data. Logistics data includes, but is not limited to, inventory data, delivery data, and order data. The data collection unit collects, for example, data on inventory status in warehouses and delivery routes. The data collection unit can collect data in real time using, for example, sensors or GPS. The data collection unit can also automatically acquire data from a company's system. For example, the data collection unit monitors inventory status in warehouses with sensors and collects data in real time. The data collection unit collects delivery route data using GPS and updates it in real time. The data collection unit automatically acquires order data from a company's system and stores it in a database. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the data using, for example, AI and proposes the optimal operating method. The analysis unit can analyze inventory data and propose the optimal inventory placement. The analysis unit can also analyze delivery data and propose the optimal delivery route. For example, the analysis unit uses AI to analyze inventory data and propose the optimal inventory placement. The analysis unit uses AI to analyze delivery data and propose the optimal delivery route. The analysis unit uses AI to analyze order data and perform demand forecasting. The proposal unit proposes the optimal operating method based on the analysis results obtained by the analysis unit. For example, the proposal unit can propose the optimal inventory placement and optimization of delivery routes. For example, the proposal unit can propose immediate replenishment if inventory is insufficient. The proposal unit can also analyze delivery route data and propose the optimal route. For example, the proposal unit can propose immediate replenishment if inventory is insufficient. The proposal unit analyzes delivery route data and proposes the optimal route. The proposal unit proposes appropriate inventory management based on demand forecasting. The monitoring unit monitors in real time based on the operating method proposed by the proposal unit. For example, the monitoring unit can immediately identify delays and problems. For example, the monitoring unit monitors inventory status in real time and immediately notifies if inventory is insufficient. The monitoring unit can also monitor delivery routes in real time and immediately notify if delays occur.For example, the monitoring unit monitors inventory status in real time and immediately notifies if inventory is insufficient. The monitoring unit monitors delivery routes in real time and immediately notifies if delays occur. The monitoring unit monitors the status of the entire system in real time and immediately notifies if problems occur. As a result, the logistics revolution digital twin system according to this embodiment enables efficient operation by collecting, analyzing, proposing, and monitoring logistics data.

[0030] The data collection unit collects logistics data. Logistics data includes, but is not limited to, inventory data, delivery data, and order data. For example, the data collection unit collects data on warehouse inventory status and delivery routes. The data collection unit can collect data in real time using, for example, sensors or GPS. The data collection unit can also automatically acquire data from a company's systems. For example, the data collection unit monitors warehouse inventory status with sensors and collects data in real time. The data collection unit collects delivery route data using GPS and updates it in real time. The data collection unit automatically acquires order data from a company's systems and stores it in a database. The data collection unit centrally manages data generated at each stage of logistics, enabling efficient data collection. For example, sensors that monitor warehouse inventory status can detect the inflow and outflow of goods in real time, allowing for immediate understanding of inventory fluctuations. This prevents inventory shortages and surpluses and enables appropriate inventory management. GPS, which collects delivery route data, tracks the location information of delivery vehicles in real time and provides basic data for selecting the optimal route. This improves delivery efficiency, reducing delays and unnecessary movement. Furthermore, order data automatically acquired from the company's systems allows for accurate understanding of customer demand and enables rapid response. For example, when an order is placed, inventory status can be checked immediately, and necessary products can be quickly picked and shipped. This improves customer satisfaction and streamlines logistics. The data collection department centrally manages this data and can link with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. In addition, by adjusting the frequency and accuracy of data collection, flexible responses can be made to specific situations and conditions. As a result, the data collection department can collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to analyze data and propose optimal operational methods. For example, the analysis unit can analyze inventory data and propose optimal inventory placement. Furthermore, the analysis unit can analyze delivery data and propose optimal delivery routes. For example, the analysis unit uses AI to analyze inventory data and propose optimal inventory placement. The analysis unit uses AI to analyze delivery data and propose optimal delivery routes. The analysis unit uses AI to analyze order data and forecast demand. The analysis unit analyzes collected data from multiple perspectives and makes concrete proposals to improve logistics efficiency. For example, in inventory data analysis, it considers product turnover and demand fluctuations to propose optimal inventory placement. This prevents inventory shortages and surpluses, enabling efficient inventory management. In addition, in delivery data analysis, it optimizes delivery routes to shorten delivery times and reduce costs. For example, it can use AI to consider traffic conditions and weather information to propose optimal delivery routes in real time. Furthermore, in order data analysis, it forecasts customer demand and develops appropriate inventory replenishment and production plans. This allows for a rapid response to fluctuations in demand and improved customer satisfaction. Based on these analysis results, the analysis department proposes specific operational methods to improve logistics efficiency and reduce costs. Furthermore, the analysis department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on historical data, it can predict fluctuations in demand during specific seasons or events and take appropriate measures. In addition, the analysis department can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing warnings early. As a result, the analysis department can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The proposal department proposes optimal operational methods based on the analysis results obtained by the analysis department. For example, the proposal department can propose optimal inventory placement and optimized delivery routes. For example, if inventory is insufficient, the proposal department will propose immediate replenishment. The proposal department can also analyze delivery route data and propose the optimal route. For example, if inventory is insufficient, the proposal department will propose immediate replenishment. The proposal department will analyze delivery route data and propose the optimal route. The proposal department proposes appropriate inventory management based on demand forecasts. Based on the data provided by the analysis department, the proposal department proposes specific operational methods to improve logistics efficiency. For example, when proposing optimal inventory placement, it considers product turnover and demand fluctuations to propose appropriate inventory placement. This prevents inventory surpluses and shortages, enabling efficient inventory management. When proposing optimized delivery routes, it considers traffic conditions and weather information to propose the optimal delivery route. This can shorten delivery times and reduce costs. Furthermore, by proposing appropriate inventory management based on demand forecasts, it is possible to respond quickly to demand fluctuations and improve customer satisfaction. Based on these proposals, the proposal department implements specific operational methods to achieve logistics efficiency and cost reduction. Furthermore, the proposal department can continuously monitor the effectiveness of the proposals and make improvements as needed. For example, by providing feedback on problems and issues that arise after the implementation of the proposals and reflecting them in future proposals, the accuracy and effectiveness of the proposals can be improved. In addition, the proposal department can simulate multiple scenarios and select the most effective operational method. As a result, the proposal department can always propose the optimal operational method, achieving logistics efficiency and cost reduction.

[0033] The monitoring department monitors in real time based on the operating methods proposed by the proposal department. For example, the monitoring department can immediately identify delays and problems. For example, the monitoring department monitors inventory status in real time and immediately notifies if inventory is insufficient. The monitoring department can also monitor delivery routes in real time and immediately notify if delays occur. For example, the monitoring department monitors inventory status in real time and immediately notifies if inventory is insufficient. The monitoring department monitors delivery routes in real time and immediately notifies if delays occur. The monitoring department monitors the status of the entire system in real time and immediately notifies if problems occur. The monitoring department monitors in real time whether the operating methods proposed by the proposal department are being properly implemented and immediately responds if problems occur. For example, by monitoring inventory status in real time, it can immediately notify if inventory is insufficient and quickly replenish it. Also, by monitoring delivery routes in real time, it can immediately notify if delays occur and propose alternative routes. This can improve logistics efficiency and customer satisfaction. Furthermore, the monitoring department monitors the status of the entire system in real time and immediately responds if problems occur. For example, if a system anomaly is detected, it can be immediately notified, allowing for a swift response. This improves the reliability and security of the system. Based on these monitoring results, the monitoring department can collaborate with the proposal and analysis departments to improve operational methods. For instance, by providing feedback on monitoring results and reviewing proposals and analysis methods, the overall system performance can be improved. The monitoring department can also collect feedback from users and use it to improve operational methods. As a result, the monitoring department can always maintain optimal operational methods, achieving increased logistics efficiency and improved customer satisfaction.

[0034] The data collection unit can collect data on warehouse inventory status and delivery routes. For example, the data collection unit can monitor warehouse inventory status with sensors and collect data in real time. The data collection unit can also collect delivery route data using GPS and update it in real time. The data collection unit can also automatically acquire order data from a company's system and store it in a database. This allows for improved logistics efficiency by collecting data on warehouse inventory status and delivery routes. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have a generating AI perform the process of monitoring warehouse inventory status with sensors and collecting data in real time.

[0035] The analysis unit can propose optimal operational methods based on the collected data. For example, the analysis unit can use AI to analyze inventory data and propose the optimal inventory placement. The analysis unit can also use AI to analyze delivery data and propose the optimal delivery route. The analysis unit can also use AI to analyze order data and perform demand forecasting. This allows for improved operational efficiency by proposing optimal operational methods based on the collected data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected data into a generating AI and have the generating AI execute a proposal for the optimal operational method.

[0036] The suggestion unit can monitor inventory levels in real time and immediately suggest replenishment if inventory levels are low. For example, if inventory levels are low, the suggestion unit can immediately suggest replenishment. The suggestion unit can also set inventory thresholds and suggest replenishment if inventory levels fall below these thresholds. The suggestion unit can also suggest methods for replenishing inventory. This allows for more efficient inventory management by monitoring inventory levels in real time and immediately suggesting replenishment if inventory levels are low. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input inventory levels into a generating AI and have the generating AI execute replenishment suggestions when inventory levels are low.

[0037] The proposal unit can analyze delivery route data and propose the optimal route. For example, the proposal unit can analyze delivery route data and propose the optimal route. The proposal unit can also propose the optimal route considering the distance, time, and traffic conditions of the delivery route. The proposal unit can also propose the optimal route considering the cost efficiency of the delivery route. In this way, by analyzing delivery route data and proposing the optimal route, the efficiency of delivery can be improved. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input delivery route data into a generating AI and have the generating AI execute the proposal of the optimal route.

[0038] The monitoring unit can immediately identify delays and problems. For example, the monitoring unit can monitor inventory status in real time and immediately notify if inventory is insufficient. The monitoring unit can also monitor delivery routes in real time and immediately notify if delays occur. The monitoring unit can also monitor the status of the entire system in real time and immediately notify if problems occur. This enables a rapid response by immediately identifying delays and problems. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input inventory status into a generating AI and have the generating AI execute a notification if inventory is insufficient.

[0039] The analysis unit can predict future demand based on past sales data. For example, the analysis unit predicts future demand based on past sales data. The analysis unit can also apply multiple forecasting algorithms to improve the accuracy of demand forecasting. The analysis unit can also propose optimizations for inventory management based on the results of the demand forecast. This improves the accuracy of demand forecasting by predicting future demand based on past sales data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past sales data into a generating AI and have the generating AI perform future demand forecasting.

[0040] The proposal unit can appropriately manage inventory based on demand forecasts. For example, the proposal unit can appropriately manage inventory based on demand forecasts. The proposal unit can also set the optimal amount of inventory and replenish inventory based on demand forecasts. The proposal unit can also propose methods for managing inventory. This makes inventory management more efficient by appropriately managing inventory based on demand forecasts. Some or all of the above processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the results of the demand forecast into a generating AI and have the generating AI execute inventory management proposals.

[0041] The data collection unit can evaluate the reliability of logistics data during collection and exclude unreliable data. For example, the data collection unit can verify the source of the data and exclude data from unreliable sources. The data collection unit can also check the consistency of the data and exclude inconsistent data. The data collection unit can also evaluate the frequency of data updates and exclude outdated data. This improves data quality by evaluating the reliability of logistics data and excluding unreliable data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the collected data into a generating AI and have the generating AI perform reliability evaluation and exclude low-reliability data.

[0042] The data collection unit can apply different collection methods depending on the type of data during collection. For example, the data collection unit can automatically collect inventory data periodically and update it in real time. The data collection unit can also collect delivery route data in real time using GPS. The data collection unit can also collect demand forecast data periodically based on past sales data. This enables efficient data collection by applying different collection methods depending on the type of data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have a generating AI execute a collection method appropriate to the type of data.

[0043] The data collection unit can prioritize the collection of highly relevant data by considering the geographical distribution of logistics data during the collection process. For example, the data collection unit can prioritize the collection of inventory data from geographically close warehouses. The data collection unit can also prioritize the collection of highly relevant data by considering the geographical distribution of delivery routes. The data collection unit can also prioritize the collection of data from geographically important locations. This enables efficient data collection by prioritizing the collection of highly relevant data while considering the geographical distribution of logistics data. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical distribution of logistics data into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0044] The data collection unit can analyze social media activity related to logistics data during collection and collect relevant data. For example, the data collection unit can analyze posts about logistics on social media and collect relevant data. The data collection unit can also collect customer feedback on social media and reflect it in the logistics data. The data collection unit can also analyze trends on social media and collect relevant logistics data. This enables efficient data collection by analyzing social media activity related to logistics data and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the logistics data during the analysis. For example, the analysis unit can analyze highly important data in detail and less important data in a simplified manner. The analysis unit can also apply multiple analysis methods to highly important data to provide detailed results. The analysis unit can also apply basic analysis methods to less important data to provide concise results. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the logistics data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the logistics data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0046] The analysis unit can apply different analysis algorithms depending on the category of logistics data during analysis. For example, the analysis unit can apply an analysis algorithm specifically for inventory management to inventory data. The analysis unit can also apply an analysis algorithm specifically for route optimization to delivery route data. The analysis unit can also apply an analysis algorithm specifically for demand forecasting to demand forecasting data. This enables efficient analysis by applying different analysis algorithms depending on the category of logistics data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of logistics data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0047] The analysis unit can determine the priority of analysis based on the submission timing of logistics data during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent data to support real-time decision-making. The analysis unit can also postpone the analysis of older data. The analysis unit can also prioritize the analysis of data where the submission timing is important, enabling a rapid response. This allows for efficient analysis by determining the priority of analysis based on the submission timing of logistics data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission timing of logistics data into a generating AI and have the generating AI determine the priority of analysis.

[0048] The analysis unit can adjust the order of analysis based on the relevance of the logistics data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data to provide efficient results. The analysis unit can also postpone the analysis of less relevant data. The analysis unit can also group highly relevant data and analyze them all at once. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the logistics data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the logistics data into a generating AI and have the generating AI adjust the order of analysis.

[0049] The proposal department can adjust the level of detail in its proposals based on the importance of the operational methods. For example, it can provide detailed proposals for highly important operational methods and simplified proposals for less important ones. For highly important operational methods, the proposal department can also provide multiple proposals and offer detailed options. For less important operational methods, the proposal department can provide basic proposals and offer concise options. This allows for efficient proposals by adjusting the level of detail in proposals based on the importance of the operational methods. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the importance of the operational methods into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0050] The proposal unit can apply different proposal algorithms depending on the category of the operational method when making a proposal. For example, the proposal unit can apply a proposal algorithm specifically for inventory management to inventory management. The proposal unit can also apply a proposal algorithm specifically for route optimization to delivery routes. The proposal unit can also apply a proposal algorithm specifically for demand forecasting to demand forecasting. This allows for efficient proposals by applying different proposal algorithms depending on the category of the operational method. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the category of the operational method into a generating AI and have the generating AI execute the application of different proposal algorithms.

[0051] The proposal department can prioritize proposals based on the submission timing of the operating methods. For example, the proposal department can prioritize the most recent operating methods to support real-time decision-making. The proposal department can also postpone proposals for older operating methods. The proposal department can also prioritize proposals for operating methods where the submission timing is critical to enable a quick response. This allows for efficient proposals by prioritizing proposals based on the submission timing of the operating methods. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the submission timing of the operating methods into a generating AI and have the generating AI determine the priority of the proposals.

[0052] The proposal department can adjust the order of proposals based on the relevance of the operating methods. For example, the proposal department can prioritize proposing highly relevant operating methods to provide efficient results. The proposal department can also postpone proposing less relevant operating methods. The proposal department can also group highly relevant operating methods and propose them all at once. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the operating methods. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the relevance of the operating methods into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0053] The monitoring unit can evaluate the reliability of logistics data during monitoring and exclude unreliable data. For example, the monitoring unit can verify the source of the data and exclude data from unreliable sources. The monitoring unit can also check the consistency of the data and exclude inconsistent data. The monitoring unit can also evaluate the frequency of data updates and exclude outdated data. This improves the quality of the data by evaluating the reliability of logistics data and excluding unreliable data. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input the collected data into a generating AI and have the generating AI perform reliability evaluation and exclude low-quality data.

[0054] The monitoring unit can apply different monitoring methods depending on the type of logistics data during monitoring. For example, the monitoring unit can automatically monitor inventory data periodically and update it in real time. The monitoring unit can also monitor delivery route data in real time using GPS. The monitoring unit can also periodically monitor demand forecast data based on past sales data. This enables efficient monitoring by applying different monitoring methods depending on the type of logistics data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can have a generating AI execute monitoring methods according to the type of data.

[0055] The monitoring unit can prioritize monitoring highly relevant data by considering the geographical distribution of logistics data during monitoring. For example, the monitoring unit can prioritize monitoring inventory data from geographically close warehouses. The monitoring unit can also prioritize monitoring highly relevant data by considering the geographical distribution of delivery routes. The monitoring unit can also prioritize monitoring data from geographically important locations. This enables efficient monitoring by prioritizing the monitoring of highly relevant data by considering the geographical distribution of logistics data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the geographical distribution of logistics data into a generating AI and have the generating AI perform priority monitoring of highly relevant data.

[0056] The monitoring unit can analyze social media activity related to logistics data and monitor relevant data during monitoring. For example, the monitoring unit can analyze posts about logistics on social media and monitor relevant data. The monitoring unit can also monitor customer feedback on social media and reflect it in the logistics data. The monitoring unit can also analyze trends on social media and monitor relevant logistics data. This enables efficient monitoring by analyzing social media activity related to logistics data and monitoring relevant data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input social media activity data into a generating AI and have the generating AI perform monitoring of the relevant data.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The analysis unit can consider environmental data when collecting logistics data. For example, the analysis unit can collect weather data and use it to optimize delivery routes. It can also collect traffic data and adjust delivery routes in real time. Furthermore, the analysis unit can collect seasonal demand fluctuation data and use it to optimize inventory management. This enables analysis that takes environmental data into account, leading to the proposal of more accurate operational methods.

[0059] The data collection unit can consider energy consumption data when collecting logistics data. For example, the data collection unit can collect fuel consumption data for each delivery vehicle and propose energy-efficient delivery routes. It can also collect energy consumption data within warehouses and propose energy-efficient inventory placement. Furthermore, the data collection unit can use the energy consumption data to reduce energy costs. This enables efficient operations that take energy consumption data into consideration.

[0060] The analysis unit can integrate data from the entire supply chain in the analysis of logistics data. For example, the analysis unit can collect delivery data from suppliers and use it to optimize inventory management. It can also collect order data from customers and improve the accuracy of demand forecasting. Furthermore, the analysis unit can integrate data from the entire supply chain to optimize the entire system. This enables efficient operation that takes the entire supply chain into consideration.

[0061] The proposal department can consider sustainability when proposing logistics data. For example, it can propose eco-friendly delivery routes to reduce environmental impact. It can also propose the use of recyclable packaging materials to reduce waste. Furthermore, it can propose the construction of sustainable supply chains to fulfill corporate social responsibility. This enables efficient operations that take sustainability into consideration.

[0062] The monitoring department can consider security data when monitoring logistics data. For example, the monitoring department can monitor the location information of delivery vehicles in real time to prevent theft. It can also monitor security camera data within warehouses to detect fraudulent activity early. Furthermore, the monitoring department can monitor cybersecurity data to ensure data security. This enables secure operations that take security data into consideration.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The collection unit collects logistics data. This logistics data includes inventory data, delivery data, and order data. The collection unit collects real-time data on warehouse inventory status and delivery routes using sensors and GPS. It can also automatically acquire data from the company's systems. Step 2: The analysis unit analyzes the data collected by the data collection unit. The analysis unit uses AI to analyze inventory data, delivery data, and order data, and proposes optimal operational methods. For example, it can propose the optimal inventory placement and optimal delivery routes. Step 3: The proposal department proposes the optimal operating method based on the analysis results obtained by the analysis department. The proposal department proposes the optimal placement of inventory and optimization of delivery routes, and proposes immediate replenishment if inventory is insufficient. It also proposes appropriate inventory management based on demand forecasts. Step 4: The monitoring department monitors in real time based on the operating methods proposed by the proposal department. The monitoring department monitors inventory status and delivery routes in real time and immediately notifies if delays or problems occur. This enables real-time monitoring of the overall system status and efficient operation.

[0065] (Example of form 2) The Logistics Revolution Digital Twin System according to an embodiment of the present invention is a system that reproduces actual logistics processes using digital twin technology and performs simulations in real time. Based on logistics data held by a company, this Logistics Revolution Digital Twin System uses AI to propose the optimal operating method. For example, the Logistics Revolution Digital Twin System monitors warehouse inventory status and delivery routes in real time and immediately identifies delays and problems. This enables rapid decision-making, resulting in cost reductions and shorter delivery times. Furthermore, the Logistics Revolution Digital Twin System also optimizes demand forecasting and inventory management by analyzing historical data. For example, the Logistics Revolution Digital Twin System can predict future demand based on past sales data and manage inventory appropriately. This prevents excess inventory and stockouts, enabling efficient operations. Thus, the Logistics Revolution Digital Twin System is an innovative solution for companies to enhance their competitiveness. For example, the Logistics Revolution Digital Twin System monitors warehouse inventory status and delivery routes in real time and immediately identifies delays and problems, enabling rapid decision-making. Furthermore, the Logistics Revolution Digital Twin System also optimizes demand forecasting and inventory management by analyzing historical data. This results in cost reductions and shorter delivery times, improving the competitiveness of companies. This enables the logistics revolution through digital twin systems, which can streamline a company's logistics processes and enhance its competitiveness.

[0066] The logistics revolution digital twin system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a monitoring unit. The data collection unit collects logistics data. Logistics data includes, but is not limited to, inventory data, delivery data, and order data. The data collection unit collects, for example, data on inventory status in warehouses and delivery routes. The data collection unit can collect data in real time using, for example, sensors or GPS. The data collection unit can also automatically acquire data from a company's system. For example, the data collection unit monitors inventory status in warehouses with sensors and collects data in real time. The data collection unit collects delivery route data using GPS and updates it in real time. The data collection unit automatically acquires order data from a company's system and stores it in a database. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the data using, for example, AI and proposes the optimal operating method. The analysis unit can analyze inventory data and propose the optimal inventory placement. The analysis unit can also analyze delivery data and propose the optimal delivery route. For example, the analysis unit uses AI to analyze inventory data and propose the optimal inventory placement. The analysis unit uses AI to analyze delivery data and propose the optimal delivery route. The analysis unit uses AI to analyze order data and perform demand forecasting. The proposal unit proposes the optimal operating method based on the analysis results obtained by the analysis unit. For example, the proposal unit can propose the optimal inventory placement and optimization of delivery routes. For example, the proposal unit can propose immediate replenishment if inventory is insufficient. The proposal unit can also analyze delivery route data and propose the optimal route. For example, the proposal unit can propose immediate replenishment if inventory is insufficient. The proposal unit analyzes delivery route data and proposes the optimal route. The proposal unit proposes appropriate inventory management based on demand forecasting. The monitoring unit monitors in real time based on the operating method proposed by the proposal unit. For example, the monitoring unit can immediately identify delays and problems. For example, the monitoring unit monitors inventory status in real time and immediately notifies if inventory is insufficient. The monitoring unit can also monitor delivery routes in real time and immediately notify if delays occur.For example, the monitoring unit monitors inventory status in real time and immediately notifies if inventory is insufficient. The monitoring unit monitors delivery routes in real time and immediately notifies if delays occur. The monitoring unit monitors the status of the entire system in real time and immediately notifies if problems occur. As a result, the logistics revolution digital twin system according to this embodiment enables efficient operation by collecting, analyzing, proposing, and monitoring logistics data.

[0067] The data collection unit collects logistics data. Logistics data includes, but is not limited to, inventory data, delivery data, and order data. For example, the data collection unit collects data on warehouse inventory status and delivery routes. The data collection unit can collect data in real time using, for example, sensors or GPS. The data collection unit can also automatically acquire data from a company's systems. For example, the data collection unit monitors warehouse inventory status with sensors and collects data in real time. The data collection unit collects delivery route data using GPS and updates it in real time. The data collection unit automatically acquires order data from a company's systems and stores it in a database. The data collection unit centrally manages data generated at each stage of logistics, enabling efficient data collection. For example, sensors that monitor warehouse inventory status can detect the inflow and outflow of goods in real time, allowing for immediate understanding of inventory fluctuations. This prevents inventory shortages and surpluses and enables appropriate inventory management. GPS, which collects delivery route data, tracks the location information of delivery vehicles in real time and provides basic data for selecting the optimal route. This improves delivery efficiency, reducing delays and unnecessary movement. Furthermore, order data automatically acquired from the company's systems allows for accurate understanding of customer demand and enables rapid response. For example, when an order is placed, inventory status can be checked immediately, and necessary products can be quickly picked and shipped. This improves customer satisfaction and streamlines logistics. The data collection department centrally manages this data and can link with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. In addition, by adjusting the frequency and accuracy of data collection, flexible responses can be made to specific situations and conditions. As a result, the data collection department can collect data efficiently and effectively, improving the overall performance of the system.

[0068] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to analyze data and propose optimal operational methods. For example, the analysis unit can analyze inventory data and propose optimal inventory placement. Furthermore, the analysis unit can analyze delivery data and propose optimal delivery routes. For example, the analysis unit uses AI to analyze inventory data and propose optimal inventory placement. The analysis unit uses AI to analyze delivery data and propose optimal delivery routes. The analysis unit uses AI to analyze order data and forecast demand. The analysis unit analyzes collected data from multiple perspectives and makes concrete proposals to improve logistics efficiency. For example, in inventory data analysis, it considers product turnover and demand fluctuations to propose optimal inventory placement. This prevents inventory shortages and surpluses, enabling efficient inventory management. In addition, in delivery data analysis, it optimizes delivery routes to shorten delivery times and reduce costs. For example, it can use AI to consider traffic conditions and weather information to propose optimal delivery routes in real time. Furthermore, in order data analysis, it forecasts customer demand and develops appropriate inventory replenishment and production plans. This allows for a rapid response to fluctuations in demand and improved customer satisfaction. Based on these analysis results, the analysis department proposes specific operational methods to improve logistics efficiency and reduce costs. Furthermore, the analysis department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on historical data, it can predict fluctuations in demand during specific seasons or events and take appropriate measures. In addition, the analysis department can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing warnings early. As a result, the analysis department can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0069] The proposal department proposes optimal operational methods based on the analysis results obtained by the analysis department. For example, the proposal department can propose optimal inventory placement and optimized delivery routes. For example, if inventory is insufficient, the proposal department will propose immediate replenishment. The proposal department can also analyze delivery route data and propose the optimal route. For example, if inventory is insufficient, the proposal department will propose immediate replenishment. The proposal department will analyze delivery route data and propose the optimal route. The proposal department proposes appropriate inventory management based on demand forecasts. Based on the data provided by the analysis department, the proposal department proposes specific operational methods to improve logistics efficiency. For example, when proposing optimal inventory placement, it considers product turnover and demand fluctuations to propose appropriate inventory placement. This prevents inventory surpluses and shortages, enabling efficient inventory management. When proposing optimized delivery routes, it considers traffic conditions and weather information to propose the optimal delivery route. This can shorten delivery times and reduce costs. Furthermore, by proposing appropriate inventory management based on demand forecasts, it is possible to respond quickly to demand fluctuations and improve customer satisfaction. Based on these proposals, the proposal department implements specific operational methods to achieve logistics efficiency and cost reduction. Furthermore, the proposal department can continuously monitor the effectiveness of the proposals and make improvements as needed. For example, by providing feedback on problems and issues that arise after the implementation of the proposals and reflecting them in future proposals, the accuracy and effectiveness of the proposals can be improved. In addition, the proposal department can simulate multiple scenarios and select the most effective operational method. As a result, the proposal department can always propose the optimal operational method, achieving logistics efficiency and cost reduction.

[0070] The monitoring department monitors in real time based on the operating methods proposed by the proposal department. For example, the monitoring department can immediately identify delays and problems. For example, the monitoring department monitors inventory status in real time and immediately notifies if inventory is insufficient. The monitoring department can also monitor delivery routes in real time and immediately notify if delays occur. For example, the monitoring department monitors inventory status in real time and immediately notifies if inventory is insufficient. The monitoring department monitors delivery routes in real time and immediately notifies if delays occur. The monitoring department monitors the status of the entire system in real time and immediately notifies if problems occur. The monitoring department monitors in real time whether the operating methods proposed by the proposal department are being properly implemented and immediately responds if problems occur. For example, by monitoring inventory status in real time, it can immediately notify if inventory is insufficient and quickly replenish it. Also, by monitoring delivery routes in real time, it can immediately notify if delays occur and propose alternative routes. This can improve logistics efficiency and customer satisfaction. Furthermore, the monitoring department monitors the status of the entire system in real time and immediately responds if problems occur. For example, if a system anomaly is detected, it can be immediately notified, allowing for a swift response. This improves the reliability and security of the system. Based on these monitoring results, the monitoring department can collaborate with the proposal and analysis departments to improve operational methods. For instance, by providing feedback on monitoring results and reviewing proposals and analysis methods, the overall system performance can be improved. The monitoring department can also collect feedback from users and use it to improve operational methods. As a result, the monitoring department can always maintain optimal operational methods, achieving increased logistics efficiency and improved customer satisfaction.

[0071] The data collection unit can collect data on warehouse inventory status and delivery routes. For example, the data collection unit can monitor warehouse inventory status with sensors and collect data in real time. The data collection unit can also collect delivery route data using GPS and update it in real time. The data collection unit can also automatically acquire order data from a company's system and store it in a database. This allows for improved logistics efficiency by collecting data on warehouse inventory status and delivery routes. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have a generating AI perform the process of monitoring warehouse inventory status with sensors and collecting data in real time.

[0072] The analysis unit can propose optimal operational methods based on the collected data. For example, the analysis unit can use AI to analyze inventory data and propose the optimal inventory placement. The analysis unit can also use AI to analyze delivery data and propose the optimal delivery route. The analysis unit can also use AI to analyze order data and perform demand forecasting. This allows for improved operational efficiency by proposing optimal operational methods based on the collected data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected data into a generating AI and have the generating AI execute a proposal for the optimal operational method.

[0073] The suggestion unit can monitor inventory levels in real time and immediately suggest replenishment if inventory levels are low. For example, if inventory levels are low, the suggestion unit can immediately suggest replenishment. The suggestion unit can also set inventory thresholds and suggest replenishment if inventory levels fall below these thresholds. The suggestion unit can also suggest methods for replenishing inventory. This allows for more efficient inventory management by monitoring inventory levels in real time and immediately suggesting replenishment if inventory levels are low. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input inventory levels into a generating AI and have the generating AI execute replenishment suggestions when inventory levels are low.

[0074] The proposal unit can analyze delivery route data and propose the optimal route. For example, the proposal unit can analyze delivery route data and propose the optimal route. The proposal unit can also propose the optimal route considering the distance, time, and traffic conditions of the delivery route. The proposal unit can also propose the optimal route considering the cost efficiency of the delivery route. In this way, by analyzing delivery route data and proposing the optimal route, the efficiency of delivery can be improved. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input delivery route data into a generating AI and have the generating AI execute the proposal of the optimal route.

[0075] The monitoring unit can immediately identify delays and problems. For example, the monitoring unit can monitor inventory status in real time and immediately notify if inventory is insufficient. The monitoring unit can also monitor delivery routes in real time and immediately notify if delays occur. The monitoring unit can also monitor the status of the entire system in real time and immediately notify if problems occur. This enables a rapid response by immediately identifying delays and problems. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input inventory status into a generating AI and have the generating AI execute a notification if inventory is insufficient.

[0076] The analysis unit can predict future demand based on past sales data. For example, the analysis unit predicts future demand based on past sales data. The analysis unit can also apply multiple forecasting algorithms to improve the accuracy of demand forecasting. The analysis unit can also propose optimizations for inventory management based on the results of the demand forecast. This improves the accuracy of demand forecasting by predicting future demand based on past sales data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past sales data into a generating AI and have the generating AI perform future demand forecasting.

[0077] The proposal unit can appropriately manage inventory based on demand forecasts. For example, the proposal unit can appropriately manage inventory based on demand forecasts. The proposal unit can also set the optimal amount of inventory and replenish inventory based on demand forecasts. The proposal unit can also propose methods for managing inventory. This makes inventory management more efficient by appropriately managing inventory based on demand forecasts. Some or all of the above processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the results of the demand forecast into a generating AI and have the generating AI execute inventory management proposals.

[0078] The data collection unit can estimate the user's emotions and adjust the timing of logistics data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to reduce the user's burden. If the user is relaxed, the data collection unit can also advance the collection timing for efficient data collection. If the user is in a hurry, the data collection unit can immediately set the collection timing for rapid data collection. This allows for efficient data collection by adjusting the collection timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into the generative AI and have the generative AI adjust the collection timing.

[0079] The data collection unit can evaluate the reliability of logistics data during collection and exclude unreliable data. For example, the data collection unit can verify the source of the data and exclude data from unreliable sources. The data collection unit can also check the consistency of the data and exclude inconsistent data. The data collection unit can also evaluate the frequency of data updates and exclude outdated data. This improves data quality by evaluating the reliability of logistics data and excluding unreliable data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the collected data into a generating AI and have the generating AI perform reliability evaluation and exclude low-reliability data.

[0080] The data collection unit can apply different collection methods depending on the type of data during collection. For example, the data collection unit can automatically collect inventory data periodically and update it in real time. The data collection unit can also collect delivery route data in real time using GPS. The data collection unit can also collect demand forecast data periodically based on past sales data. This enables efficient data collection by applying different collection methods depending on the type of data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have a generating AI execute a collection method appropriate to the type of data.

[0081] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting high-priority data. If the user is relaxed, the data collection unit can collect all data equally. If the user is in a hurry, the data collection unit can prioritize collecting data that is immediately needed. This enables efficient data collection by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0082] The data collection unit can prioritize the collection of highly relevant data by considering the geographical distribution of logistics data during the collection process. For example, the data collection unit can prioritize the collection of inventory data from geographically close warehouses. The data collection unit can also prioritize the collection of highly relevant data by considering the geographical distribution of delivery routes. The data collection unit can also prioritize the collection of data from geographically important locations. This enables efficient data collection by prioritizing the collection of highly relevant data while considering the geographical distribution of logistics data. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical distribution of logistics data into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0083] The data collection unit can analyze social media activity related to logistics data during collection and collect relevant data. For example, the data collection unit can analyze posts about logistics on social media and collect relevant data. The data collection unit can also collect customer feedback on social media and reflect it in the logistics data. The data collection unit can also analyze trends on social media and collect relevant logistics data. This enables efficient data collection by analyzing social media activity related to logistics data and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0084] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit provides simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results. This allows for efficient analysis by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the logistics data during the analysis. For example, the analysis unit can analyze highly important data in detail and less important data in a simplified manner. The analysis unit can also apply multiple analysis methods to highly important data to provide detailed results. The analysis unit can also apply basic analysis methods to less important data to provide concise results. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the logistics data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the logistics data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0086] The analysis unit can apply different analysis algorithms depending on the category of logistics data during analysis. For example, the analysis unit can apply an analysis algorithm specifically for inventory management to inventory data. The analysis unit can also apply an analysis algorithm specifically for route optimization to delivery route data. The analysis unit can also apply an analysis algorithm specifically for demand forecasting to demand forecasting data. This enables efficient analysis by applying different analysis algorithms depending on the category of logistics data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of logistics data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. If the user is excited, the analysis unit can also provide an analysis with visually stimulating effects. This allows for efficient analysis by adjusting the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0088] The analysis unit can determine the priority of analysis based on the submission timing of logistics data during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent data to support real-time decision-making. The analysis unit can also postpone the analysis of older data. The analysis unit can also prioritize the analysis of data where the submission timing is important, enabling a rapid response. This allows for efficient analysis by determining the priority of analysis based on the submission timing of logistics data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission timing of logistics data into a generating AI and have the generating AI determine the priority of analysis.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the logistics data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data to provide efficient results. The analysis unit can also postpone the analysis of less relevant data. The analysis unit can also group highly relevant data and analyze them all at once. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the logistics data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the logistics data into a generating AI and have the generating AI adjust the order of analysis.

[0090] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. This allows for efficient suggestions by adjusting the presentation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the suggestions.

[0091] The proposal department can adjust the level of detail in its proposals based on the importance of the operational methods. For example, it can provide detailed proposals for highly important operational methods and simplified proposals for less important ones. For highly important operational methods, the proposal department can also provide multiple proposals and offer detailed options. For less important operational methods, the proposal department can provide basic proposals and offer concise options. This allows for efficient proposals by adjusting the level of detail in proposals based on the importance of the operational methods. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the importance of the operational methods into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0092] The proposal unit can apply different proposal algorithms depending on the category of the operational method when making a proposal. For example, the proposal unit can apply a proposal algorithm specifically for inventory management to inventory management. The proposal unit can also apply a proposal algorithm specifically for route optimization to delivery routes. The proposal unit can also apply a proposal algorithm specifically for demand forecasting to demand forecasting. This allows for efficient proposals by applying different proposal algorithms depending on the category of the operational method. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the category of the operational method into a generating AI and have the generating AI execute the application of different proposal algorithms.

[0093] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on those emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. This allows for efficient suggestions by adjusting the length of suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of suggestions.

[0094] The proposal department can prioritize proposals based on the submission timing of the operating methods. For example, the proposal department can prioritize the most recent operating methods to support real-time decision-making. The proposal department can also postpone proposals for older operating methods. The proposal department can also prioritize proposals for operating methods where the submission timing is critical to enable a quick response. This allows for efficient proposals by prioritizing proposals based on the submission timing of the operating methods. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the submission timing of the operating methods into a generating AI and have the generating AI determine the priority of the proposals.

[0095] The proposal department can adjust the order of proposals based on the relevance of the operating methods. For example, the proposal department can prioritize proposing highly relevant operating methods to provide efficient results. The proposal department can also postpone proposing less relevant operating methods. The proposal department can also group highly relevant operating methods and propose them all at once. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the operating methods. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the relevance of the operating methods into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0096] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring based on the estimated user emotions. For example, if the user is tense, the monitoring unit can provide a simple and highly visible display method. If the user is relaxed, the monitoring unit can also provide a display method that includes detailed information. If the user is in a hurry, the monitoring unit can also provide a display method that gets straight to the point. This allows for efficient monitoring by adjusting the display method of the monitoring based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the monitoring.

[0097] The monitoring unit can evaluate the reliability of logistics data during monitoring and exclude unreliable data. For example, the monitoring unit can verify the source of the data and exclude data from unreliable sources. The monitoring unit can also check the consistency of the data and exclude inconsistent data. The monitoring unit can also evaluate the frequency of data updates and exclude outdated data. This improves the quality of the data by evaluating the reliability of logistics data and excluding unreliable data. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input the collected data into a generating AI and have the generating AI perform reliability evaluation and exclude low-quality data.

[0098] The monitoring unit can apply different monitoring methods depending on the type of logistics data during monitoring. For example, the monitoring unit can automatically monitor inventory data periodically and update it in real time. The monitoring unit can also monitor delivery route data in real time using GPS. The monitoring unit can also periodically monitor demand forecast data based on past sales data. This enables efficient monitoring by applying different monitoring methods depending on the type of logistics data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can have a generating AI execute monitoring methods according to the type of data.

[0099] The monitoring unit can estimate the user's emotions and determine the priority of data to monitor based on the estimated emotions. For example, if the user is stressed, the monitoring unit will prioritize monitoring high-priority data. If the user is relaxed, the monitoring unit can monitor all data equally. If the user is in a hurry, the monitoring unit can prioritize monitoring data that is immediately needed. This enables efficient monitoring by determining the priority of data to monitor based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI determine the priority of data to monitor.

[0100] The monitoring unit can prioritize monitoring highly relevant data by considering the geographical distribution of logistics data during monitoring. For example, the monitoring unit can prioritize monitoring inventory data from geographically close warehouses. The monitoring unit can also prioritize monitoring highly relevant data by considering the geographical distribution of delivery routes. The monitoring unit can also prioritize monitoring data from geographically important locations. This enables efficient monitoring by prioritizing the monitoring of highly relevant data by considering the geographical distribution of logistics data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the geographical distribution of logistics data into a generating AI and have the generating AI perform priority monitoring of highly relevant data.

[0101] The monitoring unit can analyze social media activity related to logistics data and monitor relevant data during monitoring. For example, the monitoring unit can analyze posts about logistics on social media and monitor relevant data. The monitoring unit can also monitor customer feedback on social media and reflect it in the logistics data. The monitoring unit can also analyze trends on social media and monitor relevant logistics data. This enables efficient monitoring by analyzing social media activity related to logistics data and monitoring relevant data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input social media activity data into a generating AI and have the generating AI perform monitoring of the relevant data.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The analysis unit can consider environmental data when collecting logistics data. For example, the analysis unit can collect weather data and use it to optimize delivery routes. It can also collect traffic data and adjust delivery routes in real time. Furthermore, the analysis unit can collect seasonal demand fluctuation data and use it to optimize inventory management. This enables analysis that takes environmental data into account, leading to the proposal of more accurate operational methods.

[0104] The data collection unit can consider energy consumption data when collecting logistics data. For example, the data collection unit can collect fuel consumption data for each delivery vehicle and propose energy-efficient delivery routes. It can also collect energy consumption data within warehouses and propose energy-efficient inventory placement. Furthermore, the data collection unit can use the energy consumption data to reduce energy costs. This enables efficient operations that take energy consumption data into consideration.

[0105] The analysis unit can integrate data from the entire supply chain in the analysis of logistics data. For example, the analysis unit can collect delivery data from suppliers and use it to optimize inventory management. It can also collect order data from customers and improve the accuracy of demand forecasting. Furthermore, the analysis unit can integrate data from the entire supply chain to optimize the entire system. This enables efficient operation that takes the entire supply chain into consideration.

[0106] The proposal department can consider sustainability when proposing logistics data. For example, it can propose eco-friendly delivery routes to reduce environmental impact. It can also propose the use of recyclable packaging materials to reduce waste. Furthermore, it can propose the construction of sustainable supply chains to fulfill corporate social responsibility. This enables efficient operations that take sustainability into consideration.

[0107] The monitoring department can consider security data when monitoring logistics data. For example, the monitoring department can monitor the location information of delivery vehicles in real time to prevent theft. It can also monitor security camera data within warehouses to detect fraudulent activity early. Furthermore, the monitoring department can monitor cybersecurity data to ensure data security. This enables secure operations that take security data into consideration.

[0108] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on those emotions. For example, if the user is stressed, the analysis timing can be delayed to reduce the user's burden. If the user is relaxed, the analysis unit can also speed up the analysis to provide quick results. Furthermore, if the user is in a hurry, the analysis can be started immediately to support rapid decision-making. This allows for efficient analysis by adjusting the timing of the analysis based on the user's emotions.

[0109] The suggestion function can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, it will prioritize providing high-priority suggestions. If the user is relaxed, the suggestion function can also provide all suggestions equally. Furthermore, if the user is in a hurry, it can prioritize providing suggestions that are immediately needed. This allows for efficient suggestions by prioritizing them based on the user's emotions.

[0110] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on those estimates. For example, if the user is stressed, the monitoring frequency can be reduced to lessen the user's burden. If the user is relaxed, the monitoring unit can increase the monitoring frequency to provide more detailed information. Furthermore, if the user is in a hurry, monitoring can be started immediately to support a quick response. This allows for efficient monitoring by adjusting the monitoring frequency based on the user's emotions.

[0111] The data collection unit can estimate the user's emotions and determine the type of data to collect based on those emotions. For example, if the user is stressed, it will prioritize collecting high-priority data. If the user is relaxed, the data collection unit can collect all data equally. Furthermore, if the user is in a hurry, it can prioritize collecting data that is immediately needed. This allows for efficient data collection by determining the type of data to collect based on the user's emotions.

[0112] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on those emotions. For example, if the user is stressed, the analysis accuracy can be increased to provide more reliable results. The analysis unit can also adjust the accuracy of the analysis to provide faster results if the user is relaxed. Furthermore, if the user is in a hurry, the analysis accuracy can be optimized to provide fast and reliable results. This allows for more efficient analysis by adjusting the accuracy of the analysis based on the user's emotions.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The collection unit collects logistics data. This logistics data includes inventory data, delivery data, and order data. The collection unit collects real-time data on warehouse inventory status and delivery routes using sensors and GPS. It can also automatically acquire data from the company's systems. Step 2: The analysis unit analyzes the data collected by the data collection unit. The analysis unit uses AI to analyze inventory data, delivery data, and order data, and proposes optimal operational methods. For example, it can propose the optimal inventory placement and optimal delivery routes. Step 3: The proposal department proposes the optimal operating method based on the analysis results obtained by the analysis department. The proposal department proposes the optimal placement of inventory and optimization of delivery routes, and proposes immediate replenishment if inventory is insufficient. It also proposes appropriate inventory management based on demand forecasts. Step 4: The monitoring department monitors in real time based on the operating methods proposed by the proposal department. The monitoring department monitors inventory status and delivery routes in real time and immediately notifies if delays or problems occur. This enables real-time monitoring of the overall system status and efficient operation.

[0115] 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.

[0116] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0117] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and monitoring unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects data in real time using the sensors and GPS of the smart device 14, and the specific processing unit 290 of the data processing unit 12 automatically acquires data from the company's system. The analysis unit analyzes the data using AI, for example, the specific processing unit 290 of the data processing unit 12, and proposes the optimal operating method. The proposal unit proposes the optimal operating method based on the analysis results, for example, the specific processing unit 290 of the data processing unit 12. The monitoring unit monitors in real time based on the proposed operating method, for example, the control unit 46A of the smart device 14, and immediately identifies delays and problems. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] 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.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0122] 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.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0124] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] 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.

[0126] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and monitoring unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects data in real time using the sensors and GPS of the smart glasses 214, and the specific processing unit 290 of the data processing unit 12 automatically acquires data from the company's system. The analysis unit analyzes the data using AI, for example, the specific processing unit 290 of the data processing unit 12, and proposes the optimal operating method. The proposal unit proposes the optimal operating method based on the analysis results, for example, the specific processing unit 290 of the data processing unit 12. The monitoring unit monitors in real time based on the proposed operating method, for example, the control unit 46A of the smart glasses 214, and immediately identifies delays and problems. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] 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.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0138] 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.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0140] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] 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.

[0142] 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.

[0143] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] 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.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and monitoring unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the collection unit collects data in real time using the sensors and GPS of the headset terminal 314, and the specific processing unit 290 of the data processing unit 12 automatically acquires data from the company's system. The analysis unit analyzes the data using AI, for example, the specific processing unit 290 of the data processing unit 12, and proposes the optimal operating method. The proposal unit proposes the optimal operating method based on the analysis results, for example, the specific processing unit 290 of the data processing unit 12. The monitoring unit monitors in real time based on the proposed operating method, for example, the control unit 46A of the headset terminal 314, and immediately identifies delays and problems. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0154] 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.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0156] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] 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.

[0158] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0159] 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.

[0160] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] 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.

[0165] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and monitoring unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects data in real time using the robot 414's sensors and GPS, and the data processing unit 12 automatically acquires data from the company's system using its specific processing unit 290. The analysis unit analyzes the data using AI, for example, the specific processing unit 290 of the data processing unit 12, and proposes the optimal operating method. The proposal unit proposes the optimal operating method based on the analysis results, for example, the specific processing unit 290 of the data processing unit 12. The monitoring unit monitors in real time based on the proposed operating method, for example, the control unit 46A of the robot 414, and immediately identifies delays and problems. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0168] 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.

[0169] Figure 9 shows the 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.

[0170] 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.

[0171] 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.

[0172] 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, and motorcycles, 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 based, for example, 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.

[0173] 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."

[0174] 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.

[0175] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0184] 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 other things 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.

[0185] 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.

[0186] (Note 1) A data collection unit that collects logistics data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes the optimal operating method. The system includes a monitoring unit that monitors in real time based on the operating method proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data on inventory status and delivery routes within the warehouse. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected data, we propose the optimal operating method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We monitor inventory levels in real time and immediately suggest replenishment if stock levels are low. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We analyze delivery route data and propose the optimal route. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned monitoring unit, Identify delays and problems immediately. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, We predict future demand based on past sales data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned proposal section is, Manage inventory appropriately based on demand forecasts. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We estimate user emotions and adjust the timing of logistics data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the reliability of logistics data is evaluated, and unreliable data is excluded. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, apply different collection methods depending on the type of data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the geographical distribution of logistics data is taken into consideration, and highly relevant data is prioritized for collection. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is During data collection, analyze social media activity related to logistics data and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the logistics data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of logistics data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on the timing of the submission of logistics data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the logistics data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the operational method. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting a proposal, different proposal algorithms are applied depending on the category of the operational method. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When submitting proposals, the priority of proposals will be determined based on the timing of submission of operational methods. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the operational methods. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned monitoring unit, It estimates the user's emotions and adjusts how monitoring is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned monitoring unit, During monitoring, the reliability of logistics data is evaluated, and unreliable data is excluded. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned monitoring unit, During monitoring, different monitoring methods are applied depending on the type of logistics data. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned monitoring unit, It estimates user sentiment and prioritizes data to monitor based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned monitoring unit, During monitoring, the geographical distribution of logistics data is taken into consideration, and highly relevant data is monitored preferentially. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned monitoring unit, During monitoring, analyze social media activity related to logistics data and monitor relevant data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects logistics data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes the optimal operating method. The system includes a monitoring unit that monitors in real time based on the operating method proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect data on inventory status and delivery routes within the warehouse. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected data, we propose the optimal operating method. The system according to feature 1.

4. The aforementioned proposal section is, We monitor inventory levels in real time and immediately suggest replenishment if stock levels are low. The system according to feature 1.

5. The aforementioned proposal section is, We analyze delivery route data and propose the optimal route. The system according to feature 1.

6. The aforementioned monitoring unit, Identify delays and problems immediately. The system according to feature 1.

7. The aforementioned analysis unit, We predict future demand based on past sales data. The system according to feature 1.

8. The aforementioned proposal section is, Manage inventory appropriately based on demand forecasts. The system according to feature 1.

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