system

Abstract

The system according to this embodiment aims to generate and provide optimal delivery routes to delivery personnel by utilizing real-time data. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a chatbot unit. The collection unit collects data such as real-time traffic information, inventory levels, and demand forecasts. The analysis unit analyzes the data collected by the collection unit. The generation unit generates the optimal delivery route based on the data analyzed by the analysis unit. The chatbot unit provides the delivery route generated by the generation unit to the delivery person.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been fully carried out to utilize real-time data to generate an optimal delivery route and provide it to the delivery staff, and there is room for improvement.

[0005] The system according to the embodiment aims to utilize real-time data to generate an optimal delivery route and provide it to the delivery staff.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a data generation unit, and a chatbot unit. The data collection unit collects data such as real-time traffic information, inventory levels, and demand forecasts. The analysis unit analyzes the data collected by the data collection unit. The data generation unit generates the optimal delivery route based on the data analyzed by the analysis unit. The chatbot unit provides the delivery route generated by the data generation unit to the delivery person. [Effects of the Invention]

[0007] The system according to this embodiment can generate and provide optimal delivery routes to delivery personnel by utilizing real-time data. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. 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 delivery efficiency system according to an embodiment of the present invention is a system that combines generative AI and IoT technology to analyze real-time traffic information, inventory levels, and demand forecasts, and automatically generates delivery routes. This delivery efficiency system collects real-time data such as traffic information, inventory levels, and demand forecasts, and the generative AI analyzes this data to automatically generate the optimal delivery route. Furthermore, by adding a chatbot function, the delivery person and the AI ​​can interact and instantly generate the optimal route. This mechanism makes it possible to improve delivery speed and efficiency while minimizing environmental impact. For example, data is acquired from traffic sensors, inventory management systems, demand forecasting systems, etc. This makes it possible to grasp the latest traffic conditions, inventory conditions, and demand forecasts. Next, the generative AI analyzes the collected data and automatically generates the optimal delivery route. The generative AI calculates the most efficient route considering traffic information, inventory levels, and demand forecasts. For example, it can generate routes that avoid traffic congestion or priority delivery routes to stores with low inventory. Furthermore, by adding a chatbot function, the delivery person and the AI ​​can interact and instantly generate the optimal route. When a delivery person enters a question into the chatbot, the generative AI analyzes the data in real time and proposes the optimal route. For example, in response to a question such as, "Considering current traffic conditions, what is the fastest route to arrive?", the generating AI suggests the optimal route. This system can improve delivery speed and efficiency while minimizing environmental impact. For instance, avoiding traffic congestion reduces fuel consumption and mitigates environmental impact. Prioritizing deliveries to stores with low stock prevents stockouts and improves customer satisfaction. Furthermore, the chatbot function allows delivery personnel to understand the optimal route in real time, improving delivery efficiency. In this way, the delivery efficiency system can achieve increased efficiency and reduced environmental impact in the delivery industry.

[0029] The delivery efficiency system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a chatbot unit. The collection unit collects data such as real-time traffic information, inventory levels, and demand forecasts. The collection unit collects data from sources such as traffic sensors, inventory management systems, and demand forecasting systems. The collection unit can grasp the traffic situation on the road in real time using traffic sensors. For example, traffic sensors include cameras on the road and sensors mounted on vehicles. The collection unit can also grasp the inventory level of each store using an inventory management system. For example, an inventory management system includes a barcode scanning system and an RFID system. Furthermore, the collection unit can forecast future demand using a demand forecasting system. For example, a demand forecasting system forecasts demand by considering past sales data, seasonal fluctuations, and the impact of promotions. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit generates an optimal delivery route considering traffic information, inventory levels, and demand forecasts. The analysis unit analyzes the collected data using a generation AI. The generation AI can use, for example, a deep learning model or a reinforcement learning model. The generation unit generates the optimal delivery route based on the data analyzed by the analysis unit. The generation unit generates, for example, routes that avoid traffic congestion or priority delivery routes to stores with low inventory. The generation unit generates the optimal delivery route using a generation AI. The generation AI calculates the optimal route considering, for example, traffic information, inventory levels, and demand forecasts. The chatbot unit provides the delivery route generated by the generation unit to the delivery person. For example, when a delivery person inputs a question into the chatbot, the generation AI analyzes the data in real time and proposes the optimal route. The chatbot unit proposes the optimal route to the delivery person using the generation AI. As a result, the delivery efficiency system according to this embodiment can achieve increased efficiency in the delivery industry and a reduction in environmental impact.

[0030] The data collection unit collects real-time traffic information, inventory levels, and demand forecasts. For example, it collects data from traffic sensors, inventory management systems, and demand forecasting systems. Specifically, traffic sensors include cameras on roads and sensors mounted on vehicles, and the data obtained from these devices is collected in real time. Traffic sensors detect vehicle speed, traffic volume, and congestion levels, and transmit this information to a central database. This allows the data collection unit to understand road congestion and traffic flow in real time. The inventory management system uses barcode scanning systems and RFID systems to track inventory levels at each store. These systems update product inbound and outbound information in real time and transmit it to the central database. This allows the data collection unit to accurately understand the inventory status at each store and prevent stockouts and excess inventory. Furthermore, the demand forecasting system predicts future demand by considering past sales data, seasonal fluctuations, and the impact of promotions. The demand forecasting system uses machine learning algorithms to learn patterns from past data and predict future demand with high accuracy. This allows the data collection unit to respond quickly to fluctuations in demand and achieve efficient inventory management and delivery planning. The data collection unit centrally manages this diverse data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. As a result, the data collection unit 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 generates the optimal delivery route by considering traffic information, inventory levels, and demand forecasts. The analysis unit uses generative AI to analyze the collected data. Generative AI can use, for example, deep learning models or reinforcement learning models. Specifically, deep learning models analyze image data obtained from traffic sensors to understand road congestion and traffic flow. This allows the analysis unit to understand traffic conditions in real time and calculate the optimal delivery route. Reinforcement learning models learn the optimal delivery route based on past delivery data and demand forecast data. Reinforcement learning models can find the optimal route through trial and error. This allows the analysis unit to generate efficient delivery routes, shortening delivery times and reducing fuel consumption. Furthermore, the analysis unit can also utilize past data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict traffic fluctuations in specific areas and time periods based on past traffic data and formulate future delivery plans. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The generation unit generates the optimal delivery route based on the data analyzed by the analysis unit. For example, the generation unit generates routes that avoid traffic congestion and priority delivery routes to stores with low inventory. The generation unit uses a generation AI to generate the optimal delivery route. The generation AI calculates the optimal route considering, for example, traffic information, inventory levels, and demand forecasts. Specifically, the generation AI analyzes real-time traffic information obtained from traffic sensors and calculates the optimal route to avoid congestion. It also considers inventory levels obtained from the inventory management system and generates priority delivery routes to stores with low inventory. Furthermore, it generates a delivery route that takes into account future demand fluctuations based on demand forecast data obtained from the demand forecasting system. As a result, the generation unit can generate efficient and effective delivery routes, achieving shorter delivery times and reduced fuel consumption. The generation unit can update the generated delivery routes in real time to respond to the latest traffic conditions and inventory levels. For example, if traffic congestion occurs, the generation unit immediately calculates a new route and notifies the delivery personnel. Also, if inventory levels change, the generation unit recalculates the priority delivery route to achieve efficient delivery. This allows the generation unit to always provide the optimal delivery route based on the latest information, supporting a quick and appropriate response.

[0033] The chatbot unit provides delivery drivers with delivery routes generated by the generation unit. For example, when a delivery driver inputs a question into the chatbot, the generation AI analyzes the data in real time and proposes the optimal route. Specifically, if a delivery driver asks the chatbot, "What is the best delivery route right now?", the generation AI calculates the optimal route based on collected traffic information, inventory levels, and demand forecast data, and provides it to the delivery driver through the chatbot. The chatbot unit uses the generation AI to propose the best route to the delivery driver. For example, the generation AI calculates routes that avoid traffic congestion or priority delivery routes to stores with low inventory, and notifies the delivery driver through the chatbot. This allows delivery drivers to understand the optimal route in real time and achieve efficient deliveries. Furthermore, the chatbot unit can collect feedback from delivery drivers and continuously improve the accuracy and suggestions of the generation AI. For example, if a delivery driver provides feedback such as, "This route was congested," the generation AI learns that information and reflects it in the next route suggestion. In addition, the chatbot unit can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through chatbot notifications, but also through voice calls, SMS, and email. This allows the chatbot to quickly and reliably provide delivery personnel with the optimal route, improving delivery efficiency and reducing environmental impact.

[0034] The data collection unit can collect data from sources such as traffic sensors, inventory management systems, and demand forecasting systems. For example, the data collection unit can use traffic sensors to grasp real-time traffic conditions on roads. For example, traffic sensors include cameras on roads and sensors mounted on vehicles. The data collection unit can also use inventory management systems to grasp inventory levels at each store. For example, inventory management systems include barcode scanning systems and RFID systems. Furthermore, the data collection unit can use demand forecasting systems to predict future demand. For example, demand forecasting systems predict demand by considering past sales data, seasonal fluctuations, and the impact of promotions. As a result, by collecting data from traffic sensors, inventory management systems, and demand forecasting systems, it is possible to grasp the latest traffic conditions, inventory conditions, and demand forecasts. 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 data acquired from traffic sensors into a generating AI and have the generating AI perform traffic condition analysis.

[0035] The analysis unit can analyze collected data and generate optimal delivery routes considering traffic information, inventory levels, and demand forecasts. For example, the analysis unit can analyze traffic information and generate routes that avoid traffic congestion. For instance, it can calculate the optimal route based on real-time traffic congestion information. The analysis unit can also analyze inventory levels and generate priority delivery routes to stores with low inventory. For example, it can identify stores that should be prioritized for delivery based on each store's inventory level. Furthermore, the analysis unit can analyze demand forecasts and generate priority delivery routes to areas with high demand. For example, it can identify areas with high demand based on demand forecast data and generate priority delivery routes to those areas. This enables efficient delivery by analyzing collected data and generating optimal delivery routes considering traffic information, inventory levels, and demand forecasts. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input collected data into the generation AI and have the generation AI generate the optimal delivery route.

[0036] The generation unit can generate routes that avoid traffic congestion and priority delivery routes to stores with low inventory using generation AI. For example, the generation unit can generate routes that avoid traffic congestion. For example, the generation unit calculates the optimal route to avoid traffic congestion based on real-time traffic information. The generation unit can also generate priority delivery routes to stores with low inventory. For example, the generation unit calculates priority delivery routes to stores with low inventory based on the inventory levels of each store. Furthermore, the generation unit can also generate priority delivery routes to areas with high demand. For example, the generation unit calculates priority delivery routes to areas with high demand based on demand forecast data. As a result, by generating routes that avoid traffic congestion and priority delivery routes to stores with low inventory using generation AI, delivery efficiency is improved. Some or all of the above processing in the generation unit is performed using generation AI. For example, the generation unit can input traffic information, inventory levels, and demand forecast data into the generation AI and have the generation AI execute the generation of the optimal delivery route.

[0037] The chatbot system allows delivery drivers to input questions, and the generating AI analyzes the data in real time to suggest the optimal route. For example, if a delivery driver asks, "Considering current traffic conditions, what is the fastest route to get there?", the generating AI will analyze traffic information in real time and suggest the optimal route. Similarly, if a delivery driver asks, "What is the priority delivery route to stores with low stock?", the generating AI will analyze stock levels and suggest a priority delivery route. Furthermore, if a delivery driver asks, "What is the priority delivery route to areas with high demand?", the generating AI will analyze demand forecast data and suggest a priority delivery route. This improves delivery efficiency by allowing delivery drivers to input questions into the chatbot, and the generating AI to analyze data in real time and suggest the optimal route. Some or all of the above processes in the chatbot system are performed using the generating AI. For example, the chatbot system can input a delivery driver's question into the generating AI and have the AI ​​suggest the optimal route.

[0038] The chatbot unit can provide the generated delivery route to the delivery person. For example, the chatbot unit can provide the delivery person with the optimal delivery route generated by the generation AI. For example, the chatbot unit can display the generated delivery route to the delivery person in text format. The chatbot unit can also provide the generated delivery route to the delivery person in voice format. For example, the chatbot unit can use speech synthesis technology to convey the generated delivery route to the delivery person by voice. Furthermore, the chatbot unit can display the generated delivery route to the delivery person in map format. For example, the chatbot unit can use a map application to display the generated delivery route on a map. This allows the delivery person to understand the optimal route by providing them with the generated delivery route. Some or all of the above processing in the chatbot unit is performed using the generation AI. For example, the chatbot unit can input the generated delivery route into the generation AI and have the generation AI execute the optimal format for providing it to the delivery person.

[0039] The data collection unit can dynamically change the types of data it collects based on the delivery person's past behavioral history. For example, the data collection unit can prioritize collecting traffic information for routes that the delivery person has frequently used in the past. For example, the data collection unit can analyze the delivery person's past route history and prioritize collecting traffic information for frequently used routes. The data collection unit can also prioritize collecting inventory information for stores that the delivery person has visited in the past. For example, the data collection unit can analyze the delivery person's past visit history and prioritize collecting inventory information for stores that are frequently visited. Furthermore, the data collection unit can analyze the delivery person's past behavioral patterns and dynamically select the necessary data. For example, the data collection unit can analyze the delivery person's past behavioral patterns using a machine learning model and dynamically select the necessary data. This allows for efficient collection of necessary data by dynamically changing the types of data collected based on the delivery person's past behavioral history. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past behavioral history data of delivery personnel into a generating AI, and have the generating AI dynamically change the types of data to be collected.

[0040] The data collection unit can evaluate the reliability of the data during collection and prioritize the collection of highly reliable data. For example, the data collection unit can evaluate the reliability of data sources and prioritize the collection of data from highly rated sources. For example, the data collection unit can use an algorithm to evaluate the reliability of data sources to identify highly reliable data sources and prioritize the collection of data from those sources. The data collection unit can also check the consistency of the data and prioritize the collection of consistent data. For example, the data collection unit can use an algorithm to evaluate the consistency of the data to identify consistent data and prioritize the collection of that data. Furthermore, the data collection unit can evaluate the recency of the data and prioritize the collection of the latest data. For example, the data collection unit can use an algorithm to evaluate the recency of the data to identify the latest data and prioritize the collection of that data. By evaluating the reliability of the data and prioritizing the collection of highly reliable data, the accuracy of the analysis results is improved. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the reliability evaluation of the data sources into a generating AI and have the generating AI perform the priority collection of highly reliable data.

[0041] The data collection unit can collect additional weather information during the collection process and incorporate it into the generation of delivery routes. For example, during rainy weather, the data collection unit can collect information on covered routes and underground passages. For example, based on weather data, the data collection unit can identify routes suitable for rainy weather. The data collection unit can also collect information on scenic routes during sunny weather. For example, based on weather data, the data collection unit can identify routes suitable for sunny weather. Furthermore, on snowy days, the data collection unit can collect information on routes that are less slippery. For example, based on weather data, the data collection unit can identify routes suitable for snowy days. By collecting additional weather information and incorporating it into the generation of delivery routes, the optimal route according to the weather can be generated. 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 weather information into a generation AI and have the generation AI perform analysis to incorporate it into the generation of delivery routes.

[0042] The data collection unit can collect and compare the delivery data of competitors during the collection process. For example, the data collection unit can collect the delivery routes of competitors and compare them to its own routes. For example, the data collection unit can collect the delivery routes of competitors through publicly available data or data sharing through partnerships and compare them to its own routes. The data collection unit can also collect the delivery speeds of competitors and compare them to its own speed. For example, the data collection unit can collect the delivery speeds of competitors through publicly available data or data sharing through partnerships and compare them to its own speed. Furthermore, the data collection unit can collect the delivery costs of competitors and compare them to its own costs. For example, the data collection unit can collect the delivery costs of competitors through publicly available data or data sharing through partnerships and compare them to its own costs. By collecting and comparing the delivery data of competitors, the company can improve its own delivery efficiency. 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 competitor delivery data into a generating AI and have the generating AI perform the comparative analysis.

[0043] The analysis unit can improve the accuracy of its analysis by combining historical data and real-time data during the analysis process. For example, the analysis unit can combine historical traffic data with real-time traffic data for analysis. For instance, it can combine historical traffic congestion data with current traffic congestion data to calculate the optimal route. The analysis unit can also combine historical inventory data with real-time inventory data for analysis. For example, it can combine historical inventory level data with current inventory level data to optimize inventory management. Furthermore, the analysis unit can combine historical demand forecast data with real-time demand forecast data for analysis. For example, it can combine historical demand forecast data with current demand forecast data to improve the accuracy of demand forecasting. In this way, the accuracy of the analysis is improved by combining historical data and real-time data for analysis. Some or all of the above processes in the analysis unit are performed using a generation AI. For example, the analysis unit can input historical data and real-time data into the generation AI and have the generation AI perform the task of improving the accuracy of the analysis.

[0044] The analysis unit can automatically detect and exclude outliers from the analysis results during the analysis process. For example, the analysis unit can automatically detect and exclude outliers in traffic data. For example, the analysis unit can use a statistical outlier detection algorithm to identify outliers in traffic data and exclude them from the analysis results. The analysis unit can also automatically detect and exclude outliers in inventory data. For example, the analysis unit can use a machine learning-based outlier detection algorithm to identify outliers in inventory data and exclude them from the analysis results. Furthermore, the analysis unit can automatically detect and exclude outliers in demand forecast data. For example, the analysis unit can use an outlier threshold setting to identify outliers in demand forecast data and exclude them from the analysis results. This improves the accuracy of the analysis results by automatically detecting and excluding outliers. Some or all of the above processes in the analysis unit are performed using a generative AI. For example, the analysis unit can have the generative AI perform the detection and exclusion of outliers.

[0045] The analysis unit can further analyze energy consumption data during the analysis process to reduce environmental impact. For example, the analysis unit can analyze energy consumption data of delivery routes and propose the most energy-efficient route. For example, the analysis unit can identify an energy-efficient route based on the energy consumption data of delivery routes. The analysis unit can also analyze fuel consumption data of delivery vehicles and propose a fuel-efficient route. For example, the analysis unit can identify a fuel-efficient route based on the fuel consumption data of delivery vehicles. Furthermore, the analysis unit can analyze energy consumption data of distribution centers and propose efficient operational methods. For example, the analysis unit can identify an efficient operational method based on the energy consumption data of distribution centers. By further analyzing energy consumption data and reducing environmental impact, sustainable delivery becomes possible. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input energy consumption data into the generation AI and have the generation AI perform an analysis to reduce environmental impact.

[0046] The analysis unit can generate the optimal route by considering the health status data of delivery personnel during analysis. For example, the analysis unit can analyze the fatigue level data of delivery personnel and propose a route that reduces fatigue. For example, the analysis unit can identify a route that reduces fatigue based on the fatigue level data of delivery personnel. The analysis unit can also analyze the health status data of delivery personnel and propose a route that takes health into consideration. For example, the analysis unit can identify a route that takes health into consideration based on the health status data of delivery personnel. Furthermore, the analysis unit can analyze the rest data of delivery personnel and propose a route that includes appropriate rest points. For example, the analysis unit can identify appropriate rest points based on the rest data of delivery personnel. In this way, by considering the health status data of delivery personnel and generating the optimal route, efficient delivery becomes possible while maintaining the health of delivery personnel. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input the health status data of delivery personnel into the generation AI and have the generation AI execute the generation of the optimal route.

[0047] The generation unit can generate multiple delivery routes and perform comparative evaluation during the generation process. For example, the generation unit can generate multiple routes and select the route with less traffic congestion. For example, the generation unit can generate multiple routes based on real-time traffic information and identify the route with less traffic congestion. The generation unit can also generate multiple routes and select the route with the least fuel consumption. For example, the generation unit can generate multiple routes based on fuel consumption data and identify the route with the least fuel consumption. Furthermore, the generation unit can generate multiple routes and select the route with the shortest delivery time. For example, the generation unit can generate multiple routes based on delivery time data and identify the route with the shortest delivery time. In this way, by generating multiple delivery routes and performing comparative evaluation, the optimal route can be selected. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can have the generation AI perform the generation of multiple routes and comparative evaluation.

[0048] The generation unit can perform a risk assessment of delivery routes during generation and prioritize routes with lower risk. For example, the generation unit can assess the risk of traffic accidents and prioritize routes with lower risk. For example, the generation unit can identify and prioritize routes with lower risk based on traffic accident data. The generation unit can also assess weather risks and prioritize routes with lower risk. For example, the generation unit can identify and prioritize routes with lower risk based on weather data. Furthermore, the generation unit can assess the risk of road construction and prioritize routes with lower risk. For example, the generation unit can identify and prioritize routes with lower risk based on road construction data. By performing a risk assessment of delivery routes and prioritizing routes with lower risk, safe delivery becomes possible. Some or all of the above processing in the generation unit is performed using generation AI. For example, the generation unit can have the generation AI perform risk assessment and route selection.

[0049] The generation unit can generate the optimal route by considering the fuel efficiency data of the delivery vehicle during the generation process. For example, the generation unit can generate a fuel-efficient route to minimize fuel consumption. For example, the generation unit identifies a fuel-efficient route based on the fuel efficiency data of the delivery vehicle. The generation unit can also avoid fuel-inefficient routes and generate an efficient route. For example, the generation unit identifies and avoids fuel-inefficient routes based on fuel efficiency data. Furthermore, the generation unit can generate a route that includes optimal rest stops based on fuel efficiency data. For example, the generation unit identifies optimal rest stops based on fuel efficiency data and incorporates them into the route. In this way, fuel consumption can be minimized by generating the optimal route while considering the fuel efficiency data of the delivery vehicle. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input fuel efficiency data into the generation AI and have the generation AI execute the generation of the optimal route.

[0050] The generation unit can generate the optimal route by considering the recipient's availability time during the generation process. For example, the generation unit can set the optimal delivery time by considering the recipient's availability time. For example, the generation unit can identify the optimal delivery time based on the recipient's availability time data. The generation unit can also generate an efficient route in accordance with the recipient's availability time. For example, the generation unit can identify an efficient route based on the recipient's availability time data. Furthermore, the generation unit can generate a route that efficiently visits multiple delivery destinations based on the recipient's availability time. For example, the generation unit can identify a route that efficiently visits multiple delivery destinations based on the recipient's availability time data. By considering the recipient's availability time and generating the optimal route, efficient delivery becomes possible. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit can input the recipient's availability time data into the generation AI and have the generation AI generate the optimal route.

[0051] The chatbot can provide more appropriate answers by referring to the delivery person's past question history. For example, the chatbot can provide relevant answers based on the content of questions the delivery person has asked in the past. For example, the chatbot can analyze the delivery person's past question history and identify relevant answers. The chatbot can also prioritize answers to frequently asked questions from the delivery person's past question history. For example, the chatbot can analyze the delivery person's past question history, identify frequently asked questions, and prioritize answers to them. Furthermore, the chatbot can analyze the delivery person's past question history and provide the optimal answer. For example, the chatbot can analyze the delivery person's past question history using a machine learning model and identify the optimal answer. This allows the chatbot to provide more appropriate answers by referring to the delivery person's past question history. Some or all of the above processing in the chatbot may be performed using AI or not. For example, the chatbot can input the delivery person's past question history data into a generating AI and have the generating AI perform the task of providing the optimal answer.

[0052] The chatbot can grasp the delivery driver's current status in real time and propose the optimal route. For example, the chatbot can grasp the delivery driver's current location in real time and propose the optimal route. For example, the chatbot can identify the delivery driver's current location based on GPS data and propose the optimal route. The chatbot can also grasp the delivery driver's current traffic situation and propose the optimal route. For example, the chatbot can identify the delivery driver's current traffic situation based on real-time traffic information and propose the optimal route. Furthermore, the chatbot can grasp the delivery driver's current delivery status and propose an efficient route. For example, the chatbot can identify the delivery driver's current delivery status based on the delivery management system and propose an efficient route. In this way, by grasping the delivery driver's current status in real time, the optimal route can be proposed. Some or all of the above processes in the chatbot may be performed using AI or not. For example, the chatbot can input the delivery driver's current status data into a generating AI and have the generating AI execute the optimal route proposal.

[0053] The chatbot unit can respond to voice input from delivery personnel and suggest the optimal route by voice. For example, when a delivery person inputs a question by voice, the chatbot unit will suggest the optimal route by voice. For example, the chatbot unit can use speech recognition technology to convert the delivery person's voice input into text and suggest the optimal route. The chatbot unit can also analyze the delivery person's voice input and provide relevant information. For example, the chatbot unit can use speech recognition technology to analyze the delivery person's voice input and identify relevant information. Furthermore, the chatbot unit can analyze the delivery person's voice input in real time and respond quickly. For example, the chatbot unit can use speech recognition technology to analyze the delivery person's voice input in real time and respond quickly. In this way, by responding to the delivery person's voice input, it can suggest the optimal route by voice. Some or all of the above processing in the chatbot unit may be performed using AI or not. For example, the chatbot unit can input the delivery person's voice input data into a generating AI and have the generating AI perform the optimal route suggestion.

[0054] The chatbot unit can work in conjunction with the delivery driver's smartwatch to suggest routes that take their health into consideration. For example, the chatbot unit can acquire health data from the delivery driver's smartwatch and suggest a route that suits their health. For instance, the chatbot unit can identify a route that suits their health based on heart rate data and fatigue data acquired from the smartwatch. The chatbot unit can also consider the delivery driver's heart rate and fatigue level and suggest a route that includes appropriate rest points. For example, the chatbot unit can identify appropriate rest points based on heart rate data and fatigue data acquired from the smartwatch and incorporate them into the route. Furthermore, the chatbot unit can monitor the delivery driver's health status in real time and suggest the optimal route. For example, the chatbot unit can analyze health data acquired from the smartwatch in real time and identify the optimal route. This allows the chatbot unit to suggest the optimal route that takes the delivery driver's health status into consideration. Some or all of the above processes in the chatbot unit may be performed using AI or not. For example, the chatbot unit can input health data acquired from a smartwatch into a generating AI, which can then generate the AI ​​to suggest the optimal route.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The data collection unit can dynamically change the types of data collected based on the delivery person's past behavior history. For example, it can prioritize collecting traffic information for routes frequently used by the delivery person in the past. It can also prioritize collecting inventory information for stores visited by the delivery person in the past. Furthermore, it can analyze the delivery person's past behavior patterns and dynamically select the necessary data. This allows for efficient collection of necessary data by dynamically changing the types of data collected based on the delivery person's past behavior history.

[0057] The data collection unit can evaluate the reliability of the data during collection and prioritize the collection of highly reliable data. For example, it can evaluate the reliability of data sources and prioritize the collection of data from highly-rated sources. It can also check the consistency of the data and prioritize the collection of consistent data. Furthermore, it can evaluate the timeliness of the data and prioritize the collection of the most recent data. As a result, by evaluating the reliability of the data and prioritizing the collection of highly reliable data, the accuracy of the analysis results is improved.

[0058] The analysis unit can improve analysis accuracy by combining historical data and real-time data during the analysis process. For example, it can combine historical traffic data with real-time traffic data for analysis. It can also combine historical inventory data with real-time inventory data for analysis. Furthermore, it can combine historical demand forecast data with real-time demand forecast data for analysis. By combining historical and real-time data, the accuracy of the analysis is improved.

[0059] The generation unit can generate multiple delivery routes and perform comparative evaluations during the generation process. For example, it can generate multiple routes and select the one with less traffic congestion. It can also generate multiple routes and select the one with the lowest fuel consumption. Furthermore, it can generate multiple routes and select the one with the shortest delivery time. In this way, by generating multiple delivery routes and performing comparative evaluations, the optimal route can be selected.

[0060] The generation unit can assess the risks of delivery routes during generation and prioritize routes with lower risk. For example, it can assess the risk of traffic accidents and prioritize routes with lower risk. It can also assess weather risks and prioritize routes with lower risk. Furthermore, it can assess road construction risks and prioritize routes with lower risk. By assessing the risks of delivery routes and prioritizing routes with lower risk, safe delivery becomes possible.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects real-time traffic information, inventory levels, demand forecasts, and other data. The data collection unit collects data from traffic sensors, inventory management systems, demand forecasting systems, etc. For example, traffic sensors include cameras on roads and sensors mounted on vehicles, and inventory management systems include barcode scanning systems and RFID systems. The demand forecasting system forecasts demand by taking into account historical sales data, seasonal fluctuations, and the impact of promotions. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses generative AI, such as deep learning models and reinforcement learning models, to generate the optimal delivery route, taking into account traffic information, inventory levels, and demand forecasts. Step 3: The generation unit generates the optimal delivery route based on the data analyzed by the analysis unit. The generation unit uses generation AI to generate routes that avoid traffic congestion and priority delivery routes to stores with low inventory. Step 4: The chatbot unit provides the delivery route generated by the generation unit to the delivery driver. When the delivery driver enters a question into the chatbot, the generation AI analyzes the data in real time and suggests the optimal route.

[0063] (Example of form 2) The delivery efficiency system according to an embodiment of the present invention is a system that combines generative AI and IoT technology to analyze real-time traffic information, inventory levels, and demand forecasts, and automatically generates delivery routes. This delivery efficiency system collects real-time data such as traffic information, inventory levels, and demand forecasts, and the generative AI analyzes this data to automatically generate the optimal delivery route. Furthermore, by adding a chatbot function, the delivery person and the AI ​​can interact and instantly generate the optimal route. This mechanism makes it possible to improve delivery speed and efficiency while minimizing environmental impact. For example, data is acquired from traffic sensors, inventory management systems, demand forecasting systems, etc. This makes it possible to grasp the latest traffic conditions, inventory conditions, and demand forecasts. Next, the generative AI analyzes the collected data and automatically generates the optimal delivery route. The generative AI calculates the most efficient route considering traffic information, inventory levels, and demand forecasts. For example, it can generate routes that avoid traffic congestion or priority delivery routes to stores with low inventory. Furthermore, by adding a chatbot function, the delivery person and the AI ​​can interact and instantly generate the optimal route. When a delivery person enters a question into the chatbot, the generative AI analyzes the data in real time and proposes the optimal route. For example, in response to a question such as, "Considering current traffic conditions, what is the fastest route to arrive?", the generating AI suggests the optimal route. This system can improve delivery speed and efficiency while minimizing environmental impact. For instance, avoiding traffic congestion reduces fuel consumption and mitigates environmental impact. Prioritizing deliveries to stores with low stock prevents stockouts and improves customer satisfaction. Furthermore, the chatbot function allows delivery personnel to understand the optimal route in real time, improving delivery efficiency. In this way, the delivery efficiency system can achieve increased efficiency and reduced environmental impact in the delivery industry.

[0064] The delivery efficiency system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a chatbot unit. The collection unit collects data such as real-time traffic information, inventory levels, and demand forecasts. The collection unit collects data from sources such as traffic sensors, inventory management systems, and demand forecasting systems. The collection unit can grasp the traffic situation on the road in real time using traffic sensors. For example, traffic sensors include cameras on the road and sensors mounted on vehicles. The collection unit can also grasp the inventory level of each store using an inventory management system. For example, an inventory management system includes a barcode scanning system and an RFID system. Furthermore, the collection unit can forecast future demand using a demand forecasting system. For example, a demand forecasting system forecasts demand by considering past sales data, seasonal fluctuations, and the impact of promotions. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit generates an optimal delivery route considering traffic information, inventory levels, and demand forecasts. The analysis unit analyzes the collected data using a generation AI. The generation AI can use, for example, a deep learning model or a reinforcement learning model. The generation unit generates the optimal delivery route based on the data analyzed by the analysis unit. The generation unit generates, for example, routes that avoid traffic congestion or priority delivery routes to stores with low inventory. The generation unit generates the optimal delivery route using a generation AI. The generation AI calculates the optimal route considering, for example, traffic information, inventory levels, and demand forecasts. The chatbot unit provides the delivery route generated by the generation unit to the delivery person. For example, when a delivery person inputs a question into the chatbot, the generation AI analyzes the data in real time and proposes the optimal route. The chatbot unit proposes the optimal route to the delivery person using the generation AI. As a result, the delivery efficiency system according to this embodiment can achieve increased efficiency in the delivery industry and a reduction in environmental impact.

[0065] The data collection unit collects real-time traffic information, inventory levels, and demand forecasts. For example, it collects data from traffic sensors, inventory management systems, and demand forecasting systems. Specifically, traffic sensors include cameras on roads and sensors mounted on vehicles, and the data obtained from these devices is collected in real time. Traffic sensors detect vehicle speed, traffic volume, and congestion levels, and transmit this information to a central database. This allows the data collection unit to understand road congestion and traffic flow in real time. The inventory management system uses barcode scanning systems and RFID systems to track inventory levels at each store. These systems update product inbound and outbound information in real time and transmit it to the central database. This allows the data collection unit to accurately understand the inventory status at each store and prevent stockouts and excess inventory. Furthermore, the demand forecasting system predicts future demand by considering past sales data, seasonal fluctuations, and the impact of promotions. The demand forecasting system uses machine learning algorithms to learn patterns from past data and predict future demand with high accuracy. This allows the data collection unit to respond quickly to fluctuations in demand and achieve efficient inventory management and delivery planning. The data collection unit centrally manages this diverse data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0066] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit generates the optimal delivery route by considering traffic information, inventory levels, and demand forecasts. The analysis unit uses generative AI to analyze the collected data. Generative AI can use, for example, deep learning models or reinforcement learning models. Specifically, deep learning models analyze image data obtained from traffic sensors to understand road congestion and traffic flow. This allows the analysis unit to understand traffic conditions in real time and calculate the optimal delivery route. Reinforcement learning models learn the optimal delivery route based on past delivery data and demand forecast data. Reinforcement learning models can find the optimal route through trial and error. This allows the analysis unit to generate efficient delivery routes, shortening delivery times and reducing fuel consumption. Furthermore, the analysis unit can also utilize past data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict traffic fluctuations in specific areas and time periods based on past traffic data and formulate future delivery plans. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0067] The generation unit generates the optimal delivery route based on the data analyzed by the analysis unit. For example, the generation unit generates routes that avoid traffic congestion and priority delivery routes to stores with low inventory. The generation unit uses a generation AI to generate the optimal delivery route. The generation AI calculates the optimal route considering, for example, traffic information, inventory levels, and demand forecasts. Specifically, the generation AI analyzes real-time traffic information obtained from traffic sensors and calculates the optimal route to avoid congestion. It also considers inventory levels obtained from the inventory management system and generates priority delivery routes to stores with low inventory. Furthermore, it generates a delivery route that takes into account future demand fluctuations based on demand forecast data obtained from the demand forecasting system. As a result, the generation unit can generate efficient and effective delivery routes, achieving shorter delivery times and reduced fuel consumption. The generation unit can update the generated delivery routes in real time to respond to the latest traffic conditions and inventory levels. For example, if traffic congestion occurs, the generation unit immediately calculates a new route and notifies the delivery personnel. Also, if inventory levels change, the generation unit recalculates the priority delivery route to achieve efficient delivery. This allows the generation unit to always provide the optimal delivery route based on the latest information, supporting a quick and appropriate response.

[0068] The chatbot unit provides delivery drivers with delivery routes generated by the generation unit. For example, when a delivery driver inputs a question into the chatbot, the generation AI analyzes the data in real time and proposes the optimal route. Specifically, if a delivery driver asks the chatbot, "What is the best delivery route right now?", the generation AI calculates the optimal route based on collected traffic information, inventory levels, and demand forecast data, and provides it to the delivery driver through the chatbot. The chatbot unit uses the generation AI to propose the best route to the delivery driver. For example, the generation AI calculates routes that avoid traffic congestion or priority delivery routes to stores with low inventory, and notifies the delivery driver through the chatbot. This allows delivery drivers to understand the optimal route in real time and achieve efficient deliveries. Furthermore, the chatbot unit can collect feedback from delivery drivers and continuously improve the accuracy and suggestions of the generation AI. For example, if a delivery driver provides feedback such as, "This route was congested," the generation AI learns that information and reflects it in the next route suggestion. In addition, the chatbot unit can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through chatbot notifications, but also through voice calls, SMS, and email. This allows the chatbot to quickly and reliably provide delivery personnel with the optimal route, improving delivery efficiency and reducing environmental impact.

[0069] The data collection unit can collect data from sources such as traffic sensors, inventory management systems, and demand forecasting systems. For example, the data collection unit can use traffic sensors to grasp real-time traffic conditions on roads. For example, traffic sensors include cameras on roads and sensors mounted on vehicles. The data collection unit can also use inventory management systems to grasp inventory levels at each store. For example, inventory management systems include barcode scanning systems and RFID systems. Furthermore, the data collection unit can use demand forecasting systems to predict future demand. For example, demand forecasting systems predict demand by considering past sales data, seasonal fluctuations, and the impact of promotions. As a result, by collecting data from traffic sensors, inventory management systems, and demand forecasting systems, it is possible to grasp the latest traffic conditions, inventory conditions, and demand forecasts. 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 data acquired from traffic sensors into a generating AI and have the generating AI perform traffic condition analysis.

[0070] The analysis unit can analyze collected data and generate optimal delivery routes considering traffic information, inventory levels, and demand forecasts. For example, the analysis unit can analyze traffic information and generate routes that avoid traffic congestion. For instance, it can calculate the optimal route based on real-time traffic congestion information. The analysis unit can also analyze inventory levels and generate priority delivery routes to stores with low inventory. For example, it can identify stores that should be prioritized for delivery based on each store's inventory level. Furthermore, the analysis unit can analyze demand forecasts and generate priority delivery routes to areas with high demand. For example, it can identify areas with high demand based on demand forecast data and generate priority delivery routes to those areas. This enables efficient delivery by analyzing collected data and generating optimal delivery routes considering traffic information, inventory levels, and demand forecasts. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input collected data into the generation AI and have the generation AI generate the optimal delivery route.

[0071] The generation unit can generate routes that avoid traffic congestion and priority delivery routes to stores with low inventory using generation AI. For example, the generation unit can generate routes that avoid traffic congestion. For example, the generation unit calculates the optimal route to avoid traffic congestion based on real-time traffic information. The generation unit can also generate priority delivery routes to stores with low inventory. For example, the generation unit calculates priority delivery routes to stores with low inventory based on the inventory levels of each store. Furthermore, the generation unit can also generate priority delivery routes to areas with high demand. For example, the generation unit calculates priority delivery routes to areas with high demand based on demand forecast data. As a result, by generating routes that avoid traffic congestion and priority delivery routes to stores with low inventory using generation AI, delivery efficiency is improved. Some or all of the above processing in the generation unit is performed using generation AI. For example, the generation unit can input traffic information, inventory levels, and demand forecast data into the generation AI and have the generation AI execute the generation of the optimal delivery route.

[0072] The chatbot system allows delivery drivers to input questions, and the generating AI analyzes the data in real time to suggest the optimal route. For example, if a delivery driver asks, "Considering current traffic conditions, what is the fastest route to get there?", the generating AI will analyze traffic information in real time and suggest the optimal route. Similarly, if a delivery driver asks, "What is the priority delivery route to stores with low stock?", the generating AI will analyze stock levels and suggest a priority delivery route. Furthermore, if a delivery driver asks, "What is the priority delivery route to areas with high demand?", the generating AI will analyze demand forecast data and suggest a priority delivery route. This improves delivery efficiency by allowing delivery drivers to input questions into the chatbot, and the generating AI to analyze data in real time and suggest the optimal route. Some or all of the above processes in the chatbot system are performed using the generating AI. For example, the chatbot system can input a delivery driver's question into the generating AI and have the AI ​​suggest the optimal route.

[0073] The chatbot unit can provide the generated delivery route to the delivery person. For example, the chatbot unit can provide the delivery person with the optimal delivery route generated by the generation AI. For example, the chatbot unit can display the generated delivery route to the delivery person in text format. The chatbot unit can also provide the generated delivery route to the delivery person in voice format. For example, the chatbot unit can use speech synthesis technology to convey the generated delivery route to the delivery person by voice. Furthermore, the chatbot unit can display the generated delivery route to the delivery person in map format. For example, the chatbot unit can use a map application to display the generated delivery route on a map. This allows the delivery person to understand the optimal route by providing them with the generated delivery route. Some or all of the above processing in the chatbot unit is performed using the generation AI. For example, the chatbot unit can input the generated delivery route into the generation AI and have the generation AI execute the optimal format for providing it to the delivery person.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the burden. For example, the data collection unit can estimate the user's stress level using an emotion recognition algorithm and adjust the frequency of data collection. The data collection unit can also increase the frequency of data collection to obtain more detailed information if the user is relaxed. For example, the data collection unit can estimate the user's level of relaxation using sensor data and adjust the frequency of data collection. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting only important data and process it quickly. For example, the data collection unit can estimate the user's hurried state using an emotion recognition algorithm and prioritize collecting important data. This reduces the user's burden and enables efficient data collection by adjusting the timing of data collection 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 includes, 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 data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI adjust the timing of data collection.

[0075] The data collection unit can dynamically change the types of data it collects based on the delivery person's past behavioral history. For example, the data collection unit can prioritize collecting traffic information for routes that the delivery person has frequently used in the past. For example, the data collection unit can analyze the delivery person's past route history and prioritize collecting traffic information for frequently used routes. The data collection unit can also prioritize collecting inventory information for stores that the delivery person has visited in the past. For example, the data collection unit can analyze the delivery person's past visit history and prioritize collecting inventory information for stores that are frequently visited. Furthermore, the data collection unit can analyze the delivery person's past behavioral patterns and dynamically select the necessary data. For example, the data collection unit can analyze the delivery person's past behavioral patterns using a machine learning model and dynamically select the necessary data. This allows for efficient collection of necessary data by dynamically changing the types of data collected based on the delivery person's past behavioral history. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past behavioral history data of delivery personnel into a generating AI, and have the generating AI dynamically change the types of data to be collected.

[0076] The data collection unit can evaluate the reliability of the data during collection and prioritize the collection of highly reliable data. For example, the data collection unit can evaluate the reliability of data sources and prioritize the collection of data from highly rated sources. For example, the data collection unit can use an algorithm to evaluate the reliability of data sources to identify highly reliable data sources and prioritize the collection of data from those sources. The data collection unit can also check the consistency of the data and prioritize the collection of consistent data. For example, the data collection unit can use an algorithm to evaluate the consistency of the data to identify consistent data and prioritize the collection of that data. Furthermore, the data collection unit can evaluate the recency of the data and prioritize the collection of the latest data. For example, the data collection unit can use an algorithm to evaluate the recency of the data to identify the latest data and prioritize the collection of that data. By evaluating the reliability of the data and prioritizing the collection of highly reliable data, the accuracy of the analysis results is improved. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the reliability evaluation of the data sources into a generating AI and have the generating AI perform the priority collection of highly reliable data.

[0077] The data collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting only important data. For example, the data collection unit will estimate the user's stress level using an emotion recognition algorithm and prioritize collecting important data. The data collection unit can also prioritize collecting detailed data if the user is relaxed. For example, the data collection unit will estimate the user's level of relaxation using sensor data and prioritize collecting detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. For example, the data collection unit will estimate the user's hurried state using an emotion recognition algorithm and prioritize collecting data that can be collected quickly. This enables efficient data collection by prioritizing the data to be collected 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 includes, 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 data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI determine the priority of the data to be collected.

[0078] The data collection unit can collect additional weather information during the collection process and incorporate it into the generation of delivery routes. For example, during rainy weather, the data collection unit can collect information on covered routes and underground passages. For example, based on weather data, the data collection unit can identify routes suitable for rainy weather. The data collection unit can also collect information on scenic routes during sunny weather. For example, based on weather data, the data collection unit can identify routes suitable for sunny weather. Furthermore, on snowy days, the data collection unit can collect information on routes that are less slippery. For example, based on weather data, the data collection unit can identify routes suitable for snowy days. By collecting additional weather information and incorporating it into the generation of delivery routes, the optimal route according to the weather can be generated. 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 weather information into a generation AI and have the generation AI perform analysis to incorporate it into the generation of delivery routes.

[0079] The data collection unit can collect and compare the delivery data of competitors during the collection process. For example, the data collection unit can collect the delivery routes of competitors and compare them to its own routes. For example, the data collection unit can collect the delivery routes of competitors through publicly available data or data sharing through partnerships and compare them to its own routes. The data collection unit can also collect the delivery speeds of competitors and compare them to its own speed. For example, the data collection unit can collect the delivery speeds of competitors through publicly available data or data sharing through partnerships and compare them to its own speed. Furthermore, the data collection unit can collect the delivery costs of competitors and compare them to its own costs. For example, the data collection unit can collect the delivery costs of competitors through publicly available data or data sharing through partnerships and compare them to its own costs. By collecting and comparing the delivery data of competitors, the company can improve its own delivery efficiency. 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 competitor delivery data into a generating AI and have the generating AI perform the comparative analysis.

[0080] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, the analysis unit can use an algorithm that performs a detailed analysis. For example, the analysis unit can estimate the user's level of relaxation using an emotion recognition algorithm and select an algorithm that performs a detailed analysis. The analysis unit can also use an algorithm that performs a rapid analysis if the user is in a hurry. For example, the analysis unit can estimate the user's state of urgency using an emotion recognition algorithm and select an algorithm that performs a rapid analysis. Furthermore, if the user is stressed, the analysis unit can use an algorithm that performs a concise analysis. For example, the analysis unit can estimate the user's stress level using an emotion recognition algorithm and select an algorithm that performs a concise analysis. By adjusting the analysis algorithm based on the user's emotions, it becomes possible to perform an optimal analysis according to the user's situation. 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-described processes in the analysis unit are performed using generative AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust the analysis algorithm.

[0081] The analysis unit can improve the accuracy of its analysis by combining historical data and real-time data during the analysis process. For example, the analysis unit can combine historical traffic data with real-time traffic data for analysis. For instance, it can combine historical traffic congestion data with current traffic congestion data to calculate the optimal route. The analysis unit can also combine historical inventory data with real-time inventory data for analysis. For example, it can combine historical inventory level data with current inventory level data to optimize inventory management. Furthermore, the analysis unit can combine historical demand forecast data with real-time demand forecast data for analysis. For example, it can combine historical demand forecast data with current demand forecast data to improve the accuracy of demand forecasting. In this way, the accuracy of the analysis is improved by combining historical data and real-time data for analysis. Some or all of the above processes in the analysis unit are performed using a generation AI. For example, the analysis unit can input historical data and real-time data into the generation AI and have the generation AI perform the task of improving the accuracy of the analysis.

[0082] The analysis unit can automatically detect and exclude outliers from the analysis results during the analysis process. For example, the analysis unit can automatically detect and exclude outliers in traffic data. For example, the analysis unit can use a statistical outlier detection algorithm to identify outliers in traffic data and exclude them from the analysis results. The analysis unit can also automatically detect and exclude outliers in inventory data. For example, the analysis unit can use a machine learning-based outlier detection algorithm to identify outliers in inventory data and exclude them from the analysis results. Furthermore, the analysis unit can automatically detect and exclude outliers in demand forecast data. For example, the analysis unit can use an outlier threshold setting to identify outliers in demand forecast data and exclude them from the analysis results. This improves the accuracy of the analysis results by automatically detecting and excluding outliers. Some or all of the above processes in the analysis unit are performed using a generative AI. For example, the analysis unit can have the generative AI perform the detection and exclusion of outliers.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. For example, the analysis unit can estimate the user's level of tension using an emotion recognition algorithm and select a simple display method. The analysis unit can also provide a display method that includes detailed information if the user is relaxed. For example, the analysis unit can estimate the user's level of relaxation using sensor data and select a detailed display method. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. For example, the analysis unit can estimate the user's state of being in a hurry using an emotion recognition algorithm and select a display method that gets straight to the point. By adjusting the display method of the analysis results based on the user's emotions, it becomes possible to provide the optimal display for the user. 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-described processing in the analysis unit is performed using generative AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust how the analysis results are displayed.

[0084] The analysis unit can further analyze energy consumption data during the analysis process to reduce environmental impact. For example, the analysis unit can analyze energy consumption data of delivery routes and propose the most energy-efficient route. For example, the analysis unit can identify an energy-efficient route based on the energy consumption data of delivery routes. The analysis unit can also analyze fuel consumption data of delivery vehicles and propose a fuel-efficient route. For example, the analysis unit can identify a fuel-efficient route based on the fuel consumption data of delivery vehicles. Furthermore, the analysis unit can analyze energy consumption data of distribution centers and propose efficient operational methods. For example, the analysis unit can identify an efficient operational method based on the energy consumption data of distribution centers. By further analyzing energy consumption data and reducing environmental impact, sustainable delivery becomes possible. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input energy consumption data into the generation AI and have the generation AI perform an analysis to reduce environmental impact.

[0085] The analysis unit can generate the optimal route by considering the health status data of delivery personnel during analysis. For example, the analysis unit can analyze the fatigue level data of delivery personnel and propose a route that reduces fatigue. For example, the analysis unit can identify a route that reduces fatigue based on the fatigue level data of delivery personnel. The analysis unit can also analyze the health status data of delivery personnel and propose a route that takes health into consideration. For example, the analysis unit can identify a route that takes health into consideration based on the health status data of delivery personnel. Furthermore, the analysis unit can analyze the rest data of delivery personnel and propose a route that includes appropriate rest points. For example, the analysis unit can identify appropriate rest points based on the rest data of delivery personnel. In this way, by considering the health status data of delivery personnel and generating the optimal route, efficient delivery becomes possible while maintaining the health of delivery personnel. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input the health status data of delivery personnel into the generation AI and have the generation AI execute the generation of the optimal route.

[0086] The generation unit can estimate the user's emotions and adjust the generation algorithm based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a route that proceeds at a leisurely pace. For example, the generation unit can estimate the user's level of relaxation using an emotion recognition algorithm and select a route that proceeds at a leisurely pace. Also, if the user is in a hurry, the generation unit can generate a route that emphasizes the shortest route. For example, the generation unit can estimate the user's state of urgency using an emotion recognition algorithm and select a route that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a route with visually stimulating effects. For example, the generation unit can estimate the user's level of excitement using an emotion recognition algorithm and select a route with visually stimulating effects. In this way, by adjusting the generation algorithm based on the user's emotions, the optimal route according to the user's situation can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the generation unit are performed using a generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the generation algorithm.

[0087] The generation unit can generate multiple delivery routes and perform comparative evaluation during the generation process. For example, the generation unit can generate multiple routes and select the route with less traffic congestion. For example, the generation unit can generate multiple routes based on real-time traffic information and identify the route with less traffic congestion. The generation unit can also generate multiple routes and select the route with the least fuel consumption. For example, the generation unit can generate multiple routes based on fuel consumption data and identify the route with the least fuel consumption. Furthermore, the generation unit can generate multiple routes and select the route with the shortest delivery time. For example, the generation unit can generate multiple routes based on delivery time data and identify the route with the shortest delivery time. In this way, by generating multiple delivery routes and performing comparative evaluation, the optimal route can be selected. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can have the generation AI perform the generation of multiple routes and comparative evaluation.

[0088] The generation unit can perform a risk assessment of delivery routes during generation and prioritize routes with lower risk. For example, the generation unit can assess the risk of traffic accidents and prioritize routes with lower risk. For example, the generation unit can identify and prioritize routes with lower risk based on traffic accident data. The generation unit can also assess weather risks and prioritize routes with lower risk. For example, the generation unit can identify and prioritize routes with lower risk based on weather data. Furthermore, the generation unit can assess the risk of road construction and prioritize routes with lower risk. For example, the generation unit can identify and prioritize routes with lower risk based on road construction data. By performing a risk assessment of delivery routes and prioritizing routes with lower risk, safe delivery becomes possible. Some or all of the above processing in the generation unit is performed using generation AI. For example, the generation unit can have the generation AI perform risk assessment and route selection.

[0089] The generation unit can estimate the user's emotions and adjust the display method of the generated route based on the estimated user emotions. For example, if the user is tense, the generation unit can provide a simple and highly visible display method. For example, the generation unit can estimate the user's level of tension using an emotion recognition algorithm and select a simple display method. The generation unit can also provide a display method that includes detailed information if the user is relaxed. For example, the generation unit can estimate the user's level of relaxation using sensor data and select a detailed display method. Furthermore, if the user is in a hurry, the generation unit can provide a concise display method. For example, the generation unit can estimate the user's hurried state using an emotion recognition algorithm and select a concise display method. By adjusting the display method of the route generated based on the user's emotions, the optimal display for the user becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 generation unit is performed using a generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust how the route is displayed.

[0090] The generation unit can generate the optimal route by considering the fuel efficiency data of the delivery vehicle during the generation process. For example, the generation unit can generate a fuel-efficient route to minimize fuel consumption. For example, the generation unit identifies a fuel-efficient route based on the fuel efficiency data of the delivery vehicle. The generation unit can also avoid fuel-inefficient routes and generate an efficient route. For example, the generation unit identifies and avoids fuel-inefficient routes based on fuel efficiency data. Furthermore, the generation unit can generate a route that includes optimal rest stops based on fuel efficiency data. For example, the generation unit identifies optimal rest stops based on fuel efficiency data and incorporates them into the route. In this way, fuel consumption can be minimized by generating the optimal route while considering the fuel efficiency data of the delivery vehicle. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input fuel efficiency data into the generation AI and have the generation AI execute the generation of the optimal route.

[0091] The generation unit can generate the optimal route by considering the recipient's availability time during the generation process. For example, the generation unit can set the optimal delivery time by considering the recipient's availability time. For example, the generation unit can identify the optimal delivery time based on the recipient's availability time data. The generation unit can also generate an efficient route in accordance with the recipient's availability time. For example, the generation unit can identify an efficient route based on the recipient's availability time data. Furthermore, the generation unit can generate a route that efficiently visits multiple delivery destinations based on the recipient's availability time. For example, the generation unit can identify a route that efficiently visits multiple delivery destinations based on the recipient's availability time data. By considering the recipient's availability time and generating the optimal route, efficient delivery becomes possible. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit can input the recipient's availability time data into the generation AI and have the generation AI generate the optimal route.

[0092] The chatbot can estimate the user's emotions and adjust its response method based on the estimated emotions. For example, if the user is nervous, the chatbot can respond in a calm voice. For example, the chatbot can estimate the user's level of tension using an emotion recognition algorithm and respond in a calm voice. The chatbot can also respond in a cheerful voice if the user is relaxed. For example, the chatbot can estimate the user's level of relaxation using sensor data and respond in a cheerful voice. Furthermore, if the user is in a hurry, the chatbot can provide a quick and concise response. For example, the chatbot can estimate the user's hurried state using an emotion recognition algorithm and provide a quick and concise response. By adjusting the chatbot's response method based on the user's emotions, the optimal response for the user becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the chatbot is performed using generative AI. For example, the chatbot unit can input user emotion data into a generating AI and have the generating AI adjust the response method.

[0093] The chatbot can provide more appropriate answers by referring to the delivery person's past question history. For example, the chatbot can provide relevant answers based on the content of questions the delivery person has asked in the past. For example, the chatbot can analyze the delivery person's past question history and identify relevant answers. The chatbot can also prioritize answers to frequently asked questions from the delivery person's past question history. For example, the chatbot can analyze the delivery person's past question history, identify frequently asked questions, and prioritize answers to them. Furthermore, the chatbot can analyze the delivery person's past question history and provide the optimal answer. For example, the chatbot can analyze the delivery person's past question history using a machine learning model and identify the optimal answer. This allows the chatbot to provide more appropriate answers by referring to the delivery person's past question history. Some or all of the above processing in the chatbot may be performed using AI or not. For example, the chatbot can input the delivery person's past question history data into a generating AI and have the generating AI perform the task of providing the optimal answer.

[0094] The chatbot can grasp the delivery driver's current status in real time and propose the optimal route. For example, the chatbot can grasp the delivery driver's current location in real time and propose the optimal route. For example, the chatbot can identify the delivery driver's current location based on GPS data and propose the optimal route. The chatbot can also grasp the delivery driver's current traffic situation and propose the optimal route. For example, the chatbot can identify the delivery driver's current traffic situation based on real-time traffic information and propose the optimal route. Furthermore, the chatbot can grasp the delivery driver's current delivery status and propose an efficient route. For example, the chatbot can identify the delivery driver's current delivery status based on the delivery management system and propose an efficient route. In this way, by grasping the delivery driver's current status in real time, the optimal route can be proposed. Some or all of the above processes in the chatbot may be performed using AI or not. For example, the chatbot can input the delivery driver's current status data into a generating AI and have the generating AI execute the optimal route proposal.

[0095] The chatbot unit can estimate the user's emotions and adjust its response speed based on the estimated emotions. For example, if the user is nervous, the chatbot unit will respond quickly. For example, the chatbot unit will estimate the user's level of tension using an emotion recognition algorithm and respond quickly. The chatbot unit can also respond slowly if the user is relaxed. For example, the chatbot unit will estimate the user's level of relaxation using sensor data and respond slowly. Furthermore, the chatbot unit can respond immediately if the user is in a hurry. For example, the chatbot unit will estimate the user's hurried state using an emotion recognition algorithm and respond immediately. By adjusting the chatbot's response speed based on the user's emotions, the optimal response for the user can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the chatbot unit is performed using generative AI. For example, the chatbot can input user emotion data into a generating AI and have the AI ​​adjust the response speed.

[0096] The chatbot unit can respond to voice input from delivery personnel and suggest the optimal route by voice. For example, when a delivery person inputs a question by voice, the chatbot unit will suggest the optimal route by voice. For example, the chatbot unit can use speech recognition technology to convert the delivery person's voice input into text and suggest the optimal route. The chatbot unit can also analyze the delivery person's voice input and provide relevant information. For example, the chatbot unit can use speech recognition technology to analyze the delivery person's voice input and identify relevant information. Furthermore, the chatbot unit can analyze the delivery person's voice input in real time and respond quickly. For example, the chatbot unit can use speech recognition technology to analyze the delivery person's voice input in real time and respond quickly. In this way, by responding to the delivery person's voice input, it can suggest the optimal route by voice. Some or all of the above processing in the chatbot unit may be performed using AI or not. For example, the chatbot unit can input the delivery person's voice input data into a generating AI and have the generating AI perform the optimal route suggestion.

[0097] The chatbot unit can work in conjunction with the delivery driver's smartwatch to suggest routes that take their health into consideration. For example, the chatbot unit can acquire health data from the delivery driver's smartwatch and suggest a route that suits their health. For instance, the chatbot unit can identify a route that suits their health based on heart rate data and fatigue data acquired from the smartwatch. The chatbot unit can also consider the delivery driver's heart rate and fatigue level and suggest a route that includes appropriate rest points. For example, the chatbot unit can identify appropriate rest points based on heart rate data and fatigue data acquired from the smartwatch and incorporate them into the route. Furthermore, the chatbot unit can monitor the delivery driver's health status in real time and suggest the optimal route. For example, the chatbot unit can analyze health data acquired from the smartwatch in real time and identify the optimal route. This allows the chatbot unit to suggest the optimal route that takes the delivery driver's health status into consideration. Some or all of the above processes in the chatbot unit may be performed using AI or not. For example, the chatbot unit can input health data acquired from a smartwatch into a generating AI, which can then generate the AI ​​to suggest the optimal route.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is stressed, the frequency of data collection can be reduced to lessen the burden. Conversely, if the user is relaxed, the frequency of data collection can be increased to obtain more detailed information. Furthermore, if the user is in a hurry, only important data can be prioritized and processed quickly. In this way, by adjusting the timing of data collection based on the user's emotions, the burden on the user is reduced and efficient data collection becomes possible.

[0100] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on those emotions. For example, if the user is relaxed, a detailed analysis algorithm can be used. If the user is in a hurry, a rapid analysis algorithm can be used. Furthermore, if the user is stressed, a concise analysis algorithm can be used. By adjusting the analysis algorithm based on the user's emotions, it becomes possible to perform the optimal analysis according to the user's situation.

[0101] The generation unit can estimate the user's emotions and adjust the generation algorithm based on those emotions. For example, if the user is relaxed, it can generate a route that proceeds at a leisurely pace. If the user is in a hurry, it can generate a route that emphasizes the shortest route. Furthermore, if the user is excited, it can generate a route with visually stimulating effects. In this way, by adjusting the generation algorithm based on the user's emotions, it is possible to generate the optimal route according to the user's situation.

[0102] The chatbot can estimate the user's emotions and adjust its response based on those emotions. For example, if the user is nervous, it can respond in a calm voice. If the user is relaxed, it can respond in a cheerful voice. Furthermore, if the user is in a hurry, it can provide a quick and concise response. By adjusting the chatbot's response based on the user's emotions, it can provide the most optimal response for the user.

[0103] The chatbot can estimate the user's emotions and adjust its response speed based on those emotions. For example, if the user is nervous, it can respond quickly. If the user is relaxed, it can respond slowly. Furthermore, if the user is in a hurry, it can respond immediately. By adjusting the chatbot's response speed based on the user's emotions, it is possible to provide the optimal response for the user.

[0104] The data collection unit can dynamically change the types of data collected based on the delivery person's past behavior history. For example, it can prioritize collecting traffic information for routes frequently used by the delivery person in the past. It can also prioritize collecting inventory information for stores visited by the delivery person in the past. Furthermore, it can analyze the delivery person's past behavior patterns and dynamically select the necessary data. This allows for efficient collection of necessary data by dynamically changing the types of data collected based on the delivery person's past behavior history.

[0105] The data collection unit can evaluate the reliability of the data during collection and prioritize the collection of highly reliable data. For example, it can evaluate the reliability of data sources and prioritize the collection of data from highly-rated sources. It can also check the consistency of the data and prioritize the collection of consistent data. Furthermore, it can evaluate the timeliness of the data and prioritize the collection of the most recent data. As a result, by evaluating the reliability of the data and prioritizing the collection of highly reliable data, the accuracy of the analysis results is improved.

[0106] The analysis unit can improve analysis accuracy by combining historical data and real-time data during the analysis process. For example, it can combine historical traffic data with real-time traffic data for analysis. It can also combine historical inventory data with real-time inventory data for analysis. Furthermore, it can combine historical demand forecast data with real-time demand forecast data for analysis. By combining historical and real-time data, the accuracy of the analysis is improved.

[0107] The generation unit can generate multiple delivery routes and perform comparative evaluations during the generation process. For example, it can generate multiple routes and select the one with less traffic congestion. It can also generate multiple routes and select the one with the lowest fuel consumption. Furthermore, it can generate multiple routes and select the one with the shortest delivery time. In this way, by generating multiple delivery routes and performing comparative evaluations, the optimal route can be selected.

[0108] The generation unit can assess the risks of delivery routes during generation and prioritize routes with lower risk. For example, it can assess the risk of traffic accidents and prioritize routes with lower risk. It can also assess weather risks and prioritize routes with lower risk. Furthermore, it can assess road construction risks and prioritize routes with lower risk. By assessing the risks of delivery routes and prioritizing routes with lower risk, safe delivery becomes possible.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data collection unit collects real-time traffic information, inventory levels, demand forecasts, and other data. The data collection unit collects data from traffic sensors, inventory management systems, demand forecasting systems, etc. For example, traffic sensors include cameras on roads and sensors mounted on vehicles, and inventory management systems include barcode scanning systems and RFID systems. The demand forecasting system forecasts demand by taking into account historical sales data, seasonal fluctuations, and the impact of promotions. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses generative AI, such as deep learning models and reinforcement learning models, to generate the optimal delivery route, taking into account traffic information, inventory levels, and demand forecasts. Step 3: The generation unit generates the optimal delivery route based on the data analyzed by the analysis unit. The generation unit uses generation AI to generate routes that avoid traffic congestion and priority delivery routes to stores with low inventory. Step 4: The chatbot unit provides the delivery route generated by the generation unit to the delivery driver. When the delivery driver enters a question into the chatbot, the generation AI analyzes the data in real time and suggests the optimal route.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, and chatbot unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects traffic information and inventory levels using the camera 42 and communication I / F 44 of the smart device 14, and collects demand forecast data using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates the optimal delivery route. The chatbot unit is implemented in the control unit 46A of the smart device 14 and provides the optimal route to the delivery person. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, and chatbot unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects traffic information and inventory levels using the camera 42 and communication I / F 44 of the smart glasses 214, and collects demand forecast data using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The generation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and generates the optimal delivery route. The chatbot unit is implemented, for example, in the control unit 46A of the smart glasses 214, and provides the optimal route to the delivery person. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, and chatbot unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects traffic information and inventory levels using the camera 42 and communication I / F 44 of the headset terminal 314, and collects demand forecast data using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates the optimal delivery route. The chatbot unit is implemented in the control unit 46A of the headset terminal 314 and provides the optimal route to the delivery person. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, and chatbot unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects traffic information and inventory levels using the camera 42 and communication I / F 44 of the robot 414, and collects demand forecast data using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12 and generates the optimal delivery route. The chatbot unit is implemented in, for example, the control unit 46A of the robot 414 and provides the optimal route to the delivery person. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A data collection unit that collects real-time traffic information, inventory levels, demand forecasts, and other data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates an optimal delivery route based on the data analyzed by the analysis unit, The system includes a chatbot unit that provides the delivery route generated by the generation unit to the delivery person. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data from traffic sensors, inventory management systems, demand forecasting systems, and other sources. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to generate the optimal delivery route, taking into account traffic information, inventory levels, and demand forecasts. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is The AI ​​generates routes that avoid traffic congestion and prioritizes delivery routes to stores with low stock. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned chatbot unit is When a delivery driver enters a question into the chatbot, the generating AI analyzes the data in real time and suggests the optimal route. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned chatbot unit is Provide the generated delivery route to the delivery person. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The types of data collected are dynamically changed based on the delivery person's past behavioral history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, the reliability of the data is evaluated, and reliable data is prioritized for collection. The system described in Appendix 1, characterized by the features described herein. (Note 10) 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 11) The aforementioned collection unit is During data collection, additional weather information will be collected and incorporated into the generation of delivery routes. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, we collect and compare delivery data from competitors. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, historical data and real-time data are combined to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, outliers are automatically detected and excluded from the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, additional energy consumption data will be analyzed to reduce the environmental impact. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the system considers the health status data of delivery personnel to generate the optimal route. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts the generation algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, multiple delivery routes are generated and compared for evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, the system performs a risk assessment of the delivery route and prioritizes routes with lower risk. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts how the generated routes are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the system considers the fuel efficiency data of delivery vehicles to generate the optimal route. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the system considers the recipient's availability time to receive the delivery and generates the optimal route. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned chatbot unit is It estimates the user's emotions and adjusts the chatbot's response based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned chatbot unit is Referencing the delivery person's past question history will provide a more appropriate answer. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned chatbot unit is We monitor the current status of delivery personnel in real time and suggest the optimal route. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned chatbot unit is It estimates the user's emotions and adjusts the chatbot's response speed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned chatbot unit is It supports voice input from delivery personnel and suggests the optimal route by voice. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned chatbot unit is The system integrates with delivery drivers' smartwatches to suggest routes that take their health into consideration. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 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 real-time traffic information, inventory levels, demand forecasts, and other data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates an optimal delivery route based on the data analyzed by the analysis unit, The system includes a chatbot unit that provides the delivery route generated by the generation unit to the delivery person. A system characterized by the following features.

2. The aforementioned collection unit is We collect data from traffic sensors, inventory management systems, demand forecasting systems, and other sources. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to generate the optimal delivery route, taking into account traffic information, inventory levels, and demand forecasts. The system according to feature 1.

4. The generating unit is The AI ​​generates routes that avoid traffic congestion and prioritizes delivery routes to stores with low stock. The system according to feature 1.

5. The aforementioned chatbot unit is When a delivery driver enters a question into the chatbot, the AI ​​analyzes the data in real time and suggests the optimal route. The system according to feature 1.

6. The aforementioned chatbot unit is Provide the generated delivery route to the delivery person. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is The types of data collected are dynamically changed based on the delivery person's past behavioral history. The system according to feature 1.

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