system

Abstract

The system according to the embodiment aims to enhance logistics prediction, optimization, and tracking, and to build an efficient and sustainable logistics process. [Solution] A system according to an embodiment includes a prediction unit, an optimization unit, and a tracking unit. The prediction unit analyzes past data to predict demand fluctuations and seasonal trends. The optimization unit calculates delivery routes based on the predicted data generated by the prediction unit. The tracking unit monitors delivery status in real time based on the delivery route calculated by the optimization unit and responds immediately if an abnormality occurs.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately predict, optimize, and track logistics, leaving room for improvement in building efficient logistics processes.

[0005] The system according to the embodiment aims to enhance logistics prediction, optimization, and tracking, and to build an efficient and sustainable logistics process. [Means for solving the problem]

[0006] The system according to the embodiment includes a prediction unit, an optimization unit, and a tracking unit. The prediction unit analyzes past data to predict demand fluctuations and seasonal trends. The optimization unit calculates delivery routes based on the prediction data generated by the prediction unit. The tracking unit monitors delivery status in real time based on the delivery routes calculated by the optimization unit and responds immediately if an abnormality occurs. [Effects of the Invention]

[0007] The system according to the embodiment can enhance the prediction, optimization, and tracking of logistics, and can build an efficient and sustainable logistics process. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A logistics system according to an embodiment of the present invention utilizes generative AI technology to revolutionize the logistics industry and build efficient and sustainable logistics processes. This logistics system includes a forecasting unit that analyzes past data and predicts demand fluctuations and seasonal trends; an optimization unit that calculates optimal delivery routes based on the forecast data generated by the forecasting unit; and a tracking unit that monitors delivery status in real time based on the delivery routes calculated by the optimization unit and responds immediately if anomalies occur. For example, the generative AI predicts periods of increased demand for a specific product based on past shipping and sales data and optimizes inventory. The generative AI then calculates the optimal delivery route, minimizing fuel consumption and delivery time. For example, when there are multiple delivery destinations, the generative AI calculates the shortest route and creates an efficient delivery plan. Furthermore, the generative AI monitors delivery status in real time and responds immediately if anomalies occur. For example, if a delay occurs during delivery, the generative AI proposes an alternative route, enabling a rapid response. This system enables the logistics industry to achieve efficient and sustainable processes, potentially reducing costs and environmental impact. This logistics system enables efficient and sustainable logistics processes, potentially reducing costs and environmental impact.

[0029] A logistics system according to an embodiment includes a prediction unit, an optimization unit, and a tracking unit. The prediction unit analyzes past data to predict demand fluctuations and seasonal trends. Examples of past data include, but are not limited to, sales data, shipping data, and inventory data. The prediction unit predicts when demand for a particular product will increase based on past shipping and sales data, thereby optimizing inventory. The generative AI can predict demand fluctuations and seasonal trends using, for example, a deep learning model or a generative adversarial network (GAN). The optimization unit calculates an optimal delivery route based on the prediction data generated by the prediction unit. The calculation of the optimal delivery route uses criteria such as, but are not limited to, shortest distance, minimum time, and cost minimization. The optimization unit calculates the shortest route when there are multiple delivery destinations, and creates an efficient delivery plan. The generative AI can calculate the optimal delivery route using, for example, a deep learning model or a generative adversarial network (GAN), and minimize fuel consumption and delivery time. The tracking unit monitors the delivery status in real time based on the delivery route calculated by the optimization unit and responds immediately if an abnormality occurs. Real-time monitoring can be performed, for example, with an update frequency of every second or every minute, but is not limited to such examples. For example, if a delay occurs during delivery, the tracking unit uses a generative AI to propose an alternative route, enabling a rapid response. The generative AI can detect abnormalities and propose an alternative route using, for example, a deep learning model or a generative adversarial network (GAN). As a result, the logistics system according to the embodiment realizes an efficient and sustainable logistics process, and is expected to reduce costs and environmental impact.

[0030] The prediction unit can predict demand fluctuations and seasonal trends based on past shipping data or sales data. The prediction unit predicts demand fluctuations and seasonal trends based on, for example, past shipping data. Past shipping data includes, for example, shipping date and time, shipping volume, and shipping destination, but is not limited to these examples. The prediction unit can also predict demand fluctuations and seasonal trends based on past sales data. Sales data includes, for example, sales date and time, sales volume, and sales destination, but is not limited to these examples. The generative AI can analyze past shipping data and sales data using, for example, a deep learning model or a generative adversarial network (GAN) to predict demand fluctuations and seasonal trends. This allows for accurate prediction of demand fluctuations and seasonal trends based on past data.

[0031] The optimization unit can calculate the shortest route when there are multiple delivery destinations and create an efficient delivery plan. For example, the optimization unit calculates the shortest route when there are multiple delivery destinations and creates an efficient delivery plan. The multiple delivery destinations include, but are not limited to, stores, warehouses, customer addresses, etc. Criteria used to calculate the shortest route include, but are not limited to, distance, time, cost, etc. For example, the generation AI can calculate the shortest route when there are multiple delivery destinations and create an efficient delivery plan using, for example, a deep learning model or a generative adversarial network (GAN). This makes it possible to create an efficient delivery plan even when there are multiple delivery destinations.

[0032] The tracking unit can monitor the delivery status in real time and propose an alternative route if an abnormality occurs. The tracking unit, for example, monitors the delivery status in real time and proposes an alternative route if an abnormality occurs. Real-time monitoring can be performed using, for example, but not limited to, an update frequency on a second or minute basis. Abnormalities can include, for example, but not limited to, delays, failures, unexpected events, etc. The alternative route can be proposed using, for example, but not limited to, criteria such as shortest distance, minimum time, and cost minimization. The generative AI can monitor the delivery status in real time using, for example, a deep learning model or a generative adversarial network (GAN), and propose an alternative route if an abnormality occurs. This makes it possible to monitor the delivery status in real time and respond quickly if an abnormality occurs.

[0033] The prediction unit can analyze past data using a generative AI and predict demand fluctuations and seasonal trends. The prediction unit can analyze past data using a generative AI, for example, and predict demand fluctuations and seasonal trends. Examples of generative AI include, but are not limited to, deep learning models and generative adversarial networks (GANs). The generative AI can predict demand fluctuations and seasonal trends based on past data. Examples of past data include, but are not limited to, sales data, shipping data, and inventory data. As a result, the use of generative AI improves the accuracy of demand forecasting.

[0034] The optimization unit calculates the optimal delivery route using a generative AI, thereby minimizing fuel consumption and delivery time. The optimization unit calculates the optimal delivery route using, for example, a generative AI, thereby minimizing fuel consumption and delivery time. Examples of the generative AI include, but are not limited to, deep learning models and generative adversarial networks (GANs). Criteria used to calculate the optimal delivery route include, but are not limited to, shortest distance, minimum time, and cost minimization. The generative AI calculates the optimal delivery route, thereby minimizing fuel consumption and delivery time. As a result, fuel consumption and delivery time can be minimized by using the generative AI.

[0035] The prediction unit can predict abnormal demand fluctuations by referring to past abnormal data during prediction. The prediction unit, for example, predicts abnormal demand fluctuations by referring to past abnormal data during prediction. Past abnormal data includes, for example, past delay data, failure data, etc., but is not limited to these examples. The generation AI can, for example, analyze past abnormal demand data, detect similar patterns, and predict future abnormal demand. The generation AI can also identify the cause of abnormal demand fluctuations and reflect them in the prediction model. Furthermore, the generation AI can make corrections based on the abnormal data to improve the accuracy of the demand forecast. This makes it possible to predict abnormal demand fluctuations by referring to past abnormal data.

[0036] The forecasting unit can forecast demand based on the impact of specific events or campaigns at the time of forecasting. For example, the forecasting unit forecasts demand based on the impact of specific events or campaigns at the time of forecasting. Specific events include, but are not limited to, sales, promotions, and holidays. Campaigns include, but are not limited to, discount campaigns and point campaigns. The generation AI can, for example, analyze past event data and forecast the impact of specific events on demand. The generation AI can also evaluate the effectiveness of campaigns and reflect this in the demand forecast. Furthermore, the generation AI can forecast peaks in demand by taking into account the schedules of events and campaigns. This improves the accuracy of demand forecasting by taking into account the impact of specific events and campaigns.

[0037] The forecasting unit can forecast demand for each region based on geographical factors during forecasting. The forecasting unit, for example, forecasts demand for each region based on geographical factors during forecasting. Geographical factors include, but are not limited to, the population density and traffic conditions of each region. The generation AI can, for example, analyze past demand data for each region and forecast demand taking geographical factors into account. The generation AI can also take into account the demographic trends and economic conditions of each region and reflect them in the demand forecast. Furthermore, the generation AI can forecast demand fluctuations taking into account seasonal trends for each region. This makes it possible to forecast demand for each region by taking geographical factors into account.

[0038] The prediction unit can predict demand by analyzing social media trends during prediction. For example, the prediction unit can predict demand by analyzing social media trends during prediction. Social media trends include, but are not limited to, for example, the frequency of hashtag appearances and analysis of post content. The generation AI can, for example, analyze social media post data to identify trends that will affect demand. The generation AI can also analyze social media hashtags and keywords and reflect them in the demand forecast. Furthermore, the generation AI can predict peaks in demand based on social media engagement data. As a result, analyzing social media trends improves the accuracy of the demand forecast.

[0039] The optimization unit can calculate a route that avoids traffic congestion based on past traffic data during optimization. For example, the optimization unit calculates a route that avoids traffic congestion based on past traffic data during optimization. Past traffic data includes, but is not limited to, past congestion information, traffic accident data, etc. For example, the generation AI can analyze past traffic congestion data and calculate an optimal route that avoids traffic congestion. The generation AI can also predict traffic congestion occurrence patterns and propose an optimal route. Furthermore, the generation AI can calculate a route that avoids traffic congestion by taking real-time traffic information into consideration. This makes it possible to calculate an optimal route that avoids traffic congestion by referring to past traffic data.

[0040] The optimization unit can calculate an optimal route based on the priority of the delivery destination during optimization. The optimization unit, for example, calculates an optimal route based on the priority of the delivery destination during optimization. The priority of the delivery destination includes, for example, but is not limited to, the importance of the customer and delivery time constraints. The generation AI can calculate an optimal route based on, for example, the priority of the delivery destination. The generation AI can also propose an optimal route taking into account the importance and urgency of the delivery destination. Furthermore, the generation AI can calculate an efficient route taking into account the time constraints of the delivery destination. This enables efficient delivery planning by taking into account the priority of the delivery destination.

[0041] The optimization unit can calculate an optimal route based on weather data during optimization. The optimization unit, for example, calculates an optimal route based on weather data during optimization. Weather data includes, but is not limited to, temperature, precipitation, and wind speed. The generation AI can, for example, analyze real-time weather data and calculate an optimal route. The generation AI can also propose a route that takes into account the effects of weather based on past weather data. Furthermore, the generation AI can calculate an optimal route taking into account weather forecasts. In this way, by taking into account weather data, an optimal route that is less affected by weather can be calculated.

[0042] The optimization unit can calculate an optimal route based on fuel efficiency data of the delivery vehicles during optimization. For example, the optimization unit calculates an optimal route based on fuel efficiency data of the delivery vehicles during optimization. Fuel efficiency data includes, for example, fuel efficiency and mileage for each vehicle, but is not limited to these examples. For example, the generation AI can analyze fuel efficiency data of the delivery vehicles and calculate a route that minimizes fuel consumption. The generation AI can also propose an efficient route based on the fuel efficiency data. Furthermore, the generation AI can calculate a route that aims to reduce costs by taking fuel efficiency data into account. As a result, an optimal route that minimizes fuel consumption can be calculated by taking fuel efficiency data into account.

[0043] The tracking unit can predict delays based on past delay data during tracking. The tracking unit, for example, can predict delays based on past delay data during tracking. Past delay data includes, for example, but is not limited to, the date and time of delay occurrence and the cause of the delay. The generation AI can, for example, analyze past delay data and predict delay occurrence patterns. The generation AI can also identify the cause of the delay and reflect it in a prediction model. Furthermore, the generation AI can evaluate the risk of delays based on the delay data and make predictions. This makes it possible to predict delays and take appropriate measures by referring to past delay data.

[0044] The tracking unit can monitor the status of the delivery vehicle during tracking and detect abnormalities. The tracking unit, for example, monitors the status of the delivery vehicle during tracking and detects abnormalities. The status of the delivery vehicle includes, for example, vehicle location information, remaining fuel, engine status, etc., but is not limited to these examples. The generation AI can, for example, analyze sensor data from the delivery vehicle to detect abnormalities. The generation AI can also predict signs of abnormalities based on vehicle operation data. Furthermore, the generation AI can monitor the vehicle status in real time and immediately notify of abnormalities. As a result, by monitoring the status of the delivery vehicle, abnormalities can be detected early and a prompt response can be made.

[0045] The tracking unit can provide a notification based on the receiving status of the delivery destination during tracking. The tracking unit, for example, provides a notification based on the receiving status of the delivery destination during tracking. The receiving status of the delivery destination includes, for example, the receiving date and time, confirmation of the recipient, etc., but is not limited to these examples. The generation AI can, for example, monitor the receiving status of the delivery destination in real time and provide a notification at an appropriate time. The generation AI can also predict the optimal notification timing based on past receiving data. Furthermore, the generation AI can adjust the notification content taking the receiving status into consideration. This allows for a notification to be provided at an appropriate time by taking the receiving status of the delivery destination into consideration.

[0046] The tracking unit can display the location information of the delivery vehicle in real time during tracking. The tracking unit, for example, displays the location information of the delivery vehicle in real time during tracking. Location information of the delivery vehicle includes, for example, GPS data, real-time update frequency, etc., but is not limited to these examples. The generation AI can, for example, analyze the GPS data of the delivery vehicle and display the location information in real time. The generation AI can also propose an optimal route based on the location information of the delivery vehicle. Furthermore, the generation AI can update the location information in real time and notify the user. As a result, by displaying the location information of the delivery vehicle in real time, the delivery status can be accurately understood.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The prediction unit can also analyze a user's purchasing history and perform individual demand predictions. For example, based on data on products a specific user has purchased in the past, it can predict products that the user is likely to purchase again. It can also analyze a user's purchasing patterns and predict demand during specific seasons or events. Furthermore, it can predict demand for related products or new products based on the user's purchasing history, optimizing inventory management. This makes it possible to perform individual demand predictions and achieve more accurate inventory management.

[0049] The optimization unit can also calculate optimal routes taking into account the maintenance schedules of delivery vehicles. For example, if a specific vehicle is unavailable due to maintenance, it calculates a route that avoids that vehicle. It can also propose routes that minimize the use of vehicles that require maintenance. Furthermore, it can calculate routes that even out the frequency of vehicle use based on the maintenance schedule, thereby extending the lifespan of vehicles. This allows for efficient vehicle operation by taking maintenance schedules into account.

[0050] The tracking unit can also monitor the health of delivery vehicle drivers and arrange for a substitute driver if an abnormality is detected. For example, it can monitor the driver's heart rate and body temperature with sensors and issue an alert if an abnormality is detected. It can also analyze the driver's fatigue level and notify them when they need to take a break. Furthermore, it can suggest optimal resting points based on the driver's health condition, supporting safe operation. In this way, monitoring the driver's health condition enables safe and efficient deliveries.

[0051] The forecasting unit can also analyze weather data and make demand forecasts based on weather fluctuations. For example, it can combine past weather data with sales data to predict demand under specific weather conditions. It can also predict demand fluctuations in real time based on weather forecasts and adjust inventory management. Furthermore, if abnormal weather is predicted, it can propose measures to deal with sudden increases or decreases in demand. This makes it possible to make more accurate demand forecasts by taking weather data into account.

[0052] The optimization unit can also calculate the optimal route by taking into account the available pickup times at the delivery destination. For example, it can consider the time periods when a specific delivery destination can receive the package and calculate a route that matches that time period. It can also propose routes that prioritize delivery destinations with shorter pickup times. Furthermore, if the available pickup times change, it can recalculate the route in real time to support efficient delivery. In this way, taking the available pickup times into account can improve customer satisfaction.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The forecasting unit analyzes past data to predict demand fluctuations and seasonal trends. Past data includes sales data, shipping data, inventory data, etc. For example, the forecasting unit predicts when demand for a specific product will increase based on past shipping and sales data, and aims to optimize inventory. The generative AI can predict demand fluctuations and seasonal trends using deep learning models and generative adversarial networks (GANs). Step 2: The optimization unit calculates the optimal delivery route based on the forecast data generated by the prediction unit. Criteria such as shortest distance, minimum time, and cost minimization are used to calculate the optimal delivery route. The optimization unit calculates the shortest route when there are multiple delivery destinations and creates an efficient delivery plan. The generation AI uses deep learning models and generative adversarial networks (GANs) to calculate the optimal delivery route and minimize fuel consumption and delivery time. Step 3: The tracking unit monitors the delivery status in real time based on the delivery route calculated by the optimization unit, and responds immediately if an abnormality occurs. Real-time monitoring is performed with updates on a second or minute basis. If a delay occurs during delivery, the tracking unit's generative AI proposes an alternative route, enabling a rapid response. The generative AI can detect anomalies and propose alternative routes using deep learning models and generative adversarial networks (GANs).

[0055] (Example 2) A logistics system according to an embodiment of the present invention utilizes generative AI technology to revolutionize the logistics industry and build efficient and sustainable logistics processes. This logistics system includes a forecasting unit that analyzes past data and predicts demand fluctuations and seasonal trends; an optimization unit that calculates optimal delivery routes based on the forecast data generated by the forecasting unit; and a tracking unit that monitors delivery status in real time based on the delivery routes calculated by the optimization unit and responds immediately if anomalies occur. For example, the generative AI predicts periods of increased demand for a specific product based on past shipping and sales data and optimizes inventory. The generative AI then calculates the optimal delivery route, minimizing fuel consumption and delivery time. For example, when there are multiple delivery destinations, the generative AI calculates the shortest route and creates an efficient delivery plan. Furthermore, the generative AI monitors delivery status in real time and responds immediately if anomalies occur. For example, if a delay occurs during delivery, the generative AI proposes an alternative route, enabling a rapid response. This system enables the logistics industry to achieve efficient and sustainable processes, potentially reducing costs and environmental impact. This logistics system enables efficient and sustainable logistics processes, potentially reducing costs and environmental impact.

[0056] A logistics system according to an embodiment includes a prediction unit, an optimization unit, and a tracking unit. The prediction unit analyzes past data to predict demand fluctuations and seasonal trends. Examples of past data include, but are not limited to, sales data, shipping data, and inventory data. The prediction unit predicts when demand for a particular product will increase based on past shipping and sales data, thereby optimizing inventory. The generative AI can predict demand fluctuations and seasonal trends using, for example, a deep learning model or a generative adversarial network (GAN). The optimization unit calculates an optimal delivery route based on the prediction data generated by the prediction unit. The calculation of the optimal delivery route uses criteria such as, but are not limited to, shortest distance, minimum time, and cost minimization. The optimization unit calculates the shortest route when there are multiple delivery destinations, and creates an efficient delivery plan. The generative AI can calculate the optimal delivery route using, for example, a deep learning model or a generative adversarial network (GAN), and minimize fuel consumption and delivery time. The tracking unit monitors the delivery status in real time based on the delivery route calculated by the optimization unit and responds immediately if an abnormality occurs. Real-time monitoring can be performed, for example, with an update frequency of every second or every minute, but is not limited to such examples. For example, if a delay occurs during delivery, the tracking unit uses a generative AI to propose an alternative route, enabling a rapid response. The generative AI can detect abnormalities and propose an alternative route using, for example, a deep learning model or a generative adversarial network (GAN). As a result, the logistics system according to the embodiment realizes an efficient and sustainable logistics process, and is expected to reduce costs and environmental impact.

[0057] The prediction unit can predict demand fluctuations and seasonal trends based on past shipping data or sales data. The prediction unit predicts demand fluctuations and seasonal trends based on, for example, past shipping data. Past shipping data includes, for example, shipping date and time, shipping volume, and shipping destination, but is not limited to these examples. The prediction unit can also predict demand fluctuations and seasonal trends based on past sales data. Sales data includes, for example, sales date and time, sales volume, and sales destination, but is not limited to these examples. The generative AI can analyze past shipping data and sales data using, for example, a deep learning model or a generative adversarial network (GAN) to predict demand fluctuations and seasonal trends. This allows for accurate prediction of demand fluctuations and seasonal trends based on past data.

[0058] The optimization unit can calculate the shortest route when there are multiple delivery destinations and create an efficient delivery plan. For example, the optimization unit calculates the shortest route when there are multiple delivery destinations and creates an efficient delivery plan. The multiple delivery destinations include, but are not limited to, stores, warehouses, customer addresses, etc. Criteria used to calculate the shortest route include, but are not limited to, distance, time, cost, etc. For example, the generation AI can calculate the shortest route when there are multiple delivery destinations and create an efficient delivery plan using, for example, a deep learning model or a generative adversarial network (GAN). This makes it possible to create an efficient delivery plan even when there are multiple delivery destinations.

[0059] The tracking unit can monitor the delivery status in real time and propose an alternative route if an abnormality occurs. The tracking unit, for example, monitors the delivery status in real time and proposes an alternative route if an abnormality occurs. Real-time monitoring can be performed using, for example, but not limited to, an update frequency on a second or minute basis. Abnormalities can include, for example, but not limited to, delays, failures, unexpected events, etc. The alternative route can be proposed using, for example, but not limited to, criteria such as shortest distance, minimum time, and cost minimization. The generative AI can monitor the delivery status in real time using, for example, a deep learning model or a generative adversarial network (GAN), and propose an alternative route if an abnormality occurs. This makes it possible to monitor the delivery status in real time and respond quickly if an abnormality occurs.

[0060] The prediction unit can analyze past data using a generative AI and predict demand fluctuations and seasonal trends. The prediction unit can analyze past data using a generative AI, for example, and predict demand fluctuations and seasonal trends. Examples of generative AI include, but are not limited to, deep learning models and generative adversarial networks (GANs). The generative AI can predict demand fluctuations and seasonal trends based on past data. Examples of past data include, but are not limited to, sales data, shipping data, and inventory data. As a result, the use of generative AI improves the accuracy of demand forecasting.

[0061] The optimization unit calculates the optimal delivery route using a generative AI, thereby minimizing fuel consumption and delivery time. The optimization unit calculates the optimal delivery route using, for example, a generative AI, thereby minimizing fuel consumption and delivery time. Examples of the generative AI include, but are not limited to, deep learning models and generative adversarial networks (GANs). Criteria used to calculate the optimal delivery route include, but are not limited to, shortest distance, minimum time, and cost minimization. The generative AI calculates the optimal delivery route, thereby minimizing fuel consumption and delivery time. As a result, fuel consumption and delivery time can be minimized by using the generative AI.

[0062] The prediction unit can estimate the user's emotions and adjust the accuracy of the demand forecast based on the estimated user emotions. The prediction unit, for example, estimates the user's emotions and adjusts the accuracy of the demand forecast based on the estimated user emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and hurry. The generation AI can estimate the user's emotions using, for example, survey results or social media post analysis. The generation AI can adjust the accuracy of the demand forecast based on the user's emotions. For example, if the user is stressed, the generation AI increases the accuracy of the demand forecast and minimizes risk. Furthermore, if the user is relaxed, the generation AI sets the accuracy of the demand forecast to a normal level, emphasizing efficiency. Furthermore, if the user is in a hurry, the generation AI quickly adjusts the accuracy of the demand forecast to support immediate decision-making. This enables more accurate forecasts by adjusting the accuracy of the demand forecast according to the user's emotions.

[0063] The prediction unit can predict abnormal demand fluctuations by referring to past abnormal data during prediction. The prediction unit, for example, predicts abnormal demand fluctuations by referring to past abnormal data during prediction. Past abnormal data includes, for example, past delay data, failure data, etc., but is not limited to these examples. The generation AI can, for example, analyze past abnormal demand data, detect similar patterns, and predict future abnormal demand. The generation AI can also identify the cause of abnormal demand fluctuations and reflect them in the prediction model. Furthermore, the generation AI can make corrections based on the abnormal data to improve the accuracy of the demand forecast. This makes it possible to predict abnormal demand fluctuations by referring to past abnormal data.

[0064] The forecasting unit can forecast demand based on the impact of specific events or campaigns at the time of forecasting. For example, the forecasting unit forecasts demand based on the impact of specific events or campaigns at the time of forecasting. Specific events include, but are not limited to, sales, promotions, and holidays. Campaigns include, but are not limited to, discount campaigns and point campaigns. The generation AI can, for example, analyze past event data and forecast the impact of specific events on demand. The generation AI can also evaluate the effectiveness of campaigns and reflect this in the demand forecast. Furthermore, the generation AI can forecast peaks in demand by taking into account the schedules of events and campaigns. This improves the accuracy of demand forecasting by taking into account the impact of specific events and campaigns.

[0065] The prediction unit can estimate the user's emotions and adjust the display method of the prediction results based on the estimated user emotions. The prediction unit, for example, estimates the user's emotions and adjusts the display method of the prediction results based on the estimated user emotions. User emotions include, but are not limited to, tension, relaxation, and hurry. The generation AI can estimate the user's emotions using, for example, survey results or social media post analysis. The generation AI can adjust the display method of the prediction results based on the user's emotions. For example, if the user is tension, a simple, highly visible display method is provided. If the user is relaxed, a display method including detailed information is provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points is provided. This makes it possible to provide more appropriate information by adjusting the display method of the prediction results according to the user's emotions.

[0066] The forecasting unit can forecast demand for each region based on geographical factors during forecasting. The forecasting unit, for example, forecasts demand for each region based on geographical factors during forecasting. Geographical factors include, but are not limited to, the population density and traffic conditions of each region. The generation AI can, for example, analyze past demand data for each region and forecast demand taking geographical factors into account. The generation AI can also take into account the demographic trends and economic conditions of each region and reflect them in the demand forecast. Furthermore, the generation AI can forecast demand fluctuations taking into account seasonal trends for each region. This makes it possible to forecast demand for each region by taking geographical factors into account.

[0067] The prediction unit can predict demand by analyzing social media trends during prediction. For example, the prediction unit can predict demand by analyzing social media trends during prediction. Social media trends include, but are not limited to, for example, the frequency of hashtag appearances and analysis of post content. The generation AI can, for example, analyze social media post data to identify trends that will affect demand. The generation AI can also analyze social media hashtags and keywords and reflect them in the demand forecast. Furthermore, the generation AI can predict peaks in demand based on social media engagement data. As a result, analyzing social media trends improves the accuracy of the demand forecast.

[0068] The optimization unit can estimate the user's emotions and adjust the optimization algorithm based on the estimated user emotions. For example, the optimization unit can estimate the user's emotions and adjust the optimization algorithm based on the estimated user emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and hurry. The generation AI can estimate the user's emotions using, for example, survey results or social media post analysis. The generation AI can adjust the optimization algorithm based on the user's emotions. For example, if the user is stressed, the generation AI adjusts the optimization algorithm to minimize risk. Furthermore, if the user is relaxed, the generation AI sets the optimization algorithm to a normal level and emphasizes efficiency. Furthermore, if the user is in a hurry, the generation AI quickly adjusts the optimization algorithm to support immediate decision-making. This enables more appropriate optimization by adjusting the optimization algorithm according to the user's emotions.

[0069] The optimization unit can calculate a route that avoids traffic congestion based on past traffic data during optimization. For example, the optimization unit calculates a route that avoids traffic congestion based on past traffic data during optimization. Past traffic data includes, but is not limited to, past congestion information, traffic accident data, etc. For example, the generation AI can analyze past traffic congestion data and calculate an optimal route that avoids traffic congestion. The generation AI can also predict traffic congestion occurrence patterns and propose an optimal route. Furthermore, the generation AI can calculate a route that avoids traffic congestion by taking real-time traffic information into consideration. This makes it possible to calculate an optimal route that avoids traffic congestion by referring to past traffic data.

[0070] The optimization unit can calculate an optimal route based on the priority of the delivery destination during optimization. The optimization unit, for example, calculates an optimal route based on the priority of the delivery destination during optimization. The priority of the delivery destination includes, for example, but is not limited to, the importance of the customer and delivery time constraints. The generation AI can calculate an optimal route based on, for example, the priority of the delivery destination. The generation AI can also propose an optimal route taking into account the importance and urgency of the delivery destination. Furthermore, the generation AI can calculate an efficient route taking into account the time constraints of the delivery destination. This enables efficient delivery planning by taking into account the priority of the delivery destination.

[0071] The optimization unit can estimate the user's emotions and adjust the display method of the optimization results based on the estimated user emotions. The optimization unit, for example, estimates the user's emotions and adjusts the display method of the optimization results based on the estimated user emotions. User emotions include, but are not limited to, tension, relaxation, and hurry. The generation AI can estimate the user's emotions using, for example, survey results or social media post analysis. The generation AI can adjust the display method of the optimization results based on the user's emotions. For example, if the user is tension, a simple, highly visible display method is provided. If the user is relaxed, a display method including detailed information is provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points is provided. This makes it possible to provide more appropriate information by adjusting the display method of the optimization results according to the user's emotions.

[0072] The optimization unit can calculate an optimal route based on weather data during optimization. The optimization unit, for example, calculates an optimal route based on weather data during optimization. Weather data includes, but is not limited to, temperature, precipitation, and wind speed. The generation AI can, for example, analyze real-time weather data and calculate an optimal route. The generation AI can also propose a route that takes into account the effects of weather based on past weather data. Furthermore, the generation AI can calculate an optimal route taking into account weather forecasts. In this way, by taking into account weather data, an optimal route that is less affected by weather can be calculated.

[0073] The optimization unit can calculate an optimal route based on fuel efficiency data of the delivery vehicles during optimization. For example, the optimization unit calculates an optimal route based on fuel efficiency data of the delivery vehicles during optimization. Fuel efficiency data includes, for example, fuel efficiency and mileage for each vehicle, but is not limited to these examples. For example, the generation AI can analyze fuel efficiency data of the delivery vehicles and calculate a route that minimizes fuel consumption. The generation AI can also propose an efficient route based on the fuel efficiency data. Furthermore, the generation AI can calculate a route that aims to reduce costs by taking fuel efficiency data into account. As a result, an optimal route that minimizes fuel consumption can be calculated by taking fuel efficiency data into account.

[0074] The tracking unit can estimate the user's emotions and adjust the tracking notification method based on the estimated user emotions. The tracking unit, for example, estimates the user's emotions and adjusts the tracking notification method based on the estimated user emotions. User emotions include, but are not limited to, tension, relaxation, and hurry. The generation AI can estimate the user's emotions using, for example, survey results or social media post analysis. The generation AI can adjust the tracking notification method based on the user's emotions. For example, if the user is tension, a simple and highly visible notification method is provided. Also, if the user is relaxed, a notification method including detailed information is provided. Furthermore, if the user is in a hurry, a notification method that focuses on the main points is provided. This makes it possible to provide more appropriate information by adjusting the tracking notification method according to the user's emotions.

[0075] The tracking unit can predict delays based on past delay data during tracking. The tracking unit, for example, can predict delays based on past delay data during tracking. Past delay data includes, for example, but is not limited to, the date and time of delay occurrence and the cause of the delay. The generation AI can, for example, analyze past delay data and predict delay occurrence patterns. The generation AI can also identify the cause of the delay and reflect it in a prediction model. Furthermore, the generation AI can evaluate the risk of delays based on the delay data and make predictions. This makes it possible to predict delays and take appropriate measures by referring to past delay data.

[0076] The tracking unit can monitor the status of the delivery vehicle during tracking and detect abnormalities. The tracking unit, for example, monitors the status of the delivery vehicle during tracking and detects abnormalities. The status of the delivery vehicle includes, for example, vehicle location information, remaining fuel, engine status, etc., but is not limited to these examples. The generation AI can, for example, analyze sensor data from the delivery vehicle to detect abnormalities. The generation AI can also predict signs of abnormalities based on vehicle operation data. Furthermore, the generation AI can monitor the vehicle status in real time and immediately notify of abnormalities. As a result, by monitoring the status of the delivery vehicle, abnormalities can be detected early and a prompt response can be made.

[0077] The tracking unit can estimate the user's emotions and adjust the display method of the tracking results based on the estimated user emotions. The tracking unit, for example, estimates the user's emotions and adjusts the display method of the tracking results based on the estimated user emotions. User emotions include, but are not limited to, tension, relaxation, and hurry. The generation AI can estimate the user's emotions using, for example, survey results or social media post analysis. The generation AI can adjust the display method of the tracking results based on the user's emotions. For example, if the user is tension, a simple, highly visible display method is provided. If the user is relaxed, a display method including detailed information is provided. Furthermore, if the user is hurrying, a display method that focuses on the main points is provided. This makes it possible to provide more appropriate information by adjusting the display method of the tracking results according to the user's emotions.

[0078] The tracking unit can provide a notification based on the receiving status of the delivery destination during tracking. The tracking unit, for example, provides a notification based on the receiving status of the delivery destination during tracking. The receiving status of the delivery destination includes, for example, the receiving date and time, confirmation of the recipient, etc., but is not limited to these examples. The generation AI can, for example, monitor the receiving status of the delivery destination in real time and provide a notification at an appropriate time. The generation AI can also predict the optimal notification timing based on past receiving data. Furthermore, the generation AI can adjust the notification content taking the receiving status into consideration. This allows for a notification to be provided at an appropriate time by taking the receiving status of the delivery destination into consideration.

[0079] The tracking unit can display the location information of the delivery vehicle in real time during tracking. The tracking unit, for example, displays the location information of the delivery vehicle in real time during tracking. Location information of the delivery vehicle includes, for example, GPS data, real-time update frequency, etc., but is not limited to these examples. The generation AI can, for example, analyze the GPS data of the delivery vehicle and display the location information in real time. The generation AI can also propose an optimal route based on the location information of the delivery vehicle. Furthermore, the generation AI can update the location information in real time and notify the user. As a result, by displaying the location information of the delivery vehicle in real time, the delivery status can be accurately understood. === Hard Collateral 1-1 === Each of the multiple elements including the prediction unit, optimization unit, and tracking unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the prediction unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes past shipping data and sales data to predict demand fluctuations and seasonal trends. The optimization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates the optimal delivery route based on the predicted data. The tracking unit is realized, for example, by the control unit 46A of the smart device 14 and monitors the delivery status in real time and immediately responds if an abnormality occurs. === Hard Collateral 1-2 === Each of the multiple elements, including the prediction unit, optimization unit, and tracking unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the prediction unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes past shipping data and sales data to predict demand fluctuations and seasonal trends. The optimization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates an optimal delivery route based on the predicted data. The tracking unit is realized, for example, by the control unit 46A of the smart glasses 214, and monitors the delivery status in real time and immediately responds if an abnormality occurs. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned prediction unit, optimization unit, and tracking unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the prediction unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes past shipping data and sales data to predict demand fluctuations and seasonal trends. The optimization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates the optimal delivery route based on the predicted data. The tracking unit is realized, for example, by the control unit 46A of the headset type terminal 314, and monitors the delivery status in real time and immediately responds if an abnormality occurs. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned prediction unit, optimization unit, and tracking unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the prediction unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes past shipping data and sales data to predict demand fluctuations and seasonal trends. The optimization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates the optimal delivery route based on the predicted data. The tracking unit is realized, for example, by the control unit 46A of the robot 414, and monitors the delivery status in real time and immediately responds if an abnormality occurs.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The prediction unit can also analyze a user's purchasing history and perform individual demand predictions. For example, based on data on products a specific user has purchased in the past, it can predict products that the user is likely to purchase again. It can also analyze a user's purchasing patterns and predict demand during specific seasons or events. Furthermore, it can predict demand for related products or new products based on the user's purchasing history, optimizing inventory management. This makes it possible to perform individual demand predictions and achieve more accurate inventory management.

[0082] The optimization unit can also calculate optimal routes taking into account the maintenance schedules of delivery vehicles. For example, if a specific vehicle is unavailable due to maintenance, it calculates a route that avoids that vehicle. It can also propose routes that minimize the use of vehicles that require maintenance. Furthermore, it can calculate routes that even out the frequency of vehicle use based on the maintenance schedule, thereby extending the lifespan of vehicles. This allows for efficient vehicle operation by taking maintenance schedules into account.

[0083] The tracking unit can also monitor the health of delivery vehicle drivers and arrange for a substitute driver if an abnormality is detected. For example, it can monitor the driver's heart rate and body temperature with sensors and issue an alert if an abnormality is detected. It can also analyze the driver's fatigue level and notify them when they need to take a break. Furthermore, it can suggest optimal resting points based on the driver's health condition, supporting safe operation. In this way, monitoring the driver's health condition enables safe and efficient deliveries.

[0084] The forecasting unit can also analyze weather data and make demand forecasts based on weather fluctuations. For example, it can combine past weather data with sales data to predict demand under specific weather conditions. It can also predict demand fluctuations in real time based on weather forecasts and adjust inventory management. Furthermore, if abnormal weather is predicted, it can propose measures to deal with sudden increases or decreases in demand. This makes it possible to make more accurate demand forecasts by taking weather data into account.

[0085] The optimization unit can also calculate the optimal route by taking into account the available pickup times at the delivery destination. For example, it can consider the time periods when a specific delivery destination can receive the package and calculate a route that matches that time period. It can also propose routes that prioritize delivery destinations with shorter pickup times. Furthermore, if the available pickup times change, it can recalculate the route in real time to support efficient delivery. In this way, taking the available pickup times into account can improve customer satisfaction.

[0086] The prediction unit can estimate the user's emotions and adjust the accuracy of the demand forecast based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI will increase the accuracy of the demand forecast and minimize risk. If the user is relaxed, the generation AI will set the accuracy of the demand forecast to a normal level, emphasizing efficiency. Furthermore, if the user is in a hurry, the generation AI will quickly adjust the accuracy of the demand forecast to support immediate decision-making. This allows for more accurate forecasts by adjusting the accuracy of the demand forecast according to the user's emotions.

[0087] The optimization unit can estimate the user's emotions and adjust the optimization algorithm based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI adjusts the optimization algorithm to minimize risk. If the user is relaxed, the generation AI sets the optimization algorithm to a normal level, emphasizing efficiency. Furthermore, if the user is in a hurry, the generation AI quickly adjusts the optimization algorithm to support immediate decision-making. This allows for more appropriate optimization by adjusting the optimization algorithm according to the user's emotions.

[0088] The tracking unit can estimate the user's emotions and adjust the tracking notification method based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible notification method is provided. If the user is relaxed, a notification method including detailed information is provided. Furthermore, if the user is in a hurry, a notification method that focuses on the main points is provided. In this way, by adjusting the tracking notification method according to the user's emotions, more appropriate information can be provided.

[0089] The prediction unit can estimate the user's emotions and adjust the display method of the prediction results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method is provided. If the user is relaxed, a display method including detailed information is provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points is provided. In this way, by adjusting the display method of the prediction results according to the user's emotions, it is possible to provide more appropriate information.

[0090] The optimization unit can estimate the user's emotions and adjust the display method of the optimization results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method is provided. If the user is relaxed, a display method including detailed information is provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points is provided. In this way, by adjusting the display method of the optimization results according to the user's emotions, it is possible to provide more appropriate information.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The forecasting unit analyzes past data to predict demand fluctuations and seasonal trends. Past data includes sales data, shipping data, inventory data, etc. For example, the forecasting unit predicts when demand for a specific product will increase based on past shipping and sales data, and aims to optimize inventory. The generative AI can predict demand fluctuations and seasonal trends using deep learning models and generative adversarial networks (GANs). Step 2: The optimization unit calculates the optimal delivery route based on the forecast data generated by the prediction unit. Criteria such as shortest distance, minimum time, and cost minimization are used to calculate the optimal delivery route. The optimization unit calculates the shortest route when there are multiple delivery destinations and creates an efficient delivery plan. The generation AI uses deep learning models and generative adversarial networks (GANs) to calculate the optimal delivery route and minimize fuel consumption and delivery time. Step 3: The tracking unit monitors the delivery status in real time based on the delivery route calculated by the optimization unit, and responds immediately if an abnormality occurs. Real-time monitoring is performed with updates on a second or minute basis. If a delay occurs during delivery, the tracking unit's generative AI proposes an alternative route, enabling a rapid response. The generative AI can detect anomalies and propose alternative routes using deep learning models and generative adversarial networks (GANs).

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0095] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0111] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0115] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0127] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0137] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0143] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0147] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0148] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0149] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0150] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0151] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0152] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0153] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0154] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0155] 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.

[0156] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0157] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0158] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0159] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0160] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0161] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0162] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0163] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0164] [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A forecasting department that analyzes past data and predicts demand fluctuations and seasonal trends; an optimization unit that calculates a delivery route based on the prediction data generated by the prediction unit; a tracking unit that monitors the delivery status in real time based on the delivery route calculated by the optimization unit and responds immediately if an abnormality occurs. A system characterized by:

2. The prediction unit Predict demand fluctuations and seasonal trends based on historical shipping or sales data 2. The system of claim 1.

3. The optimization unit Calculate the shortest route when there are multiple delivery destinations and create an efficient delivery plan 2. The system of claim 1.

4. The tracking unit Monitor delivery status in real time and suggest alternative routes if anomalies occur 2. The system of claim 1.

5. The prediction unit Generative AI analyzes past data to predict demand fluctuations and seasonal trends 2. The system of claim 1.

6. The optimization unit Generative AI calculates optimal delivery routes to minimize fuel consumption and delivery times 2. The system of claim 1.

7. The prediction unit Estimate user emotions and adjust the accuracy of demand forecasts based on the estimated user emotions 2. The system of claim 1.

8. The prediction unit When forecasting, refer to past abnormal data to predict abnormal demand fluctuations.

2. The system of claim 1.

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