system

Abstract

The system according to this embodiment aims to optimize the routes of the transport vehicles and achieve efficient transport. [Solution] The system according to the embodiment comprises a collection unit, an environmental collection unit, an analysis unit, a generation unit, and a transmission unit. The collection unit collects location information of the transport vehicle. The environmental collection unit collects environmental data. The analysis unit analyzes the information collected by the collection unit and the environmental collection unit. The generation unit generates a transport route based on the information analyzed by the analysis unit. The transmission unit transmits the transport route generated by the generation unit.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system. ​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​The system according to the embodiment comprises a collection unit, an environmental collection unit, an analysis unit, a generation unit, and a transmission unit. The collection unit collects location information of the transport vehicle. The environmental collection unit collects environmental data. The analysis unit analyzes the information collected by the collection unit and the environmental collection unit. The generation unit generates a transport route based on the information analyzed by the analysis unit. The transmission unit transmits the transport route generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can optimize the route of the transport vehicle and achieve efficient transport. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) The transportation route optimization system according to an embodiment of the present invention is a multimodal generation AI service for optimizing transportation routes for nursing care facilities. This transportation route optimization system takes in data such as the number of pedestrians, weather, and traffic conditions in real time and generates the most efficient transportation route based on each user's schedule and cancellation information. For example, the transportation route optimization system collects real-time location information from each transportation vehicle. Next, the transportation route optimization system collects environmental data such as the number of pedestrians, weather, and traffic conditions. This data is obtained from various sensors and external data sources. Next, the transportation route optimization system uses a generation AI to analyze the collected data. The generation AI considers each user's schedule and cancellation information and generates the most efficient transportation route. The generated transportation route is transmitted to each transportation vehicle in real time. This allows the transportation vehicles to operate according to the optimal route. Furthermore, if a change in the transportation route is necessary, the generation AI generates a new route in real time and transmits it to the transportation vehicle. This mechanism improves the efficiency of transportation and makes the operation of nursing care facilities smoother. For example, by avoiding times when there are many pedestrians, transportation time is shortened and user satisfaction is improved. Furthermore, safety is ensured through route adjustments based on weather and traffic conditions. In addition, the AI ​​generation system leverages AI development capabilities to improve the accuracy of the route generation algorithm and add and enhance features. This ensures continuous improvement in service quality, making it a more valuable service for users. Thus, the transportation route optimization system is a multimodal AI generation service for optimizing transportation routes for nursing care facilities. It incorporates real-time data such as pedestrian traffic, weather, and traffic conditions to generate the most efficient transportation routes based on each user's schedule and cancellation information. This improves the efficiency of transportation and ensures smoother operation of nursing care facilities.

[0029] The shuttle route optimization system according to this embodiment comprises a collection unit, an environmental collection unit, an analysis unit, a generation unit, and a transmission unit. The collection unit collects location information of shuttle vehicles. The collection unit acquires location information of shuttle vehicles in real time, for example, using GPS data. The collection unit can also acquire location information using beacon data. Furthermore, the collection unit can also collect data from sensors mounted on the shuttle vehicles. The environmental collection unit collects environmental data such as the number of pedestrians, weather, and traffic conditions. The environmental collection unit detects the number of pedestrians, for example, using a camera. Furthermore, the environmental collection unit can acquire weather data from a weather database. Furthermore, the environmental collection unit can also collect traffic condition data using traffic sensors. The analysis unit analyzes the information collected by the collection unit and the environmental collection unit. The analysis unit analyzes the collected information, for example, using a data analysis algorithm. Furthermore, the analysis unit can also consider each user's schedule and cancellation information. Furthermore, the analysis unit can use AI to improve the accuracy of the analysis. The generation unit generates a shuttle route based on the information analyzed by the analysis unit. The generation unit generates a shuttle route based on, for example, the shortest distance. The generation unit can also generate a shuttle route based on the optimal time. Furthermore, the generation unit can generate a shuttle route using a generation AI. The transmission unit transmits the shuttle route generated by the generation unit. The transmission unit transmits the shuttle route in, for example, real time. Furthermore, the transmission unit can perform batch transmission. Furthermore, the transmission unit can use AI to transmit the shuttle route to the shuttle vehicle. As a result, the shuttle route optimization system according to the embodiment improves the efficiency of shuttle services by collecting and analyzing the location information and environmental data of the shuttle vehicle to generate and transmit the optimal shuttle route.

[0030] The data collection unit collects location information of the transport vehicles. For example, the data collection unit obtains real-time location information of the transport vehicles using GPS data. Specifically, a GPS module installed in the transport vehicle periodically acquires location information and transmits this data to a central server. This allows the system to constantly know the current location of the transport vehicles. The data collection unit can also acquire location information using beacon data. A beacon is a mechanism that emits a signal within a specific area, and a device that receives that signal identifies the location. This makes it possible to obtain accurate location information even in urban areas or indoors where GPS signals are difficult to receive. Furthermore, the data collection unit can also collect data from sensors installed in the transport vehicles. For example, it can collect data from the vehicle's speed sensor, acceleration sensor, fuel sensor, etc., to understand the vehicle's operating status and fuel consumption. In this way, the data collection unit can collect information from diverse data sources and determine the location information of the transport vehicles with high accuracy.

[0031] The Environmental Data Collection Unit collects environmental data such as pedestrian traffic, weather, and traffic conditions. For example, the Environmental Data Collection Unit uses cameras to detect pedestrian traffic. Specifically, cameras installed on streets and at intersections capture images, and these images are analyzed to count the number of pedestrians. By using image analysis technology, the movement and density of pedestrians can be grasped in real time. The Environmental Data Collection Unit can also obtain weather data from a weather database. The weather database provides detailed weather information such as temperature, precipitation, and wind speed, and this data can be used to optimize pick-up and drop-off routes. Furthermore, the Environmental Data Collection Unit can collect traffic condition data using traffic sensors. Traffic sensors are installed on roads and measure the volume and speed of vehicles. This allows for real-time understanding of traffic congestion and road congestion levels. By collecting this environmental data, the Environmental Data Collection Unit can provide the information necessary to optimize pick-up and drop-off routes and improve the efficiency of pick-up and drop-off services.

[0032] The analysis unit analyzes the information collected by the data collection unit and the environmental data collection unit. For example, the analysis unit uses data analysis algorithms to analyze the collected information. Specifically, it integrates location information and environmental data to extract patterns and trends necessary for optimizing transportation routes. The analysis unit can also consider each user's schedule and cancellation information. User schedules are obtained, for example, from smartphone calendar apps or reservation systems and reflected in the transportation route plan. Furthermore, the analysis unit can use AI to improve the accuracy of the analysis. The AI ​​uses machine learning algorithms to analyze large amounts of data and propose optimal transportation routes. For example, it can learn from past transportation data to predict the optimal route for specific time periods or regions. This allows the analysis unit to efficiently analyze the collected data and provide the information necessary for optimizing transportation routes.

[0033] The generation unit generates transportation routes based on the information analyzed by the analysis unit. For example, the generation unit generates transportation routes based on the shortest distance. Specifically, it calculates the shortest distance based on the current location of the transportation vehicle and the destination, and proposes that route. The generation unit can also generate transportation routes based on the optimal time. For example, it considers traffic conditions and the number of pedestrians and proposes a route that allows for transportation during the most efficient time of day. Furthermore, the generation unit can also generate transportation routes using a generation AI. The generation AI uses advanced algorithms to generate the optimal route by considering multiple factors. For example, it integrates weather data, traffic data, and the user's schedule to propose the most efficient transportation route. As a result, the generation unit can generate the optimal transportation route based on the analyzed information, thereby improving the efficiency of transportation.

[0034] The transmitting unit transmits the shuttle routes generated by the generating unit. The transmitting unit transmits the shuttle routes in real time, for example. Specifically, it transmits the generated shuttle routes directly to the navigation system of the shuttle vehicle and displays them to the driver. The transmitting unit can also perform batch transmissions. For example, it can transmit multiple shuttle routes at once to efficiently convey information. Furthermore, the transmitting unit can use AI to transmit shuttle routes to the shuttle vehicles. The AI ​​considers the current location and operating status of the shuttle vehicles and transmits the shuttle routes at the optimal timing. This allows the transmitting unit to quickly and accurately transmit the generated shuttle routes to the shuttle vehicles, improving the efficiency of the shuttle service. In addition, the transmitting unit can quickly respond when changes or updates to the shuttle routes are necessary. For example, it can regenerate the shuttle routes in response to changes in conditions such as traffic congestion or sudden changes in weather and transmit them immediately to the shuttle vehicles. This allows the transmitting unit to always provide the optimal shuttle routes based on the latest information, improving the efficiency and safety of the shuttle service.

[0035] The environmental data collection unit can collect environmental data such as the number of pedestrians, weather, and traffic conditions. For example, the environmental data collection unit can detect the number of pedestrians using cameras. For example, cameras can count the number of pedestrians and collect that data. The environmental data collection unit can also obtain weather data from a weather database. For example, it can obtain current weather information from a weather database and collect that data. The environmental data collection unit can also collect traffic condition data using traffic sensors. For example, traffic sensors can detect traffic volume and collect that data. By collecting this environmental data, the unit can provide the information necessary for generating pick-up and drop-off routes. Some or all of the above processing in the environmental data collection unit may be performed using AI, for example, or without AI. For example, the environmental data collection unit can input pedestrian data acquired by cameras into a generation AI and have the generation AI perform an analysis of the number of pedestrians.

[0036] The data collection unit can collect real-time location information from each shuttle vehicle. For example, the data collection unit can acquire real-time location information of shuttle vehicles using GPS data. For example, a GPS device identifies the location of a shuttle vehicle and collects that data. The data collection unit can also acquire location information using beacon data. For example, a beacon detects the location of a shuttle vehicle and collects that data. The data collection unit can also collect data from sensors mounted on the shuttle vehicles. For example, a sensor detects the location of a shuttle vehicle and collects that data. By collecting real-time location information in this way, the data collection unit provides the information necessary for generating shuttle routes. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input location information acquired from a GPS device into a generating AI and have the generating AI perform analysis of the location information.

[0037] The analysis unit can analyze the collected information and take into account each user's schedule and cancellation information. For example, the analysis unit can analyze the collected information using a data analysis algorithm. For example, the data analysis algorithm analyzes the collected data and extracts the necessary information. The analysis unit can also take into account each user's schedule and cancellation information. For example, it can analyze the user's calendar information and reservation information and take into account the schedule and cancellation information. The analysis unit can also use AI to improve the accuracy of the analysis. For example, the AI ​​can analyze the collected information using the data analysis algorithm and provide highly accurate analysis results. This allows for the generation of more efficient transportation routes by taking into account each user's schedule and cancellation information. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can input the user's calendar information into a generating AI and have the generating AI perform the analysis of the schedule and cancellation information.

[0038] The generation unit can generate the most efficient pick-up and drop-off route based on the analyzed information. For example, the generation unit can generate a pick-up and drop-off route based on the shortest distance. For example, the generation unit calculates the shortest distance and generates a pick-up and drop-off route based on the result. The generation unit can also generate a pick-up and drop-off route based on the optimal time. For example, the generation unit calculates the optimal time and generates a pick-up and drop-off route based on the result. The generation unit can also generate a pick-up and drop-off route using a generation AI. For example, the generation AI generates the most efficient pick-up and drop-off route based on the analyzed information. This improves the efficiency of pick-up and drop-off by generating the most efficient route. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the analyzed information into a generation AI and have the generation AI perform the pick-up and drop-off route generation.

[0039] The transmission unit can transmit the generated shuttle routes to each shuttle vehicle in real time. For example, the transmission unit can transmit the shuttle routes in real time. For example, the transmission unit can transmit the generated shuttle routes to the shuttle vehicles in real time. The transmission unit can also perform batch transmission. For example, the transmission unit can transmit the shuttle routes at regular intervals. The transmission unit can also use AI to transmit the shuttle routes to the shuttle vehicles. For example, the AI ​​can optimize the transmission of the shuttle routes and transmit them efficiently. This allows the shuttle vehicles to operate according to the optimal route by transmitting the generated shuttle routes in real time. Some or all of the above processing in the transmission unit may be performed using AI, for example, or without AI. For example, the transmission unit can input the generated shuttle routes into a generating AI and have the generating AI perform the transmission of the shuttle routes.

[0040] The generation unit can improve the accuracy of the route generation algorithm and add or improve its functions. For example, the generation unit can improve the algorithm. For example, the generation unit can improve the route generation algorithm and increase its accuracy. The generation unit can also cleanse the data to improve the accuracy of the data. For example, the generation unit can cleanse the data and remove noise. The generation unit can also add new functions. For example, the generation unit can add a new route generation function to improve the quality of the service. As a result, the quality of the service is continuously improved by improving the accuracy of the route generation algorithm and adding or improving its functions. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can have a generation AI perform improvements to the route generation algorithm.

[0041] The data collection unit can analyze the past operation history of the shuttle vehicle and select the optimal method for acquiring location information. For example, the data collection unit can determine from the past operation history whether it is necessary to acquire location information frequently during a particular time period. The data collection unit can also optimize the location information acquisition interval for a particular route based on the past operation history. The data collection unit can also optimize the amount of data transmitted by analyzing the past operation history and adjusting the frequency of location information acquisition. This allows the optimal method for acquiring location information to be selected by analyzing the past operation history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past operation history data into a generating AI and have the generating AI select the optimal method for acquiring location information.

[0042] The data collection unit can filter data when acquiring location information, taking into account the fuel consumption status of the transport vehicle. For example, if fuel consumption is high, the data collection unit can reduce the frequency of acquiring location information to conserve fuel. The data collection unit can also acquire location information frequently to collect detailed operational data when fuel consumption is low. The data collection unit can also adjust the timing of acquiring location information according to the fuel consumption status to perform efficient data collection. This allows for efficient data collection by considering the fuel consumption status. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input fuel consumption data into a generating AI and have the generating AI perform data filtering.

[0043] The data collection unit can acquire location information while considering the driving style of the vehicle driver. For example, if the driver frequently uses sudden braking, the data collection unit can acquire location information frequently to understand the driving situation in detail. For example, the data collection unit considers the driver's driving style and acquires location information frequently when sudden braking is frequent. The data collection unit can also save data by widening the interval between location information acquisitions if the driver drives smoothly. For example, the data collection unit considers the driver's driving style and widens the interval between location information acquisitions when the driver drives smoothly. The data collection unit can also adjust the timing of location information acquisition according to the driver's driving style to perform efficient data collection. For example, the data collection unit adjusts the timing of location information acquisition according to the driver's driving style. This makes efficient data collection possible by considering the driver's driving style. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the driver's driving style data into a generating AI and have the generating AI perform the data acquisition.

[0044] The data collection unit can acquire location information while considering the maintenance status of the transport vehicle. For example, if a vehicle requires maintenance, the data collection unit can acquire location information frequently to understand its operational status in detail. For example, the data collection unit acquires location information frequently if the vehicle requires maintenance. The data collection unit can also save data by widening the interval between location information acquisitions if the vehicle has completed maintenance. For example, the data collection unit widens the interval between location information acquisitions if the vehicle has completed maintenance. The data collection unit can also adjust the timing of location information acquisition according to the maintenance status to perform efficient data collection. For example, the data collection unit adjusts the timing of location information acquisition according to the maintenance status. This makes efficient data collection possible by considering the maintenance status. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input maintenance status data into a generating AI and have the generating AI perform data acquisition.

[0045] The environmental data collection unit can optimize its data collection method by referring to past weather data when collecting environmental data. For example, the environmental data collection unit can optimize the data collection method under specific weather conditions based on past weather data. For example, the environmental data collection unit can refer to past weather data and optimize the data collection method under specific weather conditions. The environmental data collection unit can also refer to past weather data and adjust the collection timing to perform efficient data collection. For example, the environmental data collection unit can refer to past weather data and adjust the collection timing. The environmental data collection unit can also analyze past weather data and optimize the data collection frequency under specific weather conditions. For example, the environmental data collection unit can analyze past weather data and optimize the data collection frequency under specific weather conditions. This makes efficient data collection possible by referring to past weather data. Some or all of the above processing in the environmental data collection unit may be performed using AI, for example, or without AI. For example, the environmental data collection unit can input past weather data into a generating AI and have the generating AI perform the optimization of the collection method.

[0046] The environmental data collection unit can filter data when collecting environmental data, taking into account the impact of specific events or activities. For example, if a specific event is held, the environmental data collection unit will collect environmental data while considering its impact. The environmental data collection unit can also adjust the timing of data collection, taking into account the impact of events, to perform efficient data collection. The environmental data collection unit can also analyze the impact of events and activities and optimize the data collection method. This makes efficient data collection possible by taking into account the impact of specific events and activities. Some or all of the above processing in the environmental data collection unit may be performed using AI, for example, or without AI. For example, the environmental data collection unit can input event and activity data into a generating AI and have the generating AI perform data filtering.

[0047] The environmental data collection unit can collect environmental data while considering local characteristics and culture. For example, the environmental data collection unit can prioritize the collection of specific environmental data according to local characteristics. The environmental data collection unit can also optimize the data collection method while considering local culture. The environmental data collection unit can also determine the type of environmental data to collect based on local characteristics and culture. This enables efficient data collection by considering local characteristics and culture. Some or all of the above processing in the environmental data collection unit may be performed using AI, for example, or without AI. For example, the environmental data collection unit can input local characteristics and cultural data into a generating AI and have the generating AI perform the data collection.

[0048] The environmental data collection unit can collect environmental data while considering the occurrence of traffic accidents. For example, the environmental data collection unit can collect detailed environmental data in areas where traffic accidents frequently occur. The environmental data collection unit can also adjust the timing of data collection while considering the occurrence of traffic accidents to ensure efficient data collection. The environmental data collection unit can also analyze the occurrence of traffic accidents and optimize the data collection method. This enables efficient data collection by considering the occurrence of traffic accidents. Some or all of the above processing in the environmental data collection unit may be performed using AI, for example, or without AI. For example, the environmental data collection unit can input traffic accident data into a generating AI and have the generating AI perform data collection.

[0049] The analysis unit can optimize the analysis algorithm by referring to past analysis results during the analysis. For example, the analysis unit optimizes the analysis algorithm based on past analysis results. For example, the analysis unit optimizes the analysis algorithm by referring to past analysis results. The analysis unit can also adjust the analysis method by referring to past analysis results to perform efficient analysis. For example, the analysis unit adjusts the analysis method by referring to past analysis results. The analysis unit can also improve the accuracy of the analysis algorithm by analyzing past analysis results. For example, the analysis unit improves the accuracy of the analysis algorithm by analyzing past analysis results. In this way, the analysis algorithm can be optimized by referring to past analysis results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past analysis result data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0050] The analysis unit can perform analysis while considering each user's health status and special needs. For example, the analysis unit can select the optimal analysis method considering the user's health status. The analysis unit can also adjust the analysis method according to the user's special needs. The analysis unit can also provide analysis results based on the user's health status and special needs. This makes it possible to perform more appropriate analysis by considering each user's health status and special needs. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user health status and special needs data into a generating AI and have the generating AI perform the analysis.

[0051] The analysis unit can perform analysis while taking into account the experience and skills of the driver of the transport vehicle. For example, the analysis unit can adjust the analysis method according to the driver's experience. The analysis unit can also select the optimal analysis method considering the driver's skills. The analysis unit can also provide analysis results based on the driver's experience and skills. This makes it possible to perform more appropriate analysis by taking the driver's experience and skills into account. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input driver experience and skill data into a generating AI and have the generating AI perform the analysis.

[0052] The analysis unit can perform analysis while considering the operating costs of the transport vehicles. For example, the analysis unit can select an analysis method that minimizes operating costs. The analysis unit can also provide an optimal analysis method that takes operating costs into consideration. The analysis unit can also provide analysis results based on operating costs. This makes it possible to perform more efficient analysis by considering operating costs. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input operating cost data into a generating AI and have the generating AI perform the analysis.

[0053] The generation unit can optimize its algorithm by referring to past route generation results when generating shuttle routes. For example, the generation unit can select the optimal route generation algorithm based on past route generation results. The generation unit can also improve the accuracy of its algorithm by referring to past route generation results. The generation unit can also analyze past route generation results and adjust the algorithm parameters. In this way, the algorithm can be optimized by referring to past route generation results. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input past route generation result data into a generation AI and have the generation AI perform algorithm optimization.

[0054] The generation unit can generate routes while considering each user's special requests and constraints. For example, if a user wants to pass through a specific location, the generation unit can generate a route that takes that request into account. For example, the generation unit can consider the user's special request and generate a route that passes through that specific location. The generation unit can also generate routes while considering the constraint of if a user wants to arrive at a specific time. For example, the generation unit can consider the user's special constraint and generate a route that arrives at a specific time. The generation unit can also generate routes while considering the user's special request and generate a route that uses a specific mode of transportation. For example, the generation unit can consider the user's special request and generate a route that uses a specific mode of transportation. This makes it possible to generate more appropriate routes by considering each user's special requests and constraints. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's special request and constraint data into the generation AI and have the generation AI perform route generation.

[0055] The generation unit can generate routes while considering the fuel efficiency of the transport vehicles. For example, the generation unit can prioritize generating routes with high fuel efficiency. The generation unit can also generate routes that minimize fuel consumption. The generation unit can also generate the optimal route while considering fuel efficiency. This makes it possible to generate more efficient routes by considering fuel efficiency. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input fuel efficiency data into a generation AI and have the generation AI perform route generation.

[0056] The generation unit can generate routes while considering the driver's rest time when generating shuttle routes. For example, the generation unit can generate the optimal route by considering the driver's rest time. The generation unit can also generate routes that include driver rest points. For example, the generation unit can generate routes that include driver rest points. The generation unit can also adjust the route generation method based on the driver's rest time. For example, the generation unit can adjust the route generation method based on the driver's rest time. This makes it possible to generate more appropriate routes by considering the driver's rest time. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input driver rest time data into a generation AI and have the generation AI perform route generation.

[0057] The transmitting unit can optimize the transmission method when transmitting the transportation route, taking into account the communication status of the transport vehicle. For example, if the communication status is good, the transmitting unit can transmit the transportation route in real time. The transmitting unit can also optimize the amount of data transmitted by adjusting the transmission frequency if the communication status is unstable. The transmitting unit can also select the optimal transmission method, taking the communication status into consideration. This makes it possible to provide information more efficiently by taking the communication status into consideration. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or without using AI. For example, the transmitting unit can input communication status data into a generating AI and have the generating AI perform the optimization of the transmission method.

[0058] The transmitting unit can transmit transportation routes while considering the schedule of the transportation vehicle driver. For example, the transmitting unit can transmit the optimal transportation route according to the driver's schedule. The transmitting unit can also adjust the transmission timing while considering the driver's schedule. The transmitting unit can also optimize the method of transmitting transportation routes based on the driver's schedule. This makes it possible to provide information more efficiently by considering the driver's schedule. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or without AI. For example, the transmitting unit can input driver schedule data into a generating AI and have the generating AI perform the transmission.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The shuttle route optimization system can further generate routes while considering the fuel consumption of the shuttle vehicles. For example, if fuel consumption is high, the generation unit can prioritize generating fuel-efficient routes. If fuel consumption is low, it can also prioritize generating the shortest route. Furthermore, the route generation method can be adjusted according to the fuel consumption situation. This makes it possible to generate more efficient routes by considering fuel consumption. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input fuel consumption data into a generation AI and have the generation AI perform route generation.

[0061] The shuttle route optimization system can further generate routes while considering the rest times of the shuttle vehicle drivers. The generation unit can, for example, generate the optimal route by considering the driver's rest times. It can also generate routes that include driver rest points. Furthermore, it can adjust the route generation method based on the driver's rest times. This makes it possible to generate more appropriate routes by considering the driver's rest times. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input driver rest time data into a generation AI and have the generation AI perform route generation.

[0062] The shuttle route optimization system can further generate routes while considering the schedules of the shuttle vehicle drivers. The generation unit can, for example, generate the optimal route according to the driver's schedule. It can also adjust the transmission timing while considering the driver's schedule. Furthermore, it can optimize the route generation method based on the driver's schedule. This makes it possible to generate routes more efficiently by considering the driver's schedule. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input driver schedule data into a generation AI and have the generation AI perform route generation.

[0063] The shuttle route optimization system can further generate routes while considering the maintenance status of the shuttle vehicles. For example, the generation unit can prioritize generating routes closer to maintenance facilities for vehicles requiring maintenance. It can also prioritize generating the shortest route for vehicles whose maintenance has been completed. Furthermore, the route generation method can be adjusted according to the maintenance status. This allows for more efficient route generation by considering the maintenance status. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input maintenance status data into the generation AI and have the generation AI perform route generation.

[0064] The shuttle route optimization system can further generate routes while considering the driving style of the shuttle vehicle driver. For example, if the driver frequently uses sudden braking, the generation unit can prioritize generating a safe route. Conversely, if the driver drives smoothly, it can prioritize generating the shortest route. Furthermore, the route generation method can be adjusted according to the driver's driving style. This makes it possible to generate more efficient routes by considering the driver's driving style. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the driver's driving style data into the generation AI and have the generation AI perform route generation.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The data collection unit collects location information of the transport vehicle. The data collection unit can, for example, obtain the location information of the transport vehicle in real time using GPS data. The data collection unit can also obtain location information using beacon data. Furthermore, the data collection unit can collect data from sensors installed in the transport vehicle. Step 2: The environmental data collection unit collects environmental data such as pedestrian traffic, weather, and traffic conditions. For example, the environmental data collection unit can use cameras to detect pedestrian traffic. It can also obtain weather data from a weather database. Furthermore, it can collect traffic condition data using traffic sensors. Step 3: The analysis unit analyzes the information collected by the data collection unit and the environment data collection unit. The analysis unit analyzes the collected information using, for example, a data analysis algorithm. The analysis unit can also take into account each user's schedule and cancellation information. Furthermore, the analysis unit can use AI to improve the accuracy of the analysis. Step 4: The generation unit generates a pick-up / drop-off route based on the information analyzed by the analysis unit. For example, the generation unit generates a pick-up / drop-off route based on the shortest distance. The generation unit can also generate a pick-up / drop-off route based on the optimal time. Furthermore, the generation unit can also generate a pick-up / drop-off route using a generation AI. Step 5: The transmitting unit transmits the transportation routes generated by the generating unit. The transmitting unit can transmit transportation routes in real time, for example. The transmitting unit can also perform batch transmissions. Furthermore, the transmitting unit can use AI to transmit transportation routes to the transport vehicles.

[0067] (Example of form 2) The transportation route optimization system according to an embodiment of the present invention is a multimodal generation AI service for optimizing transportation routes for nursing care facilities. This transportation route optimization system takes in data such as the number of pedestrians, weather, and traffic conditions in real time and generates the most efficient transportation route based on each user's schedule and cancellation information. For example, the transportation route optimization system collects real-time location information from each transportation vehicle. Next, the transportation route optimization system collects environmental data such as the number of pedestrians, weather, and traffic conditions. This data is obtained from various sensors and external data sources. Next, the transportation route optimization system uses a generation AI to analyze the collected data. The generation AI considers each user's schedule and cancellation information and generates the most efficient transportation route. The generated transportation route is transmitted to each transportation vehicle in real time. This allows the transportation vehicles to operate according to the optimal route. Furthermore, if a change in the transportation route is necessary, the generation AI generates a new route in real time and transmits it to the transportation vehicle. This mechanism improves the efficiency of transportation and makes the operation of nursing care facilities smoother. For example, by avoiding times when there are many pedestrians, transportation time is shortened and user satisfaction is improved. Furthermore, safety is ensured through route adjustments based on weather and traffic conditions. In addition, the AI ​​generation system leverages AI development capabilities to improve the accuracy of the route generation algorithm and add and enhance features. This ensures continuous improvement in service quality, making it a more valuable service for users. Thus, the transportation route optimization system is a multimodal AI generation service for optimizing transportation routes for nursing care facilities. It incorporates real-time data such as pedestrian traffic, weather, and traffic conditions to generate the most efficient transportation routes based on each user's schedule and cancellation information. This improves the efficiency of transportation and ensures smoother operation of nursing care facilities.

[0068] The shuttle route optimization system according to this embodiment comprises a collection unit, an environmental collection unit, an analysis unit, a generation unit, and a transmission unit. The collection unit collects location information of shuttle vehicles. The collection unit acquires location information of shuttle vehicles in real time, for example, using GPS data. The collection unit can also acquire location information using beacon data. Furthermore, the collection unit can also collect data from sensors mounted on the shuttle vehicles. The environmental collection unit collects environmental data such as the number of pedestrians, weather, and traffic conditions. The environmental collection unit detects the number of pedestrians, for example, using a camera. Furthermore, the environmental collection unit can acquire weather data from a weather database. Furthermore, the environmental collection unit can also collect traffic condition data using traffic sensors. The analysis unit analyzes the information collected by the collection unit and the environmental collection unit. The analysis unit analyzes the collected information, for example, using a data analysis algorithm. Furthermore, the analysis unit can also consider each user's schedule and cancellation information. Furthermore, the analysis unit can use AI to improve the accuracy of the analysis. The generation unit generates a shuttle route based on the information analyzed by the analysis unit. The generation unit generates a shuttle route based on, for example, the shortest distance. The generation unit can also generate a shuttle route based on the optimal time. Furthermore, the generation unit can generate a shuttle route using a generation AI. The transmission unit transmits the shuttle route generated by the generation unit. The transmission unit transmits the shuttle route in, for example, real time. Furthermore, the transmission unit can perform batch transmission. Furthermore, the transmission unit can use AI to transmit the shuttle route to the shuttle vehicle. As a result, the shuttle route optimization system according to the embodiment improves the efficiency of shuttle services by collecting and analyzing the location information and environmental data of the shuttle vehicle to generate and transmit the optimal shuttle route.

[0069] The data collection unit collects location information of the transport vehicles. For example, the data collection unit obtains real-time location information of the transport vehicles using GPS data. Specifically, a GPS module installed in the transport vehicle periodically acquires location information and transmits this data to a central server. This allows the system to constantly know the current location of the transport vehicles. The data collection unit can also acquire location information using beacon data. A beacon is a mechanism that emits a signal within a specific area, and a device that receives that signal identifies the location. This makes it possible to obtain accurate location information even in urban areas or indoors where GPS signals are difficult to receive. Furthermore, the data collection unit can also collect data from sensors installed in the transport vehicles. For example, it can collect data from the vehicle's speed sensor, acceleration sensor, fuel sensor, etc., to understand the vehicle's operating status and fuel consumption. In this way, the data collection unit can collect information from diverse data sources and determine the location information of the transport vehicles with high accuracy.

[0070] The Environmental Data Collection Unit collects environmental data such as pedestrian traffic, weather, and traffic conditions. For example, the Environmental Data Collection Unit uses cameras to detect pedestrian traffic. Specifically, cameras installed on streets and at intersections capture images, and these images are analyzed to count the number of pedestrians. By using image analysis technology, the movement and density of pedestrians can be grasped in real time. The Environmental Data Collection Unit can also obtain weather data from a weather database. The weather database provides detailed weather information such as temperature, precipitation, and wind speed, and this data can be used to optimize pick-up and drop-off routes. Furthermore, the Environmental Data Collection Unit can collect traffic condition data using traffic sensors. Traffic sensors are installed on roads and measure the volume and speed of vehicles. This allows for real-time understanding of traffic congestion and road congestion levels. By collecting this environmental data, the Environmental Data Collection Unit can provide the information necessary to optimize pick-up and drop-off routes and improve the efficiency of pick-up and drop-off services.

[0071] The analysis unit analyzes the information collected by the data collection unit and the environmental data collection unit. For example, the analysis unit uses data analysis algorithms to analyze the collected information. Specifically, it integrates location information and environmental data to extract patterns and trends necessary for optimizing transportation routes. The analysis unit can also consider each user's schedule and cancellation information. User schedules are obtained, for example, from smartphone calendar apps or reservation systems and reflected in the transportation route plan. Furthermore, the analysis unit can use AI to improve the accuracy of the analysis. The AI ​​uses machine learning algorithms to analyze large amounts of data and propose optimal transportation routes. For example, it can learn from past transportation data to predict the optimal route for specific time periods or regions. This allows the analysis unit to efficiently analyze the collected data and provide the information necessary for optimizing transportation routes.

[0072] The generation unit generates transportation routes based on the information analyzed by the analysis unit. For example, the generation unit generates transportation routes based on the shortest distance. Specifically, it calculates the shortest distance based on the current location of the transportation vehicle and the destination, and proposes that route. The generation unit can also generate transportation routes based on the optimal time. For example, it considers traffic conditions and the number of pedestrians and proposes a route that allows for transportation during the most efficient time of day. Furthermore, the generation unit can also generate transportation routes using a generation AI. The generation AI uses advanced algorithms to generate the optimal route by considering multiple factors. For example, it integrates weather data, traffic data, and the user's schedule to propose the most efficient transportation route. As a result, the generation unit can generate the optimal transportation route based on the analyzed information, thereby improving the efficiency of transportation.

[0073] The transmitting unit transmits the shuttle routes generated by the generating unit. The transmitting unit transmits the shuttle routes in real time, for example. Specifically, it transmits the generated shuttle routes directly to the navigation system of the shuttle vehicle and displays them to the driver. The transmitting unit can also perform batch transmissions. For example, it can transmit multiple shuttle routes at once to efficiently convey information. Furthermore, the transmitting unit can use AI to transmit shuttle routes to the shuttle vehicles. The AI ​​considers the current location and operating status of the shuttle vehicles and transmits the shuttle routes at the optimal timing. This allows the transmitting unit to quickly and accurately transmit the generated shuttle routes to the shuttle vehicles, improving the efficiency of the shuttle service. In addition, the transmitting unit can quickly respond when changes or updates to the shuttle routes are necessary. For example, it can regenerate the shuttle routes in response to changes in conditions such as traffic congestion or sudden changes in weather and transmit them immediately to the shuttle vehicles. This allows the transmitting unit to always provide the optimal shuttle routes based on the latest information, improving the efficiency and safety of the shuttle service.

[0074] The environmental data collection unit can collect environmental data such as the number of pedestrians, weather, and traffic conditions. For example, the environmental data collection unit can detect the number of pedestrians using cameras. For example, cameras can count the number of pedestrians and collect that data. The environmental data collection unit can also obtain weather data from a weather database. For example, it can obtain current weather information from a weather database and collect that data. The environmental data collection unit can also collect traffic condition data using traffic sensors. For example, traffic sensors can detect traffic volume and collect that data. By collecting this environmental data, the unit can provide the information necessary for generating pick-up and drop-off routes. Some or all of the above processing in the environmental data collection unit may be performed using AI, for example, or without AI. For example, the environmental data collection unit can input pedestrian data acquired by cameras into a generation AI and have the generation AI perform an analysis of the number of pedestrians.

[0075] The data collection unit can collect real-time location information from each shuttle vehicle. For example, the data collection unit can acquire real-time location information of shuttle vehicles using GPS data. For example, a GPS device identifies the location of a shuttle vehicle and collects that data. The data collection unit can also acquire location information using beacon data. For example, a beacon detects the location of a shuttle vehicle and collects that data. The data collection unit can also collect data from sensors mounted on the shuttle vehicles. For example, a sensor detects the location of a shuttle vehicle and collects that data. By collecting real-time location information in this way, the data collection unit provides the information necessary for generating shuttle routes. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input location information acquired from a GPS device into a generating AI and have the generating AI perform analysis of the location information.

[0076] The analysis unit can analyze the collected information and take into account each user's schedule and cancellation information. For example, the analysis unit can analyze the collected information using a data analysis algorithm. For example, the data analysis algorithm analyzes the collected data and extracts the necessary information. The analysis unit can also take into account each user's schedule and cancellation information. For example, it can analyze the user's calendar information and reservation information and take into account the schedule and cancellation information. The analysis unit can also use AI to improve the accuracy of the analysis. For example, the AI ​​can analyze the collected information using the data analysis algorithm and provide highly accurate analysis results. This allows for the generation of more efficient transportation routes by taking into account each user's schedule and cancellation information. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can input the user's calendar information into a generating AI and have the generating AI perform the analysis of the schedule and cancellation information.

[0077] The generation unit can generate the most efficient pick-up and drop-off route based on the analyzed information. For example, the generation unit can generate a pick-up and drop-off route based on the shortest distance. For example, the generation unit calculates the shortest distance and generates a pick-up and drop-off route based on the result. The generation unit can also generate a pick-up and drop-off route based on the optimal time. For example, the generation unit calculates the optimal time and generates a pick-up and drop-off route based on the result. The generation unit can also generate a pick-up and drop-off route using a generation AI. For example, the generation AI generates the most efficient pick-up and drop-off route based on the analyzed information. This improves the efficiency of pick-up and drop-off by generating the most efficient route. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the analyzed information into a generation AI and have the generation AI perform the pick-up and drop-off route generation.

[0078] The transmission unit can transmit the generated shuttle routes to each shuttle vehicle in real time. For example, the transmission unit can transmit the shuttle routes in real time. For example, the transmission unit can transmit the generated shuttle routes to the shuttle vehicles in real time. The transmission unit can also perform batch transmission. For example, the transmission unit can transmit the shuttle routes at regular intervals. The transmission unit can also use AI to transmit the shuttle routes to the shuttle vehicles. For example, the AI ​​can optimize the transmission of the shuttle routes and transmit them efficiently. This allows the shuttle vehicles to operate according to the optimal route by transmitting the generated shuttle routes in real time. Some or all of the above processing in the transmission unit may be performed using AI, for example, or without AI. For example, the transmission unit can input the generated shuttle routes into a generating AI and have the generating AI perform the transmission of the shuttle routes.

[0079] The generation unit can improve the accuracy of the route generation algorithm and add or improve its functions. For example, the generation unit can improve the algorithm. For example, the generation unit can improve the route generation algorithm and increase its accuracy. The generation unit can also cleanse the data to improve the accuracy of the data. For example, the generation unit can cleanse the data and remove noise. The generation unit can also add new functions. For example, the generation unit can add a new route generation function to improve the quality of the service. As a result, the quality of the service is continuously improved by improving the accuracy of the route generation algorithm and adding or improving its functions. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can have a generation AI perform improvements to the route generation algorithm.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of acquiring the vehicle's location information based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit will acquire location information frequently and update it in real time. For example, if the data collection unit estimates the user's emotions and they are feeling anxious, it will acquire location information frequently. The data collection unit can also save data by widening the interval between location information acquisitions if the user is relaxed. For example, if the data collection unit estimates the user's emotions and they are relaxed, it will widen the interval between location information acquisitions. The data collection unit can also acquire location information at shorter intervals if the user is in a hurry, enabling a quicker response. For example, if the data collection unit estimates the user's emotions and they are in a hurry, it will acquire location information at shorter intervals. This allows for more appropriate data collection by adjusting the timing of location information acquisition based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0081] The data collection unit can analyze the past operation history of the shuttle vehicle and select the optimal method for acquiring location information. For example, the data collection unit can determine from the past operation history whether it is necessary to acquire location information frequently during a particular time period. The data collection unit can also optimize the location information acquisition interval for a particular route based on the past operation history. The data collection unit can also optimize the amount of data transmitted by analyzing the past operation history and adjusting the frequency of location information acquisition. This allows the optimal method for acquiring location information to be selected by analyzing the past operation history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past operation history data into a generating AI and have the generating AI select the optimal method for acquiring location information.

[0082] The data collection unit can filter data when acquiring location information, taking into account the fuel consumption status of the transport vehicle. For example, if fuel consumption is high, the data collection unit can reduce the frequency of acquiring location information to conserve fuel. The data collection unit can also acquire location information frequently to collect detailed operational data when fuel consumption is low. The data collection unit can also adjust the timing of acquiring location information according to the fuel consumption status to perform efficient data collection. This allows for efficient data collection by considering the fuel consumption status. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input fuel consumption data into a generating AI and have the generating AI perform data filtering.

[0083] The data collection unit can estimate the user's emotions and determine the priority of location information to acquire based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit will prioritize acquiring important location information. For example, if the data collection unit estimates the user's emotions and they are feeling anxious, it will prioritize acquiring important location information. The data collection unit can also acquire normal location information if the user is relaxed. For example, if the data collection unit estimates the user's emotions and they are relaxed, it will acquire normal location information. The data collection unit can also prioritize acquiring the most important location information if the user is in a hurry, enabling a quick response. For example, if the data collection unit estimates the user's emotions and they are in a hurry, it will prioritize acquiring the most important location information. This allows for more appropriate data collection by prioritizing location information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0084] The data collection unit can acquire location information while considering the driving style of the vehicle driver. For example, if the driver frequently uses sudden braking, the data collection unit can acquire location information frequently to understand the driving situation in detail. For example, the data collection unit considers the driver's driving style and acquires location information frequently when sudden braking is frequent. The data collection unit can also save data by widening the interval between location information acquisitions if the driver drives smoothly. For example, the data collection unit considers the driver's driving style and widens the interval between location information acquisitions when the driver drives smoothly. The data collection unit can also adjust the timing of location information acquisition according to the driver's driving style to perform efficient data collection. For example, the data collection unit adjusts the timing of location information acquisition according to the driver's driving style. This makes efficient data collection possible by considering the driver's driving style. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the driver's driving style data into a generating AI and have the generating AI perform the data acquisition.

[0085] The data collection unit can acquire location information while considering the maintenance status of the transport vehicle. For example, if a vehicle requires maintenance, the data collection unit can acquire location information frequently to understand its operational status in detail. For example, the data collection unit acquires location information frequently if the vehicle requires maintenance. The data collection unit can also save data by widening the interval between location information acquisitions if the vehicle has completed maintenance. For example, the data collection unit widens the interval between location information acquisitions if the vehicle has completed maintenance. The data collection unit can also adjust the timing of location information acquisition according to the maintenance status to perform efficient data collection. For example, the data collection unit adjusts the timing of location information acquisition according to the maintenance status. This makes efficient data collection possible by considering the maintenance status. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input maintenance status data into a generating AI and have the generating AI perform data acquisition.

[0086] The environmental data collection unit can estimate the user's emotions and adjust the timing of environmental data collection based on the estimated emotions. For example, if the user is feeling anxious, the environmental data collection unit will collect environmental data frequently and update it in real time. For example, if the environmental data collection unit estimates the user's emotions and they are feeling anxious, it will collect environmental data frequently. The environmental data collection unit can also widen the interval between environmental data collections to save data usage if the user is relaxed. For example, if the environmental data collection unit estimates the user's emotions and they are relaxed, it will widen the interval between environmental data collections. The environmental data collection unit can also collect environmental data at shorter intervals if the user is in a hurry, enabling a quicker response. For example, if the environmental data collection unit estimates the user's emotions and they are in a hurry, it will collect environmental data at shorter intervals. This allows for more appropriate data collection by adjusting the timing of environmental data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the environmental data collection unit may be performed using AI, for example, or without AI. For example, the environmental data collection unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0087] The environmental data collection unit can optimize its data collection method by referring to past weather data when collecting environmental data. For example, the environmental data collection unit can optimize the data collection method under specific weather conditions based on past weather data. For example, the environmental data collection unit can refer to past weather data and optimize the data collection method under specific weather conditions. The environmental data collection unit can also refer to past weather data and adjust the collection timing to perform efficient data collection. For example, the environmental data collection unit can refer to past weather data and adjust the collection timing. The environmental data collection unit can also analyze past weather data and optimize the data collection frequency under specific weather conditions. For example, the environmental data collection unit can analyze past weather data and optimize the data collection frequency under specific weather conditions. This makes efficient data collection possible by referring to past weather data. Some or all of the above processing in the environmental data collection unit may be performed using AI, for example, or without AI. For example, the environmental data collection unit can input past weather data into a generating AI and have the generating AI perform the optimization of the collection method.

[0088] The environmental data collection unit can filter data when collecting environmental data, taking into account the impact of specific events or activities. For example, if a specific event is held, the environmental data collection unit will collect environmental data while considering its impact. The environmental data collection unit can also adjust the timing of data collection, taking into account the impact of events, to perform efficient data collection. The environmental data collection unit can also analyze the impact of events and activities and optimize the data collection method. This makes efficient data collection possible by taking into account the impact of specific events and activities. Some or all of the above processing in the environmental data collection unit may be performed using AI, for example, or without AI. For example, the environmental data collection unit can input event and activity data into a generating AI and have the generating AI perform data filtering.

[0089] The environmental data collection unit can estimate the user's emotions and determine the priority of environmental data to collect based on the estimated emotions. For example, if the user is feeling anxious, the environmental data collection unit will prioritize collecting important environmental data. For example, if the environmental data collection unit estimates the user's emotions and they are feeling anxious, it will prioritize collecting important environmental data. The environmental data collection unit can also perform normal environmental data collection when the user is relaxed. For example, if the environmental data collection unit estimates the user's emotions and they are relaxed, it will perform normal environmental data collection. Furthermore, if the user is in a hurry, the environmental data collection unit can prioritize collecting the most important environmental data to enable a quick response. For example, if the environmental data collection unit estimates the user's emotions and they are in a hurry, it will prioritize collecting the most important environmental data. This allows for more appropriate data collection by prioritizing environmental data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the environmental data collection unit may be performed using AI, for example, or without AI. For example, the environmental data collection unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0090] The environmental data collection unit can collect environmental data while considering local characteristics and culture. For example, the environmental data collection unit can prioritize the collection of specific environmental data according to local characteristics. The environmental data collection unit can also optimize the data collection method while considering local culture. The environmental data collection unit can also determine the type of environmental data to collect based on local characteristics and culture. This enables efficient data collection by considering local characteristics and culture. Some or all of the above processing in the environmental data collection unit may be performed using AI, for example, or without AI. For example, the environmental data collection unit can input local characteristics and cultural data into a generating AI and have the generating AI perform the data collection.

[0091] The environmental data collection unit can collect environmental data while considering the occurrence of traffic accidents. For example, the environmental data collection unit can collect detailed environmental data in areas where traffic accidents frequently occur. The environmental data collection unit can also adjust the timing of data collection while considering the occurrence of traffic accidents to ensure efficient data collection. The environmental data collection unit can also analyze the occurrence of traffic accidents and optimize the data collection method. This enables efficient data collection by considering the occurrence of traffic accidents. Some or all of the above processing in the environmental data collection unit may be performed using AI, for example, or without AI. For example, the environmental data collection unit can input traffic accident data into a generating AI and have the generating AI perform data collection.

[0092] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can perform a detailed analysis to provide reassurance. For example, if the analysis unit estimates the user's emotions and determines that the user is feeling anxious, it can perform a detailed analysis. The analysis unit can also use a normal analysis method if the user is relaxed. For example, if the analysis unit estimates the user's emotions and determines that the user is relaxed, it can use a normal analysis method. The analysis unit can also perform a rapid analysis if the user is in a hurry, enabling a quick response. For example, if the analysis unit estimates the user's emotions and determines that the user is in a hurry, it can perform a rapid analysis. This allows for more appropriate analysis by adjusting the analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0093] The analysis unit can optimize the analysis algorithm by referring to past analysis results during the analysis. For example, the analysis unit optimizes the analysis algorithm based on past analysis results. For example, the analysis unit optimizes the analysis algorithm by referring to past analysis results. The analysis unit can also adjust the analysis method by referring to past analysis results to perform efficient analysis. For example, the analysis unit adjusts the analysis method by referring to past analysis results. The analysis unit can also improve the accuracy of the analysis algorithm by analyzing past analysis results. For example, the analysis unit improves the accuracy of the analysis algorithm by analyzing past analysis results. In this way, the analysis algorithm can be optimized by referring to past analysis results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past analysis result data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0094] The analysis unit can perform analysis while considering each user's health status and special needs. For example, the analysis unit can select the optimal analysis method considering the user's health status. The analysis unit can also adjust the analysis method according to the user's special needs. The analysis unit can also provide analysis results based on the user's health status and special needs. This makes it possible to perform more appropriate analysis by considering each user's health status and special needs. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user health status and special needs data into a generating AI and have the generating AI perform the analysis.

[0095] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can provide a simple and easy-to-understand display method. For example, if the analysis unit estimates the user's emotions and they are feeling anxious, it can provide a simple and easy-to-understand display method. The analysis unit can also provide a display method that includes detailed information if the user is relaxed. For example, if the analysis unit estimates the user's emotions and they are relaxed, it can provide a display method that includes detailed information. The analysis unit can also provide a concise display method if the user is in a hurry. For example, if the analysis unit estimates the user's emotions and they are in a hurry, it can provide a concise display method. By adjusting the display method of the analysis results based on the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0096] The analysis unit can perform analysis while taking into account the experience and skills of the driver of the transport vehicle. For example, the analysis unit can adjust the analysis method according to the driver's experience. The analysis unit can also select the optimal analysis method considering the driver's skills. The analysis unit can also provide analysis results based on the driver's experience and skills. This makes it possible to perform more appropriate analysis by taking the driver's experience and skills into account. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input driver experience and skill data into a generating AI and have the generating AI perform the analysis.

[0097] The analysis unit can perform analysis while considering the operating costs of the transport vehicles. For example, the analysis unit can select an analysis method that minimizes operating costs. The analysis unit can also provide an optimal analysis method that takes operating costs into consideration. The analysis unit can also provide analysis results based on operating costs. This makes it possible to perform more efficient analysis by considering operating costs. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input operating cost data into a generating AI and have the generating AI perform the analysis.

[0098] The generation unit can estimate the user's emotions and adjust the route generation method based on the estimated emotions. For example, if the user is feeling anxious, the generation unit will prioritize generating a safe route to provide a sense of security. For example, if the generation unit estimates the user's emotions and they are feeling anxious, it will prioritize generating a safe route. The generation unit can also prioritize generating a route with good scenery if the user is relaxed. For example, if the generation unit estimates the user's emotions and they are relaxed, it will prioritize generating a route with good scenery. The generation unit can also prioritize generating the shortest route if the user is in a hurry. For example, if the generation unit estimates the user's emotions and they are in a hurry, it will prioritize generating the shortest route. In this way, by adjusting the route generation method based on the user's emotions, it becomes possible to generate a more appropriate route. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation.

[0099] The generation unit can optimize its algorithm by referring to past route generation results when generating shuttle routes. For example, the generation unit can select the optimal route generation algorithm based on past route generation results. The generation unit can also improve the accuracy of its algorithm by referring to past route generation results. The generation unit can also analyze past route generation results and adjust the algorithm parameters. In this way, the algorithm can be optimized by referring to past route generation results. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input past route generation result data into a generation AI and have the generation AI perform algorithm optimization.

[0100] The generation unit can generate routes while considering each user's special requests and constraints. For example, if a user wants to pass through a specific location, the generation unit can generate a route that takes that request into account. For example, the generation unit can consider the user's special request and generate a route that passes through that specific location. The generation unit can also generate routes while considering the constraint of if a user wants to arrive at a specific time. For example, the generation unit can consider the user's special constraint and generate a route that arrives at a specific time. The generation unit can also generate routes while considering the user's special request and generate a route that uses a specific mode of transportation. For example, the generation unit can consider the user's special request and generate a route that uses a specific mode of transportation. This makes it possible to generate more appropriate routes by considering each user's special requests and constraints. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's special request and constraint data into the generation AI and have the generation AI perform route generation.

[0101] The generation unit can estimate the user's emotions and adjust the display method of the generated route based on the estimated user emotions. For example, if the user is feeling anxious, the generation unit can provide a simple and highly visible display method. For example, if the generation unit estimates the user's emotions and provides a simple and highly visible display method when the user is feeling anxious, the generation unit can also provide a display method that includes detailed information when the user is relaxed. For example, if the generation unit estimates the user's emotions and provides a display method that includes detailed information when the user is relaxed, the generation unit can also provide a display method that gets to the point when the user is in a hurry. For example, if the generation unit estimates the user's emotions and provides a display method that gets to the point when the user is in a hurry, the generation unit can also provide a display method that gets to the point when the user is in a hurry. In this way, by adjusting the display method of the route based on the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation.

[0102] The generation unit can generate routes while considering the fuel efficiency of the transport vehicles. For example, the generation unit can prioritize generating routes with high fuel efficiency. The generation unit can also generate routes that minimize fuel consumption. The generation unit can also generate the optimal route while considering fuel efficiency. This makes it possible to generate more efficient routes by considering fuel efficiency. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input fuel efficiency data into a generation AI and have the generation AI perform route generation.

[0103] The generation unit can generate routes while considering the driver's rest time when generating shuttle routes. For example, the generation unit can generate the optimal route by considering the driver's rest time. The generation unit can also generate routes that include driver rest points. For example, the generation unit can generate routes that include driver rest points. The generation unit can also adjust the route generation method based on the driver's rest time. For example, the generation unit can adjust the route generation method based on the driver's rest time. This makes it possible to generate more appropriate routes by considering the driver's rest time. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input driver rest time data into a generation AI and have the generation AI perform route generation.

[0104] The transmission unit can estimate the user's emotions and adjust the timing of sending the pick-up / drop-off route based on the estimated emotions. For example, if the user is feeling anxious, the transmission unit will send the pick-up / drop-off route more frequently and update it in real time. For example, if the transmission unit estimates the user's emotions and they are feeling anxious, it will send the pick-up / drop-off route more frequently. The transmission unit can also widen the transmission interval to save data usage if the user is relaxed. For example, if the transmission unit estimates the user's emotions and they are relaxed, it will widen the transmission interval. The transmission unit can also send the pick-up / drop-off route at shorter intervals if the user is in a hurry, enabling a quicker response. For example, if the transmission unit estimates the user's emotions and they are in a hurry, it will send the pick-up / drop-off route at shorter intervals. This allows for more appropriate information to be provided by adjusting the transmission timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the transmission unit may be performed using AI, for example, or without AI. For example, the transmission unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0105] The transmitting unit can optimize the transmission method when transmitting the transportation route, taking into account the communication status of the transport vehicle. For example, if the communication status is good, the transmitting unit can transmit the transportation route in real time. The transmitting unit can also optimize the amount of data transmitted by adjusting the transmission frequency if the communication status is unstable. The transmitting unit can also select the optimal transmission method, taking the communication status into consideration. This makes it possible to provide information more efficiently by taking the communication status into consideration. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or without using AI. For example, the transmitting unit can input communication status data into a generating AI and have the generating AI perform the optimization of the transmission method.

[0106] The transmission unit can estimate the user's emotions and determine the priority of sending transportation routes based on the estimated emotions. For example, if the user is feeling anxious, the transmission unit will prioritize sending important transportation routes. For example, if the transmission unit estimates the user's emotions and they are feeling anxious, it will prioritize sending important transportation routes. The transmission unit can also send normal transportation routes if the user is relaxed. For example, if the transmission unit estimates the user's emotions and they are relaxed, it will send normal transportation routes. The transmission unit can also prioritize sending the most important transportation routes if the user is in a hurry, enabling a quick response. For example, if the transmission unit estimates the user's emotions and they are in a hurry, it will prioritize sending the most important transportation routes. This allows for the provision of more appropriate information by determining the transmission priority based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the transmission unit may be performed using AI, for example, or without AI. For example, the transmission unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0107] The transmitting unit can transmit transportation routes while considering the schedule of the transportation vehicle driver. For example, the transmitting unit can transmit the optimal transportation route according to the driver's schedule. The transmitting unit can also adjust the transmission timing while considering the driver's schedule. The transmitting unit can also optimize the method of transmitting transportation routes based on the driver's schedule. This makes it possible to provide information more efficiently by considering the driver's schedule. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or without AI. For example, the transmitting unit can input driver schedule data into a generating AI and have the generating AI perform the transmission.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The transportation route optimization system may further include a health monitoring unit that monitors the user's health status. The health monitoring unit collects vital data such as the user's heart rate and blood pressure, and transmits it to an analysis unit. Based on the collected vital data, the analysis unit evaluates the user's health status and can adjust the transportation route as needed. For example, if the user's heart rate is high, the analysis unit can select a quiet route to reduce stress. Also, if the user's blood pressure is high, the analysis unit can select a route closer to a medical facility. This makes it possible to optimize the transportation route according to the user's health status. Some or all of the above processing in the health monitoring unit may be performed using AI, for example, or without AI. For example, the health monitoring unit can input vital data into a generating AI and have the generating AI perform a health status evaluation.

[0110] The shuttle route optimization system may further include a preference learning unit that learns the user's preferences. The preference learning unit, for example, collects routes and ratings previously selected by the user and transmits them to the analysis unit. Based on the collected data, the analysis unit can learn the user's preferences and take them into consideration when generating shuttle routes. For example, if the user prefers routes with good scenery, the analysis unit can generate a route that reflects that preference. Also, if the user wants to avoid a particular facility, the analysis unit can generate a route that takes that information into consideration. This makes it possible to optimize shuttle routes according to the user's preferences. Some or all of the above processing in the preference learning unit may be performed using AI, for example, or without AI. For example, the preference learning unit can input the user's preference data into a generation AI and have the generation AI perform preference learning.

[0111] The shuttle route optimization system can further estimate the user's emotions and adjust the route generation method based on the estimated emotions. For example, if the user is feeling anxious, the generation unit can prioritize generating a safe route to provide a sense of security. If the user is relaxed, it can prioritize generating a route with good scenery. Furthermore, if the user is in a hurry, it can prioritize generating the shortest route. By adjusting the route generation method based on the user's emotions, more appropriate route generation becomes possible. Emotion estimation is achieved, for example, using an emotion engine or a generative AI. Some or all of the above-described processing in the generation unit may be performed using a generative AI, or not using a generative AI. For example, the generation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0112] The shuttle route optimization system can further estimate the user's emotions and adjust the way the shuttle route is displayed based on those emotions. For example, if the user is feeling anxious, the generation unit can provide a simple and highly visible display. If the user is relaxed, it can provide a display that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display that gets straight to the point. By adjusting the way the route is displayed based on the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved, for example, using an emotion engine or a generative AI. Some or all of the above-described processing in the generation unit may be performed using a generative AI, or not using a generative AI. For example, the generation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0113] The shuttle route optimization system can further estimate the user's emotions and adjust the timing of shuttle route transmission based on the estimated emotions. For example, if the user is feeling anxious, the transmission unit can transmit shuttle routes frequently and update them in real time. Conversely, if the user is relaxed, the transmission interval can be widened to save data usage. Furthermore, if the user is in a hurry, the shuttle route can be transmitted at short intervals to enable a quick response. This allows for the provision of more appropriate information by adjusting the transmission timing based on the user's emotions. Emotion estimation is achieved, for example, using an emotion engine or generative AI. Some or all of the above processing in the transmission unit may be performed using AI, for example, or without AI. For example, the transmission unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0114] The shuttle route optimization system can further generate routes while considering the fuel consumption of the shuttle vehicles. For example, if fuel consumption is high, the generation unit can prioritize generating fuel-efficient routes. If fuel consumption is low, it can also prioritize generating the shortest route. Furthermore, the route generation method can be adjusted according to the fuel consumption situation. This makes it possible to generate more efficient routes by considering fuel consumption. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input fuel consumption data into a generation AI and have the generation AI perform route generation.

[0115] The shuttle route optimization system can further generate routes while considering the rest times of the shuttle vehicle drivers. The generation unit can, for example, generate the optimal route by considering the driver's rest times. It can also generate routes that include driver rest points. Furthermore, it can adjust the route generation method based on the driver's rest times. This makes it possible to generate more appropriate routes by considering the driver's rest times. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input driver rest time data into a generation AI and have the generation AI perform route generation.

[0116] The shuttle route optimization system can further generate routes while considering the schedules of the shuttle vehicle drivers. The generation unit can, for example, generate the optimal route according to the driver's schedule. It can also adjust the transmission timing while considering the driver's schedule. Furthermore, it can optimize the route generation method based on the driver's schedule. This makes it possible to generate routes more efficiently by considering the driver's schedule. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input driver schedule data into a generation AI and have the generation AI perform route generation.

[0117] The shuttle route optimization system can further generate routes while considering the maintenance status of the shuttle vehicles. For example, the generation unit can prioritize generating routes closer to maintenance facilities for vehicles requiring maintenance. It can also prioritize generating the shortest route for vehicles whose maintenance has been completed. Furthermore, the route generation method can be adjusted according to the maintenance status. This allows for more efficient route generation by considering the maintenance status. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input maintenance status data into the generation AI and have the generation AI perform route generation.

[0118] The shuttle route optimization system can further generate routes while considering the driving style of the shuttle vehicle driver. For example, if the driver frequently uses sudden braking, the generation unit can prioritize generating a safe route. Conversely, if the driver drives smoothly, it can prioritize generating the shortest route. Furthermore, the route generation method can be adjusted according to the driver's driving style. This makes it possible to generate more efficient routes by considering the driver's driving style. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the driver's driving style data into the generation AI and have the generation AI perform route generation.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The data collection unit collects location information of the transport vehicle. The data collection unit can, for example, obtain the location information of the transport vehicle in real time using GPS data. The data collection unit can also obtain location information using beacon data. Furthermore, the data collection unit can collect data from sensors installed in the transport vehicle. Step 2: The environmental data collection unit collects environmental data such as pedestrian traffic, weather, and traffic conditions. For example, the environmental data collection unit can use cameras to detect pedestrian traffic. It can also obtain weather data from a weather database. Furthermore, it can collect traffic condition data using traffic sensors. Step 3: The analysis unit analyzes the information collected by the data collection unit and the environment data collection unit. The analysis unit analyzes the collected information using, for example, a data analysis algorithm. The analysis unit can also take into account each user's schedule and cancellation information. Furthermore, the analysis unit can use AI to improve the accuracy of the analysis. Step 4: The generation unit generates a pick-up / drop-off route based on the information analyzed by the analysis unit. For example, the generation unit generates a pick-up / drop-off route based on the shortest distance. The generation unit can also generate a pick-up / drop-off route based on the optimal time. Furthermore, the generation unit can also generate a pick-up / drop-off route using a generation AI. Step 5: The transmitting unit transmits the transportation routes generated by the generating unit. The transmitting unit can transmit transportation routes in real time, for example. The transmitting unit can also perform batch transmissions. Furthermore, the transmitting unit can use AI to transmit transportation routes to the transport vehicles.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0123] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements described above, including the collection unit, environmental collection unit, analysis unit, generation unit, and transmission unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit acquires the location information of the transport vehicle in real time using GPS data and beacon data from the smart device 14. The environmental collection unit collects data on the number of pedestrians and weather data using the camera of the smart device 14 and data from a weather database. The analysis unit analyzes the information collected by the identification processing unit 290 of the data processing unit 12, and the generation unit generates the optimal transport route based on the analysis results. The transmission unit transmits the generated transport route to the smart device 14 in real time. Furthermore, the collection unit can estimate the user's emotions and adjust the timing of location information acquisition based on the estimated emotions. For example, if the user is feeling anxious, the collection unit acquires location information frequently and updates it in real time. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the collection unit, environmental collection unit, analysis unit, generation unit, and transmission unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit acquires the location information of the transport vehicle in real time using GPS data and beacon data from the smart glasses 214. The environmental collection unit collects pedestrian traffic and weather data using the camera of the smart glasses 214 and data from a weather database. The analysis unit analyzes the information collected by the identification processing unit 290 of the data processing unit 12, and the generation unit generates the optimal transport route based on the analysis results. The transmission unit transmits the generated transport route to the smart glasses 214 in real time. Furthermore, the collection unit can estimate the user's emotions and adjust the timing of location information acquisition based on the estimated emotions. For example, if the user is feeling anxious, the collection unit acquires location information frequently and updates it in real time. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the collection unit, environmental collection unit, analysis unit, generation unit, and transmission unit, is implemented in at least one of the following: a headset terminal 314 and a data processing unit 12. For example, the collection unit acquires real-time location information of the transport vehicle using GPS data and beacon data from the headset terminal 314. The environmental collection unit collects pedestrian traffic and weather data using the camera of the headset terminal 314 and data from a weather database. The analysis unit analyzes the information collected by the identification processing unit 290 of the data processing unit 12, and the generation unit generates an optimal transport route based on the analysis results. The transmission unit transmits the generated transport route to the headset terminal 314 in real time. Furthermore, the collection unit can estimate the user's emotions and adjust the timing of location information acquisition based on the estimated emotions. For example, if the user is feeling anxious, the collection unit acquires location information frequently and updates it in real time. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0165] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the collection unit, environment collection unit, analysis unit, generation unit, and transmission unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit acquires the location information of the transport vehicle in real time using GPS data and beacon data from the robot 414. The environment collection unit collects data on the number of pedestrians and weather data using the camera on the robot 414 and data from a weather database. The analysis unit analyzes the information collected by the identification processing unit 290 of the data processing unit 12, and the generation unit generates the optimal transport route based on the analysis results. The transmission unit transmits the generated transport route to the robot 414 in real time. Furthermore, the collection unit can estimate the user's emotions and adjust the timing of acquiring location information based on the estimated emotions. For example, if the user is feeling anxious, the collection unit acquires location information frequently and updates it in real time. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0174] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0176] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0177] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0178] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0182] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0183] 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.

[0184] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0185] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0186] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0187] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0189] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0190] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0191] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0192] (Note 1) A collection unit that collects location information of the transport vehicle, The Environmental Data Collection Department collects environmental data, An analysis unit that analyzes the information collected by the collection unit and the environmental collection unit, A generation unit generates a pick-up / drop-off route based on the information analyzed by the analysis unit, The system includes a transmission unit that transmits the transportation route generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned environmental collection unit is Collect environmental data such as the number of pedestrians, weather, and traffic conditions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Collect real-time location information from each shuttle vehicle. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, The collected information is analyzed, and each user's schedule and cancellation information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Based on the analyzed information, the most efficient pick-up and drop-off route is generated. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned transmitting unit The generated shuttle routes are transmitted to each shuttle vehicle in real time. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is We will improve the accuracy of the route generation algorithm and add and improve its functionality. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of acquiring the location information of the transport vehicle based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the past operation history of the shuttle vehicles and select the optimal method for obtaining location information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When acquiring location information, the data is filtered to take into account the fuel consumption status of the transport vehicle. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is The system estimates the user's emotions and determines the priority of location information to acquire based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When acquiring location information, the data is collected while taking into account the driving style of the driver of the shuttle vehicle. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When acquiring location information, the maintenance status of the shuttle vehicle is taken into consideration when selecting data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned environmental collection unit is It estimates the user's emotions and adjusts the timing of environmental data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned environmental collection unit is When collecting environmental data, we optimize the collection method by referring to historical weather data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned environmental collection unit is When collecting environmental data, filter the data to take into account the impact of specific events or occurrences. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned environmental collection unit is It estimates user sentiment and prioritizes the environmental data to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned environmental collection unit is When collecting environmental data, the data should be collected while taking into account local characteristics and culture. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned environmental collection unit is When collecting environmental data, the data should be collected while taking into account the occurrence of traffic accidents. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to past analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, During the analysis, the health status and special needs of each user will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, During the analysis, the experience and skills of the shuttle vehicle drivers will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit, During the analysis, the operating costs of the shuttle vehicles will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is The system estimates the user's emotions and adjusts the method of generating transportation routes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is When generating shuttle routes, the algorithm is optimized by referring to past route generation results. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is When generating shuttle routes, the system takes into account each user's special requests and constraints. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is It estimates the user's emotions and adjusts how the generated routes are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The generating unit is When generating shuttle routes, the route is generated while taking into account the fuel efficiency of the shuttle vehicles. The system described in Appendix 1, characterized by the features described herein. (Note 31) The generating unit is When generating shuttle routes, the route is generated taking into account the rest time of the shuttle vehicle driver. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned transmitting unit The system estimates the user's emotions and adjusts the timing of sending the pick-up / drop-off route based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned transmitting unit When sending the pick-up / drop-off route, the transmission method is optimized considering the communication status of the pick-up / drop-off vehicle. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned transmitting unit The system estimates the user's emotions and determines the priority of sending pick-up / drop-off routes based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned transmitting unit When sending the pick-up / drop-off route, please take into consideration the driver's schedule for the vehicle. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects location information of the transport vehicle, The Environmental Data Collection Department collects environmental data, An analysis unit that analyzes the information collected by the collection unit and the environmental collection unit, A generation unit generates a pick-up / drop-off route based on the information analyzed by the analysis unit, The system includes a transmission unit that transmits the transportation route generated by the generation unit. A system characterized by the following features.

2. The aforementioned environmental collection unit is Collect environmental data such as the number of pedestrians, weather, and traffic conditions. The system according to feature 1.

3. The aforementioned collection unit is Collect real-time location information from each shuttle vehicle. The system according to feature 1.

4. The aforementioned analysis unit, The collected information is analyzed, and each user's schedule and cancellation information is taken into consideration. The system according to feature 1.

5. The generating unit is Based on the analyzed information, the most efficient pick-up and drop-off route is generated. The system according to feature 1.

6. The aforementioned transmitting unit The generated shuttle routes are transmitted to each shuttle vehicle in real time. The system according to feature 1.

7. The generating unit is We will improve the accuracy of the route generation algorithm and add and improve its functionality. The system according to feature 1.

8. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of acquiring the location information of the transport vehicle based on the estimated user emotions. The system according to feature 1.

9. The aforementioned collection unit is Analyze the past operation history of the shuttle vehicles and select the optimal method for obtaining location information. The system according to feature 1.

10. The aforementioned collection unit is When acquiring location information, the data is filtered to take into account the fuel consumption status of the transport vehicle. The system according to feature 1.

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